A New Reference miRNA IN TUMOR AND NORMAL BREAST TISSUES
Publish place: the fourth International and 16th National Genetics Congress
Publish Year: 1399
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
CIGS16_011
تاریخ نمایه سازی: 14 اردیبهشت 1400
Abstract:
Background & aim: One of the most widely used evaluation methods in miRNA experiments is qRT-PCR. However, selecting suitable internal controls (IC) is crucial for qRT-PCR experiments. Currently, there is no consensus on the ICs for miRNA qRT-PCR experiments in breast cancer. High-throughput miRNA-Seq data is a great source to discover stable ICs. To this end, we tried to identify the most stable (the least expression alteration) and promising miRNAs in normal and tumor breast tissues by employing TCGA miRNA-Seq data.Materials and methods: A multi-component scoring system was used which takes into account multiple expression stability criteria as well as correlation with clinical characteristics. Furthermore, we extended the scoring system for more than two biological subgroups. The top ۱۰ most stable miRNAs were further investigated by differential expression and survival analysis. Then, we examined the expression level of the top scored miRNA (hsa-miR-۳۶۱-۵p) along with two commonly used ICs hsa-miR-۱۶-۵p and U۴۸ on ۳۴ pairs of Primary breast tumor and their adjacent normal tissues using qRT-PCR.Results: While U۴۸ was the most abundant IC, hsa-miR-۳۶۱-۵p had a lower standard deviation and also was the only IC capable of detecting a significant up-regulation of hsa-miR-۲۱-۵p as an oncomiR in tumor vs. normal tissues of the breast. Conclusion: Our results demonstrated that hsa-miR-۳۶۱-۵p is a highly stable miRNA in tumor and non-tumor breast tissue and we recommend it as a suitable reference gene for miRNA expression studies in breast cancer. Additionally, although hsa-miR-۱۶-۵p is a commonly used IC, it's not a suitable one for breast cancer studies.
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Authors
Sogol Ghanbari
Molecular Genetics Department, Biological Sciences Faculty, Tarbiat Modares University, Tehran, Iran
Adel Salimi
Computer Engineering department, Sharif University of Technology, Tehran, Iran
Saeed Rahmani
Computer Engineering department, Sharif University of Technology, Tehran, Iran
Nahid Nafisi
Surgical Department, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
ali Sharifi-Zarchi
Computer Engineering department, Sharif University of Technology, Tehran, Iran
Seyed Javad Mowla
Molecular Genetics Department, Biological Sciences Faculty, Tarbiat Modares University, Tehran, Iran