研究动态
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通过scRNA-seq和bulk RNA-seq的整合分析建立卵巢癌耗竭CD8 T细胞相关基因模型。

Establishment of an ovarian cancer exhausted CD8+T cells-related genes model by integrated analysis of scRNA-seq and bulk RNA-seq.

发表日期:2024 Jul 05
作者: Tian Hua, Deng-Xiang Liu, Xiao-Chong Zhang, Shao-Teng Li, Jian-Lei Wu, Qun Zhao, Shu-Bo Chen
来源: Cellular & Molecular Immunology

摘要:

卵巢癌(OC)是癌症死亡的第五大原因,也是女性最致命的妇科癌症。这很大程度上归因于其诊断较晚、治疗耐药性高以及缺乏有效的治疗方法。临床和临床前研究表明,肿瘤浸润的CD8 T细胞经常失去效应功能,CD8 T细胞的功能失调状态被称为衰竭。我们的目标是确定与耗竭的 CD8 T 细胞 (CD8TEXG) 相关的基因及其在 OC 中的预后意义。我们从癌症基因组图谱 (TCGA) 和基因表达综合 (GEO) 数据库下载了 RNA 测序和临床数据。 CD8TEXG 最初是从单细胞 RNA 序列 (scRNA-seq) 数据集中识别出来的,然后利用单变量 Cox 回归、最小绝对收缩和选择算子 (LASSO) 以及多变量 Cox 回归来计算风险评分并开发 CD8TEXG 风险签名。进行 Kaplan-Meier 分析、单变量 Cox 回归、多变量 Cox 回归、时间依赖性受试者工作特征 (ROC)、列线图和校准来验证和评估风险特征。使用风险组中的基因集富集分析(GSEA)来找出与风险组密切相关的通路。风险评分在同源重组修复缺陷(HRD)、BRAC1/2基因突变和肿瘤突变负荷(TMB)中的作用得到了进一步探讨。最终在 TCGA 数据库中建立了 OC 中 4 个 CD8TEXG 的风险特征,并在大型 GEO 队列中得到进一步验证。该签名还证明了在泛癌分析中对各种类型癌症的广泛适用性。高风险评分与较差的预后显着相关,并且风险评分被证明是独立的预后生物标志物。 1年、3年和5年的ROC值、列线图、校准以及与之前发布的模型的比较证实了该模型出色的预测能力。低风险组患者往往表现出较高的HRD评分、BRCA1/2基因突变率和TMB。低风险组患者对聚ADP核糖聚合酶抑制剂(PARPi)更敏感。我们对 CD8TEXG 在预后和药物反应中的预后价值的研究结果为 OC 的分子机制和临床管理提供了宝贵的见解。© 2024。作者。
Ovarian cancer (OC) was the fifth leading cause of cancer death and the deadliest gynecological cancer in women. This was largely attributed to its late diagnosis, high therapeutic resistance, and a dearth of effective treatments. Clinical and preclinical studies have revealed that tumor-infiltrating CD8+T cells often lost their effector function, the dysfunctional state of CD8+T cells was known as exhaustion. Our objective was to identify genes associated with exhausted CD8+T cells (CD8TEXGs) and their prognostic significance in OC. We downloaded the RNA-seq and clinical data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. CD8TEXGs were initially identified from single-cell RNA-seq (scRNA-seq) datasets, then univariate Cox regression, the least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression were utilized to calculate risk score and to develop the CD8TEXGs risk signature. Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, time-dependent receiver operating characteristics (ROC), nomogram, and calibration were conducted to verify and evaluate the risk signature. Gene set enrichment analyses (GSEA) in the risk groups were used to figure out the closely correlated pathways with the risk group. The role of risk score has been further explored in the homologous recombination repair deficiency (HRD), BRAC1/2 gene mutations and tumor mutation burden (TMB). A risk signature with 4 CD8TEXGs in OC was finally built in the TCGA database and further validated in large GEO cohorts. The signature also demonstrated broad applicability across various types of cancer in the pan-cancer analysis. The high-risk score was significantly associated with a worse prognosis and the risk score was proven to be an independent prognostic biomarker. The 1-, 3-, and 5-years ROC values, nomogram, calibration, and comparison with the previously published models confirmed the excellent prediction power of this model. The low-risk group patients tended to exhibit a higher HRD score, BRCA1/2 gene mutation ratio and TMB. The low-risk group patients were more sensitive to Poly-ADP-ribose polymerase inhibitors (PARPi). Our findings of the prognostic value of CD8TEXGs in prognosis and drug response provided valuable insights into the molecular mechanisms and clinical management of OC.© 2024. The Author(s).