Department of Obstetrics and Gynecology, Benxi Central Hospital of China Medical University, Benxi, Liaoning 117022, China.
Clinical Laboratory Department, Benxi Central Hospital of China Medical University, Benxi, Liaoning 117022, China.
Genet Res (Camb). 2022 Mar 16;2022:5401106. doi: 10.1155/2022/5401106. eCollection 2022.
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.
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).
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.
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.
子宫内膜癌(EC)是一种常见的生殖系统肿瘤,但治疗选择有限。我们旨在发现 EC 的新预后生物标志物。
我们使用 mRNA-seq 数据检测 EC 和对照组织之间的差异表达基因(DEG)。收集详细的临床病理信息,并分析 EC 中关键 DEG 的 mRNA 和蛋白水平变化。还评估了拷贝数变异(CNV)与 EC 发病机制的关系。进行基因集富集分析(GSEA)以丰富由关键基因驱动的显著途径。使用 Cox 回归分析选择变量以创建列线图。通过应用一致性指数(C-index)校准列线图,并使用决策曲线分析(DCA)量化列线图在不同阈值概率下的净收益。
差异表达分析确定了 24 个 DEG 作为 EC 的潜在危险因素。生存分析显示,TPX2 表达与晚期 EC 患者总生存率恶化相关。高 CNV 与 TPX2 的过表达相关;这表明细胞周期途径的改变可能在 EC 的进展中至关重要。此外,还开发了包含临床因素的 TPX2 个体化列线图来评估其预测 EC 的能力。校准和 DCA 分析证实了列线图的稳健性和临床实用性。
我们提供了关于 EC 发病机制和分子机制的新见解。TPX2 的过表达与预后较差相关,可作为预测 EC 患者预后结果的生物标志物。