Multi-Omics and Machine Learning Analyses Reveal PIK3CG, PRKCD, and TRIM22 as Potential Markers of Poor Prognosis and Immune Activation in Glioblastoma.

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Title: Multi-Omics and Machine Learning Analyses Reveal PIK3CG, PRKCD, and TRIM22 as Potential Markers of Poor Prognosis and Immune Activation in Glioblastoma.
Authors: Han MH; Department of Neurosurgery, Hanyang University Guri Hospital, Hanyang University College of Medicine, Guri, Korea., Noh YK; Department of Computer Science, Hanyang University, Seoul, Korea.; School of Computational Sciences, Korea Institute for Advanced Study, Seoul, Korea., Kim H; Department of Neurology, Hanyang University Seoul Hospital, Hanyang University College of Medicine, Seoul, Korea., Kim KS; CardiOmics Program, Center for Heart and Vascular Research, Division of Cardiovascular Medicine, Department of Cellular and Integrative Physiology, University of Nebraska Medical Center, Omaha, NE, USA., Kim DH; Department of Pathology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea., Jung US; Department of Obstetrics and Gynecology, Hanyang University Guri Hospital, Hanyang University College of Medicine, Guri, Korea., Lee KS; Department of Pediatrics, Hanyang University Guri Hospital, Hanyang University College of Medicine, Guri, Korea., Kwon MJ; Department of Pathology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Korea., Chae SW; Department of Pathology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea. chae_sw@hanmail.net., Min KW; Department of Pathology, Uijeongbu Eulji Medical Center, Eulji University School of Medicine, Uijeongbu, Korea. kyueng@hanyang.ac.kr.
Source: Journal of Korean medical science [J Korean Med Sci] 2026 Apr 27; Vol. 41 (16), pp. e130. Date of Electronic Publication: 2026 Apr 27.
Publication Type: Journal Article
Journal Info: Publisher: Korean Academy of Medical Science Country of Publication: Korea (South) NLM ID: 8703518 Publication Model: Electronic Cited Medium: Internet ISSN: 1598-6357 (Electronic) Linking ISSN: 10118934 NLM ISO Abbreviation: J Korean Med Sci Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1598-6357
DOI:10.3346/jkms.2026.41.e130