Department of Infectious Disease, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China.
Infectious Disease Clinical Research Center of Ningxia, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China.
Front Immunol. 2024 Oct 9;15:1444091. doi: 10.3389/fimmu.2024.1444091. eCollection 2024.
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.
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.
Among the 18 DE-TCRs, six genes (, , , , , and ) 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 and harbored missense mutations. exhibited an increased copy number, whereas exhibited a decreased copy number. The prognostic genes were significantly enriched in tryptophan metabolism pathways.
This bioinformatics analysis identified six TCR genes associated with HCC prognosis that can serve as diagnostic markers and therapeutic targets for HCC.
T 细胞增殖调节剂（TCRs）在肝细胞癌（HCC）中的预后价值和免疫意义尚未被报道。本研究旨在基于 HCC 患者的 TCRs 建立一种新的预后模型。
本研究使用了 The Cancer Genome Atlas-Liver Hepatocellular Carcinoma（TCGA-LIHC）和 International Cancer Genome Consortium-Liver Cancer-Riken，Japan（ICGC-LIRI-JP）数据集以及 TCRs。通过 TCRs 和 HCC 与非癌样本之间差异表达基因的交集来识别差异表达 TCR（DE-TCR）。使用 Cox 回归分析确定预后基因，并用于构建 HCC 的风险模型。通过 Kaplan-Meier 生存分析评估高危和低危组之间的生存差异。采用受试者工作特征曲线评估风险模型的有效性，并在 ICGC-LIRI-JP 数据集上进行测试。此外，使用多变量 Cox 回归分析和比例风险假设确定独立预后因素，并用于构建列线图模型。TCGA-LIHC 数据集进行肿瘤微环境分析、药物敏感性分析、基因集变异分析和免疫相关性分析。使用共识聚类分析、突变分析、拷贝数变异分析、基因集富集分析和分子预测分析对预后基因进行分析。
在 18 个 DE-TCR 中，有 6 个基因（、、、、和）可以预测 HCC 的预后。基于这些基因建立了一个可以准确预测 HCC 预后的风险模型。还使用临床特征和风险评分开发了一个有效的列线图模型。免疫相关分析显示，39 个免疫检查点在高危和低危组之间存在差异表达。属于高危组的患者免疫治疗反应率较低。根据预后基因，将 HCC 患者进一步分为聚类 1 和聚类 2。突变分析显示和携带错义突变。表现出拷贝数增加，而表现出拷贝数减少。预后基因在色氨酸代谢途径中显著富集。
本生物信息学分析确定了与 HCC 预后相关的 6 个 TCR 基因，它们可以作为 HCC 的诊断标志物和治疗靶点。
