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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1444091
This article is part of the Research Topic Clinical prediction models in cancer through bioinformatics View all articles

Prognostic Modeling of Hepatocellular Carcinoma Based on T-Cell Proliferation Regulators: A Bioinformatics Approach

Provisionally accepted
Long Hai Long Hai 1*Xiao-Yang Bai Xiao-Yang Bai 1*Xia Luo Xia Luo 1,2*Shuai-Wei Liu Shuai-Wei Liu 1,2*Zi-Min Ma Zi-Min Ma 3*Li-Na Ma Li-Na Ma 1,2*Xiang-Chun Ding Xiang-Chun Ding 1,2*
  • 1 General Hospital of Ningxia Medical University, Yinchuan, China
  • 2 Infectious Disease Clinical Research Center of Ningxia, Yinchuan, Henan Province, China
  • 3 Weiluo Microbial Pathogens Monitoring Technology Co., Ltd. of Beijing, Beijing, China

The final, formatted version of the article will be published soon.

    Background: The prognostic value and immune significance of T-cell proliferation regulators (TCRs) in hepatocellular carcinoma (HCC) have not been previously reported. This study aimed to develop a new prognostic model based on TCRs in patients with HCC. Method: This study used The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) and International Cancer Genome Consortium-Liver Cancer-Riken, Japan (ICGC-LIRI-JP) datasets along with TCRs. Differentially expressed TCRs (DE-TCRs) were identified by intersecting TCRs and differentially expressed genes between HCC and non-cancerous samples. Prognostic genes were determined using Cox regression analysis and were used to construct a risk model for HCC. Kaplan-Meier survival analysis was performed to assess the difference in survival between high-risk and low-risk groups. Receiver operating characteristic curve was used to assess the validity of risk model, as well as for testing in the ICGC-LIRI-JP dataset. Additionally, independent prognostic factors were identified using multivariate Cox regression analysis and proportional hazards assumption, and they were used to construct a nomogram model. TCGA-LIHC dataset was subjected to tumor microenvironment analysis, drug sensitivity analysis, gene set variation analysis, and immune correlation analysis. The prognostic genes were analyzed using consensus clustering analysis, mutation analysis, copy number variation analysis, gene set enrichment analysis, and molecular prediction analysis. Results: Among the 18 DE-TCRs, six genes (DCLRE1B, RAN, HOMER1, ADA, CDK1, and IL1RN) could predict the prognosis of HCC. A risk model that can accurately predict HCC prognosis was established based on these genes. An efficient nomogram model was also developed using clinical traits and risk scores. Immune-related analyses revealed that 39 immune checkpoints exhibited differential expression between the high-risk and low-risk groups. The rate of immunotherapy response was low in patients belonging to the high-risk group. Patients with HCC were further divided into cluster 1 and cluster 2 based on prognostic genes. Mutation analysis revealed that HOMER1 and CDK1 harbored missense mutations. DCLRE1B exhibited an increased copy number, whereas RAN exhibited a decreased copy number. The prognostic genes were significantly enriched in tryptophan metabolism pathways. Conclusions: This bioinformatics analysis identified six TCR genes associated with HCC prognosis that can serve as diagnostic markers and therapeutic targets for HCC.

    Keywords: T-cell proliferation regulators, Hepatocellular Carcinoma, BioInformatic, GEO, Prognostic model

    Received: 05 Jun 2024; Accepted: 23 Sep 2024.

    Copyright: © 2024 Hai, Bai, Luo, Liu, Ma, Ma and Ding. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Long Hai, General Hospital of Ningxia Medical University, Yinchuan, China
    Xiao-Yang Bai, General Hospital of Ningxia Medical University, Yinchuan, China
    Xia Luo, General Hospital of Ningxia Medical University, Yinchuan, China
    Shuai-Wei Liu, General Hospital of Ningxia Medical University, Yinchuan, China
    Zi-Min Ma, Weiluo Microbial Pathogens Monitoring Technology Co., Ltd. of Beijing, Beijing, China
    Li-Na Ma, General Hospital of Ningxia Medical University, Yinchuan, China
    Xiang-Chun Ding, General Hospital of Ningxia Medical University, Yinchuan, China

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