Yun-De Dou1, Tao Huang1, Qun Wang1, Xin Shu1, Shi-Gang Zhao1, Lei Li2, Tao Liu3, Gang Lu4, Wai-Yee Chan4, Hong-Bin Liu5. 1. Center for Reproductive Medicine, Shandong University, National Research Center for Assisted Reproductive Technology and Reproductive Genetics, PR China; The Key Laboratory of Reproductive Endocrinology (Shandong University), Ministry of Education, PR China. 2. Center for Reproductive Medicine, Shandong University, National Research Center for Assisted Reproductive Technology and Reproductive Genetics, PR China; The Key Laboratory of Reproductive Endocrinology (Shandong University), Ministry of Education, PR China; Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA. 3. Center for Reproductive Medicine, Tai'an Central Hospital, Tai'an, PR China. 4. The Chinese University of Hong Kong-Shandong University Joint Laboratory on Reproductive Genetics, School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong SAR, PR China. 5. Center for Reproductive Medicine, Shandong University, National Research Center for Assisted Reproductive Technology and Reproductive Genetics, PR China; The Key Laboratory of Reproductive Endocrinology (Shandong University), Ministry of Education, PR China; The Chinese University of Hong Kong-Shandong University Joint Laboratory on Reproductive Genetics, School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong SAR, PR China. Electronic address: liuhongbin@sduivf.com.
Abstract
PURPOSE: Characterization of the genetic landscapes of familial ovarian cancer through integrated analysis of microRNA and mRNA by partial least squares (PLS) and Monte Carlo technique based on genome-wide association studies (GWAS). METHODS: The miRNA and mRNA transcriptional data in familial ovarian cancer were characterized from the Gene Expression Omnibus (GEO) database. The miRNA and mRNA expression profiles in peripheral blood lymphocytes (PBLs) of 74 familial ovarian cancer patients and 47 control subjects were analyzed with the integration of partial least squares (PLS) and Monte Carlo techniques. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were also performed. RESULTS: Total of 16 miRNA-mRNA pairs were identified with the target gene prediction results of miRNAs and mRNAs. An innovated miRNA-mRNA integrated network was constructed in which 6 downregulated miRNAs and 1 upregulated miRNAs were included. KEGG and GO pathway enrichment analysis revealed over-representation of dysregulated miRNAs in various biological processes especially in cancer pathology. Hsa-miR-34b played a pivotal role in this network and interacted with other miRNAs. Hsa-miR-136 and hsa-miR-335 were associated with p53 and Erk1/2 pathways and tumor suppressors, such as PTEN. CONCLUSIONS: The results from this research provide insights on miRNA-mRNA networks and offer new tools for studying transcriptional variants in familial ovarian cancer.
PURPOSE: Characterization of the genetic landscapes of familial ovarian cancer through integrated analysis of microRNA and mRNA by partial least squares (PLS) and Monte Carlo technique based on genome-wide association studies (GWAS). METHODS: The miRNA and mRNA transcriptional data in familial ovarian cancer were characterized from the Gene Expression Omnibus (GEO) database. The miRNA and mRNA expression profiles in peripheral blood lymphocytes (PBLs) of 74 familial ovarian cancerpatients and 47 control subjects were analyzed with the integration of partial least squares (PLS) and Monte Carlo techniques. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were also performed. RESULTS: Total of 16 miRNA-mRNA pairs were identified with the target gene prediction results of miRNAs and mRNAs. An innovated miRNA-mRNA integrated network was constructed in which 6 downregulated miRNAs and 1 upregulated miRNAs were included. KEGG and GO pathway enrichment analysis revealed over-representation of dysregulated miRNAs in various biological processes especially in cancer pathology. Hsa-miR-34b played a pivotal role in this network and interacted with other miRNAs. Hsa-miR-136 and hsa-miR-335 were associated with p53 and Erk1/2 pathways and tumor suppressors, such as PTEN. CONCLUSIONS: The results from this research provide insights on miRNA-mRNA networks and offer new tools for studying transcriptional variants in familial ovarian cancer.