Endometrial cancer (EC) is the most common gynecologic cancer with increasing incidence. The dysregulation of intracellular calcium plays a crucial role in cancer progression. However, the relationship between calcium-related genes and prognosis remains unclear. In this study, we aimed to establish a risk model based on calcium-related genes for prognosis prediction in patients with EC. The TCGA-total set was divided into a training set and a testing set (1:1). The four-gene prognostic signature (CACNA2D1, SLC8A1, TRPM4 and CCL2) was established and classified all EC patients into a low-risk or high-risk group. This model was validated in both the testing dataset and the total set. The EC patients with high RiskScores showed significantly shorter overall survival than those with low RiskScores, and this trend was consistent among most subgroups. Moreover, an enrichment analysis confirmed that calcium-related and estrogen-response signalings were significantly enriched in the high-risk group. The knockdown of CACNA2D1 by siRNA or its blocker, amlodipine (AM) inhibited cell proliferation and induced cycle arrest in vitro. The calcium channel blocker AM inhibited cell proliferation and induced cycle arrest in vitro. AM also showed marked tumor inhibition effects in vivo. In summary, the prognostic model constructed by four calcium-related genes can reliably predict the outcomes of EC patients, and a calcium channel blocker, AM, has significant potential for EC treatment.
Background Lymph node metastasis (LNM) is an important factor affecting endometrial cancer (EC) prognosis. Current controversy exists as to how to accurately assess the risk of lymphatic metastasis. Metabolic syndrome has been considered a risk factor for endometrial cancer, yet its effect on LNM remains elusive. We developed a nomogram integrating metabolic syndrome indicators with other crucial variables to predict lymph node metastasis in endometrial cancer. Methods This study is based on patients diagnosed with EC in Peking University People’s Hospital between January 2004 and December 2020. A total of 1076 patients diagnosed with EC and who underwent staging surgery were divided into training and validation cohorts according to the ratio of 2:1. Univariate and multivariate logistic regression analyses were used to determine the significant predictive factors. Results The prediction nomogram included MSR, positive peritoneal cytology, lymph vascular space invasion, endometrioid histological type, tumor size > = 2 cm, myometrial invasion > = 50%, cervical stromal invasion, and tumor grade. In the training group, the area under the curve (AUC) of the nomogram and Mayo criteria were 0.85 (95% CI: 0.81–0.90) and 0.77 (95% CI: 0.77–0.83), respectively (P < 0.01). In the validation group (N = 359), the AUC was 0.87 (95% CI: 0.82–0.93) and 0.80 (95% CI: 0.74–0.87) for the nomogram and the Mayo criteria, respectively (P = 0.01). Calibration plots revealed the satisfactory performance of the nomogram. Decision curve analysis showed a positive net benefit of this nomogram, which indicated clinical value. Conclusion This model may promote risk stratification and individualized treatment, thus improving the prognosis.
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