{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T20:51:31Z","timestamp":1781643091668,"version":"3.54.5"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:00:00Z","timestamp":1773273600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T00:00:00Z","timestamp":1776643200000},"content-version":"vor","delay-in-days":39,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Hum Genomics"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Parkinson's disease (PD) is a multifactorial neurodegenerative disease that results from the interplay of genetic, environmental, and immunological factors. In order to find biomarkers linked to PD, the current study intends to thoroughly explore the interaction network of immunomodulatory-related differentially expressed genes (IMRDEGs).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>GeneCards database was used to obtain immunomodulation-related genes (IMRGs), and GEO database was used to obtain the expression profile dataset of GSE224913 and GSE99039 in patients with Parkinson\u2019s disease. Using the limma package, we performed differential analysis to identify differentially expressed genes. Subsequently, we used GO, KEGG and GSEA for enrichment analysis of these differentially expressed genes. In addition, we constructed interaction networks between MRnas and transcription factors (TF), mrnas and drugs, and investigated protein\u2013protein interaction networks (PPIs). Finally, CIBERSORT method was used for immunoinfiltration analysis to estimate the composition and abundance of immune cells.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 6 IMRDEGs were identified in the GSE224913 and GSE99039 datasets. The main focus of enrichment analysis is on immune-related pathways, including dendritic cell development and negative regulation of leucocyte mediated immunity. Most immune cells were associated with 6 IMRDEGs; The associations were highest with neutrophils, static NK cells, and T cells with active CD4 memory. In addition, regulatory networks of mRNA-TF, mRNA-miRNA and mRNA-Drugs target genes were established.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      In summary, the six core genes (\n                      <jats:italic>HLA-B, HNRNPA, LILRB1, PARK7, S100A9, and SPI1<\/jats:italic>\n                      ) play pivotal roles in the progression of PD. The interaction networks encompassing mRNA-TF and mRNA-drugs contribute significantly to our understanding of disease progression and the optimization of treatment strategies.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s40246-026-00929-8","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T09:15:55Z","timestamp":1773306955000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A comprehensive analysis of the interaction network of immunomodulatory-related differentially expressed genes, aiming to identify biomarkers associated with Parkinson\u2019s disease"],"prefix":"10.1186","volume":"20","author":[{"given":"Jianying","family":"Gao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,12]]},"reference":[{"issue":"1","key":"929_CR1","doi-asserted-by":"publisher","DOI":"10.1186\/s40035-023-00354-0","volume":"12","author":"P Naren","year":"2023","unstructured":"Naren P, Samim KS, Tryphena KP, Vora LK, Srivastava S, Singh SB, et al. 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