Development and Validation of an Anoikis-Related Gene Signature for Prognostic Prediction in Cervical Cancer.

Abstract

Background: Cervical cancer still has high incidence and mortality rates worldwide. This study aimed to evaluate the prognostic value of anoikis-related genes (ARGs) and develop a risk scoring model for accurate survival prediction in cervical cancer patients.

Methods: The expression profiles of cervical cancer tissue and survival data were downloaded from TCGA-CESC and CGCI-HTMCP-CC. We identified 83 ARGs significantly associated with patients' survival. Subsequently, we developed a risk-scoring model based on 10 key genes. We assessed the predictive performance of our model by survival analysis, ROC curve analysis, and a nomogram that incorporated clinical factors. Additionally, we validated the expression of Granzyme B (GZMB) by immunohistochemical staining. Furthermore, we compared the biological processes and pathway enrichment in high-risk and low-risk patient groups, using differential gene expression and functional enrichment analysis. Finally, we investigated the immune microenvironment of patients in both high-risk and low-risk groups.

Results: Patients in the high-risk group had significantly poorer survival compared to those in the low-risk group. The immunohistochemical results suggested that GZMB was associated with the prognosis of cervical cancer patients. The risk scoring model showed high accuracy in predicting the prognosis of cervical cancer patients. Differential gene expression analysis revealed enriched pathways related to tumor invasion and metastasis in the high-risk group. Conversely, the low-risk group showed a strong association with the activation of immune response pathways.

Conclusion: This study concluded that anoikis-related genes played a crucial role in determining the prognosis of individuals with cervical cancer. This discovery not only presented potential biomarkers but also provided valuable insights for informing treatment strategies. The risk scoring model may assist clinicians in better identifying high-risk patients and personalizing treatment plans.