Long Noncoding RNA NEAT1 as a Potential Candidate Biomarker for Prostate Cancer.
Diana NituscaAnca MarcuAlis Liliana Carmen DemaLoredana BalacescuOvidiu BălăcescuRăzvan BardanAlin Adrian CumpanasIoan-Ovidiu SirbuBogdan PetrutEdward SeclamanCatalin MarianPublished in: Life (Basel, Switzerland) (2021)
Background: Prostate cancer (PCa) remains one of the leading causes of cancer-related mortality in men worldwide, mainly due to unsatisfactory diagnostic methods used at present, which lead to overdiagnosis, unnecessary biopsies and treatment, or misdiagnosis in early asymptomatic stages. New diagnostic biomarkers are needed for a correct and early diagnosis. Long noncoding RNAs (lncRNAs) have been broadly studied for their involvement in PCa biology, as well as for their potential role as diagnostic biomarkers. Methods: We conducted lncRNA profiling in plasma and microdissected formalin-fixed paraffin-embedded (FFPE) tissues of PCa patients and attempted validation for commonly dysregulated individual lncRNAs. Results: Plasma profiling revealed eight dysregulated lncRNAs, while microarray analysis revealed 717 significantly dysregulated lncRNAs, out of which only nuclear-enriched abundant transcript 1 (NEAT1) was commonly upregulated in plasma samples and FFPE tissues. NEAT1's individual validation revealed statistically significant upregulation (FC = 2.101, p = 0.009). Receiver operating characteristic (ROC) analysis showed an area under the curve (AUC) value of 0.7298 for NEAT1 (95% CI = 0.5812-0.8785), suggesting a relatively high diagnostic value, thus having a potential biomarker role for this malignancy. Conclusions: We present herein data suggesting that NEAT1 could serve as a diagnostic biomarker for PCa. Additional studies of larger cohorts are needed to confirm our findings, as well as the oncogenic mechanism of NEAT1 in the development of PCa.
Keyphrases
- prostate cancer
- long noncoding rna
- single cell
- end stage renal disease
- gene expression
- newly diagnosed
- network analysis
- ejection fraction
- chronic kidney disease
- risk factors
- type diabetes
- cardiovascular events
- machine learning
- prognostic factors
- deep learning
- peritoneal dialysis
- patient reported outcomes
- ultrasound guided
- climate change
- data analysis
- clinical evaluation