Unleash multifunctional role of long noncoding RNAs biomarker panel in breast cancer: a predictor classification model.
Eman Ali ToraihAya El-WazirEssam Al AgeeliMohammad H HusseinMohamed Meselhy EltoukhyMary T KillackeyEmad KandilManal S FawzyPublished in: Epigenomics (2020)
Aim: We aimed to explore the circulating expression profile of nine lncRNAs (MALAT1, HOTAIR, PVT1, H19, ROR, GAS5, ANRIL, BANCR, MIAT) in breast cancer (BC) patients relative to normal and risky individuals. Methods: Serum relative expressions of the specified long non-coding RNAs were quantified in 155 consecutive women, using quantitative reverse-transcription PCR. Random Forest (RF) and decision tree were also applied. Results: Significant MALAT1 upregulation and GAS5 downregulation could discriminate risky women from healthy controls. Overexpression of the other genes showed good diagnostic performances. Lower GAS5 levels were associated with metastasis and recurrence. RF model revealed a better performance when combining gene expression patterns with risk factors. Conclusion: The studied panel could be utilized as diagnostic/prognostic biomarkers in BC, providing promising epigenetic-based therapeutic targets.
Keyphrases
- gene expression
- long non coding rna
- end stage renal disease
- risk factors
- room temperature
- polycystic ovary syndrome
- dna methylation
- poor prognosis
- cell proliferation
- breast cancer risk
- ejection fraction
- newly diagnosed
- chronic kidney disease
- pregnancy outcomes
- transcription factor
- signaling pathway
- machine learning
- deep learning
- peritoneal dialysis
- carbon dioxide
- prognostic factors
- drug delivery
- genome wide
- type diabetes
- cervical cancer screening
- high resolution
- climate change
- pregnant women
- patient reported outcomes
- single cell
- network analysis
- adipose tissue
- free survival
- real time pcr
- solid state