Validation of common reference genes stability in exosomal mRNA-isolated from liver and breast cancer cell lines.
Gilar Gorji-BahriNiloofar MoradtabriziFaezeh VakhshitehAtieh HashemiPublished in: Cell biology international (2021)
Accurate relative gene expression analysis by reverse transcription-quantitative polymerase chain reaction relies on the usage of suitable reference genes for data normalization. The RNA content of small extracellular vesicles including exosomes is growingly considered as cancer biomarkers. So, reliable relative quantification of exosomal messenger RNA (mRNA) is essential for cancer diagnosis and prognosis applications. However, suitable reference genes for accurate normalization of a target gene in exosomes derived from cancer cells are not depicted yet. Here, we analyzed the expression and stability of eight well-known reference genes namely GAPDH, B2M, HPRT1, ACTB, YWHAZ, UBC, RNA18S, and TBP in exosomes-isolated from the liver (Huh7, HepG2, PLC/PRF/5) and breast (SK-BR-3) cancer cell lines using five different algorithms including geNorm, BestKeeper, Delta Ct, NormFinder, and RefFinder. Our results showed that ACTB, TBP, and HPRT1 were not expressed in exosomes-isolated from studied liver and breast cancer cell lines. The geNorm and BestKeeper algorithms indicated GAPDH and UBC as the most stable candidates. Moreover, Delta Ct and NormFinder algorithms showed YWHAZ as the most stable reference genes. Comprehensive ranking calculated by the RefFinder algorithm also pointed out GAPDH, YWHAZ, and UBC as the first three stable reference genes. Taken together, this study validated the common reference genes stability in exosomal mRNA derived from liver and breast cancer cell lines for the first time. We believe that this study would be the first step in finding more stable reference genes in exosomes that triggers more accurate detection of exosomal biomarkers.
Keyphrases
- genome wide
- genome wide identification
- mesenchymal stem cells
- gene expression
- bioinformatics analysis
- machine learning
- stem cells
- genome wide analysis
- papillary thyroid
- dna methylation
- deep learning
- high resolution
- magnetic resonance imaging
- magnetic resonance
- poor prognosis
- binding protein
- copy number
- childhood cancer
- bone marrow
- young adults
- long non coding rna
- lymph node metastasis
- loop mediated isothermal amplification