Development of an Immune Prognostic Model for Clear Cell Renal Cell Carcinoma Based on Tumor Microenvironment

作者全名："Wang, Munan; Song, Qianqian; Song, Zhijie; Xie, Yuduan"

作者地址："[Wang, Munan; Song, Qianqian] Chongqing Med Univ, Coll Tradit Chinese Med, Chongqing, Peoples R China; [Song, Zhijie] Univ Tradit Chinese Med, Sch Integrated Tradit Chinese & Western Med, Tianjin, Peoples R China; [Xie, Yuduan] Chinese Acad Tradit Chinese Med, Wangjing Hosp, Lab Dept, Beijing, Peoples R China; [Xie, Yuduan] Chinese Acad Tradit Chinese Med, Wangjing Hosp, Lab Dept, 6 Zhonghuan South Rd, Beijing 100020, Peoples R China"

通信作者："Xie, YD (通讯作者)，Chinese Acad Tradit Chinese Med, Wangjing Hosp, Lab Dept, 6 Zhonghuan South Rd, Beijing 100020, Peoples R China."

来源：HORMONE AND METABOLIC RESEARCH

ESI学科分类：BIOLOGY & BIOCHEMISTRY

WOS号：WOS:000989048400001

JCR分区：Q3

影响因子：2

年份：2023

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期号：　

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文献类型：Article; Early Access

关键词：TCGA; clear cell renal cell carcinoma; immune microenvironment; prognosis; signature

摘要："Immune infiltration remains at a high level in clear cell renal cell carcinoma (ccRCC). It has been confirmed that immune cell infiltration in tumor microenvironment (TME) is intimately bound up with the progression and the clinical outcome of ccRCC. The prognostic model, developed based on different immune subtypes of ccRCC, has a predictive value in patients' prognosis. RNA sequencing data, somatic mutation data of ccRCC and clinical information were acquired from the cancer genome atlas (TCGA) database. The key immune-related genes (IRGs) were selected and by univariate Cox, LASSO, and multivariate Cox regression analyses. Then the ccRCC prognostic model was developed. The applicability of this model was verified in the independent dataset GSE29609. Thirteen IRGs including CCL7, ATP6V1C2, ATP2B3, ELAVL2, SLC22A8, DPP6, EREG, SERPINA7, PAGE2B, ADCYAP1, ZNF560, MUC20, and ANKRD30A were finally selected and a 13-IRGs prognostic model was developed. Survival analysis demonstrated that when compared with the low-risk group, patients in the high-risk group had a lower overall survival (p<0.05). AUC values based on the 13-IRGs prognostic model used to predict 3- and 5-year survival of ccRCC patients were greater than 0.70. And risk score was an independent prognostic factor (p<0.001). In addition, nomogram could accurately predict ccRCC patient's prognosis. This 13-IRGs model can effectively evaluate the prognosis of ccRCC patients, and also provide guidance for the treatment and prognosis of ccRCC patients."

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