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Prognostic model establishment and immune microenvironment analysis based on transcriptomic data of long-term survivors of pancreatic ductal adenocarcinoma

  • 0Department of Surgery, Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden.
Biochemistry and Biophysics Reports +

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October 13, 2025

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October 13, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Researchers identified key genes and tumor characteristics to predict pancreatic cancer survival and drug response. This model aids in developing new prognostic and therapeutic strategies for patients.

Area Of Science

  • Oncology
  • Genomics
  • Cancer Biology

Background

  • Pancreatic cancer is a leading global cause of cancer mortality.
  • Understanding long-term survivor (LTS) tumor characteristics offers prognostic and therapeutic insights.
  • Few studies have comprehensively analyzed molecular and microenvironment differences between LTS and short-term survivors (STS).

Purpose Of The Study

  • To identify differentially expressed genes (DEGs) in pancreatic tumors of LTS versus STS.
  • To develop a prognostic model predicting tumor risk and drug sensitivity.
  • To characterize the genetic, molecular, and tumor microenvironment distinctions between LTS and STS.

Main Methods

  • RNA sequencing to identify DEGs.
  • LASSO-Cox regression for prognostic gene selection and model development.
  • Kaplan-Meier survival analysis, KEGG pathway analysis, ESTIMATE, and drug sensitivity analysis.

Main Results

  • A prognostic model using 4 DEGs and tumor stage identified high-risk tumors with significantly worse survival.
  • High-risk tumors showed pathway amplifications (e.g., focal adhesion) and increased stromal infiltration.
  • Low-risk tumors exhibited upregulated metabolic pathways; high-risk tumors had altered immune cell profiles and higher drug sensitivity.

Conclusions

  • A novel model effectively predicts pancreatic cancer survival and drug sensitivity.
  • Distinct molecular and tumor microenvironment features differentiate LTS from STS.
  • Findings provide a foundation for targeted therapies and improved patient stratification.

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