Samaneh Khazaei; Sedigheh Gharbi; Seyed Javad Mowla
Abstract
Esophageal squamous cell carcinoma (ESCC) is a deadly cancer with poor prognosis. In this regard, early diagnosis is of vital importance to cure the tumor in its early stages. Novel cancer diagnostic and therapeutic approaches have been recently introduced based on microRNAs (miRNAs). Also, accurate ...
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Esophageal squamous cell carcinoma (ESCC) is a deadly cancer with poor prognosis. In this regard, early diagnosis is of vital importance to cure the tumor in its early stages. Novel cancer diagnostic and therapeutic approaches have been recently introduced based on microRNAs (miRNAs). Also, accurate normalization using appropriate reference genes is a critical step in miRNA expression studies. In this study, we aimed to identify appropriate reference genes for miRNA quantification in serum samples of ESCC. In this case and control experimental study, two statistical algorithms including GeNorm and NormFinder were used to evaluate the suitability of miR-16 and 5S rRNA and their geometric mean as reference genes. Then, relative expression of miR-451 and miR-24 were evaluated while different normalizer including miR-16, 5S rRNA and their geometric mean were applied. Both GeNorm and NormFinder analyses showed that geometric mean of miR-16 and 5S rRNA is the most stable reference gene in these samples. Also, our data showed that choosing an inappropriate normalizer could change the relative expression of target genes of miR-451 and miR-24 in ESCC samples which emphasize on the importance of selecting a reliable internal control in expression analyses. We demonstrated that geometric mean of two reference genes could increase the reliability of normalizers and also by using geometric mean as reference gene, relative expression of different target is closer to reality.
Sedigheh Gharbi; Fatemh Mirzadeh; Shahriar Khatrei; Mohammad Reza Soroush; Mahmood Tavallaie; Mohammad Reza Nourani; mehdi Sahmsara; Seyyed Javad Mowla
Abstract
MicroRNAs constitute a group of small non-coding RNAs that negatively regulate gene expression. Aside from their contribution to biological and pathological pathways, altered expression of microRNAs is reported in bio-fluid samples, such as serum. To employ serum's microRNAs as potential biomarkers, ...
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MicroRNAs constitute a group of small non-coding RNAs that negatively regulate gene expression. Aside from their contribution to biological and pathological pathways, altered expression of microRNAs is reported in bio-fluid samples, such as serum. To employ serum's microRNAs as potential biomarkers, it is crucial to develop an efficient method for microRNA quantification, avoiding pre-analytical and analytical variations which could affect the accuracy of data analysis. Here, we optimized a real-time PCR quantification procedure for microRNA detection in serum samples. Serum's total RNA was extracted using two different RNA isolation methods, one based on phenol-chloroform and the other based on silica column. To investigate a potential PCR inhibitory effect, different RNA amounts were subjected to reverse transcription. Moreover to assess the enzymatic efficiency, synthetic exogenous microRNAs was spiked into the mixture. Moreover, to find a reliable internal control gene for normalizing the microRNA quantification, the amounts of 8 candidate non-coding RNAs including SNORD38B, SNORD49A, U6, 5S rRNA, miR-423-5p, miR-191, miR-16 and miR-103 were assessed on serum samples. Altogether, our data demonstrated that the silica-based method was more efficient for microRNA recovery. Furthermore, increasing the input volume of the extracted RNA would dramatically increase inhibitors' amounts which could end up in a larger Cq values. Therefore, the best input volume of RNA turned out to be 1.5 microliter/reaction. Among the 8 aforementioned internal controls, U6, SNORD38B and SNORD49A showed low levels of expression, and were undetectable in some samples. Amongst the others, 5s rRNA, had the biggest standard deviation which could significantly affect data analysis. MiR-103 with the least variation appeared to be the best normalizer gene.
