Lymph node (LN) metastasis is a lethal independent risk factor for patients with bladder cancer (BLCA). Accurate evaluation of LN metastasis is of vital importance for disease staging, treatment selection, and prognosis prediction. Several histopathologic parameters are available to predict LN metastasis postoperatively. To date, medical imaging techniques have made a great contribution to preoperatively diagnosis of LN metastasis, but it also exhibits substantial false positives. Therefore, a reliable and robust method to preoperatively predict LN metastasis is urgently needed. Here, we selected 19 candidate genes related to epithelial-mesenchymal transition (EMT) across the LN metastasis samples, which was previously reported to be responsible for the subtype transition and correlation with malignancy and prog nosis of BLCA, to establish an EMT-LN signature through LASSO logistic regression analysis. The EMT-LN signature could significantly predict LN metastasis with high accuracy in the TCGA-BLCA cohort, as well as several independent cohorts. As integrating with C3orf70 mutation, we developed an individualized prediction nomogram based on the EMT-LN signature. The nomogram exhibited good discrimination on LN metastasis status, with AUC of 71.7% and 75.9% in training and testing datasets of the TCGA-BLCA cohort. Moreover, the EMT-LN nomogram displayed good calibration with in the Hosmer-Lemeshow goodness of fit test. Decision curve analysis (DCA) revealed that the EMT-LN nomogram was of high potential for clinical utility. In summary, we established an EMT-LN nomogram integrating an EMT-LN signature and C3orf70 mutation status, which acted as an easy-to-use tool to facilitate preoperative prediction of LN metastasis in BLCA individuals.
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