{
  "abstract": "Purpose: To investigate whether quantitative nuclear morphology from routine H&E images can be used as a biomarker for glioblastoma (GBM) biology and prognosis. Methods: Nuclear eccentricity, circularity, density, and heterogeneity were measured from H&E whole-slide images in the TCGA-GBM cohort and independently validated in the CPTAC-GBM cohort. A multivariable Cox model was used to test the survival associations in both cohorts, and transcriptome-wide and gene set enrichment analyses were conducted in TCGA RNA data and validated using CPTAC RNA and proteomic data. Results: In TCGA-GBM, greater nuclear eccentricity was associated with better survival (HR, 0.85; 95% CI, 0.71-1.02; p = 0.089), with the same favorable association independently observed in CPTAC-GBM (HR, 0.74; 95% CI, 0.60-0.90; p = 0.003). No individual gene association with eccentricity was statistically significant in the TCGA cohort, but pathway analysis identified its association with epithelial-mesenchymal transition (EMT), inflammatory, hypoxic, and stress-related programs. Across the TCGA RNA, CPTAC RNA and proteomic datasets, greater eccentricity was consistently associated with increased NF-κB signaling, collagen biosynthesis, and ER-to-Golgi transport and decreased L1CAM-related and neuronal-projection programs, with EMT showing the strongest cross-cohort consistency. Conclusions: Nuclear eccentricity from patient H&E slide images is a potential biomarker associated with survival and transcriptional/translational programs in GBM. Integrating quantitative pathology with RNA and proteomic data may provide an accessible approach for GBM prognosis and tumor biology characterization.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Anatomy and Neurobiology, Boston University Aram V. Chobanian & Edward Avedisian School of Medicine, Boston, MA 02118, USA."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Luna Han"
    },
    {
      "affs": [
        0
      ],
      "name": "Teresia John",
      "orcid": "0009-0006-6846-150X"
    },
    {
      "affs": [
        0
      ],
      "name": "Mohid Hassan",
      "orcid": "0009-0007-5483-8756"
    },
    {
      "affs": [
        0
      ],
      "name": "Jonathan J Wisco",
      "orcid": "0000-0003-3689-5937"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1ychkhpvvwtnw.bib",
  "cited_by": 0,
  "doi": "10.3390/curroncol33100627",
  "fulltext_url": "https://europepmc.org/articles/PMC13702909",
  "id": "1ychkhpvvwtnw",
  "issns": [
    "1198-0052",
    "1718-7729"
  ],
  "landing_url": "https://europepmc.org/article/MED/42930194",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
    ]
  ],
  "openalex": "W7204988752",
  "pdf_url": "https://europepmc.org/articles/PMC13702909?pdf=render",
  "pmcid": "PMC13702909",
  "pmid": "42930194",
  "published": "2026-10-17",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1ychkhpvvwtnw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:01Z",
      "native_id": "MED:42930194",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42930194"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Nuclear Eccentricity as a Potential Biomarker for Tumor Biology and Survival in Glioblastoma.",
  "title_text": "Nuclear Eccentricity as a Potential Biomarker for Tumor Biology and Survival in Glioblastoma.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:22:01Z",
  "url": "https://research.draconitic.com/paper/1ychkhpvvwtnw",
  "venue": "Current oncology (Toronto, Ont.)",
  "versions": []
}