Efferocytosis-related biomarkers and immune infiltration in pancreatic ductal adenocarcinoma based on single-cell sequencing and machine learning: a comprehensive bioinformatics analysis and experimental validation

Clinical Snapshot

Key Findings

• P Value: Not specified

• Effect Size: Not specified in abstract

• Primary Outcome: Overall survival prediction using six-gene efferocytosis model

• Nnt Or Sensitivity: Notable AUC reported but specific values not provided

• Confidence Interval: Not provided

Clinical Application

Requires gene expression profiling technology; may be challenging in routine practice Could inform PBS funding decisions for targeted therapies; relevant to Cancer Australia guidelines for PDAC management; may guide referral patterns to specialist centres Patients with pancreatic ductal adenocarcinoma requiring prognostic stratification

Abstract

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a highly invasive and lethal malignancy, with its complex tumor microenvironment (TME) severely limiting therapeutic efficacy. Efferocytosis, a key process by which macrophages clear apoptotic cells, has been demonstrated to influence tumor initiation and progression by modulating immune responses, promoting M2 polarization, and facilitating immune evasion. Yet, the function and predictive significance of genes linked to efferocytosis in PDAC are still not fully understood. METHODS: This study integrated TCGA-PDAC, GTEx, and GEO datasets with single-cell transcriptomics to screen and analyze expression patterns and functions of efferocytosis-related genes (ERGs) in PDAC. Differential analysis, single-cell clustering, cell communication, and pseudo-time analysis revealed heterogeneity in macrophage efferocytosis. Subsequently, a six-gene prognostic model (CD36, ITGAV, PANX1, PPARG, S100A4, THBS1) was constructed and validated using Cox and LASSO regression. Concurrently, GSEA pathway analysis, immune infiltration assessment, and immunotherapy response prediction were conducted. Key gene functions were validated through molecular docking, RT-qPCR, western blotting, and functional experiments. RESULTS: Single-cell analysis revealed that macrophages exhibit the highest efferocytic activity within the PDAC microenvironment, demonstrating significant subpopulation heterogeneity. Differential analysis identified 23 key intersecting genes, ultimately yielding a risk model comprising six core genes. The model exhibited strong predictive accuracy in both the training and validation groups (notable AUC). Additional examination showed that patients at high risk showed a heightened presence of pathways linked to the reprogramming of energy metabolism and inflammatory reactions, along with heightened genomic instability and immunosuppressive TME traits, suggesting a grim prognosis. Experimental validation confirmed that knockdown of THBS1 significantly inhibited the proliferation, migration, and invasion of PDAC cells. CONCLUSION: The research clarifies the diverse nature of macrophage efferocytosis in PDAC and its crucial function in the restructuring of TME. It establishes and validates a six-gene risk prediction model, clarifying the functional significance of core genes, including THBS1. These findings provide novel molecular tools for PDAC prognosis assessment and identify potential targets for immunotherapy and personalized treatment strategies.