{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T06:04:52Z","timestamp":1790661892147,"version":"4.1.0"},"reference-count":33,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"vor","delay-in-days":270,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BioMed Research International"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>\n                    Antiphospholipid syndrome (APS) and systemic sclerosis (SSc) are distinct autoimmune diseases, but both are characterized by immune dysregulation, persistent inflammation, and vascular injury. To explore whether they also share blood transcriptional features, we analyzed peripheral blood datasets from GEO (GSE102215, APS: nine patients\/nine controls; GSE231691, SSc: 49 patients\/18 controls). Differential expression analysis performed separately in each cohort using edgeR with cohort\u2010specific fold\u2010change thresholds and nominal\n                    <jats:italic>p<\/jats:italic>\n                    &lt; 0.05 identified 281 genes that were altered in the same direction in both diseases, including 100 upregulated and 181 downregulated genes. Enrichment analyses in both cohorts consistently pointed to interferon\u2010related, cytokine, and inflammatory pathways. We then combined random forest and a single\u2010hidden\u2010layer artificial neural network as exploratory feature\u2010prioritization approaches and identified five shared candidate genes: S100A8, IER5L\u2010AS1, LTK, PRR5\u2010ARHGAP8, and PCDH1. All five genes showed significant case\u2010control expression differences in both datasets (\n                    <jats:italic>p<\/jats:italic>\n                    &lt; 0.001). Given the small APS cohort, the machine\u2010learning analyses were considered exploratory and were not interpreted as evidence of validated diagnostic performance. CIBERSORT suggested that both diseases have a blood immune profile enriched for myeloid signals, particularly neutrophils and monocyte\/macrophage populations, whereas SSc showed relatively stronger CD4+ T\u2010cell and NK\u2010cell signals. S100A8 expression was positively correlated with inferred neutrophil abundance in both datasets (APS\n                    <jats:italic>r<\/jats:italic>\n                    = 0.62; SSc\n                    <jats:italic>r<\/jats:italic>\n                    = 0.58;\n                    <jats:italic>p<\/jats:italic>\n                    &lt; 0.05). Finally, miRNA prediction and DSigDB enrichment pointed to possible upstream regulators and candidate compounds, including miR\u2010155, miR\u2010146a, celecoxib, tamibarotene, HMN\u2010176, and XMD14\u201099. These findings represent concordant signals observed across two parallel disease cohorts rather than independent cross\u2010disease validation and therefore require confirmation in independent cohorts.\n                  <\/jats:p>","DOI":"10.1155\/bmri\/8727103","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T05:45:22Z","timestamp":1790660722000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploratory Machine Learning Prioritizes Shared Blood Transcriptional Candidate Genes and Immune Correlates Across Antiphospholipid Syndrome and Systemic Sclerosis"],"prefix":"10.1155","volume":"2026","author":[{"given":"Renju","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinchan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yawen","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8824-8762","authenticated-orcid":false,"given":"Fei","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,28]]},"reference":[{"key":"e_1_2_12_1_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.15387836.2006.01753.x"},{"key":"e_1_2_12_2_2","first-page":"202","article-title":"Scleroderma (Systemic Sclerosis): Classification, Subsets and Pathogenesis","volume":"15","author":"LeRoy E. 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