Abstract

Background The prognosis of cervical cancer (CESC) is closely associated with the differentiation of myeloid cells within the tumor microenvironment (TME), including myeloid-derived suppressor cells (MDSCs) and tumor-associated macrophages (TAMs). These myeloid cells modulate CESC progression and treatment response by regulating immunosuppressive and activating pathways. The current study aimed to identify prognostic gene signatures and elucidate the biological pathways involved in myeloid cell differentiation (MCD) in CESC. Methods CESC-related datasets and an MCD-related gene set were utilized in this study. Prognostic genes were identified using an integrated approach combined with differential expression analysis, Venn diagram intersection, univariate Cox regression, and least absolute shrinkage and selection operator (LASSO) for feature selection. A risk model was constructed and validated. Additionally, functional enrichment analysis, immune microenvironment profiling, drug sensitivity assessment, and single-cell RNA sequencing (scRNA-seq) were employed to explore the molecular mechanisms underlying the prognostic genes and risk model in CESC development. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) was used to validate the expression levels of the prognostic genes. Results The risk score, derived from four prognostic genes (TNF, PTPN6, FASN, and TFRC), effectively classified CESC samples into high-risk group (HRG) and low-risk group (LRG). The risk model and nomogram accurately predicted the prognosis of patients with CESC. Synapse organization-related pathways were significantly enriched in the HRG compared to the LRG. Notably, 11 immune cell populations, including immature B cells and macrophages, were significantly different between HRG and LRG. Moreover, the half-maximum inhibitory concentration (IC50) values for 85 drugs, such as roscovitine and embelin, were markedly distinct between the two risk groups. Pseudotime analysis