Cellular subpopulation remodeling and biphasic molecular dynamics in glioma recurrence: a multi-omics analysis and...

Glioblastoma (GBM) is a highly aggressive brain tumor with a median survival of about 15 months and frequent recurrence. Liquid biopsy of cerebrospinal fluid (CSF) and plasma enables minimally invasive detection of tumor-associated molecular signals and may improve early diagnosis of recurrence. This study aims to elucidate the distinct molecular landscapes of cerebrospinal fluid and plasma in glioblastoma, and to identify recurrence-associated biomarkers through an integrated multi-omics framework.

Using integrated bioinformatic frameworks, gene set enrichment analysis, and receiver operating characteristic curves, we systematically mapped differential gene expression patterns and their associated regulatory mechanisms.

Cerebrospinal fluid yielded substantially more differentially expressed genes than plasma (3,362 vs. 1,329), with upregulated genes predominating in both compartments. We identified four genes (THNSL2, SYT5, RUVBL1, and GSKIP) that showed significant expression differences between primary and recurrent tumors, with RUVBL1 and GSKIP demonstrating the strongest functional enrichment signals. These genes were preferentially linked to glioblastoma cell subpopulations in both fluid types. The plasma-based multigene prognostic model achieved area under the curve values of 1.0 for 1-, 2-, and 3-year survival prediction in the training cohort, which requires further validation in independent cohorts.

Collectively, by using patients' CSF and plasma samples, this study identifies candidate molecular markers and regulatory networks associated with primary and recurrent GBM through integrated analysis of CSF and plasma samples, and highlights complementary molecular signatures between the two fluid compartments. Our findings provide a preliminary molecular basis and candidate targets for future research on precision diagnosis, prognostic stratification, and targeted therapy of GBM.