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α-2-巨球蛋白与特征基因:透明细胞肾细胞癌预后及免疫治疗的预测生物标志物

Alpha-2-Macroglobulin and Signature Genes: Predictive Biomarkers for Prognosis and Immunotherapy in Clear Cell Renal Cell Carcinoma.

作者信息

Li Ming, Luo Xin, Zhou Renyu, Liu Minting, Wang Guang, Zhang Xiaotan

机构信息

Department of Pathology, First Affiliated Hospital of Jinan University, School of Medicine, Jinan University, Guangzhou 510632, China.

International Joint Laboratory for Embryonic Development & Prenatal Medicine, Division of Histology and Embryology, School of Medicine, Jinan University, Guangzhou 510632, China.

出版信息

J Cancer. 2025 Jul 10;16(10):3141-3162. doi: 10.7150/jca.113242. eCollection 2025.

Abstract

Alpha-2-macroglobulin (A2M) is a broad-spectrum protease inhibitor that plays a role in maintaining coagulation balance and immune regulation. Previous studies have demonstrated a strong association between A2M and various kidney diseases. However, little is known about the role of A2M in clear cell renal cell carcinoma (ccRCC). In this study, through pan-cancer analysis based on data from multiple public databases such as The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx), a unique prognostic relationship between A2M and ccRCC was identified. A2M expression in three common RCCs and the prognosis were detected, which further proved that A2M was closely related to the prognosis of ccRCC, and the diagnostic value of A2M in ccRCC was determined. Additionally, the results found that A2M in ccRCC was regulated by methylation and affected vascularization and immune invasion. Subsequently, A2M-related genes were analyzed and 42 co-related gene expressions were identified in four public databases. Furthermore, a prognostic model [A2M gene-associated prognostic index (A2M-GPI)] composed of 7 genes [TIE1, VWF, TCF4, PTPRB, ICAM2, DOCK6, and RAMP3] was constructed using machine learning to predict the prognosis of ccRCC. Additionally, A2M-GPI combined with independent predictors (such as age, pathologic stage, and TNM stage) were used to create a survival Nomogram. This study is the first to systematically analyze the multiple mechanisms of A2M in the pathogenesis and progression of ccRCC. Machine learning was used to construct a prognostic model based on A2M to confirm that A2M is a valuable prognostic biomarker for ccRCC. Based on these findings, we created a publicly accessible website for its application (https://A2Mgpinomogram.shinyapps.io/ccRCC_prognosis_prediction/).

摘要

α-2-巨球蛋白(A2M)是一种广谱蛋白酶抑制剂,在维持凝血平衡和免疫调节中发挥作用。先前的研究表明A2M与多种肾脏疾病之间存在密切关联。然而,关于A2M在肾透明细胞癌(ccRCC)中的作用知之甚少。在本研究中,通过基于来自多个公共数据库(如癌症基因组图谱(TCGA)和基因型-组织表达(GTEx))的数据进行泛癌分析,确定了A2M与ccRCC之间独特的预后关系。检测了三种常见肾细胞癌中A2M的表达及其预后情况,进一步证明A2M与ccRCC的预后密切相关,并确定了A2M在ccRCC中的诊断价值。此外,研究结果发现ccRCC中的A2M受甲基化调控,并影响血管生成和免疫浸润。随后,对与A2M相关的基因进行了分析,并在四个公共数据库中鉴定出42个共相关基因的表达。此外,利用机器学习构建了一个由7个基因(TIE1、VWF、TCF4、PTPRB、ICAM2、DOCK6和RAMP3)组成的预后模型[A2M基因相关预后指数(A2M-GPI)],以预测ccRCC的预后。此外,将A2M-GPI与独立预测因子(如年龄、病理分期和TNM分期)相结合,创建了生存列线图。本研究首次系统分析了A2M在ccRCC发病机制和进展中的多种机制。利用机器学习构建基于A2M的预后模型,证实A2M是ccRCC有价值的预后生物标志物。基于这些发现,我们创建了一个可供公众访问的应用网站(https://A2Mgpinomogram.shinyapps.io/ccRCC_prognosis_prediction/)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b152/12305615/4640bf1ee77a/jcav16p3141g007.jpg

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