T 细胞耗竭 (TEX) 和癌症干细胞 (CSC) 的高度异质性与肝细胞癌 (HCC) 的进展、转移和治疗耐药性相关。在这里，我们的目标是表征 TEX 干相关基因 (TEXSRG) 并筛选对免疫治疗更敏感的 HCC 患者。利用免疫细胞丰度识别器（ImmuCellAI）精确评估TEX的丰度并筛选TEX相关基因。通过一类逻辑回归（OCLR）算法分析样本的干性指数（mRNAsi）。应用非负矩阵分解算法（NMF）进行 HCC 样本亚型识别。评估不同亚型的预后、肿瘤微环境（TME）景观和免疫治疗反应的差异。然后，通过 LASSO-Cox 和多元 Cox 回归建立了可以准确预测 HCC 患者生存结果的 TEXSRGS 评分，并针对最重要的 TEXSRG 进行了实验验证。我们还使用 qRT-PCR 和免疫组织化学 (IHC) 分析了临床样本中 TEXSRG 的表达和 CD8 T 细胞的浸润。基于146个TEXSRG，我们通过非负矩阵分解算法（NMF）发现了两种具有不同TEX浸润丰度、肿瘤干性指数、富集途径、突变景观和免疫细胞浸润的不同临床表型，并在ICGC数据集中得到了证实。利用与临床结果相关的八个 TEXSRG，我们创建了 TEXSRG 评分模型，以进一步提高临床适用性。患者可分为两组，在免疫细胞浸润特征、TEX 浸润丰度和生存结果方面存在显着差异。 qRT-PCR和IHC分析结果显示，PAFAH1B3、ZIC2和ESR1在HCC和正常组织中存在差异表达，并且TEXSRGs分数高的患者具有更高的TEX浸润丰度和肿瘤干性基因表达。关于免疫治疗反应和免疫细胞浸润，不同 TEXSRGs 评分水平的患者具有不同的临床特征。 TEXSRGs 评分低的患者的结果和免疫治疗效果良好。总之，我们基于 TEXSRGs 确定了具有不同预后、TEX 浸润丰度、肿瘤细胞干性指数和免疫治疗反应的两种临床亚型，并开发和验证了能够通过综合生物信息学分析准确预测 HCC 患者生存结果的 TEXSRGs 评分。我们相信 TEXSRGs 评分对于预后评估具有前瞻性临床相关性，并且可以帮助医生优先选择潜在的反应者，而不是当前的免疫检查点抑制剂 (ICIs)。© 2023。作者。

T-cell exhaustion (TEX) and high heterogeneity of cancer stem cells (CSCs) are associated with progression, metastasis, and treatment resistance in hepatocellular carcinoma (HCC). Here, we aim to characterize TEX-stemness-related genes (TEXSRGs) and screen for HCC patients who are more sensitive to immunotherapy. The immune cell abundance identifier (ImmuCellAI) was utilized to precisely evaluate the abundance of TEX and screen TEX-related genes. The stemness index (mRNAsi) of samples was analyzed through the one-class logistic regression (OCLR) algorithm. Application of the non-negative matrix decomposition algorithm (NMF) for subtype identification of HCC samples. The different subtypes were assessed for differences in prognosis, tumor microenvironment (TME) landscape, and immunotherapy treatment response. Then, the TEXSRGS-score, which can accurately forecast the survival outcome of HCC patients, was built by LASSO-Cox and multivariate Cox regression, and experimentally validated for the most important TEXSRGs. We also analyzed the expression of TEXSRGs and the infiltration of CD8+ T cells in clinical samples using qRT-PCR and immunohistochemistry (IHC). Based on 146 TEXSRGs, we found two distinct clinical phenotypes with different TEX infiltration abundance, tumor stemness index, enrichment pathways, mutational landscape, and immune cell infiltration through the non-negative matrix decomposition algorithm (NMF), which were confirmed in the ICGC dataset. Utilizing eight TEXSRGs linked to clinical outcome, we created a TEXSRGs-score model to further improve the clinical applicability. Patients can be divided into two groups with substantial differences in the characteristics of immune cell infiltration, TEX infiltration abundance, and survival outcomes. The results of qRT-PCR and IHC analysis showed that PAFAH1B3, ZIC2, and ESR1 were differentially expressed in HCC and normal tissues and that patients with high TEXSRGs-scores had higher TEX infiltration abundance and tumor stemness gene expression. Regarding immunotherapy reaction and immune cell infiltration, patients with various TEXSRGs-score levels had various clinical traits. The outcome and immunotherapy efficacy of patients with low TEXSRGs-score was favorable. In conclusion, we identified two clinical subtypes with different prognoses, TEX infiltration abundance, tumor cell stemness index, and immunotherapy response based on TEXSRGs, and developed and validated a TEXSRGs-score capable of accurately predicting survival outcomes in HCC patients by comprehensive bioinformatics analysis. We believe that the TEXSRGs-score has prospective clinical relevance for prognostic assessment and may help physicians select prospective responders in preference to current immune checkpoint inhibitors (ICIs).© 2023. The Author(s).