Journal articles: 'Integrative multiomics'

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Rotroff, Daniel M., and Alison A. Motsinger-Reif. "Embracing Integrative Multiomics Approaches." International Journal of Genomics 2016 (2016): 1–5. http://dx.doi.org/10.1155/2016/1715985.

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As “-omics” data technology advances and becomes more readily accessible to address complex biological questions, increasing amount of cross “-omics” dataset is inspiring the use and development of integrative bioinformatics analysis. In the current review, we discuss multiple options for integrating data across “-omes” for a range of study designs. We discuss established methods for such analysis and point the reader to in-depth discussions for the various topics. Additionally, we discuss challenges and new directions in the area.

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Lee, Jeongwoo, Do Young Hyeon, and Daehee Hwang. "Single-cell multiomics: technologies and data analysis methods." Experimental & Molecular Medicine 52, no. 9 (September 2020): 1428–42. http://dx.doi.org/10.1038/s12276-020-0420-2.

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Abstract Advances in single-cell isolation and barcoding technologies offer unprecedented opportunities to profile DNA, mRNA, and proteins at a single-cell resolution. Recently, bulk multiomics analyses, such as multidimensional genomic and proteogenomic analyses, have proven beneficial for obtaining a comprehensive understanding of cellular events. This benefit has facilitated the development of single-cell multiomics analysis, which enables cell type-specific gene regulation to be examined. The cardinal features of single-cell multiomics analysis include (1) technologies for single-cell isolation, barcoding, and sequencing to measure multiple types of molecules from individual cells and (2) the integrative analysis of molecules to characterize cell types and their functions regarding pathophysiological processes based on molecular signatures. Here, we summarize the technologies for single-cell multiomics analyses (mRNA-genome, mRNA-DNA methylation, mRNA-chromatin accessibility, and mRNA-protein) as well as the methods for the integrative analysis of single-cell multiomics data.

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Dai, Ling-Yun, Rong Zhu, and Juan Wang. "Joint Nonnegative Matrix Factorization Based on Sparse and Graph Laplacian Regularization for Clustering and Co-Differential Expression Genes Analysis." Complexity 2020 (November 16, 2020): 1–10. http://dx.doi.org/10.1155/2020/3917812.

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The explosion of multiomics data poses new challenges to existing data mining methods. Joint analysis of multiomics data can make the best of the complementary information that is provided by different types of data. Therefore, they can more accurately explore the biological mechanism of diseases. In this article, two forms of joint nonnegative matrix factorization based on the sparse and graph Laplacian regularization (SG-jNMF) method are proposed. In the method, the graph regularization constraint can preserve the local geometric structure of data. L 2,1 -norm regularization can enhance the sparsity among the rows and remove redundant features in the data. First, SG-jNMF1 projects multiomics data into a common subspace and applies the multiomics fusion characteristic matrix to mine the important information closely related to diseases. Second, multiomics data of the same disease are mapped into the common sample space by SG-jNMF2, and the cluster structures are detected clearly. Experimental results show that SG-jNMF can achieve significant improvement in sample clustering compared with existing joint analysis frameworks. SG-jNMF also effectively integrates multiomics data to identify co-differentially expressed genes (Co-DEGs). SG-jNMF provides an efficient integrative analysis method for mining the biological information hidden in heterogeneous multiomics data.

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Wang, Jinkai. "Integrative analyses of transcriptome data reveal the mechanisms of post-transcriptional regulation." Briefings in Functional Genomics 20, no. 4 (February 22, 2021): 207–12. http://dx.doi.org/10.1093/bfgp/elab004.

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Abstract Post-transcriptional processing of RNAs plays important roles in a variety of physiological and pathological processes. These processes can be precisely controlled by a series of RNA binding proteins and cotranscriptionally regulated by transcription factors as well as histone modifications. With the rapid development of high-throughput sequencing techniques, multiomics data have been broadly used to study the mechanisms underlying the important biological processes. However, how to use these high-throughput sequencing data to elucidate the fundamental regulatory roles of post-transcriptional processes is still of great challenge. This review summarizes the regulatory mechanisms of post-transcriptional processes and the general principles and approaches to dissect these mechanisms by integrating multiomics data as well as public resources.

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He, Yong, Hao Chen, Hao Sun, Jiadong Ji, Yufeng Shi, Xinsheng Zhang, and Lei Liu. "High‐dimensional integrative copula discriminant analysis for multiomics data." Statistics in Medicine 39, no. 30 (October 15, 2020): 4869–84. http://dx.doi.org/10.1002/sim.8758.

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Bisht, Vartika, Katrina Nash, Yuanwei Xu, Prasoon Agarwal, Sofie Bosch, Georgios V. Gkoutos, and Animesh Acharjee. "Integration of the Microbiome, Metabolome and Transcriptomics Data Identified Novel Metabolic Pathway Regulation in Colorectal Cancer." International Journal of Molecular Sciences 22, no. 11 (May 28, 2021): 5763. http://dx.doi.org/10.3390/ijms22115763.

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Integrative multiomics data analysis provides a unique opportunity for the mechanistic understanding of colorectal cancer (CRC) in addition to the identification of potential novel therapeutic targets. In this study, we used public omics data sets to investigate potential associations between microbiome, metabolome, bulk transcriptomics and single cell RNA sequencing datasets. We identified multiple potential interactions, for example 5-aminovalerate interacting with Adlercreutzia; cholesteryl ester interacting with bacterial genera Staphylococcus, Blautia and Roseburia. Using public single cell and bulk RNA sequencing, we identified 17 overlapping genes involved in epithelial cell pathways, with particular significance of the oxidative phosphorylation pathway and the ACAT1 gene that indirectly regulates the esterification of cholesterol. These findings demonstrate that the integration of multiomics data sets from diverse populations can help us in untangling the colorectal cancer pathogenesis as well as postulate the disease pathology mechanisms and therapeutic targets.

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Lin, Dan‐Yu, Donglin Zeng, and David Couper. "A general framework for integrative analysis of incomplete multiomics data." Genetic Epidemiology 44, no. 7 (July 21, 2020): 646–64. http://dx.doi.org/10.1002/gepi.22328.

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Wang, Jun, Peng Chen, Mingyang Su, Guocheng Zhong, Shasha Zhang, and Deming Gou. "Integrative Modeling of Multiomics Data for Predicting Tumor Mutation Burden in Patients with Lung Cancer." BioMed Research International 2022 (January 20, 2022): 1–14. http://dx.doi.org/10.1155/2022/2698190.

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Immunotherapy has been widely used in the treatment of lung cancer, and one of the most effective biomarkers for the prognosis of immunotherapy currently is tumor mutation burden (TMB). Although whole-exome sequencing (WES) could be utilized to assess TMB, several problems prevent its routine clinical application. To develop a simplified TMB prediction model, patients with lung adenocarcinoma (LUAD) in The Cancer Genome Atlas (TCGA) were randomly split into training and validation cohorts and categorized into the TMB-high (TMB-H) and TMB-low (TMB-L) groups, respectively. Based on the 610 differentially expressed genes, 50 differentially expressed miRNAs and 58 differentially methylated CpG sites between TMB-H and TMB-L patients, we constructed 4 predictive signatures and established TMB prediction model through machine learning methods that integrating the expression or methylation profiles of 7 genes, 7 miRNAs, and 6 CpG sites. The multiomics model exhibited excellent performance in predicting TMB with the area under curve (AUC) of 0.911 in the training cohort and 0.859 in the validation cohort. Besides, the significant correlation between the multiomics model score and TMB was observed. In summary, we developed a prognostic TMB prediction model by integrating multiomics data in patients with LUAD, which might facilitate the further development of quantitative real time-polymerase chain reaction- (qRT-PCR-) based TMB prediction assay.

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Du, Yinhao, Kun Fan, Xi Lu, and Cen Wu. "Integrating Multi–Omics Data for Gene-Environment Interactions." BioTech 10, no. 1 (January 29, 2021): 3. http://dx.doi.org/10.3390/biotech10010003.

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Gene-environment (G×E) interaction is critical for understanding the genetic basis of complex disease beyond genetic and environment main effects. In addition to existing tools for interaction studies, penalized variable selection emerges as a promising alternative for dissecting G×E interactions. Despite the success, variable selection is limited in terms of accounting for multidimensional measurements. Published variable selection methods cannot accommodate structured sparsity in the framework of integrating multiomics data for disease outcomes. In this paper, we have developed a novel variable selection method in order to integrate multi-omics measurements in G×E interaction studies. Extensive studies have already revealed that analyzing omics data across multi-platforms is not only sensible biologically, but also resulting in improved identification and prediction performance. Our integrative model can efficiently pinpoint important regulators of gene expressions through sparse dimensionality reduction, and link the disease outcomes to multiple effects in the integrative G×E studies through accommodating a sparse bi-level structure. The simulation studies show the integrative model leads to better identification of G×E interactions and regulators than alternative methods. In two G×E lung cancer studies with high dimensional multi-omics data, the integrative model leads to an improved prediction and findings with important biological implications.

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Wang, Biqi, Kathryn L. Lunetta, Josée Dupuis, Steven A. Lubitz, Ludovic Trinquart, Lixia Yao, Patrick T. Ellinor, Emelia J. Benjamin, and Honghuang Lin. "Integrative Omics Approach to Identifying Genes Associated With Atrial Fibrillation." Circulation Research 126, no. 3 (January 31, 2020): 350–60. http://dx.doi.org/10.1161/circresaha.119.315179.

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Rationale: GWAS (Genome-Wide Association Studies) have identified hundreds of genetic loci associated with atrial fibrillation (AF). However, these loci explain only a small proportion of AF heritability. Objective: To develop an approach to identify additional AF-related genes by integrating multiple omics data. Methods and Results: Three types of omics data were integrated: (1) summary statistics from the AFGen 2017 GWAS; (2) a whole blood EWAS (Epigenome-Wide Association Study) of AF; and (3) a whole blood TWAS (Transcriptome-Wide Association Study) of AF. The variant-level GWAS results were collapsed into gene-level associations using fast set-based association analysis. The CpG-level EWAS results were also collapsed into gene-level associations by an adapted SNP-set Kernel Association Test approach. Both GWAS and EWAS gene-based associations were then meta-analyzed with TWAS using a fixed-effects model weighted by the sample size of each data set. A tissue-specific network was subsequently constructed using the NetWAS (Network-Wide Association Study). The identified genes were then compared with the AFGen 2018 GWAS that contained more than triple the number of AF cases compared with AFGen 2017 GWAS. We observed that the multiomics approach identified many more relevant AF-related genes than using AFGen 2018 GWAS alone (1931 versus 206 genes). Many of these genes are involved in the development and regulation of heart- and muscle-related biological processes. Moreover, the gene set identified by multiomics approach explained much more AF variance than those identified by GWAS alone (10.4% versus 3.5%). Conclusions: We developed a strategy to integrate multiple omics data to identify AF-related genes. Our integrative approach may be useful to improve the power of traditional GWAS, which might be particularly useful for rare traits and diseases with limited sample size.

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Pak, Kyoungjune, Sae-Ock Oh, Tae Sik Goh, Hye Jin Heo, Myoung-Eun Han, Dae Cheon Jeong, Chi-Seung Lee, et al. "A User-Friendly, Web-Based Integrative Tool (ESurv) for Survival Analysis: Development and Validation Study." Journal of Medical Internet Research 22, no. 5 (May 5, 2020): e16084. http://dx.doi.org/10.2196/16084.

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Background Prognostic genes or gene signatures have been widely used to predict patient survival and aid in making decisions pertaining to therapeutic actions. Although some web-based survival analysis tools have been developed, they have several limitations. Objective Taking these limitations into account, we developed ESurv (Easy, Effective, and Excellent Survival analysis tool), a web-based tool that can perform advanced survival analyses using user-derived data or data from The Cancer Genome Atlas (TCGA). Users can conduct univariate analyses and grouped variable selections using multiomics data from TCGA. Methods We used R to code survival analyses based on multiomics data from TCGA. To perform these analyses, we excluded patients and genes that had insufficient information. Clinical variables were classified as 0 and 1 when there were two categories (for example, chemotherapy: no or yes), and dummy variables were used where features had 3 or more outcomes (for example, with respect to laterality: right, left, or bilateral). Results Through univariate analyses, ESurv can identify the prognostic significance for single genes using the survival curve (median or optimal cutoff), area under the curve (AUC) with C statistics, and receiver operating characteristics (ROC). Users can obtain prognostic variable signatures based on multiomics data from clinical variables or grouped variable selections (lasso, elastic net regularization, and network-regularized high-dimensional Cox-regression) and select the same outputs as above. In addition, users can create custom gene signatures for specific cancers using various genes of interest. One of the most important functions of ESurv is that users can perform all survival analyses using their own data. Conclusions Using advanced statistical techniques suitable for high-dimensional data, including genetic data, and integrated survival analysis, ESurv overcomes the limitations of previous web-based tools and will help biomedical researchers easily perform complex survival analyses.

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Reilly, Muredach P., and Karin E. Bornfeldt. "Integrative Multiomics Approaches for Discovery of New Drug Targets for Cardiovascular Disease." Circulation 143, no. 25 (June 22, 2021): 2471–74. http://dx.doi.org/10.1161/circulationaha.121.054900.

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Ge, Siqi, Youxin Wang, Manshu Song, Xingang Li, Xinwei Yu, Hao Wang, Jing Wang, Qiang Zeng, and Wei Wang. "Type 2 Diabetes Mellitus: Integrative Analysis of Multiomics Data for Biomarker Discovery." OMICS: A Journal of Integrative Biology 22, no. 7 (July 2018): 514–23. http://dx.doi.org/10.1089/omi.2018.0053.

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Yamaguchi, Takefumi, Kazunari Higa, Yukari Yagi-Yaguchi, Koji Ueda, Hisashi Noma, Shinsuke Shibata, Toshihiro Nagai, et al. "Pathological processes in aqueous humor due to iris atrophy predispose to early corneal graft failure in humans and mice." Science Advances 6, no. 20 (May 2020): eaaz5195. http://dx.doi.org/10.1126/sciadv.aaz5195.

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Corneal endothelial cell (CEnC) loss after corneal transplantation is the major cause of graft failure and remains a clinically relevant challenge to overcome. Accumulated knowledge derived from long-term clinical outcomes suggested that elevated protein levels in the aqueous humor are associated with CEnC loss. However, the full spectrum of driver proteins and molecular processes remains to be determined. Here, we defined the somatic microenvironmental landscape and cellular response across human aqueous humor in samples with poor corneal transplantation clinical outcomes using multiomics analyses and clarified specific driver alterations, including complement activation and disturbed energy homeostasis. These driver alterations were also confirmed in aqueous humor from a novel murine model that spontaneously develops iris atrophy, leading to CEnC loss. The application of the integrative multiomics performed in human samples to the novel murine model will help the development of therapeutic modalities for patients with CEnC loss after corneal transplantation.

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Tan, Yao, Liming Wang, Jian Gao, Junhong Ma, Haiyang Yu, Yi Zhang, Tao Wang, and Lifeng Han. "Multiomics Integrative Analysis for Discovering the Potential Mechanism of Dioscin against Hyperuricemia Mice." Journal of Proteome Research 20, no. 1 (October 27, 2020): 645–60. http://dx.doi.org/10.1021/acs.jproteome.0c00584.

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Ma, Yawen, and Zhuo Xi. "Integrated Analysis of Multiomics Data Identified Molecular Subtypes and Oxidative Stress-Related Prognostic Biomarkers in Glioblastoma Multiforme." Oxidative Medicine and Cellular Longevity 2022 (September 22, 2022): 1–15. http://dx.doi.org/10.1155/2022/9993319.

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Glioblastoma multiforme (GBM) is a glioma in IV stage, which is one of the most common primary malignant brain tumors in adults. GBM has the characters of high invasiveness, high recurrence rate, and low survival rate and with a poor prognosis. GBM implicates various genetic changes and epigenetic and gene transcription disorders, which are crucial in developing GBM. With the progression and enhancement of high-throughput sequencing technologies, the acquirement and administering approaches of diverse biological omics data on distinctive levels are developing more advanced. However, the research of GBM with multiomics remains largely unknown. We identified GBM-related molecular subtypes by integrated multiomics data and exploring the connections of gene copy number variation (CNV) and methylation gene (MET) change data. The expression of CNV and MET genes was examined through cluster integration analysis. The present study confirmed three clusters (iC1, iC2, and iC3) with distinctive prognosis and molecule peculiarities. We also recognized three oxidative stress protecting molecules (OSMR, IGFBP6, and MYBPH) by contrasting gene expression, MET, and CNV in the three subtypes. OSMR, IGFBP6, and MYBPH were differentially expressed in the clusters, suggesting they might be recognized as characteristic markers for the three clusters in GBM. Through integrative investigation of genomics, epigenomics, and transcriptomics, we offer novel visions into the multilayered molecules of GBM and facilitate the accuracy remedy for GBM sufferers.

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Madrid, Laura, Sonia Moreno-Grau, Shahzad Ahmad, Antonio González-Pérez, Itziar de Rojas, Rui Xia, Pamela V. Martino Adami, et al. "Multiomics integrative analysis identifies APOE allele-specific blood biomarkers associated to Alzheimer’s disease etiopathogenesis." Aging 13, no. 7 (April 12, 2021): 9277–329. http://dx.doi.org/10.18632/aging.202950.

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Chen, Canbin, Fangping Li, Fangfang Xie, Jiaxuan Chen, Qingzhu Hua, Jianye Chen, Zhijiang Wu, et al. "Pitaya Genome and Multiomics Database (PGMD): A Comprehensive and Integrative Resource of Selenicereus undatus." Genes 13, no. 5 (April 24, 2022): 745. http://dx.doi.org/10.3390/genes13050745.

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Pitaya (Selenicereus) is a kind of novel fruit with a delicious taste and superior horticulture ornamental value. The potential economic impact of the pitaya lies in its diverse uses not only as agricultural produce and processed foods but also in industrial and medicinal products. It is also an excellent plant material for basic and applied biological research. A comprehensive database of pitaya would facilitate studies of pitaya and the other Cactaceae plant species. Here, we constructed pitaya genome and multiomics database, which is a collection of the most updated and high-quality pitaya genomic assemblies. The database contains various information such as genomic variation, gene expression, miRNA profiles, metabolite and proteomic data from various tissues and fruit developmental stages of different pitaya cultivars. In PGMD, we also uploaded videos on the flowering process and planting tutorials for practical usage of pitaya. Overall, these valuable data provided in the PGMD will significantly facilitate future studies on population genetics, molecular breeding and function research of pitaya.

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Laganà, Alessandro, Itai Beno, David Melnekoff, Violetta Leshchenko, Deepu Madduri, Dennis Ramdas, Larysa Sanchez, et al. "Precision Medicine for Relapsed Multiple Myeloma on the Basis of an Integrative Multiomics Approach." JCO Precision Oncology, no. 2 (November 2018): 1–17. http://dx.doi.org/10.1200/po.18.00019.

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Purpose Multiple myeloma (MM) is a malignancy of plasma cells, with a median survival of 6 years. Despite recent therapeutic advancements, relapse remains mostly inevitable, and the disease is fatal in the majority of patients. A major challenge in the treatment of patients with relapsed MM is the timely identification of treatment options in a personalized manner. Current approaches in precision oncology aim at matching specific DNA mutations to drugs, but incorporation of genome-wide RNA profiles has not yet been clinically assessed. Methods We have developed a novel computational platform for precision medicine of relapsed and/or refractory MM on the basis of DNA and RNA sequencing. Our approach expands on the traditional DNA-based approaches by integrating somatic mutations and copy number alterations with RNA-based drug repurposing and pathway analysis. We tested our approach in a pilot precision medicine clinical trial with 64 patients with relapsed and/or refractory MM. Results We generated treatment recommendations in 63 of 64 patients. Twenty-six patients had treatment implemented, and 21 were assessable. Of these, 11 received a drug that was based on RNA findings, eight received a drug that was based on DNA, and two received a drug that was based on both RNA and DNA. Sixteen of the 21 evaluable patients had a clinical response (ie, reduction of disease marker ≥ 25%), giving a clinical benefit rate of 76% and an overall response rate of 66%, with five patients having ongoing responses at the end of the trial. The median duration of response was 131 days. Conclusion Our results show that a comprehensive sequencing approach can identify viable options in patients with relapsed and/or refractory myeloma, and they represent proof of principle of how RNA sequencing can contribute beyond DNA mutation analysis to the development of a reliable drug recommendation tool.

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Wang, Xiaqiong, and Yalu Wen. "A U-statistics for integrative analysis of multilayer omics data." Bioinformatics 36, no. 8 (January 8, 2020): 2365–74. http://dx.doi.org/10.1093/bioinformatics/btaa004.

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Abstract Motivation The emerging multilayer omics data provide unprecedented opportunities for detecting biomarkers that are associated with complex diseases at various molecular levels. However, the high-dimensionality of multiomics data and the complex disease etiologies have brought tremendous analytical challenges. Results We developed a U-statistics-based non-parametric framework for the association analysis of multilayer omics data, where consensus and permutation-based weighting schemes are developed to account for various types of disease models. Our proposed method is flexible for analyzing different types of outcomes as it makes no assumptions about their distributions. Moreover, it explicitly accounts for various types of underlying disease models through weighting schemes and thus provides robust performance against them. Through extensive simulations and the application to dataset obtained from the Alzheimer’s Disease Neuroimaging Initiatives, we demonstrated that our method outperformed the commonly used kernel regression-based methods. Availability and implementation The R-package is available at https://github.com/YaluWen/Uomic. Supplementary information Supplementary data are available at Bioinformatics online.

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Nalbantoglu, Sinem, and Abdullah Karadag. "Metabolomics bridging proteomics along metabolites/oncometabolites and protein modifications: Paving the way toward integrative multiomics." Journal of Pharmaceutical and Biomedical Analysis 199 (May 2021): 114031. http://dx.doi.org/10.1016/j.jpba.2021.114031.

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Tasaki, Shinya, Katsuya Suzuki, Ayumi Nishikawa, Yoshiaki Kassai, Maiko Takiguchi, Rina Kurisu, Yuumi Okuzono, et al. "Multiomic disease signatures converge to cytotoxic CD8 T cells in primary Sjögren’s syndrome." Annals of the Rheumatic Diseases 76, no. 8 (May 18, 2017): 1458–66. http://dx.doi.org/10.1136/annrheumdis-2016-210788.

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ObjectivesMultiomics study was conducted to elucidate the crucial molecular mechanisms of primary Sjögren’s syndrome (SS) pathology.MethodsWe generated multiple data set from well-defined patients with SS, which includes whole-blood transcriptomes, serum proteomes and peripheral immunophenotyping. Based on our newly generated data, we performed an extensive bioinformatic investigation.ResultsOur integrative analysis identified SS gene signatures (SGS) dysregulated in widespread omics layers, including epigenomes, mRNAs and proteins. SGS predominantly involved the interferon signature and ADAMs substrates. Besides, SGS was significantly overlapped with SS-causing genes indicated by a genome-wide association study and expression trait loci analyses. Combining the molecular signatures with immunophenotypic profiles revealed that cytotoxic CD8 T cells were associated with SGS. Further, we observed the activation of SGS in cytotoxic CD8 T cells isolated from patients with SS.ConclusionsOur multiomics investigation identified gene signatures deeply associated with SS pathology and showed the involvement of cytotoxic CD8 T cells. These integrative relations across multiple layers will facilitate our understanding of SS at the system level.

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Chen, Qiling, Xiangke Yang, Qiang Meng, Lili Zhao, Yuxin Yuan, Wei Chi, Ling He, Kan Shi, and Shuwen Liu. "Integrative multiomics analysis of the acid stress response of Oenococcus oeni mutants at different growth stages." Food Microbiology 102 (April 2022): 103905. http://dx.doi.org/10.1016/j.fm.2021.103905.

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Dai, Yongmei, Wenhan Chen, Junpeng Huang, Lijing Zheng, Qing Lin, Tongjian Cui, and Chen Huang. "Multiomics Integrative Analysis Identifying EPC1 as a Prognostic Biomarker in Head and Neck Squamous Cell Carcinoma." BioMed Research International 2022 (September 16, 2022): 1–15. http://dx.doi.org/10.1155/2022/1074412.

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Background. Biomarker research in head and neck squamous cell carcinoma (HNSCC) is constantly revealing promising findings. An enhancer of polycomb homolog 1 (EPC1) was found to play a procancer role in nasopharyngeal carcinoma (NPC), but its role in HNSCC with strong heterogeneity is still unclear. Herein, we investigated the prognostic significance and related mechanisms of EPC1 in HNSCC. Methods. The Kaplan-Meier plotter was used to evaluate the prognostic significance of EPC1. Based on a range of published public databases, the multiomics expression of EPC1 in HNSCC was explored to investigate the mechanisms affecting prognosis. Results. According to the clinical data, high EPC1 expression in HNSCC was a predictor of patient prognosis (hazard ratio HR = 0.64 ; 95% confidence interval (CI) 0.49-0.83; P < 0.01 ). EPC1 expression varied among clinical subtypes and was related to key factors, such as TP53 and human papillomavirus (HPV) ( P < 0.05 ). At the genetic level, EPC1 expression level may be associated with protein phosphorylation, cell adhesion, cancer-related pathways, etc. For the noncoding region, a competing endogenous RNA network was constructed, and 6 microRNAs and 12 long noncoding RNAs were identified. At the protein level, a protein-protein interaction (PPI) network related to EPC1 expression was constructed and found to be involved in HPV infection, endocrine resistance, and multiple cancer pathways. At the immune level, EPC1 expression was correlated with a variety of immune cells and immune molecules, which together constituted the immune microenvironments of tumors. Conclusion. High EPC1 expression may predict a better prognosis in HNSCC, as it is more frequently found in HNSCC with HPV infection. EPC1 may participate in the genomics, transcriptomics, proteomics, and immunomics of HNSCC, and the results can provide a reference for the development of targeted drugs and evaluation of patient prognosis.

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Papiez, Anna, Omid Azimzadeh, Tamara Azizova, Maria Moseeva, Natasa Anastasov, Jan Smida, Soile Tapio, and Joanna Polanska. "Integrative multiomics study for validation of mechanisms in radiation-induced ischemic heart disease in Mayak workers." PLOS ONE 13, no. 12 (December 31, 2018): e0209626. http://dx.doi.org/10.1371/journal.pone.0209626.

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Dagher, Julien, Angelique Brunot, Bertrand Evrard, Solene-Florence Kammerer-Jacquet, Marion Beaumont, Laurence Cornevin, Fanny Derquin, et al. "Multiple metastatic clones assessed by an integrative multiomics strategy in clear cell renal carcinoma: a case study." Journal of Clinical Pathology 75, no. 6 (March 25, 2021): 426–30. http://dx.doi.org/10.1136/jclinpath-2020-207326.

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The dynamics of metastatic evolution in clear cell renal cell carcinoma (ccRCC) are complex. We report a case study where tumour heterogeneity resulting from clonal evolution is a frequent feature and could play a role in metastatic dissemination.We used an integrative multiomics strategy combining genomic and transcriptomic data to classify fourteen specimens from spatially different areas of a kidney tumour and three non-primary sites including a vein thrombus and two adrenal metastases.All sites were heterogeneous and polyclonal, each tumour site containing two different aggressive subclonal populations, with differentially expressed genes implicated in distinct biological functions. These are rare primary metastatic samples prior to any medical treatment, where we showed a multiple metastatic seeding of two subclonal populations.Multiple interdependent lineages could be the source of metastatic heterogeneity in ccRCC. By sampling metastases, patients with resistance to therapies could benefit a combination of targeted therapies based on more than one aggressive clone.

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Abolfazi, Razi, Afghah Fatemeh, Singh Salendra, and Varadan Vinay. "Network-Based Enriched Gene Subnetwork Identification: A Game-Theoretic Approach." Biomedical Engineering and Computational Biology 7s2 (January 2016): BECB.S38244. http://dx.doi.org/10.4137/becb.s38244.

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Identifying subsets of genes that jointly mediate cancer etiology, progression, or therapy response remains a challenging problem due to the complexity and heterogeneity in cancer biology, a problem further exacerbated by the relatively small number of cancer samples profiled as compared with the sheer number of potential molecular factors involved. Pure data-driven methods that merely rely on multiomics data have been successful in discovering potentially functional genes but suffer from high false-positive rates and tend to report subsets of genes whose biological interrelationships are unclear. Recently, integrative data-driven models have been developed to integrate multiomics data with signaling pathway networks in order to identify pathways associated with clinical or biological phenotypes. However, these approaches suffer from an important drawback of being restricted to previously discovered pathway structures and miss novel genomic interactions as well as potential crosstalk among the pathways. In this article, we propose a novel coalition-based game-theoretic approach to overcome the challenge of identifying biologically relevant gene subnetworks associated with disease phenotypes. The algorithm starts from a set of seed genes and traverses a protein–protein interaction network to identify modulated subnetworks. The optimal set of modulated subnetworks is identified using Shapley value that accounts for both individual and collective utility of the subnetwork of genes. The algorithm is applied to two illustrative applications, including the identification of subnetworks associated with (i) disease progression risk in response to platinum-based therapy in ovarian cancer and (ii) immune infiltration in triple-negative breast cancer. The results demonstrate an improved predictive power of the proposed method when compared with state-of-the-art feature selection methods, with the added advantage of identifying novel potentially functional gene subnetworks that may provide insights into the mechanisms underlying cancer progression.

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Zhang, Jinzhong, Xiuzhi Zhang, Lingxiao Wang, Chunyan Kang, Ningning Li, Zhefeng Xiao, and Liping Dai. "Multiomics-based analyses of KPNA2 highlight its multiple potentials in hepatocellular carcinoma." PeerJ 9 (September 21, 2021): e12197. http://dx.doi.org/10.7717/peerj.12197.

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Dysregulation and prognostic roles of Karyopherin α2 (KPNA2) were reported in many malignancies including hepatocellular carcinoma (HCC). A multi-omics analysis of KPNA2 is needed to gain a deeper understanding of its multilevel molecular characteristics and provide novel clues for HCC diagnosis, prognosis, and target therapy. Herein multi-omic alterations of KPNA2 were analyzed at genetic, epigenetic, transcript, and protein levels with evaluation of their relevance with clinicopathological features of HCC by integrative analyses. The significant correlations of KPNA2 expression with its gene copy number variation (CNV) and methylation status were shown through Spearman correlation analyses. With Cox regression, Kaplan-Meier survival, and receiver operating characteristic (ROC) analyses, based on the factors of KPNA2 CNV, methylation, expression, and tumor stage, risk models for HCC overall survival (OS) and disease-free survival (DFS) were constructed which could discriminate the 1-year, 3-year, and 5-year OS/DFS status effectively. With Microenvironment Cell Populations-counter (MCP-counter), the immune infiltrations of HCC samples were evaluated and their associations with KPNA2 were shown. KPNA2 expression in liver was found to be influenced by low fat diet and presented significant correlations with fatty acid metabolism and fatty acid synthase activity in HCC. KPNA2 was detected lowered in HCC patient’s plasma by enzyme linked immunosorbent assay (ELISA), consistent with its translocation to nuclei of HCC cells. In conclusion, KPNA2 multilevel dysregulation in HCC and its correlations with immune infiltration and the fatty acid metabolism pathway indicated its multiple roles in HCC. The clinicopathological significance of KPNA2 was highlighted through the in-depth analyses at multilevels.

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Song, Won‐Suk, Sung Gyu Shin, Sung‐Hyun Jo, Jae‐Seung Lee, Hyo‐Jin Jeon, Ji‐Eun Kwon, Ji‐Hyeon Park, et al. "Development of an in vitro coculture device for the investigation of host–microbe interactions via integrative multiomics approaches." Biotechnology and Bioengineering 118, no. 4 (January 25, 2021): 1593–604. http://dx.doi.org/10.1002/bit.27676.

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Quist, Jelmar, Lawson Taylor, Johan Staaf, and Anita Grigoriadis. "Random Forest Modelling of High-Dimensional Mixed-Type Data for Breast Cancer Classification." Cancers 13, no. 5 (February 27, 2021): 991. http://dx.doi.org/10.3390/cancers13050991.

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Advances in high-throughput technologies encourage the generation of large amounts of multiomics data to investigate complex diseases, including breast cancer. Given that the aetiologies of such diseases extend beyond a single biological entity, and that essential biological information can be carried by all data regardless of data type, integrative analyses are needed to identify clinically relevant patterns. To facilitate such analyses, we present a permutation-based framework for random forest methods which simultaneously allows the unbiased integration of mixed-type data and assessment of relative feature importance. Through simulation studies and machine learning datasets, the performance of the approach was evaluated. The results showed minimal multicollinearity and limited overfitting. To further assess the performance, the permutation-based framework was applied to high-dimensional mixed-type data from two independent breast cancer cohorts. Reproducibility and robustness of our approach was demonstrated by the concordance in relative feature importance between the cohorts, along with consistencies in clustering profiles. One of the identified clusters was shown to be prognostic for clinical outcome after standard-of-care adjuvant chemotherapy and outperformed current intrinsic molecular breast cancer classifications.

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Birga, Anteneh M., Liping Ren, Huaichao Luo, Yang Zhang, and Jian Huang. "Prediction of New Risk Genes and Potential Drugs for Rheumatoid Arthritis from Multiomics Data." Computational and Mathematical Methods in Medicine 2022 (January 31, 2022): 1–11. http://dx.doi.org/10.1155/2022/6783659.

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Rheumatoid arthritis (RA) is an autoimmune and inflammatory disease for which there is a lack of therapeutic options. Genome-wide association studies (GWASs) have identified over 100 genetic loci associated with RA susceptibility; however, the most causal risk genes (RGs) associated with, and molecular mechanism underlying, RA remain unknown. In this study, we collected 95 RA-associated loci from multiple GWASs and detected 87 candidate high-confidence risk genes (HRGs) from these loci via integrated multiomics data (the genome-scale chromosome conformation capture data, enhancer-promoter linkage data, and gene expression data) using the Bayesian integrative risk gene selector (iRIGS). Analysis of these HRGs indicates that these genes were indeed, markedly associated with different aspects of RA. Among these, 36 and 46 HRGs have been reported to be related to RA and autoimmunity, respectively. Meanwhile, most novel HRGs were also involved in the significantly enriched RA-related biological functions and pathways. Furthermore, drug repositioning prediction of the HRGs revealed three potential targets (ERBB2, IL6ST, and MAPK1) and nine possible drugs for RA treatment, of which two IL-6 receptor antagonists (tocilizumab and sarilumab) have been approved for RA treatment and four drugs (trastuzumab, lapatinib, masoprocol, and arsenic trioxide) have been reported to have a high potential to ameliorate RA. In summary, we believe that this study provides new clues for understanding the pathogenesis of RA and is important for research regarding the mechanisms underlying RA and the development of therapeutics for this condition.

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Bei, Jiaxin, Shaoping Zhu, Minqun Du, Zhihui Hu, Zheng Tang, Cailing Chen, Kevin Yang, et al. "Integrative analysis of multiomics data identified acetylation as key variable of excessive energy metabolism in hyperthyroidism-induced osteoporosis rats." Journal of Proteomics 252 (February 2022): 104451. http://dx.doi.org/10.1016/j.jprot.2021.104451.

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Lee, Jae‐Seung, Won‐Suk Song, Jun Woo Lim, Tae‐Rim Choi, Sung‐Hyun Jo, Hyo‐Jin Jeon, Ji‐Eun Kwon, et al. "An integrative multiomics approach to characterize anti‐adipogenic and anti‐lipogenic effects of Akkermansia muciniphila in adipocytes." Biotechnology Journal 17, no. 2 (December 29, 2021): 2100397. http://dx.doi.org/10.1002/biot.202100397.

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Marquardt, Jens U. "The Role of Transforming Growth Factor-β in Human Hepatocarcinogenesis: Mechanistic and Therapeutic Implications From an Integrative Multiomics Approach." Gastroenterology 154, no. 1 (January 2018): 17–20. http://dx.doi.org/10.1053/j.gastro.2017.11.015.

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XIAO, GUODONG, FENG CHENG, JING YUAN, WEIPING LU, PEILI WANG, and HUIJIE FAN. "Integrative multiomics analysis identifies a metastasis-related gene signature and the potential oncogenic role of EZR in breast cancer." Oncology Research 30, no. 1 (2022): 35–51. http://dx.doi.org/10.32604/or.2022.026616.

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Piña, Benjamin, Tamar Ziv, Melissa Faria, Shani Ben-Lulu, Eva Prats, Mark A. Arick II, Cristian Gómez-Canela, Natàlia García-Reyero, Arie Admon, and Demetrio Raldúa. "Multiomic Analysis of Zebrafish Models of Acute Organophosphorus Poisoning With Different Severity." Toxicological Sciences 171, no. 1 (June 19, 2019): 211–20. http://dx.doi.org/10.1093/toxsci/kfz133.

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Abstract Organophosphorus compounds are acetylcholinesterase inhibitors used as pesticides and chemical warfare nerve agents. Acute organophosphorus poisoning (acute OPP) affects 3 million people, with 300 000 deaths annually worldwide. Severe acute OPP effects include overstimulation of cholinergic neurons, seizures, status epilepticus, and finally, brain damage. In a previous study, we developed 3 different chemical models of acute OPP in zebrafish larvae. To elucidate the complex pathophysiological pathways related to acute OPP, we used integrative omics (proteomic, transcriptomics, and metabolomics) on these 3 animal models. Our results show that these stochastic, apparently disparate morphological phenotypes can result from almost linear concentration-response variations in molecular levels. Results from the multiomics analysis strongly suggest that endoplasmic reticulum stress might play a central role in the pathophysiology of severe acute OPP, emphasizing the urgent need of further research on this molecular pathway. Endoplasmic reticulum stress could be an important therapeutic target to be included in the treatment of patients with severe acute OPP.

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Osawa, Tsuyoshi. "Tolerance of cancer cells against amino acids deprivation in tumour microenvironments." Impact 2021, no. 8 (October 28, 2021): 9–11. http://dx.doi.org/10.21820/23987073.2021.8.9.

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New approaches for treating cancers are required and advances in 'omics' technologies including genomics, epigenomics, transcriptomics could provide valuable treatment options. Associate Professor Tsuyoshi Osawa, Research Center for Advanced Science and Technology (RCAST), University of Tokyo, believes that integrative techniques are essential in combating cancer. Osawa's lab is utilising a pioneering approach called nutriomics that involves applying multiple omics technologies to cancer biology. These omics approaches can be used to generate detailed genetic and molecular profiles of whole tumours, allowing researchers to discover important information about the tumour cells. In addition, they provide an opportunity to explore the healthy cells surrounding the tumour, thereby establishing a picture of the interactions between the tumour and the microenvironment in which it exists, which is important information that could be exploited for treatments methods. Using the omics approach, the researchers have been able to identify and describe the functions of the metabolites contributing to the malignant progression of cancer cells. They found that hypoxia, nutrient starvation and acidic pH all induce tumour aggressiveness by epigenetic regulation. Osawa and the team now want to identify further cancer metabolites that lead to malignancy and, ultimately, develop therapeutics for metastasis and recurrent advanced cancer from the viewpoint of an integrative multiomics approach.

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Shen, Yiqing, Wensong Yang, Xin Xiong, Xinhui Li, Zhongsong Xiao, Jialun Yu, Fangyu Liu, et al. "Integrated Multiomics Analysis Identifies a Novel Biomarker Associated with Prognosis in Intracerebral Hemorrhage." Oxidative Medicine and Cellular Longevity 2021 (December 14, 2021): 1–20. http://dx.doi.org/10.1155/2021/2510847.

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Existing treatments for intracerebral hemorrhage (ICH) are unable to satisfactorily prevent development of secondary brain injury after ICH and multiple pathological mechanisms are involved in the development of the injury. In this study, we aimed to identify novel genes and proteins and integrated their molecular alternations to reveal key network modules involved in ICH pathology. A total of 30 C57BL/6 male mice were used for this study. The collagenase model of ICH was employed, 3 days after ICH animals were tested neurological. After it, animals were euthanized and perihematomal brain tissues were collected for transcriptome and TMT labeling-based quantitative proteome analyses. Protein-protein interaction (PPI) network, Gene Set Enrichment Analysis (GSEA), and regularized Canonical Correlation Analysis (rCCA) were performed to integrated multiomics data. For validation of hub genes and proteins, qRT-PCR and Western blot were carried out. The candidate biomarkers were further measured by ELISA in the plasma of ICH patients and the controls. A total of 2218 differentially expressed genes (DEGs) and 353 differentially expressed proteins (DEPs) between the ICH model group and control group were identified. GSEA revealed that immune-related gene sets were prominently upregulated and significantly enriched in pathways of inflammasome complex, negative regulation of interleukin-12 production, and pyroptosis during the ICH process. The rCCA network presented two highly connective clusters which were involved in the sphingolipid catabolic process and inflammatory response. Among ten hub genes screened out by integrative analysis, significantly upregulated Itgb2, Serpina3n, and Ctss were validated in the ICH group by qRT-PCR and Western blot. Plasma levels of human SERPINA3 (homologue of murine Serpina3n) were elevated in ICH patients compared with the healthy controls (SERPINA3: 13.3 ng/mL vs. 11.2 ng/mL, p = 0.015 ). Within the ICH group, higher plasma SERPINA3 levels with a predictive threshold of 14.31 ng/mL ( sensitivity = 64.3 % ; specificity = 80.8 % ; AUC = 0.742 , 95% CI: 0.567-0.916) were highly associated with poor outcome (mRS scores 4-6). Taken together, the results of our study exhibited molecular changes related to ICH-induced brain injury by multidimensional analysis and effectively identified three biomarker candidates in a mouse ICH model, as well as pointed out that Serpina3n/SERPINA3 was a potential biomarker associated with poor functional outcome in ICH patients.

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Pang, Lijun, Yuhua Xiong, Zhongwen Feng, Cuiyu Li, Bin Fang, Quanfang Huang, and Xing Lin. "Integrative Analysis of Transcriptome and Metabolome to Illuminate the Protective Effects of Didymin against Acute Hepatic Injury." Mediators of Inflammation 2023 (January 12, 2023): 1–22. http://dx.doi.org/10.1155/2023/6051946.

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Based on the multiomics analysis, this study is aimed at investigating the underlying mechanism of didymin against acute liver injury (ALI). The mice were administrated with didymin for 2 weeks, followed by injection with lipopolysaccharide (LPS) plus D-galactosamine (D-Gal) to induce ALI. The pathological examination revealed that didymin significantly ameliorated LPS/D-Gal-induced hepatic damage. Also, it markedly reduced proinflammatory cytokines release by inhibiting the TLR4/NF-κB pathway activation, alleviating inflammatory injury. A transcriptome analysis proved 2680 differently expressed genes (DEGs) between the model and didymin groups and suggested that the PI3K/Akt and metabolic pathways might be the most relevant targets. Meanwhile, the metabolome analysis revealed 67 differently expressed metabolites (DEMs) between the didymin and model groups that were mainly clustered into the glycerophospholipid metabolism, which was consistent with the transcriptome study. Importantly, a comprehensive analysis of both the omics indicated a strong correlation between the DEGs and DEMs, and an in-depth study demonstrated that didymin alleviated metabolic disorder and hepatocyte injury likely by inhibiting the glycerophospholipid metabolism pathway through the regulation of PLA2G4B, LPCAT3, and CEPT1 expression. In conclusion, this study demonstrates that didymin can ameliorate LPS/D-Gal-induced ALI by inhibiting the glycerophospholipid metabolism and PI3K/Akt and TLR4/NF-κB pathways.

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Prince, Claire, Ruth E. Mitchell, and Tom G. Richardson. "Integrative multiomics analysis highlights immune-cell regulatory mechanisms and shared genetic architecture for 14 immune-associated diseases and cancer outcomes." American Journal of Human Genetics 108, no. 12 (December 2021): 2259–70. http://dx.doi.org/10.1016/j.ajhg.2021.10.003.

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Moldogazieva, Nurbubu, Innokenty Mokhosoev, Sergey Zavadskiy, and Alexander Terentiev. "Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine." Biomedicines 9, no. 2 (February 6, 2021): 159. http://dx.doi.org/10.3390/biomedicines9020159.

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Hepatocellular carcinoma (HCC) is the most common primary cancer of the liver with high morbidity and mortality rates worldwide. Since 1963, when alpha-fetoprotein (AFP) was discovered as a first HCC serum biomarker, several other protein biomarkers have been identified and introduced into clinical practice. However, insufficient specificity and sensitivity of these biomarkers dictate the necessity of novel biomarker discovery. Remarkable advancements in integrated multiomics technologies for the identification of gene expression and protein or metabolite distribution patterns can facilitate rising to this challenge. Current multiomics technologies lead to the accumulation of a huge amount of data, which requires clustering and finding correlations between various datasets and developing predictive models for data filtering, pre-processing, and reducing dimensionality. Artificial intelligence (AI) technologies have an enormous potential to overcome accelerated data growth, complexity, and heterogeneity within and across data sources. Our review focuses on the recent progress in integrative proteomic profiling strategies and their usage in combination with machine learning and deep learning technologies for the discovery of novel biomarker candidates for HCC early diagnosis and prognosis. We discuss conventional and promising proteomic biomarkers of HCC such as AFP, lens culinaris agglutinin (LCA)-reactive L3 glycoform of AFP (AFP-L3), des-gamma-carboxyprothrombin (DCP), osteopontin (OPN), glypican-3 (GPC3), dickkopf-1 (DKK1), midkine (MDK), and squamous cell carcinoma antigen (SCCA) and highlight their functional significance including the involvement in cell signaling such as Wnt/β-catenin, PI3K/Akt, integrin αvβ3/NF-κB/HIF-1α, JAK/STAT3 and MAPK/ERK-mediated pathways dysregulated in HCC. We show that currently available computational platforms for big data analysis and AI technologies can both enhance proteomic profiling and improve imaging techniques to enhance the translational application of proteomics data into precision medicine.

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Raposo de Magalhães, Cláudia, Ana Paula Farinha, Gavin Blackburn, Phillip D. Whitfield, Raquel Carrilho, Denise Schrama, Marco Cerqueira, and Pedro M. Rodrigues. "Gilthead Seabream Liver Integrative Proteomics and Metabolomics Analysis Reveals Regulation by Different Prosurvival Pathways in the Metabolic Adaptation to Stress." International Journal of Molecular Sciences 23, no. 23 (December 6, 2022): 15395. http://dx.doi.org/10.3390/ijms232315395.

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The study of the molecular mechanisms of stress appraisal on farmed fish is paramount to ensuring a sustainable aquaculture. Stress exposure can either culminate in the organism’s adaptation or aggravate into a metabolic shutdown, characterized by irreversible cellular damage and deleterious effects on fish performance, welfare, and survival. Multiomics can improve our understanding of the complex stressed phenotype in fish and the molecular mediators that regulate the underlying processes of the molecular stress response. We profiled the stress proteome and metabolome of Sparus aurata responding to different challenges common to aquaculture production, characterizing the disturbed pathways in the fish liver, i.e., the central organ in mounting the stress response. Label-free shotgun proteomics and untargeted metabolomics analyses identified 1738 proteins and 120 metabolites, separately. Mass spectrometry data have been made fully accessible via ProteomeXchange, with the identifier PXD036392, and via MetaboLights, with the identifier MTBLS5940. Integrative multivariate statistical analysis, performed with data integration analysis for biomarker discovery using latent components (DIABLO), depicted the 10 most-relevant features. Functional analysis of these selected features revealed an intricate network of regulatory components, modulating different signaling pathways related to cellular stress, e.g., the mTORC1 pathway, the unfolded protein response, endocytosis, and autophagy to different extents according to the stress nature. These results shed light on the dynamics and extent of this species’ metabolic reprogramming under chronic stress, supporting future studies on stress markers’ discovery and fish welfare research.

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Ko, Tun Kiat, Asif Javed, Kian Leong Lee, Thushangi N. Pathiraja, Xingliang Liu, Simeen Malik, Sheila Xinxuan Soh, et al. "An integrative model of pathway convergence in genetically heterogeneous blast crisis chronic myeloid leukemia." Blood 135, no. 26 (June 25, 2020): 2337–53. http://dx.doi.org/10.1182/blood.2020004834.

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Abstract Targeted therapies against the BCR-ABL1 kinase have revolutionized treatment of chronic phase (CP) chronic myeloid leukemia (CML). In contrast, management of blast crisis (BC) CML remains challenging because BC cells acquire complex molecular alterations that confer stemness features to progenitor populations and resistance to BCR-ABL1 tyrosine kinase inhibitors. Comprehensive models of BC transformation have proved elusive because of the rarity and genetic heterogeneity of BC, but are important for developing biomarkers predicting BC progression and effective therapies. To better understand BC, we performed an integrated multiomics analysis of 74 CP and BC samples using whole-genome and exome sequencing, transcriptome and methylome profiling, and chromatin immunoprecipitation followed by high-throughput sequencing. Employing pathway-based analysis, we found the BC genome was significantly enriched for mutations affecting components of the polycomb repressive complex (PRC) pathway. While transcriptomically, BC progenitors were enriched and depleted for PRC1- and PRC2-related gene sets respectively. By integrating our data sets, we determined that BC progenitors undergo PRC-driven epigenetic reprogramming toward a convergent transcriptomic state. Specifically, PRC2 directs BC DNA hypermethylation, which in turn silences key genes involved in myeloid differentiation and tumor suppressor function via so-called epigenetic switching, whereas PRC1 represses an overlapping and distinct set of genes, including novel BC tumor suppressors. On the basis of these observations, we developed an integrated model of BC that facilitated the identification of combinatorial therapies capable of reversing BC reprogramming (decitabine+PRC1 inhibitors), novel PRC-silenced tumor suppressor genes (NR4A2), and gene expression signatures predictive of disease progression and drug resistance in CP.

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Assidi, Mourad. "Infertility in Men: Advances towards a Comprehensive and Integrative Strategy for Precision Theranostics." Cells 11, no. 10 (May 22, 2022): 1711. http://dx.doi.org/10.3390/cells11101711.

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Male infertility is an increasing and serious medical concern, though the mechanism remains poorly understood. Impaired male reproductive function affects approximately half of infertile couples worldwide. Multiple factors related to the environment, genetics, age, and comorbidities have been associated with impaired sperm function. Present-day clinicians rely primarily on standard semen analysis to diagnose male reproductive potential and develop treatment strategies. To address sperm quality assessment bias and enhance analysis accuracy, the World Health Organization (WHO) has recommended standardized sperm testing; however, conventional diagnostic and therapeutic options for male infertility, including physical examination and semen standard analysis, remain ineffective in relieving the associated social burden. Instead, assisted reproductive techniques are becoming the primary therapeutic approach. In the post-genomic era, multiomics technologies that deeply interrogate the genome, transcriptome, proteome, and/or the epigenome, even at single-cell level, besides the breakthroughs in robotic surgery, stem cell therapy, and big data, offer promises towards solving semen quality deterioration and male factor infertility. This review highlights the complex etiology of male infertility, especially the roles of lifestyle and environmental factors, and discusses advanced technologies/methodologies used in characterizing its pathophysiology. A comprehensive combination of these innovative approaches in a global and multi-centric setting and fulfilling the suitable ethical consent could ensure optimal reproductive and developmental outcomes. These combinatorial approaches should allow for the development of diagnostic markers, molecular stratification classes, and personalized treatment strategies. Since lifestyle choices and environmental factors influence male fertility, their integration in any comprehensive approach is required for safe, proactive, cost-effective, and noninvasive precision male infertility theranostics that are affordable, accessible, and facilitate couples realizing their procreation dream.

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Jha, Prakash, Prithvi Singh, Shweta Arora, Armiya Sultan, Arnab Nayek, Kalaiarasan Ponnusamy, Mansoor Ali Syed, Ravins Dohare, and Madhu Chopra. "Integrative multiomics and in silico analysis revealed the role of ARHGEF1 and its screened antagonist in mild and severe COVID‐19 patients." Journal of Cellular Biochemistry 123, no. 3 (January 17, 2022): 673–90. http://dx.doi.org/10.1002/jcb.30213.

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Kammer, Michael, Andreas Heinzel, Jill A. Willency, Kevin L. Duffin, Gert Mayer, Kai Simons, Mathias J. Gerl, et al. "Integrative analysis of prognostic biomarkers derived from multiomics panels helps discrimination of chronic kidney disease trajectories in people with type 2 diabetes." Kidney International 96, no. 6 (December 2019): 1381–88. http://dx.doi.org/10.1016/j.kint.2019.07.025.

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Núñez-Lillo, Gerardo, Excequel Ponce, Camila Arancibia-Guerra, Sebastien Carpentier, Alegría Carrasco-Pancorbo, Lucía Olmo-García, Rosana Chirinos, et al. "A multiomics integrative analysis of color de-synchronization with softening of ‘Hass’ avocado fruit: A first insight into a complex physiological disorder." Food Chemistry 408 (May 2023): 135215. http://dx.doi.org/10.1016/j.foodchem.2022.135215.

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Rinaldi de Alvarenga, José Fernando, Mar Garcia-Aloy, Marynka Ulaszewska, Sebastian Zagmutt, Marta Perez-Montero, Urska Vrhovsek, Rosa M. Lamuela-Raventós, and Rosalia Rodriguez-Rodriguez. "Integrated Metabolomics, Lipidomics, and Genomics Reveal the Presence of a New Biomarker, Butanediol Glucuronide, Associated with the Activation of Liver Ketogenesis and Lipid Oxidation by Tomato-Based Sofrito in Obese Rats." Antioxidants 11, no. 11 (October 31, 2022): 2165. http://dx.doi.org/10.3390/antiox11112165.

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The increasing prevalence of obesity worldwide has promoted research on human metabolism and foods such as sofrito, a tomato and olive oil-based sauce from the Mediterranean diet, has shown beneficial effects on obesity and related complications. Sofrito has been associated with better cardiovascular health, metabolic syndrome, and anti-inflammatory effects. The aim of this study was to understand how sofrito intake could contribute to the control of energy metabolism in obese rats. For this purpose, integrative untargeted lipidomics, metabolomics, and targeted gene expression approaches were used in the liver and adipose tissue to identify metabolic changes and the mechanism of action promoted by sofrito intake. A new biomarker was identified in the liver, butanediol glucuronide, an indicator of ketogenic activation and lipid oxidation after the sofrito intervention. Gene expression analysis revealed an increase in the uptake and liver oxidation of lipids for energy production and ketogenesis activation as fuel for other tissues in sofrito-fed animals. Sofrito altered the lipidomic profile in the fat depots of obese rats. This multiomics study identifies a new biomarker linked to the beneficial actions of sofrito against obesity and provides further insight into the beneficial effect of the Mediterranean diet components.

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Wen, Chen-Yueh, Kuan-Hao Tsui, Chiung-Hung Chang, Yi-Han Chiu, Shu-Chuan Amy Lin, Ching-Yu Chu, and Chia-Jung Li. "Integrative Multiomics Evaluation of IIDH1 Metabolic Enzyme as a Candidate Oncogene That is Correlated with Poor Prognosis and Immune Infiltration in Prostate Adenocarcinoma." Journal of Oncology 2022 (January 29, 2022): 1–13. http://dx.doi.org/10.1155/2022/9854788.

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Mutations in the isocitrate dehydrogenase gene (IDH1) are involved in the progression of tumors. Although IDH1 has a role in various tumors, its clinical relevance and its expression in response to the immune response have not been investigated in prostate adenocarcinoma (PRAD). In the present study, we investigated the utility of IDH1 as a prognostic biomarker for PRAD by analyzing IDH1 mRNA expression and its association with patient survival and immune cell infiltration. IDH1 mRNA expression was significantly higher in PRAD tissue than in normal tissue, and Kaplan–Meier survival analysis showed that IDH1 expression was significantly associated with poor prognosis in PRAD patients. To elucidate the mechanisms involved, the correlation between IDH1 expression and the level of immune cell infiltration, in particular of immunosuppressive cells such as CD8+ T-cells, CD4+ T-cells, and macrophages, was further analyzed by single-cell RNA sequencing. We also screened a pharmacogenetic database for IDH1-specific drugs that inhibited high expression in PRAD. In the present study, we used a combination of databases to identify a significant correlation between IDH1 expression and cellular infiltration and to explain the mechanism by which IDH1 confers poor prognosis in PRAD, thus demonstrating the relevance of IDH1 expression as a prognostic biomarker with clinical utility in PRAD patients.

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Gupta, Sonam, Prithvi Singh, Alvea Tasneem, Ahmad Almatroudi, Arshad Husain Rahmani, Ravins Dohare, and Shama Parveen. "Integrative Multiomics and Regulatory Network Analyses Uncovers the Role of OAS3, TRAFD1, miR-222-3p, and miR-125b-5p in Hepatitis E Virus Infection." Genes 14, no. 1 (December 23, 2022): 42. http://dx.doi.org/10.3390/genes14010042.

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The hepatitis E virus (HEV) is a long-ignored virus that has spread globally with time. It ranked 6th among the top risk-ranking viruses with high zoonotic spillover potential; thus, considering its viral threats is a pressing priority. The molecular pathophysiology of HEV infection or the underlying cause is limited. Therefore, we incorporated an unbiased, systematic methodology to get insights into the biological heterogeneity associated with the HEV. Our study fetched 93 and 2016 differentially expressed genes (DEGs) from chronic HEV (CHEV) infection in kidney-transplant patients, followed by hub module selection from a weighted gene co-expression network (WGCN). Most of the hub genes identified in this study were associated with interferon (IFN) signaling pathways. Amongst the genes induced by IFNs, the 2′-5′-oligoadenylate synthase 3 (OAS3) protein was upregulated. Protein-protein interaction (PPI) modular, functional enrichment, and feed-forward loop (FFL) analyses led to the identification of two key miRNAs, i.e., miR-222-3p and miR-125b-5p, which showed a strong association with the OAS3 gene and TRAF-type zinc finger domain containing 1 (TRAFD1) transcription factor (TF) based on essential centrality measures. Further experimental studies are required to substantiate the significance of these FFL-associated genes and miRNAs with their respective functions in CHEV. To our knowledge, it is the first time that miR-222-3p has been described as a reference miRNA for use in CHEV sample analyses. In conclusion, our study has enlightened a few budding targets of HEV, which might help us understand the cellular and molecular pathways dysregulated in HEV through various factors. Thus, providing a novel insight into its pathophysiology and progression dynamics.

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Journal articles on the topic 'Integrative multiomics'

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