Development and Validation of Prognostic and Diagnostic Model for Pancreatic Ductal Adenocarcinoma Based on scRNA-Seq and Bulk-Seq Datasets

Abstract

Background: The 5-year overall survival (OS) of pancreatic ductal adenocarcinoma (PDAC) is only 10%, partly due to the lack of reliable diagnostic and prognostic biomarkers. This study aimed to establish and validate prognostic and diagnostic model for PDAC based on scRNA-seq and bulk-seq datasets. Methods: The raw gene-cell matrix for scRNA-seq analysis was downloaded from GSA (Genome Sequence Archive) database. We drew cell atlas for PDAC and normal pancreatic tissues using t-SNE clustering, and identified cell subpopulations according to known cell markers. Then we isolated ductal cell subpopulations and conducted t-SNE analysis again. The inferCNV analysis was used to distinguish tumor cells from normal ductal cells. We identified differential expression genes (DEGs) by comparing tumor cells and normal ductal cells using FindMarkers algorithm. The common DEGs were used to conducted prognostic and diagnostic model using univariate and multivariate Cox or Logistic regression analysis. Finally, we drew an easy-to-use nomogram to predict 2-year and 3-year overall survival (OS) for PDAC. Results: We identified 9 cell types including stellate cells, macrophages, endothelial cells, ductal cells, T cells, fibroblast cells, B cells, acinar cells and endocrine cells in PDAC. The 222 common DEGs were identified to screen prognosis-related genes. Four genes, including MET, KLK10, PSMB9 and ITGB6, were utilized to create risk score formula to predict OS and establish diagnostic model for PDAC. Finally, we validated prognosis nomogram combining N stage, margin status and risk score. Conclusions: We developed and validated the prognostic and diagnostic model for PDAC based on scRNA-seq and bulk-seq datasets, which will help identify high-risk PDAC patients. Funding Information: This study was supported by The Natural Science Foundation of China (NO.81871954, 81672353) and Beijing Municipal Natural Science Foundation (NO.7212111). Declaration of Interests: We declare there are no any competing financial interests in relation to this work. Ethics Approval Statement: This study was approved by Ethics Committee of Peking University First Hospital (Approval No. 2019-147) and conducted in accordance with ethical guidelines (Declaration of Helsinki). Written informed consent was obtained from all participants.