The Identification of Gamma-Glutamyl Hydrolase in Uterine Corpus Endometrial Carcinoma: a Predictive Model and Machine Learning

BackgroundPoor neoplastic differentiation contributes to the rapid progression of uterine corpus endometrial carcinoma (UCEC). Thus, it is essential to identify candidate genes, clarifying the carcinogenesis and progression of UCEC.MethodsWe screened genes that affect differentiation and prognosis in UCEC. Least absolute selection and shrinkage operator (LASSO) regression, univariate Cox, and multivariate Cox proportional risk regression analyses were performed to screen out gamma-glutamyl hydrolase (GGH) as the candidate gene. The clinical value of GGH on prognosis was evaluated. The relationship between GGH and immune infiltration was assessed by CIBERSORT, EPIC, ssGSEA, unsupervised clustering and immunohistochemistry (IHC). Additionally, we investigated the effect of GGH knockdown in vitro.ResultsAmong the GGH, CDKN2A, and SIX1 genes, the impact of GGH was predominant on immune infiltration in UCEC. A nomogram containing GGH and other clinical features showed good predictive performance via curve analysis (DCA). In the functional analysis, GGH affected differentiation, tumour proliferation, and immune regulation. The immunosuppressive components were enriched in the GGH-high group, with poor immunotherapy efficacy. The study suggests that GGH may influence the progression of UCEC by regulating the glycolytic process.ConclusionsGGH is closely associated with various immune cell infiltrations. Our study demonstrates the prognostic role of GGH in carcinogenesis in UCEC.

Automated summary

The study identifies gamma-glutamyl hydrolase (GGH) as a candidate gene that may influence the progression of uterine corpus endometrial carcinoma (UCEC) by regulating the glycolytic process and is closely associated with various immune cell infiltrations. GGH plays a significant role in the prognosis of UCEC.