Comprehensive analysis of immune-related gene signature based on ssGSEA algorithms in the prognosis and immune landscape of hepatocellular carcinoma - Wang, Liangliang; Wang, Li; He, Peihong (Frontiers in Genetics, 2022)
Background: Hepatocellular carcinoma is a gastrointestinal malignancy with dismal prognosis. This study aimed to distinguish HCC patients with distinct tumor immune microenvironment features and to construct an immune-related gene signature (IRGs) to assess prognosis and provide a basis for individualized therapies. Methods: Transcriptomic data for patients with HCC were downloaded from the TCGA database and the GEO database. We assessed the immune cell infiltration in each specimen according to ssGSEA and classified all HCC patients into high- and low-immune clusters by a hierarchical clustering algorithm. The ESTIMATE and CIBERSORT algorithms were employed to verify the stability and effectiveness of the clusters. The differentially expressed genes of the high- and low-immune clusters and the immune-related genes were taken to intersect to obtain the immune-related DEGs. The LASSO was then employed to screen the optimal genes for the construction of a signature. The predictive efficacy of the IRGs was further confirmed by Kaplan-Meier curves, Cox regression, and ROC curves in the TCGA and GSE14520 cohorts. The GO analysis and the GSVA were used for biological function and pathway exploration. Lastly, drug sensitivity analyses were employed to investigate prospective therapeutics. Results: Immune cluster analysis based on ssGSEA could well distinguish the TIME characteristics of patients with HCC. Meanwhile, most of the immune checkpoint-related genes and HLA family genes were overexpressed in the high-immune cluster, suggesting that that this cluster may be a beneficial population for immune checkpoint inhibitors therapy. There were 1617 DEGs between the two immune clusters, of which 414 genes were intersecting genes with immune-associated genes. Then 19 DEGs were screened by the LASSO algorithm for IRGs construction and patients were classified into high- and low-risk groups. Both the constructed signature and nomogram had good prognostic predictive efficacy. The signature-based risk score was an independent prognostic predictor in both the TCGA and GSE14520 cohorts. Lastly, the half-maximal inhibitory concentrations (IC50) of certain chemotherapeutic and targeted therapeutic agents differed between the two risk groups. Conclusions: Our study provides a personalized tool for predicting the prognosis and TIME landscape of HCC and provides a basis for the selection of individualized treatment regimens.