Epithelial-mesenchymal transition classification based on machine learning for predicting prognosis and treatment response in clear cell renal cell carcinoma.

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 mod

SUBMITTER: Zhu G 

PROVIDER: S-EPMC12271937 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature