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Computational Analysis of Fused Co-expression Networks for the Identification of Candidate Cancer Gene Biomarkers by Pidò, Sara (author);Ceddia, Gaia (author);Masseroli, Marco (author) is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

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## Abstract The complexity of cancer has always been a huge issue in understanding the source of this disease. However, by appreciating its complexity, we can shed some light on crucial gene associations across and in specific cancer types. In this study, we develop a general framework to infer relevant gene biomarkers and their gene-to-gene associations using multiple gene co-expression networks for each cancer type. Specifically, we infer computationally and biologically interesting communities of genes from kidney renal clear cell carcinoma, liver hepatocellular carcinoma, and prostate adenocarcinoma data sets of The Cancer Genome Atlas (TCGA) database. The gene communities are extracted through a data-driven pipeline and then evaluated through both functional analyses and literature findings. Furthermore, we provide a computational validation of their relevance for each cancer type by comparing the performance of normal/cancer classification for our identified gene sets and other gene signatures, including the typically-used differentially expressed genes. The hallmark of this study is its approach based on gene co-expression networks from different similarity measures: using a

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Author

Pidò, Sara (author);Ceddia, Gaia (author);Masseroli, Marco (author)

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)