{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T17:14:54Z","timestamp":1787505294983,"version":"build-2736575974"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T00:00:00Z","timestamp":1781740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Aging Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      Parkinson\u2019s disease (PD) is a neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra (SN), primarily due to\n                      <jats:italic>\u03b1<\/jats:italic>\n                      -synuclein aggregation, posing a major health threat to the elderly population. Current treatment options remain limited, necessitating the discovery of novel diagnostic and therapeutic targets.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to identify a novel diagnostic and druggable target in the SN of PD patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We first nominated PD risk-associated differentially expressed genes (DEGs) in the SN bulk profile (GSE7621) of PD patients using Limma, weighted gene co-expression network analysis (WGCNA), and summary-data-based Mendelian randomization (SMR). Next, three machine learning algorithms [random forest (RF), least absolute shrinkage and selection operator LASSO, and support vector machine (SVM)] were performed for the identification of central pathogenic factors among the risk DEGs in the training PD patient SN bulk profile (integrated GSE20163 and GSE20164). In addition, the diagnostic performance of the identified central pathogenic factor for PD was evaluated in GSE7621, integrated GSE20163 and GSE20164, and independent PD patient SN bulk profiles (GSE140231). In addition, the molecular and immune patterns of the central pathogenic factor were assessed in SN single-cell data from PD patients (GSE7621) were estimated by a cutting-edge analytical framework in temporal and spatial manners. Furthermore, network-based drug screening and molecular docking were subsequently performed to identify potential therapeutic agents targeting the central pathogenic factors. Finally,\n                      <jats:italic>in vitro<\/jats:italic>\n                      assays estimated the expression patterns of the central pathogenic factor.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Multimerin 1 (MMRN1) was identified as an upregulated pathogenic factor predominantly expressed in neurons. Quercetin was highlighted as a promising repurposed drug candidate targeting MMRN1.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study illustrated a novel diagnostic and therapeutic target for PD, which provides novel clues into clinical applications of PD patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnagi.2026.1761327","type":"journal-article","created":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T05:30:44Z","timestamp":1781760644000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Deciphering MMRN1 diagnostic and therapeutic implications in the substantia nigra of Parkinson\u2019s disease patients via integrative bioinformatic analysis and multi-omics studies"],"prefix":"10.3389","volume":"18","author":[{"given":"Yu","family":"Ning","sequence":"first","affiliation":[{"name":"General Surgery, Affiliated Hospital of Beihua University","place":["Jilin City, Jilin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Gao","sequence":"additional","affiliation":[{"name":"Ophthalmology Department, Jilin People's Hospital","place":["Jilin City, Jilin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Gao","sequence":"additional","affiliation":[{"name":"Neurology Department, Affiliated Hospital of Beihua University","place":["Jilin City, Jilin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,18]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1016\/j.cmet.2020.11.007","article-title":"SCENITH: a flow cytometry-based method to functionally profile energy metabolism with single-cell resolution","volume":"32","author":"Arg\u00fcello","year":"2020","journal-title":"Cell Metab."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"2284","DOI":"10.1016\/S0140-6736(21)00218-X","article-title":"Parkinson's disease","volume":"397","author":"Bloem","year":"2021","journal-title":"Lancet"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1186\/s12864-023-09677-8","article-title":"plotnineSeqSuite: a Python package for visualizing sequence data using ggplot2 style","volume":"24","author":"Cao","year":"2023","journal-title":"BMC Genomics"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"1846","DOI":"10.1093\/bioinformatics\/btm254","article-title":"GEOquery: a bridge between the gene expression omnibus (GEO) and BioConductor","volume":"23","author":"Davis","year":"2007","journal-title":"Bioinformatics"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1111\/bcpt.14011","article-title":"Effects of quercetin in preclinical models of Parkinson's disease: a systematic review","volume":"135","author":"de Oliveira Vian","year":"2024","journal-title":"Basic Clin. Pharmacol. Toxicol."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"1484","DOI":"10.1038\/s41596-020-0292-x","article-title":"CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes","volume":"15","author":"Efremova","year":"2020","journal-title":"Nat. Protoc."},{"key":"ref7","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1007\/s00702-020-02167-1","article-title":"Parkinson's disease treatment: past, present, and future","volume":"127","author":"Elsworth","year":"2020","journal-title":"J. Neural Transm. (Vienna)"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"4016","DOI":"10.1111\/ejn.15316","article-title":"Bioinformatic analysis of the human brain extracellular matrix proteome in neurodegenerative disorders","volume":"53","author":"Freitas","year":"2021","journal-title":"Eur. J. Neurosci."},{"key":"ref9","doi-asserted-by":"publisher","first-page":"e43","DOI":"10.1002\/imt2.43","article-title":"Complex heatmap visualization","volume":"1","author":"Gu","year":"2022","journal-title":"iMeta"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"10331","DOI":"10.1038\/ncomms10331","article-title":"Network-based in silico drug efficacy screening","volume":"7","author":"Guney","year":"2016","journal-title":"Nat. Commun."},{"key":"ref11","doi-asserted-by":"publisher","first-page":"802","DOI":"10.1016\/j.amjmed.2019.03.001","article-title":"Parkinson's disease and parkinsonism","volume":"132","author":"Hayes","year":"2019","journal-title":"Am. J. Med."},{"key":"ref12","doi-asserted-by":"publisher","first-page":"7","DOI":"10.7554\/eLife.34408","article-title":"The MR-base platform supports systematic causal inference across the human phenome","volume":"7","author":"Hemani","year":"2018","journal-title":"eLife"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"1410158","DOI":"10.3389\/fimmu.2024.1410158","article-title":"Identification of novel biomarkers related to neutrophilic inflammation in COPD","volume":"15","author":"Huang","year":"2024","journal-title":"Front. Immunol."},{"key":"ref14","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1186\/1471-2105-9-559","article-title":"WGCNA: an R package for weighted correlation network analysis","volume":"9","author":"Langfelder","year":"2008","journal-title":"BMC Bioinformatics"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"3187","DOI":"10.1158\/1078-0432.CCR-14-2684","article-title":"Multimerin-1 (MMRN1) as novel adverse marker in pediatric acute myeloid leukemia: a report from the children's oncology group","volume":"21","author":"Laszlo","year":"2015","journal-title":"Clin. Cancer Res."},{"key":"ref16","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1111\/jth.15171","article-title":"Multimerin 1 supports platelet function in vivo and binds to specific GPAGPOGPX motifs in fibrillar collagens that enhance platelet adhesion","volume":"19","author":"Leatherdale","year":"2021","journal-title":"J. Thromb. Haemost."},{"key":"ref17","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1093\/bioinformatics\/bts034","article-title":"The sva package for removing batch effects and other unwanted variation in high-throughput experiments","volume":"28","author":"Leek","year":"2012","journal-title":"Bioinformatics"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"8176","DOI":"10.1038\/s41598-025-93068-4","article-title":"Identification of biomarkers and potential drug targets for esophageal cancer: a Mendelian randomization study","volume":"15","author":"Li","year":"2025","journal-title":"Sci. Rep."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"1037318","DOI":"10.3389\/fimmu.2022.1037318","article-title":"Identification of diagnostic genes for both Alzheimer's disease and metabolic syndrome by the machine learning algorithm","volume":"13","author":"Li","year":"2022","journal-title":"Front. Immunol."},{"key":"ref20","doi-asserted-by":"publisher","first-page":"52","DOI":"10.3390\/molecules23010052","article-title":"Selecting feature subsets based on SVM-RFE and the overlapping ratio with applications in bioinformatics","volume":"23","author":"Lin","year":"2017","journal-title":"Molecules"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"100434","DOI":"10.1016\/j.patter.2022.100434","article-title":"scTenifoldKnk: an efficient virtual knockout tool for gene function predictions via single-cell gene regulatory network perturbation","volume":"3","author":"Osorio","year":"2022","journal-title":"Patterns"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1111\/jnc.15288","article-title":"Exosomes in Parkinson disease","volume":"157","author":"Pinnell","year":"2021","journal-title":"J. Neurochem."},{"key":"ref23","doi-asserted-by":"publisher","first-page":"11085","DOI":"10.3390\/ijms231911085","article-title":"The role of extracellular matrix in human neurodegenerative diseases","volume":"23","author":"Pint\u00e9r","year":"2022","journal-title":"Int. J. Mol. Sci."},{"key":"ref24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/1878766","article-title":"FAM171B as a novel biomarker mediates tissue immune microenvironment in pulmonary arterial hypertension","volume":"2022","author":"Qu","year":"2022","journal-title":"Mediat. Inflamm."},{"key":"ref25","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/j.mcna.2018.10.014","article-title":"Parkinson's disease","volume":"103","author":"Reich","year":"2019","journal-title":"Med. Clin. North Am."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"e47","DOI":"10.1093\/nar\/gkv007","article-title":"Limma powers differential expression analyses for RNA-sequencing and microarray studies","volume":"43","author":"Ritchie","year":"2015","journal-title":"Nucleic Acids Res."},{"key":"ref27","doi-asserted-by":"publisher","first-page":"860","DOI":"10.1007\/s00415-020-09705-7","article-title":"Precision medicine in Parkinson's disease: emerging treatments for genetic Parkinson's disease","volume":"267","author":"Schneider","year":"2020","journal-title":"J. Neurol."},{"key":"ref28","doi-asserted-by":"publisher","first-page":"15545","DOI":"10.1073\/pnas.0506580102","article-title":"Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles","volume":"102","author":"Subramanian","year":"2005","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1056\/NEJMra2401857","article-title":"Parkinson's disease","volume":"391","author":"Tanner","year":"2024","journal-title":"N. Engl. J. Med."},{"key":"ref30","doi-asserted-by":"publisher","first-page":"102081","DOI":"10.1016\/j.artmed.2021.102081","article-title":"Artificial intelligence in neurodegenerative diseases: a review of available tools with a focus on machine learning techniques","volume":"117","author":"T\u0103u\u0163an","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"ref31","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1016\/S1474-4422(21)00030-2","article-title":"Challenges in the diagnosis of Parkinson's disease","volume":"20","author":"Tolosa","year":"2021","journal-title":"Lancet Neurol."},{"key":"ref32","doi-asserted-by":"publisher","first-page":"105454","DOI":"10.1016\/j.compbiomed.2022.105454","article-title":"Mechanisms underlying the therapeutic effects of Qingfeiyin in treating acute lung injury based on GEO datasets, network pharmacology and molecular docking","volume":"145","author":"Wang","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref33","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/s00108-022-01444-3","article-title":"Parkinson's disease","volume":"64","author":"Warnecke","year":"2023","journal-title":"Inn Med"},{"key":"ref34","doi-asserted-by":"publisher","first-page":"fcae190","DOI":"10.1093\/braincomms\/fcae190","article-title":"Investigation of the genetic aetiology of Lewy body diseases with and without dementia","volume":"6","author":"Wu","year":"2024","journal-title":"Brain Commun"},{"key":"ref35","first-page":"1307","article-title":"miRNA signature identification of retinoblastoma and the correlations between differentially expressed miRNAs during retinoblastoma progression","volume":"21","author":"Yang","year":"2015","journal-title":"Mol. Vis."},{"key":"ref36","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1186\/s12915-023-01728-6","article-title":"Single-cell Mayo map (scMayoMap): an easy-to-use tool for cell type annotation in single-cell RNA-sequencing data analysis","volume":"21","author":"Yang","year":"2023","journal-title":"BMC Biol."},{"key":"ref37","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1089\/omi.2011.0118","article-title":"clusterProfiler: an R package for comparing biological themes among gene clusters","volume":"16","author":"Yu","year":"2012","journal-title":"OMICS"},{"key":"ref38","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1186\/s41065-026-00659-w","article-title":"A prognostic model for gastric cancer based on histamine-associated prognostic genes","volume":"163","author":"Zhang","year":"2026","journal-title":"Hereditas"},{"key":"ref39","doi-asserted-by":"publisher","first-page":"2214","DOI":"10.1080\/21655979.2021.1933743","article-title":"Identification of TYR, TYRP1, DCT and LARP7 as related biomarkers and immune infiltration characteristics of vitiligo via comprehensive strategies","volume":"12","author":"Zhang","year":"2021","journal-title":"Bioengineered"},{"key":"ref40","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1038\/s41698-026-01324-1","article-title":"Kinic index: an artificial intelligence-driven predictive model and multitarget drug discovery framework for hepatocellular carcinoma patients","volume":"10","author":"Zhou","year":"2026","journal-title":"NPJ Precis Oncol"},{"key":"ref41","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1038\/ng.3538","article-title":"Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets","volume":"48","author":"Zhu","year":"2016","journal-title":"Nat. Genet."}],"container-title":["Frontiers in Aging Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnagi.2026.1761327\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T05:30:45Z","timestamp":1781760645000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnagi.2026.1761327\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,18]]},"references-count":41,"alternative-id":["10.3389\/fnagi.2026.1761327"],"URL":"https:\/\/doi.org\/10.3389\/fnagi.2026.1761327","relation":{},"ISSN":["1663-4365"],"issn-type":[{"value":"1663-4365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,18]]},"article-number":"1761327"}}