Prediction of prognosis and immune landscape in cervical cancer based on heat shock protein-related genes

Abstract

Objective: Heat shock proteins (HSPs) play key roles in the malignant transformation and progression of many tumors. However, the effectiveness of using HSP-related genes to predict the prognosis of patients with cervical cancer (CC) remains elusive. We aimed to delineate the prognosis and biological significance of HSP-related genes in CC. Methods: We collected the transcriptional and clinical data of CC patients from The Cancer Genome Atlas (TCGA) and searched for HSP-related genes in the literature. LASSO and univariate/multivariate Cox regression analyses were utilized to screen genes; 12 genes were found to be related to CC survival, and a prediction model was built. The effectiveness of the model was confirmed using TCGA and GEO, and it was found to be an independent predictor of CC. The nomogram is plotted. The prognostic model was further visualized using calibration curves, which showed good agreement with the predicted outcomes at 1-, 3, and 5 years. Results: We found that low-risk patients had higher immune cell infiltration and stronger immune function, and according to the immunophenoscore and TIDE scores, the low-risk group tended to respond more to immunotherapy. Additionally, we used the GDSC database to predict drug sensitivity in patients with different prognostic risks. Conclusion: In summary, we built a good model to help predict the prognosis of CC patients and provide a reference for personalized treatment and medication for different patients.