Mining key circadian biomarkers for major depressive disorder by integrating bioinformatics and machine learning.

ABSTRACT:

Objective

This study aimed to identify key clock genes closely associated with major depressive disorder (MDD) using bioinformatics and machine learning approaches.

Methods

Gene expression data of 128 MDD patients and 64 healthy controls from blood samples were obtained. Differentially expressed were identified and weighted gene co-expression network analysis (WGCNA) was first performed to screen MDD-related key genes. These genes were then intersected with 1475 known circadian rhythm genes to identify circadian rhythm genes associated with MDD. Finally, multiple machine learning algorithms were applied for further selection, to determine the most critical 4 circadian rhythm biomarkers.

Results

Four key circadian rhythm genes (ABCC2, APP, HK2 and RORA) were identified

SUBMITTER: Shi Y 

PROVIDER: S-EPMC11236317 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature