Peer-reviewedCC BY 4.0Frontiers in Oncology23 Oct 2026

SPP1-associated pathomics features for prognostic modeling in pancreatic ductal adenocarcinoma

Jinming Zhao¹
Mingjun Li²
Jialiang Xing³
Yunsong Chen⁴
Xingguo Song¹

4 affiliations

¹Department of Clinical Laboratory, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences
²Department of Research and Education, Jinan Center for Disease Control and Prevention
³General Surgery, The 5th People’s Hospital of Jinan
⁴Shandong Provincial Key Laboratory of Precision Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences

Abstract

Background: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy characterized by an immunosuppressive tumor microenvironment (TME).

Methods: In this study, we investigated the prognostic and immunological relevance of SPP1 in PDAC through integrative analyses of public transcriptomic datasets, single-cell resources, and tissue-based assessments. Furthermore, leveraging high-throughput pathomics via whole-slide imaging and advanced computational analytics, we developed and internally validated a prognostic model incorporating two pathomics features (Zernike_2_2, Hematoxylin_4of4) together with age.

Results: SPP1 was found to be upregulated in PDAC overall compared to normal tissue, whereas its expression was lower in TLS-enriched regions relative to adjacent normal tissues. Multidimensional analysis revealed correlations between SPP1 expression and macrophage infiltration estimates. Moreover, low SPP1 expression was associated with favorable prognosis in PDAC patients. The pathomics-based prognostic model demonstrated good discriminative performance (bootstrap-corrected C-index = 0.715; corrected 12-month AUC = 0.766; apparent AUC = 0.866) and significant survival stratification between high- and low-risk groups (log-rank P < 0.0001).

Conclusions: These findings suggest that SPP1-associated histoarchitectural patterns captured by pathomics may provide prognostic information, and that SPP1 represents a potential immunomodulatory factor and a candidate prognostic biomarker for PDAC.

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BibTeX RIS

BibTeX

@article{zhao2026spp1associated,
  title = {{SPP1-associated pathomics features for prognostic modeling in pancreatic ductal adenocarcinoma}},
  author = {Jinming Zhao and Mingjun Li and Jialiang Xing and Yunsong Chen and Xingguo Song},
  journal = {Frontiers in Oncology},
  year = {2026},
  month = oct,
  doi = {10.3389/fonc.2026.1923860},
  url = {https://www.frontiersin.org/articles/10.3389/fonc.2026.1923860/full},
  note = {CC BY 4.0},
}

RIS

TY  - JOUR
TI  - SPP1-associated pathomics features for prognostic modeling in pancreatic ductal adenocarcinoma
AU  - Jinming Zhao
AU  - Mingjun Li
AU  - Jialiang Xing
AU  - Yunsong Chen
AU  - Xingguo Song
PY  - 2026
DA  - 2026/10/23
JO  - Frontiers in Oncology
DO  - 10.3389/fonc.2026.1923860
UR  - https://www.frontiersin.org/articles/10.3389/fonc.2026.1923860/full
AB  - Background: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy characterized by an immunosuppressive tumor microenvironment (TME).  Methods: In this study, we investigated the prognostic and immunological relevance of SPP1 in PDAC through integrative analyses of public transcriptomic datasets, single-cell resources, and tissue-based assessments. Furthermore, leveraging high-throughput pathomics via whole-slide imaging and advanced computational analytics, we developed and internally validated a prognostic model incorporating two pathomics features (Zernike_2_2, Hematoxylin_4of4) together with age.  Results: SPP1 was found to be upregulated in PDAC overall compared to normal tissue, whereas its expression was lower in TLS-enriched regions relative to adjacent normal tissues. Multidimensional analysis revealed correlations between SPP1 expression and macrophage infiltration estimates. Moreover, low SPP1 expression was associated with favorable prognosis in PDAC patients. The pathomics-based prognostic model demonstrated good discriminative performance (bootstrap-corrected C-index = 0.715; corrected 12-month AUC = 0.766; apparent AUC = 0.866) and significant survival stratification between high- and low-risk groups (log-rank P &lt; 0.0001).  Conclusions: These findings suggest that SPP1-associated histoarchitectural patterns captured by pathomics may provide prognostic information, and that SPP1 represents a potential immunomodulatory factor and a candidate prognostic biomarker for PDAC.
SN  - 2234-943X
ER  -

Abstract

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