Nie Shiwen, Huili Youlong, He Yadong, Hu Junchao, Kang Shaosan, Cao Fenghong
Department of Urology, North China University of Science and Technology Affiliated Hospital, Tangshan, China.
Department of General Practice, North China University of Science and Technology Affiliated Hospital, Tangshan, China.
Front Surg. 2022 Apr 11;9:860857. doi: 10.3389/fsurg.2022.860857. eCollection 2022.
Necroptosis is associated with the development of many tumors but in bladder cancer the tumor microenvironment (TME) and prognosis associated with necroptosis is unclear.
We classified patients into different necroptosis subtypes by the expression level of NRGS (necroptosis-related genes) and analyzed the relationship between necroptosis subtypes of bladder cancer and TME, then extracted differentially expressed genes (DEGS) of necroptosis subtypes, classified patients into different gene subtypes according to DEGS, and performed univariate COX analysis on DEGS to obtain prognosis-related DEGS. All patients included in the analysis were randomized into the Train and Test groups in a 1:1 ratio, and the prognostic model was obtained using the LASSO algorithm and multivariate COX analysis with the Train group as the sample, and external validation of the model was conducted using the GSE32894.
Two necroptosis subtypes and three gene subtypes were obtained by clustering analysis and the prognosis-related DEGS was subjected to the LASSO algorithm and multivariate COX analysis to determine six predictors to construct the prognostic model using the formula: riskScore = CERCAM × 0.0035 + POLR1H × -0.0294 + KCNJ15 × -0.0172 + GSDMB × -0.0109 + EHBP1 × 0.0295 + TRIM38 × -0.0300. The results of the survival curve, roc curve, and risk curve proved the reliability of the prognostic model by validating the model with the test group and the results of the calibration chart of the Nomogram applicable to the clinic also showed its good accuracy. Necroptosis subtype A with high immune infiltration had a higher risk score than necroptosis subtype B, gene subtype B with low immune infiltration had a lower risk score than gene subtypes A and C, CSC index was negatively correlated with the risk score and drug sensitivity prediction showed that commonly used chemotherapeutic agents were highly sensitive to the high-risk group.
Our analysis of NRGS in bladder cancer reveals their potential role in TME, immunity, and prognosis. These findings may improve our understanding of necroptosis in bladder cancer and provide some reference for predicting prognosis and developing immunotherapies.
坏死性凋亡与多种肿瘤的发生发展相关，但在膀胱癌中，与坏死性凋亡相关的肿瘤微环境（TME）及预后尚不清楚。
根据坏死性凋亡相关基因（NRGS）的表达水平将患者分为不同的坏死性凋亡亚型，分析膀胱癌坏死性凋亡亚型与TME的关系，然后提取坏死性凋亡亚型的差异表达基因（DEGS），根据DEGS将患者分为不同的基因亚型，并对DEGS进行单因素COX分析以获得与预后相关的DEGS。分析纳入的所有患者按1:1比例随机分为训练组和测试组，以训练组为样本，使用LASSO算法和多因素COX分析获得预后模型，并使用GSE32894对模型进行外部验证。
通过聚类分析获得两种坏死性凋亡亚型和三种基因亚型，对与预后相关的DEGS进行LASSO算法和多因素COX分析，确定六个预测因子，使用公式riskScore = CERCAM × 0.0035 + POLR1H × -0.0294 + KCNJ15 × -0.0172 + GSDMB × -0.0109 + EHBP1 × 0.0295 + TRIM38 × -0.0300构建预后模型。生存曲线、roc曲线和风险曲线的结果通过测试组对模型进行验证，证明了预后模型的可靠性，适用于临床的列线图校准图结果也显示出其良好的准确性。免疫浸润高的坏死性凋亡亚型A的风险评分高于坏死性凋亡亚型B，免疫浸润低的基因亚型B的风险评分低于基因亚型A和C，CSC指数与风险评分呈负相关，药物敏感性预测显示常用化疗药物对高危组高度敏感。
我们对膀胱癌中NRGS的分析揭示了它们在TME、免疫和预后中的潜在作用。这些发现可能会提高我们对膀胱癌坏死性凋亡的理解，并为预测预后和开发免疫疗法提供一些参考。
