A Novel 14-Gene Panel Associated With Efferocytosis for Predicting Pancreatic Cancer Prognosis Through Bulk and Single-Cell Databases.

BACKGROUND

Efferocytosis (ER) plays a crucial role in the programmed clearance of dead cells, a process that is mediated by phagocytic immune cells. However, further exploration is needed to determine the full extent of its impact on the progression of pancreatic ductal adenocarcinoma (PDAC), particularly through interactions among tumor cells, stromal cells, and immune cells within the tumor microenvironment (TME).

METHODOLOGY AND RESULTS

In this study, we comprehensively analyzed the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, as well as additional databases from multiple bioinformatics websites, utilizing 167 ER features derived from the integration of single-cell RNA sequencing (scRNA-seq) and bulk transcriptomic data. A set of 14 ER-associated prognostic signatures, referred to as the "14-gene panel" genes, was identified based on overall survival (OS)/disease-free survival (DFS) data, Pearson correlation coefficients, and multivariate Cox regression analyses. The model pathways enriched by the four-gene combination represented by "LEAF" and the 14-gene combination represented by the "14-gene panel" presented a high degree of similarity, including among the adhesion, mitotic, G2/M checkpoint, and epithelial‒mesenchymal transition (EMT) signaling pathways. Least absolute shrinkage and selection operator (LASSO) regression was subsequently employed to construct an ER risk scoring system using deep learning, based on the following formula: , , , and , collectively termed the "LEAF" panel. Additionally, random survival forest (RSF) algorithms facilitated the identification of a key panel of genes, designated "LEAP" genes, including , , , and ; three of which genes (, , and ) were identified as key factors influencing the behaviors of PDAC tumors, tumor-associated stroma, and macrophages. Finally, we utilized experimental methods, including Boyden chamber analyses, immunohistochemical staining, and cell cycle analyses, to demonstrate that interference with suppresses the malignant properties of tumors, including proliferation and migration. Multiplex immunofluorescence staining was employed to identify as highly relevant to the M2 macrophage subpopulation.

CONCLUSION

Our findings underscore the importance of considering a novel prognostic signature comprising 14 ER genes in the context of the TME when investigating the biology of PDAC. Future studies may explore how modulating these interactions could lead to novel therapeutic opportunities.