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Updated: Jul 29, 2026
Adaptation of Semiautomated Circulating Tumor Cell CTC Assays for Clinical and Preclinical Research Applications
Published on: February 28, 2014
Modelo pronóstico basado en quimiocinas que presenta CXCL8, ITGA5, BACE2, CCR7, CERS4 y MEI1 para el cáncer de cuello
Liang Wang1,2,3, Liying Wang1, Yisen Cao4
1Laboratory of Gynecologic Oncology, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, 350001, China.
Background:
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.
Methods:
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.
Results:
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.
Conclusions:
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.
