Construction of a prognostic model based on efferocytosis for predicting survival and immunotherapy efficacy in cervical cancer – Research

Background:

Cervical cancer (CC) is the foremost contributor to cancer-related mortality among women. Identifying and investigating genes associated with CC prognosis are crucial for improving patient outcomes. Recent advances in high-throughput data analysis methods have revealed a correlation between CC prognosis and the tumor immune microenvironment (TIME). But as one of the most frequent events in the TIME, the relationship between efferocytosis and the prognosis of CC is still unknown. The aim of this research is to examine the effect of efferocytosis-related genes (ERGs) on the treatment outcomes and prognosis of CC.

Methods:

Datasets pertaining to CC were obtained from the Cancer Genome Characterization Initiative (CGCI), The Cancer Genome Atlas (TCGA), and the Gene Expression Omnibus (GEO) databases. Differentially expressed ERGs (DE-ERGs) were subsequently identified. A prognostic risk model for CC was developed and validated using univariate and multivariate Cox regression as well as least absolute shrinkage and selection operator (LASSO) regression analyses. Patients were stratified into distinct risk groups as per the median risk score. Variations in the tumor microenvironment (TME), angiogenesis score, invasion score, epithelial-mesenchymal transition (EMT) score, and stemness index were compared between high- and low-risk groups of patients with CC. Pearson correlation analysis was utilized to explore the association between key genes and RNA methylation regulators. Furthermore, microRNAs (miRNAs) and lncRNAs (long non-coding RNAs) associated with the identified characteristic genes were retrieved. Tissue specimens from 10 patients with human papillomavirus (HPV) 16-positive cervical squamous cell carcinoma (stage IA-IIA) were collected to validate RNA expression of the characteristic genes utilizing reverse transcription-quantitative polymerase chain reaction (RT-qPCR).

Results:

A total of 19 DE-ERGs were identified in the GSE9750 dataset, and EPO and UCP2 were selected from these for constructing a prognostic risk model. Significant differences in age, T stage, and tumor stage were observed between the two risk groups. Notably, immune score, ESTIMATE score, stromal score, and tumor purity were all correlated with the high-risk group. In particular, EPO showed a strong positive correlation with resting mast cells, whereas UCP2 was negatively correlated with activated mast cells. Patients in the low-risk group were more likely to benefit from immunotherapy. Most methylation-related regulators were found to be associated with the characteristic genes. Additionally, only hsa-miR-125b-5p was predicted to regulate EPO within the lncRNA-miRNA-ceRNA (competing endogenous RNA) network. Subsequent RT-qPCR analysis confirmed the downregulation of EPO in CC tissues, consistent with the findings in the GSE9750 dataset.

Conclusions:

This systematic analysis of ERGs led to the development of a prognostic risk model incorporating EPO and UCP2 for patients with CC. For the specific cohort of HPV16-positive, early-stage (IA-IIA) patients, this tool has potential clinical utility in identifying high-risk individuals who may benefit from intensified post-operative management or immunotherapy.