An integrative analysis to identify pancancer epigenetic biomarkers

IF 2.6 4区生物学 Q2 BIOLOGY Computational Biology and Chemistry Pub Date : 2024-10-23 DOI:10.1016/j.compbiolchem.2024.108260

Panchami V.U. , Manish T.I. , Manesh K.K.

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Abstract

Integrating and analyzing the pancancer data collected from different experiments is crucial for gaining insights into the common mechanisms in the molecular level underlying the development and progression of cancers. Epigenetic study of the pancancer data can provide promising results in biomarker discovery. The genes that are epigenetically dysregulated in different cancers are powerful biomarkers for drug-related studies. This paper identifies the genes having altered expression due to aberrant methylation patterns using differential analysis of TCGA pancancer data of 12 different cancers. We identified a comprehensive set of 115 epigenetic biomarker genes out of which 106 genes having pancancer properties. The correlation analysis, gene set enrichment, protein–protein interaction analysis, pancancer characteristics analysis, and diagnostic modeling were performed on these biomarkers to illustrate the power of this signature and found to be important in different molecular operations related to cancer. An accuracy of 97.56% was obtained on TCGA pancancer gene expression dataset for predicting the binary class tumor or normal. The source code and dataset of this work are available at https://github.com/panchamisuneeth/EpiPanCan.git.

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综合分析确定胰腺癌表观遗传生物标志物。

整合和分析从不同实验中收集到的胰腺癌数据，对于深入了解癌症发生和发展的分子水平共同机制至关重要。对胰腺癌数据进行表观遗传学研究可为生物标志物的发现提供有希望的结果。在不同癌症中表观遗传失调的基因是药物相关研究的有力生物标志物。本文通过对 12 种不同癌症的 TCGA 胰腺癌数据进行差异分析，确定了因甲基化模式异常而导致表达改变的基因。我们鉴定出了一整套 115 个表观遗传生物标记基因，其中 106 个基因具有胰腺癌特性。我们对这些生物标志基因进行了相关性分析、基因组富集、蛋白-蛋白相互作用分析、胰腺癌特征分析和诊断模型分析，以说明该特征基因的强大功能，并发现它们在与癌症有关的不同分子操作中具有重要作用。在 TCGA 胰腺癌基因表达数据集上，预测二元类肿瘤或正常的准确率达到 97.56%。这项工作的源代码和数据集可在 https://github.com/panchamisuneeth/EpiPanCan.git 网站上查阅。

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来源期刊

Computational Biology and Chemistry 生物-计算机：跨学科应用

CiteScore

6.10

自引率

3.20%

发文量

142

审稿时长

24 days

期刊介绍： Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.