Wei Guo-Hao, Wei Xi-Yi, Fan Ling-Yao, Zhou Wen-Zheng, Sun Ming, Zhu Chuan-Dong
Department of Oncology, The Second Hospital of Nanjing, Affiliated to Nanjing University of Chinese Medicine, Nanjing 210003, Jiangsu Province, China.
The State Key Laboratory of Reproductive, Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210003, Jiangsu Province, China.
World J Clin Oncol. 2024 Oct 24;15(10):1280-1292. doi: 10.5306/wjco.v15.i10.1280.
According to current statistics, renal cancer accounts for 3% of all cancers worldwide. Renal cell carcinoma (RCC) is the most common solid lesion in the kidney and accounts for approximately 90% of all renal malignancies. Increasing evidence has shown an association between immune infiltration in RCC and clinical outcomes. To discover possible targets for the immune system, we investigated the link between tumor-infiltrating immune cells (TIICs) and the prognosis of RCC.
To investigate the effects of 22 TIICs on the prognosis of RCC patients and identify potential therapeutic targets for RCC immunotherapy.
The CIBERSORT algorithm partitioned the 22 TIICs from the Cancer Genome Atlas cohort into proportions. Cox regression analysis was employed to evaluate the impact of 22 TIICs on the probability of developing RCC. A predictive model for immunological risk was developed by analyzing the statistical relationship between the subpopulations of TIICs and survival outcomes. Furthermore, multivariate Cox regression analysis was used to investigate independent factors for the prognostic prediction of RCC. A value of < 0.05 was regarded as statistically significant.
Compared to normal tissues, RCC tissues exhibited a distinct infiltration of immune cells. An immune risk score model was established and univariate Cox regression analysis revealed a significant association between four immune cell types and the survival risk connected to RCC. High-risk individuals were correlated to poorer outcomes according to the Kaplan-Meier survival curve ( = 1). The immunological risk score model was demonstrated to be a dependable predictor of survival risk (area under the curve = 0.747) the receiver operating characteristic curve. According to multivariate Cox regression analysis, the immune risk score model independently predicted RCC patients' prognosis (hazard ratio = 1.550, 95%CI: 1.342-1.791; < 0.001). Finally, we established a nomogram that accurately and comprehensively forecast the survival of patients with RCC.
TIICs play various roles in RCC prognosis. The immunological risk score is an independent predictor of poor survival in kidney cancer cases.
根据当前统计数据，肾癌占全球所有癌症的3%。肾细胞癌（RCC）是肾脏中最常见的实体病变，约占所有肾恶性肿瘤的90%。越来越多的证据表明RCC中的免疫浸润与临床结果之间存在关联。为了发现免疫系统的潜在靶点，我们研究了肿瘤浸润免疫细胞（TIICs）与RCC预后之间的联系。
研究22种TIICs对RCC患者预后的影响，并确定RCC免疫治疗的潜在治疗靶点。
使用CIBERSORT算法将来自癌症基因组图谱队列的22种TIICs划分为不同比例。采用Cox回归分析评估22种TIICs对发生RCC概率的影响。通过分析TIICs亚群与生存结果之间的统计关系，建立了免疫风险预测模型。此外 multivariate Cox回归分析用于研究RCC预后预测的独立因素。P<0.05被认为具有统计学意义。
与正常组织相比，RCC组织表现出明显的免疫细胞浸润。建立了免疫风险评分模型，单变量Cox回归分析显示四种免疫细胞类型与RCC相关的生存风险之间存在显著关联。根据Kaplan-Meier生存曲线（P=1），高危个体与较差的结果相关。免疫风险评分模型被证明是生存风险的可靠预测指标（曲线下面积=） 0.747） 受试者工作特征曲线。根据多变量Cox回归分析，免疫风险评分模型独立预测RCC患者的预后（风险比=1.5） 50，95%CI：1.342-1.791；P<0.001）。最后，我们建立了一个列线图，能够准确、全面地预测RCC患者的生存情况。
TIICs在RCC预后中发挥着多种作用。免疫风险评分是肾癌患者生存不良的独立预测指标。
