%0 Journal Article %T Identification of Key Candidate Genes and Pathways for Relationship between Ovarian Cancer and Diabetes Mellitus Using Bioinformatical Analysis %J Asian Pacific Journal of Cancer Prevention %I West Asia Organization for Cancer Prevention (WAOCP), APOCP's West Asia Chapter. %Z 1513-7368 %A Sun, Yi %A Xiaoyan, Huang %A Yun, Liu %A Chaoqun, Liu %A Jialing, Wen %A Liu, Yang %A Yingqi, Zhao %A Peipei, Yi %A Junjun, Peng %A Yuanming, Lu %D 2019 %\ 01/01/2019 %V 20 %N 1 %P 145-155 %! Identification of Key Candidate Genes and Pathways for Relationship between Ovarian Cancer and Diabetes Mellitus Using Bioinformatical Analysis %K Bioinformatics analysis %K Microarray %K Ovarian Cancer %K Diabetes Mellitus %R 10.31557/APJCP.2019.20.1.145 %X Ovarian cancer is one of the three major gynecologic cancers in the world. The aim of this study is to find therelationship between ovarian cancer and diabetes mellitus by using the genetic screening technique. By GEO databasequery and related online tools of analysis, we analyzed 185 cases of ovarian cancer and 10 control samples fromGSE26712, and a total of 379 different genes were identified, including 104 up-regulated genes and 275 down-regulatedgenes. The up-regulated genes were mainly enriched in biological processes, including cell adhesion, transcription ofnucleic acid and biosynthesis, and negative regulation of cell metabolism. The down-regulated genes were enriched incell proliferation, migration, angiogenesis and macromolecular metabolism. Protein-protein interaction was analyzedby network diagram and module synthesis analysis. The top ten hub genes (CDC20, H2AFX, ENO1, ACTB, ISG15,KAT2B, HNRNPD, YWHAE, GJA1 and CAV1) were identified, which play important roles in critical signalingpathways that regulate the process of oxidation-reduction reaction and carboxylic acid metabolism. CTD analysisshowed that the hub genes were involved in 1,128 distinct diseases (bonferroni-corrected P<0.05). Further analysis bydrawing the Kaplan-Meier survival curve indicated that CDC20 and ISG15 were statistically significant (P<0.05). Inconclusion, glycometabolism was related to ovarian cancer and genes and proteins in glycometabolism could serve aspotential targets in ovarian cancer treatment. %U https://journal.waocp.org/article_81666_21ec5aabfa24eba655698d1694814448.pdf