Computational and Bioinformatics Analysis of Gallbladder Cancer Gene Expression Profiles

Background: Gallbladder cancer (GBC) is a highly aggressive malignancy with limited therapeutic options and poor prognosis. This study integrates bioinformatics and machine learning (ML) approaches to analyze the transcriptional changes in GBC and identify potential therapeutic targets. Methods: The NCBI GEO database was used to gather the published microarray data gene expression patterns of GBC cells stimulated with chenodeoxycholic acid (CDCA), analyzed using RNA-seq to identify differentially expressed genes (DEGs). R software was used to process the data, Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database for the enrichment of pathways and their function in DEGs, and string database was used to study protein-protein interactions (PPIs). ML algorithms, including support vector machine, random forest, XG boost, Least Absolute Shrinkage and Selection Operat (LASSO), Elastic Net, and neural networks, were applied to predict the outcomes based on gene expression profiles. Results: A total of 11,009 DEGs were identified, including 6663 upregulated and 4346 downregulated genes, with key downregulated genes such as PADI1 , CXCL8 , and MUC5AC significantly associated with immune modulation, epithelial barrier function, and tumor invasion. Pathway enrichment analysis using GO and KEGG highlighted critical pathways, including IL-17 signaling, glycosylation defects, and epithelial cell signaling, emphasizing their roles in tumor progression and immune evasion. PPI network analysis identified functional clusters with high connectivity, suggesting significant biological roles in GBC. ML techniques, including Gradient Boosting, Random Forest, and XGBoost, were employed for predictive modeling. These models achieved exceptional accuracy (area under the curve = 1.0), with LASSO and ElasticNet feature selection pinpointing critical genes driving GBC progression. Principal Component Analysis captured 50.7% of the transcriptional variability, confirming distinct gene expression profiles between CDCA-treated and control samples. Hierarchical clustering validated these findings, highlighting clear segregation of experimental conditions. Conclusion: The results support the pivotal role of FGF19-FGFR4 signaling in GBC progression and provide novel insights into transcriptional disruptions associated with the disease. The findings underscore the potential of targeting immune modulation and glycosylation pathways as therapeutic strategies in GBC. This study offers novel insights into the molecular landscape of GBC.