■透明细胞肾细胞癌(ccRCC)是一种发病机制复杂的肾皮质恶性肿瘤。确定理想的生物标志物以建立更准确的有前途的预后模型对于肾癌患者的生存至关重要。
■SeuratR包用于单细胞RNA测序(scRNA-seq)数据过滤,降维,聚类,和差异表达基因分析。进行基因共表达网络分析(WGCNA)以鉴定细胞毒性相关模块。通过生存R包建立独立的细胞毒性相关风险模型,采用Kaplan-Meier(KM)生存分析和带曲线下面积(AUC)的时间ROC来确认风险模型的预后和有效性。通过建立列线图预测ccRCC患者的风险和预后。使用CIBERSORT比较不同风险组和亚型的免疫浸润水平,MCP计数器,和TIMER方法,以及使用pRrophetic包装评估风险人群对常规化学治疗剂的药物敏感性,是制造的。
■通过来自GSE224630数据集的单细胞测序数据鉴定了11个ccRCC亚群。鉴定的细胞毒性相关的T细胞簇和模块基因定义了三种细胞毒性相关的分子亚型。六个关键基因(SOWAHB,SLC16A12,IL20RB,SLC12A8,PLG,和HHLA2)影响预后的风险基因被选择用于建立风险模型。包含RiskScore和分期的列线图显示,RiskScore在校准图和决策曲线分析(DCA)中对预后的贡献最大,并表现出出色的预测性能。值得注意的是,ccRCC高危患者预后较差,具有较高的免疫浸润特征和TIDE评分,而低风险患者更有可能从免疫治疗中获益.
■基于细胞毒性相关特征,建立了ccRCC生存预后模型,具有重要的临床意义,可为ccRCC的治疗提供指导。
UNASSIGNED: Clear cell renal cell carcinoma (ccRCC) is a renal cortical malignancy with a complex pathogenesis. Identifying ideal biomarkers to establish more accurate promising prognostic models is crucial for the survival of kidney cancer patients.
UNASSIGNED: Seurat R package was used for single-cell RNA-sequencing (scRNA-seq) data filtering, dimensionality reduction, clustering, and differentially expressed genes analysis. Gene coexpression network analysis (WGCNA) was performed to identify the cytotoxicity-related module. The independent cytotoxicity-related risk model was established by the survival R package, and Kaplan-Meier (KM) survival analysis and timeROC with area under the curve (AUC) were employed to confirm the prognosis and effectiveness of the risk model. The risk and prognosis in patients suffering from ccRCC were predicted by establishing a nomogram. A comparison of the level of immune infiltration in different risk groups and subtypes using the CIBERSORT, MCP-counter, and TIMER methods, as well as assessment of drug sensitivity to conventional chemotherapeutic agents in risk groups using the pRRophetic package, was made.
UNASSIGNED: Eleven ccRCC subpopulations were identified by single-cell sequencing data from the GSE224630 dataset. The identified cytotoxicity-related T-cell cluster and module genes defined three cytotoxicity-related molecular subtypes. Six key genes (SOWAHB, SLC16A12, IL20RB, SLC12A8, PLG, and HHLA2) affecting prognosis risk genes were selected for developing a risk model. A nomogram containing the RiskScore and stage revealed that the RiskScore contributed the most and exhibited excellent predicted performance for prognosis in the calibration plots and decision curve analysis (DCA). Notably, high-risk patients with ccRCC demonstrate a poorer prognosis with higher immune infiltration characteristics and TIDE scores, whereas low-risk patients are more likely to benefit from immunotherapy.
UNASSIGNED: A ccRCC survival prognostic model was produced based on the cytotoxicity-related signature, which had important clinical significance and may provide guidance for ccRCC treatment.