ArticleScientific reports2018
Identification and validation of an 18-gene signature highly-predictive of bladder cancer metastasis.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 13 citations in OpenAlex.
- Improving data interpretability with new differential sample variance gene set tests.BMC bioinformatics · 2025Article
- A clinically practical model for the preoperative prediction of lymph node metastasis in bladder cancer: a multicohort study.British journal of cancer · 2023Article
- Review
- Predictive signature of response to neoadjuvant chemotherapy in muscle-invasive bladder cancer integrating mRNA expression, taxonomic subtypes, and clinicopathological features.Frontiers in oncology · 2023Article
- Transcriptomics and solid tumors: The next frontier in precision cancer medicine.Seminars in cancer biology · 2022Review
- KDELC1 and TRMT1 Serve as Prognosis-Related SARS-CoV-2 Proteins Binding Human mRNAs and Promising Biomarkers in Clear Cell Renal Cell Carcinoma.International journal of general medicine · 2021Article
- Transcriptomic Analysis of Glycolysis-Related Genes Reveals an Independent Signature of Bladder Carcinoma.Frontiers in genetics · 2020Article
- Communication Of Cancer Cells And Lymphatic Vessels In Cancer: Focus On Bladder Cancer.OncoTargets and therapy · 2019Article
- Evaluation of gene expression-based predictors of lymph node metastasis in bladder cancer.Bladder cancer (Amsterdam, Netherlands)Article
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We found two deviant groups that were unpredictable with clinical models predicting bladder cancer metastasis. The group G consists of patients at high risk of pN+ , but they have pN0. The group P consists of patients at low risk of pN+ , but they have pN+ . We aimed to determine the genetic differences between these two groups. 1603 patients from SEER database were enrolled to build a multivariate model. This model was applied to patients from the TCGA database to distinguish groups G and P. Differentially expressed genes between the two groups were identified. RT-qPCR was used to validate the results in a cohort from FUSCC. Two deviant groups were identified both in the SEER population and the TCGA population. Expression of 183 genes was significantly different between the two groups. 18 genes achieved significant statistical power in predicting lymph node metastasis excluding these two deviant groups. The 18-gene signature outperformed 3 other bladder cancer lymph node prediction tools in 2 external GEO datasets. RT-qPCR results of our own cohort identified NECTIN2 (P = 0.036) as the only gene that could predict metastasis. Our study showed a novel gene screening method and proposed an 18-gene signature highly predictive of bladder cancer metastasis.
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