Integrated Multi-Omics and Radiogenomic Analysis Identifies S100B and ITGB5 as Complementary Candidate Biomarkers of Glioblastoma Heterogeneity.

Overview

1. Wenzhou Medical University, Wenzhou, China
2. Department of Neurosurgery, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
3. The Key Laboratory of Pediatric Hematology and Oncology Diseases of Wenzhou, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China

Abstract

Glioblastoma (GBM) is characterized by marked intratumoral heterogeneity, diffuse invasion, and a profoundly immunosuppressive tumor microenvironment. In this study, we applied an integrative multi‐omics and radiogenomic framework to identify candidate biomarkers reflecting complementary dimensions of GBM heterogeneity. Public bulk transcriptomic datasets were integrated to define GBM‐related differentially expressed genes, which were intersected with curated microbiota‐associated gene sets as a hypothesis‐generating screening strategy. Machine‐learning models with SHAP interpretation were used for candidate‐gene prioritization, followed by survival‐association analysis, pan‐cancer comparison, Human Protein Atlas immunohistochemistry, CPTAC proteomics, immune infiltration analysis, single‐cell transcriptomics, spatial transcriptomics, in silico perturbation analysis, and MRI‐based radiogenomics. GBM samples showed enrichment of extracellular matrix organization, proliferative programs, immune‐related signaling, and vascular or endothelial pathways, with relative reductions in neural, synaptic, and myelin‐associated signatures. S100B and ITGB5 emerged as survival‐associated candidate markers with different biological contexts. Multi‐omics analyses suggested that S100B may reflect broadly distributed glial‐lineage and malignant‐state programs, whereas ITGB5 was more closely associated with focal extracellular matrix remodeling, stromal‐vascular interactions, and immunoregulatory niches. Radiogenomic analysis further suggested distinct MRI‐derived associations for the two genes, with the ITGB5‐related model retaining a broader radiomic signature and showing a more heterogeneous habitat pattern than the S100B‐related model. These exploratory findings support S100B and ITGB5 as complementary candidate biomarkers of GBM heterogeneity and provide a basis for future experimental, multicenter, and prospective validation.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• geo:GSE116520 — at NCBI GEO; found in “Data Availability Statement”

Data Availability Statement

The transcriptomic and single‐cell RNA sequencing datasets analyzed during the current study are available in public repositories, including TCGA, CGGA, GTEx, and the Gene Expression Omnibus (GEO) database under the accession numbers GSE116520 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE116520), GSE263588 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE263588), GSE83300 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE83300), GSE119834 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE119834), GSE137900 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137900), GSE135045 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE135045), and GSE237183 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE237183). All custom scripts, computational pipelines, and processed analytical data underlying the results reported in this manuscript are available from the corresponding authors upon reasonable request.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 3, 28 September 2026

• Publisher: — → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 10 keywords, 11 MeSH terms, 40 references.

Cite

This paper

Yang, X., Zhang, Y., Zheng, Y., & Lin, J. (2026). Integrated Multi-Omics and Radiogenomic Analysis Identifies S100B and ITGB5 as Complementary Candidate Biomarkers of Glioblastoma Heterogeneity. Chemical biology & drug design, 108(3), e70396. https://doi.org/10.1111/cbdd.70396

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