Prognostic model establishment and immune microenvironment analysis based on transcriptomic data of long-term survivors of pancreatic ductal adenocarcinoma
Year: 2025
Type: article
Abstract: Pancreatic cancer continues to be a major cause of cancer deaths worldwide. Characterizing the tumors of long-term survivors (≥5 years survival) would create opportunities in prognostic and therapeutic strategies. In this study, RNA sequencing data was used to identify differentially expressed genes (DEGs) in tumors of long-term survivors (LTS) vs short-term survivors (STS). Using LASSO-Cox regression, 4 prognostic DEGs, along with tumor stage, were utilized to develop a model for identifying high- and low-risk tumors. In Kaplan-Meier survival analysis, the high-risk group had significantly worse prognosis in both the training and validation cohorts. Using KEGG pathway gene signature sets, the high-risk group was found to have amplification of pathways, such as focal adhesion and ECM receptor interaction. The low-risk group, meanwhile, showed upregulation of specific metabolic pathways. Using ESTIMATE analysis, the high-risk group was found to have more stromal cell infiltration. Increased unpolarized macrophages and decreased inflammatory/anti-tumoral macrophages were also found in the high-risk group. Lastly, drug sensitivities were calculated and found to be generally higher in the high-risk group. This study reveals a model for predicting survival and drug sensitivity in pancreatic cancer. Genetic, molecular and tumor microenvironment characteristics of tumors from LTS and STS have been identified, highlighting opportunities for further research. • The study of long-term survivors of pancreatic cancer may offer unique insights into tumor biology. • We identified tumor gene signatures and immune microenvironmental features differentiating long- vs short-term survivors. • We developed a gene-based risk score predicting prognosis and drug sensitivity.
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,700
OpenAlex ID: W4415146944