CC/CXC chemokine risk signature at single-cell resolution: a machine learning model for precision stratification in
Bing Xu1, Xinyan Shi1, Yanfei Liu1
1Department of Clinical Laboratory, Hangzhou Women's Hospital, Hangzhou Maternity and Child Health Care Hospital, Hangzhou, China.
Background:
Advanced cervical cancer has a poor prognosis due to chemoresistance and immunosuppression, while the prognostic value of chemokines-related genes (CRGs) remains underexplored. This study aimed to develop a prognostic signature based on CRGs and explore its clinical utility in cervical cancer risk stratification, microenvironment characterization, and therapeutic response prediction.
Methods:
We integrated bulk transcriptomic data from The Cancer Genome Atlas Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (TCGA-CESC) cohort and Gene Expression Omnibus (GEO) datasets, immune infiltration analysis, drug sensitivity prediction, and single-cell RNA sequencing (scRNA-seq) analysis. Machine learning algorithms were employed to identify prognostic CRGs and construct a risk signature. Validation was performed using an independent GEO cohort. Immune cell infiltration was quantified using CIBERSORT. Enrichment analyses [Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Hallmark] were conducted on differentially expressed genes (DEGs) between risk groups. scRNA-seq data were processed using Seurat for cell type annotation and CRG expression profiling, while CellChat was used to analyze chemokine-mediated cell-cell communication.
Results:
Univariate Cox analysis identified 15 CRGs associated with cervical cancer prognosis. A robust 14-gene CRG-derived risk signature was constructed. The signature demonstrated high prognostic accuracy for overall survival (OS) in the TCGA-CESC cohort [1-year area under the curve (AUC): 0.966; 3-year AUC: 0.980; 5-year AUC: 0.976] and was validated in the GEO cohorts. High-risk patients exhibited worse OS, disease-specific survival (DSS), and progression-free survival (PFI). Risk scores correlated significantly with advanced T stage (P<0.05), International Federation of Gynecology and Obstetrics (FIGO) stage IV (P<0.05), and older age (≤55 years, P<0.05). High-risk patients displayed an immunosuppressive microenvironment characterized by reduced CD8+ T cells and M1 macrophages, along with increased chemoresistance. Enrichment analysis linked high-risk profiles to cytokine signaling, epithelial-mesenchymal transition, and glycolysis. scRNA-seq analysis revealed distinct CRG expression patterns localized to specific cellular niches: CCL22 in granulocyte-monocyte progenitors (GMPs), CCL5 in natural killer (NK)/T cells, CXCL9 in dendritic cells (DCs)/macrophages, and CXCL2/CXCL3 in endothelial cells/macrophages. Cell-cell communication identified active CXCL/CCL-mediated communication networks, particularly between epithelial cells and stromal components (smooth muscle cells, fibroblasts), highlighting their role in tumor-stroma crosstalk. A nomogram incorporating the risk score showed high predictive accuracy for 1-, 3-, and 5-year OS.
Conclusions:
This study constructed the first CRGs-derived risk signature and revealed its role in tumor-immune-stromal crosstalk at single-cell resolution. The signature reflects tumor-immune interactions and therapeutic vulnerabilities, providing a basis for clinical risk stratification and personalized immunotherapy strategies.
