Machine learning-based screening and validation of pyroptosis-associated prognostic genes and potential drugs in cervical cancer.

Pyroptosis is a newly discovered form of programmed cell death, but its mechanism in the development of cervical cancer has not been elucidated. Cervical cancer differentially expressed pyroptosis-related genes were identified via bioinformatic analysis Gene Expression Omnibus (GEO) dataset GSE7803, GSE9750, GSE63514 and GSE67522. The correlation between the expression of pyroptosis-related genes in normal cervical tissue and cervical cancer tissue was analyzed through the TCGA database. Using LASSO regression algorithm to establish a prediction model for the obtained genes related to pyroptosis. Exploring the functions of differentially expressed genes through GO and KEGG pathway analysis. Using PPI network analysis to screen hub genes, using CIBERSORT method for immune infiltration analysis of prognostic genes, and finally predicting drug-gene interactions in DGIdb database. A total of 19 pyroptosis-related genes were screened from the GEO dataset of cervical cancer tissues, revealing their regulation of endopeptidase activity, inflammation response, positive regulation of cytokine production and cellular response to environmental stimuli. LASSO regression algorithm was used to establish prediction models for 7 of these genes, and 3 pyroptosis-related genes (SPP1, VEGFA, and CXCL8) closely associated with the prognosis of cervical cancer were identified. qRT-PCR confirmed that compared with normal cervical tissue, the expression of SPP1, VEGFA, and CXCL8 was increased in cervical cancer (P<0.05). SPP1, VEGFA, and CXCL8 are most closely related to macrophages, Th2, and neutrophils, respectively. 148 potential targeted drugs targeting key genes were predicted, providing a possible basis for predicting the prognosis and treatment of cervical cancer. Knocking down SPP1 can inhibit cell proliferation and migration in cervical cancer cells in vitro. In conclusion, our study has identified key genes related to pyroptosis in cervical cancer, which potentially become effective clinical prognostic biomarkers, and further research is needed to explore their underlying mechanisms.