Experimental verification and bioinformatics analysis of miRNAs for the differentiation between HER۲ positive and HER۲ negative Breast cancer

Backgrounds: Breast cancer is the most frequent malignancy cancer among women. Clinically,breast cancers are classified by hormone receptor status including estrogen receptor (ER),progesterone receptor (PR), and human EGF-like receptor ۲ (HER۲) receptor expression. HER۲is over-expressed or amplified in ۳۰% breast cancers which are associated with poor prognosisamong all subtypes of breast cancer. microRNAs are a class of short non-coding regulatoryRNAs that can be proposed to be used as biomarkers for cancers. Several studies showed thatmicroRNAs could be considered as potential biomarkers for early detection of breast cancer.Materials and Methods: The Gene Expression Omnibus database (GEO,http://www.ncbi.nlm.nih.gov/geo/) in the National Center for Biotechnology Information (NCBI)is currently the largest fully public gene expression resource. In the study, several bioinformaticstools were utilized to investigate the most important biomarkers. Microarray data set GSE۶۸۰۸۵was downloaded from GEO. Differentially expressed miRNAs were obtained by using Rpackage “limma” in microarray datasets. The expression levels of miRNAs were confirmed byreal-time PCR.Results: Bioinformatics algorithms are predicted signature miRNAs as biological markers inbreast. Real-time PCR confirmed that the expression level of these miRNAs ware significantlydifferent in HER۲ positive breast cancer tissues compared to HER۲ negative breast cancertissues.Conclusion: However, it is not clear whether the changes in miRNA expression are a cause oreffect of the disease for many miRNA species, they could be potential biomarker in cancersbecause many studies on the expression of various miRNAs. This study demonstrate thatoptimized high-throughput microRNA expression profiling offers novel biomarker in HER۲positive breast cancer and Her۲ negative ones.

کلیدواژه ها:

Breast cancer ، HER۲ ، microRNAs ، Biomarkers ، Bioinformatics