Integrative Machine Learning and Bioinformatics Approach for Identifying Key Biomarkers in Gallbladder Cancer Diagnosis and Progression

Gallbladder cancer (GBC) is the most common biliary tract neoplasm. Identifying biomarkers for GBC initiation and progression remains a challenge. This study aimed to identify GBC biomarkers using machine learning and bioinformatics. Differentially expressed genes (DEGs) were identified from two microarray datasets (GSE100363, GSE139682) from the GEO database. Gene Ontology and pathway analyses were performed using DAVID. A protein-protein interaction network was constructed using STRING, and hub genes were identified via three ranking algorithms (degree, MNC and closeness centrality). Feature selection methods (Pearson correlation, recursive feature elimination) were applied to extract key gene subsets. Machine learning models (SVM, NB and RF) were trained on GSE100363 and validated on GSE139682 to assess predictive performance. Biomarkers were further validated using the GEPIA database. A total of 146 DEGs were identified, including 39 upregulated and 107 downregulated genes. Eleven hub genes were identified, with SLIT3, COL7A1 and CLDN4 strongly correlated with GBC. Machine learning results confirmed their diagnostic potential. The study highlights NTRK2, COL14A1, SCN4B, ATP1A2, SLC17A7, SLIT3, COL7A1, CLDN4, CLEC3B, ADCYAP1R1 and MFAP4 as crucial genes associated with GBC. SLIT3, COL7A1 and CLDN4 serve as highly predictive biomarkers, and findings can improve early diagnosis and prognosis, aiding clinical decision-making.