A Protocol for Weighted Gene Co-expression Network Analysis With Module Preservation and Functional Enrichment Analysis for Tumor and Normal Transcriptomic Data

Weighted gene co-expression network analysis (WGCNA) is widely used in transcriptomic studies to identify groups of highly correlated genes, aiding in the understanding of disease mechanisms. Although numerous protocols exist for constructing WGCNA networks from gene expression data, many focus on single datasets and do not address how to compare module stability across conditions. Here, we present a protocol for constructing and comparing WGCNA modules in paired tumor and normal datasets, enabling the identification of modules involved in both core biological processes and those specifically related to cancer pathogenesis. By incorporating module preservation analysis, this approach allows researchers to gain deeper insights into the molecular underpinnings of oral cancer, as well as other diseases. Overall, this protocol provides a framework for module preservation analysis in paired datasets, enabling researchers to identify which gene co-expression modules are conserved or disrupted between conditions, thereby advancing our understanding of disease-specific vs. universal biological processes. Key features • Presents a step-by-step WGCNA protocol with module preservation and functional enrichment analysis [1,2] using TCGA cancer data, demonstrating network differences between normal and tumor tissues. • Preprocesses gene expression data and conducts downstream analysis for constructed networks. • Requires 2-3 h hands-on time and 8-12 h total computational time, depending on dataset size and permutation number used for module preservation analysis.