Work

Uncovering dark biomarkers of glioblastoma survival through regulatory-deviation–based transcriptomic modeling

Year: 2026

Type: preprint

Abstract: Glioblastoma (GBM) displays extreme survival heterogeneity that remains poorly resolved by conventional gene-level transcriptomic analyses, which often fail to identify robust survival-associated biomarkers. Here, we introduce mqTrans, a regulatory deviation modeling framework that shifts the analytical focus from absolute gene-level expression abundance to the deviation between observed expression and transcription-factor-inferred regulatory expectations. Applying mqTrans to two independent GBM cohorts (CGGA and CPTAC-GBM), we systematically defined and identified “dark biomarkers”—genes that show no significant expression changes in traditional differential expression analysis yet exhibit significant and reproducible regulatory deviations linked to survival. We uncovered 19 such dark biomarkers that consistently distinguished short-term from long-term survivors across both cohorts. These biomarkers converged on metabolic pathways (cholesterol homeostasis) and post-transcriptional regulatory programs (miRNA targets) and were enriched in genomically complex loci with overlapping non-coding transcripts, explaining their invisibility in abundance-based analyses. A LASSO-derived five-gene Cox model based on these dark biomarkers provided more stable and generalizable survival stratification than models built from conventional expression features. Our findings demonstrate that regulatory-deviation modeling can reveal hidden prognostic signals in GBM, offering a complementary regulatory-layer view of the transcriptome that captures regulatory perturbations that remain obscured under gene-expression abundance–based quantification.

Source: Research Square

DOI: 10.21203/rs.3.rs-9640328/v1