ArticleScientific reports2024
Identifying MSMO1, ELOVL6, AACS, and CERS2 related to lipid metabolism as biomarkers of Parkinson's disease.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Integrative bulk and single-cell transcriptome analyses reveal RNA modification-related biomarkers of spinal cord injury.Neural regeneration research · 2026Article
- Multi-omic insight into the molecular mechanism of cuproptosis-related genes in the pathogenesis of Parkinson's disease.NPJ Parkinson's disease · 2026Article
- Modelling Ferroptosis in a Human Microglial Line by Sequential Exposure to Iron and GPX4 Inhibition.Oxidative medicine and cellular longevity · 2026Article
- Systematic Analysis of Alternative Splicing in Transcriptomes of Multiple Sclerosis Patient Brain Samples.International journal of molecular sciences · 2025Article
- Identification of new candidate genes affecting drip loss in pigs based on genomics and transcriptomics data.Journal of animal science · 2025Article
- Zinc sulfate improves insulin resistance, oxidative stress and apoptosis in liver tissues of PCOS rats through the NF-κB pathway.Frontiers in endocrinology · 2025Article
- Neuron stress-related genes serve as new biomarkers in hypothalamic tissue following high fat diet.Frontiers in endocrinology · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
The mechanisms underlying lipid metabolic disorders in Parkinson's diseases (PD) remain unclear. Weighted Gene Co-Expression Network Analysis (WGCNA) was conducted to identify PD-related modular genes and differentially expressed genes (DEGs). Lipid metabolism-related genes (LMRGs) were extracted from Molecular Signatures Database. Candidate genes were assessed with overlapping modular genes, DEGs, and LMRGs for the purpose of building protein-protein interaction (PPI) networks. Then, biomarkers were generated by machine learning and Backpropagation Neural Network development according to candidate genes. Biomarker-based enrichment and network modulation analyses were executed to investigate related signaling pathways. Following dimensionality reduction clustering and annotation, scRNA-seq was submitted to cellular interactions and trajectory analysis to analyze regulatory mechanisms of critical cells. Finally, qRT-PCR was conducted to confirm the expression of biomarkers in PD patients. Four biomarkers (MSMO1, ELOVL6, AACS, and CERS2) were obtained and highly predictive after analysis mentioned above. Then, OPC, Oli, and Neu cells were the primary expression sites for biomarkers according to scRNA-seq studies. Finally, we confirmed mRNA of MSMO1, ELOVL6 and AACS were downregulated in PD patients comparing with control, while CERS2 was upregulated. In conclusion, MSMO1, ELOVL6, AACS, and CERS2 related to LMRGs could be new biomarkers for diagnosing and treating PD.
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