Identification of signature genes for renal ischemia‒reperfusion injury based on machine learning and WGCNA.

ABSTRACT:

Background

As an inevitable event after kidney transplantation, ischemia‒reperfusion injury (IRI) can lead to a decrease in kidney transplant success. The search for signature genes of renal ischemia‒reperfusion injury (RIRI) is helpful in improving the diagnosis and guiding clinical treatment.

Methods

We first downloaded 3 datasets from the GEO database. Then, differentially expressed genes (DEGs) were identified and applied for functional enrichment analysis. After that, we performed three machine learning methods, including random forest (RF), Lasso regression analysis, and support vector machine recursive feature elimination (SVM-RFE), to further predict candidate genes. WGCNA was also executed to screen candidate genes from DEGs. Then, we took the intersection of candid

SUBMITTER: Lu Z 

PROVIDER: S-EPMC10622618 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature