Статті в журналах: "Cancer bioinformatics"

1

Desany, Brian, and Zemin Zhang. "Bioinformatics and cancer target discovery." Drug Discovery Today 9, no. 18 (September 2004): 795–802. http://dx.doi.org/10.1016/s1359-6446(04)03224-6.

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Brenner, Chad. "Applications of Bioinformatics in Cancer." Cancers 11, no. 11 (October 24, 2019): 1630. http://dx.doi.org/10.3390/cancers11111630.

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Blekherman, Grigoriy, Reinhard Laubenbacher, Diego F. Cortes, Pedro Mendes, Frank M. Torti, Steven Akman, Suzy V. Torti, and Vladimir Shulaev. "Bioinformatics tools for cancer metabolomics." Metabolomics 7, no. 3 (January 12, 2011): 329–43. http://dx.doi.org/10.1007/s11306-010-0270-3.

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4

Puig, Oscar, Eugene Joseph, Malgorzata Jaremko, Gregory Kellogg, Robert Wisotzkey, Roman Shraga, Bonny Patel, et al. "Comprehensive next generation sequencing assay and bioinformatic pipeline for identifying pathogenic variants associated with hereditary cancers." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): e13105-e13105. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e13105.

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e13105 Background: Diagnosis of hereditary cancer syndromes involves time-consuming comprehensive clinical and laboratory work-up, however, timely and accurate diagnosis is pivotal to the clinical management of cancer patients. Germline genetic testing has shown to facilitate the diagnostic process, allowing for identification and management of individuals at risk for inherited cancers. However, the laboratory diagnostics process requires not only development and validation of comprehensive gene panels to improve diagnostic yields, but a quality driven workflow including an end-to-end bioinformatics pipeline, and a robust process for variant classification. We will present a gene panel for the evaluation of hereditary cancer syndromes, conducted utilizing our novel end-to-end workflow, and validated in the CLIA-approved environment. Methods: A targeted Next-Generation Sequencing (NGS) panel consisting of 130 genes, including exons, promoters, 5’-UTRs, 3’-UTRs and selected introns, was designed to include genes associated with hereditary cancers. The assay was validated using samples from the 1000 genomes project and samples with known pathogenic variants. Elements software was utilized for end-to-end bioinformatic process ensuring adherence with the CLIA quality standards, and supporting manual curation of sequence variants. Results: Preliminary data from our current panel of genes associated with hereditary cancer syndromes revealed high sensitivity, specificity, and positive predictive value. Accuracy was confirmed by analysis of known SNVs, indels, and CNVs using 1000 Genomes and samples carrying pathogenic variants. The bioinformatics software allowed for an end-to-end quality controlled process of handling and analyzing of the NGS data, showing applicability for a clinical laboratory workflow. Conclusions: We have developed a comprehensive and accurate genetic testing process based on an automated and quality driven bioinformatics workflow that can be used to identify clinically important variants in genes associated with hereditary cancers. It's performance allows for implementation in the clinical laboratory setting.

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UMAR, ASAD. "Applications of Bioinformatics in Cancer Detection: A Lexicon of Bioinformatics Terms." Annals of the New York Academy of Sciences 1020, no. 1 (May 2004): 263–76. http://dx.doi.org/10.1196/annals.1310.021.

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Fenstermacher, David A. "Book Review: Bioinformatics in Cancer and Cancer Therapy." Cancer Control 16, no. 4 (October 2009): 349. http://dx.doi.org/10.1177/107327480901600411.

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Xu, Chaobo, and Ming Liu. "Integrative bioinformatics analysis of KPNA2 in six major human cancers." Open Medicine 16, no. 1 (January 1, 2021): 498–511. http://dx.doi.org/10.1515/med-2021-0257.

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Abstract Background Malignant tumors were considered as the leading causes of cancer-related mortality globally. More and more studies found that dysregulated genes played an important role in carcinogenesis. The aim of this study was to explore the significance of KPNA2 in human six major cancers including non-small cell lung cancer (NSCLC), gastric cancer, colorectal cancer, breast cancer, hepatocellular carcinoma, and bladder cancer based on bioinformatics analysis. Methods The data were collected and comprehensively analyzed based on multiple databases. KPNA2 mRNA expression in six major cancers was investigated in Oncomine, the human protein atlas, and GEPIA databases. The mutation status of KPNA2 in the six major cancers was evaluated by online data analysis tool Catalog of Somatic Mutations in Cancer (COSMIC) and cBioPortal. Co-expressed genes with KPNA2 were identified by using LinkedOmics and made pairwise correlation by Cancer Regulome tools. Protein-protein interaction (PPI) network relevant to KPNA2 was constructed by STRING database and KEGG pathway of the included proteins of the PPI network was explored and demonstrated by circus plot. Survival analysis-relevant KPNA2 of the six cancers was performed by GEPIA online data analysis tool based on TCGA database. Results Compared with paired normal tissue, KPNA2 mRNA was upregulated in all of the six types of cancers. KPNA2 mutations, especially missense substitution, were widely identified in six cancers and interact with different genes in different cancer types. Genes involved in PPI network were mainly enriched in p53 signaling pathway, cell cycle, viral carcinogenesis, and Foxo signaling pathway. KPNA2 protein was mainly expressed in nucleoplasm and cytosol in cancer cells. Immunohistochemistry assay indicated that KPNA2 protein was also positively expressed in nucleoplasm with brownish yellow staining. Overall survival (OS) and progression free survival (PFS) were different between KPNA2 high and low expression groups. Conclusions KPNA2 was widely dysregulated and mutated in carcinomas and correlated with the patients prognosis which may be potential target for cancer treatment and biomarker for prognosis.

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Van Neste, Leander, James G. Herman, Kornel E. Schuebel, Leslie Cope, Stephen B. Baylin, Wim Van Criekinge, and Nita Ahuja. "A Bioinformatics Pipeline for Cancer Epigenetics." Current Bioinformatics 5, no. 3 (September 1, 2010): 153–63. http://dx.doi.org/10.2174/157489310792006710.

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YANG, HOWARD H., and MAXWELL P. LEE. "Application of Bioinformatics in Cancer Epigenetics." Annals of the New York Academy of Sciences 1020, no. 1 (May 2004): 67–76. http://dx.doi.org/10.1196/annals.1310.008.

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Charoentong, Pornpimol, Mihaela Angelova, Mirjana Efremova, Ralf Gallasch, Hubert Hackl, Jerome Galon, and Zlatko Trajanoski. "Bioinformatics for cancer immunology and immunotherapy." Cancer Immunology, Immunotherapy 61, no. 11 (September 18, 2012): 1885–903. http://dx.doi.org/10.1007/s00262-012-1354-x.

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Olsen, Lars Rønn, Benito Campos, Mike Stein Barnkob, Ole Winther, Vladimir Brusic, and Mads Hald Andersen. "Bioinformatics for cancer immunotherapy target discovery." Cancer Immunology, Immunotherapy 63, no. 12 (October 26, 2014): 1235–49. http://dx.doi.org/10.1007/s00262-014-1627-7.

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12

Dopazo, Joaquín. "Bioinformatics and cancer: an essential alliance." Clinical and Translational Oncology 8, no. 6 (June 2006): 409–15. http://dx.doi.org/10.1007/s12094-006-0194-6.

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13

Marsili, Stefania, Ailone Tichon, Deepali Kundnani, and Francesca Storici. "Gene Co-Expression Analysis of Human RNASEH2A Reveals Functional Networks Associated with DNA Replication, DNA Damage Response, and Cell Cycle Regulation." Biology 10, no. 3 (March 13, 2021): 221. http://dx.doi.org/10.3390/biology10030221.

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Ribonuclease (RNase) H2 is a key enzyme for the removal of RNA found in DNA-RNA hybrids, playing a fundamental role in biological processes such as DNA replication, telomere maintenance, and DNA damage repair. RNase H2 is a trimer composed of three subunits, RNASEH2A being the catalytic subunit. RNASEH2A expression levels have been shown to be upregulated in transformed and cancer cells. In this study, we used a bioinformatics approach to identify RNASEH2A co-expressed genes in different human tissues to underscore biological processes associated with RNASEH2A expression. Our analysis shows functional networks for RNASEH2A involvement such as DNA replication and DNA damage response and a novel putative functional network of cell cycle regulation. Further bioinformatics investigation showed increased gene expression in different types of actively cycling cells and tissues, particularly in several cancers, supporting a biological role for RNASEH2A but not for the other two subunits of RNase H2 in cell proliferation. Mass spectrometry analysis of RNASEH2A-bound proteins identified players functioning in cell cycle regulation. Additional bioinformatic analysis showed that RNASEH2A correlates with cancer progression and cell cycle related genes in Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA) Pan Cancer datasets and supported our mass spectrometry findings.

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Solmaz, Mustafa, Adam Lane, Bilal Gonen, Ogulsheker Akmamedova, Mehmet H. Gunes, and Kakajan Komurov. "Graphical data mining of cancer mechanisms with SEMA." Bioinformatics 35, no. 21 (May 9, 2019): 4413–18. http://dx.doi.org/10.1093/bioinformatics/btz303.

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Abstract Motivation An important goal of cancer genomics initiatives is to provide the research community with the resources for the unbiased query of cancer mechanisms. Several excellent web platforms have been developed to enable the visual analyses of molecular alterations in cancers from these datasets. However, there are few tools to allow the researchers to mine these resources for mechanisms of cancer processes and their functional interactions in an intuitive unbiased manner. Results To address this need, we developed SEMA, a web platform for building and testing of models of cancer mechanisms from large multidimensional cancer genomics datasets. Unlike the existing tools for the analyses and query of these resources, SEMA is explicitly designed to enable exploratory and confirmatory analyses of complex cancer mechanisms through a suite of intuitive visual and statistical functionalities. Here, we present a case study of the functional mechanisms of TP53-mediated tumor suppression in various cancers, using SEMA, and identify its role in the regulation of cell cycle progression, DNA repair and signal transduction in different cancers. SEMA is a first-in-its-class web application designed to allow visual data mining and hypothesis testing from the multidimensional cancer datasets. The web application, an extensive tutorial and several video screencasts with case studies are freely available for academic use at https://sema.research.cchmc.org/. Availability and implementation SEMA is freely available at https://sema.research.cchmc.org. The web site also contains a detailed Tutorial (also in Supplementary Information), and a link to the YouTube channel for video screencasts of analyses, including the analyses presented here. The Shiny and JavaScript source codes have been deposited to GitHub: https://github.com/msolmazm/sema. Supplementary information Supplementary data are available at Bioinformatics online.

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Li, Kening, Yuxin Du, Lu Li, and Dong-Qing Wei. "Bioinformatics Approaches for Anti-cancer Drug Discovery." Current Drug Targets 21, no. 1 (December 20, 2019): 3–17. http://dx.doi.org/10.2174/1389450120666190923162203.

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Drug discovery is important in cancer therapy and precision medicines. Traditional approaches of drug discovery are mainly based on in vivo animal experiments and in vitro drug screening, but these methods are usually expensive and laborious. In the last decade, omics data explosion provides an opportunity for computational prediction of anti-cancer drugs, improving the efficiency of drug discovery. High-throughput transcriptome data were widely used in biomarkers’ identification and drug prediction by integrating with drug-response data. Moreover, biological network theory and methodology were also successfully applied to the anti-cancer drug discovery, such as studies based on protein-protein interaction network, drug-target network and disease-gene network. In this review, we summarized and discussed the bioinformatics approaches for predicting anti-cancer drugs and drug combinations based on the multi-omic data, including transcriptomics, toxicogenomics, functional genomics and biological network. We believe that the general overview of available databases and current computational methods will be helpful for the development of novel cancer therapy strategies.

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Cheng, Phil F. "Medical bioinformatics in melanoma." Current Opinion in Oncology 30, no. 2 (March 2018): 113–17. http://dx.doi.org/10.1097/cco.0000000000000428.

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Liu, Zhenqiu, Dechang Chen, Xuewen Chen, and Haomiao Jia. "Computational Data Mining in Cancer Bioinformatics and Cancer Epidemiology." Journal of Biomedicine and Biotechnology 2009 (2009): 1–2. http://dx.doi.org/10.1155/2009/582697.

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Li, Ju-Yueh, Chia-Jung Li, Li-Te Lin, and Kuan-Hao Tsui. "Multi-Omics Analysis Identifying Key Biomarkers in Ovarian Cancer." Cancer Control 27, no. 1 (January 1, 2020): 107327482097667. http://dx.doi.org/10.1177/1073274820976671.

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Ovarian cancer is one of the most common malignant tumors. Here, we aimed to study the expression and function of the CREB1 gene in ovarian cancer via the bioinformatic analyses of multiple databases. Previously, the prognosis of ovarian cancer was based on single-factor or single-gene studies. In this study, different bioinformatics tools (such as TCGA, GEPIA, UALCAN, MEXPRESS, and Metascape) have been used to assess the expression and prognostic value of the CREB1 gene. We used the Reactome and cBioPortal databases to identify and analyze CREB1 mutations, copy number changes, expression changes, and protein–protein interactions. By analyzing data on the CREB1 differential expression in ovarian cancer tissues and normal tissues from 12 studies collected from the “Human Protein Atlas” database, we found a significantly higher expression of CREB1 in normal ovarian tissues. Using this database, we collected information on the expression of 25 different CREB-related proteins, including TP53, AKT1, and AKT3. The enrichment of these factors depended on tumor metabolism, invasion, proliferation, and survival. Individualized tumors based on gene therapy related to prognosis have become a new possibility. In summary, we established a new type of prognostic gene profile for ovarian cancer using the tools of bioinformatics.

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Gensterblum-Miller, Elizabeth, and J. Chad Brenner. "Protecting Tumors by Preventing Human Papilloma Virus Antigen Presentation: Insights from Emerging Bioinformatics Algorithms." Cancers 11, no. 10 (October 12, 2019): 1543. http://dx.doi.org/10.3390/cancers11101543.

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Recent developments in bioinformatics technologies have led to advances in our understanding of how oncogenic viruses such as the human papilloma virus drive cancer progression and evade the host immune system. Here, we focus our review on understanding how these emerging bioinformatics technologies influence our understanding of how human papilloma virus (HPV) drives immune escape in cancers of the head and neck, and how these new informatics approaches may be generally applicable to other virally driven cancers. Indeed, these tools enable researchers to put existing data from genome wide association studies, in which high risk alleles have been identified, in the context of our current understanding of cellular processes regulating neoantigen presentation. In the future, these new bioinformatics approaches are highly likely to influence precision medicine-based decision making for the use of immunotherapies in virally driven cancers.

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Lin, Chih-Hsu, and Olivier Lichtarge. "Using interpretable deep learning to model cancer dependencies." Bioinformatics 37, no. 17 (May 27, 2021): 2675–81. http://dx.doi.org/10.1093/bioinformatics/btab137.

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Abstract Motivation Cancer dependencies provide potential drug targets. Unfortunately, dependencies differ among cancers and even individuals. To this end, visible neural networks (VNNs) are promising due to robust performance and the interpretability required for the biomedical field. Results We design Biological visible neural network (BioVNN) using pathway knowledge to predict cancer dependencies. Despite having fewer parameters, BioVNN marginally outperforms traditional neural networks (NNs) and converges faster. BioVNN also outperforms an NN based on randomized pathways. More importantly, dependency predictions can be explained by correlating with the neuron output states of relevant pathways, which suggest dependency mechanisms. In feature importance analysis, BioVNN recapitulates known reaction partners and proposes new ones. Such robust and interpretable VNNs may facilitate the understanding of cancer dependency and the development of targeted therapies. Availability and implementation Code and data are available at https://github.com/LichtargeLab/BioVNN Supplementary information Supplementary data are available at Bioinformatics online.

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Kim, Jiwoong, Yun-Gyeong Lee, and Namshin Kim. "Bioinformatics Interpretation of Exome Sequencing: Blood Cancer." Genomics & Informatics 11, no. 1 (2013): 24. http://dx.doi.org/10.5808/gi.2013.11.1.24.

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Hanauer, David, Daniel Rhodes, Chandan Sinha-Kumar, and Arul Chinnaiyan. "Bioinformatics Approaches in the Study of Cancer." Current Molecular Medicine 7, no. 1 (February 1, 2007): 133–41. http://dx.doi.org/10.2174/156652407779940431.

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Senior, Kathryn. "Bioinformatics: a tangled web for cancer researchers." Lancet Oncology 7, no. 3 (March 2006): 208. http://dx.doi.org/10.1016/s1470-2045(06)70611-8.

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Bensmail, Halima, and Abdelali Haoudi. "Postgenomics: Proteomics and Bioinformatics in Cancer Research." Journal of Biomedicine and Biotechnology 2003, no. 4 (2003): 217–30. http://dx.doi.org/10.1155/s1110724303209207.

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Now that the human genome is completed, the characterization of the proteins encoded by the sequence remains a challenging task. The study of the complete protein complement of the genome, the “proteome,” referred to as proteomics, will be essential if new therapeutic drugs and new disease biomarkers for early diagnosis are to be developed. Research efforts are already underway to develop the technology necessary to compare the specific protein profiles of diseased versus nondiseased states. These technologies provide a wealth of information and rapidly generate large quantities of data. Processing the large amounts of data will lead to useful predictive mathematical descriptions of biological systems which will permit rapid identification of novel therapeutic targets and identification of metabolic disorders. Here, we present an overview of the current status and future research approaches in defining the cancer cell's proteome in combination with different bioinformatics and computational biology tools toward a better understanding of health and disease.

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Simon, Richard. "Bioinformatics in cancer therapeutics—hype or hope?" Nature Clinical Practice Oncology 2, no. 5 (May 2005): 223. http://dx.doi.org/10.1038/ncponc0176.

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Dimond, Patricia Fitzpatrick. "Future Cancer Care: Anticipating “Panomics” with Bioinformatics." Clinical OMICs 1, no. 3 (May 15, 2014): 8–11. http://dx.doi.org/10.1089/clinomi.01.03.04.

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Ruan, Peifeng, Shuang Wang, and Hua Liang. "mirPLS: a partial linear structure identifier method for cancer subtyping using microRNAs." Bioinformatics 36, no. 19 (July 1, 2020): 4902–9. http://dx.doi.org/10.1093/bioinformatics/btaa606.

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Abstract Motivation MicroRNAs (miRNAs) are small non-coding RNAs that have been successfully identified to be differentially expressed in various cancers. However, some miRNAs were reported to be up-regulated in one subtype of a cancer but down-regulated in another, making overall associations between these miRNAs and the heterogeneous cancer non-linear. These non-linearly associated miRNAs, if identified, are thus informative for cancer subtyping. Results Here, we propose mirPLS, a Partial Linear Structure identifier for miRNA data that simultaneously identifies miRNAs of linear or non-linear associations with cancer status when non-linearly associated miRNAs can then be used for subsequent cancer subtyping. Simulation studies showed that mirPLS can identify both non-linearly and linearly outcome-associated miRNAs more accurately than the comparison methods. Using the identified non-linearly associated miRNAs much improves the cancer subtyping accuracy. Applications to miRNA data of three different cancer types suggest that the cancer subtypes defined by the non-linearly associated miRNAs identified by mirPLS are consistently more predictive of patient survival and more biological meaningful. Availability and implementation The R package mirPLS is available for downloading from https://github.com/pfruan/mirPLS. Supplementary information Supplementary data are available at Bioinformatics online.

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Lu, Mingbei, Suping Wu, Guoxiong Cheng, Chaobo Xu, and Zhengwei Chen. "Integrative Bioinformatics Analysis of iNOS/NOS2 in gastric and colorectal cancer." Pteridines 31, no. 1 (December 17, 2020): 174–84. http://dx.doi.org/10.1515/pteridines-2020-0011.

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AbstractObjective The aim of the present work was to investigate the expression of nitric oxide synthase 2 (iNOS/ NOS2) in colorectal and gastric cancers and evaluate its association with patient’s prognosis by integrated bioinformatics analysis.Methods The data for present study was obtained from the TCGA, GTEx, and STRING database. iNOS/NOS2 mRNA expression in normal tissue and colorectal, and gastric cancer tissuea were investigated through the GTEx and TCGA database. iNOS/NOS2 gene mutations and frequency were analyzed in the TCGA database using the cBioPortal online data analysis tool. The protein-protein interaction (PPI) network of iNOS/NOS2 was constructed by STRING database. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of iNOS/NOS2 and relevant proteins involved in the PPI network were enriched and demonstrated by the bubble plot. Comparison of the overall survival(OS) and disease free survival(DFS) between samples expressing high and low levels of iNOS/NOS2 was analysis based on the TCGA databases through the GEPIA online data analysis tool.Results For colon adenocarcinoma (COAD) and rectal adenocarcinoma(READ) iNOS/NOS2 mRNA expression levels in tumor tissue were significant higher than those of corresponding normal colorectal tissue (p<0.05). iNOS/NOS2 mutations were identified in both colorectal cancer and gastric cancer. Missense substitutions and synonymous substitution were the top two mutation types for colorectal and gastric cancer. The top positive and negative co-expressed genes correlated with iNOS/ NOS2 were TRIM40 (rpearson=0.56, p<0.05) and GDPD5 (rpearson=-0.41, p<0.05) in colorectal cancer respectively andCASP5 (rpearson=0.63,p<0.05) and PIAS3 (rpearson=-0.43,p<0.05) in gastric cancer. Twenty one proteins were included in the PPI network with 51 nodes and 345 edges which indicated the PPI enrichment wassignificant (p=1.0e-16). The KEGG of the included genes were mainly enriched in metabolic pathway and Jak-STAT signaling pathway. There was a significant difference indisease free survival (DFS) between samples expressing high and low iNOS/NOS2 (HR=0.37, p=0.044) in rectal cancer. The difference was not statistical between iNOS/NOS2 high and low expressing groups for overall survival(OS) or DFS in the colon cancer or gastric cancer(p>0.05).Conclusions iNOS/NOS2 mRNA isup-regulated in tumor tissue compared to corresponding normal tissue in colorectal and gastric cancer which implement it in the development of colorectal and gastric cancers.

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Hicks, C. "Bioinformatics Project Streamlines Data Exchange." JNCI Journal of the National Cancer Institute 96, no. 8 (April 20, 2004): 580. http://dx.doi.org/10.1093/jnci/96.8.580.

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Coleman, William B. "Cancer Bioinformatics: Addressing the Challenges of Integrated Postgenomic Cancer Research." Cancer Investigation 22, no. 1 (January 2004): 171–73. http://dx.doi.org/10.1081/cnv-120027591.

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Gadaleta, Emanuela, Stefano Pirrò, Abu Zafer Dayem Ullah, Jacek Marzec, and Claude Chelala. "BCNTB bioinformatics: the next evolutionary step in the bioinformatics of breast cancer tissue banking." Nucleic Acids Research 46, no. D1 (October 9, 2017): D1055—D1061. http://dx.doi.org/10.1093/nar/gkx913.

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Han, Jie, Yihui Rong, and Xudong Gao. "Multiomic analysis of the function of SPOCK1 across cancers: an integrated bioinformatics approach." Journal of International Medical Research 49, no. 6 (June 2021): 030006052096265. http://dx.doi.org/10.1177/0300060520962659.

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Objective To investigate SPARC (osteonectin), cwcv and kazal like domains proteoglycan 1 ( SPOCK1) gene expression across The Cancer Genome Atlas (TCGA) cancers, both in cancer versus normal tissues and in different stages across the cancer types. Methods This integrated bioinformatics study used data from several bioinformatics databases (Cancer Cell Line Encyclopedia, Genotype-Tissue Expression, TCGA, Tumor Immune Estimation Resource [TIMER]) to define the expression pattern of the SPOCK1 gene. A survival analysis was undertaken across the cancers. The search tool for retrieval of interacting genes (STRING) database was used to identify proteins that interacted with SPOCK1. Gene Set Enrichment Analysis was conducted to determine pathway enrichment. The TIMER database was used to explore the correlation between SPOCK1 and immune cell infiltration. Results This multiomic analysis showed that the SPOCK1 gene was expressed differently between normal tissues and tumours in several cancers and that it was involved in cancer progression. The overexpression of the SPOCK1 gene was associated with poor clinical outcomes. Analysis of gene expression and tumour-infiltrating immune cells showed that SPOCK1 correlated with several immune cells across cancers. Conclusions This research showed that SPOCK1 might serve as a new target for several cancer therapies in the future.

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Pettini, Francesco, Anna Visibelli, Vittoria Cicaloni, Daniele Iovinelli, and Ottavia Spiga. "Multi-Omics Model Applied to Cancer Genetics." International Journal of Molecular Sciences 22, no. 11 (May 27, 2021): 5751. http://dx.doi.org/10.3390/ijms22115751.

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In this review, we focus on bioinformatic oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. Before providing a deeper insight into the bioinformatics approach and utilities involved in oncology, we must understand what is a system biology framework and the genetic connection, because of the high heterogenicity of the backgrounds of people approaching precision medicine. In fact, it is essential to providing general theoretical information on genomics, epigenomics, and transcriptomics to understand the phases of multi-omics approach. We consider how to create a multi-omics model. In the last section, we describe the new frontiers and future perspectives of this field.

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Kihara, Daisuke, Yifeng David Yang, and Troy Hawkins. "Bioinformatics Resources for Cancer Research with an Emphasis on Gene Function and Structure Prediction Tools." Cancer Informatics 2 (January 2006): 117693510600200. http://dx.doi.org/10.1177/117693510600200020.

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The immensely popular fields of cancer research and bioinformatics overlap in many different areas, e.g. large data repositories that allow for users to analyze data from many experiments (data handling, databases), pattern mining, microarray data analysis, and interpretation of proteomics data. There are many newly available resources in these areas that may be unfamiliar to most cancer researchers wanting to incorporate bioinformatics tools and analyses into their work, and also to bioinformaticians looking for real data to develop and test algorithms. This review reveals the interdependence of cancer research and bioinformatics, and highlight the most appropriate and useful resources available to cancer researchers. These include not only public databases, but general and specific bioinformatics tools which can be useful to the cancer researcher. The primary foci are function and structure prediction tools of protein genes. The result is a useful reference to cancer researchers and bioinformaticians studying cancer alike.

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He, Yongxiong, Yongfei Cao, Xiaolei Wang, Wu Jisiguleng, Mingkai Tao, Jianfeng Liu, Fei Wang, et al. "Identification of Hub Genes to Regulate Breast Cancer Spinal Metastases by Bioinformatics Analyses." Computational and Mathematical Methods in Medicine 2021 (May 12, 2021): 1–12. http://dx.doi.org/10.1155/2021/5548918.

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Breast cancer (BC) had been one of the deadliest types of cancers in women worldwide. More than 65% of advanced-stage BC patients were identified to have bone metastasis. However, the molecular mechanisms involved in the BC spinal metastases remained largely unclear. This study screened dysregulated genes in the progression of BC spinal metastases by analyzing GSE22358. Moreover, we constructed PPI networks to identify key regulators in this progression. Bioinformatics analysis showed that these key regulators were involved in regulating the metabolic process, cell proliferation, Toll-like receptor and RIG-I-like receptor signaling, and mRNA surveillance. Furthermore, our analysis revealed that key regulators, including C1QB, CEP55, HIST1H2BO, IFI6, KIAA0101, PBK, SPAG5, SPP1, DCN, FZD7, KRT5, and TGFBR3, were correlated to the OS time in BC patients. In addition, we analyzed TCGA database to further confirm the expression levels of these hub genes in breast cancer. Our results showed that these regulators were significantly differentially expressed in breast cancer, which were consistent with GSE22358 dataset analysis. Furthermore, our analysis demonstrated that CEP55 was remarkably upregulated in the advanced stage of breast cancer compared to the stage I breast cancer sample and was significantly upregulated in triple-negative breast cancers (TNBC) compared to other types of breast cancers, including luminal and HER2-positive cancers, demonstrating CEP55 may have a regulatory role in TNBC. Finally, our results showed that CEP55 was the most highly expressed in Basal-like 1 TNBC and Basal-like 2 TNBC samples but the most lowly expressed in mesenchymal stem-like TNBC samples. Although more studies are still needed to understand the functions of key regulators in BC, this study provides useful information to understand the mechanisms underlying BC spinal metastases.

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36

Robinson, Welles, Roded Sharan, and Mark D. M. Leiserson. "Modeling clinical and molecular covariates of mutational process activity in cancer." Bioinformatics 35, no. 14 (July 2019): i492—i500. http://dx.doi.org/10.1093/bioinformatics/btz340.

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Abstract Motivation Somatic mutations result from processes related to DNA replication or environmental/lifestyle exposures. Knowing the activity of mutational processes in a tumor can inform personalized therapies, early detection, and understanding of tumorigenesis. Computational methods have revealed 30 validated signatures of mutational processes active in human cancers, where each signature is a pattern of single base substitutions. However, half of these signatures have no known etiology, and some similar signatures have distinct etiologies, making patterns of mutation signature activity hard to interpret. Existing mutation signature detection methods do not consider tumor-level clinical/demographic (e.g. smoking history) or molecular features (e.g. inactivations to DNA damage repair genes). Results To begin to address these challenges, we present the Tumor Covariate Signature Model (TCSM), the first method to directly model the effect of observed tumor-level covariates on mutation signatures. To this end, our model uses methods from Bayesian topic modeling to change the prior distribution on signature exposure conditioned on a tumor’s observed covariates. We also introduce methods for imputing covariates in held-out data and for evaluating the statistical significance of signature-covariate associations. On simulated and real data, we find that TCSM outperforms both non-negative matrix factorization and topic modeling-based approaches, particularly in recovering the ground truth exposure to similar signatures. We then use TCSM to discover five mutation signatures in breast cancer and predict homologous recombination repair deficiency in held-out tumors. We also discover four signatures in a combined melanoma and lung cancer cohort—using cancer type as a covariate—and provide statistical evidence to support earlier claims that three lung cancers from The Cancer Genome Atlas are misdiagnosed metastatic melanomas. Availability and implementation TCSM is implemented in Python 3 and available at https://github.com/lrgr/tcsm, along with a data workflow for reproducing the experiments in the paper. Supplementary information Supplementary data are available at Bioinformatics online.

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37

Katoh, Masuko, and Masaru Katoh. "Bioinformatics for Cancer Management in the Post-Genome Era." Technology in Cancer Research & Treatment 5, no. 2 (April 2006): 169–75. http://dx.doi.org/10.1177/153303460600500208.

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Human cancer is caused by multiple factors, such as genetic predisposition, chronic persistent inflammation, environmental factors, life style, and aging. Dysregulated proliferation, dysregulated adhesion, resistance to apoptosis, resistance to senescence, and resistance to anti-cancer drugs are features of cancer cells. Accumulation of multiple epigenetic changes and genetic alterations of cancer-associated genes during multi-stage carcinogenesis results in more malignant phenotypes. Post-genome science is characterized by omics data related to genome, transcriptome, proteome, metabolome, interactome, and epigenome as well as by high-throughput technology, such as whole-genome tiling oligonucleotide array, array CGH with 32,433 overlapping BAC clones, transcriptome microarray, mass spectrometry, tissue-based expression array, and cell-based transfection array. Benchtop oncology supplies Desktop oncology with large amounts of omics data produced by high-throughput technology. Desktop oncology establishes knowledge on cancer-related biomarkers, such as predisposition markers, diagnostic markers, prognostic markers, and therapeutic markers, by using bioinformatics and human intelligence of experts for data mining and text mining. Bedside oncology applies the knowledge established by Desktop oncology to determine therapeutics for cancer patients. Antibody drugs (Trastuzumab/Herceptin, Cetuximab/Erbitux, Bevacizumab/Avastin, et cetera), small molecule inhibitors for tyrosine kinases (Gefitinib/Iressa, Erlotinib/Tarceva, Imatinib/Gleevec, et cetera), conventional cytotoxic drugs, and anti-hormonal drugs are used for cancer chemotherapy. Biomarker monitoring contributes to therapeutic optional choice and drug dosage determination for cancer patients. Knowledge on biomarkers is feedforwarded from desktop to bedside in the translational research, and then biomarker monitoring is feedbacked from bedside to desktop in the reverse translational research. Desktop oncology is indispensable for cancer research in the post-genome era. Combination of genetic screening for cancer predisposition in the general population and precise selection of therapeutic options during cancer management could contribute to the realization of personalized prevention and to dramatically improve the prognosis of cancer patients in the future.

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38

Abdalwahid, Shadan Mohammed Jihad, Sami Ismael, and Shahab Wahhab Kareem. "Pre-Cancer Diagnosis via TP53 Gene Mutations Applied Ensemble Algorithms." Technium BioChemMed 2, no. 4 (September 9, 2021): 9–16. http://dx.doi.org/10.47577/biochemmed.v2i4.4654.

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According to current study, individuals with cancer who have a gene mutation have a bad prognosis. Young women with breast cancer had a poorer prognosis than older women, although it is unknown if the p53 gene mutation contributed to this. Due in part to the devastation of cancer, the appropriate technology may help cancer sufferers in regaining their lives. Researchers seek for mutations in cancer-causing gene sequences in order to identify the precancerous stage. While genetic testing may be used to forecast some kinds of cancer, there is presently no effective technique for identifying all cancers caused by TP53 gene mutations. It is one of the most often discovered genetic anomalies in human cancer is a malfunction in the action of the protein P53. As a consequence, the Universal Mutation Database is used to identify gene mutations (UMDCell-line2010). The issue is that, although many basic databases (for example, Excel format databases) exist that include datasets of TP53 gene mutations associated with disease (cancer), this huge database is incapable of detecting cancer. Thus, the purpose the objective of this study is to create an approach for data mining that utilizes a neural network to ascertain the pre-cancerous state. To begin, bioinformatics techniques such as BLAST, CLUSTALW, and NCBI were used to determine whether or not there were any malignant mutations; second, the proposed method was carried out in two stages: to begin, bioinformatics techniques such as BLAST, CLUSTALW, and NCBI were used to determine whether or not there were any malignant mutations; and third, the proposed method was carried out in two stages: to begin, bioinformatics techniques such as To begin, bioinformatics tools such as BLAST and CLUSTAL Vote Algorithms were utilized to classify pre-cancer by malignant mutations in the disease's early stages. The writers teach and evaluate their subjects using a variety of situations, including cross validation and percentages. This page contains a review of the algorithms discussed before.

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McConkey, David J., and Woonyoung Choi. "Subtyping Bladder Cancers: Biology vs Bioinformatics." JNCI: Journal of the National Cancer Institute 110, no. 5 (January 12, 2018): 439–40. http://dx.doi.org/10.1093/jnci/djx254.

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40

Zhang, Chuan, Mandy Berndt-Paetz, and Jochen Neuhaus. "Bioinformatics Analysis Identifying Key Biomarkers in Bladder Cancer." Data 5, no. 2 (April 16, 2020): 38. http://dx.doi.org/10.3390/data5020038.

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Our goal was to find new diagnostic and prognostic biomarkers in bladder cancer (BCa), and to predict molecular mechanisms and processes involved in BCa development and progression. Notably, the data collection is an inevitable step and time-consuming work. Furthermore, identification of the complementary results and considerable literature retrieval were requested. Here, we provide detailed information of the used datasets, the study design, and on data mining. We analyzed differentially expressed genes (DEGs) in the different datasets and the most important hub genes were retrieved. We report on the meta-data information of the population, such as gender, race, tumor stage, and the expression levels of the hub genes. We include comprehensive information about the gene ontology (GO) enrichment analyses and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. We also retrieved information about the up- and down-regulation of genes. All in all, the presented datasets can be used to evaluate potential biomarkers and to predict the performance of different preclinical biomarkers in BCa.

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41

Sankar, Shanju, Sangeetha K. Nayanar, and Satheesan Balasubramanian. "Current Trends in Cancer Vaccines - a Bioinformatics Perspective." Asian Pacific Journal of Cancer Prevention 14, no. 7 (July 30, 2013): 4041–47. http://dx.doi.org/10.7314/apjcp.2013.14.7.4041.

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42

Grafström, Roland C., Rebecca Ceder, Bengt Fadeel, Karin Roberg, and Egon Willighagen. "Bioinformatics-based cancer research have wide toxicological applicability." Toxicology Letters 211 (June 2012): S160. http://dx.doi.org/10.1016/j.toxlet.2012.03.580.

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43

Pienta, K. J., and A. M. Chinnaiyan. "186 Bioinformatics and gene discovery in prostate cancer." European Journal of Cancer Supplements 7, no. 2 (September 2009): 47. http://dx.doi.org/10.1016/s1359-6349(09)70165-x.

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44

Hoheisel, Jörg. "Bioinformatics tools for molecular cancer diagnostics on microarrays." European Journal of Cancer Supplements 4, no. 6 (June 2006): 9. http://dx.doi.org/10.1016/j.ejcsup.2006.04.018.

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45

Manning, A. T., J. T. Garvin, R. I. Shahbazi, N. Miller, R. E. McNeill, and M. J. Kerin. "Molecular profiling techniques and bioinformatics in cancer research." European Journal of Surgical Oncology (EJSO) 33, no. 3 (April 2007): 255–65. http://dx.doi.org/10.1016/j.ejso.2006.09.002.

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46

Malik, Adeel, Hemajit Singh, Munazah Andrabi, Syed Akhtar Husain, and Shandar Ahmad. "Databases and QSAR for Cancer Research." Cancer Informatics 2 (January 2006): 117693510600200. http://dx.doi.org/10.1177/117693510600200002.

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In this review, we take a survey of bioinformatics databases and quantitative structure-activity relationship studies reported in published literature. Databases from the most general to special cancer-related ones have been included. Most commonly used methods of structure-based analysis of molecules have been reviewed, along with some case studies where they have been used in cancer research. This article is expected to be of use for general bioinformatics researchers interested in cancer and will also provide an update to those who have been actively pursuing this field of research.

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47

Huang, Chiang-Ching, Meijun Du, and Liang Wang. "Bioinformatics Analysis for Circulating Cell-Free DNA in Cancer." Cancers 11, no. 6 (June 11, 2019): 805. http://dx.doi.org/10.3390/cancers11060805.

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Molecular analysis of cell-free DNA (cfDNA) that circulates in plasma and other body fluids represents a “liquid biopsy” approach for non-invasive cancer screening or monitoring. The rapid development of sequencing technologies has made cfDNA a promising source to study cancer development and progression. Specific genetic and epigenetic alterations have been found in plasma, serum, and urine cfDNA and could potentially be used as diagnostic or prognostic biomarkers in various cancer types. In this review, we will discuss the molecular characteristics of cancer cfDNA and major bioinformatics approaches involved in the analysis of cfDNA sequencing data for detecting genetic mutation, copy number alteration, methylation change, and nucleosome positioning variation. We highlight specific challenges in sensitivity to detect genetic aberrations and robustness of statistical analysis. Finally, we provide perspectives regarding the standard and continuing development of bioinformatics analysis to move this promising screening tool into clinical practice.

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48

Kim, So Yeon, Eun Kyung Choe, Manu Shivakumar, Dokyoon Kim, and Kyung-Ah Sohn. "Multi-layered network-based pathway activity inference using directed random walks: application to predicting clinical outcomes in urologic cancer." Bioinformatics 37, no. 16 (February 5, 2021): 2405–13. http://dx.doi.org/10.1093/bioinformatics/btab086.

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Abstract Motivation To better understand the molecular features of cancers, a comprehensive analysis using multi-omics data has been conducted. In addition, a pathway activity inference method has been developed to facilitate the integrative effects of multiple genes. In this respect, we have recently proposed a novel integrative pathway activity inference approach, iDRW and demonstrated the effectiveness of the method with respect to dichotomizing two survival groups. However, there were several limitations, such as a lack of generality. In this study, we designed a directed gene–gene graph using pathway information by assigning interactions between genes in multiple layers of networks. Results As a proof-of-concept study, it was evaluated using three genomic profiles of urologic cancer patients. The proposed integrative approach achieved improved outcome prediction performances compared with a single genomic profile alone and other existing pathway activity inference methods. The integrative approach also identified common/cancer-specific candidate driver pathways as predictive prognostic features in urologic cancers. Furthermore, it provides better biological insights into the prioritized pathways and genes in an integrated view using a multi-layered gene–gene network. Our framework is not specifically designed for urologic cancers and can be generally applicable for various datasets. Availability and implementation iDRW is implemented as the R software package. The source codes are available at https://github.com/sykim122/iDRW. Supplementary information Supplementary data are available at Bioinformatics online.

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49

Zhang, Yuwei, Yang Tao, Huihui Ji, Wei Li, Xingli Guo, Derry Minyao Ng, Maria Haleem, et al. "Genome-wide identification of the essential protein-coding genes and long non-coding RNAs for human pan-cancer." Bioinformatics 35, no. 21 (March 27, 2019): 4344–49. http://dx.doi.org/10.1093/bioinformatics/btz230.

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Abstract Motivation Genome-scale CRISPR/Cas9 system has been a democratized gene editing technique and widely used to investigate gene functions in some biological processes and diseases especially cancers. Aiming to characterize gene aberrations and assess their effects on cancer, we designed a pipeline to identify the essential genes for pan-cancer. Methods CRISPR screening data were used to identify the essential genes that were collected from published data and integrated by Robust Rank Aggregation algorithm. Then, hypergeometrics test and random walks with restart (RWR) were used to predict additional essential genes on broader scale. Finally, the expression status and potential roles of these genes were explored based on TCGA portal and regulatory network analysis. Results We collected 926 samples from 10 CRISPR-based screening studies involving 33 different types of cancer to identify cancer-essential genes, which consists of 799 protein-coding genes (PCGs) and 97 long non-coding RNAs (lncRNAs). Then, we constructed a ‘bi-colored’ network with both PCGs and lncRNAs and applied it to predict additional essential genes including 495 PCGs and 280 lncRNAs on a broader scale using hypergeometrics test and RWR. After obtaining all essential genes, we further investigated their potential roles in cancer and found that essential genes have higher and more stable expression levels, and are associated with multiple cancer-associated biological processes and survival time. The regulatory network analysis detected two intriguing modules of essential genes participating in the regulation of cell cycle and ribosome biogenesis in cancer. Availability and implementation Supplementary information Supplementary data are available at Bioinformatics online.

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Finney, Richard, and Daoud Meerzaman. "Chromatic: WebAssembly-Based Cancer Genome Viewer." Cancer Informatics 17 (January 1, 2018): 117693511877197. http://dx.doi.org/10.1177/1176935118771972.

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Chromatic is a novel web-browser tool that enables researchers to visually inspect genomic variations identified through next-generation sequencing of cancer data sets to determine whether such calls are, in fact, valid. It is the first cancer bioinformatics tool developed using WebAssembly technology, which comprises a portable, low-level byte code format that provides for the rapid execution of programs within supported web browsers. It has been designed expressly for ease of use by scientists without extensive expertise in bioinformatics.

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