Identification and Validation of Tumour Microenvironment-Based Immune Related Signature for Hepatocellular Carcioma: Immunotherapeutic Implications

Background: Even though treatment outcomes for hepatocellular carcinoma patients have significantly improved, prognostic clinical evaluation remains a substantial challenge due to the heterogeneity and complexity of cancer. Accumulating evidence has revealed that the tumor immune microenvironment is critical for progression and prognosis of hepatocellular carcinoma. A powerful predictive model could assist physicians to better monitor patient treatment outcomes and improve overall survival rates. Therefore, we introduced tumor immune-related genes into a model that could be used for patient risk classification. Results: First, the Single-sample gene set enrichment analysis (ssGSEA) and Weighted gene co-expression networks construction (WGCNA) methods were applied to identify highly associated immunity genes. Following this, a multi-immune-related gene-based signature determined by The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to determine risk stratification. In addition, this predictive model was evaluated according to its performance as a prognostic model in the training and testing datasets. Furthermore, tumor mutation burden and biological enrichment analysis were applied to reveal the potential mechanisms through which the gene signature functions. Conclusion: In conclusion, our four-gene signature model may be clinically applied in hepatocellular carcinoma patients at high risk of mortality for personalized therapy.