Integrated transcriptomic and machine learning analysis reveals novel diagnostic biomarkers for adolescent major depressive disorder.

ABSTRACT:

Introduction

The lack of objective biomarkers and mechanistic understanding of adolescent Major Depressive Disorder (MDD) impedes early diagnosis and targeted intervention.

Methods

To elucidate peripheral molecular biomarkers for adolescent MDD, we performed RNA sequencing on peripheral blood mononuclear cells (PBMCs) from 15 adolescents with MDD and 15 age- and sex-matched healthy controls. Differential expression analysis and protein-protein interaction (PPI) network construction were utilized to identify key regulatory genes. The expression of core targets was validated using RT-qPCR and ELISA. To establish a robust diagnostic model, an integrated feature selection strategy combining Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine-Recursive

SUBMITTER: Yang R 

PROVIDER: S-EPMC12900750 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature