Multiple chromosomal aberrations, nucleotide substitutions, and epigenetic modifications may occur in human cancer cells, which drive malignant transformation. The Cancer Genome Atlas (TCGA) project aims to promote large-scale multi-dimensional analysis of these molecular characteristics in human cancer and rapidly provide data to researchers. In this study, we introduce four flow paths of the production of TCGA data, the collections of various cancer types, the data category and level, and the standardized pipeline of data analysis, as well as several existing data analytical tools. We used ovarian cancer as an example to introduce the application of the TCGA data in the analyses of mutation, copy number, analysis, and expression. We summarized the important findings of glioblasto-ma by TCGA teams.

AIM: To investigate the role of GATA-3 in the pathogenesis of airway inflammation in a Wistar rat asthma model.METHODS: The Wistar rat asthma model was made with conventional method and animals were divided into five groups(10 rats in each group): asthma group(A group),dexamethasone group(D group),antisense oligonucleotide group(AS group),nonsense oligonucleotide group(NS group) and normal control group(N group).Antisense,nonsense oligonucleotide were administered intranasally,and the dexamethasone was injected intraperitoneally.The airway inflammation was observed with HE staining method.The GATA-3 positive cells were stained immunohistochemically.The GATA-3 mRNA expression in pulmonary tissue was investigated with RT-PCR.The GATA-3 protein in pulmonary tissue was detected by Western blotting.RESULTS: In contrast to N group,the expression of GATA-3 mRNA, protein and the amount of inflammatory cells in pulmonary tissue in group A were increased significantly(P

OBJECTIVETo explore the pathogenesis of ovarian cancer from the perspective of molecular genetic variation and changes in mRNA expression profiles.

METHODThe data of DNA copy number and mRNA expression profiles of high-grade serious ovarian cancer were obtained from TCGA. The significant copy number variation regions were identified using the bioinformatics tool GISTIC, and the differentially expressed genes in these regions were identified using the samr package of SAM. The selected genes were subjected to bioinformatics analysis using GSEA tools.

RESULTSGISTIC analysis identified 45 significant copy number amplification regions in ovarian cancer, and SAM and Fisher's exact test found that 40 of these genes showed altered expression levels. GSEA enrichment analysis revealed that most of these genes were reported in several published studies describing genetic study of tumorigenesis.

CONCLUSIONAn integrative bioinformatics study of DNA copy number variation data and microarray data can identify genes involved in tumor pathogenesis. and offer new clues for studying early diagnosis and therapeutic target of ovarian cancer.

Computational Biology ; DNA Copy Number Variations ; Female ; Gene Expression Regulation, Neoplastic ; Humans ; Ovarian Neoplasms ; genetics

Objective To explore the pathogenesis of ovarian cancer from the perspective of molecular genetic variation and changes in mRNA expression profiles. Method The data of DNA copy number and mRNA expression profiles of high-grade serious ovarian cancer were obtained from TCGA. The significant copy number variation regions were identified using the bioinformatics tool GISTIC, and the differentially expressed genes in these regions were identified using the samr package of SAM. The selected genes were subjected to bioinformatics analysis using GSEA tools. Results GISTIC analysis identified 45 significant copy number amplification regions in ovarian cancer, and SAM and Fisher's exact test found that 40 of these genes showed altered expression levels. GSEA enrichment analysis revealed that most of these genes were reported in several published studies describing genetic study of tumorigenesis. Conclusion An integrative bioinformatics study of DNA copy number variation data and microarray data can identify genes involved in tumor pathogenesis. and offer new clues for studying early diagnosis and therapeutic target of ovarian cancer.

Objective To explore the pathogenesis of ovarian cancer from the perspective of molecular genetic variation and changes in mRNA expression profiles. Method The data of DNA copy number and mRNA expression profiles of high-grade serious ovarian cancer were obtained from TCGA. The significant copy number variation regions were identified using the bioinformatics tool GISTIC, and the differentially expressed genes in these regions were identified using the samr package of SAM. The selected genes were subjected to bioinformatics analysis using GSEA tools. Results GISTIC analysis identified 45 significant copy number amplification regions in ovarian cancer, and SAM and Fisher's exact test found that 40 of these genes showed altered expression levels. GSEA enrichment analysis revealed that most of these genes were reported in several published studies describing genetic study of tumorigenesis. Conclusion An integrative bioinformatics study of DNA copy number variation data and microarray data can identify genes involved in tumor pathogenesis. and offer new clues for studying early diagnosis and therapeutic target of ovarian cancer.