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Peer-reviewedCC BY 4.0Frontiers in Oncology

Integrated transcriptomic and single-cell analyses define neuronal heterogeneity and PDIA4-associated malignancy in glioblastoma

  • Zhixin Liu1,2,3
  • Xiaojuan Luo4
  • Xuetao Deng5
  • Yihao Liu2
  • Wenrui Wu1
5 affiliations
  1. 1Nanchang County People’s Hospital
  2. 2Department of Neurosurgery, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science
  3. 3Department of Neurosurgery, The First Affiliated Hospital, Fujian Medical University, Fuzhou
  4. 4Department of Geriatrics, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science
  5. 5Department of Neurosurgery, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University
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Abstract

Background: Glioblastoma (GBM) is characterized by extensive cellular heterogeneity and complex interactions between malignant cells and the surrounding microenvironment. The contribution of neuronal populations to GBM progression and prognosis, however, remains incompletely understood.

Methods: Bulk transcriptomic data from TCGA-GBM and GTEx were integrated with two single-cell RNA-seq datasets. Differential expression analysis, WGCNA, machine-learning-based survival modeling, CIBERSORT, GSEA, CopyKAT, MiloR, pseudotime analysis, GSVA, CellChat, and pySCENIC were used to characterize prognostic genes and their cellular context. PDIA4 was further examined in U251 and U87 cells using siRNA-mediated knockdown followed by Western blotting, qPCR, Transwell invasion, wound-healing, colony-formation, and CCK-8 assays.

Results: Nine prognostic genes—PDIA4, CAMK2N1, RPH3A, RAB11FIP4, DMTN, EPHB6, SLC47A2, ESM1, and EMILIN2—were retained in the final prognostic model. Single-cell analysis showed distinct cellular distributions of these genes and identified a tumor-associated neuronal population enriched in GBM. Pseudotime analysis revealed transcriptional differences between normal- and tumor-associated neuronal states, while CellChat predicted altered NRXN–NLGN communication involving neuronal populations and GBM cells. Immune deconvolution showed associations between several prognostic genes and immune-cell composition, including an inverse correlation between PDIA4 and activated NK cells. Experimentally, PDIA4 was expressed at higher levels in U251 and U87 cells than in SVG p12 cells. PDIA4 knockdown reduced clonogenic growth, invasion, wound closure, and CCK-8 metabolic activity readouts in both GBM cell lines.

Conclusions: Integrative bulk and single-cell analyses linked GBM prognosis to distinct neuronal and immune features of the tumor microenvironment. Together, these analyses place the nine-gene prognostic signature within the cellular architecture of GBM and highlight tumor-associated neuronal states as a potential component of the prognostic tumor ecosystem. PDIA4 knockdown provided complementary functional support for one component of the signature, while the predicted NRXN–NLGN network nominates a candidate route of neuron–GBM communication for future investigation.

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Cite

BibTeXRIS
BibTeX
@article{liu2026integrated,
  title = {{Integrated transcriptomic and single-cell analyses define neuronal heterogeneity and PDIA4-associated malignancy in glioblastoma}},
  author = {Zhixin Liu and Xiaojuan Luo and Xuetao Deng and Yihao Liu and Wenrui Wu},
  journal = {Frontiers in Oncology},
  year = {2026},
  month = oct,
  doi = {10.3389/fonc.2026.1971414},
  url = {https://www.frontiersin.org/articles/10.3389/fonc.2026.1971414/full},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Integrated transcriptomic and single-cell analyses define neuronal heterogeneity and PDIA4-associated malignancy in glioblastoma
AU  - Zhixin Liu
AU  - Xiaojuan Luo
AU  - Xuetao Deng
AU  - Yihao Liu
AU  - Wenrui Wu
PY  - 2026
DA  - 2026/10/13
JO  - Frontiers in Oncology
DO  - 10.3389/fonc.2026.1971414
UR  - https://www.frontiersin.org/articles/10.3389/fonc.2026.1971414/full
AB  - Background: Glioblastoma (GBM) is characterized by extensive cellular heterogeneity and complex interactions between malignant cells and the surrounding microenvironment. The contribution of neuronal populations to GBM progression and prognosis, however, remains incompletely understood.  Methods: Bulk transcriptomic data from TCGA-GBM and GTEx were integrated with two single-cell RNA-seq datasets. Differential expression analysis, WGCNA, machine-learning-based survival modeling, CIBERSORT, GSEA, CopyKAT, MiloR, pseudotime analysis, GSVA, CellChat, and pySCENIC were used to characterize prognostic genes and their cellular context. PDIA4 was further examined in U251 and U87 cells using siRNA-mediated knockdown followed by Western blotting, qPCR, Transwell invasion, wound-healing, colony-formation, and CCK-8 assays.  Results: Nine prognostic genes—PDIA4, CAMK2N1, RPH3A, RAB11FIP4, DMTN, EPHB6, SLC47A2, ESM1, and EMILIN2—were retained in the final prognostic model. Single-cell analysis showed distinct cellular distributions of these genes and identified a tumor-associated neuronal population enriched in GBM. Pseudotime analysis revealed transcriptional differences between normal- and tumor-associated neuronal states, while CellChat predicted altered NRXN–NLGN communication involving neuronal populations and GBM cells. Immune deconvolution showed associations between several prognostic genes and immune-cell composition, including an inverse correlation between PDIA4 and activated NK cells. Experimentally, PDIA4 was expressed at higher levels in U251 and U87 cells than in SVG p12 cells. PDIA4 knockdown reduced clonogenic growth, invasion, wound closure, and CCK-8 metabolic activity readouts in both GBM cell lines.  Conclusions: Integrative bulk and single-cell analyses linked GBM prognosis to distinct neuronal and immune features of the tumor microenvironment. Together, these analyses place the nine-gene prognostic signature within the cellular architecture of GBM and highlight tumor-associated neuronal states as a potential component of the prognostic tumor ecosystem. PDIA4 knockdown provided complementary functional support for one component of the signature, while the predicted NRXN–NLGN network nominates a candidate route of neuron–GBM communication for future investigation.
SN  - 2234-943X
ER  -