Definition and verification of novel metastasis and recurrence related signatures of ccRCC: A multicohort study.

ABSTRACT:

Background

Cancer metastasis and recurrence remain major challenges in renal carcinoma patient management. There are limited biomarkers to predict the metastatic probability of renal cancer, especially in the early-stage subgroup. Here, our study applied robust machine-learning algorithms to identify metastatic and recurrence-related signatures across multiple renal cancer cohorts, which reached high accuracy in both training and testing cohorts.

Methods

Clear cell renal cell carcinoma (ccRCC) patients with primary or metastatic site sequencing information from eight cohorts, including one out-house cohort, were enrolled in this study. Three robust machine-learning algorithms were applied to identify metastatic signatures. Then, two distinct metastatic-related subtypes were

SUBMITTER: Jiang A 

PROVIDER: S-EPMC10686128 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature