ArticleTranslational andrology and urology2025
Epithelial-mesenchymal transition classification based on machine learning for predicting prognosis and treatment response in clear cell renal cell carcinoma.
Article in Translational andrology and urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Renal cell carcinoma (RCC) is the third most common cancer in the genitourinary system. However, factors such as postoperative metastasis, recurrence, and advanced inoperable conditions contribute to its high mortality rate. Epithelial-mesenchymal transition (EMT) is the initial process that enables cells to metastasize. It is crucial for initiating and promoting both tumor cell invasion and metastasis. This study aims to construct a prognostic prediction model for clear cell renal cell carcinoma (ccRCC) patients using EMT-association genes (EAGs) based on public database data to improve the management of ccRCC. Methods: EAGs were identified through clustering and differential expression analysis, and machine learning methods were used to construct a prognostic model. External dataset E-MTAB-1980 was used for model construction and validation. Tumor microenvironment scores, enrichment analysis, and drug sensitivity analysis were used to predict treatment efficacy in different risk score groups. Finally, the "scissors" analysis linked high- and low-risk patients to individual cells and further explored the regulatory relationships between high- and low-risk cells through the cell communication network. Results: A final set of 12 EAGs was used for model construction. Risk scores showed statistically significant differences in different stages. Risk scores were independent prognostic factors for ccRCC patients. Significant differences were observed in the infiltration levels of various immune cells, expression levels of immune checkpoint genes, tumor mutation burden, and drug sensitivity between high- and low-risk groups. Validation tests demonstrated that our EAGs model showed good predictive performance in ccRCC. Cell communication analysis indicated that high-risk and low-risk cell subpopulations had different regulatory networks. Conclusions: A new EAGs prognostic signature was constructed, which can be used to assess the prognosis and treatment response of ccRCC.
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