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Publications

Journal Article Epublish

PubMed: 40486509 DOI: 10.3389/fimmu.2025.1559373

Innate immune cell barrier-related genes inform precision prognosis in pancreatic cancer.

Front Immunol 16:1559373

Authors: Qiang Luo, Tingting Jiang, Dacheng Xie, Xiaojia Li, Keping Xie

Affiliations

  • Center for Pancreatic Cancer Research, The South China University of Technology School of Medicine, Guangzhou, Guangdong, China. Center for Pancreatic Cancer Center, The South China University of Technology Comprehensive Cancer Center, Guangzhou, Guangdong, China.
  • Department of Medical Oncology, Shanghai Pulmonary Hospital & Thoracic Cancer Institute, Tongji University School of Medicine, Shanghai, China.
  • Center for Pancreatic Cancer Research, The South China University of Technology School of Medicine, Guangzhou, Guangdong, China.
  • Center for Pancreatic Cancer Center, The South China University of Technology Comprehensive Cancer Center, Guangzhou, Guangdong, China.

Abstract

Introduction

Pancreatic cancer (PC) remains a lethal malignancy with limited treatment options. The role of innate immune cell barrier-related genes in PC prognosis is poorly defined. This study aimed to identify prognostic biomarkers, develop a predictive model, and uncover novel targets for personalized therapy.

Methods

Innate immune cell barrier-related genes were curated from KEGG, ImmPort, MSigDB, and InnateDB. Differential expression analysis was performed using TCGA and GTEx datasets. Univariate Cox regression identified survival-associated genes. Prognostic modeling of PC was developed using 14 machine learning algorithms, with performance validated through long-term survival metrics, functional enrichment, immune infiltration analysis, and drug sensitivity profiling. Core genes were prioritized via the "mime1" package, and single-cell RNA sequencing (scRNA-seq) data explored UBASH3B's functional role.

Results

352 differentially expressed genes of Innate immune cell barrier-related were identified, with NK cell pathways linked to PC immunity. Univariate Cox analysis revealed 8 protective and 84 risk genes. The RSF model (trained on risk genes) showed strong 3- and 5-year survival prediction. High-risk patients exhibited elevated tumor mutation burden (TMB), reduced NK/CD8+ T cell infiltration, and resistance to Erlotinib/Oxaliplatin but sensitivity to 5-Fluorouracil. Five key genes (ITGB6, COL17A1, MMP28, DIAPH3, UBASH3B) were highlighted. UBASH3B, a novel marker, correlated negatively with NK cell activation and mediated immune signaling and drug resistance.

Discussion

This study established the CDRG-RSF model, a robust prognostic tool leveraging innate immune genes. UBASH3B's dual role in immune suppression and drug resistance highlights its potential for stratifying PC patients into tailored treatment groups. The findings underscore the importance of integrating machine learning with immune profiling to advance precision oncology for PC.

MeSH

Cells, Genes, Pancreatic Neoplasms, Pharmaceutical Preparations, Neoplasms, Fluorouracil, Machine Learning, Therapeutics, Learning, Prognosis, Drug Resistance