Immunotherapy and Immune Infiltration in Patients with Clear Cell Renal Cell Carcinoma: A Comprehensive Analysis.

IF 1.4 4区 生物学 Q4 GENETICS & HEREDITY Genetics research Pub Date : 2023-01-01 DOI:10.1155/2023/3898610
Lin Hou, Xinyue Liu
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Abstract

On a global scale, renal cell carcinoma (RCC) is the second most common form of cancer and the 10th leading cause of cancer-related deaths. There are about 70% of cases of RCC that are clear cell renal cell carcinomas (ccRCCs). This study explores possible targets for immune therapy in patients with RCC. In the recent years, immunotherapy has been applied to RCC patients. In order to identify genes that are closely associated with immune cells, a weighted gene coexpression network analysis (WGCNA) was conducted. A close association was found between genes involved in MEred and M0 macrophages, M1 macrophages, and M2 macrophages. A prognostic prediction model is subsequently developed by incorporating the OS and the expression level of key genes from the RCC cohort into a univariate COX regression analysis, a multivariate COX regression analysis, and a combined COX regression analysis. We finally discovered that 6 genes are closely associated with the prognosis of RCC patients, including SLC16A12, SLC2A9, IGF2BP2, EMX2, ANK3, and METTL7A. The survival analysis proved the prognostic prediction value of the model. The 1-year, 3-year, and 5-year AUC of ROC curves are 0.759, 0.723, and 0.733, respectively. For clinical ROC curves, the AUC score for risk score, stage, grade, and T stage is 0.759, 0.824, 0722, and 0.736, respectively. The nomogram was constructed for better prognosis prediction of RCC patients. In addition, GSVA and GO enrichment analysis was performed to explore the potential pathways that are closely associated with genes involved in the prognostic prediction model. Accordingly, our study demonstrates that immune cells play a crucial role in RCC infiltration. The development of a prognostic prediction model is a potential new prognostic biomarker and potential immunotherapy target for tumors.

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透明细胞肾细胞癌患者的免疫治疗和免疫浸润:综合分析。
在全球范围内,肾细胞癌(RCC)是第二常见的癌症形式,也是癌症相关死亡的第十大原因。大约70%的肾细胞癌是透明细胞肾细胞癌(ccrcc)。本研究探讨了RCC患者免疫治疗的可能靶点。近年来,免疫疗法已被应用于RCC患者。为了鉴定与免疫细胞密切相关的基因,我们进行了加权基因共表达网络分析(WGCNA)。med与M0巨噬细胞、M1巨噬细胞和M2巨噬细胞相关的基因密切相关。随后,将RCC队列的OS和关键基因表达水平纳入单因素COX回归分析、多因素COX回归分析和组合COX回归分析,建立预后预测模型。我们最终发现6个基因与RCC患者的预后密切相关,包括SLC16A12、SLC2A9、IGF2BP2、EMX2、ANK3、METTL7A。生存分析证实了该模型的预后预测价值。ROC曲线1年、3年、5年AUC分别为0.759、0.723、0.733。临床ROC曲线上,风险评分、分期、分级、T分期的AUC评分分别为0.759、0.824、0722、0.736。为了更好地预测RCC患者的预后,构建了nomogram。此外,我们还进行了GSVA和GO富集分析,以探索与预后预测模型中涉及的基因密切相关的潜在途径。因此,我们的研究表明免疫细胞在RCC浸润中起着至关重要的作用。肿瘤预后预测模型的发展是一种潜在的新的预后生物标志物和潜在的肿瘤免疫治疗靶点。
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来源期刊
Genetics research
Genetics research 生物-遗传学
自引率
6.70%
发文量
74
审稿时长
>12 weeks
期刊介绍: Genetics Research is a key forum for original research on all aspects of human and animal genetics, reporting key findings on genomes, genes, mutations and molecular interactions, extending out to developmental, evolutionary, and population genetics as well as ethical, legal and social aspects. Our aim is to lead to a better understanding of genetic processes in health and disease. The journal focuses on the use of new technologies, such as next generation sequencing together with bioinformatics analysis, to produce increasingly detailed views of how genes function in tissues and how these genes perform, individually or collectively, in normal development and disease aetiology. The journal publishes original work, review articles, short papers, computational studies, and novel methods and techniques in research covering humans and well-established genetic organisms. Key subject areas include medical genetics, genomics, human evolutionary and population genetics, bioinformatics, genetics of complex traits, molecular and developmental genetics, Evo-Devo, quantitative and statistical genetics, behavioural genetics and environmental genetics. The breadth and quality of research make the journal an invaluable resource for medical geneticists, molecular biologists, bioinformaticians and researchers involved in genetic basis of diseases, evolutionary and developmental studies.
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