Abstract
Background
The aim of this study was to construct a clinically practical model to precisely predict lymph node (LN) metastasis in bladder cancer patients.
Methods
Four independent cohorts were included. The least absolute shrinkage and selection operator regression with multivariate logistic regression were applied. The diagnostic efficacy of LN score and CT/MRI was compared by accuracy, sensitivity, specificity, and area under curve (AUC).
Results
A total of 606 patients were included to develop a basic prediction model. After multistep gene selection, the LN metastasis prediction model was constructed with 5 genes. The model can accurately predict LN metastasis with an AUC of 0.781. For clinically practical use, we transformed the model into a Fast LN Scoring System using the SYSMH cohort (n = 105). High LN score patients exhibited a 72.2% LN metastasis rate, while low LN score patients showed a 3.4% LN metastasis rate. The LN score achieved a superior accuracy than CT/MRI (0.882 vs. 0.727). Application of LN score can correct the diagnosis of 88% (22/25) patients who were misdiagnosed by CT/MRI.
Discussion
The clinically practical LN score can precisely, rapidly, and conveniently predict LN status, which will assist preoperative diagnosis for LN metastasis and guide precise therapy.
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Data availability
The raw RNA-seq data can be acquired from the TCGA database (The Cancer Genome Atlas, https://xenabrowser.net/datapages/) and GEO database (Gene Expression Omnibus, https://www.ncbi.nlm.nih.gov/). The anonymous patient characteristics and qPCR results can be obtained in the Supplementary Materials.
Code availability
The code used in this study can be obtained at https://github.com/JunlinLu123/LNscore.
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Funding
This study was supported by the National Key Research and Development Program of China (Grant No. 2022YFC2408300), the National Natural Science Foundation of China (Grant Nos. 82273421, 81825016, 82072827, U21A20383), Guangdong Basic and Applied Basic Research Foundation (Grant No. 2021B1515020009), Key Research and Development Program of Guangdong (Grant No. 2018B010109006), Science and Technology Program of Guangzhou (Grant No. 2023A03J0718, 202102010002), Fundamental Research Funds for the Central Universities, Sun Yat-sen University (23ykbj002 for XC), Guangdong Provincial Clinical Research Center for Urological Diseases (2020B1111170006), and Guangdong Science and Technology Department (2020B1212060018, 2018B030317001, 2017B030314026).
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J Lu and XC conceived and designed the study. J Lai and KX downloaded and maintained the public data. J Lu and QX performed bioinformatics analysis. SP and YZ collected patient samples. J Lai conducted PCR. J Lu, XC, and TL wrote the first draft. YC, QZ, and SL reviewed and revised the manuscript. XC and TL provided fundings for the research. All authors read and approved the final version of the manuscript. The authorship order among the co-first authors was assigned based on their relative contributions.
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The ethical consent of this study was approved by Sun Yat‐sen University Committees for Ethical Review of Research involving Human Subjects. All human tissue samples were obtained from patients with written informed consent.
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Lu, J., Lai, J., Xiao, K. et al. A clinically practical model for the preoperative prediction of lymph node metastasis in bladder cancer: a multicohort study. Br J Cancer 129, 1166–1175 (2023). https://doi.org/10.1038/s41416-023-02383-y
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DOI: https://doi.org/10.1038/s41416-023-02383-y
