Identification of Cancer Associated Fibroblasts Related Genes Signature to Facilitate Improved Prediction of Prognosis and Responses to Therapy in Patients with Pancreatic Cancer

Zhou Y, Lu Y, Czubayko F, Chen J, Zheng S, Mo H, Liu R, Weber G, Grützmann R, Pilarsky C, David P (2025)

Publication Language: English

Publication Type: Journal article

Publication year: 2025

Journal

International Journal of Molecular Sciences MDPI

Book Volume: 26

Article Number: 4876

Journal Issue: 10

DOI: 10.3390/ijms26104876

Abstract

Pancreatic cancer (PC) is highly aggressive, with a 5-year survival rate of 12.8%, making early detection vital. However, non-specific symptoms and precursor lesions complicate diagnosis. Existing tools for the early detection of PC are limited. CAFs are crucial in cancer progression, invasion, and metastasis, yet their role in PC is poorly understood. This study analyzes mRNA data from PC samples to identify CAF-related genes and drugs for PC treatment using algorithms like EPIC, xCell, MCP-counter, and TIDE to quantify CAF infiltration. Weighted gene co-expression network analysis (WGCNA) identified 26 hub genes. Our analyses revealed eight prognostic genes, leading to establishing a six-gene model for assessing prognosis. Correlation analysis showed that the CAF risk score correlates with CAF infiltration and related markers. We also identified six potential drugs, observing significant differences between high-CAF and low-CAF risk groups. High CAF risk scores were associated with lower responses to immunotherapy and higher tumor mutation burdens. GSEA indicated that these scores are enriched in tumor microenvironment pathways. In summary, these six model genes can predict overall survival and responses to chemotherapy and immunotherapy for pancreatic cancer, offering valuable insights for future clinical strategies.

Authors with CRIS profile

Yong Zhou Professur für Chirurgische Forschung Yanxi Lu Department of Surgery Franziska Czubayko Department of Surgery Jisheng Chen Professur für Molekulare und Experimentelle Chirurgie Shuwen Zheng Professur für Chirurgische Forschung Huaqing Mo Professur für Chirurgische Forschung Rui Liu Professur für Chirurgische Forschung Georg Weber Department of Surgery Robert Grützmann Lehrstuhl für Allgemein- und Viszeralchirurgie Christian Pilarsky Professur für Chirurgische Forschung Paul David Department of Surgery

How to cite

APA:

Zhou, Y., Lu, Y., Czubayko, F., Chen, J., Zheng, S., Mo, H.,... David, P. (2025). Identification of Cancer Associated Fibroblasts Related Genes Signature to Facilitate Improved Prediction of Prognosis and Responses to Therapy in Patients with Pancreatic Cancer. International Journal of Molecular Sciences, 26(10). https://doi.org/10.3390/ijms26104876

MLA:

Zhou, Yong, et al. "Identification of Cancer Associated Fibroblasts Related Genes Signature to Facilitate Improved Prediction of Prognosis and Responses to Therapy in Patients with Pancreatic Cancer." International Journal of Molecular Sciences 26.10 (2025).

BibTeX: Download