Single-cell transcriptomic analysis reveals efferocytosis signature predicting immunotherapy response in hepatocellular carcinoma

Abstract

Background

Hepatocellular carcinoma (HCC) is a substantial global health challenge owing to its high mortality rate and limited therapeutic options. We aimed to develop an efferocytosis-related gene signature (ER.Sig) and conduct a transcriptomic analysis to predict the prognosis and immunotherapeutic responses of patients with HCC.

Methods

Single-cell RNA sequencing data and bulk RNA sequencing data were obtained from public databases. Based on single-sample gene set enrichment analysis and Weighted Gene Co-expression Network analyses, efferocytosis-related genes (ERGs) were selected at both the single-cell and bulk transcriptome levels. A machine-learning framework employing ten different algorithms was used to develop the ER.Sig. Subsequently, a multi-omics approach (encompassing genomic analysis, single-cell transcriptomics, and bulk transcriptomics) was employed to thoroughly elucidate the prognostic signatures.

Results

Analysis of the HCC single-cell transcriptomes revealed significant efferocytotic activity in macrophages, endothelial cells, and fibroblasts within the HCC microenvironment. We then constructed a weighted co-expression network and identified six modules, among which the brown module (168 genes) was most highly correlated with the efferocytosis score (cor = 0.84). Using the univariate Cox regression analysis, 33 prognostic ERGs were identified. Subsequently, a predictive model was constructed using 10 machine-learning algorithms, with the random survival forest model showing the highest predictive performance. The final model, ER.Sig, comprised nine genes and demonstrated robust prognostic capabilities across multiple datasets. High-risk patients exhibited greater intratumoral heterogeneity and higher TP53 mutation frequencies than did low-risk patients. Immune landscape analysis revealed that compared with high-risk patients, low-risk patients exhibited a more favorable immune environment, characterized by higher proportions of CD8+ T and B cells, tumor microenvironment score, immunophenoscore, and lower Tumor Immune Dysfunction and Exclusion scores, indicating better responses to immunotherapy. Additionally, an examination of an independent immunotherapy cohort (IMvigor210) demonstrated that low-risk patients exhibited more favorable responses to immunotherapy and improved prognoses than did their high-risk counterparts.

Conclusions

The developed ER.Sig effectively predicted the prognosis of patients with HCC and revealed significant differences in tumor biology and treatment responses between the risk groups.