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A clinically practical model for the preoperative prediction of lymph node metastasis in bladder cancer: a multicohort study

British Journal of Cancer volumeĀ 129,Ā pages 1166ā€“1175 (2023)Cite this article

Subjects

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 (nā€‰=ā€‰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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Fig. 1: Flow chart of establishing basic model and clinically practical model in this study.
Fig. 2: Screening key genes predicting lymph node metastasis.
Fig. 3: Establishing and evaluating the lymph node metastasis prediction model.
Fig. 4: The performance of the prediction model in different patient subgroups.
Fig. 5: Establishing a Fast LN Scoring System using the SYSMH cohort.
Fig. 6: Comparison of the diagnostic efficacies of the Fast LN Scoring System and CT/MRI in predicting LN metastasis.

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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.

References

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209ā€“49.

    ArticleĀ  PubMedĀ  Google ScholarĀ 

  2. Chen X, Zhang J, Ruan W, Huang M, Wang C, Wang H, et al. Urine DNA methylation assay enables early detection and recurrence monitoring for bladder cancer. J Clin Investig. 2020;130:6278ā€“89.

    ArticleĀ  CASĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  3. Liu S, Chen X, Lin T. Emerging strategies for the improvement of chemotherapy in bladder cancer: current knowledge and future perspectives. J Adv Res. 2022;39:187ā€“202.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  4. Zhang Q, Liu S, Wang H, Xiao K, Lu J, Chen S, et al. ETV4 mediated tumor-associated neutrophil infiltration facilitates lymphangiogenesis and lymphatic metastasis of bladder cancer. Adv Sci. 2023;10:e2205613.

    ArticleĀ  Google ScholarĀ 

  5. Youssef RF, Raj GV. Lymphadenectomy in management of invasive bladder cancer. Int J Surg Oncol. 2011;2011:758189.

    PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  6. Karl A, Carroll PR, Gschwend JE, KnĆ¼chel R, Montorsi F, Stief CG, et al. The impact of lymphadenectomy and lymph node metastasis on the outcomes of radical cystectomy for bladder cancer. Eur Urol. 2009;55:826ā€“35.

    ArticleĀ  PubMedĀ  Google ScholarĀ 

  7. Del Bene G, CalabrĆ² F, Giannarelli D, Plimack ER, Harshman LC, Yu EY, et al. Neoadjuvant vs. adjuvant chemotherapy in muscle invasive bladder cancer (MIBC): analysis from the RISC database. Front Oncol. 2018;8:463.

    ArticleĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  8. McKibben MJ, Woods ME. Preoperative imaging for staging bladder cancer. Curr Urol Rep. 2015;16:22.

    ArticleĀ  PubMedĀ  Google ScholarĀ 

  9. Liu S, Chen X, Lin T. Lymphatic metastasis of bladder cancer: molecular mechanisms, diagnosis and targeted therapy. Cancer Lett. 2021;505:13ā€“23.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  10. Huang M, Dong W, Xie R, Wu J, Su Q, Li W, et al. HSF1 facilitates the multistep process of lymphatic metastasis in bladder cancer via a novel PRMT5-WDR5-dependent transcriptional program. Cancer Commun. 2022;42:447ā€“70.

    ArticleĀ  Google ScholarĀ 

  11. Xie R, Chen X, Cheng L, Huang M, Zhou Q, Zhang J, et al. NONO inhibits lymphatic metastasis of bladder cancer via alternative splicing of SETMAR. Mol Ther. 2021;29:291ā€“307.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  12. Luo C, Huang B, Wu Y, Xu Y, Ou W, Chen J, et al. Identification of lymph node metastasis-related key genes and prognostic risk model in bladder cancer by co-expression analysis. Front Mol Biosci. 2021;8:633299.

    ArticleĀ  CASĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  13. Cao R, Ma B, Wang G, Xiong Y, Tian Y, Yuan L. An epithelial-mesenchymal transition (EMT) preoperative nomogram for prediction of lymph node metastasis in bladder cancer (BLCA). Dis Markers. 2020;2020:8833972.

    ArticleĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  14. Wagner GP, Kin K, Lynch VJ. Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci. 2012;131:281ā€“5.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  15. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8:118ā€“27.

    ArticleĀ  PubMedĀ  Google ScholarĀ 

  16. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47.

    ArticleĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  17. Tibshirani R. The lasso method for variable selection in the Cox model. Stat Med. 1997;16:385ā€“95.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  18. Simon N, Friedman J, Hastie T, Tibshirani R. Regularization paths for Coxā€™s proportional hazards model via coordinate descent. J Stat Softw. 2011;39:1ā€“13.

    ArticleĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  19. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77.

    ArticleĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  20. Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Mak. 2006;26:565ā€“74.

    ArticleĀ  Google ScholarĀ 

  21. Peng S, Chen X, Huang C, Yang C, Situ M, Zhou Q, et al. UBE2S as a novel ubiquitinated regulator of p16 and Ī²-catenin to promote bone metastasis of prostate cancer. Int J Biol Sci. 2022;18:3528ā€“43.

    ArticleĀ  CASĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  22. Chen X, Xie R, Gu P, Huang M, Han J, Dong W, et al. Long noncoding RNA LBCS inhibits self-renewal and chemoresistance of bladder cancer stem cells through epigenetic silencing of SOX2. Clin Cancer Res. 2019;25:1389ā€“403.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  23. Wang B, Wan F, Sheng H, Zhu Y, Shi G, Zhang H, et al. Identification and validation of an 18-gene signature highly-predictive of bladder cancer metastasis. Sci Rep. 2018;8:374.

    ArticleĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  24. Smith SC, Baras AS, Dancik G, Ru Y, Ding KF, Moskaluk CA, et al. A 20-gene model for molecular nodal staging of bladder cancer: development and prospective assessment. Lancet Oncol. 2011;12:137ā€“43.

    ArticleĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  25. Liu J, Zheng Z, Zhang W, Wan M, Ma W, Wang R, et al. Dysregulation of tumor microenvironment promotes malignant progression and predicts risk of metastasis in bladder cancer. Ann Transl Med. 2021;9:1438.

    ArticleĀ  CASĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  26. Lu X, Wang Y, Jiang L, Gao J, Zhu Y, Hu W, et al. A pre-operative nomogram for prediction of lymph node metastasis in bladder urothelial carcinoma. Front Oncol. 2019;9:488.

    ArticleĀ  CASĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  27. Dobrocky T, Grunder L, Breiding PS, Branca M, Limacher A, Mosimann PJ, et al. Assessing spinal cerebrospinal fluid leaks in spontaneous intracranial hypotension with a scoring system based on brain magnetic resonance imaging findings. JAMA Neurol. 2019;76:580ā€“7.

    ArticleĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  28. Lin KC, Chen TM, Yuan KS, Wu ATH, Wu SY. Assessment of predictive scoring system for 90-day mortality among patients with locally advanced head and neck squamous cell carcinoma who have completed concurrent chemoradiotherapy. JAMA Netw Open. 2020;3:e1920671.

    ArticleĀ  PubMedĀ  Google ScholarĀ 

  29. Wang L, Yang C, Li F, Mu D, Ran P, Shen H, et al. High levels of MESP1 expression in non-small cell lung cancer can facilitate cell proliferation, metastasis and suppresses cell apoptosis. Transl Cancer Res. 2020;9:5956ā€“68.

    ArticleĀ  CASĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  30. Yin X, Fang S, Wang M, Wang Q, Fang R, Chen J. EFEMP1 promotes ovarian cancer cell growth, invasion and metastasis via activated the AKT pathway. Oncotarget. 2016;7:47938ā€“53.

    ArticleĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  31. Zhang D, Han S, Pan X, Li H, Zhao H, Gao X, et al. EFEMP1 binds to STEAP1 to promote osteosarcoma proliferation and invasion via the Wnt/Ī²-catenin and TGF-Ī²/Smad2/3 signal pathways. J Bone Oncol. 2022;37:100458.

    ArticleĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  32. Gao X, Yang J. Identification of genes related to clinicopathological characteristics and prognosis of patients with colorectal cancer. DNA Cell Biol. 2020;39:690ā€“9.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  33. Yang B, Li M, Tang W, Liu W, Zhang S, Chen L, et al. Dynamic network biomarker indicates pulmonary metastasis at the tipping point of hepatocellular carcinoma. Nat Commun. 2018;9:678.

    ArticleĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  34. Zhou Y, Fu X, Zheng Z, Ren Y, Zheng Z, Zhang B, et al. Identification of gene co-expression modules and hub genes associated with the invasiveness of pituitary adenoma. Endocrine. 2020;68:377ā€“89.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  35. Wu YM, Sa Y, Guo Y, Li QF, Zhang N. Identification of WHO II/III gliomas by 16 prognostic-related gene signatures using machine learning methods. Curr Med Chem. 2022;29:1622ā€“39.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

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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).

Author information

Author notes

  1. These authors contributed equally: Junlin Lu, Jiajian Lai, Kanghua Xiao.

Authors and Affiliations

  1. Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, Guangdong, P. R. China

    Junlin Lu,Ā Jiajian Lai,Ā Kanghua Xiao,Ā Shengmeng Peng,Ā Yangjie Zhang,Ā Sen Liu,Ā Liang Cheng,Ā Qiang Zhang,Ā Yuelong Chen,Ā Xu ChenĀ &Ā Tianxin Lin

  2. Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 510120, Guangzhou, Guangdong, P. R. China

    Junlin Lu,Ā Jiajian Lai,Ā Kanghua Xiao,Ā Shengmeng Peng,Ā Yangjie Zhang,Ā Sen Liu,Ā Liang Cheng,Ā Qiang Zhang,Ā Yuelong Chen,Ā Xu ChenĀ &Ā Tianxin Lin

  3. Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, Hubei, P. R. China

    Qidong Xia

  4. Guangdong Provincial Clinical Research Center for Urological Diseases, 510120, Guangzhou, Guangdong, P. R. China

    Xu ChenĀ &Ā Tianxin Lin

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Contributions

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.

Corresponding authors

Correspondence to Xu Chen or Tianxin Lin.

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Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

The ethical consent of this study was approved by Sun Yatā€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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The consent for publication has been acquired from the participants.

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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, 1166ā€“1175 (2023). https://doi.org/10.1038/s41416-023-02383-y

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