Validation of Reference Genes for Silver Nanotoxicity Analysis in MCF-7 Cell Line

Uncovering the stability status of the reputed reference genes in breast and hepatic cancer cell lines

Accurate and reliable relative gene expression analysis via the Reverse Transcription-quantitative Real Time PCR (RT-qPCR) method strongly depends on employing several stable reference genes as normalizers. Utilization of the reference genes without analyzing their expression stability under each experimental condition causes RT-qPCR analysis error as well as false output. Similar to cancerous tissues, cancer cell lines also exhibit various gene expression profiles. It is crucial to recognize stable reference genes for well-known cancer cell lines to minimize RT-qPCR analysis error. In this study, we showed the expression level and investigated the expression stability of eight common reference genes that are ACTB, YWHAZ, HPRT1, RNA18S, TBP, GAPDH, UBC, and B2M, in two sets of cancerous cell lines. One set contains MCF7, SKBR3, and MDA-MB231 as breast cancer cell lines. Another set includes three hepatic cancer cell lines, including Huh7, HepG2, and PLC-PRF5. Three excel-based softwares comprising geNorm, BestKeeper, and NormFinder, and an online tool, namely RefFinder were used for stability analysis. Although all four algorithms did not show the same stability ranking of nominee genes, the overall results showed B2M and ACTB as the least stable reference genes for the studied breast cancer cell lines. While TBP had the lowest expression stability in the three hepatic cancer cell lines. Moreover, YWHAZ, UBC, and GAPDH showed the highest stability in breast cancer cell lines. Besides that, a panel of five nominees, including ACTB, HPRT1, UBC, YWHAZ, and B2M showed higher stability than others in hepatic cancer cell lines. We believe that our results would help researchers to find and to select the best combination of the reference genes for their own experiments involving the studied breast and hepatic cancer cell lines. To further analyze the reference genes stability for each experimental condition, we suggest researchers to consider the provided stability ranking emphasizing the unstable reference genes.

Validation of common reference genes stability in exosomal mRNA-isolated from liver and breast cancer cell lines

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.

RAB5A is associated with genes involved in exosome secretion: Integration of bioinformatics analysis and experimental validation

Exosomes, as cell-cell communicators with an endosomal origin, are involved in the progression of various diseases. RAB5A, a member of the small Rab GTPases family, which is well known as a key regulator of cellular endocytosis, is expected to be involved in exosome secretion. Here, we found the impact of RAB5A on exosome secretion from human hepatocellular carcinoma cell line using a rapid yet reliable bioinformatics approach followed by experimental analysis. Initially, RAB5A and exosome secretion-related genes were gathered from bioinformatics tools, namely, CTD, COREMINE, and GeneMANIA; and published papers. Protein-protein interaction (PPI) was then constructed by the Search Tool for Retrieval of Interacting Genes (STRING) database. Among them, several genes with different combined scores were validated by the real-time quantitative polymerase chain reaction (RT-qPCR) in stable RAB5A knockdown cells. Thereafter, to validate the bioinformatics results functionally, the impact of RAB5A knockdown on exosome secretion was evaluated. Bioinformatics analysis showed that RAB5A interacts with 37 genes involved in exosome secretion regulatory pathways. Validation by RT-qPCR confirmed the association of RAB5A with candidate interacted genes and interestingly showed that even medium to low combined scores of the STRING database could be experimentally valid. Moreover, the functional analysis demonstrated that the stable silencing of RAB5A could experimentally decrease exosome secretion. In conclusion, we suggest RAB5A as a regulator of exosome secretion based on our bioinformatics approach and experimental analysis. Also, we propose the usage of PPI-derived from the STRING database regardless of their combined scores in advanced bioinformatics analysis.