Potential diagnostic markers and therapeutic targets for non-alcoholic fatty liver disease and ulcerative colitis based on bioinformatics analysis and machine learning - Luo, Zheng; Huang, Cong; Chen, Jilan; Chen, Yunhui; Yang, Hongya; Wu, Qiaofeng; Lu, Fating; Zhang, Tian E. (Frontiers in Medicine, 2024)
Background: Non-alcoholic fatty liver disease (NAFLD) and ulcerative colitis (UC) are common health issues with a potential connection, though the underlying pathophysiology is unclear. This study explores shared genes, diagnostic markers, and therapeutic targets between NAFLD and UC. Methods: Datasets for NAFLD (GSE89632) and UC (GSE87466) were obtained from the GEO database. Differentially expressed genes (DEGs) were identified, and WGCNA was applied to find gene modules in both datasets. Shared genes between NAFLD and UC were obtained by intersecting DEGs and modular genes. Functional enrichment analysis and a protein-protein interaction (PPI) network were performed using the STRING database and Cytoscape software. Central genes were identified using the Cytohubba algorithm. Diagnostic biomarkers were screened using LASSO regression and SVM, with ROC curve analysis used to confirm diagnostic genes in both training and validation sets. A nomogram evaluated diagnostic efficacy, and the CIBERSORT algorithm was used to explore immune infiltration patterns. Correlations between hub genes, diagnostic genes, and immune infiltration were also examined. Results: A total of 34 shared genes were identified, with enrichment in pathways like IL-17 signaling. CCL2 emerged as an optimal candidate gene through LASSO and SVM. The ROC curve confirmed CCL2 as a diagnostic marker in both training and validation sets. Its expression levels correlated significantly with immune cell infiltration. Conclusions: CCL2 was identified as a potential biomarker for both NAFLD and UC, offering insights into disease progression and potential diagnostic and therapeutic strategies.