WGCNA and Machine Learning-Based Integrative Bioinformatics Analysis for Identifying Key Genes of Colorectal Cancer

Abstract

Colorectal cancer (CC) is a significant public health concern and make it necessary to identify reliable biomarkers and elucidate their molecular and biological mechanisms. This study proposed a system by integrating weighted gene co-expression network analysis (WGCNA) and machine learning-based integrative bioinformatics (ML-IB) analysis to identify key genes for CC. WGCNA was implemented to find a co-expression network of genes and identify important genes by intersecting gene sets obtained using module membership and gene significance criteria across datasets. WGCNA-based significant genes were determined by intersecting important genes between two datasets. ML-IB based approach primarily identified differentially expressed genes (DEGs), then employed support vector machine to determine differentially expressed discriminative genes (DEDGs) and took their common DEDGs across datasets. Protein-protein interaction networks were built and identified hub genes based on the degrees of connectivity and hub module genes using MCODE scores. The ML-IB based significant genes were determined by intersecting hub genes and hub module genes. Four common significant genes were found by intersecting significant genes derived from WGCNA and ML-IB based perspectives. Finally, two genes (AURKA and CCNA2) were determined as key genes for showing strong correlation with survival of CC patients and validated their discriminative capability on an independent test dataset using AUC analysis. The key genes of AURKA and CCNA2 may be used for the early detection of patients with CC. This study will helpful for physicians and doctors to determine and understand the associated the molecular mechanisms and pathway of patients with CC.

Keywords

• Colorectal cancer
• WGCNA
• machine learning-based models
• differentially expressed discriminative genes
• bioinformatics analysis
• key genes