{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T12:03:09Z","timestamp":1788436989938,"version":"build-2803163510"},"reference-count":35,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T00:00:00Z","timestamp":1745971200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["OMICS: A Journal of Integrative Biology"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:p>\n                    Traditional paradigms of pharmaceutical innovation have long relied on the \u201cone drug, one disease\u201d premise. However, a network mindset in unpacking disease mechanisms can be fruitful to move toward a \u201cone drug, polydisease\u201d paradigm of drug discovery and development. A case in point is obstructive sleep apnea (OSA) and lung cancer, which are two prevalent respiratory disorders that share common risk factors and may potentially exhibit overlapping molecular mechanisms. The putative mechanistic linkages between OSA and lung cancer remain underexplored; however, this study offers new evidence on overlapping genetic signatures between OSA and lung cancer with an in-silico approach. Bioinformatics analysis of the publicly available datasets (GSE135917 and GSE268175) identified 123 upregulated and 13 downregulated genes in OSA and 3175 upregulated and 2272 downregulated genes in lung cancer. A total of four genes (\n                    <jats:italic toggle=\"yes\">C1GALT1<\/jats:italic>\n                    ,\n                    <jats:italic toggle=\"yes\">TMEM106B<\/jats:italic>\n                    ,\n                    <jats:italic toggle=\"yes\">ZNF117<\/jats:italic>\n                    , and\n                    <jats:italic toggle=\"yes\">ZNF486<\/jats:italic>\n                    ) were significantly upregulated with both disorders, highlighting potentially shared genetic and molecular mechanisms. Pathway and cell enrichment analysis indicated that mucin type O-glycan biosynthesis pathway and endothelial cells are strongly associated with these shared genes, lending support for their potential roles in both diseases. Moreover, hsa-miR-34a-5p, hsa-let-7g-5p, and hsa-miR-19a-3p were found to be associated with these common genes. Validation using the GEPIA2 tool confirmed the consistent expression patterns of these four genes in lung cancer. Machine learning analysis highlighted\n                    <jats:italic toggle=\"yes\">TMEM106B<\/jats:italic>\n                    as the most significant biomarker candidate for distinguishing OSA and lung cancer from controls. In summary, this study supports the overarching concept that human diseases can have shared mechanistic pathways in the specific example of OSA and lung cancer. While these findings call for further research and validation, they invite rethinking the current pharmaceutical innovation paradigms to move beyond the \u201cone drug, one disease\u201d concept.\n                  <\/jats:p>","DOI":"10.1089\/omi.2025.0010","type":"journal-article","created":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T05:12:45Z","timestamp":1744089165000},"page":"221-228","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":7,"title":["Identification of Overlapping Genetic Signatures Between Obstructive Sleep Apnea and Lung Cancer: Moving Beyond \u201cOne Drug, One Disease\u201d Paradigm of Pharmaceutical Innovation"],"prefix":"10.1177","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9085-2658","authenticated-orcid":false,"given":"Sanjukta","family":"Dasgupta","sequence":"first","affiliation":[{"name":"Department of Biotechnology, Center for Multidisciplinary Research &amp; Innovations, Brainware University, Barasat, India."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,4,30]]},"reference":[{"key":"e_1_3_7_2_1","doi-asserted-by":"crossref","first-page":"D991","DOI":"10.1093\/nar\/gks1193","article-title":"NCBI GEO: Archive for functional genomics data sets \u2013 Update","volume":"41","author":"Barrett T","year":"2013","unstructured":"Barrett T, , Wilhite SE, , Ledoux P, et al. 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