Y Luo1, Y Wu2, Y Peng2, X Liu2, J Bie3, S Li3. 1. Department of Gynaecology and Obstetrics, Nanchong Central Hospital, North Sichuan Medical College, NO.97 South Renmin Road, Nanchong, 637000, Sichuan, China. lyxnanchong@126.com. 2. Department of Gynaecology and Obstetrics, Nanchong Central Hospital, North Sichuan Medical College, NO.97 South Renmin Road, Nanchong, 637000, Sichuan, China. 3. Department of Oncology, Nanchong Central Hospital, North Sichuan Medical College, NO.112 Chenshou Road Nanchong, Nanchong, 637000, Sichuan, China.
Abstract
OBJECTIVE: This study aims to identify corresponding differentially expressed genes in cervical cancer by comparing gene expression profiles between normal and cervical cancer samples. METHOD: To identify differentially expressed genes in cervical cancer, two groups of Affymetrix microarray data available online were analyzed. One group consisted of 43 carcinomatous cervical epithelial cell samples, and the other was composed of 17 healthy cervical epithelial cell samples, both from the Amerindian. R packages-GO.db, KEGG.db and KEGGREST were used to detect GO categories and KEGG pathways with significant overrepresentation in differentially expressed genes comparing with the whole genome. Cytoscape was utilized to construct biological networks. RESULTS: By comparing gene expression profile of normal and cervical cancer samples, 122 differentially expressed genes were identified including 46 up-regulated genes and 76 down-regulated genes. Using the identified differentially expressed genes, a large and a small biological network was constructed. In addition, 402 GO biological processes and 9 KEGG pathways were over-represented. Top significant biological processes included cell cycle and cell proliferation. Moreover, top significant KEGG pathways were oocyte meiosis, cell cycle and progesterone-mediated oocyte maturation. Most importantly, CDK1 frequently appeared in these processes and pathways, which indicated its significant role in the progression of cervical cancer. CONCLUSION: CDK1 plays a comprehensive role in mediating genetic networks implicated in the progression of cervical cancer. Novel therapeutics targeting CDK1 or its related pathways might help improve prognosis of advanced stage cervical cancer.
OBJECTIVE: This study aims to identify corresponding differentially expressed genes in cervical cancer by comparing gene expression profiles between normal and cervical cancer samples. METHOD: To identify differentially expressed genes in cervical cancer, two groups of Affymetrix microarray data available online were analyzed. One group consisted of 43 carcinomatous cervical epithelial cell samples, and the other was composed of 17 healthy cervical epithelial cell samples, both from the Amerindian. R packages-GO.db, KEGG.db and KEGGREST were used to detect GO categories and KEGG pathways with significant overrepresentation in differentially expressed genes comparing with the whole genome. Cytoscape was utilized to construct biological networks. RESULTS: By comparing gene expression profile of normal and cervical cancer samples, 122 differentially expressed genes were identified including 46 up-regulated genes and 76 down-regulated genes. Using the identified differentially expressed genes, a large and a small biological network was constructed. In addition, 402 GO biological processes and 9 KEGG pathways were over-represented. Top significant biological processes included cell cycle and cell proliferation. Moreover, top significant KEGG pathways were oocyte meiosis, cell cycle and progesterone-mediated oocyte maturation. Most importantly, CDK1 frequently appeared in these processes and pathways, which indicated its significant role in the progression of cervical cancer. CONCLUSION:CDK1 plays a comprehensive role in mediating genetic networks implicated in the progression of cervical cancer. Novel therapeutics targeting CDK1 or its related pathways might help improve prognosis of advanced stage cervical cancer.
Entities:
Keywords:
Cervical cancer development; Gene expression profiles; Systematic analysis
Authors: Stuart Emanuel; Catherine A Rugg; Robert H Gruninger; Ronghui Lin; Angel Fuentes-Pesquera; Peter J Connolly; Steven K Wetter; Beth Hollister; Walter W Kruger; Cheryl Napier; Linda Jolliffe; Steven A Middleton Journal: Cancer Res Date: 2005-10-01 Impact factor: 12.701
Authors: Yali Zhai; Kevin B Hotary; Bin Nan; F Xavier Bosch; Nubia Muñoz; Stephen J Weiss; Kathleen R Cho Journal: Cancer Res Date: 2005-08-01 Impact factor: 12.701