Abstract

This study aimstodevelopariskpredictionmodel of pyroptosis-related genes based on its impact on immunotherapy sensitivityof uterine corpus endometrial carcinoma (UCEC), one of the most common and threatening gynecological malignancies. Through multiple bioinformatics analysis, we obtained raw counts of RNA-sequencing data and corresponding clinical information related to UCEC from The Cancer Genome Atlas (TCGA) and Gene Expression Profiling Interactive Analysis (GEPIA) to investigate the potential mechanisms of differentially expressed pyroptosis-related genes (DEPRGs), including the correlation between DEPRGs and prognosis, tumor immune microenvironment and the immunotherapy sensitivity of UCEC patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment Analysis were used to figure out the functional differences. Furthermore, a mRNA-miRNA-lncRNA network was constructed to identify potentialimpact of pyroptosis ontumorprogression. In this study, we achieved six DEPRGs (CASP3, GPX4, GSDMD, NOD2, PYCARD and TIRAP) and constructed a 6-gene signature which classified UCEC patients in the TCGA cohort into a low-risk group or a high-risk group. Patients in the low-risk group showed significantly longer survival time (p=0.000373). Theriskscorewasalso confirmedasanindependentprognosticfactorcombining with the clinical characteristics. GO and KEGG functional analysis revealed the possible molecular mechanisms by which six DEPRGs influence anti-tumor immunity in UCEC patients. In addition, we found that two DEPRGs (GPX4, TIRAP) were not only significantly associated with tumor mutational burden (TMB) or microsatellite Instability (MSI), but also involved in regulating the number and function of CD8+ cells. Upon comprehensive bioinformatics analysis, it was concluded that pyroptosis-related genes (PRGs) could predict the prognosis of EC patients and be affected in modulating theanti-tumorimmune responsesforpatientswithEC.

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