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Article Open Access

Immune infiltration and stromal heterogeneity in pancreatic cancer: A prognostic model guiding immunotherapy response

  • Authors:
    • Zhilong Zhao
    • Jinlong Zhang
    • Jiayi Zhang
    • Fuyong Liu
    • Bao Wang
    • Yanlin Zhou
    • Xiaomeng Li
    • Rui Li
    • Jinwen Si Ma
    • Hao Wu
    • Yongsheng Xie
    • Xuejiao Li
  • View Affiliations / Copyright

    Affiliations: Department of Basic Medicine Science, North Henan Medical University, Xinxiang, Henan 453003, P.R. China, Department of Respiratory Intervention, Affiliated Cancer Institute of Zhengzhou University, Zhengzhou, Henan 450008, P.R. China, Shenzhen Key Laboratory for Systems Medicine in Inflammatory Diseases, School of Medicine, Shenzhen Campus of Sun Yat‑Sen University, Sun Yat‑Sen University, Shenzhen, Guangdong 518107, P.R. China
    Copyright: © Zhao et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 465
    |
    Published online on: July 29, 2025
       https://doi.org/10.3892/ol.2025.15211
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Abstract

Pancreatic adenocarcinoma (PAAD) is among the most common malignant tumors of the gastrointestinal tract and has a poor prognosis. Research on its pathogenesis and treatment remains insufficient; therefore, the present study aimed to develop a novel model for predicting PAAD prognosis. Transcriptome data for pancreatic cancer were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases (GSE28735), with TCGA data serving as the training set and GSE28735 data as the validation set. Differential expression analysis of GSE28735 data was performed using the limma package, followed by the identification of potential prognostic genes through univariate Cox regression and least absolute shrinkage and selection operator regression. Patients were stratified into high‑ and low‑risk groups based on risk scores, followed by survival analysis, nomogram construction, immune infiltration analysis and mutation analysis. Reverse transcription‑quantitative PCR (RT‑qPCR) experiments were performed for validation. A prognostic prediction model for PAAD was constructed using TCGA patient samples and comprised 12 genes. Kaplan‑Meier analysis indicated that the model effectively distinguished between high‑ and low‑risk groups, which corresponded with poor and favorable prognoses, respectively. Receiver operating characteristic curve analysis demonstrated high predictive accuracy. Additionally, significant differences in immune infiltration and mutation levels were observed between the two risk groups. RT‑qPCR results further demonstrated that the expression of prognostic genes differed significantly between pancreatic cancer (PANC‑1) and normal (HPDE‑6) cell lines. In conclusion, the prognostic model developed in the present study may contribute to an improved understanding of PAAD prognosis and provide new tools for predicting prognosis and immune response in patients with PAAD.
View Figures

Figure 1

Development history of TME treatment
strategies. TME, tumor microenvironment; FAK, focal adhesion
kinase; FAP, fibroblast activation protein-α; SHH, sonic
hedgehog.

Figure 2

Flow chart of the present study.
LASSO, least absolute shrinkage and selection operator; TCGA, The
Cancer Genome Atlas; TME, tumor microenvironment; RT-qPCR, reverse
transcription-quantitative PCR.

Figure 3

PAAD gene expression analysis. (A)
Heat map and (B) volcano plot of differentially expressed RNA in
the TCGA dataset (Blue, downregulated expression; red, upregulated
expression). (C) Upregulated gene GO enrichment analysis. (D)
Upregulated gene KEGG enrichment analysis. (E) Downregulated gene
GO enrichment analysis. (F) Downregulated gene KEGG enrichment
analysis. PAAD, pancreatic adenocarcinoma; TCGA, The Cancer Genome
Atlas; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and
Genomes; FC, fold change; FDR, false discovery rate; BP, Biological
Process; CC, Cellular Component; MF, Molecular Function.

Figure 4

Establishment of The Cancer Genome
Atlas data set patient prediction model. (A) Univariate Cox
analysis of differentially expressed genes. (B and C) Least
absolute shrinkage and selection operator regression analysis: (B)
Optimal λ selection and (C) Coefficient paths. (D) Kaplan-Meier
curve analysis of high- and low-risk groups. (E) Riskscore clinical
features timeROC curve. (F) Common clinical features ROC curve.
ROC, receiver operating characteristic; AUC, area under the curve;
T, tumor; N, node; M, metastasis.

Figure 5

Nomogram construction and performance
evaluation. (A) Cox regression analysis of clinical features. (B)
Construction of the nomogram. (C) Nomogram timeROC curve. (D)
Nomogram calibration curve. ***P<0.001. ROC, receiver operating
characteristic; T, tumor; N, node; AUC, area under the curve; OS,
overall survival.

Figure 6

Verification results of external data
set GSE28735. (A) Kaplan-Meier curve analysis of high- and low-risk
groups in the GSE28735 cohort. (B) TimeROC curve. (C) Risk Score:
Univariate and multivariate Cox regression analyses. ***P<0.001.
AUC, area under the curve.

Figure 7

Immunoassay of high- and low-risk
groups. (A) ssGSEA immune infiltration analysis. (B) ssGSEA immune
function analysis. (C) Immune scoring box plot. (D) TIDE box line
diagram for the high- and low-risk groups. (E) TIDE and RiskScore
scatter plot. *P<0.05; **P<0.01; ***P<0.001. ssGSEA,
single sample Gene Set Enrichment Analysis; TIDE, Tumor Immune
Dysfunction and Exclusion; MDSC, myeloid-derived suppressor cell;
HLA, human leukocyte antigen; MHC, major histocompatibility
complex; TME, tumor microenvironment; APC, antigen-presenting cell;
CCR, C-C motif chemokine receptor.

Figure 8

Analysis of abrupt landscape changes.
Mutation landscape of the (A) high-risk and (B) low-risk groups.
TMB, tumor mutation burden.

Figure 9

Reverse transcription-quantitative
PCR analysis of CHGB, DHRS9, ESM1, FNDC1, GLS2, HPGD, IGFL2, KYNU,
MMP9, MT1M, SLC16A10 and TTN expression. *P<0.05; **P<0.01;
***P<0.001. CHGB, chromogranin B; DHRS9, dehydrogenase/reductase
9; ESM1, endothelial cell specific molecule 1; FNDC1, fibronectin
type III domain containing 1; GLS2, glutaminase 2; HPGD,
15-hydroxyprostaglandin dehydrogenase; IGFL2, insulin growth
factor-like family member 2; KYNU, kynureninase; MMP9, matrix
metallopeptidase 9; MT1M, metallothionein 1M; SLC16A10, solute
carrier family 16 member 10; TTN, titin.
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Copy and paste a formatted citation
Spandidos Publications style
Zhao Z, Zhang J, Zhang J, Liu F, Wang B, Zhou Y, Li X, Li R, Ma JS, Wu H, Wu H, et al: Immune infiltration and stromal heterogeneity in pancreatic cancer: A prognostic model guiding immunotherapy response. Oncol Lett 30: 465, 2025.
APA
Zhao, Z., Zhang, J., Zhang, J., Liu, F., Wang, B., Zhou, Y. ... Li, X. (2025). Immune infiltration and stromal heterogeneity in pancreatic cancer: A prognostic model guiding immunotherapy response. Oncology Letters, 30, 465. https://doi.org/10.3892/ol.2025.15211
MLA
Zhao, Z., Zhang, J., Zhang, J., Liu, F., Wang, B., Zhou, Y., Li, X., Li, R., Ma, J. S., Wu, H., Xie, Y., Li, X."Immune infiltration and stromal heterogeneity in pancreatic cancer: A prognostic model guiding immunotherapy response". Oncology Letters 30.4 (2025): 465.
Chicago
Zhao, Z., Zhang, J., Zhang, J., Liu, F., Wang, B., Zhou, Y., Li, X., Li, R., Ma, J. S., Wu, H., Xie, Y., Li, X."Immune infiltration and stromal heterogeneity in pancreatic cancer: A prognostic model guiding immunotherapy response". Oncology Letters 30, no. 4 (2025): 465. https://doi.org/10.3892/ol.2025.15211
Copy and paste a formatted citation
x
Spandidos Publications style
Zhao Z, Zhang J, Zhang J, Liu F, Wang B, Zhou Y, Li X, Li R, Ma JS, Wu H, Wu H, et al: Immune infiltration and stromal heterogeneity in pancreatic cancer: A prognostic model guiding immunotherapy response. Oncol Lett 30: 465, 2025.
APA
Zhao, Z., Zhang, J., Zhang, J., Liu, F., Wang, B., Zhou, Y. ... Li, X. (2025). Immune infiltration and stromal heterogeneity in pancreatic cancer: A prognostic model guiding immunotherapy response. Oncology Letters, 30, 465. https://doi.org/10.3892/ol.2025.15211
MLA
Zhao, Z., Zhang, J., Zhang, J., Liu, F., Wang, B., Zhou, Y., Li, X., Li, R., Ma, J. S., Wu, H., Xie, Y., Li, X."Immune infiltration and stromal heterogeneity in pancreatic cancer: A prognostic model guiding immunotherapy response". Oncology Letters 30.4 (2025): 465.
Chicago
Zhao, Z., Zhang, J., Zhang, J., Liu, F., Wang, B., Zhou, Y., Li, X., Li, R., Ma, J. S., Wu, H., Xie, Y., Li, X."Immune infiltration and stromal heterogeneity in pancreatic cancer: A prognostic model guiding immunotherapy response". Oncology Letters 30, no. 4 (2025): 465. https://doi.org/10.3892/ol.2025.15211
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