A chemokine-based prognostic model featuring CXCL8, ITGA5, BACE2, CCR7, CERS4, and MEI1 for cervical cancer
Cervical cancer, often caused by persistent high-risk HPV infection, remains a significant public health challenge with limited treatment options. Chemokines play crucial roles in immune cell recruitment and tumor microenvironment modulation. This study proposes a chemokine-based predictive model to optimize personalized treatment and prognosis. Bulk RNA-seq and clinical data from TCGA-CESC and GSE52903 cohorts were analyzed along with 152 chemokine-related genes (CRGs). Prognostic CRGs were identified via Cox regression, consensus clustering was used for subtypes. Differentially expressed genes (DEGs) were analyzed with "limma". Then, a RiskScore model was developed through Lasso algorithm, and ROC curves were performed for the model performance. CIBERSORT, TIMER, MCP-counter and TIDE were used for the immune differences. Drug sensitivity was analyzed via IMvigor210 cohort and the "oncoPredict" package. GSEA identified enriched pathways, and single-cell RNA-seq (GSE168652) explored the special cell types in tumor and normal tissues. Finally, we examined the model gene expression by qPCR, and cell proliferation, migration and invasion through CCK-8, wound healing and trans-well assay. Through the analysis of the TCGA-CESC and GSE52903 cohorts, we first identified 39 prognostic CRGs and subsequently stratified cervical cancer patients into two molecular subtypes (C1/C2) based on their expression profiles. These subtypes exhibited significant differences in overall survival (C1 with a more favorable prognosis than C2) and distinct immune infiltration patterns. We then developed and validated a robust risk score model based on subtype-specific differentially expressed genes, which incorporated six genes (CXCL8, ITGA5, BACE2, CCR7, CERS4, and MEI1). Furthermore, the risk score revealed profound heterogeneity in the tumor immune microenvironment: the low-risk group was characterized by enhanced infiltration of immune cells (e.g., CD8 + T cells, activated CD4 + T cells, and M1 macrophages) and a higher predicted response to immunotherapy, whereas the high-risk group was associated with an immunosuppressive phenotype and T cell dysfunction. Mechanistically, the high-risk signature was linked to the activation of pro-tumorigenic pathways, including focal adhesion, ECM-receptor interaction, and the IL-17 signaling pathway. Single-cell transcriptomic analysis further pinpointed the cellular origins of key model genes, revealing that CXCL8 was predominantly highly expressed in tumor-associated monocytes/macrophages. Finally, in vitro functional assays confirmed that CXCL8 was significantly upregulated in cervical cancer cells, and its knockdown potently suppressed cell proliferation, migration, and invasion. This study identified a distinct chemokine-driven molecular classification and constructed a chemokine-related prognostic model for cervical cancer. These findings offer novel insights into tumor immunobiology and provide a promising tool for improving individualized risk stratification and therapeutic strategies for cervical cancer.
Authors
Funding
National Natural Science Foundation of China (NSFC) Grant 82303734Fujian Medical University Training Program of Innovation and Entrepreneurship for Undergraduates Grant C2025072Science and Technology Innovation Joint Fund Project of Fujian Provincial Health Commission Grant 2024Y9535Fujian Provincial Natural Science Foundation Grant 2025J01201