ArticleHereditas2025
Identification and experimental validation of biomarkers associated with the endocannabinoid system in major depressive disorder.
Article in Hereditas, 2025. 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
4 authors.
Funding
Abstract
backgroundThe endocannabinoid system (ES) plays a pivotal role in modulating central nervous system activity in response to emotional stimuli. This study aimed to identify and validate biomarkers associated with ES-related genes (ES-RGs) in major depressive disorder (MDD), providing insights into potential therapeutic targets.
methodsDatasets GSE52790 and GSE38206 were analyzed in this study. Overlapping differential expression analysis and weighted gene co-expression network analysis (WGCNA) were integrated to identify intersecting genes. Candidate genes were selected through protein-protein interaction (PPI) analysis. Biomarker identification involved the integration of machine learning techniques, gene expression data, and receiver operating characteristic (ROC) analysis. A nomogram was developed and evaluated using these biomarkers as key indicators. Comprehensive analyses, including functional exploration, immune infiltration assessment, regulatory network construction, and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) validation, were conducted.
resultsMitochondrial ribosome protein S11 (MRPS11) and mitochondrial serine hydroxymethyltransferase2 (SHMT2) were identified as significant biomarkers for MDD, with markedly reduced expression in patient samples. These findings were validated by RT-qPCR analysis. The development of a biomarker-based nomogram successfully predicted MDD risk. Enrichment analysis highlighted the co-enrichment of both biomarkers in the "ribosome" pathway. Differential immune cell analysis revealed four immune cell types distinguishing MDD from control samples. Moreover, five key miRNAs targeting these biomarkers were predicted, along with 31 lncRNAs targeting the miRNAs, establishing an lncRNA-miRNA-mRNA network. Ten transcription factors (TFs) targeting the biomarkers were also identified, leading to the construction of a TF-mRNA network. Furthermore, 15 drugs targeting MRPS11 and 56 drugs targeting SHMT2 were identified, resulting in the formation of a biomarker-drug network. These findings may inform more precise and personalized therapeutic strategies for MDD.
conclusionMRPS11 and SHMT2 were identified as biomarkers for MDD through the validation of their expression patterns in clinical samples. This study provides a theoretical foundation for the development of targeted therapies for MDD.
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.