ArticleMolecular neurobiology2023
Identification and Experimental Validation of Parkinson's Disease with Major Depressive Disorder Common Genes.
Article in Molecular neurobiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 22 citations in OpenAlex.
- Integrative analysis via bioinformatics and machine learning identifies SERPING1 as a biomarker candidate for major depressive disorder.BMC psychiatry · 2026Article
- Identifying the biomarkers associated with G protein-coupled receptors of parkinson's disease.Scientific reports · 2025Article
- Parkinson's disease: an integrative bioinformatics and machine learning analysis reveals tryptophan metabolism-associated gene biomarkers.BMC neurology · 2025Article
- Comparative Multi-Omics Analysis Identifies Shared Transcriptomic Signatures and Therapeutic Targets in Alzheimer's, Parkinson's, and Huntington's Diseases.Current issues in molecular biology · 2025Article
- Chromosome 12 and Environmental Factors in Parkinson's Disease: An All of Us Data Analysis.Genes · 2025Article
- Serum sirtuin 3 levels and multimodal abnormalities in brain structure and function in Parkinson's disease patients with depression.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Article
- Multi-omics machine learning classifier and blood transcriptomic signature of Parkinson's disease.Research square · 2025Article
- An integrated bioinformatics and machine learning approach to identifying biomarkers connecting parkinson's disease with purine metabolism-related genes.BMC neurology · 2025Article
- Sex-specific gene expression and weighted co-expression network analysis suggest distinct sex-specific molecular signatures in acutely suicidal MDD-patients without somatic comorbidities.Frontiers in genetics · 2025Article
- Exploring Gut Microbe-Host Genes in Postpartum Depression: Mendelian Randomization and Transcriptomic Analysis.International journal of women's health · 2025Article
- Machine learning study on predicting depressive symptoms and genetic correlations in Parkinson's disease.Frontiers in aging neuroscience · 2025Article
- Transcriptional pathobiology and multi-omics predictors for Parkinson's disease.bioRxiv : the preprint server for biology · 2024Article
- Integration of machine learning to identify diagnostic genes in leukocytes for acute myocardial infarction patients.Journal of translational medicine · 2023Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disease that affects about 10 million people worldwide. Non-motor and motor symptoms usually accompany PD. Major depressive disorder (MDD) is one of the non-motor manifestations of PD it remains unrecognized and undertreated effectively. MDD in PD has complicated pathophysiologies and remains unclear. The study aimed to explore the candidate genes and molecular mechanisms of PD with MDD. PD (GSE6613) and MDD (GSE98793) gene expression profiles were downloaded from Gene Expression Omnibus (GEO). Above all, the data of the two datasets were standardized separately, and differentially expressed genes (DEGs) were obtained by using the Limma package of R. Take the intersection of the two differential genes and remove the genes with inconsistent expression trends. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were investigated to explore the function of the common DEGs. Additionally, the construction of the protein-protein interaction (PPI) network was to search the hub genes, and then the least absolute shrinkage and selection operator (LASSO) regression was used to further identify the key genes. GSE99039 for PD and GSE201332 for MDD were performed to validate the hub genes by the violin plot and receiver operating characteristic (ROC) curve. Last but not least, immune cell dysregulation in PD was investigated by immune cell infiltration. As a result, a total of 45 common genes with the same trend. Functional analysis revealed that they were enriched in neutrophil degranulation, secretory granule membrane, and leukocyte activation. LASSO was performed on 8 candidate hub genes after CytoHubba filtered 14 node genes. Finally, AQP9, SPI1, and RPH3A were validated by GSE99039 and GSE201332. Additionally, the three genes were also detected by the qPCR in vivo model and all increased compared to the control. The co-occurrence of PD and MDD can be attributed to AQP9, SPI1, and RPH3A genes. Neutrophils and monocyte infiltration play important roles in the development of PD and MDD. Novel insights may be gained from the findings for the study of mechanisms.
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