Abstract
Background
Pancreatic cancer is one of the most malignant gastrointestinal tumors. Due to the difficulty of early diagnosis and limited treatment, the prognosis of pancreatic cancer patients is very poor. Pancreatic cancer is characterized by high interstitial fibrosis, in which activation of cancer-associated fibroblasts (CAFs) plays a key role. CAFs is the most abundant cell in the pancreatic tumor microenvironment, with a high degree of plasticity, and participates in various processes of tumor development through crosstalk with tumor cells and other cells in the microenvironment. Elucidate the heterogeneity of CAFs and its mechanism of action, which helps find a new effective treatment for pancreatic cancer.
Methods
We used single-cell RNA sequencing (scRNA-seq) transcriptomics to analyze fibroblasts from pancreatic cancer patient specimens. This approach was able to identify key subpopulations of fibroblasts and elucidate their contribution to pancreatic cancer progression. Subsequently, we established a prediction model for pancreatic cancer using Cox regression and the LASSO algorithm and conducted cell experiments to verify it.
Results
Our study identified a BNIP3 + tumor-associated fibroblast and used this cell-associated gene to construct a prognostic model of pancreatic cancer, a feature that effectively divided PDAC patients into high-risk and low-risk groups and outperformed traditional clinicopathological features in predicting survival outcomes in pancreatic cancer patients. In vitro co-culture experiments showed that BNIP3 + fibroblasts could have more effects on pancreatic cancer cells.
Conclusion
We screened C1 BNIP3 + pancreatic cancer-associated fibroblasts, which advanced our knowledge and understanding of CAFs heterogeneity. The prognostic model we constructed can effectively predict the prognosis and treatment response of pancreatic cancer.