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<p>Palmitoylation may represent a new therapeutic target for psychiatric disorders. However, the specific role of palmitoylation in major depressive disorder (MDD) remains unclear. Therefore, this study aimed to explore biomarkers related to palmitoylation-related genes (PRGs) in MDD using bioinformatics methods. In this study, we used GSE52790, GSE38206, and PRGs for analysis. First, we performed weighted gene co-expression network analysis and differential expression analysis between MDD and control samples to obtain key module genes and differentially expressed genes (DEGs). These genes were overlapped to obtain differentially expressed PRGs (DE-PRGs). Additionally, the least absolute shrinkage and selection operator (LASSO), support vector machine–recursive feature elimination (SVM-RFE), and Boruta algorithms were used to screen candidate genes from the DE-PRGs. Receiver operating characteristic (ROC) curve analysis and expression validation were then conducted to obtain biomarkers for functional and immune analyses. In addition, regulatory networks were built to explore the mechanisms underlying MDD. Moreover, the obtained biomarkers were evaluated in a chronic unpredictable mild stress (CUMS) mouse model. Hematoxylin–eosin staining was used to characterize pathological changes in the hippocampus. Finally, biomarker expression was detected using RT-qPCR, WB, and IHC. A total of 256 DE-PRGs were obtained, and two candidate genes <i>H2AC14</i> and <i>H2AC20</i> were selected by LASSO, SVM-RFE, and Boruta algorithms. These two candidate genes were considered potential biomarkers due to their high diagnostic accuracy for MDD, significant differential expression, and consistent expression trends in GSE52790 and GSE38206 datasets. Functional enrichment analysis revealed that the two biomarkers were co-enriched in pathways such as RIBOSOME and SPLICEOSOME. Immune infiltration revealed that <i>H2AC20</i> had the strongest positive correlation with immature dendritic cells, while <i>H2AC14</i> was strongly positively correlated with CD4 + central memory T cells. The gene–drug network showed that valproic acid may be a potential therapeutic agent for MDD. In the CUMS mouse model, pathological damage was observed in brain tissues, and RT-qPCR, WB, and IHC results showed reduced expression levels of <i>H2AC14</i> and <i>H2AC20</i>. <i>H2AC14</i> and <i>H2AC20</i> were identified as palmitoylation-associated biomarkers for MDD, providing a theoretical basis for their potential roles in MDD diagnosis and therapeutic development.</p>

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Identification and validation of palmitoylation-associated biomarkers in major depressive disorder

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月1日

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  • Xin Wang,
  • Xiajin Ren,
  • Ying Bai

摘要

Palmitoylation may represent a new therapeutic target for psychiatric disorders. However, the specific role of palmitoylation in major depressive disorder (MDD) remains unclear. Therefore, this study aimed to explore biomarkers related to palmitoylation-related genes (PRGs) in MDD using bioinformatics methods. In this study, we used GSE52790, GSE38206, and PRGs for analysis. First, we performed weighted gene co-expression network analysis and differential expression analysis between MDD and control samples to obtain key module genes and differentially expressed genes (DEGs). These genes were overlapped to obtain differentially expressed PRGs (DE-PRGs). Additionally, the least absolute shrinkage and selection operator (LASSO), support vector machine–recursive feature elimination (SVM-RFE), and Boruta algorithms were used to screen candidate genes from the DE-PRGs. Receiver operating characteristic (ROC) curve analysis and expression validation were then conducted to obtain biomarkers for functional and immune analyses. In addition, regulatory networks were built to explore the mechanisms underlying MDD. Moreover, the obtained biomarkers were evaluated in a chronic unpredictable mild stress (CUMS) mouse model. Hematoxylin–eosin staining was used to characterize pathological changes in the hippocampus. Finally, biomarker expression was detected using RT-qPCR, WB, and IHC. A total of 256 DE-PRGs were obtained, and two candidate genes H2AC14 and H2AC20 were selected by LASSO, SVM-RFE, and Boruta algorithms. These two candidate genes were considered potential biomarkers due to their high diagnostic accuracy for MDD, significant differential expression, and consistent expression trends in GSE52790 and GSE38206 datasets. Functional enrichment analysis revealed that the two biomarkers were co-enriched in pathways such as RIBOSOME and SPLICEOSOME. Immune infiltration revealed that H2AC20 had the strongest positive correlation with immature dendritic cells, while H2AC14 was strongly positively correlated with CD4 + central memory T cells. The gene–drug network showed that valproic acid may be a potential therapeutic agent for MDD. In the CUMS mouse model, pathological damage was observed in brain tissues, and RT-qPCR, WB, and IHC results showed reduced expression levels of H2AC14 and H2AC20. H2AC14 and H2AC20 were identified as palmitoylation-associated biomarkers for MDD, providing a theoretical basis for their potential roles in MDD diagnosis and therapeutic development.