{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T18:02:17Z","timestamp":1784916137015,"version":"3.55.0"},"reference-count":57,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T00:00:00Z","timestamp":1784851200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The genesis of Parkinson\u2019s disease (PD), a common central neurodegenerative disorder, involves dysregulation of protein posttranslational modifications (PTM). The primary objective of this study was to screen key PTM-associated genes (PTMGs) serving as diagnostic indicators and potential therapeutic targets in PD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Peripheral blood transcriptomic data for PD cohorts and healthy controls were retrieved from publicly accessible repositories. Candidate genes were identified by overlapping differentially expressed genes (DEGs) with established PTMGs via differential expression profiling. Machine learning-based screening approaches were used for the selection of feature genes. Key genes were validated via receiver operating characteristic curve assessment combined with verification of expression levels. Subsequently, enrichment analysis, immune infiltration assessment, chromosome mapping, prediction of ribonucleic acid (RNA) modification sites, and compound screening were further explored.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 404 DEGs were identified, 19 of which overalpped with PTMGs and were thus selected as candidate genes. ML-based analysis narrowed these to eight feature genes, among which those coding for beta-1,4-galactosyltransferase 3 (\n                      <jats:italic>B4GALT3<\/jats:italic>\n                      ), ring finger and FYVE-like domain-containing E3 ubiquitin protein ligase (\n                      <jats:italic>RFFL<\/jats:italic>\n                      ), and GABA type A receptor-associated protein (\n                      <jats:italic>GABARAP<\/jats:italic>\n                      ) were validated as key genes based on their diagnostic performance and consistent downregulation in the PD group (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05). Gene set enrichment analysis demonstrated significant enrichment within immune signaling cascades. Analysis of immune cell infiltration revealed diminished populations of activated B lymphocytes, activated CD4-positive T cells, and natural killer T cell subsets in the PD group, which exhibited predominantly positive associations with the identified key genes (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05). Chromosome mapping localized\n                      <jats:italic>B4GALT3<\/jats:italic>\n                      to chromosome 1 and\n                      <jats:italic>RFFL<\/jats:italic>\n                      \/\n                      <jats:italic>GABARAP<\/jats:italic>\n                      to chromosome 17. High-confidence m\n                      <jats:sup>6<\/jats:sup>\n                      A methylation sites were predicted for\n                      <jats:italic>B4GALT3<\/jats:italic>\n                      and\n                      <jats:italic>RFFL<\/jats:italic>\n                      . Compound screening identified 34 potential compounds targeting these genes, including valproic acid and phenobarbital.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      This study identified\n                      <jats:italic>B4GALT3<\/jats:italic>\n                      ,\n                      <jats:italic>RFFL<\/jats:italic>\n                      , and\n                      <jats:italic>GABARAP<\/jats:italic>\n                      as key PTMGs in PD, highlighting their roles in PD genesis and potential as diagnostic biomarkers.\n                    <\/jats:p>\n                  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