Construction of a liver cancer prognostic model based on IFN-γ-related genes for revealing the immune landscape and predicting drugs

Background: IFN-γ exerts anti-tumor effects, but there is currently no reliable and comprehensive study on the predictive function of IFN-γ-related genes in liver cancer. Methods: In this study, IFN-γ-related differentially expressed genes (DEGs) in liver cancer were obtained through GO/KEGG databases and open-access literature. Based on these genes, liver cancer individuals were clustered. A liver cancer prognostic model was built based on the intersection genes between differential genes in clusters and in liver cancer. Then, the predictive accuracy of the model was analyzed and validated in the GEO dataset. Regression analysis was fulfilled on the model, and a nomogram was used to evaluate the ability of the model as an independent prognostic factor and its clinical application value. An immune-related analysis was conducted on both the H- and L-groups, with an additional investigation into the correlation between model genes and drug sensitivity. Results: Significant differential expression of IFN-γ-related genes was observed between the liver cancer and control groups. Subsequently, liver cancer individuals were classified into two subtypes based on these genes, which displayed a notable difference in survival between the two subtypes. A 10-gene liver cancer prognostic model was constructed, with good predictive accuracy and was an independent prognostic indicator for patient analysis. L-group patients possessed higher immune infiltration levels, immune checkpoint expression levels, and immunophenoscore, as well as lower TIDE scores. Drugs that had high correlations with the feature genes included SPANXB1: PF-04217903, SGX-523, MMP1: PF-04217903, DUSP13: Imat