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Background <p>Pancreatic cancer (PC) is associated with a poor prognosis and limited therapeutic options because most patients are diagnosed at an advanced stage. Hypoxia- and lipid metabolism-related genes (HLPGs) play important roles in cancer progression. This study aimed to identify HLPGs-based prognostic genes in PC and explore their potential mechanisms, thereby providing a scientific basis for clinical management.</p> Methods <p>Univariate and multivariate Cox regression analyses were performed using public transcriptomic data to identify hypoxia- and lipid metabolism-related prognostic genes in PC, and a prognostic model was subsequently constructed. The underlying mechanisms were further investigated using gene set enrichment analysis (GSEA), immune microenvironment profiling, and drug sensitivity analysis in the high-risk group (HRG) and low-risk group (LRG). In addition, single-cell RNA sequencing was used to identify key cell populations in PC.</p> Results <p>INPP4B, SLCO1B3, LIPH, TGM2, ACSL5, SLC2A1, and EPHX2 were identified as prognostic genes. EPHX2 was significantly downregulated in PC, whereas the other six genes were upregulated. The prognostic model showed moderate predictive performance in the validation datasets. Pathway enrichment analysis revealed significant enrichment in the ribosome, ECM-receptor interaction, and focal adhesion pathways. Immune infiltration analysis showed that regulatory T cells were positively correlated with both myeloid-derived suppressor cells (MDSCs) and T follicular helper cells. LIPH showed the strongest correlations with type 17 T helper cells and central memory CD4 T cells. HRG patients were predicted to be more sensitive to the top 10 candidate drugs, including entinostat and sorafenib, and LIPH was positively correlated with sabutoclax_1849 and AZD8055_1059. ScRNA-seq identified ductal cells as the key cell type in PC, which were further clustered into 8 subsets.</p> Conclusion <p>INPP4B, SLCO1B3, LIPH, TGM2, ACSL5, SLC2A1, and EPHX2 were identified as hypoxia- and lipid metabolism-related prognostic genes in PC. Distinct immune cell subsets were also characterized during PC progression. These findings provide a foundation for further mechanistic studies and potential prognostic applications.</p> Graphical Abstract <p></p>

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Identification of prognostic genes and development of a risk model for pancreatic cancer based on hypoxia- and lipid metabolism-related genes

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月25日

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  • Xiangshu Guo,
  • Jianqi Xiong,
  • Longxing Niu,
  • Shaolin Li

摘要

Background

Pancreatic cancer (PC) is associated with a poor prognosis and limited therapeutic options because most patients are diagnosed at an advanced stage. Hypoxia- and lipid metabolism-related genes (HLPGs) play important roles in cancer progression. This study aimed to identify HLPGs-based prognostic genes in PC and explore their potential mechanisms, thereby providing a scientific basis for clinical management.

Methods

Univariate and multivariate Cox regression analyses were performed using public transcriptomic data to identify hypoxia- and lipid metabolism-related prognostic genes in PC, and a prognostic model was subsequently constructed. The underlying mechanisms were further investigated using gene set enrichment analysis (GSEA), immune microenvironment profiling, and drug sensitivity analysis in the high-risk group (HRG) and low-risk group (LRG). In addition, single-cell RNA sequencing was used to identify key cell populations in PC.

Results

INPP4B, SLCO1B3, LIPH, TGM2, ACSL5, SLC2A1, and EPHX2 were identified as prognostic genes. EPHX2 was significantly downregulated in PC, whereas the other six genes were upregulated. The prognostic model showed moderate predictive performance in the validation datasets. Pathway enrichment analysis revealed significant enrichment in the ribosome, ECM-receptor interaction, and focal adhesion pathways. Immune infiltration analysis showed that regulatory T cells were positively correlated with both myeloid-derived suppressor cells (MDSCs) and T follicular helper cells. LIPH showed the strongest correlations with type 17 T helper cells and central memory CD4 T cells. HRG patients were predicted to be more sensitive to the top 10 candidate drugs, including entinostat and sorafenib, and LIPH was positively correlated with sabutoclax_1849 and AZD8055_1059. ScRNA-seq identified ductal cells as the key cell type in PC, which were further clustered into 8 subsets.

Conclusion

INPP4B, SLCO1B3, LIPH, TGM2, ACSL5, SLC2A1, and EPHX2 were identified as hypoxia- and lipid metabolism-related prognostic genes in PC. Distinct immune cell subsets were also characterized during PC progression. These findings provide a foundation for further mechanistic studies and potential prognostic applications.

Graphical Abstract