Zhiyuan Chen, Xiaoxiao Tang, Chao Gu, Shaohong Zou
{"title":"通过生物信息学分析研究重度抑郁症线粒体和衰老相关基因的生物标志物。","authors":"Zhiyuan Chen, Xiaoxiao Tang, Chao Gu, Shaohong Zou","doi":"10.3389/fpsyt.2025.1653998","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Major depressive disorder (MDD) is a prevalent mental health condition in which mitochondrial dysfunction and cellular senescence contribute to its pathogenesis. This study aims to identify biomarkers related to mitochondria-associated genes (MRGs) and aging-related genes (ARGs) in MDD using bioinformatics.</p><p><strong>Methods: </strong>This study utilized data from GSE201332 and GSE52790, including 1,136 MRGs and 866 ARGs. Initially, candidate genes were selected by intersecting MRGs, ARGs, and differentially expressed genes (DEGs) derived from differential expression analysis in GSE201332. Biomarkers were identified through LASSO regression analysis of the candidate genes. The biomarkers were then evaluated using ROC curves, and artificial neural network (ANN) models were constructed. Subsequently, functional enrichment, immune-related analyses, drug predictions, and molecular docking were performed. Finally, the expression of biomarkers was validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR).</p><p><strong>Results: </strong>Seven candidate genes were identified from the intersection of 4,041 DEGs, 1,136 MRGs, and 866 ARGs, with SLC25A5, ALDH2, CPT1C, and IMMT identified as potential biomarkers for MDD through LASSO regression analysis. ROC curve analysis in both GSE201332 and GSE52790 showed that these biomarkers effectively distinguished between MDD and control samples, with AUC values exceeding 0.7. ANN models further confirmed the diagnostic potential of these biomarkers. Gene set enrichment analysis (GSEA) revealed significant enrichment of SLC25A5, CPT1C, and IMMT in pathways related to cellular protein complex assembly and chromatin organization. Immune infiltration analysis demonstrated significant positive correlations between SLC25A5, ALDH2, and IMMT and most of the 18 immune cell types. Molecular docking predictions identified ALDH2 and SLC25A5 as potential targets for specific drugs, with NITROGLYCERIN showing the best binding affinity to ALDH2 (-6.4 kcal/mol). RT-qPCR validation showed significantly lower expression of SLC25A5 and IMMT, and higher expression of CPT1C, in patients with MDD compared to controls (p < 0.05), consistent with bioinformatics predictions.</p><p><strong>Conclusion: </strong>This study identified SLC25A5, ALDH2, CPT1C, and IMMT as biomarkers associated with MDD, offering insights into its molecular mechanisms.</p>","PeriodicalId":12605,"journal":{"name":"Frontiers in Psychiatry","volume":"16 ","pages":"1653998"},"PeriodicalIF":3.2000,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12504309/pdf/","citationCount":"0","resultStr":"{\"title\":\"Investigating biomarkers of mitochondrial and aging-related genes in major depressive disorder through bioinformatics analysis.\",\"authors\":\"Zhiyuan Chen, Xiaoxiao Tang, Chao Gu, Shaohong Zou\",\"doi\":\"10.3389/fpsyt.2025.1653998\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Major depressive disorder (MDD) is a prevalent mental health condition in which mitochondrial dysfunction and cellular senescence contribute to its pathogenesis. This study aims to identify biomarkers related to mitochondria-associated genes (MRGs) and aging-related genes (ARGs) in MDD using bioinformatics.</p><p><strong>Methods: </strong>This study utilized data from GSE201332 and GSE52790, including 1,136 MRGs and 866 ARGs. Initially, candidate genes were selected by intersecting MRGs, ARGs, and differentially expressed genes (DEGs) derived from differential expression analysis in GSE201332. Biomarkers were identified through LASSO regression analysis of the candidate genes. The biomarkers were then evaluated using ROC curves, and artificial neural network (ANN) models were constructed. Subsequently, functional enrichment, immune-related analyses, drug predictions, and molecular docking were performed. Finally, the expression of biomarkers was validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR).</p><p><strong>Results: </strong>Seven candidate genes were identified from the intersection of 4,041 DEGs, 1,136 MRGs, and 866 ARGs, with SLC25A5, ALDH2, CPT1C, and IMMT identified as potential biomarkers for MDD through LASSO regression analysis. ROC curve analysis in both GSE201332 and GSE52790 showed that these biomarkers effectively distinguished between MDD and control samples, with AUC values exceeding 0.7. ANN models further confirmed the diagnostic potential of these biomarkers. Gene set enrichment analysis (GSEA) revealed significant enrichment of SLC25A5, CPT1C, and IMMT in pathways related to cellular protein complex assembly and chromatin organization. Immune infiltration analysis demonstrated significant positive correlations between SLC25A5, ALDH2, and IMMT and most of the 18 immune cell types. Molecular docking predictions identified ALDH2 and SLC25A5 as potential targets for specific drugs, with NITROGLYCERIN showing the best binding affinity to ALDH2 (-6.4 kcal/mol). RT-qPCR validation showed significantly lower expression of SLC25A5 and IMMT, and higher expression of CPT1C, in patients with MDD compared to controls (p < 0.05), consistent with bioinformatics predictions.</p><p><strong>Conclusion: </strong>This study identified SLC25A5, ALDH2, CPT1C, and IMMT as biomarkers associated with MDD, offering insights into its molecular mechanisms.</p>\",\"PeriodicalId\":12605,\"journal\":{\"name\":\"Frontiers in Psychiatry\",\"volume\":\"16 \",\"pages\":\"1653998\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2025-09-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12504309/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Psychiatry\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3389/fpsyt.2025.1653998\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"PSYCHIATRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Psychiatry","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3389/fpsyt.2025.1653998","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"PSYCHIATRY","Score":null,"Total":0}
Investigating biomarkers of mitochondrial and aging-related genes in major depressive disorder through bioinformatics analysis.
Background: Major depressive disorder (MDD) is a prevalent mental health condition in which mitochondrial dysfunction and cellular senescence contribute to its pathogenesis. This study aims to identify biomarkers related to mitochondria-associated genes (MRGs) and aging-related genes (ARGs) in MDD using bioinformatics.
Methods: This study utilized data from GSE201332 and GSE52790, including 1,136 MRGs and 866 ARGs. Initially, candidate genes were selected by intersecting MRGs, ARGs, and differentially expressed genes (DEGs) derived from differential expression analysis in GSE201332. Biomarkers were identified through LASSO regression analysis of the candidate genes. The biomarkers were then evaluated using ROC curves, and artificial neural network (ANN) models were constructed. Subsequently, functional enrichment, immune-related analyses, drug predictions, and molecular docking were performed. Finally, the expression of biomarkers was validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR).
Results: Seven candidate genes were identified from the intersection of 4,041 DEGs, 1,136 MRGs, and 866 ARGs, with SLC25A5, ALDH2, CPT1C, and IMMT identified as potential biomarkers for MDD through LASSO regression analysis. ROC curve analysis in both GSE201332 and GSE52790 showed that these biomarkers effectively distinguished between MDD and control samples, with AUC values exceeding 0.7. ANN models further confirmed the diagnostic potential of these biomarkers. Gene set enrichment analysis (GSEA) revealed significant enrichment of SLC25A5, CPT1C, and IMMT in pathways related to cellular protein complex assembly and chromatin organization. Immune infiltration analysis demonstrated significant positive correlations between SLC25A5, ALDH2, and IMMT and most of the 18 immune cell types. Molecular docking predictions identified ALDH2 and SLC25A5 as potential targets for specific drugs, with NITROGLYCERIN showing the best binding affinity to ALDH2 (-6.4 kcal/mol). RT-qPCR validation showed significantly lower expression of SLC25A5 and IMMT, and higher expression of CPT1C, in patients with MDD compared to controls (p < 0.05), consistent with bioinformatics predictions.
Conclusion: This study identified SLC25A5, ALDH2, CPT1C, and IMMT as biomarkers associated with MDD, offering insights into its molecular mechanisms.
期刊介绍:
Frontiers in Psychiatry publishes rigorously peer-reviewed research across a wide spectrum of translational, basic and clinical research. Field Chief Editor Stefan Borgwardt at the University of Basel is supported by an outstanding Editorial Board of international researchers. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide.
The journal''s mission is to use translational approaches to improve therapeutic options for mental illness and consequently to improve patient treatment outcomes.