基于 CD8 Tex 相关 lncRNA 标志的新型 HCC 亚型可预测肝细胞癌的预后、免疫学和药物敏感性特征
IF 4.2
3区 医学
Q2 ONCOLOGY
引用次数: 0
摘要
目的：肝细胞癌已成为威胁人类健康的严重疾病之一。T细胞衰竭被认为是免疫治疗耐药的原因之一。然而，人们对CD8 Tex相关lncRNA在HCC中的作用知之甚少：我们通过单细胞RNA测序来鉴定CD8 Tex相关基因。根据CD8 Tex相关lncRNA与mRNA的相关性对其进行鉴定。采用无监督聚类方法确定了CD8 Tex相关lncRNA的分子聚类。探讨了不同群组之间预后和免疫浸润的差异。使用机器学习算法构建预后特征。根据风险评分将样本分为低风险组和高风险组。我们确定了与预后相关的lncRNA，并构建了一个ceRNA网络。体外实验研究了CD8 Tex相关lncRNA对HCC细胞增殖和凋亡的影响：我们明确了两个HCC单细胞数据集中的细胞类型。我们确定了 CD8 Tex 细胞的特定标记物，并分析了它们的潜在功能。28个lncRNA被鉴定为与CD8 Tex相关。根据与CD8 Tex相关的lncRNA，样本被分为两个不同的群组，这两个群组在存活率和免疫浸润方面存在显著差异。我们采用了96种算法组合来建立预后特征。其中，RSF 的 C 指数最高。高风险组和低风险组的患者在预后、富集通路、突变和药物敏感性方面存在明显差异。MCM3AP-AS1、MAPKAPK5-AS1和PART1被认为是与预后相关的lncRNA。根据与CD8 Tex相关的lncRNA和mRNA构建了ceRNA网络。细胞系和器官组织实验表明，下调MCM3AP-AS1、MAPKAPK5-AS1和PART1可抑制细胞增殖并诱导细胞凋亡：结论：CD8 Tex相关lncRNA在HCC进展中起着关键作用。我们的研究结果为CD8 Tex相关lncRNAs在HCC中的调控机制提供了新的见解。 关键词：肝细胞癌；lncRNA；T细胞衰竭；单细胞RNA-seq；机器学习；预后特征本文章由计算机程序翻译，如有差异，请以英文原文为准。
New HCC Subtypes Based on CD8 Tex-Related lncRNA Signature Could Predict Prognosis, Immunological and Drug Sensitivity Characteristics of Hepatocellular Carcinoma
Purpose: Hepatocellular carcinoma has become one of the severe diseases threatening human health. T cell exhaustion is deemed as a reason for immunotherapy resistance. However, little is known about the roles of CD8 Tex-related lncRNAs in HCC.
Materials and Methods: We processed single-cell RNA sequencing to identify CD8 Tex-related genes. CD8 Tex-related lncRNAs were identified based on their correlations with mRNAs. Unsupervised clustering approach was used to identify molecular clusters of CD8 Tex-related lncRNAs. Differences in prognosis and immune infiltration between the clusters were explored. Machine learning algorithms were used to construct a prognostic signature. Samples were classified as low- and high-risk groups based on their risk scores. We identified prognosis-related lncRNAs and constructed a ceRNA network. In vitro experiments were conducted to investigate the impacts of CD8 Tex-related lncRNAs on proliferation and apoptosis of HCC cells.
Results: We clarified cell types within two HCC single-cell datasets. We identified specific markers of CD8 Tex cells and analyzed their potential functions. Twenty-eight lncRNAs were identified as CD8 Tex-related. Based on CD8 Tex-related lncRNAs, samples were categorized into two distinct clusters, which exhibited significant differences in survival rates and immune infiltration. Ninety-six algorithm combinations were employed to establish a prognostic signature. RSF emerged as the one with the highest C-index. Patients in high- and low-risk groups exhibited marked differences in prognosis, enriched pathways, mutations and drug sensitivities. MCM3AP-AS1, MAPKAPK5-AS1 and PART1 were regarded as prognosis-related lncRNAs. A ceRNA network was constructed based on CD8 Tex-related lncRNAs and mRNAs. Experiments on cell lines and organoids indicated that downregulation of MCM3AP-AS1, MAPKAPK5-AS1 and PART1 suppressed cell proliferation and induced apoptosis.
Conclusion: CD8 Tex-related lncRNAs played crucial roles in HCC progression. Our findings provided new insights into the regulatory mechanisms of CD8 Tex-related lncRNAs in HCC.
Keywords: hepatocellular carcinoma, lncRNA, T cell exhaustion, single-cell RNA-seq, machine learning, prognostic signature
Materials and Methods: We processed single-cell RNA sequencing to identify CD8 Tex-related genes. CD8 Tex-related lncRNAs were identified based on their correlations with mRNAs. Unsupervised clustering approach was used to identify molecular clusters of CD8 Tex-related lncRNAs. Differences in prognosis and immune infiltration between the clusters were explored. Machine learning algorithms were used to construct a prognostic signature. Samples were classified as low- and high-risk groups based on their risk scores. We identified prognosis-related lncRNAs and constructed a ceRNA network. In vitro experiments were conducted to investigate the impacts of CD8 Tex-related lncRNAs on proliferation and apoptosis of HCC cells.
Results: We clarified cell types within two HCC single-cell datasets. We identified specific markers of CD8 Tex cells and analyzed their potential functions. Twenty-eight lncRNAs were identified as CD8 Tex-related. Based on CD8 Tex-related lncRNAs, samples were categorized into two distinct clusters, which exhibited significant differences in survival rates and immune infiltration. Ninety-six algorithm combinations were employed to establish a prognostic signature. RSF emerged as the one with the highest C-index. Patients in high- and low-risk groups exhibited marked differences in prognosis, enriched pathways, mutations and drug sensitivities. MCM3AP-AS1, MAPKAPK5-AS1 and PART1 were regarded as prognosis-related lncRNAs. A ceRNA network was constructed based on CD8 Tex-related lncRNAs and mRNAs. Experiments on cell lines and organoids indicated that downregulation of MCM3AP-AS1, MAPKAPK5-AS1 and PART1 suppressed cell proliferation and induced apoptosis.
Conclusion: CD8 Tex-related lncRNAs played crucial roles in HCC progression. Our findings provided new insights into the regulatory mechanisms of CD8 Tex-related lncRNAs in HCC.
Keywords: hepatocellular carcinoma, lncRNA, T cell exhaustion, single-cell RNA-seq, machine learning, prognostic signature
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