Identification of Cervical Cancer Biomarkers Using Gene Co-Expression Networks and Machine Learning Methods

Cervical cancer (CC) causes significant mortality due to late diagnosis and limited understanding of its molecular drivers. The complex gene co-expression patterns associated with CC remain poorly characterized. Identifying key genes that distinguish tumors from normal tissue can significantly improve early diagnosis and treatment strategies. This study combines Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning to detect potential biomarkers. Bulk RNA-seq data from 228 cervical tumors and 49 normal tissues from the TCGA-CESC and GTEx datasets are analyzed. After batch correction with ComBat-seq, Differentially Expressed Genes (DEGs) are identified using |log2FC| > 2.0 and adjusted p < 0.05. These DEGs are grouped into modules using WGCNA, which are then linked to cervical cancer traits. Key genes are selected based on strong module membership and gene-trait significance. Gene Ontology (GO) analysis is performed, and then a random forest model is trained to identify biomarker genes. WGCNA groups 5,165 DEGs into four modules: Green (1,320), Black (150), Blue (1,686), and Yellow (258), excluding unassigned genes. The Green and Blue modules show strong correlations with CC. Within these modules, 513 genes in the green module and 12 genes in the blue module meet the selection criteria as candidate genes. GO analysis reveals that these key genes are associated with muscle cell differentiation, tissue migration, renal system development, cell adhesion, and vascular processes. Finally, the random forest model identifies LRRN4CL, CNRIP1, and CDCA3 as the top genes for distinguishing tumors from normal samples. This study identifies key gene modules and biomarker genes strongly linked to cervical cancer. These genes reveal critical biological processes involved in tumor progression and have potential for early diagnosis and targeted therapies.