Evidence map›Paper›PMID 40719833›Full record

ArticleDigestive diseases and sciences2025

A Bioinformatics Analysis on the Subtypes of Hepatocellular Carcinoma Related to RNA Processing Genes to Reveal Prognosis and Immune Microenvironment Features.

Bin Hu, Yanfei Zhang, Bingjing Jiang, Angcheng Li

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Article in Digestive diseases and sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 authors.

Bin HuPathology 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.
Yanfei ZhangPathology Department, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, No.365 Renmin East Road, Wucheng District, Jinhua City, 321000, Zhejiang Province, China.
Bingjing JiangPathology Department, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, No.365 Renmin East Road, Wucheng District, Jinhua City, 321000, Zhejiang Province, China.
Angcheng LiPathology Department, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, No.365 Renmin East Road, Wucheng District, Jinhua City, 321000, Zhejiang Province, China.

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6 · The paper itself

Abstract

backgroundAlterations 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).

methodsWe 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.

resultsBased 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.

conclusionThe 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.

Indexed as

Carcinoma, HepatocellularComputational BiologyLiver NeoplasmsRNA Processing, Post-TranscriptionalTumor MicroenvironmentBiomarkers, TumorGene Expression ProfilingGene Expression Regulation, NeoplasticHumansPrognosisTranscriptomeBiomarkers, TumorHepatocellular carcinomaPrognostic modelRNA processing genesSingle cellTumor immune microenvironment

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.