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Tanabe, Shihori, Sabina Quader, Ryuichi Ono, Horacio Cabral, Kazuhiko Aoyagi, Akihiko Hirose, Hiroshi Yokozaki, and Hiroki Sasaki. "Molecular Network Profiling in Intestinal- and Diffuse-Type Gastric Cancer." Cancers 12, no. 12 (December 18, 2020): 3833. http://dx.doi.org/10.3390/cancers12123833.
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Epithelial-mesenchymal transition (EMT) plays an important role in the acquisition of cancer stem cell (CSC) feature and drug resistance, which are the main hallmarks of cancer malignancy. Although previous findings have shown that several signaling pathways are activated in cancer progression, the precise mechanism of signaling pathways in EMT and CSCs are not fully understood. In this study, we focused on the intestinal and diffuse-type gastric cancer (GC) and analyzed the gene expression of public RNAseq data to understand the molecular pathway regulation in different subtypes of gastric cancer. Network pathway analysis was performed by Ingenuity Pathway Analysis (IPA). A total of 2815 probe set IDs were significantly different between intestinal- and diffuse-type GC data in cBioPortal Cancer Genomics. Our analysis uncovered 10 genes including male-specific lethal 3 homolog (Drosophila) pseudogene 1 (MSL3P1), CDC28 protein kinase regulatory subunit 1B (CKS1B), DEAD-box helicase 27 (DDX27), golgi to ER traffic protein 4 (GET4), chromosome segregation 1 like (CSE1L), translocase of outer mitochondrial membrane 34 (TOMM34), YTH N6-methyladenosine RNA binding protein 1 (YTHDF1), ribonucleic acid export 1 (RAE1), par-6 family cell polarity regulator beta (PARD6B), and MRG domain binding protein (MRGBP), which have differences in gene expression between intestinal- and diffuse-type GC. A total of 463 direct relationships with three molecules (MYC, NTRK1, UBE2M) were found in the biomarker-filtered network generated by network pathway analysis. The networks and features in intestinal- and diffuse-type GC have been investigated and profiled in bioinformatics. Our results revealed the signaling pathway networks in intestinal- and diffuse-type GC, bringing new light for the elucidation of drug resistance mechanisms in CSCs.
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Sahajpal, Nikhil Shri, Ashis Mondal, Meenakshi Ahluwalia, Allan Njoroge Njau, Vamsi Kota, Nwogbo Okechukwu, Gretchen Purcell Jackson, et al. "Clinical utility of comprehensive genomic pathway and integrated network analyses in personalized oncology." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e14051-e14051. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e14051.
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e14051 Background: Adoption of next-generation sequencing (NGS) technology in routine clinical practice has enabled the detection of genetic aberrations such as single nucleotide variants, copy number alterations, and gene fusions. Pathway and network analyses (PNA) are key components for evaluation of NGS data in a clinical setting to explain findings involving thousands of altered genes and proteins with a smaller and more interpretable set of altered processes. Though PNA have been applied to identify driver genes and pathways in cohort-based analyses, its application in precision oncology remains unexplored. We investigate the potential utility of the Watson for Genomics (WfG) pathway analyses tool in interpreting complex and multiple genomic alterations in individual cancers. Methods: DNA and RNA isolated from 70 patient tumors across 30 different cancer types were processed with Illumina’s TST170 NGS platform. WfG’s feature of pathway analyses was used to identify gene variants, signaling pathways, networks, and the drugs targeting these alterations based on evidence in the clinical literature and FDA drug databases. Results: Analyses defined 5 different pathway/network models: 1) downstream therapeutic targets, 2) synthetic lethality, 3) combinatorial downstream targets + synthetic lethality, 4) two or more pathways converging to downstream targets, and 5) complex profile analyses. The five PNA models are illustrated by the following unique cases. 1) A thyroid cancer case with HRAS variant and activated RAF1 downstream pathway showed MAPK1/3 were suggestive of relevant targets. 2) An acute myeloid leukemia case with BRCA1, BRCA2 and PTEN variants, targeting a common synthetic lethal partner PARP1 was ideal for therapy. 3) A penile carcinoma case with BRAF, CDKN2A and TP53 variants, targeting the BRAF downstream pathway in combination with either CDKN2A or TP53 were the likely choice for therapy. 4) A glioma case with activated PI3K and MEK downstream pathway, targeting a common downstream marker would block both pathways. 5) A breast carcinoma case with a complex pathogenic variant profile provided relevant clinical information and levels of evidence for multiple drug targets. Conclusions: We discovered that the integrated WfG pathway analyses tool is ideal for visualization of the variants with levels of evidence from clinical literature and FDA drug databases that can help inform treatment options and provides a holistic understanding of a specific tumor profile allowing the treating clinician to select personalized targeted therapy.
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Yuan, Mengxia, Qi He, Zhiyong Long, Xiaofei Zhu, Wang Xiang, Yonghe Wu, and Shibin Lin. "Exploring the Pharmacological Mechanism of Liuwei Dihuang Decoction for Diabetic Retinopathy: A Systematic Biological Strategy-Based Research." Evidence-Based Complementary and Alternative Medicine 2021 (August 2, 2021): 1–20. http://dx.doi.org/10.1155/2021/5544518.
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Objective. To explore the pharmacological mechanism of Liuwei Dihuang decoction (LDD) for diabetic retinopathy (DR). Methods. The potential targets of LDD were predicted by PharmMapper. GeneCards and other databases were used to collect DR genes. Cytoscape was used to construct and analyze network DR and LDD’s network, and DAVID was used for Gene Ontology (GO) and pathway enrichment analysis. Finally, animal experiments were carried out to verify the results of systematic pharmacology. Results. Five networks were constructed and analyzed: (1) diabetic retinopathy genes’ PPI network; (2) compound-compound target network of LDD; (3) LDD-DR PPI network; (4) compound-known target network of LDD; (5) LDD known target-DR PPI network. Several DR and treatment-related targets, clusters, signaling pathways, and biological processes were found. Animal experiments found that LDD can improve the histopathological changes of the retina. LDD can also increase erythrocyte filtration rate and decrease the platelet adhesion rate ( P < 0.05 ) and decrease MDA and TXB2 ( P < 0.05 ). Compared with the model group, the retinal VEGF and HIF-1α expression in the LDD group decreased significantly ( P < 0.05 ). Conclusion. The therapeutic effect of LDD on DR may be achieved by interfering with the biological processes (such as response to insulin, glucose homeostasis, and regulation of angiogenesis) and signaling pathways (such as insulin, VEGF, HIF-1, and ErbB signaling pathway) related to the development of DR that was found in this research.
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Tanabe, Shihori, Sabina Quader, Ryuichi Ono, Horacio Cabral, Kazuhiko Aoyagi, Akihiko Hirose, Hiroshi Yokozaki, and Hiroki Sasaki. "Cell Cycle Regulation and DNA Damage Response Networks in Diffuse- and Intestinal-Type Gastric Cancer." Cancers 13, no. 22 (November 18, 2021): 5786. http://dx.doi.org/10.3390/cancers13225786.
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Dynamic regulation in molecular networks including cell cycle regulation and DNA damage response play an important role in cancer. To reveal the feature of cancer malignancy, gene expression and network regulation were profiled in diffuse- and intestinal-type gastric cancer (GC). The results of the network analysis with Ingenuity Pathway Analysis (IPA) showed that the activation states of several canonical pathways related to cell cycle regulation were altered. The G1/S checkpoint regulation pathway was activated in diffuse-type GC compared to intestinal-type GC, while canonical pathways of the cell cycle control of chromosomal replication, and the cyclin and cell cycle regulation, were activated in intestinal-type GC compared to diffuse-type GC. A canonical pathway on the role of BRCA1 in the DNA damage response was activated in intestinal-type GC compared to diffuse-type GC, where gene expression of BRCA1, which is related to G1/S phase transition, was upregulated in intestinal-type GC compared to diffuse-type GC. Several microRNAs (miRNAs), such as mir-10, mir-17, mir-19, mir-194, mir-224, mir-25, mir-34, mir-451 and mir-605, were identified to have direct relationships in the G1/S cell cycle checkpoint regulation pathway. Additionally, cell cycle regulation may be altered in epithelial-mesenchymal transition (EMT) conditions. The alterations in the activation states of the pathways related to cell cycle regulation in diffuse- and intestinal-type GC highlighted the significance of cell cycle regulation in EMT.
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Sirico, Marianna, Alberto D’Angelo, Caterina Gianni, Chiara Casadei, Filippo Merloni, and Ugo De Giorgi. "Current State and Future Challenges for PI3K Inhibitors in Cancer Therapy." Cancers 15, no. 3 (January 23, 2023): 703. http://dx.doi.org/10.3390/cancers15030703.
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The phosphoinositide 3 kinase (PI3K)-protein kinase B (PKB/AKT)-mammalian target of the rapamycin (mTOR) axis is a key signal transduction system that links oncogenes and multiple receptor classes which are involved in many essential cellular functions. Aberrant PI3K signalling is one of the most commonly mutated pathways in cancer. Consequently, more than 40 compounds targeting key components of this signalling network have been tested in clinical trials among various types of cancer. As the oncogenic activation of the PI3K/AKT/mTOR pathway often occurs alongside mutations in other signalling networks, combination therapy should be considered. In this review, we highlight recent advances in the knowledge of the PI3K pathway and discuss the current state and future challenges of targeting this pathway in clinical practice.
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Vandermeulen, Matthew D., and Paul J. Cullen. "Gene by Environment Interactions reveal new regulatory aspects of signaling network plasticity." PLOS Genetics 18, no. 1 (January 4, 2022): e1009988. http://dx.doi.org/10.1371/journal.pgen.1009988.
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Phenotypes can change during exposure to different environments through the regulation of signaling pathways that operate in integrated networks. How signaling networks produce different phenotypes in different settings is not fully understood. Here, Gene by Environment Interactions (GEIs) were used to explore the regulatory network that controls filamentous/invasive growth in the yeast Saccharomyces cerevisiae. GEI analysis revealed that the regulation of invasive growth is decentralized and varies extensively across environments. Different regulatory pathways were critical or dispensable depending on the environment, microenvironment, or time point tested, and the pathway that made the strongest contribution changed depending on the environment. Some regulators even showed conditional role reversals. Ranking pathways’ roles across environments revealed an under-appreciated pathway (OPI1) as the single strongest regulator among the major pathways tested (RAS, RIM101, and MAPK). One mechanism that may explain the high degree of regulatory plasticity observed was conditional pathway interactions, such as conditional redundancy and conditional cross-pathway regulation. Another mechanism was that different pathways conditionally and differentially regulated gene expression, such as target genes that control separate cell adhesion mechanisms (FLO11 and SFG1). An exception to decentralized regulation of invasive growth was that morphogenetic changes (cell elongation and budding pattern) were primarily regulated by one pathway (MAPK). GEI analysis also uncovered a round-cell invasion phenotype. Our work suggests that GEI analysis is a simple and powerful approach to define the regulatory basis of complex phenotypes and may be applicable to many systems.
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Hayne, Victoria, Leah A. Stein, Carole Kathleen Tremonti, Elizabeth H. Baldini, John G. Phillips, Susanna C. Hilfer, David Michael Jackman, et al. "Implementing radiation oncology pathways at Dana-Farber Cancer Institute/Brigham and Women’s Hospital." Journal of Clinical Oncology 36, no. 30_suppl (October 20, 2018): 301. http://dx.doi.org/10.1200/jco.2018.36.30_suppl.301.
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301 Background: Modern cancer care faces increasing complexity and the challenge of delivering consistent, high-quality care across growing networks. Dana-Farber Pathways address these concerns by translating expert content into treatment algorithms delivered through a web-based platform and implemented across the network. We had previously built 31 Medical Oncology (MO) pathways. Our goal was to build and implement Radiation Oncology (RO) pathways for common cancer conditions in 18 months. Methods: Partnering with lead clinicians from each disease group, we chose the most appropriate framework for each pathway: expand previously established RO pathway; use corresponding MO pathway; or if no framework was available, develop RO pathway in its entirety. We worked with each disease group to gain consensus about the recommended on-pathway selections that reflect the latest research and institutional standard of care. Implementation consisted of pre- and post-launch department communications about metric goals and individual provider training on the system. Because the program was launched without provider incentivization, the usage rate goal was navigations for at least 25% of patients receiving radiation treatment. The on-pathway rate goal range was 70-80%. Results: We exceeded our goal: we built and implemented 25 pathways in 12 months and constructed all 27 pathways in 18 months. We have met both key rate goals across all disease pathways since time of launch: usage rate is 63%; on-pathway rate is 85% (39 providers across 3 sites). Conclusions: A preliminary analysis of RO Pathway data demonstrates collective adoption across all sites. Qualitative surveys show it to be a useful resource in radiation treatment decision-making and in palliative care service. We will continue to analyze the use of RO Pathways and develop strategies to collaborate with MO to further guide multi-disciplinary decision-making.[Table: see text]
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Brougher, Laura Ione, Rocky Lee Billups, Tonya Cox, Therese Dodd, and Charles F. LeMaistre. "Development and implementation of clinical pathways across a network of blood cancer programs." Journal of Clinical Oncology 35, no. 8_suppl (March 10, 2017): 161. http://dx.doi.org/10.1200/jco.2017.35.8_suppl.161.
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161 Background: The Sarah Cannon Blood Cancer Network (SCBCN) is comprised of 7 blood cancer and Hematopoietic Cell Therapy (HCT) programs providing care for complex blood cancer patients. Network commitment to achieving standardization of care for quality and clinical platforms led to process design for development of evidence-based pathways. Methods: Standard operating procedures and a document control process were developed by the network quality management (QM). With oversight by physician leaders, initial efforts focused on standardizing patient selection criteria for HCT followed by formation of disease-specific work groups (leukemia, lymphoma, multiple myeloma, HCT). Physician-led and composed of key network team members (PharmDs, QM professionals, SCBCN leaders, research staff), the groups are responsible for developing evidence-based diagnostic and treatment pathways/algorithms for hematologic malignancies. Members participate in monthly teleconferences to develop the pathways. Annual review and deviation tracking are conducted, providing a mechanism for subsequent pathway revisions reflecting changing treatment paradigms and updated clinical evidence. QM oversees implementation and tracking across the network. Deviation tracking is managed locally using pathway-associated algorithms, with attending physician attestation to either compliance or pathway deviation/reason (e.g. co-morbidity, age, disease status, enrolled on clinical trial, other). Results are reported quarterly with review by QM. Results: See table. To date, 13 HCT pathways (18 algorithms) have been developed and released to SCBCN programs. Final approval is pending for 5 disease-based pathways (19 algorithms). Conclusions: The first compliance report will be submitted to QM in December, 2016. While deviation tracking has been paper-based, work efforts are underway to implement an electronic solution, enabling automated real-time monitoring. Implementation is expected in early 2017. [Table: see text]
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Li, Jianjie, Yuqi Gao, and Xuan Yu. "A structural analysis of the hypoxia response network." PeerJ 9 (April 6, 2021): e10985. http://dx.doi.org/10.7717/peerj.10985.
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Background The hypoxia-inducible factor-1 (HIF-1) signaling pathway is an important topic in high-altitude medicine. Network analysis is a novel method for integrating information on different aspects and levels of biological networks. However, this method has not been used in research on the HIF-1 signaling pathway network. To introduce this method into HIF-1-related research fields and verify its feasibility and effectiveness, we used a network analytical method to explore the structural attributes of the HIF-1 signaling pathway network. Methods First, we analyzed the overall network of the HIF-1 signaling pathway using information retrieved from the Kyoto Encyclopedia of Genes and Genomes (KEGG). We performed topology analysis, centrality analysis, and subgroup analysis of the network. Then, we analyzed the core network based on the overall network analysis. We analyzed the properties of the topology, the bow-tie structure, and the structural complexity of the core network. Results We obtained topological structure diagrams and quantitative indicators of the overall and core networks of the HIF-1 signaling pathway. For the structure diagrams, we generated topology diagrams of the network and the bow-tie structure of the core network. As quantitative indicators, we identified topology, centrality, subgroups, the bow-tie structure, and structural complexity. The topology indicators were the number of nodes, the number of lines, the network diameter, and the network density. The centrality indicators were the degree, closeness, and betweenness. The cohesive subgroup indicator was the components of the network. The bow-tie structure indicators included the core, input, and tendril-like structures. The structural complexity indicators included a power-law fitting model and its scale parameter. Conclusions The core network could be extracted based on the subgroup analysis of the overall network of the HIF-1 signaling pathway. The critical elements of the network could be identified in the centrality analysis. The results of the study show the feasibility and effectiveness of the network analytical method used to explore the network properties of the HIF-1 signaling pathway and provide support for further research.
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He, Qiaoyu, Xiaopeng Chen, Jing Liu, Chunxia Li, Hong Xing, Yumeng Shi, and Qian Tang. "Combining Network Pharmacology with Molecular Docking for Mechanistic Research on Thyroid Dysfunction Caused by Polybrominated Diphenyl Ethers and Their Metabolites." BioMed Research International 2021 (November 17, 2021): 1–12. http://dx.doi.org/10.1155/2021/2961747.
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Network pharmacology was used to illuminate the targets and pathways of polybrominated diphenyl ethers (PBDEs) causing thyroid dysfunction. A protein-protein interaction (PPI) network was constructed. Molecular docking was applied to analyze PBDEs and key targets according to the network pharmacology results. A total of 247 targets were found to be related to 16 PBDEs. Ten key targets with direct action were identified, including the top five PIK3R1, MAPK1, SRC, RXRA, and TP53. Gene Ontology (GO) functional enrichment analysis identified 75 biological items. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis identified 62 pathways mainly related to the regulation of the thyroid hormone signaling pathway, MAPK signaling pathway, PI3K-Akt signaling, pathways in cancer, proteoglycans in cancer, progesterone-mediated oocyte maturation, and others. The molecular docking results showed that BDE-99, BDE-153, 5-OH-BDE47, 5 ′ -OH-BDE99, 5-BDE47 sulfate, and 5 ′ -BDE99 sulfate have a good binding effect with the kernel targets. PBDEs could interfere with the thyroid hormone endocrine through multiple targets and biological pathways, and metabolites demonstrated stronger effects than the prototypes. This research provides a basis for further research on the toxicological effects and molecular mechanisms of PBDEs and their metabolites. Furthermore, the application of network pharmacology to the study of the toxicity mechanisms of environmental pollutants provides a new methodology for environmental toxicology.
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Wang, Fei, Qiheng Zhao, Wenping Liu, and Daliang Kong. "CENPE, PRC1, TTK, and PLK4 May Play Crucial Roles in the Osteosarcoma Progression." Technology in Cancer Research & Treatment 19 (January 1, 2020): 153303382097327. http://dx.doi.org/10.1177/1533033820973278.
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Osteosarcoma (OS) is a cancerous tumor in a bone. We aimed to identify the critical genes involved in OS progression, and then try to elucidate the molecular mechanisms of this disease. The microarray data of GSE32395 was used for the present study. We analyzed differentially expressed genes (DEGs) in OS cells compared with control group by Student’s t-test. The significant enriched gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) pathways were analyzed for upregulated genes and downregulated genes, respectively. In addition, a protein-protein interaction (PPI) network was constructed. GO and KEGG enrichment analyses were conducted for genes in the PPI network. In total, 183 DEGs, including 100 upregulated DEGs and 83 downregulated DEGs were screened. The upregulated DEGs were significantly enriched in 2 KEGG pathways, such as “Glycosaminoglycan biosynthesis-chondroitin sulfate” and the downregulated DEGs were significantly enriched in 12 pathways, including “cell adhesion molecules,” “pentose phosphate pathway” and “allograft rejection.” GO enrichment analysis indicated that the upregulated DEGs were significantly involved in biological process, such as “multicellular organismal metabolic process” and “limb morphogenesis,” while the downregulated DEGs were significantly enriched in biological process, such as “Positive regulation of pathway-restricted SMAD protein phosphorylation.” The PPI network included 84 interactions and 51 nodes. The “glycosaminoglycan biosynthesis-chondroitin sulfate pathway,” “microtubule motor activityfunction,” and “regulation of mitosis process” were significantly enriched by genes in PPI network. In particular, CENPE, PRC1, TTK, and PLK4 had higher degrees in the PPI network. The interactions between TTK and PLK4 as well as CENPE and PRC1 may involve in the OS development. These 4 genes might be possible biomarkers for the treatment and diagnosis of OS.
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Bott, Thomas, Nanor Bozoyan, Trisha Babcock, Kristen Cushman, Akanksha Sharma, Naveed Wagle, Jose Carrillo, Tiffany Juarez, and Santosh Kesari. "QLTI-26. INTEGRATION OF NEURO-ONCOLOGY CLINICAL PATHWAYS IN PROVIDENCE SOUTHERN CALIFORNIA CLINICAL RESEARCH NETWORK." Neuro-Oncology 24, Supplement_7 (November 1, 2022): vii240. http://dx.doi.org/10.1093/neuonc/noac209.928.
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Abstract INTRODUCTION ClinicalPath is an evidence-based oncology decision support and analytics tool for cancer care. ClinicalPath’s treatment recommendations are prioritized based on efficacy, toxicity, and cost by a nationwide committee of oncologists. The pathways are updated quarterly and are expected to speed the integration of new treatments into practice, standardize therapy, improve quality, and decrease cost. The pathway system also allows for local clinical trial mapping to promote clinical trial awareness and increase enrollment. ClinicalPath provides clinical pathways delivering personalized, evidence-based oncology guidance at the point of care. ClinicalPath’s network in North America serves more than 2,000 cancer physicians, within 54 practices in 31 states (15 academic medical centers, 29 hospital systems, and 9 community practices). Population: Our population is derived from multiple hospitals in Southern California within the Providence Health System. Methodology: Medical oncologists and Advanced Practice Providers received training on ClinicalPath before go-live. ClinicalPath was integrated into the Epic EHR in multiple Southern CA hospitals in a single month. RESULTS After 3 months of utilization within our Southern California region, 342 treatment decisions were made across all cancers, and 85.1% of cancer patients were treated on pathway. Of which, 7 treatment decisions were made within the neuro-oncology specialty, and 85.7% of those cancer patients were treated on pathway. CONCLUSION We successfully integrated and initiated ClinicalPath in a multiple hospital-affiliated community oncology clinical trial network. We are actively working across our Southern California Region to map locally available clinical trials to promote awareness and increase enrollment. Provider utilization and patient on-pathway rates are actively monitored and will be updated.
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Ruane, Thomas, Philip J. Stella, Jeffrey A. Scott, Bruce A. Feinberg, and Joseph Cooper. "Physician participation and compliance with cancer clinical care pathways: A successful model of collaboration between payers and providers." Journal of Clinical Oncology 30, no. 15_suppl (May 20, 2012): e16552-e16552. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.e16552.
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e16552 Background: Blue Cross Blue Shield of Michigan (BCBSM), Physician Resource Management (PRM), and Cardinal Health Specialty Solutions (CHSS), partnered to develop a clinical pathway program that would benefit all parties by improving the consistency and quality of patient care while reducing costs. A major obstacle to clinical pathway adoption is physician participation and compliance. Incentives were provided to physicians to join and comply with this clinical pathways program. We report the rates of physician participation and compliance after 1 year. Methods: In 2009, treatment and supportive cancer care clinical pathways moderated by PRM and CHSS chief medical officers were developed by a 12-member steering committee of BCBSM network oncologists. The non-steering committee physicians within the BCBSM network then provided input and review prior to implementation. Physician financial incentives were provided by BCBSM to encourage adoption of the cancer pathways: (1)$5,000 (2) an increased reimbursement rate for certain generic therapies associated with the pathways (3) a share of the savings realized in expenditures for chemotherapy and supportive medications directly attributable to pathways. Rates of participation and compliance by physicians were evaluated after the first year. Compliance was measured using insurance claims data from BCBSM and a proprietary CHSS claims management software tool. Results: Out of 228 Michigan private practice medical oncologists, 187 (82%) participated in the first year of BCBSM’s cancer pathways program. Average compliance rates for the cancer treatment and supportive care pathways over the year 2010 were 96% and 92.5% respectively. Conclusions: Effective clinical care pathway programs require robust physician participation and compliance. Involving physicians in the pathway development process and providing financial incentives led to high rates of physician involvement and “buy-in”, a crucial step in developing our successful clinical care pathway program.
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Ghulam, Ali, Xiujuan Lei, Min Guo, and Chen Bian. "Comprehensive Analysis of Features and Annotations of Pathway Databases." Current Bioinformatics 15, no. 8 (January 1, 2021): 803–20. http://dx.doi.org/10.2174/1574893615999200413123352.
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This study focused on describing the necessary information related to pathway mechanisms, characteristics, and databases feature annotations. Various difficulties related to data storage and retrieval in biological pathway databases are discussed. These focus on different techniques for retrieving annotations, features, and methods of digital pathway databases for biological pathway analysis. Furthermore, many pathway databases annotations, features, and search databases were also examined (which are reasonable for the integration into microarray examination). The investigation was performed on the databases, which contain human pathways to understand the hidden components of cells applied in this process. Three different domain-specific pathways were selected for this study and the information of pathway databases was extracted from the existing literature. The research compared different pathways and performed molecular level relations. Moreover, the associations between pathway networks were also evaluated. The study involved datasets for gene pathway matrices and pathway scoring techniques. Additionally, different pathways techniques, such as metabolomics and biochemical pathways, translation, control, and signaling pathways and signal transduction, were also considered. We also analyzed the list of gene sets and constructed a gene pathway network. This article will serve as a useful manual for storing a repository of specific biological data and disease pathways.
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Jusufi, Ilir, Christian Klukas, Andreas Kerren, and Falk Schreiber. "Guiding the interactive exploration of metabolic pathway interconnections." Information Visualization 11, no. 2 (September 19, 2011): 136–50. http://dx.doi.org/10.1177/1473871611405677.
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Approaches to investigate biological processes have been of strong interest in the past few years and are the focus of several research areas, especially Systems Biology. Biochemical networks as representations of processes are very important for a comprehensive understanding of living beings. Drawings of these networks are often visually overloaded and do not scale. A common solution to deal with this complexity is to divide the complete network, for example, the metabolism, into a large set of single pathways that are hierarchically structured. If those pathways are visualized, this strategy generates additional navigation and exploration problems as the user loses the context within the complete network. In this article, we present a general solution to this problem of visualizing interconnected pathways and discuss it in context of biochemical networks. Our new visualization approach supports the analyst in obtaining an overview to related pathways if they are working within a particular pathway of interest. By using glyphs, brushing, and topological information of the related pathways, our interactive visualization is able to intuitively guide the exploration and navigation process, and thus the analysis processes too. To deal with real data and current networks, our tool has been implemented as a plugin for the VANTED system.
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Wang, Li, Nicole Pearson, Yuning Xiong, Santosh Renuse, Ran Cheng, Jodi M. Carter, Akhilesh Pandey, and Xinyan Wu. "Abstract 3913: Quantitative phosphoproteomic analysis of AXL signaling network in breast cancer." Cancer Research 82, no. 12_Supplement (June 15, 2022): 3913. http://dx.doi.org/10.1158/1538-7445.am2022-3913.
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Abstract Background: As a key receptor tyrosine kinase (RTK) regulating tumorigenesis, AXL is overexpressed in multiple different types of cancers. AXL can be activated by homodimerization after binding with its ligand, growth arrest specific 6 protein (Gas6) or by heterodimerization with other RTKs. AXL overexpression and/or activation have been reported to promote cancer cell proliferation, migration/invasion and to induce resistance to targeted therapy. However, the AXL downstream signaling network has not yet been fully elucidated. Methods: We employed a CRISPR-CAS9-based knockout technology to create AXL inducible knockout cells in a human triple negative breast cancer (TNBC) cell line, HCC1395. In order to explore the downstream signaling events driven by AXL, we induced AXL knockout for 72 hours and used a 6-plex TMT-labeling-based quantitative proteomic approach to characterize the protein phosphorylation alterations induced by AXL knockout. IMAC and anti-phosphotyrosine antibody (pY1000)-based phosphopeptide enrichment methods were used to comprehensively quantify the phosphoproteome changes induced by AXL knockout. Results: In this study, we identified 38,525 phospho serine/threonine sites and 1,427 phosphotyrosine sites. In response to AXL knockout, 1,581 phospho serine/threonine sites and 40 phosphotyrosine sites showed significant differences between AXL-KO and AXL-WT groups with >1.5-fold change and p<0.05 as the cutoff. Among them, 982 phosphoserine/threonine sites and 30 phosphotyrosine sites were decreased, while 599 phospho serine/threonine sites and 10 phosphotyrosine sites were increased in AXL-KO cells. Signaling pathway enrichment analysis revealed that suppressing AXL expression could inhibit multiple important cancer related signaling pathways, including cell cycle, focal adhesion, MAPK signaling pathway, p53 signaling pathway and mTOR signaling pathway. Conclusion: Quantitative analysis of the phosphoproteome driven by AXL demonstrated that AXL plays a pivotal role in regulating canonical oncogenic signaling but also signaling pathways involved in RNA processing and DNA repair. Our data also suggest that targeting AXL could suppress these oncogenic signaling pathways and have therapeutic potential to improve clinical outcomes for patients with AXL overexpression. Citation Format: Li Wang, Nicole Pearson, Yuning Xiong, Santosh Renuse, Ran Cheng, Jodi M. Carter, Akhilesh Pandey, Xinyan Wu. Quantitative phosphoproteomic analysis of AXL signaling network in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3913.
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He, Li, Xian-Xu Song, Mei Wang, and Ben-Zhuo Zhang. "Screening feature modules and pathways in glioma using EgoNet." Open Life Sciences 12, no. 1 (October 23, 2017): 277–84. http://dx.doi.org/10.1515/biol-2017-0032.
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AbstractBackgroundTo investigate differential egonetwork modules and pathways in glioma using EgoNet algorithm.MethodologyBased on microarray data, EgoNet algorithm mainly comprised three stages: construction of differential co-expression network (DCN); EgoNet algorithm used to identify candidate ego-network modules based on the increased classification accuracy; statistical significance for candidate modules using random permutation testing. After that, pathway enrichment analysis for differential ego-network modules was implemented to illuminate the biological processes.ResultsWe obtained 109 ego genes. From every ego gene, we progressively grew the ego-networks by levels; we extracted 109 ego-networks and the mean node size in an ego-network was 6. By setting the classification accuracy threshold at 0.90 and the count of nodes in an ego-network module at 10, we extracted 8 candidate ego-network modules. After random permutation test with 1000 times, 5 modules including module 59, 72, 78, 86, and 90 were identified to be significant. Of note, the genes of module 90 and 86 were enriched in the pathway of resolution of sister chromatid cohesion and mitotic prometaphase, respectively.ConclusionThe identified modules and their corresponding ego genes might be beneficial in revealing the pathology underlying glioma and give insight for future research of glioma.
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Gao, Shan-shan, Ji-jia Sun, Xin Wang, Yi-yang Hu, Qin Feng, and Xiao-jun Gou. "Research on the Mechanism of Qushi Huayu Decoction in the Intervention of Nonalcoholic Fatty Liver Disease Based on Network Pharmacology and Molecular Docking Technology." BioMed Research International 2020 (November 4, 2020): 1–12. http://dx.doi.org/10.1155/2020/1704960.
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Objective. To use network pharmacology and molecular docking technology in predicting the main active ingredients and targets of Qushi Huayu Decoction (QHD) treatment in Nonalcoholic Fatty Liver Disease (NAFLD) and explore the potential mechanisms of its multi-component-multi-target-multi-pathway. Materials and Methods. The main chemical components of QHD were searched using traditional Chinese medicine system pharmacology technology platform (TCMSP) and PubChem database. The main chemical components of the prescription were ADMET screened by the ACD/Labs software. The main active ingredient was screened by 60% oral bioavailability, and 60% of “bad” ingredients were removed from the drug-like group. Swiss Target Prediction, the SEA, and HitPick systems were sequentially used to search for the target of each active ingredient, and a network map of the QHD’s target of the active ingredient was constructed. Genome annotation database platforms (GeneCards, OMIM, and DisGeNET) were used to predict action targets related to fatty liver disease. “Drug-Disease-Target” network diagram could be visualized with the help of Cytoscape (3.7.1) software. UniProt and STRING database platforms were used to build a protein interaction network. The KEGG signal pathway and DAVID platform were analyzed for biological process enrichment. Results. A total of 128 active ingredients and 275 corresponding targets in QHD were discovered through screening. 55 key target targets and 27 important signaling pathways were screened, such as the cancer pathway, P13K-AKT signaling pathway, PPAR signaling pathway, and other related signaling pathways. Conclusions. The present study revealed the material basis of QHD and discussed the pharmacological mechanism of QHD in fatty liver, thus providing a scientific basis for the clinical application and experimental research of QHD in the future.
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Paley, Suzanne, Richard Billington, James Herson, Markus Krummenacker, and Peter D. Karp. "Pathway Tools Visualization of Organism-Scale Metabolic Networks." Metabolites 11, no. 2 (January 22, 2021): 64. http://dx.doi.org/10.3390/metabo11020064.
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Metabolomics, synthetic biology, and microbiome research demand information about organism-scale metabolic networks. The convergence of genome sequencing and computational inference of metabolic networks has enabled great progress toward satisfying that demand by generating metabolic reconstructions from the genomes of thousands of sequenced organisms. Visualization of whole metabolic networks is critical for aiding researchers in understanding, analyzing, and exploiting those reconstructions. We have developed bioinformatics software tools that automatically generate a full metabolic-network diagram for an organism, and that enable searching and analyses of the network. The software generates metabolic-network diagrams for unicellular organisms, for multi-cellular organisms, and for pan-genomes and organism communities. Search tools enable users to find genes, metabolites, enzymes, reactions, and pathways within a diagram. The diagrams are zoomable to enable researchers to study local neighborhoods in detail and to see the big picture. The diagrams also serve as tools for comparison of metabolic networks and for interpreting high-throughput datasets, including transcriptomics, metabolomics, and reaction fluxes computed by metabolic models. These data can be overlaid on the metabolic charts to produce animated zoomable displays of metabolic flux and metabolite abundance. The BioCyc.org website contains whole-network diagrams for more than 18,000 sequenced organisms. The ready availability of organism-specific metabolic network diagrams and associated tools for almost any sequenced organism are useful for researchers working to better understand the metabolism of their organism and to interpret high-throughput datasets in a metabolic context.
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Xu, Yu-Bin. "The Research on Huanglian Jiedu Decoction against Atopic Dermatitis." Scientific Programming 2021 (February 22, 2021): 1–6. http://dx.doi.org/10.1155/2021/5557908.
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Objective. Study on the pharmacodynamic basis and mechanism of Huanglian Jiedu Decoction against atopic dermatitis (AD). Methods. Based on network pharmacology, the targets of Huanglian Jiedu Decoction and AD were screened by Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), SwissTargetPrediction databases, and the database of Online Mendelian Inheritance in Man (OMIM), Therapeutic Targets Database (TTD) and the Comparative Toxicogenomics Database (CTD); then, “chemical composition-target-related pathway-disease target” network graph of Huanglian Jiedu Decoction against AD was constructed by using STRING and Cytoscape software. In combination with in vitro experiments, the levels of IL-4, IL-6, and IL-10 in T cells were determined by ELISA; the pharmacodynamic basis and mechanism of Huanglian Jiedu Decoction against AD were preliminarily explored. Results. 81 active ingredients in Huanglian Jiedu Decoction were screened by network pharmacology, 31 of which were related to atopic dermatitis, corresponding to 12 target proteins. A total of 14 pathways were obtained by KEGG pathway analysis, and 8 were associated with atopic dermatitis. Compared with the control group, 20 and 40 µg/ml of Huanglian Jiedu Decoction could significantly reduce the contents of IL-4, IL-6, and IL-10 in T lymphocytes of mice with atopic dermatitis ( p < 0.01 ). Conclusion. Huanglian Jiedu Decoction can act against AD by multicomponent, multitarget, and multichannel mode of action.
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Qiao, Bo, Yueying Wu, Xiaoya Li, Zhenyuan Xu, Weigang Duan, Yanan Hu, Wenqing Jia, Qiuyang Fan, and Haijing Xing. "A Network Pharmacology Approach to Explore the Potential Mechanisms of Yifei Sanjie Formula in Treating Pulmonary Fibrosis." Evidence-Based Complementary and Alternative Medicine 2020 (November 30, 2020): 1–15. http://dx.doi.org/10.1155/2020/8887017.
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Objective. Yifei Sanjie Formula (YFSJF) is an effective formula on pulmonary fibrosis (PF), which has been used in clinic for more than 30 years. In order to investigate the molecular mechanism of YFSJF in treating PF, network pharmacology was used to predict the cooperative ingredients and associated pathways. Methods. Firstly, we collected potential active ingredients of YFSJF by TCMSP databases. Secondly, we obtained PF-associated targets through OMIM and Genecards database. Finally, metascape was applied for the analysis of GO terms and KEGG pathways. Results. We screened out 76 potential active ingredients and 98 associated proteins. A total of 5715 items were obtained by GO enrichment analysis ( P < 0.05 ), including 4632 biological processes, 444 cellular components, and 639 molecular functions. A total of 143 related KEGG pathways were enriched ( P < 0.05 ), including IL-17 signaling pathway, T cell receptor signaling pathway, TNF signaling pathway, calcium signaling pathway, TH17 cell differentiation, HIF-1 signaling pathway, and PI3K-Akt signaling pathway. Conclusion. YFSJF can interfere with immune and inflammatory response through multiple targets and pathways, which has a certain role in the treatment of PF. This study lays a foundation for future experimental research.
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Liang, Guo-Cheng, Wen-Gui Duan, Shu-Yin Chen, and Jian-Kang Fang. "Analysis of the Composition and Anti-Rheumatoid Arthritis Mechanism of Qintengtongbi Decoction Based on Network Pharmacology." Natural Product Communications 16, no. 9 (September 2021): 1934578X2110414. http://dx.doi.org/10.1177/1934578x211041421.
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Qintengtongbi Decoction (QTTBD) is a traditional prescription for rheumatoid arthritis (RA) treatment in Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine, Nanning, southern China's Guangxi Zhuang Autonomous Region. However, there is not yet any analysis on its active compounds or action mechanism for treating RA. Moreover, the prescription has not been investigated from the perspective of network pharmacology. Therefore, this study aimed to analyze the compounds QTTBD and their potential pharmacological effects and the mechanism by which they treat RA via an integrated network pharmacology approach. With the aid of the relevant database tools and research indices, 188 compounds and 272 related drug targets genes/proteins were collected from QTTBD through the compound-target network, and 175 common gene targets between the QTTBD and RA were obtained by Venn 2.1. Finally, the top 10 gene targets and pathways were identified through the protein–protein interaction network, gene ontology, and KEGG pathway analysis: the gene targets include AKT1, IL6, TP53, VEGFA, MAPK3, TNF, CASP3, JUN, EGF, and EGFR; the pathways include oxytocin signaling pathway, amphetamine addiction, graft-versus-host disease, ovarian steroidogenesis, cGMP-PKG signaling pathway, Rap1 signaling pathway, allograft rejection, cytokine–cytokine receptor interaction, regulation of lipolysis in adipocytes and inflammatory mediator regulation of transient receptor potential channels. Therefore, it is concluded that a network pharmacology-based approach can help reveal and clarify the anti-RA role of QTTBD, and provide a scientific basis for further research into the mechanism.
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Kui, Fuguang, Wenwen Gu, Fan Gao, Yuji Niu, Wenwen Li, Yaru Zhang, Lijuan Guo, et al. "Research on Effect and Mechanism of Xuefu Zhuyu Decoction on CHD Based on Meta-Analysis and Network Pharmacology." Evidence-Based Complementary and Alternative Medicine 2021 (February 13, 2021): 1–15. http://dx.doi.org/10.1155/2021/9473531.
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Xuefu Zhuyu Decoction (XFZY) is an ancient compound widely used in the treatment of coronary heart disease. However, its efficacy evaluation is not complete and its mechanism of action is not clear enough. In an attempt to address these problems, the efficacy was evaluated by meta-analysis and the mechanism was elucidated by the network pharmacology method. We systematically searched relevant studies in PubMed, Chinese National Knowledge Infrastructure Database (CNKI), Cochrane Library, Wanfang Data, and other databases from 2007 to 2019. The association between XFZY treatment and CHD was estimated by risk ratio (RR) and corresponding 95% confidence intervals (95% CIs). The compounds and the potential protein targets of XFZY were obtained from TCMSP, and active compounds were selected according to their oral bioavailability and drug similarity. The potential genes of coronary heart disease were obtained from TTD, OMIM, and GeneCards. The potential pathways related to genes were determined by GO and KEGG pathway enrichment analyses. The compound-target and compound-target-pathway networks were constructed. Molecular docking validates the component and the target. A total of 21 studies including 1844 patients were enrolled in the present meta-analysis, indicating that XFZY has a greater beneficial on total effect (fixed effect RR = 1.30; 95% Cl: 1.24–1.36; P = 0.82 ; I2 = 0.0%) and electrocardiogram efficacy (fixed effect RR = 1.40; 95% Cl: 1.26–1.56; P = 0.96 ; I2 = 0.0%) compared with the control group. A total of 1342 components in XFZY were obtained, among which, 241 were chosen as bioactive components. GO and KEGG analyses got top 10 significantly enriched terms and 10 enriched pathways. The C-T network included 192 compounds and 3085 targets, whereas the C-T-P network included 10 compounds, 109 targets, and 5 pathways. There was a good binding activity between the components and the targets. XFZY has the curative effect on coronary heart disease, and its mechanism is related to 10 compounds, 10 core targets, and 5 pathways.
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Young, Tiffany J., Yi Cui, Claire Pfeffer, Emilie Hobbs, Wenjie Liu, Joseph Irudayaraj, and Ann L. Kirchmaier. "CAF-1 and Rtt101p function within the replication-coupled chromatin assembly network to promote H4 K16ac, preventing ectopic silencing." PLOS Genetics 16, no. 12 (December 7, 2020): e1009226. http://dx.doi.org/10.1371/journal.pgen.1009226.
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Replication-coupled chromatin assembly is achieved by a network of alternate pathways containing different chromatin assembly factors and histone-modifying enzymes that coordinate deposition of nucleosomes at the replication fork. Here we describe the organization of a CAF-1-dependent pathway in Saccharomyces cerevisiae that regulates acetylation of histone H4 K16. We demonstrate factors that function in this CAF-1-dependent pathway are important for preventing establishment of silenced states at inappropriate genomic sites using a crippled HMR locus as a model, while factors specific to other assembly pathways do not. This CAF-1-dependent pathway required the cullin Rtt101p, but was functionally distinct from an alternate pathway involving Rtt101p-dependent ubiquitination of histone H3 and the chromatin assembly factor Rtt106p. A major implication from this work is that cells have the inherent ability to create different chromatin modification patterns during DNA replication via differential processing and deposition of histones by distinct chromatin assembly pathways within the network.
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Katoh, Masuko, and Masaru Katoh. "WNT Signaling Pathway and Stem Cell Signaling Network: Fig. 1." Clinical Cancer Research 13, no. 14 (July 15, 2007): 4042–45. http://dx.doi.org/10.1158/1078-0432.ccr-06-2316.
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Xu, Wenting, Mengyu Tang, Jiahui Wang, and Lihong Wang. "Identification of the Active Constituents and Significant Pathways of Cangfu Daotan Decoction for the Treatment of PCOS Based on Network Pharmacology." Evidence-Based Complementary and Alternative Medicine 2020 (February 22, 2020): 1–15. http://dx.doi.org/10.1155/2020/4086864.
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Background. Polycystic ovary syndrome (PCOS) is the most common female endocrine disease. Cangfu Daotan Decoction (CDD) can effectively relieve the clinical symptoms of PCOS patients. Methods. To explore the active ingredients and related pathways of CDD for treating PCOS, a network pharmacology-based analysis was carried out. The active ingredients of CDD and their potential targets were obtained from the TCM system pharmacology analysis platform. The obtained PCOS-related genes from OMIM and GeneCards were imported to establish protein-protein interaction networks in STRING. Finally, GO analysis and significant pathway analysis were conducted with the RStudio (Bioconductor) database. Results. A total of 111 active compounds were obtained from 1433 ingredients present in the CDD, related to 118 protein targets. In addition, 736 genes were found to be closely related to PCOS, of which 44 overlapped with CDD and were thus considered therapeutically relevant. Pathway enrichment analysis identified the AGE-RAGE signalling pathway in diabetic complications, endocrine resistance, the IL-17 signalling pathway, the prolactin signalling pathway, and the HIF-1 signalling pathway. Moreover, PI3K-Akt, insulin resistance, Toll-like receptor, MAPK, and AGE-RAGE were related to PCOS and treatment. Conclusions. CDD can effectively improve the symptoms of PCOS, and our network pharmacological analysis lays the foundation for future clinical research.
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Ma, Chen-Yu, Yu-Qian Ma, and Min Deng. "Mechanism of Zhen Wu Decoction in the Treatment of Heart Failure Based on Network Pharmacology and Molecular Docking." Evidence-Based Complementary and Alternative Medicine 2022 (March 15, 2022): 1–10. http://dx.doi.org/10.1155/2022/4877920.
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Heart failure (HF) is a serious manifestation or advanced stage of various cardiovascular diseases, and its mortality and rehospitalization rate are still on the rise in China. Based on the network pharmacology method, 59 components of Zhen Wu decoction (ZWD) and 83 target genes related to HF were obtained. Through the PPI network, four potential therapeutic targets were identified: AKT1, IL6, JUN, and MAPK8. The beneficial components of ZWD might intervene HF through the AGE-RAGE signalling pathway in the diabetes component, fluid shear stress and atherosclerosis, the TNF signalling pathway, TB, and Kaposi sarcoma related herpesvirus infection, according to a KEGG enrichment study. The protein interaction network of candidate targets was constructed by the STRING database, and the protein interaction network was clustered by MEODE software. GO and KEGG enrichment analyses were performed on the core modules obtained by clustering. Finally, AutoDock Vina software was used for molecular docking verification of key targets and active ingredients. The result was that 75 active ingredients and 109 genes were screened as potential active ingredients and potential targets of Shengjie Tongyu decoction for CHF treatment. The main active components were quercetin, luteolin, kaempferol, dehydrated icariin, isorhamnetin, formononetin, and other flavonoids. Il-6, MAPK1, MAPK8, AKT1, VEGFA, and JUN were selected as the core targets. Molecular docking showed that the key components were well connected with the target. GO enrichment analysis showed that Shengjie Tongyu decoction could play a role through multiple biological pathways including angiogenesis, regulation of endothelial cell proliferation, binding of cytokine receptors, negative regulation of apoptotic signalling pathways, regulation of nitric oxide synthase activity, and reactive oxygen metabolism. Key pathways mainly focus on the toll-like receptor signalling pathway, nod-like receptor signalling pathway, MAPK signalling pathway, mTOR signalling pathway, JAK-STAT signalling pathway, VEGF signalling pathway, and other pathways. Through molecular docking technology, it was found that a variety of effective components in ZWD, such as kaempferol. Molecular docking technology has preliminatively verified the network pharmacology and laid a foundation for the follow-up pharmacological research.
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Zhao, Chengguo, Wenpei Ling, Chunyu Luo, Meifang Yin, and Shuzhi Qin. "Study on the Mechanism of Paeoniflorin Against Atherosclerosis Through Network Pharmacology and Molecular Docking." Journal of Biobased Materials and Bioenergy 14, no. 4 (August 1, 2020): 467–75. http://dx.doi.org/10.1166/jbmb.2020.1993.
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This study explored the mechanism of paeoniflorin (PF) against atherosclerosis (AS) at the molecular level using network pharmacology and molecular docking. The targets of PF and disease targets related to AS were obtained through literature mining and database search, the PPI network diagram was drawn, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, and the PF structure was docked with core target. In the results, 130 common target proteins of PF and AS were obtained. GO enrichment analysis found 1071 items related to biological processes, mainly related to metabolism, protein modification, regulation of cell activity, regulation of macromolecule synthesis, etc. There were 107 items related to molecular functions, mainly related to cyclic compounds, ions, nucleotides, and ribose Combine etc. KEGG analysis revealed 79 pathways, mainly Pathways in cancer, PI3K-Akt signalling pathway, Proteoglycans in cancer, Ras signalling pathway, FoxO signalling pathway, etc. The molecular docking results showed that PF had good binding activity with the screened target. In conclusion, this study indicated that PF treatment of AS involves multiple direct or indirect targets and signal pathways, providing a reference for further research on the mechanism of PF treatment of AS.
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Yan, Qingying, Jiewen Yang, Yongwei Yao, Zhen Jia, Yiqing Wang, Miao Cheng, Xiaobo Yan, and Yefeng Xu. "Research of the Active Components and Potential Mechanisms of Qingfei Gujin Decoction in the Treatment of Osteosarcoma Based on Network Pharmacology and Molecular Docking Technology." Computational and Mathematical Methods in Medicine 2022 (November 23, 2022): 1–12. http://dx.doi.org/10.1155/2022/7994425.
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Aim. Qingfei Gujin Decoction (QGD) has been shown to be effective against osteosarcoma. This research was aimed at investigating the main active ingredients and potential mechanisms of QGD acting on osteosarcoma through network pharmacology and molecular docking techniques. Methods. The active ingredients and targets of QGD were screened from the TCMSP database, and the predicted targets were obtained from the PharmMapper database. Meanwhile, the targets of osteosarcoma were collected using OMIM, PharmGKB, and DisGeNET databases. Then, GO and KEGG enrichment analyses were performed by RStudio. PPI and drug-ingredient-target networks were constructed using Cytoscape 3.2.1 to screen the major active ingredients, key networks, and targets. Finally, molecular docking of key genes and their regulatory active ingredients was performed using AutoDockTools 1.5.6 software. Results. 38 active ingredients were collected, generating 89 cross-targets; quercetin, luteolin, β-sitosterol, and kaempferol were the main active ingredients of QGD acting on osteosarcoma, and major signaling pathways such as PI3K-Akt signaling pathway, MAPK signaling pathway, and IL-17 signaling pathway were observed. TP53, SRC, and ESR1 were identified as key proteins that docked well with their regulated compounds. Conclusion. QGD is effective against osteosarcoma through multicomponent, multitarget, and multipathway. This study was helpful for finding effective targets and compounds for osteosarcoma treatment.
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Liu, Shuyu, Xiaohong Hu, Xiaotian Fan, Ruiqi Jin, Wenqian Yang, Yifei Geng, and Jiarui Wu. "A Bioinformatics Research on Novel Mechanism of Compound Kushen Injection for Treating Breast Cancer by Network Pharmacology and Molecular Docking Verification." Evidence-Based Complementary and Alternative Medicine 2020 (August 11, 2020): 1–14. http://dx.doi.org/10.1155/2020/2758640.
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Compound Kushen injection (CKI) has been extensively used in treating breast cancer (BC). However, the molecular mechanism remains unclear. In this study, 16 active compounds of CKI were obtained from 3 articles for target prediction. Then, a compound-predicted target network and a compound-BC target network were conducted by Cytoscape 3.6.1. The gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on the DAVID database. The binding energy between the key targets of CKI and the active compounds was studied by molecular docking. As a result, 16 active compounds of CKI were identified, corresponding to 285 putative targets. The key targets of CKI for BC are HSD11B1, DPP4, MMP9, CDK1, MMP2, PTGS2, and CA14. The function enrichment analysis obtained 13 GO entries and 6 KEGG pathways, including bladder cancer, cancer pathways, chemical carcinogenesis, estrogen signaling pathway, TNF signaling pathway, and leukocyte transendothelial migration. The result of molecular docking indicated that DPP4 had strong binding activity with matrine, alicyclic protein, and sophoridine, and MMP9 had strong binding activity with adenine and sophoridine. In conclusion, the therapeutic effect of CKI on BC is based on the overall pharmacological effect formed by the combined effects of multiple components, multiple targets, and multiple pathways. This study provides a theoretical basis for further experimental research in the future.
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Xiao, Wen-Ping, Yan-Fang Yang, He-Zhen Wu, and Yi-yi Xiong. "Predicting the Mechanism of the Analgesic Property of Yanhusuo Based on Network Pharmacology." Natural Product Communications 14, no. 10 (October 2019): 1934578X1988307. http://dx.doi.org/10.1177/1934578x19883071.
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Yanhusuo (Corydalis Rhizoma) extracts are widely used for the treatment of pain and inflammation. The effects of Yanhusuo in pain assays were assessed in a few studies. However, there are few studies on its analgesic mechanism. In this paper, network pharmacology was used to explore the analgesic components of Yanhusuo and its analgesic mechanism. The active components of Yanhusuo were screened by TCMSP database, combined with literature data. PharmMapper and GeneCards databases were used for screening the analgesic targets of the components. The protein interaction network diagram was drawn by String database and Cytoscape software, the gene ontology and KEGG pathway analyses of the target were performed by DAVID database, and the component–target–pathway interaction network diagram was further drawn by Cytoscape3.6.1 software. System Dock Web Site verified the molecular docking among components and targets. Finally, an interaction network of the component–target–pathway of Yanhusuo was constructed, and the functions and pathways were analyzed for preliminarily investigating the mechanism of Yanhusuo in analgesia. The results showed that the active components of analgesic in Yanhusuo were Corynoline, 13-methylpalmatrubine, dehydrocorydaline, saulatine, 2,3,9,10-tetramethoxy-13-methyl-5,6-dihydroisoquinolino[2,1-b]isoquinolin-8-on-e, and Capaurine. The mechanisms were involved in metabolic pathways, PI3k-Akt signaling pathway, pathways in cancer, and so on. The top 3 targets were NOS3, glucose-6-phosphate dehydrogenase, and glucose-6-phosphate isomerase in components-target-pathways network, and they were all enriched in metabolic pathways. Meanwhile the molecular docking showed that there was a high binding activity between the 6 components and the important target proteins, as a further certification for the subsequent network analysis. This study reveals the relationship of the components, targets, and pathways of active components in Yanhusuo, and provides new ideas and methods for further research on the analgesic mechanism of Yanhusuo.
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Li, De-Hui, Yi-Fan Su, Chun-Xia Sun, Huan-Fang Fan, and Wei-Juan Gao. "A Network Pharmacology-Based Identification Study on the Mechanism of Xiao-Xu-Ming Decoction for Cerebral Ischemic Stroke." Evidence-Based Complementary and Alternative Medicine 2020 (October 19, 2020): 1–8. http://dx.doi.org/10.1155/2020/2507074.
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Objective. We used the network pharmacological analysis method to explore the mechanism of multicomponent, multitarget, and multiway actions of Xiao-Xu-Ming decoction (XXMD) for cerebral ischemic stroke (CIS), which provided a basis on the research of innovative drugs. Method. We used the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) to retrieve the active ingredients and targets of 12 herbs of XXMD; we used the Gene Expression Omnibus (GEO) database of the National Center for Biotechnology Information (NCBI) to screen for differentially expressed genes in CIS to obtain the disease targets of CIS and to intersect it with the action targets of XXMD, and then the target drug efficacy is obtained. We used Cytoscape 3.6 software to construct the drug-active ingredient-action target interaction network of XXMD to treat CIS and conduct protein-protein interaction (PPI) network and topology analysis. The action target Gene Ontology (GO) biological processes and metabolic pathways in Kyoto Encyclopedia of Genes and Genomes (KEGG) of XXMD to treat CIS were enrichment analyzed with R software. Result. We screened out 226 active ingredients and 3646 action targets for XXMD. Among them, XXMD to treat CIS has 144 active ingredients, 12 targets, and proteins in the core network of PPI having STAT3, HIF1A, etc. Pathway enrichment analysis was based on the GO and KEGG biological processes involved in active oxygen metabolism, smooth muscle cell proliferation, cytokine production, angiogenesis, redox coenzyme metabolism, and oxidative stress. The main action processes are significantly associated with CIS signal pathways involved in microRNAs, ovarian steroid hormones, NF-кB signaling pathway, Th17 cell differentiation pathway, HIF-1 signaling pathway, folic acid synthesis pathway, galactose metabolism, and fructose and mannose metabolism. Conclusion. This study initially clarified the main targets and pathways of XXMD in the treatment of CIS, which can lay the foundation for further research on its pharmacological effects.
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Shi, Minjuan, Bo Li, Qiuzhen Yuan, Xuefeng Gan, Xiao Ren, Shanshan Jiang, and Zhuo Liu. "Network Pharmacology-Based Approach to Investigate the Mechanisms of Mahai Capsules in the Treatment of Cardiovascular Diseases." Evidence-Based Complementary and Alternative Medicine 2020 (May 13, 2020): 1–15. http://dx.doi.org/10.1155/2020/9180982.
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Background. Mahai capsules (MHC) have been deemed to be an effective herb combination for treatment of cardiovascular diseases (CVD) development and improvement of the life quality of CVD patients. To systematically explore the mechanisms of MHC in CVD, a network pharmacology approach mainly comprising target prediction, network construction, biological process and pathway analysis, and related diseases was adopted in this study. Methods. We collected the bioactive compounds and potential targets of MHC through the TCMSP servers. Candidate targets related to CVD were collected from Therapeutic Targets Database and PharmGkb database and analyzed using ClueGO plugin in Cytoscape. KEGG pathway was enriched and analyzed through the EnrichR platform, and protein-protein interaction networks were calculated by STRING platform. The compound-target, target-disease, and compound-target-disease networks were constructed using Cytoscape. Results. A total of 303 targets of the 57 active ingredients in MHC were obtained. The network analysis showed that PTGS2, PTGS1, HSP90, Scn1a, estrogen receptor, calmodulin, and thrombin were identified as key targets of MHC in the treatment of CVD. The functional enrichment analysis indicated that MHC probably produced the therapeutic effects against CVD by synergistically regulating many biological pathways, such as PI3K-Akt, TNF, HIF-1, FoxO, apoptosis, calcium, T-cell receptor, VEGF, and NF-kappa B signaling pathway. Conclusions. In summary, the analysis of the complete profile of the pharmacological properties, as well as the elucidation of targets, networks, and pathways, can further illuminate that the underlying mechanisms of MHC in CVD might be strongly associated with its synergic regulation of inflammation, apoptosis, and immune function, and provide new clues for its future development of therapeutic strategies and basic research.
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Yao, Liangliang, Xuan Zhang, Chaoming Huang, Yi Cai, and Chunpeng (Craig) Wan. "The Effect of Citrus aurantium on Non-Small-Cell Lung Cancer: A Research Based on Network and Experimental Pharmacology." BioMed Research International 2023 (January 23, 2023): 1–17. http://dx.doi.org/10.1155/2023/6407588.
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Purpose. To screen the main active components of Citrus aurantium through a network pharmacology approach, construct a component-disease target network, explore its molecular mechanism for the treatment of non-small-cell lung cancer (NSCLC), and validate it experimentally. Methods. The active ingredients in Citrus aurantium and the targets of Citrus aurantium and NSCLC were collected through the Traditional Chinese Medicine Systematic Pharmacology Database and Analysis Platform (TCMSP), GeneCards, and OMIM databases. The protein interaction network was constructed using the STRING database, and the component-disease relationship network graph was analyzed using Cytoscape 3.9.1. The Metascape database can be used for GO and KEGG enrichment analyses. The Kaplan-Meier plotter was applied for overall survival analysis of key targets of Citrus aurantium in the treatment of NSCLC. Real-time PCR (RT-PCR) and Western blotting were used to determine the mRNA and protein levels of key targets of Citrus aurantium for the treatment of NSCLC. Results. Five active ingredients of Citrus aurantium were screened, and 54 potential targets for the treatment of NSCLC were found, of which the key ingredient was nobiletin and the key targets are TP53, CXCL8, ESR1, PPAR-α, and MMP9. GO and KEGG enrichment analyses indicated that the mechanism of nobiletin in treating NSCLC may be related to the regulation of cancer signaling pathway, phosphatidylinositol-3 kinase (PI3K)/protein kinase B (Akt) signaling pathway, lipid and atherosclerosis signaling pathway, and neurodegenerative signaling pathway. The experimental results showed that nobiletin could inhibit the proliferation of NSCLC cells and upregulate the levels of P53 and PPAR-α and suppress the expression of MMP9 ( P < 0.05 ). Conclusion. Citrus aurantium can participate in the treatment of NSCLC through multiple targets and pathways.
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Fabrizio, Federico Pio, Angelo Sparaneo, and Lucia Anna Muscarella. "NRF2 Regulation by Noncoding RNAs in Cancers: The Present Knowledge and the Way Forward." Cancers 12, no. 12 (December 3, 2020): 3621. http://dx.doi.org/10.3390/cancers12123621.
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Nuclear factor erythroid 2-related factor 2 (NRF2) is the key transcription factor triggered by oxidative stress that moves in cells of the antioxidant response element (ARE)-antioxidant gene network against reactive oxygen species (ROS) cellular damage. In tumors, the NRF2 pathway represents one of the most intriguing pathways that promotes chemo- and radioresistance of neoplastic cells and its activity is regulated by genetic and epigenetic mechanisms; some of these being poorly investigated in cancer. The noncoding RNA (ncRNA) network is governed by microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) and modulates a variety of cellular mechanisms linked to cancer onset and progression, both at transcriptional and post-transcriptional levels. In recent years, the scientific findings about the effects of ncRNA landscape variations on NRF2 machines are rapidly increasing and need to be continuously updated. Here, we review the latest knowledge about the link between NRF2 and ncRNA networks in cancer, thus focusing on their potential translational significance as key tumor biomarkers.
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Liu, Weixin, Yi Feng, Suhang Yu, Zhengqi Fan, Xinlei Li, Jiyuan Li, and Hengfu Yin. "The Flavonoid Biosynthesis Network in Plants." International Journal of Molecular Sciences 22, no. 23 (November 26, 2021): 12824. http://dx.doi.org/10.3390/ijms222312824.
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Flavonoids are an important class of secondary metabolites widely found in plants, contributing to plant growth and development and having prominent applications in food and medicine. The biosynthesis of flavonoids has long been the focus of intense research in plant biology. Flavonoids are derived from the phenylpropanoid metabolic pathway, and have a basic structure that comprises a C15 benzene ring structure of C6-C3-C6. Over recent decades, a considerable number of studies have been directed at elucidating the mechanisms involved in flavonoid biosynthesis in plants. In this review, we systematically summarize the flavonoid biosynthetic pathway. We further assemble an exhaustive map of flavonoid biosynthesis in plants comprising eight branches (stilbene, aurone, flavone, isoflavone, flavonol, phlobaphene, proanthocyanidin, and anthocyanin biosynthesis) and four important intermediate metabolites (chalcone, flavanone, dihydroflavonol, and leucoanthocyanidin). This review affords a comprehensive overview of the current knowledge regarding flavonoid biosynthesis, and provides the theoretical basis for further elucidating the pathways involved in the biosynthesis of flavonoids, which will aid in better understanding their functions and potential uses.
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Xie, Xuejiao, Xingyu Ma, Siyu Zeng, Wansi Tang, Liucheng Xiao, Chenggong Zhu, and Rong Yu. "Mechanisms of Berberine for the Treatment of Atherosclerosis Based on Network Pharmacology." Evidence-Based Complementary and Alternative Medicine 2020 (March 19, 2020): 1–11. http://dx.doi.org/10.1155/2020/3568756.
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Atherosclerosis is a common metabolic disease characterized by lipid metabolic disorder. The processes of atherosclerosis include endothelial dysfunction, new endothelial layer formation, lipid sediment, foam cell formation, plaque formation, and plaque burst. Owing to the adverse effects of first-line medications, it is urgent to discover new medications to deal with atherosclerosis. Berberine is one of the most promising natural products derived from traditional Chinese medicine. However, the panoramic mechanism of berberine against atherosclerosis has not been discovered clearly. In this study, we used network pharmacology to investigate the interaction between berberine and atherosclerosis. We identified potential targets related to berberine and atherosclerosis from several databases. A total of 31 and 331 putative targets for berberine and atherosclerosis were identified, respectively. Then, we constructed berberine and atherosclerosis targets with PPI data. Berberine targets network with PPI data had 3204 nodes and 79437 edges. Atherosclerosis targets network with PPI data had 5451 nodes and 130891 edges. Furthermore, we merged the two PPI networks and obtained the core PPI network from the merged PPI network. The core PPI network had 132 nodes and 3339 edges. At last, we performed functional enrichment analyses including GO and KEGG pathway analysis in David database. GO analysis indicated that the biological processes were correlated with G1/S transition of mitotic cells cycle. KEGG pathway analysis found that the pathways directly associated with berberine against atherosclerosis were cell cycle, ubiquitin mediated proteolysis, MAPK signaling pathway, and PI3K-Akt signaling pathway. After combining the results in context with the available treatments for atherosclerosis, we considered that berberine inhibited inflammation and cell proliferation in the treatment of atherosclerosis. Our study provided a valid theoretical foundation for future research.
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Liu, Mengting, Liying Tang, Xin Liu, Jing Fang, Hao Zhan, Hongwei Wu, and Hongjun Yang. "An Evidence-Based Review of Related Metabolites and Metabolic Network Research on Cerebral Ischemia." Oxidative Medicine and Cellular Longevity 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/9162074.
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In recent years, metabolomics analyses have been widely applied to cerebral ischemia research. This paper introduces the latest proceedings of metabolomics research on cerebral ischemia. The main techniques, models, animals, and biomarkers of cerebral ischemia will be discussed. With analysis help from the MBRole website and the KEGG database, the altered metabolites in rat cerebral ischemia were used for metabolic pathway enrichment analyses. Our results identify the main metabolic pathways that are related to cerebral ischemia and further construct a metabolic network. These results will provide useful information for elucidating the pathogenesis of cerebral ischemia, as well as the discovery of cerebral ischemia biomarkers.
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Dong, Zhen, Mengting Liu, Xianglin Zou, Wenqing Sun, Xiubin Liu, Jianguo Zeng, and Zihui Yang. "Integrating Network Pharmacology and Molecular Docking to Analyse the Potential Mechanism of action of Macleaya cordata (Willd.) R. Br. in the Treatment of Bovine Hoof Disease." Veterinary Sciences 9, no. 1 (December 30, 2021): 11. http://dx.doi.org/10.3390/vetsci9010011.
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Based on network pharmacological analysis and molecular docking techniques, the main components of M. cordata for the treatment of bovine relevant active compounds in M. cordata were searched for through previous research bases and literature databases, and then screened to identify candidate compounds based on physicochemical properties, pharmacokinetic parameters, bioavailability, and drug-like criteria. Target genes associated with hoof disease were obtained from the GeneCards database. Compound−target, compound−target−pathway−disease visualization networks, and protein−protein interaction (PPI) networks were constructed by Cytoscape. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed in R language. Molecular docking analysis was done using AutoDockTools. The visual network analysis showed that four active compounds, sanguinarine, chelerythrine, allocryptopine and protopine, were associated with the 10 target genes/proteins (SRC, MAPK3, MTOR, ESR1, PIK3CA, BCL2L1, JAK2, GSK3B, MAPK1, and AR) obtained from the screen. The enrichment analysis indicated that the cAMP, PI3K-Akt, and ErbB signaling pathways may be key signaling pathways in network pharmacology. The molecular docking results showed that sanguinarine, chelerythrine, allocryptopine, and protopine bound well to MAPK3 and JAK2. A comprehensive bioinformatics-based network topology strategy and molecular docking study has elucidated the multi-component synergistic mechanism of action of M. cordata in the treatment of bovine hoof disease, offering the possibility of developing M. cordata as a new source of drugs for hoof disease treatment.
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Huang, Dan, Yan Lv, Chuansen Lu, Bo Zhang, Zongjun Fu, and Yingliu Huang. "Mechanism of Rhizoma Coptidis in epilepsy with network pharmacology." Allergologia et Immunopathologia 50, no. 3 (May 1, 2022): 138–50. http://dx.doi.org/10.15586/aei.v50i3.489.
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Network pharmacology is a bioinformatics-based research strategy aimed at identifying drug actions and facilitating drug discovery. In this study, network pharmacology was used for exploring the anti-epileptic multi-target mechanism of Rhizoma Coptidis. The possible protein targets of Rhizoma Coptidis were predicted by constructing the pathway and network of drug targets. Then, the interaction of the main active components of Rhizoma Coptidis and predicted candidate targets were verified using molecular docking technology. Finally, nine active compounds were selected from Rhizoma Coptidis. A total of 68 targets associated with Rhizoma Coptidis treating epilepsy. The key targets were AKT1, IL6, VEGFA, and TP53. According to GO functional enrichment analysis, 289 items of biological process, 33 items of cellular component, and 55 items of molecular function were obtained. A total of 89 signaling pathways were identified through KEGG pathway enrichment analysis (P < 0.05), and HIF-1, TNF, and T-cell receptor signaling pathways were mainly related to epilepsy. Molecular docking showed quercetin and (R)-canadine combined well with the key targets. The active ingredient in Rhizoma Coptidis can regulate various signaling pathways, and have therapeutic effects on epilepsy.
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Oh, Ki-Kwang, Ye-Rin Choi, Haripriya Gupta, Raja Ganesan, Satya Priya Sharma, Sung-Min Won, Jin-Ju Jeong, et al. "Identification of Gut Microbiome Metabolites via Network Pharmacology Analysis in Treating Alcoholic Liver Disease." Current Issues in Molecular Biology 44, no. 7 (July 19, 2022): 3253–66. http://dx.doi.org/10.3390/cimb44070224.
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Alcoholic liver disease (ALD) is linked to a broad spectrum of diseases, including diabetes, hypertension, atherosclerosis, and even liver carcinoma. The ALD spectrum includes alcoholic fatty liver disease (AFLD), alcoholic hepatitis, and cirrhosis. Most recently, some reports demonstrated that the pathogenesis of ALD is strongly associated with metabolites of human microbiota. AFLD was the onset of disease among ALDs, the initial cause of which is alcohol consumption. Thus, we analyzed the significant metabolites of microbiota against AFLD via the network pharmacology concept. The metabolites from microbiota were retrieved by the gutMGene database; sequentially, AFLD targets were identified by public databases (DisGeNET, OMIM). The final targets were utilized for protein–protein interaction (PPI) networks and signaling pathway analyses. Then, we performed a molecular docking test (MDT) to verify the affinity between metabolite(s) and target(s) utilizing the Autodock 1.5.6 tool. From a holistic viewpoint, we integrated the relationships of microbiota-signaling pathways-targets-metabolites (MSTM) using the R Package. We identified the uppermost six key targets (TLR4, RELA, IL6, PPARG, COX-2, and CYP1A2) against AFLD. The PPI network analysis revealed that TLR4, RELA, IL6, PPARG, and COX-2 had equivalent degrees of value (4); however, CYP1A2 had no associations with the other targets. The bubble chart showed that the PI3K-Akt signaling pathway in nine signaling pathways might be the most significant mechanism with antagonistic functions in the treatment of AFLD. The MDT confirmed that Icaritin is a promising agent to bind stably to RELA (known as NF-Κb). In parallel, Bacterium MRG-PMF-1, the PI3K-Akt signaling pathway, RELA, and Icaritin were the most significant components against AFLD in MSTM networks. In conclusion, we showed that the Icaritin–RELA complex on the PI3K-Akt signaling pathway by bacterial MRG-PMF-1 might have promising therapeutic effects against AFLD, providing crucial evidence for further research.
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Wang, Ming, Qi Wang, Yongqiang Du, and Xiansheng Zhang. "Network Pharmacology-Based Strategy to Investigate Pharmacological Mechanisms of Qiaoshao Formula for Treatment of Premature Ejaculation." Evidence-Based Complementary and Alternative Medicine 2020 (November 12, 2020): 1–13. http://dx.doi.org/10.1155/2020/1418634.
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Background. Qiaoshao (QS) formula, a traditional Chinese medicine (TCM) comprising seven herbs, has been clinically proven to have a favorable treatment effect on premature ejaculation (PE). However, its underlying pharmacological mechanisms in the treatment of PE need to be further clarified. Methods. In the present study, a network pharmacology-based strategy was adopted. The active compounds of QS formula were obtained from the Chinese medicine database, and the potential targets of these compounds were collected from the DrugBank database to construct compound-compound targets network. PE-related targets were identified from human disease databases and used to construct the protein-protein interaction (PPI) networks. Compound-disease target PPI network was constructed by merging the PPI network of disease-targets and compound-targets. Cluster and enrichment analyses were performed on the PPI network of disease targets and compound-disease targets. The influence of QS formula on serum 5-HT, NO, oxytocin, and thyroid hormones of PE patients was verified. Results. Four primary pharmacological networks of QS formula were constructed, including the compound-compound targets network, PPI network of PE-related targets and compound-disease targets, and the QS-PE mechanism network. The module and pathway enrichment analyses revealed that the QS formula had the potential to affect varieties of biological process and pathways, such as nitric oxide biosynthetic process, oxytocin, thyroid hormone, TNF, PI3K-Akt, and the HIF-1 signaling pathway, that play an important role in the pathogenesis of PE. Meanwhile, the QS formula has been clinically confirmed to regulate the serum level of 5-HT, NO, oxytocin, and TT in PE patients. Conclusion. This study preliminarily discovered the potential targets and pathways of QS formula in the treatment of PE, which laid a good foundation for further experimental research.
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Ellis, Peter G., and Kathleen Lokay. "Using a pathways process to ensure measurable evidence-based standardized care in a large cancer network." Journal of Clinical Oncology 30, no. 34_suppl (December 1, 2012): 252. http://dx.doi.org/10.1200/jco.2012.30.34_suppl.252.
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252 Background: UPMC CancerCenter includes 40 sites of services in a 100 mile radius in Western Pennsylvania. Consistency and quality of care are critical to such a diverse network. In addition, the UPMC mission includes accrual to clinical trials. To meet these challenges, UPMC developed the Via Oncology Pathways. The program has served UPMC well for over seven years to date and is now a key foundation for UPMC’s overall healthcare reform strategy for quality and accountable care. Methods: Treatment algorithms were developed for 90% of cancer types by establishing committees of academic and community specialists. These committees interpret the literature and define the most efficacious, least toxic, and economically efficient treatment regimens appropriate for highly specific disease presentations (e.g., node +, er-, her2 +, PS 0-1). Clinical trials are also imbedded into the algorithms. Quarterly, these algorithms are reviewed by the committees to assess relevance, review network feedback, add newly available trials and address emerging data. Equally important to clinical content is its presentation to the practicing physician in a manner that allows real time usage and adds value to physician workflow. This is accomplished with a web portal that presents the individual pathways status through the physician’s daily schedule. Results: Over 120 oncologists at UPMC use Via Oncology Pathways in their daily practice. In 2011, UPMC physicians confirmed a pathways status for 94% of their patient visits (195,000) and achieved an On Pathway rate of 82% for their 18,000 treatment decisions. The database also includes patient presentations, reasons for going off pathway and reasons for not accruing to clinical trial. Lower hospitalization rates and mandated adoption of personalized medicine were also observed. Conclusions: When appropriately developed and implemented, clinical pathways are a solution to improving the quality and cost effectiveness of cancer care by enhancing physician decision-making, standardizing care and ensuring access to evidence-based personalized medicine. We continue to expand the scope of our pathways to include diagnostic studies, surveillance protocols and end of life prompts.
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Schweber, Sarah J., Alicia G. Rodriguez-LaRocca, Valerie Calvert, Emanuel Petricoin, Susan Band Horwitz, Eleni Andreopoulou, and Hayley M. McDaid. "Protein pathway activation mapping guided biomarker development to identify optimal combinations of MEK inhibitor with PI3K/mTOR pathway inhibitors for the treatment of triple-negative breast cancer." Journal of Clinical Oncology 31, no. 15_suppl (May 20, 2013): 2612. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.2612.
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2612 Background: Activated MAPK and PI3K pathway signaling are associated with poor prognosis in triple negative breast cancer (TNBC). Although some TNBC cell models are sensitive to MEK inhibition, feedback activation of the PI3K pathway mediates resistance. Thus, suppression of both arms of the MAPK/PI3K/mTOR network is a rational approach to targeting TNBC. Here we explore the anti-tumor efficacy of combinations of MEK inhibitor with PI3K, AKT, or mTOR inhibitors with a focus on biomarker development. Methods: Combinations of the MEK inhibitor PD-0325901 with the PI3K inhibitor GDC-0941, AKT inhibitor MK-2206, dual mTORC 1/2 inhibitor Torin 1, or the rapalog temsirolimus were evaluated in TNBC cell lines. Synergy was assessed using the combination index method of Chou and Talalay. We utilized reverse-phase protein array to map the signaling architecture of the treated lines to verify target suppression and identify pharmacodynamic biomarkers. Results: All combinations demonstrated synergy that was mediated by both suppression of proliferation and cell death in a dose-dependent manner. Cell death was delayed, peaking at least 96 hours post-dosing, and was associated with sustained suppression of target proteins in both pathways, including pERKT202/Y204, pS6rpS235/236, p4EBP-1S65, and pPRAS40T246. However, suppression of pAKT (at T308 or S473) was variable and not consistently required for cell death. Pathway mapping identified a protein network ‘signature’ specific to all combination therapies that emerged at 72 hours and was associated with cell death. Thus, all combinations appear to share common downstream effectors. All combinations showed promising efficacy and will be evaluated in a human-in-mouse model of TNBC. Conclusions: These data support therapeutic strategies for TNBC that simultaneously inhibit both arms of the MAPK/PI3K/mTOR signaling network. For continued biomarker development, we stress the importance of studying the delayed effects of combination therapy. This strategy coupled with a protein network based approach uncovered a unique functional signaling ‘signature’.
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Kohli, Manish, Liewei Wang, Scott Dehm, David W. Hillman, Hugues Sicotte, Michael Gormley, Vipul Bhargava, et al. "Genome-wide analysis of metastases to reveal association of pathway activation with abiraterone acetate/prednisone (AA/P) primary resistance and cell cycle proliferation pathway activation with response duration in metastatic castrate resistant prostate cancer (mCRPC)." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): 5053. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.5053.
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5053 Background: Genomic aberrations associated with resistance/response to AA/P are not known. In a prospective study we assessed whole-exome/RNA-seq based aberrations in CRPC metastatic biopsies for identifying molecular markers associated with primary resistance and response duration. Methods: Sequencing of metastatic biopsies was performed for analyzing molecular aberrations that predict primary resistance (defined as progression at 12-weeks of therapy (non-responders) using PSA, RECIST, bone scan criteria per PCWG2). Gene network analysis was performed in genes mutated more frequently in non-responders and in genes differentially expressed between non-responders and responders using a “risk ratio” (RR) of ≥2. Cox regression models with multiple gene network pathways were used for determining association with time to treatment change (TTTC). Results: Of 92 enrolled pts 82 had complete whole-exome, RNA-seq & 12-week outcome data available for analysis. At 12-weeks 33/82 had progressed. Using a RR of ≥2, 113 genes were more frequently mutated in non-responders & 292 in responders. In non-responders, gene network analysis revealed frequent mutations in Wnt/β-catenin pathway genes; frequent deletion of negative regulators of Wnt pathway ( DKK4, SFRP2, LRP6). Gene expression analyses revealed significantly reduced expression levels of Wnt/β-catenin pathway inhibitors and increased expression levels of cell cycle proliferation (CCP) genes in non-responders. Median study follow up was 32 months during which time 58/82 pts progressed and switched treatments. Median TTTC was 10.1 months (IQR:4.4-24.1). In multivariate analysis CCP scores of ≥50 predicted shorter TTTC (HR = 2.11, 95% CI: 1.17-3.80; p = 0.01). Conclusions: In metastases Wnt/β-catenin pathway activation is associated with primary AA/P resistance and increased CCP with acquired drug resistance. These findings offer molecular based predictive biomarkers in CRPC stage treatment. Clinical trial information: NCT#01953640.
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Yi, Wanwan, Jin Liu, Shuping Qu, Hengwei Fan, and Zhongwei Lv. "An 8 miRNA-Based Risk Score System for Predicting the Prognosis of Patients With Papillary Thyroid Cancer." Technology in Cancer Research & Treatment 19 (January 1, 2020): 153303382096559. http://dx.doi.org/10.1177/1533033820965594.
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Background: Dysregulation of microRNAs (miRNAs) in papillary thyroid cancer (PTC) might influence prognosis of PTC. This study is aimed to develop a risk score system for predicting prognosis of PTC. Methods: The miRNA and gene expression profiles of PTC were obtained from The Cancer Genome Atlas database. PTC samples were randomly separated into training set (n = 248) and validation set (n = 248). The differentially expressed miRNAs (DE-miRNAs) in the training set were screened using limma package. The independent prognosis-associated DE-miRNAs were identified for building a risk score system. Risk score of PTC samples in the training set was calculated and samples were divided into high risk group and low risk group. Kaplan-Meier curves and receiver operating characteristic (ROC) curve were used to assess the accuracy of the risk score system in the training set, validation set and entire set. Finally, a miRNA-gene regulatory network was visualized by Cytoscape software, followed by enrichment analysis. Results: Totally, 162 DE-miRNAs between tumor and control groups in the training set were identified. An 8 independent prognosis-associated DE-miRNAs, (including miR-1179, miR-133b, miR-3194, miR-3912, miR-548j, miR-6720, miR-6734, and miR-6843) based risk score system was developed. The area under ROC curve in the training set, validation set and entire set was all above 0.93. A miRNA-gene regulatory network involving the 8 DE-miRNAs were built and functional enrichment analysis suggested the genes in the network were significantly enriched into 13 pathways, including calcium signaling pathway and hedgehog signaling pathway. Conclusion: The risk score system developed this study might be used for predicting the prognosis of PTC. Besides, the 8 miRNAs might affect the prognosis of PTC via hedgehog signaling pathway and calcium signaling pathway.
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Zhang, Lunzhong, Shu Han, Manli Zhao, Runshun Zhang, Xuebin Zhang, Jing Zhang, Xiaoqing Liu, et al. "Using the Symptom Patient Similarity Network to Explore the Difference between the Chinese and Western Medicine Pathways of Ischemic Stroke and its Comorbidities." Evidence-Based Complementary and Alternative Medicine 2021 (December 1, 2021): 1–12. http://dx.doi.org/10.1155/2021/4961738.
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Background and Objectives. The development of network medicine provides new opportunities for disease research. Ischemic stroke has a high incidence, disability, and recurrence rate, and one of the reasons is that it is often accompanied by other complex diseases, including risk factors, complications, and comorbidities. Network medicine was used to try to analyze the characteristics of IS-related diseases and find out the differences in genetic pathways between Chinese herbs and Western drugs. Methods. Individualized treatment of traditional Chinese medicine (TCM) provides a theoretical basis for the study of the personalized classification of complex diseases. Utilizing the TCM clinical electronic medical records (EMRs) of 7170 in patients with IS, a patient similarity network (PSN) with shared symptoms was constructed. Next, patient subgroups were identified using community detection methods and enrichment analyses were performed. Finally, genetic data of symptoms, herbs, and drugs were used for pathway and GO analysis to explore the characteristics of pathways of subgroups and to compare the similarities and differences in genetic pathways of herbs and drugs from the perspective of molecular pathways of symptoms. Results. We identified 34 patient modules from the PSN, of which 7 modules include 98.48% of the whole cases. The 7 patient subgroups have their own characteristics of risk factors, complications, and comorbidities and the underlying genetic pathways of symptoms, drugs, and herbs. Each subgroup has the largest number of herb pathways. For specific symptom pathways, the number of herb pathways is more than that of drugs. Conclusion. The research of disease classification based on community detection of symptom-shared patient networks is practical; the common molecular pathway of symptoms and herbs reflects the rationality of TCM herbs on symptoms and the wide range of therapeutic targets.
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Meric-Bernstam, Funda, and Ana Maria Gonzalez-Angulo. "Targeting the mTOR Signaling Network for Cancer Therapy." Journal of Clinical Oncology 27, no. 13 (May 1, 2009): 2278–87. http://dx.doi.org/10.1200/jco.2008.20.0766.
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The serine-threonine kinase mammalian target of rapamycin (mTOR) plays a major role in the regulation of protein translation, cell growth, and metabolism. Alterations of the mTOR signaling pathway are common in cancer, and thus mTOR is being actively pursued as a therapeutic target. Rapamycin and its analogs (rapalogs) have proven effective as anticancer agents in a broad range of preclinical models. Clinical trials using rapalogs have demonstrated important clinical benefits in several cancer types; however, objective response rates achieved with single-agent therapy have been modest. Rapalogs may be more effective in combination with other anticancer agents, including chemotherapy and targeted therapies. It is increasingly apparent that the mTOR signaling network is quite complex, and rapamycin treatment leads to different signaling responses in different cell types. A better understanding of mTOR signaling, the mechanism of action of rapamycin, and the identification of biomarkers of response will lead to more optimal targeting of this pathway for cancer therapy.
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Zou, Menglong, and Ying Zhu. "Exploring the Molecular Mechanism of Tong Xie Yao Fang in Treating Ulcerative Colitis Using Network Pharmacology and Molecular Docking." Evidence-Based Complementary and Alternative Medicine 2022 (September 27, 2022): 1–14. http://dx.doi.org/10.1155/2022/8141443.
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Objective. The purpose of this study was to investigate the mechanisms of action of Tong Xie Yao Fang (TXYF) against ulcerative colitis (UC) by employing a network pharmacology approach. Methods. The network pharmacology approach, including screening of the active ingredients and targets, construction of the active ingredient-drug target network, the active ingredient-diseasetarget network, the protein–protein interaction (PPI) network, enrichment analyses, molecular docking, and targets validation, was used to explore the mechanisms of TXYF against UC. Results. 34 active ingredients and 129 and 772 targets of TXYF and UC, respectively, were identified. The intersection of the active ingredient-drug target network, the active ingredient-disease target network, and the PPI network suggested that kaempferol, beta-sitosterol, wogonin, and naringenin were the core ingredients and prostaglandin-endoperoxide synthase 2 (PTGS2) was the core target. Enrichment analyses showed that regulation of exogenous protein binding and other functions were of great significance. Nuclear factor-kappa B (NF-κB) signaling pathway, interleukin-17 (IL-17) signaling pathway, and tumor necrosis factor (TNF) signaling pathway were important pathways. Results of molecular docking indicated that the core ingredients and the target molecule had strong binding affinities. We have validated the high levels of expression of PTGS2 in UC by analyzing three additional datasets from the Gene Expression Omnibus (GEO) database. Conclusions. There are multiple ingredients, targets, and pathways involved in TXYF’s effectiveness against UC, and these findings will promote further research and clinical applications.
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LIU, BING, and P. S. THIAGARAJAN. "MODELING AND ANALYSIS OF BIOPATHWAYS DYNAMICS." Journal of Bioinformatics and Computational Biology 10, no. 04 (July 23, 2012): 1231001. http://dx.doi.org/10.1142/s0219720012310014.
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Cellular processes are governed and coordinated by a multitude of biopathways. A pathway can be viewed as a complex network of biochemical reactions. The dynamics of this network largely determines the functioning of the pathway. Hence the modeling and analysis of biochemical networks dynamics is an important problem and is an active area of research. Here we review quantitative models of biochemical networks based on ordinary differential equations (ODEs). We mainly focus on the parameter estimation and sensitivity analysis problems and survey the current methods for tackling them. In this context we also highlight a recently developed probabilistic approximation technique using which these two problems can be considerably simplified.