Abstract
BACKGROUND: Genes associated efferocytosis play a critical role in tumor invasion, metastasis, and clinical outcomes in several malignancies. However, their prognostic relevance in cervical squamous carcinoma (CESC) is currently unclear. METHODS: Data from the Cancer Genome Atlas (TCGA) database were used to create the TCGA-CESC cohort. Additional CESC-related datasets (GSE44001 and GSE168652) were retrieved from the Gene Expression Omnibus database. Single-cell analysis of the GSE168652 dataset was conducted to identify differentially expressed genes (DEGs), associated with efferocytosis (ER-DEGs). CESC samples from the TCGA cohort were classified into clusters, and prognosis-related DEGs (PR-DEGs) were identified by comparing these clusters. The intersection of ER-DEGs and PR-DEGs generated a subset of prognosis-efferocytosis related DEGs (PER-DEGs). Subsequently, a risk model was developed using univariate Cox regression and least absolute shrinkage and selection operator analysis. Independent prognostic factors were further analyzed using Cox regression analysis. Functional pathways were examined through gene set enrichment analysis. RESULTS: A total of 15 cell clusters were identified from the GSE168652 dataset and categorized into seven distinct cell types. Comparative analysis indicated 774 ER-DEGs between groups with high and low ERG expression levels. From these, 332 PER-DEGs were identified. A prognostic risk model was constructed based on seven characteristic genes: ITGA5, SNRPF, EGLN1, NDUFB7, NDUFA2, SRGAP3, and PCNA. Risk score and pathologic-N emerged as independent prognostic factors. These genes were found to be associated with multiple pathways implicated in the pathogenesis of CESC. CONCLUSION: Seven efferocytosis-related prognostic genes were identified as prognostic markers for CESC, providing a scientific basis for further research on their role in disease progression.