Identification and validation of the diagnostic biomarker MFAP5 for CAVD with type 2 diabetes by bioinformatics analysis.

ABSTRACT:

Introduction

Calcific aortic valve disease (CAVD) is increasingly prevalent among the aging population, and there is a notable lack of drug therapies. Consequently, identifying novel drug targets will be of utmost importance. Given that type 2 diabetes is an important risk factor for CAVD, we identified key genes associated with diabetes - related CAVD via various bioinformatics methods, which provide further potential molecular targets for CAVD with diabetes.

Methods

Three transcriptome datasets related to CAVD and two related to diabetes were retrieved from the Gene Expression Omnibus (GEO) database. To distinguish key genes, differential expression analysis with the "Limma" package and WGCNA was applied. Machine learning (ML) algorithms were employed to screen potential b

SUBMITTER: Shen Q 

PROVIDER: S-EPMC11693595 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature