Constructing Scissor+ risk model to predict prognosis and immunotherapy responses in PAAD by integrating bulk and single-cell RNA sequencing data
Year: 2025
Type: article
Abstract: Background: This study focused on epithelial cells to construct a prognostic risk model and provide targeted insights into responses to immunotherapy. Methods: Single-cell RNA sequencing (scRNA-seq) was clustered using Uniform Manifold Approximation and Projection (UMAP) and a risk model was developed through Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Kaplan-Meier analysis was performed to evaluate the prognosis of PAAD. The biological characteristics of LIPH were assessed using CCK-8, colony formation and Transwell assays. Results: Eight major cell clusters were identified, revealing two developmental trajectories for malignant epithelial cells from primary to metastases. Epithelial cells were categorized into Scissor+ and Scissor- subtypes, with Scissor+ epithelial cells exhibiting more complex cellular communication with TME cells. Furthermore, we successfully developed a risk model for PAAD patients based on the Scissor findings. The prognosis for PAAD patients in the high-risk group was significantly poorer within both the TCGA and ICGC cohorts. Differences were observed in the populations of naïve B cells, CD8 T cells, M0 macrophages, and activated dendritic cells in different groups. Knockdown of LIPH significantly inhibited the growth and invasion of PAAD cells. Conclusion: These findings underscore the significance of this risk model in predicting prognosis and immunotherapy responses, and enhancing understanding of tumor microenvironment (TME) heterogeneity in PAAD metastases.
Source: Frontiers in Pharmacology
Authors: Gaofei Zhang, Jiao Yu, Fan Zhang, Fang Wang, Congya Zhou
Institution: Shaanxi Provincial People's Hospital
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
Funder: Yakult Bio-Science Foundation
OpenAlex ID: W4414370975
