Background
Immunogenic cell death (ICD) can reshape the immune microenvironment of tumors. Driven by stressful pressure, by directly destroying tumor cells and activating the body's adaptive immunity, ICD acts as a modulator of cell death, enabling the body to generate an anti-tumor immune response that produces a more effective therapeutic effect, while tumor cells are driven to kill. Hence, this research aimed to find and evaluate ICD-related genetic signatures as cervical cancer (CC) prognostic factors.
Conclusion
A risk model constructed from four IPGs can independently predict the prognosis of CC patients and recommend more appropriate immunotherapy strategies for patients.
Methods
Data of CC patients from the Tumor Genome Atlas (TCGA) were used as the basis to obtain immunogenic cell-death-related prognostic genes (IPGs) in patients with CC, using the least absolute shrinkage and selection operator and Cox regression screening, and the IPGs scoring system was constructed to classify patients into high- and low-risk groups, with the Gene Expression Omnibus (GEO) dataset as the validation group. Finally, the difference analysis of single-sample gene set enrichment analysis, tumor microenvironment (TME), immune cells, tumor mutational burden, and chemotherapeutic drug sensitivity between the high-risk and low-risk groups was investigated.
Results
A prognostic model with four IPGs (PDIA3, CASP8, IL1, and LY96) was developed, and it was found that the group of CC patients with a higher risk score of IPGs expression had a lower survival rate. Single and multifactor Cox regression analysis also showed that this risk score was a reliable predictor of overall survival. In comparison to the low-risk group, the high-risk group had lower TME scores and immune cell infiltration, and gene set variation analysis showed that immune-related pathways were more enriched in the high-risk group.