{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T05:44:32Z","timestamp":1776231872647,"version":"3.50.1"},"reference-count":40,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T00:00:00Z","timestamp":1776211200000},"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. Med."],"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study aimed to comprehensively investigate the shared molecular mechanisms and intercellular communication signatures of gout and metabolic syndrome (MetS), seeking to identify and validate key regulatory genes and pathways for developing precise diagnostic and therapeutic strategies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Transcriptomic datasets for gout and MetS were retrieved from the Gene Expression Omnibus (GEO) database. Common differentially expressed genes (CODEGs) were identified through integrative analysis, followed by the construction of protein\u2013protein interaction (PPI), drug\u2013gene, and competing endogenous RNA (ceRNA) networks to pinpoint hub genes and regulatory axes. Single-cell RNA sequencing data were analyzed to map hub gene expression and cell\u2013cell communication patterns. Crucially, key bioinformatic predictions were validated in established\n                      <jats:italic>in vitro<\/jats:italic>\n                      cell models of gout and MetS using quantitative real-time PCR (qPCR) and Western blot analysis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 261 CODEGs were identified, leading to the selection of 19 hub genes, including JAK1 and CSF1R. Functional enrichment analysis revealed their primary involvement in immune activation and inflammatory signaling, such as the JAK\u2013STAT pathway. Experimental validation confirmed these findings: qPCR analysis demonstrated that the mRNA levels of JAK1, CSF1R, and NAMPT were significantly elevated in cellular models simulating both gout and MetS conditions. Furthermore, Western blot analysis revealed increased protein expression of JAK1 and CSF1R, alongside a marked increase in phosphorylated STAT3 (p-STAT3), indicating activation of the JAK\u2013STAT pathway at the signaling level in both conditions. Single-cell analysis showed that JAK1 and CSF1R were predominantly expressed in natural killer (NK) cells and monocytes, respectively. Cell communication analysis highlighted monocytes and neutrophils as central hubs in gout, while smooth muscle cells and hematopoietic stem cells were dominant in MetS. Notably, the VISFATIN signaling pathway was highly active in both diseases, with NAMPT-associated ligand\u2013receptor interactions, including NAMPT\u2013(ITGA5\u202f+\u202fITGB1) in gout and NAMPT\u2013INSR in MetS.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study, through integrated multi-omics analysis and experimental validation, identifies and characterizes a shared molecular landscape between gout and MetS. We highlight the potential roles of JAK1 and CSF1R in a shared inflammatory context, associated with activation of the JAK\u2013STAT pathway at the signaling level. Our findings suggest that the VISFATIN signaling axis may represent a common link and delineate NAMPT-associated communication networks. These results provide insights into the intertwined pathophysiology of gout and MetS, and may offer promising avenues for joint therapeutic interventions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1749788","type":"journal-article","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T04:45:44Z","timestamp":1776228344000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrated multi-omics and single-cell transcriptomic analysis reveals shared molecular mechanisms and cell\u2013cell communication signatures in gout and metabolic syndrome"],"prefix":"10.3389","volume":"13","author":[{"given":"Zichen","family":"Shao","sequence":"first","affiliation":[{"name":"Jiangxi University of Chinese Medicine","place":["Nanchang, Jiangxi, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinqin","family":"Deng","sequence":"additional","affiliation":[{"name":"Jiangxi University of Chinese Medicine","place":["Nanchang, Jiangxi, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ling","family":"Cheng","sequence":"additional","affiliation":[{"name":"Jiangxi University of Chinese Medicine","place":["Nanchang, Jiangxi, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fo","family":"Yang","sequence":"additional","affiliation":[{"name":"Hongdu Hospital of Traditional Chinese Medicine Affiliated to Jiangxi University of Chinese Medicine","place":["Nanchang, Jiangxi, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,4,15]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"111932","DOI":"10.1016\/j.intimp.2024.111932","article-title":"Signaling pathways in uric acid homeostasis and gout: from pathogenesis to therapeutic interventions","volume":"132","author":"Yang","year":"2024","journal-title":"Int 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