Long non-coding RNAs enable precise diagnosis and prediction of early relapse after nephrectomy in patients with renal cell carcinoma

Logo poskytovatele

Varování

Publikace nespadá pod Ekonomicko-správní fakultu, ale pod Lékařskou fakultu. Oficiální stránka publikace je na webu muni.cz.
Autoři

BOHOŠOVÁ Júlia KOŽELKOVÁ Kateřina AL TUKMACHI Dagmar TRACHTOVÁ Karolína NAAR Ondřej RUČKOVÁ Michaela KOLÁRIKOVÁ Eva STANÍK Michal POPRACH Alexandr SLABÝ Ondřej

Rok publikování 2023
Druh Článek v odborném periodiku
Časopis / Zdroj Journal of cancer research and clinical oncology
Fakulta / Pracoviště MU

Lékařská fakulta

Citace
www https://link.springer.com/article/10.1007/s00432-023-04700-7
Doi http://dx.doi.org/10.1007/s00432-023-04700-7
Klíčová slova Long non-coding RNA; Diagnosis; Prognosis; Biomarker; Early relapse; Next-generation sequencing
Přiložené soubory
Popis PurposeRenal 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.MethodsGlobal 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.ResultsSequencing 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%.ConclusionIn 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.
Související projekty:

Používáte starou verzi internetového prohlížeče. Doporučujeme aktualizovat Váš prohlížeč na nejnovější verzi.