Identification of the prognostic value of a 2-gene signature of the WNT gene family in UCEC using bioinformatics and real-world data

Abstract

Abstract Background The WNT gene family plays an important role in the occurrence and development of malignant tumors, but its involvement has not been systematically analyzed in uterine corpus endometrial carcinoma (UCEC). This study aimed to evaluate the prognostic value of the WNT gene family in UCEC. Methods Pan-cancer transcriptome data of the UCSC Xena database and Genotype-Tissue Expression (GTEx) normal tissue data were downloaded to analyze the expression and prognosis of 19 WNT family genes in UCEC. A cohort from The Cancer Genome Atlas-Uterine Corpus Endometrial Carcinoma (TCGA-UCEC) was used to analyze the expression of the WNT gene family in different immune subtypes and clinical subgroups. The STRING database was used to analyze the interaction of the WNT gene family and its biological function. Univariate Cox regression analysis and Lasso cox analysis were used to identify the genes associated with significant prognosis and to construct multi signature prognosis model. An immunohistochemical assay was used to verify the predictive ability of the model. Risk score and the related clinical features were used to construct a nomogram. Results The expression levels of WNT2, WNT3, WNT3A, WNT5A, WNT7A, and WNT10A were significantly different among different immune subtypes and correlated with TP53 mutation. According to the WNT family genes related to the prognosis of UCEC, UCEC was classified into two subtypes (C1, C2). The prognosis of subtype C1 was significantly better than that of subtype C2. A 2-gene signature (WNT2 and WNT10A) was constructed and the two significantly prognostic groups can be divided based on median Risk score. These results were verified using real-world data, and the nomogram constructed using clinical features and Risk score had good prognostic ability. Conclusions The 2-gene signature including WNT2 and WNT10A can be used to predict the prognosis of patients with UCEC, which is important for clinical decision-making and individualized therapy for patients with UCEC.