TPX2 Serves as a Cancer Susceptibility Gene and Is Closely Associated with the Poor Prognosis of Endometrial Cancer.

Background: Endometrial cancer (EC) is a common tumor of the genital tract that affects the female reproductive system but with only limited treatment options. We aimed to discover new prognostic biomarkers for EC. Methods: We used mRNA-seq data to detect differentially expressed genes (DEGs) between EC and control tissues. Detailed clinicopathological information was collected, and changes in the mRNA and protein levels of hub DEGs were analyzed in EC. Copy number variation (CNV) was also evaluated for its association with the pathogenesis of EC. Gene set enrichment analysis (GSEA) was conducted to enrich significant pathways driven by the hub genes. Cox regression analysis was used to select variables to create a nomogram. The nomogram was calibrated by applying the concordance index (C-index), and net benefits of the nomogram at different threshold probabilities were quantified using decision curve analysis (DCA). Results: Differential expression analysis identified 24 DEGs as potential risk factors for EC. Survival analysis revealed that TPX2 expression was related to worsening overall survival in patients with advanced EC. A high CNV was associated with the overexpression of TPX2; this suggested that modifications in the cell-cycle pathway might be crucial in the advancement of EC. Moreover, an individualized nomogram was developed for TPX2 incorporating clinical factors; this was also evaluated for its ability to predict EC. Calibration and DCA analyses confirmed the robustness and clinical usefulness of the nomogram. Conclusion: We offer novel insights into the pathogenesis and molecular mechanisms of EC. The overexpression of TPX2 was related to a poorer prognosis and could serve as a biomarker for predicting prognostic outcomes in EC patients.

A Novel Four-Gene Prognostic Signature as a Risk Biomarker in Cervical Cancer.

BACKGROUND: Cervical cancer (CC) is a major malignancy affecting women worldwide, with limited treatment options for patients with advanced disease. The aim of this study was to identify novel prognostic biomarkers for CC. METHODS: RNA-Seq data from four Gene Expression Omnibus datasets (GSE5787, GSE6791, GSE26511, and GSE63514) were used to identify differentially expressed genes (DEGs) between CC and normal cervical tissues. Functional and enrichment analyses of the DEGs were performed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and the Database for Annotation, Visualization, and Integrated Discovery (DAVID). The Oncomine database, Cytoscape software, and Kaplan-Meier survival analyses were used for in-depth screening of hub DEGs. The Cox regression was then used to develop a prognostic signature, which was in turn used to create a nomogram. RESULTS: A total of 207 DEGs were identified in the tissue samples, eight of which were prognostically significant in terms of overall survival (OS). Thereafter, a novel four-gene signature consisting of DSG2, MMP1, SPP1, and MCM2 was developed and validated using stepwise Cox analysis. The area under the receiver operating characteristic (ROC) curve (AUC) values were 0.785, 0.609, and 0.686 in the training, verification, and combination groups, respectively. The protein expression levels of the four genes were well validated by the western blotting. Moreover, the nomogram analysis showed that a combination of this four-gene signature plus lymph node metastasis (LNM) status effectively predicted the 1- and 3-year OS probabilities of CC patients with accuracies of 69.01% and 83.93%, respectively. CONCLUSIONS: We developed a four-gene signature that can accurately predict the prognosis in terms of OS, of CC patients, and could be a valuable tool for designing treatment strategies.