Department of Urology The First Affiliated Hospital of Harbin Medical University, Harbin 150010, China.
Department of Oral and Maxillofacial Surgery The First Affliated Hospital of Harbin Medical University, Harbin 150010, China.
Genet Res (Camb). 2024 Aug 30;2024:3468209. doi: 10.1155/2024/3468209. eCollection 2024.
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.
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.
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.
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.
透明细胞肾细胞癌（ccRCC）是一种具有复杂发病机制的肾皮质恶性肿瘤。寻找理想的生物标志物来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。
使用 Seurat R 包对单细胞 RNA 测序（scRNA-seq）数据进行过滤、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）以识别细胞毒性相关模块。使用 survival R 包建立独立的细胞毒性相关风险模型，并通过 Kaplan-Meier（KM）生存分析和时间 ROC 与曲线下面积（AUC）来验证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、MCP-counter 和 TIMER 方法比较不同风险组和亚型的免疫浸润水平，并使用 pRRophetic 包评估风险组中常规化疗药物的敏感性。
从 GSE224630 数据集的单细胞测序数据中鉴定出 11 个 ccRCC 亚群。鉴定出的细胞毒性相关 T 细胞簇和模块基因定义了三种细胞毒性相关分子亚型。选择六个关键基因（SOWAHB、SLC16A12、IL20RB、SLC12A8、PLG 和 HHLA2）来开发风险模型，这些基因影响预后风险基因。包含 RiskScore 和分期的列线图表明，RiskScore 贡献最大，在校准图和决策曲线分析（DCA）中表现出出色的预后预测性能。值得注意的是，ccRCC 高危患者具有较差的预后，具有更高的免疫浸润特征和 TIDE 评分，而低危患者更可能受益于免疫治疗。
基于细胞毒性相关特征构建了 ccRCC 生存预后模型，具有重要的临床意义，可能为 ccRCC 治疗提供指导。
