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Updated: Aug 6, 2026
Association Between Sleep Quality and Cognitive Symptoms in Patients with Major Depressive Disorder
Published on: April 26, 2024
Identification of AICD-associated transcriptomic markers in major depressive disorder
Shengjie Xiong1, Lixin Liao1, Meng Chen2
1Department of Psychiatry, West China School of Medicine, Sichuan University, Sichuan University Affiliated Chengdu Second People's Hospital, Chengdu Second People's Hospital, Chengdu, Sichuan, China.
Background:
Major Depressive Disorder (MDD) is a complex mental disorder with unclear molecular mechanisms. This study aimed to identify key genes associated with ATP-induced cell death (AICD) in MDD and elucidate their roles in disease pathogenesis.
Methods:
MDD transcriptome data were obtained from public databases. Genes associated with AICD-related pathways were identified through differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms. A diagnostic nomogram was constructed and validated. Functional enrichment, immune infiltration, regulatory network, and molecular docking analyses were performed to explore biological functions and therapeutic potential. Gene expression was validated using RT-qPCR.
Results:
MRPL53 and RPL24 were identified as key genes with excellent diagnostic performance (AUC > 0.7 in both cohorts). The nomogram based on these genes demonstrated high accuracy (AUC = 0.967 in training cohort). Gene set enrichment analysis revealed significant enrichment in ribosome, neuroactive ligand-receptor interaction, and oxidative phosphorylation pathways. Immune infiltration analysis showed substantial alterations in the MDD immune microenvironment, with key genes strongly correlating with specific immune cell populations. Molecular docking preliminarily suggested hydralazine as a candidate compound with in silico binding affinity to MRPL53 (-5.0 kcal/mol) and RPL24 (-5.7 kcal/mol), warranting further experimental validation in cellular and animal models. RT-qPCR validation confirmed the bioinformatics findings in clinical samples (p < 0.05).
Conclusion:
MRPL53 and RPL24 were identified as candidate AICD-associated transcriptomic markers in MDD, providing novel insights into MDD pathogenesis and suggesting hydralazine as a potential therapeutic candidate. However, the training cohort (whole blood) and validation cohort (PBMCs) differ in sample type, which may affect cross-cohort comparability and should be considered when interpreting the reproducibility of these findings.
