Comprehensive Analysis Of Differently Expressed MicroRNAs Between Molecular Subtypes Of Breast Cancer And Functional Studies Of MicroRNA-9-5p

BackgroundBreast cancer is the most frequent type of cancer in women and it ranks second among all kinds of cancers. Although breast cancer just ranks as the fifth cause of death from cancer overall, it is the most frequent cause of cancer death in women in both developing and developed regions. For a long time, based on the traditional histological type, clinical and pathological staging, different breast cancer treatment options including surgical treatment, chemotherapy, radiotherapy, endocrine therapy or molecularly targeted treatment are selected. However, because of the high degree of heterogeneity, even patiens with the same histological type and TNM staging differ greatly in treatment sensitivity, metastasis, recurrence and survival prognosis. As a result, further classification of different breast cancers becomes one of the most critical job in fighting against breast cancer.With the rapid development of modern molecular biology, researchers began to realize that the different patterns of gene expression in breast cancer may lead to phenotypic diversity. High-throughput detection technologies such as gene microarrays make the analysis of genomic gene expression of different breast cancer feasible. Understanding of the molecular taxonomy of breast caner stems form classic mRNA profiling studies, which broadly sub-classified breast cancer into5major groups:Luminal A, Luminal B, Basal-like, HER2-positive, and normal-like tumors. Except the different gene expression patterns, there are significant difference in biological behavior, growth rate and the activity of specific signaling pathways between breast cancer molecular subtypes.However, the current number of clearly different molecular phenotypes observed among the breast cancers suggest that we are still far from having a complete picture of diversity of breast cancers. Therefore, researchers are still actively looking for new molecular markers of breast cancer. MicroRNAs (miRNAs) are a class of-22nucleotide noncoding small RNA molecules that regulate gene expression by targeting the3’-untranslated region of mRNAs with consequent degradation of target mRNA or inhibition of protein translation. A lot of evidence has shown that miRNA mis-expression correlate with various human cancers and indicates that miRNAs can function as tumour suppressors and oncogenes. miRNAs have been shown to repress the expression of important cancer-related genes and might prove useful in the diagnosis and treatment of cancer. Global studies of miRNA expression in normal breast tissues and tumors identified numerous miRNAs desregulated in breast cancer, and generated a signature ciscriminating between normal and malignant breast tissues. While other studies demonstrated that miRNA expression profiles may also be applied to classify breast tumors, such as Luminal A, Luminal B, Basal-like, HER2+, and normal-like.Differently expressed miRNAs among breast cancer molecular subtpes may play different biological functions by regulating target gene expression, deserving further study. The study of miRNA function depends on the identification of its target gene. Because of its short sequence characteristics, single miRNA can regulate multiple target gene, and single gene can be regulated by multiple miRNAs at the same time. Therefore, we speculate that differently expressed miRNAs and their target genes are bound to form an intricate regulatory network. As a result, it is really hard to describe the regulatory relationship between miRNAs and their target genes just by experiment methods.ObjectiveSo, this study intends to identify differently epxressed miRNAs among the molecular subtypes of breast cancer, establish a comprehensive method for analysis of cellular functions and signaling pathways affected by differently expressed miRNAs, explore the potential roles of miRNAs, and try to verify it in breast cancer cell lines.Contents There are mainly three parts in this study:Part1:Analysis of signaling pathways for differently expressed miRNAs between different ER status breast cancers.Methods:By literature metrological methods, aberrant expressed miRNAs were collected. By target prediction algorithm TargetScan, PicTar and miRanda, predicted target gene of miRNAs were acquired; by searching TarBase database, experiments validated target genes were acquired, and then all the target genes were collected. Using a two-step enrichment analysis, target gene sets of differently expressed miRNAs were built. Then, using DAVID database, Gene Ontology categories as well as biological functions and signaling pathways that are probably regulated by differently expressed microRNAs were analysed.Results:Total5sets of miRNAs including11,43,25,6and19respectively were collected. After miRNAs target gene prediction and a two-step data enrichment procedure,1553,1750,1905,1250and1826target genes of5miRNA sets were built. Gene Ontology analysis found these genes may involve in transcription, protein location and transport, RNA metabolism, cell cycle and apoptosis. Also,3BIOCARTA signal pathways correlating with ER status were found, including CARM1and regulation of the estrogen receptor, role of ERBB2in signal transduction and oncology, and activation of Src by protein-tyrosine phosphatase alpha.Conclusion:In this part, we acquired5sets of differently expressed miRNAs, and found that the number and categories of miRNAs differ greatly, deserving more reasearches. The two-step enrichment analysis could reduce the number of potential target genes effectively. Differently expressed miRNAs may play a role in various processes of the cell, and three BIOCARTA signalling pathways probably take part in the regulation of ER expression.Part2:In order to further rule out the confounding effects of HER2, Ki-67et al. we analyzed the miRNAs expression profiles of Basal and Luminal A breast cancers.Methods:A dataset of miRNAs expression profilings of Basal and Luminal A subtype of breast cancers was obtained from GEO database, and differently expressed miRNAs were acquired by analysis of BRB-ArrayTolls. Target genes were first acquired by intersection results of prediction software TargetScan and miRDB, and then collected with the experiment validated ones from TarBase database. And then Gene Ontology categories and pathways of target gene sets were further analyzed by DAVID database.Results:Primary data sets of miRNA micorarrays expression profiles from41Luminal A and15Basal breast cancers were selected in GEO database. Up-regulation of31miRNAs and down-regulation of23miRNAs were identified in Basal compared with Luminal A breast cancers (P≤0.001). Correspondingly, two gene sets of4916and3217target genes were collected, named L1and L2respectively. Further Geno Ontology analyses showed that different Geno Ontology categories were enriched between two target gene sets. There were35and39KEGG pathways (P≤0.05) were enriched separately in L1and L2. Also,5and9BIOCARTA pathways were enriched (P≤0.05).Conclusion:There are different miRNA expression patterns between Basal and Luminal A breast cancers. Different miRNA expression patterns can discriminate the subtpye of breast cancers. Function analysis indicated that differently expressed miRNAs may take part in quite different biological processes. Part3:We then selected miR-9-5p for function analysis in breast cancer cell lines.Methods:Expression of miR-9-5p was detected in normal human mammary epithelial cells MCF-10A and breast cancer cell line MCF-7, MDA-MB-231, and T47D by quantitative real-time PCR. Chemically synthesized miR-9-5p mimics and inhibitors were transfected into breast cancer cells by Lipofectamine2000. After transient transfection, MTT assay, cell cycle analysis and cell apoptosis analysis were utilized to show the efeect of over-expression or down-expresson of miR-9-5p on breast cancer cells, and wound-healing assay, transwell migration assay and transwell invasion assay were also conducted. At last, two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) and matrix assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS)were exploited to obtain protein expression profiles.Results:The results of quantitative real-time PCR showed that the epxression of miR-9-5p was decreased in all three breast cancer cell line examined, compared with the normal human mammary epithelial cells MCF-10A (F=51.307, P<0.001). After transient transfection of miR-9-5p mimics, the the epxression levels of miR-9-5p were increased by300-fold (t=-50.503, P<0.001) in MCF-7and100-fold (t=-63.598, P<0.001) in MDA-MB-231cells respectively. MTT assay results showed that miR-9-5p overexpression lead to the inhibition of cell growth rate both in MCF-7and MDA-MB-231cells. The results of cell cycle distribution showed that MCF-7cells transfected with miR-9-5p displayed an increased percentage of cells in G1phase (t=29.994, P<0.001) and decreased cell in S phase (t=-29.424, P<0.001).Cell apoptosis analysis indicated that although there is a minor increase of6%in early apoptosis rate (t=2.953, P=0.042), the late apoptosis rate did not change evidently (t=-2.289, P=0.084). Wound-healing assay showed that miR-9-5p overexpressing MCF-7cells had slower motility. Transwell migration and invasion assay found that miR-9-5p overexpression significantly decreased the migration ability and invasiveness of MDA-MB-231cells (t=10.823, P<0.001). In miR-9-5p overexressing MCF-7cells,2D-PAGE and MALDI-TOF MS analysis identified11differently expressed proteins compared with the MCF-7cells transfected with miRNA mimic negative control. There are8down-regulated proteins including OTUB1, PP4C, EIF3K, DCNL1, COPZ1, SSRD, SAHH, and GNAS2, while up-reregulated proteins includs HSPB1, TSG101, and B2MG.Conclusion:There is a reduced expression of miR-9-5p in breast cancer cell lines cmpared with normal human mammary epithelial cells. miR-9-5p displays tumor-suppressor like activity in breast cancer cells and reduced expression of OTUB1, PP4C, and EIF3K may contributes to this activity.