Identification of CD8+ T Cell-Related Genes: Correlations with Immune Phenotypes and Outcomes of Liver Cancer

Abstract

Purpose. Treatment outcomes for advanced liver cancer are poor. Immunotherapy is a treatment strategy that has been widely used to treat other cancers. Studies have shown that CD8+ T lymphocytes are essential factors affecting the efficacy of immunotherapy. We used computational biology methods to determine the coexpressed gene network that promotes CD8+ T lymphocyte infiltration. Method. We obtained the liver cancer gene matrix and clinical follow-up information data from TCGA liver hepatocellular carcinoma FPKM. We obtained single nucleotide polymorphism (SNP) data to evaluate the tumor mutation burden. The “estimate” package and the CIBERSORT algorithm were used to evaluate tumor purity and the proportion of CD8+ T lymphocytes in the liver cancer cohort. We used the gene expression matrix of liver cancer and the relative proportion of CD8+ T lymphocytes as input files and performed WGCNA based on this analysis. The weighted coexpression network identified the most CD8+ T lymphocyte-related coexpression modules in liver cancer. Then, we analyzed the biological processes involved in the module. We determined the coexpression module with CD8+ T lymphocyte infiltration in terms of data and function. We then screened the factors in the coexpression module correlated with CD8+ T lymphocyte content greater than 0.4. Finally, the expression levels of these factors were verified at the protein level using immunohistochemistry and single-cell sequencing. Results. We determined the CD8+ T lymphocyte proportions that correlated with coexpression networks. Four coexpressed genes (C1QC, CD3D, GZMA, and PSMB9) were identified as CD8+ T cell coexpression genes that promoted infiltration of CD8+ T cells. Because the factors in the coexpression network often participate in similar biological processes, we found that these factors were most related to antigen processing and presentation of peptide antigen through functional enrichment. In the clinical phenotype analysis, we found that 18 factors can be used as independent prognostic protective factors. We found that these factors were significantly negatively correlated with tumor purity and negatively correlated with M2 macrophages in the immunophenotyping analysis. Using immunohistochemistry and single-cell sequencing analysis, we found that CD3D antibody staining was weaker in tumor tissues than normal tissues and was related to CD8+ T cells. Conclusion. These coexpressed genes were positively related to the high infiltration proportion of CD8+ T lymphocytes in an antigen presentation process. The biological process might provide new directions for patients who are insensitive to immune therapy.