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Identification of Key Candidate Genes and Pathways for Relationship between Ovarian Cancer and Diabetes Mellitus Using Bioinformatical Analysis

Ovarian cancer is one of the three major gynecologic cancers in the world. The aim of this study is to find the relationship between ovarian cancer and diabetes mellitus by using the genetic screening technique. By GEO database query and related online tools of analysis, we analyzed 185 cases of ova...

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Autores principales: Sun, Yi, Xiaoyan, Huang, Yun, Liu, Chaoqun, Liu, Jialing, Wen, Liu, Yang, Yingqi, Zhao, Peipei, Yi, Junjun, Peng, Yuanming, Lu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: West Asia Organization for Cancer Prevention 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6485580/
https://www.ncbi.nlm.nih.gov/pubmed/30678426
http://dx.doi.org/10.31557/APJCP.2019.20.1.145
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author Sun, Yi
Xiaoyan, Huang
Yun, Liu
Chaoqun, Liu
Jialing, Wen
Liu, Yang
Yingqi, Zhao
Peipei, Yi
Junjun, Peng
Yuanming, Lu
author_facet Sun, Yi
Xiaoyan, Huang
Yun, Liu
Chaoqun, Liu
Jialing, Wen
Liu, Yang
Yingqi, Zhao
Peipei, Yi
Junjun, Peng
Yuanming, Lu
author_sort Sun, Yi
collection PubMed
description Ovarian cancer is one of the three major gynecologic cancers in the world. The aim of this study is to find the relationship between ovarian cancer and diabetes mellitus by using the genetic screening technique. By GEO database query and related online tools of analysis, we analyzed 185 cases of ovarian cancer and 10 control samples from GSE26712, and a total of 379 different genes were identified, including 104 up-regulated genes and 275 down-regulated genes. The up-regulated genes were mainly enriched in biological processes, including cell adhesion, transcription of nucleic acid and biosynthesis, and negative regulation of cell metabolism. The down-regulated genes were enriched in cell proliferation, migration, angiogenesis and macromolecular metabolism. Protein-protein interaction was analyzed by 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 signaling pathways that regulate the process of oxidation-reduction reaction and carboxylic acid metabolism. CTD analysis showed that the hub genes were involved in 1,128 distinct diseases (bonferroni-corrected P<0.05). Further analysis by drawing the Kaplan-Meier survival curve indicated that CDC20 and ISG15 were statistically significant (P<0.05). In conclusion, glycometabolism was related to ovarian cancer and genes and proteins in glycometabolism could serve as potential targets in ovarian cancer treatment.
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spelling pubmed-64855802019-05-13 Identification of Key Candidate Genes and Pathways for Relationship between Ovarian Cancer and Diabetes Mellitus Using Bioinformatical Analysis Sun, Yi Xiaoyan, Huang Yun, Liu Chaoqun, Liu Jialing, Wen Liu, Yang Yingqi, Zhao Peipei, Yi Junjun, Peng Yuanming, Lu Asian Pac J Cancer Prev Research Article Ovarian cancer is one of the three major gynecologic cancers in the world. The aim of this study is to find the relationship between ovarian cancer and diabetes mellitus by using the genetic screening technique. By GEO database query and related online tools of analysis, we analyzed 185 cases of ovarian cancer and 10 control samples from GSE26712, and a total of 379 different genes were identified, including 104 up-regulated genes and 275 down-regulated genes. The up-regulated genes were mainly enriched in biological processes, including cell adhesion, transcription of nucleic acid and biosynthesis, and negative regulation of cell metabolism. The down-regulated genes were enriched in cell proliferation, migration, angiogenesis and macromolecular metabolism. Protein-protein interaction was analyzed by 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 signaling pathways that regulate the process of oxidation-reduction reaction and carboxylic acid metabolism. CTD analysis showed that the hub genes were involved in 1,128 distinct diseases (bonferroni-corrected P<0.05). Further analysis by drawing the Kaplan-Meier survival curve indicated that CDC20 and ISG15 were statistically significant (P<0.05). In conclusion, glycometabolism was related to ovarian cancer and genes and proteins in glycometabolism could serve as potential targets in ovarian cancer treatment. West Asia Organization for Cancer Prevention 2019 /pmc/articles/PMC6485580/ /pubmed/30678426 http://dx.doi.org/10.31557/APJCP.2019.20.1.145 Text en Copyright: © Asian Pacific Journal of Cancer Prevention http://creativecommons.org/licenses/BY-SA/4.0 This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
spellingShingle Research Article
Sun, Yi
Xiaoyan, Huang
Yun, Liu
Chaoqun, Liu
Jialing, Wen
Liu, Yang
Yingqi, Zhao
Peipei, Yi
Junjun, Peng
Yuanming, Lu
Identification of Key Candidate Genes and Pathways for Relationship between Ovarian Cancer and Diabetes Mellitus Using Bioinformatical Analysis
title Identification of Key Candidate Genes and Pathways for Relationship between Ovarian Cancer and Diabetes Mellitus Using Bioinformatical Analysis
title_full Identification of Key Candidate Genes and Pathways for Relationship between Ovarian Cancer and Diabetes Mellitus Using Bioinformatical Analysis
title_fullStr Identification of Key Candidate Genes and Pathways for Relationship between Ovarian Cancer and Diabetes Mellitus Using Bioinformatical Analysis
title_full_unstemmed Identification of Key Candidate Genes and Pathways for Relationship between Ovarian Cancer and Diabetes Mellitus Using Bioinformatical Analysis
title_short Identification of Key Candidate Genes and Pathways for Relationship between Ovarian Cancer and Diabetes Mellitus Using Bioinformatical Analysis
title_sort identification of key candidate genes and pathways for relationship between ovarian cancer and diabetes mellitus using bioinformatical analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6485580/
https://www.ncbi.nlm.nih.gov/pubmed/30678426
http://dx.doi.org/10.31557/APJCP.2019.20.1.145
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