Bioinformatics Analysis of Inflammation Gene Signature in Indicating Cholangiocarcinoma Prognosis

AIM: We studied inflammatory response-related genes in cholangiocarcinoma by bioinformatics analysis. METHODS: The expression profiles and clinical information of cholangiocarcinoma patients were downloaded from the TCGA cohort and the Gene Expression Omnibus. The greatest absolute shrinking and selecting operator Cox analyses were utilized to build a multigene predictive signature. RESULTS: An inflammation response-related gene profile was generated using LASSO-Cox regression analysis of Homo sapiens bestrophin 1 (BEST1), Chemokine (C–C motif) ligand 2 (CCL2), and plasminogen activator, urokinase receptor (PLAUR). Individuals in the highest category had a significantly lower overall survival time than those from the low-risk group. A receiver operating curve analysis was used to demonstrate the predictive ability of the predictive gene signature. Through multivariate Cox analysis, the risk score was discovered to be a predictor of overall survival (OS). According to functional assessments, the immunological state and milieu of the two risk areas were significantly different. The expression levels of predictive genes were found to be strongly linked to the sensitivity of cancer cells to antitumor therapy. CONCLUSION: A new signature made up of three respective response-relevant genes is found to be a promising indicator of prognosis by influencing the immune condition and tumor microenvironment.