{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T06:20:37Z","timestamp":1783405237938,"version":"3.54.6"},"reference-count":55,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:00:00Z","timestamp":1783382400000},"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. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Post-treatment Lyme disease syndrome (PTLDS) and rheumatoid arthritis (RA) are both characterized by chronic inflammation and immune dysregulation; however, whether they share underlying molecular immune mechanisms remains unclear.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Peripheral blood mononuclear cell (PBMC) transcriptomic datasets from the GEO database were analyzed to identify common differentially expressed genes (DEGs) between PTLDS and RA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were subsequently performed. Feature genes were screened using least absolute shrinkage and selection operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE) machine learning algorithms. Gene set enrichment analysis (GSEA) and xCell analysis were conducted to characterize signaling pathway features and immune cell infiltration patterns in both diseases. In addition, prediction-based ceRNA regulatory network analysis and drug association analysis were performed as exploratory components.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 44 common DEGs were identified, which were primarily enriched in neutrophil chemotaxis, complement activation, and immune-inflammatory processes. Machine learning analysis ultimately identified ZNF83 as the feature gene. GSEA revealed that both diseases were associated with innate immune-related pathways and immunoregulatory processes and exhibited certain similarities in immune cell composition. ZNF83 showed preliminary discrimination between disease and control samples in both PTLDS and RA (AUC\u00a0=\u00a00.934 and 0.765, respectively), although these estimates require validation in larger independent cohorts. Furthermore, DSigDB analysis suggested potential associations between ZNF83 and several candidate therapeutic compounds.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>PTLDS and RA share certain common features at the level of immune-inflammatory regulation, and ZNF83 may serve as a potential feature regulatory molecule involved in the immune processes of both diseases. This study provides new insights into the immunopathological mechanisms of PTLDS and its relationship with autoimmune diseases.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1796178","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T05:45:44Z","timestamp":1783403144000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Bioinformatics analysis of potential molecular markers and immunological characteristics shared between post-treatment Lyme disease syndrome and rheumatoid arthritis"],"prefix":"10.3389","volume":"17","author":[{"given":"Yantong","family":"Chen","sequence":"first","affiliation":[{"name":"Faculty of Life Science and Technology & The Affiliated Anning First People\u2019s Hospital, Kunming University of Science and Technology","place":["Kunming, Yunnan, 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