Amin Moqadami; Mohammad Khalaj-Kondori
Abstract
Long non-coding RNAs (lncRNAs) have recently emerged as effective regulatory agents in biological processes as well as in the formation of tumors. LncRNAs are important regulators of cell transformation and cancer progression. LncRNA NEAT1 is one of the most important lncRNAs, and its deregulation has ...
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Long non-coding RNAs (lncRNAs) have recently emerged as effective regulatory agents in biological processes as well as in the formation of tumors. LncRNAs are important regulators of cell transformation and cancer progression. LncRNA NEAT1 is one of the most important lncRNAs, and its deregulation has been reported in a variety of human cancers. Ovarian cancer has an inverse relationship with the number of reported pregnancies and deliveries, while it has a direct relationship with infertility. This study aimed to investigate NEAT1 expression in ovarian cancer. A total of 140 tissue samples, including 70 ovarian tumors and 70 marginal samples, were included in the study. Total RNA was extracted using the RNXplus solution. The quality and quantity of the extracted RNAs were determined using gel electrophoresis and a NanoDrop device. The complementary DNA was synthesized by the reverse transcriptase enzyme, and quantitative reverse transcriptase PCR was used to quantify the expression of NEAT1. A comparison between the mean expression of NEAT1 in ovarian tumors and marginal samples showed an increase in NEAT1 expression in tumor tissue that was not statistically significant (P-value = 0.2). ROC curve analysis also showed that NEAT1 expression level might not be an informative biomarker for ovarian cancer.
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.
