ArticleJournal of Cancer2025
Alpha-2-Macroglobulin and Signature Genes: Predictive Biomarkers for Prognosis and Immunotherapy in Clear Cell Renal Cell Carcinoma.
Article in Journal of Cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Distinct endogenous retroviruses are expressed in mutational subtypes of clear cell renal cell carcinoma and are linked to improved clinical outcomes.Cell reports · 2026Article
- Comparative Analysis of Progesterone Secretion and Plasma Proteome Across the Pregnant and Non-Pregnant Luteal Phase of the Koala (Proteomes · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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/).
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