Integrated single-cell and bulk RNA sequencing unravels neutrophil heterogeneity and validates SPP1 as a prognostic biomarker in cervical cancer.

backgroundCervical cancer (CC) remains a major global health burden, with tumor microenvironment (TME) plasticity and immune evasion driving its progression. The intricate heterogeneity of neutrophils and their complex crosstalk within the TME remain poorly understood, thereby limiting the development of targeted therapeutic strategies. Single-cell RNA sequencing (scRNA-seq) provides an unprecedented level of resolution for dissecting neutrophil subpopulations and elucidating their roles in CC pathogenesis.

methodsWe integrated scRNA-seq data from CC tissues (GSE208653, n = 5) with bulk RNA-seq cohorts from The Cancer Genome Atlas - Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (TCGA-CESC) and Genotype-Tissue Expression (GTEx) database. Using Seurat-based clustering, pseudotime trajectory analysis and CellChat, we mapped neutrophil dynamics and intercellular communication networks. Differentially expressed genes (DEGs) were analyzed using the limma package, followed by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses. A prognostic model was subsequently constructed via LASSO-Cox regression. Key targets were further validated through both in vitro functional assays, immunohistochemical (IHC) and immunofluorescence (IF) analyses of human pathological specimens.

resultsFour functionally distinct neutrophil subtypes, including immature, mature, antitumor, and interferon-stimulated populations, were characterized in CC, demonstrating dynamic heterogeneity during tumorigenesis. Secreted Phosphoprotein 1 (SPP1) was identified as a critical differentially expressed gene, with the SPP1-CD44 axis serving as a key mediator of neutrophil-tumor cell crosstalk. Downregulation of SPP1 markedly suppressed CC cell migration, invasion, and proliferation. Furthermore, a prognostic signature based on risk stratification efficiently categorized patients into high- and low-risk cohorts, with validated clinical utility in predicting survival outcomes.

conclusionsOur study comprehensively characterized the TME in CC through single-cell transcriptomics. Integrated analysis with bulk RNA-seq established and validated a robust prognostic signature, identifying SPP1 as a key oncogenic driver. The SPP1-centric model demonstrates significant clinical utility for risk stratification. These findings provide new insights into neutrophil heterogeneity and establish a mechanistic foundation for precision therapeutics in CC.