{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T07:02:31Z","timestamp":1772434951049,"version":"3.50.1"},"reference-count":47,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T00:00:00Z","timestamp":1772409600000},"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. Cell Dev. Biol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This research aimed to explore key immune-related programmed cell death (PCD) genes and associated molecular mechanism on chronic rhinosinusitis with nasal polyps (CRSwNP), which further providing new perspectives for disease prognosis and therapy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>The microarray data were downloaded from the public GEO database. The immune-related PCD genes (co-genes) were identified based on DEGs of CRSwNP vs. normal, immune cells explored by WGCNA as well as PCD genes from published articles, followed by enrichment and PPI network analysis. Important signature genes were screened using machine learning methods, followed by nomogram validation. Then, the immune infiltration, GSEA pathway, target drug and clustering analysis associated with signature genes were further investigated. Finally, validation analysis based on clinical samples were performed to test the expression of signature genes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 54 co-genes were revealed based on 518 DEGs, 1127 immune genes and 520 PCD genes. By three kinds of machine learning analysis, totally five signature genes including\n                      <jats:italic>CD209, CYBB, FPR1, IL2RB<\/jats:italic>\n                      and\n                      <jats:italic>TYROBP<\/jats:italic>\n                      were explored, which showed an ideal prognostic value using nomogram investigation. Drug prediction analysis showed Sulfinpyrazone and Azacyclonol exhibited the highest combined scores with five signature genes, which were promising drug candidates for CRSwNP. Immune infiltration and GSEA analysis showed that signature genes were dramatically correlated with immune cells like macrophage and pathways associated with immune. Clustering analysis divided the data into two clusters, which primarily enriched in pathways such as cytokine-cytokine receptor interaction. Then, qRT - PCR, Western blot, and ELISA assays showed that the expression levels of all signature genes were in line with the findings of our bioinformatics analysis. This further validated the reliability of our research outcomes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      <jats:italic>CD209, CYBB, FPR1, IL2RB<\/jats:italic>\n                      and\n                      <jats:italic>TYROBP<\/jats:italic>\n                      were valuable immune-related PCD signature genes for clinical prognosis of CRSwNP. In addition, macrophages may play a key role in the chronic inflammation process of CRSwNP via activating immune-associated pathways. These findings may provide compelling evidence for early diagnosis and personalized therapeutic approaches in CRSwNP management.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcell.2026.1702613","type":"journal-article","created":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T06:32:23Z","timestamp":1772433143000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Uncovering the immune-related PCD genes in chronic rhinosinusitis with nasal polyps inflammatory progression: a machine learning and functional validation study"],"prefix":"10.3389","volume":"14","author":[{"given":"Minliang","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Otolaryngology, Sunshine Union Hospital","place":["Weifang, Shandong, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ting","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology, Sunshine Union Hospital","place":["Weifang, Shandong, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Jiao","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology, Sunshine Union Hospital","place":["Weifang, Shandong, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quanfa","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology, Sunshine Union Hospital","place":["Weifang, Shandong, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,3,2]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1356298","DOI":"10.3389\/fimmu.2024.1356298","article-title":"The interleukin-4\/interleukin-13 pathway in type 2 inflammation in chronic rhinosinusitis with nasal polyps","volume":"15","author":"Bachert","year":"2024","journal-title":"Front. 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