{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T08:09:12Z","timestamp":1779178152305,"version":"3.51.4"},"reference-count":50,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T00:00:00Z","timestamp":1778544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background: Clinically actionable biomarkers that reliably distinguish glioblastoma (GBM) from lower-grade glioma (LGG) across expression platforms remain an unmet need. Existing transcriptomic signatures are frequently confounded by batch effects, platform heterogeneity, and the inability to translate to single-patient clinical workflows. Methods: We developed a topology-aware biomarker discovery framework in which analysis-of-variance ranking defines a candidate gene pool, hypergraph co-expression analysis at correlation threshold \u03c4 = 0.75 identifies densely connected hubs within this pool, rough set reduct optimisation selects a minimal sufficient subset of these hubs, and a Random Forest classifier with stratified cross-validation performs the final discrimination. The pipeline was trained exclusively on GSE16011, a single-platform single-institution Affymetrix microarray cohort free from batch-class confound, and validated on two independent RNA-sequencing cohorts (CGGA-325 and CGGA-693). Robustness was further assessed through bootstrap optimism correction, DeLong cross-cohort equivalence testing, leave-one-gene-out analysis, and a sensitivity analysis under WHO CNS5 (2021) class definitions. Results: The pipeline identified a ten-gene biomarker panel (CSMD3, CHI3L1, PLP2, FRY, FCHSD2, ADM, MCUB, ANXA1, DUSP26, and HK2), achieving a fivefold cross-validation AUROC of 0.906 \u00b1 0.029 and a held-out AUROC of 0.831. External validation yielded AUROC = 0.838 in CGGA-325 and AUROC = 0.836 in CGGA-693. The biomarker-derived risk score demonstrated independent prognostic value in CGGA-693 (multivariate Cox hazard ratio = 9.195; p &lt; 0.001) after adjustment for WHO histological grade, with Kaplan\u2013Meier analysis confirming highly significant survival separation (log-rank p = 4.60 \u00d7 10\u221237). Class definitions in the present work follow the histology-based pre-2021 WHO classification used in the source datasets and do not directly incorporate WHO CNS5 (2021) molecular criteria, such as IDH mutation status, that distinguish IDH-wild-type glioblastoma from IDH-mutant grade-IV astrocytoma. After excluding IDH-mutant grade-IV cases from the CGGA cohorts, the classification AUROCs increased to 0.906 in CGGA-325 and 0.872 in CGGA-693, with a Cox risk-score hazard ratio of 8.57 (p = 1.4 \u00d7 10\u221213) and log-rank p = 1.4 \u00d7 10\u221232 retained on the CNS5-aligned cohort. Conclusions: The methodological contributions introduced in this study, namely, the topology-aware hypergraph candidate pool construction, the rough set combinatorial reduct selection, the fixed-reference single-sample normalisation protocol, and the nested validation regime combining bootstrap optimism correction with cross-platform DeLong testing, are platform agnostic and directly applicable to future CNS5-aligned cohorts as such resources become publicly available, supporting the prospective re-derivation of molecularly defined glioma signatures within the integrated histopathological and molecular frameworks of contemporary neuro-oncology.<\/jats:p>","DOI":"10.3390\/cancers18101576","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:04:36Z","timestamp":1778601876000},"page":"1576","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Ten-Gene Transcriptomic Biomarker Panel for Glioma Classification and Prognosis Identified via Integrative Hypergraph and Rough Set Analysis"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7061-2534","authenticated-orcid":false,"given":"\u00d6mer","family":"Akg\u00fcller","sequence":"first","affiliation":[{"name":"Department of Mathematics, Faculty of Science, Mugla Sitki Kocman University, Mu\u011fla 48000, Turkey"},{"name":"Oncology Department, Institute of Health Sciences, Dokuz Eylul University, Izmir 35340, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1465-7153","authenticated-orcid":false,"given":"Mehmet Ali","family":"Balc\u0131","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Faculty of Science, Mugla Sitki Kocman University, Mu\u011fla 48000, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1165-1389","authenticated-orcid":false,"given":"Gabriela","family":"Cioca","sequence":"additional","affiliation":[{"name":"Preclinical Department, Faculty of Medicine, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1007\/s40336-022-00489-6","article-title":"Epidemiology, risk factors, and prognostic factors of gliomas","volume":"10","author":"Pellerino","year":"2022","journal-title":"Clin. 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