{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T07:32:18Z","timestamp":1767598338398,"version":"3.41.2"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T00:00:00Z","timestamp":1738108800000},"content-version":"vor","delay-in-days":68,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"National High Level Hospital Clinical Research Funding"},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82151302"],"award-info":[{"award-number":["82151302"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CAMS Innovation Fund for Medical Sciences","award":["2021-I2M-1-014"],"award-info":[{"award-number":["2021-I2M-1-014"]}]},{"name":"National High Level Hospital Clinical Research Funding","award":["2022-PUMCH-B-113","2022-PUMCH-A-019"],"award-info":[{"award-number":["2022-PUMCH-B-113","2022-PUMCH-A-019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>This study aimed to investigate the genetic association between glioblastoma (GBM) and unsupervised deep learning\u2013derived imaging phenotypes (UDIPs). We employed a combination of genome-wide association study (GWAS) data, single-nucleus RNA sequencing (snRNA-seq), and scPagwas (pathway-based polygenic regression framework) methods to explore the genetic links between UDIPs and GBM. Two-sample Mendelian randomization analyses were conducted to identify causal relationships between UDIPs and GBM. Colocalization analysis was performed to validate genetic associations, while scPagwas analysis was used to evaluate the relevance of key UDIPs to GBM at the cellular level. Among 512 UDIPs tested, 23 were found to have significant causal associations with GBM. Notably, UDIPs such as T1-33 (OR\u2009=\u20091.007, 95% CI\u2009=\u20091.001 to 1.012, P\u2009=\u2009.022), T1-34 (OR\u2009=\u20091.012, 95% CI\u2009=\u20091.001\u20131.023, P\u2009=\u2009.028), and T1-96 (OR\u2009=\u20091.009, 95% CI\u2009=\u20091.001\u20131.019, P\u2009=\u2009.046) were found to have a genetic association with GBM. Furthermore, T1-34 and T1-96 were significantly associated with GBM recurrence, with P-values &amp;lt; .0001 and P\u2009&amp;lt;\u2009.001, respectively. In addition, scPagwas analysis revealed that T1-33, T1-34, and T1-96 are distinctively linked to different GBM subtypes, with T1-33 showing strong associations with the neural progenitor-like subtype (NPC2), T1-34 with mesenchymal (MES2) and neural progenitor (NPC1) cells, and T1-96 with the NPC2 subtype. T1-33, T1-34, and T1-96 hold significant potential for predicting tumor recurrence and aiding in the development of personalized GBM treatment strategies.<\/jats:p>","DOI":"10.1093\/bib\/bbaf037","type":"journal-article","created":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T09:38:50Z","timestamp":1738143530000},"source":"Crossref","is-referenced-by-count":4,"title":["Inferring the genetic relationships between unsupervised deep learning\u2013derived imaging phenotypes and glioblastoma through multi-omics approaches"],"prefix":"10.1093","volume":"26","author":[{"given":"Liguo","family":"Ye","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100730 ,","place":["China"]}]},{"given":"Cheng","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Xuanwu Hospital, Capital Medical University , Beijing ,","place":["People's Republic of China"]}]},{"given":"Pengtao","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100730 ,","place":["China"]}]},{"given":"Yu","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100730 ,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0636-1670","authenticated-orcid":false,"given":"Wenbin","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100730 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