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Updated: Sep 13, 2025
Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment — Challenges and Innovations in Cancer Prognosis
Published on: April 12, 2024
A Bioinformatics Analysis on the Subtypes of Hepatocellular Carcinoma Related to RNA Processing Genes to Reveal
Bin Hu1, Yanfei Zhang2, Bingjing Jiang2
1Pathology Department, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, No.365 Renmin East Road, Wucheng District, Jinhua City, 321000, Zhejiang Province, China. hbinman23@163.com.
Background:
Alterations in RNA processing or anomalies in gene expression and function can contribute to the development of cancer. This work aims to investigate the stratification and prognostic value of RNA processing-related genes (RPRGs).
Methods:
We collected single-cell and transcriptomic data from hepatocellular carcinoma (HCC) samples from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), as well as RPRGs from the Molecular Signatures Database (MSigDB). The bioinformatic analysis was undertaken to reveal differentially expressed RPRGs (DE-RPRGs) at the cellular level. Different subtypes based on RPRGs were obtained based on AUcell scores. The RPRG-related prognostic model was created through regression analysis and was validated in different datasets. Prognostic feature genes were screened, with their expression patterns observed at the single-cell level. The single-sample gene set enrichment analysis (ssGSEA) was carried out for immune infiltration analysis. Potential therapeutic approaches for different risk groups were investigated. High-sensitive drugs were identified by analyzing drug information.
Results:
Based on the AUcell scoring on DE-RPRGs, two HCC molecular subtypes with distinct RPRG characteristics were identified. Based on the regression analysis of DE-RPRGs, 8 prognostic feature genes with varying expression levels in high-risk (HR) and low-risk (LR) groups as well as distinct cell clusters were identified, including LGALS3, THOC2, TYW3, SFPQ, GTPBP4, SMAD2, DTWD1, and ZC3H13. A predictive model that exhibited robust performance across the training and validation datasets was established. Two risk groups with distinct immune characteristics and survival rates were obtained. The HR group with poorer survival exhibited a greater enrichment of Treg cells linked with immune suppression, while the LR group with better survival had a higher accumulation of anti-tumor immune cells.
Conclusion:
The work highlights the stratification and prognostic value of RPRGs in HCC patients and reveals immune and mutational characteristics of RPRGs, which may hold great implications for patient management and targeted therapy.
