{
  "abstract": "Background\n\nGlioblastoma (GBM) is characterized by marked malignant-cell plasticity and a complex tumor ecosystem composed of heterogeneous malignant and microenvironmental cell populations. We integrated single-cell and bulk transcriptomic data to characterize reproducible GBM cell and ecosystem-associated states, evaluate cross-platform concordance, and examine immune-associated transcriptional programs while distinguishing descriptive associations from mechanistic inference.Methods\n\nThe deposited Smart-seq2 (7,930 cells) and 10x (16,201 cells) expression matrices from GSE131928/SCP393 were analyzed on their verified expression scales. Official annotations and malignant-state modules were audited, four broad malignant states were recomputed, and a tumor-held-out machine-learning label-transfer framework was applied to the 10x dataset. Public TCGA-GBM RNA-seq data were used to evaluate state- and microenvironment-associated transcriptional programs and to perform nested prediction benchmarking. Selected ecosystem-associated genes were additionally examined by RT-qPCR in glioma cell lines and normal human astrocytes.Results\n\nAmong Smart-seq2 cells, 6,576 malignant cells had valid state scores, whereas 287 alias-affected records were excluded from state-score denominators. The machine-learning label-transfer model achieved an out-of-fold accuracy of 0.998, and malignant-state fractions showed moderate-to-strong concordance across six shared tumor labels. At the tumor-label level, MES-like fraction was not significantly associated with macrophage fraction (Spearman rho = 0.171, P = 0.395), indicating that ecosystem-level transcriptional association should not be interpreted as a simple directional immune-to-state mechanism. In TCGA-GBM, MES and myeloid-associated programs correlated with immune and stromal expression signatures, whereas survival and 12-month prediction performance remained modest and specification-sensitive. RT-qPCR further demonstrated increased STAT3, CHI3L1, SPP1, and VEGFA expression in glioma cell lines relative to normal human astrocytes.Conclusion\n\nIntegrated single-cell and bulk transcriptomic analysis supports reproducible characterization of malignant-cell heterogeneity and immune-associated ecosystem features in IDH-wildtype GBM. Machine-learning-assisted cross-platform analysis provides a reproducible framework for evaluating tumor ecosystem-associated states, while the observed immune-mesenchymal associations remain descriptive rather than causal. Experimental RT-qPCR provides expression-level support for selected ecosystem-associated genes and highlights testable hypotheses for future functional and spatial studies.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Neurosurgery, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University , ,",
    "Department of Neurosurgery, The First Affiliated Hospital of Wannan Medical University (Yijishan Hospital of Wannan Medical University) , ,",
    "Department of Neurosurgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine , ,",
    "Department of Neurosurgery, The First Affiliated Hospital of Wannan Medical University (Yijishan Hospita) , ,"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jun Cao"
    },
    {
      "affs": [
        0
      ],
      "name": "Hao Cheng"
    },
    {
      "affs": [
        1
      ],
      "name": "Yu Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Bing Zhao"
    },
    {
      "affs": [
        2
      ],
      "name": "Yang Wang"
    },
    {
      "affs": [
        3
      ],
      "name": "Xiaochun Jiang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0vqwmtac71x4b.bib",
  "cited_by": 0,
  "doi": "10.3389/fcell.2026.1967483",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13716947/?tool=EBI",
  "id": "0vqwmtac71x4b",
  "issns": [
    "2296-634X"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13716947",
  "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": [
    [
      "heading",
      "Original Research"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7221764170",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13716947/pdf/?tool=EBI",
  "pmcid": "PMC13716947",
  "pmid": "42959382",
  "published": "2026-10-14",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/0vqwmtac71x4b.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T00:20:37Z",
      "native_id": "PMC:PMC13716947",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13716947"
    },
    {
      "first_seen": "2026-10-31T12:21:46Z",
      "native_id": "MED:42959382",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42959382"
    },
    {
      "first_seen": "2026-11-02T12:21:21Z",
      "native_id": "PMC13716947",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fcell.2026.1967483"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Single-cell and spatial transcriptomics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Machine-learning-assisted integration of single-cell and bulk transcriptomics characterizes glioblastoma ecosystem states and immune-associated programs",
  "title_text": "Machine-learning-assisted integration of single-cell and bulk transcriptomics characterizes glioblastoma ecosystem states and immune-associated programs",
  "type": "peer-reviewed",
  "updated_at": "2026-11-02T12:21:21Z",
  "url": "https://research.draconitic.com/paper/0vqwmtac71x4b",
  "venue": "Frontiers in cell and developmental biology",
  "versions": []
}