Long non-coding RNAs enable precise diagnosis and prediction of early relapse after nephrectomy in patients with renal cell carcinoma
Jazyk angličtina Země Německo Médium print-electronic
Typ dokumentu časopisecké články
Grantová podpora
NV18-03-00554
Ministerstvo Zdravotnictví Ceské Republiky
PubMed
36988708
PubMed Central
PMC10374689
DOI
10.1007/s00432-023-04700-7
PII: 10.1007/s00432-023-04700-7
Knihovny.cz E-zdroje
- Klíčová slova
- Biomarker, Diagnosis, Early relapse, Long non-coding RNA, Next-generation sequencing, Prognosis,
- MeSH
- karcinom z renálních buněk * diagnóza genetika chirurgie MeSH
- lidé MeSH
- lokální recidiva nádoru diagnóza genetika chirurgie MeSH
- nádorové biomarkery genetika MeSH
- nádory ledvin * diagnóza genetika chirurgie MeSH
- nefrektomie MeSH
- regulace genové exprese u nádorů MeSH
- RNA dlouhá nekódující * genetika MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- nádorové biomarkery MeSH
- RNA dlouhá nekódující * MeSH
PURPOSE: Renal cell carcinoma belongs among the deadliest malignancies despite great progress in therapy and accessibility of primary care. One of the main unmet medical needs remains the possibility of early diagnosis before the tumor dissemination and prediction of early relapse and disease progression after a successful nephrectomy. In our study, we aimed to identify novel diagnostic and prognostic biomarkers using next-generation sequencing on a novel cohort of RCC patients. METHODS: Global expression profiles have been obtained using next-generation sequencing of paired tumor and non-tumor tissue of 48 RCC patients. Twenty candidate lncRNA have been selected for further validation on an independent cohort of paired tumor and non-tumor tissue of 198 RCC patients. RESULTS: Sequencing data analysis showed significant dysregulation of more than 2800 lncRNAs. Out of 20 candidate lncRNAs selected for validation, we confirmed that 14 of them are statistically significantly dysregulated. In order to yield better discriminatory results, we combined several best performing lncRNAs into diagnostic and prognostic models. A diagnostic model consisting of AZGP1P1, CDKN2B-AS1, COL18A1, and RMST achieved AUC 0.9808, sensitivity 95.96%, and specificity 90.4%. The model for prediction of early relapse after nephrectomy consists of COLCA1, RMST, SNHG3, and ZNF667-AS1 and achieved AUC 0.9241 with sensitivity 93.75% and specificity 71.07%. Notably, no combination has outperformed COLCA1 alone. Lastly, a model for stage consists of ZNF667-AS1, PVT1, RMST, LINC00955, and TCL6 and achieves AUC 0.812, sensitivity 85.71%, and specificity 69.41%. CONCLUSION: In our work, we identified several lncRNAs as potential biomarkers and developed models for diagnosis and prognostication in relation to stage and early relapse after nephrectomy.
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