Screening For Breast Cancer Recurrence Related Genes And Their Systems Biology Analysis

Breast cancer is the world’s first serious malignant neoplasm which threaten the health of female. In the past ten years, not only in developed countries the incidence of breast cancer is rising, but also in developing countries it has exceeded the cervical cancer and became the first factor of cancer deaths in women, and the age of being breast cancer in younger trend. Although the medical and treatment for breast cancer having improved significantly, the mortality rate of breast cancer has been reduced significantly, and the quality of patients’ life has been improved. However, the recurrence and metastasis of breast cancer still happened in a large number, which have been reported in the literature, the rate of breast cancer recurrence phenomenon is about 10%~30% of patients with breast cancer, and the survival rate of patients with breast cancer recurrence will be significantly reduced, and seriously affecting the rate of patients’ life.Systems biology is a new cross subject based on information, integration and interference. It consists all composition of genes, protein etc. and explores the interconnection of new biology. Systemic and integrative studies became the characteristics of this subject. It is supported by bioinformatics research, calculation of informatics research, genomics etc. Bioinformatics as the basic part of systems biology, plays an important role in the whole development of life science. It combined the research of life science, information science, statistics and computer science and other technological means, including a massive database, a variety of online analysis software, exploring the mysteries of biology. Not only for the data mining of human disease associated gene, statistical analysis, functional annotation and pathway analysis and visualization of network analysis, but also for a deeper understanding of malignant tumor.Four Databases which are the Pubmed/Medline database, Web of Science database, CNKI database and WANFANG Data, has been used to sum up the genes associated with breast cancer recurrence, bibliometrics and bioinformatics were used to analyze the interaction relationship between breast cancer recurrence related genes and micro RNA, and to build ce RNAs network, thus it will help us understand the function and role of recurrence of breast cancer related gene pathway further, and explore the occurrence and development of breast cancer recurrence of molecular mechanisms. The results will provide information and theoretical basis, and a new research direction for the prevention and treatment of breast cancer recurrence.PartⅠ: Screening for Breast Cancer Recurrence Related Genes and their Bioinformatics Analysis Background and AimsBreast cancer is one of Chinese most common malignant neoplasm, and its incidence is still rising by 3% a year, and the age of onset is more and more younger. Breast cancer is not only a hormone dependent malignancies, but also a complex multi-gene, systemic, lifelong disease, genetic and environmental factors are involved in the occurrence and development of breast cancer, and breast cancer recurrence let us recognize that this disease has the same invasive and dangerous as other malignant tumors, it will greatly reduce the breast cancer patients’ survival time, it is a serious threat to the majority of female patients’ life. The research of gene and molecular level of knowledge and understanding of the tumor make us know more function of genes and tumor cell signal pathway.The aims of this study: based on the bioinformatics analysis of breast cancer recurrence related gene, understanding the function of genes associated with breast cancer recurrence, signal pathways and the expression of protein interactions between network composition, providing theoretical basis for exploring the molecular mechanism of breast cancer recurrence. MethodAll published articles during the period of 2001.01 to 2015.01 on breast cancer recurrence related gene in both Chinese and English articles were retrieved through certain criteria, such as taking human as the defining species. Note Express software and Excel 2010 software were used to manage literatures and analyze bibliometrics, and summarize the breast cancer recurrence related genes. GATHER(Gene annotation tool to help explain relationships)online software(http://gather.genome.duke.edu/)was used to analyze the GO(Gene Ontology)function of breast cancer recurrence related gene. JEPETTO(Java Enrichment of Pathways Extended To Topology)(Version 1.3.1) which is a plug-in of Cytoscape was used to analyze the KEGG pathway. STRING(Search Tool for the Retrieval of Interacting Genes/Proteins) online software(http://string.embl.del/) was used to draw the interaction network diagram of related gene expressing protein, and Cytoscape software was used to select the key genes by calculating the network and each node topology characteristics. Results1. Through the analysis 196 articles were included, the number of research papers related genes in breast cancer recurrence significantly increased in the past 14 years, and 59 breast cancer recurrence related genes were screened.2. GO analysis showed that the breast cancer recurrence related genes and their products function mainly concentrated in the regulation of cell cycle, collagen catabolism, cell proliferation and negative regulation of cell physiological process etc.3. KEGG pathway analysis suggested that breast cancer recurrence related genes in the signal path were mainly involved in pathway in cancer, P53 signaling pathway, Erb B signaling pathway, VEGF signaling pathway etc.4. By the STING software, we screened for 52 related gene expression protein interaction, successfully constructed related gene expression protein mapping.5. By the Cytoscape software, we selected 7 key genes of the breast cancer recurrence, respectively, TP53(tumor protein p53), VEGFA(vascular endothelial growth factor A), ESR1(estrogen receptor 1), ERBB2(Her2,human epidermal growth factor receptor2), CDH1(cadherin 1, type 1, E-cadherin(epithelial)), PTEN(phosphatase and tensin homolog)and MMP2(Matrix metalloproteinases). Conclusions1. 59 breast cancer recurrence related genes were screened, and the GO functional annotations and KEGG pathway analysis were successfully applied, it will provide theoretical basis for the molecular mechanism of breast cancer recurrence.2. The network graph of breast cancer recurrence related genes was successfully constructed, and 7 key genes were selected. The results suggested that TP53 gene might be involved in the recurrence of breast cancer, which helps us to study the interaction of related genes and close relationship. It will provide a research direction for the prevention, diagnosis and treatment of breast cancer recurrence. Part Ⅱ: Screening for breast cancer recurrence related mi RNA and ce RNAs regulatory network construction Background and AimsNoncoding RNA(nc RNA) is a kind of RNA which does not code for proteins, but plays an important role in molecular function, contains micro RNA(mi RNA) and long non coding RNA( lnc RNA). Micro RNA is the floorboard of endogenous non coding small RNA, it can be combined with target gene silencing complex so as to guide the degradation of m RNA or block its translation, inhibit the post transcriptional gene expression effect. Compting endogenous RNA(ce RNA) hypothesis reveals a new mechanism of RNA interaction. Lnc RNA is one of the ce RNAs, which plays an important role in the regulation of cell cycle, differentiation epigenetics. Systems biology is a science of information, which sums the information of genomics and transcriptomics. ce RNAs network control chart was constructed for the whole process of life such as cell growth, proliferation, apoptosis and tumor. The aims of this study: The database of experimental verification and target gene prediction were used to verify mi RNA- target genes, mi RNA-lnc RNA related to the recurrence of breast cancer. Bioinformatics analysis was used to construct ce RNAs network control chart, for screening the important mi RNA related with the breast cancer recurrence. MethodPOMA algorithm, and three experiment database: mi Records(http://mirecords. biolead.org/), Tar Base(http://diana.cslab.ece.ntua.gr/tarbase/), mi RTar Base(http:// mirtarbase. mbc.nctu.edu.tw/) and 2 target gene prediction database: star Base star Base(http://www.targetscan.org/)and mi RDB(http://www.microrna.org/microrna/ home.do)were used to screen breast cancer recurrence related mi RNAs with Z = α / β(α: the number of target gene represents only regulate by the mi RNA. β:the number of all target gene represents regulate by the mi RNA). Z_score > 0.1 was taken as a threshold. mi RNAs which Z_score is bigger than 0.1 were choosen as a significant mi RNA related with breast cancer recurrence. star Base v2.0 database was used to forecast the interrelated mi RNA-lnc RNA. Cytoscape3.2.1 software was used to build breast cancer recurrence related ce RNAs network diagram for the important mi RNAs. Results1. POMA screening prediction model and the three experiments provide database and two target gene prediction database were used to select 104 breast cancer recurrence related mi RNAs. By the Z score, we got 12 important related mi RNAs which are mi R-204, mi R-29 c, mi R-29 b, mi R-146 a, mi R-9, mi R-491, mi R-222, mi R-27 b, mi R-324-5p, mi R-124, mi R-141 and mi R-122. 16 corresponding relationships between mi RNA and target genes were found.2. By Lnc RNABase which is an embedded databae in the star Base database predicted the relationship of mi RNA and lnc RNA, found 12 lnc RNA related to breast cancer recurrence which are NEAT1(nuclear paraspeckle assembly transcript 1)、MALAT1(metastasis associated lung adenocarcinoma transcript 1)、XIST(X inactive specific transcript)、HCG18(HLA complex group 18)、KCNQ1OT1(KCNQ1 opposite strand/antisense transcript 1)、SNHG7(small nucleolar RNA host gene 7)、HOTAIR(HOX transcript antisense RNA)、TUG1(taurine up-regulated 1)、GAS5(growth arrest-specific 5)、HCP5(HLA complex P5)、H19(H19, imprinted maternally expressed transcript)、MIAT(myocardial infarction associated transcript). 40 corresponding relationships between mi RNA and lnc RNA were got.3.The ce RNAs network diagram was successfully built by using the Cytoscape software, and 4 important mi RNAs related with breast cancer recurrence were screened out, they were mi R-204, mi R-29 c, mi R-29 b, and mi R-146 a. ConclusionsThe ce RNAs network diagram associated with breast cancer recurrence was built successfully, and 4 important mi RNAs related with breast cancer recurrence were screened out.