Abstract
BackgroundThe Cancer Genome Atlas (TCGA) Data portal provides a platform for researchers to search, download, and analysis data generated by TCGA. The objective of this study was to explore the molecular mechanism of ovarian cancer pathogenesis.MethodsMicroarray data of ovarian cancer were downloaded from TCGA database, and Limma package in R language was used to identify the differentially expressed genes (DEGs) between ovarian cancer and normal samples, followed by the function and pathway annotations of the DEGs. Next, NetBox software was used to for the gene-gene interaction (GGI) network construction and the corresponding modules identification, and functions of genes in the modules were screened using DAVID.ResultsOur studies identified 332 DEGs, including 146 up-regulated genes which mainly involved in the cell cycle related functions and cell cycle pathway, and 186 down-regulated genes which were enriched in extracellular region par function, and Ether lipid metabolism pathway. GGI network was constructed by 127 DEGs and their significantly interacted 209 genes (LINKERs). In the top 10 nodes ranked by degrees in the network, 5 were LINKERs. Totally, 7 functional modules in the network were selected, and they were enriched in different functions and pathways, such as mitosis process, DNA replication and DNA double-strand synthesis, lipid synthesis processes and metabolic pathways. AR, BRCA1, TFDP1, FOXM1, CDK2, and DBF4 were identified as the transcript factors of the 7 modules.Conclusionour data provides a comprehensive bioinformatics analysis of genes, functions, and pathways which may be involved in the pathogenesis of ovarian cancer.
Highlights
Ovarian cancer remains a significant public health burden, with the highest mortality rate of all the gynecological cancer, accounting for about three percent of all cancers in women [1]
Functions that related to cell cycle, nucleotide binding, and mitosis of up-regulated differentially expressed genes (DEGs) were enriched, while cell cycle pathway was enriched
LINKERs such as AHCTF1, B9D2, CASC5, and Coiled-coil domain containing 99 (CCDC99) were found to be closely related with DEGs in the databases (p < 0.05)
Summary
Ovarian cancer remains a significant public health burden, with the highest mortality rate of all the gynecological cancer, accounting for about three percent of all cancers in women [1]. Despite advances in surgery and chemotherapy, ovarian cancer in the majority of women will return and become resistant to further treatments [2]. Bioinformatics analysis provides a first large scale integrative view of the aberrations in high grade serous ovarian cancer, with surprisingly simple mutational spectrum [3]. Previous studies have examined the role of genetic variation associated with the susceptibility, progression, treatment response, and survival of ovarian cancer [4,5]. It has been shown that high grade ovarian cancer is characterized by TP53 mutations in almost all tumors [6]. Genes related with cell cycle, lipid metabolism and cytoskeletal structure are screened as the treatment targets for ovarian cancer [8]. The objective of this study was to explore the molecular mechanism of ovarian cancer pathogenesis
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