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 combination of multiple gene networks and then fusing normal and cancer networks for each cancer type, we can have better insights on the overall structure of the cancer-type-specific network.
Highlights
Cancer is a complex disease affecting various biological processes in human cells and causing abnormal cell growth, invasion, and migration[1]
We present the results obtained with our approach applied on The Cancer Genome Atlas (TCGA) data of kidney clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), and prostate adenocarcinoma (PRAD) cancer types
We report the communities of genes identified in the fused network for each cancer type and the evaluation of their relevance, both computationally and biologically
Summary
Cancer is a complex disease affecting various biological processes in human cells and causing abnormal cell growth, invasion, and migration[1]. System biology is one of the most commonly used approaches for understanding such complexity since it studies the molecular interactions determining particular biological functions within a cell[1,2,3,4,5]. Gene expression studies have shown that diverse gene expressions are associated with fundamental differences in clinical and biological features[10,11]. The majority of these studies focused on the identification of individual cancer biomarkers and their prognostic use, without addressing the main objective of system biology, i.e., a better comprehension of the functional mechanisms of the found biomarkers[6,12,13,14,15,16].
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