ArticleScientific reports2026
Identification and validation of palmitoylation-associated biomarkers in major depressive disorder.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.