Evaluating Distribution and Prognostic Value of New Tumor-Infiltrating Lymphocytes in HCC Based on a scRNA-Seq Study With CIBERSORTx

Hepatocellular Carcinoma(HCC) is a commonly diagnosed cancer with high mortality. Immune response plays important role in progression of HCC. Recent years, immunotherapies are becoming a promising tool for treating cancers. Besides, advances in scRNA-seq(single-cell RNA sequencing) allow us to identify new subsets in immune microenvironment of HCC. Yet, distribution of these new cell types and potential prognostic value in bulk samples from large cohorts remained unclear. This study is aimed to investigate the tumor-infiltration and prognostic value of new cell subsets identified by a previous scRNA-seq study in TCGA HCC cohort using CIBERSORTx, a machine learning method to estimate cell proportion and infer cell-type-specific gene expression profiles. We observed different distribution of 3 immune cell types between tumor and normal. Among these, CD4-GZMA cell subset showed association with prognosis(log-rank test, p<0.05). We further analyzed CD4-GZMA cell specific gene expression with CIBERSORTx, and found 19 prognostic genes(univariable cox regression, p<0.05). Finally, we applied Least absolute shrinkage and selection operator(LASSO) Cox regression to construct an immune risk score model and performed prognostic assessment of our model in TCGA and ICGC cohorts. Taken together, immune landscape in HCC bulk samples may be more complex with heterogeneity with different tumor-infiltration relative to scRNA-seq results. Additionally, CD4-GZMA cells and their characteristics may yield therapeutic benefits in the immune treatment of HCC.