Integrative Analysis of scRNA-Seq and Bulk RNA-Seq Identifies Plasma Cell Related Genes and Constructs a Prognostic Model for Hepatocellular Carcinoma.

Abstract

Purpose: The complexity and heterogeneity of the tumor immune microenvironment (TIME) are linked to the development and poor prognosis of hepatocellular carcinoma (HCC). However, the cell type within the TIME that is most closely associated with HCC development remains unclear. Herein, we aimed to identify cell clusters that significantly contribute to HCC development and their underlying mechanisms.

Method and results: Using single-cell RNA sequencing (scRNA-seq), we analyzed changes in the TIME of normal and tumor tissues, identifying plasma cells as the key cluster in HCC development. Based on plasma cell-related genes (PCRGs), we constructed and validated an eight-gene prognostic model (ST6GALNAC4, SEC61A1, SSR3, RPN2, PRDX4, TRAM1, SPCS2, CD79A) using internal and external datasets and a nomogram. Functional enrichment, miRNA network construction, and transcriptional regulation analyses were performed to explore underlying mechanisms. TIDE scores and the GDSC database were used to predict immunotherapy and chemotherapy sensitivity in different risk groups. Finally, SSR3's biological function was validated in vitro in HCC cell lines.

Conclusion: Plasma cells are key clusters in HCC development. A prognostic model based on the PCRGs can accurately predict the prognosis of patients with HCC and guide clinical treatment.