Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a highly heterogeneous malignancy. Single-cell sequencing (scRNA-seq) technology enables quantitative gene expression measurements that underlie the phenotypic diversity of cells within a tumor. By integrating PDAC scRNA-seq and bulk sequencing data, we aim to extract relevant biological insights into the ductal cell features that lead to different prognoses. Firstly, differentially expressed genes (DEGs) of ductal cells between normal and tumor tissues were identified through scRNA-seq data analysis. The effect of DEGs on PDAC survival was then assessed in the bulk sequencing data. Based on these DEGs (LY6D, EPS8, DDIT4, TNFSF10, RBP4, NPY1R, MYADM, SLC12A2, SPCS3, NBPF15) affecting PDAC survival, a risk score model was developed to classify patients into high-risk and low-risk groups. The results showed that the overall survival was significantly longer in the low-risk group (p < 0.05). The model also revealed reliable predictive power in different subgroups of patients. The high-risk group had a higher tumor mutational burden (TMB) (p < 0.05), with significantly higher mutation frequencies in KRAS and ADAMTS12 (p < 0.05). Meanwhile, the high-risk group had a higher tumor stemness score (p < 0.05). However, there was no significant difference in the immune cell infiltration scores between the two groups. Lastly, drug candidates targeting risk model genes were identified, and seven compounds might act against PDAC through different mechanisms. In conclusion, we have developed a validated survival assessment model, which acted as an independent risk factor for PDAC.
Highlights
Pancreatic cancer is a highly aggressive malignant tumor of the digestive system, 95% of which are pancreatic ductal adenocarcinoma (PDAC)
Bulk sequencing data in the training and validation sets were obtained from The Cancer Genome Atlas (TCGA) (TCGA- PAAD), the Gene Expression Omnibus (GEO) (GSE71729, GSE21501), the European Molecular Biology Laboratory (EMBLEBI) (E-MTAB-6134) and the International Cancer Genome Consortium Data Portal (ICGC) Canada pancreatic cancer project (PACA-CA) (ICGC-CA, https://dcc.icgc.org/ releases/current/Projects/PACA-CA)
After performing cell quality control, we performed principal component analysis using the top 2000 variable genes (Figure 1A) and identified 13 principal components for downstream analysis via elbow plots (Figure 1B). These cells were divided into 33 clusters (Supplementary Figure S1) and based on marker gene expression (Supplementary Figure S1, Supplementary Tables S1, S2) these clusters were classified into acinar cells, ductal cells, endothelial cells, endocrine cells, FIGURE 4 | Validation of the risk model for PDAC survival using GEO datasets. (A,B) Risk score analysis in the validation sets GSE71729 and GSE21501. (C,D) Kaplan-Meier survival curve of the risk score for patient overall survival (OS) in the validation sets GSE71729 and GSE21501. (E,F) The time-dependent receiver operating characteristic (ROC) curve for predicting the 1, 3, and 5-years OS rates in the validation sets GSE71729 and GSE21501
Summary
Pancreatic cancer is a highly aggressive malignant tumor of the digestive system, 95% of which are pancreatic ductal adenocarcinoma (PDAC). Its incidence and mortality have increased by an average of 0.3% per year with lifestyle changes and factors such as increased life expectancy and an aging population (Santucci et al, 2020). Diagnosis of pancreatic cancer is challenging due to the lack of specific symptoms and biological markers. Surgery remains the only possible cure for pancreatic cancer, but approximately 80–85% of patients present with either unresectable or metastatic disease at the time of diagnosis (Mizrahi et al, 2020). Molecular events in tumors usually precede the presentation of clinical features. Effective molecular markers can more accurately predict patient prognosis and suggest individualized treatment plans
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