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BackgroundLymph node metastasis (LNM) is a critical unfavorable prognostic factor in endometrial cancer (EC). At present, models involving molecular indicators that accurately predict LNM are still uncommon. We addressed this gap by developing nomograms to individualize the risk of LNM in EC and to identify a low-risk group for LNM.MethodsIn all, 776 patients who underwent comprehensive surgical staging with pelvic lymphadenectomy at the First Affiliated Hospital of Chongqing Medical University were divided into a training cohort (used for building the model) and a validation cohort (used for validating the model) according to a predefined ratio of 7:3. Logistics regression analysis was used in the training cohort to screen out predictors related to LNM, after which a nomogram was developed to predict LNM in patients with EC. A calibration curve and consistency index (C-index) were used to estimate the performance of the model. A receiver operating characteristic (ROC) curve and Youden index were used to determine the optimal threshold of the risk probability of LNM predicted by the model proposed in this study. Then, the prediction performance of different models and their discrimination abilities for identifying low-risk patients were compared.ResultLNM occurred in 87 and 42 patients in the training and validation cohorts, respectively. Multivariate logistic regression analysis showed that histological grade (P=0.022), myometrial invasion (P=0.002), lymphovascular space invasion (LVSI) (P=0.001), serum CA125 (P=0.008), Ki67 (P=0.012), estrogen receptor (ER) (0.009), and P53 (P=0.003) were associated with LNM; a nomogram was then successfully established on this basis. The internal and external calibration curves showed that the model fits well, and the C-index showed that the prediction accuracy of the model proposed in this study was better than that of the other models (the C-index of the training and validation cohorts was 0.90 and 0.91, respectively). The optimal threshold of the risk probability of LNM predicted by the model was 0.18. Based on this threshold, the model showed good discrimination for identifying low-risk patients.ConclusionCombining molecular indicators based on classical clinical parameters can predict LNM of patients with EC more accurately. The nomogram proposed in this study showed good discrimination for identifying low-risk patients with LNM.

Indirubin, one of the key components of medicinal plants including Isatis tinctoria, Polygonum tinctorium, and Strobilanthes cusia, possesses great medicinal efficacy in the treatment of chronic myelocytic leukemia (CML). Due to misidentification and similar name, materials containing indirubin and their close relatives frequently fall prey to adulteration. In this study, we selected an internal transcribed spacer 2 (ITS2) for distinguishing these indirubin-containing species from five of their usual adulterants, after assessing identification efficiency of matK, rbcL, psbA-trnH, and ITS2 among these species. The results of genetic distances and neighbor-joining (NJ) phylogenetic tree indicated that ITS2 region is a powerful DNA barcode to accurately identify these indirubin-containing species and discriminate them from their adulterants. Additionally, high performance liquid chromatography (HPLC) was used to verify indirubin in different organs of the above species. The results showed that indirubin had been detected in the leaves of Is. tinctoria, P. tinctorium, S. cusia, and Indigo Naturalis (made from their mixture), but not in their roots, or in the leaves of their adulterants. Therefore, this study provides a novel and rapid method to identify and verify indirubin-containing medicinal plants for effective natural treatment of CML.