<![CDATA[Background: Metabolic Associated Fatty Liver Disease (MAFLD) is a common liver disease associated with an increased risk of developing hepatocellular carcinoma (HCC). Current treatments for MAFLD-related HCC are limited, highlighting the need for new biomarkers and therapeutic targets to improve patient outcomes. This study employed Weighted Gene Coexpression Network Analysis (WGCNA) to uncover novel gene associations and interactions in MAFLD and HCC, providing new insights into their molecular mechanisms.Method: A dataset comprising liver biopsy tissues was obtained from 106 HCC-naive MAFLD patients (164 biopsy tissues, including 1st biopsy for all patients and 2nd follow-up biopsy for 58 PLS (Prognostic Liver Signature)-MAFLD high-risk patients). Transcriptomic data from liver tissue samples were subjected to WGCNA, facilitating the identification of gene modules associated with risk stratification.Results: Analysis of 12544 genes from the GSE193066 dataset revealed 17 distinct gene coexpression modules. Notably, the black, blue, and cyan modules exhibited strong correlations with MAFLD-related HCC. Functional enrichment analysis provided significant insights into key biological processes and pathways, including epithelial-mesenchymal transition, inflammatory response, angiogenesis, and signaling pathways such as KRAS, wnt-β-catenin, and p53 pathway. Conclusion: This study utilizes WGCNA to identify crucial genes and pathways involved in MAFLDrelated HCC. The results provide a basis for future research and call for validation in larger patient cohorts. Overcoming these limitations and exploring the clinical significance of the identified genes and pathways can enhance diagnostics and enable targeted therapies for MAFLD-related HCC.]]>
