BACKGROUND & OBJECTIVE: Ovarian cancer is the leading cause of death among gynecological malignancies. Up to now, little is known about specific tumor-suppressor genes or oncogenes involved in the ovarian cancer genesis. Thus isolation of new candidate genes and characterization of their role in ovarian cancer genesis will be helpful for understanding the molecular mechanisms and developing protocols for early diagnosis and therapy of ovarian carcinomas. This study was developed to screen and identify genes related to ovarian carcinomas. METHODS: Modified mRNA differential display PCR and reverse Northern dot blot analysis were used to screen and identify different displayed genes between ovarian carcinoma tissue and normal ovarian tissue. The genes were sequenced and analyzed by bioinformatics software. With these gene fragments as probes, in situ hybridization was used to characterize specific gene expression difference between ovarian cancer tissues and normal ovarian tissues. RESULTS: Of 12 differentially expressed genes, 5 were novel genes, 3 were chromosome genomic repeat sequences and the other 4 were known human genes. Furthermore, in situ hybridization analysis indicated that four genes, ZNF361, PSMA2, OCRC13 (a novel gene on chromosome 1) and OCRC4 (a novel gene on chromosome 9) were highly expressed in 36 samples of ovarian cancer tissues but not in 16 samples of normal ovarian tissues (P< 0.05). More interestingly, the gene OCRC4, with 99% sequence homology to mouse spindlin, a member of gene family specially expressed during gametogenesis, was highly expressed in 19 samples of ovarian cancer from total 36 cases (53%), but not expressed in control samples. CONCLUSION: Our observations indicate that the four genes ZNF361, PSMA2, OCRC4, and OCRC13 may be ovarian cancer related genes. It is speculated that novel gene OCRC4 might also be a member of specific gene family, which plays roles during early period of embryo development and tumorigenesis.
BACKGROUND & OBJECTIVE:Ovarian cancer is the leading cause of death among gynecological malignancies. Up to now, little is known about specific tumor-suppressor genes or oncogenes involved in the ovarian cancer genesis. Thus isolation of new candidate genes and characterization of their role in ovarian cancer genesis will be helpful for understanding the molecular mechanisms and developing protocols for early diagnosis and therapy of ovarian carcinomas. This study was developed to screen and identify genes related to ovarian carcinomas. METHODS: Modified mRNA differential display PCR and reverse Northern dot blot analysis were used to screen and identify different displayed genes between ovarian carcinoma tissue and normal ovarian tissue. The genes were sequenced and analyzed by bioinformatics software. With these gene fragments as probes, in situ hybridization was used to characterize specific gene expression difference between ovarian cancer tissues and normal ovarian tissues. RESULTS: Of 12 differentially expressed genes, 5 were novel genes, 3 were chromosome genomic repeat sequences and the other 4 were known human genes. Furthermore, in situ hybridization analysis indicated that four genes, ZNF361, PSMA2, OCRC13 (a novel gene on chromosome 1) and OCRC4 (a novel gene on chromosome 9) were highly expressed in 36 samples of ovarian cancer tissues but not in 16 samples of normal ovarian tissues (P< 0.05). More interestingly, the gene OCRC4, with 99% sequence homology to mouse spindlin, a member of gene family specially expressed during gametogenesis, was highly expressed in 19 samples of ovarian cancer from total 36 cases (53%), but not expressed in control samples. CONCLUSION: Our observations indicate that the four genes ZNF361, PSMA2, OCRC4, and OCRC13 may be ovarian cancer related genes. It is speculated that novel gene OCRC4 might also be a member of specific gene family, which plays roles during early period of embryo development and tumorigenesis.
Authors: Umar Wazir; Mona M A W Orakzai; Tracey Amanda Martin; Wen G Jiang; Kefah Mokbel Journal: Cancer Genomics Proteomics Date: 2019 Mar-Apr Impact factor: 4.069
Authors: Michael Hubberten; Gregor Bochenek; Hong Chen; Robert Häsler; Ricarda Wiehe; Philip Rosenstiel; Søren Jepsen; Henrik Dommisch; Arne S Schaefer Journal: Eur J Hum Genet Date: 2018-08-14 Impact factor: 4.246
Authors: Lynn A Beer; Huan Wang; Hsin-Yao Tang; Zhijun Cao; Tony Chang-Wong; Janos L Tanyi; Rugang Zhang; Qin Liu; David W Speicher Journal: PLoS One Date: 2013-03-27 Impact factor: 3.240
Authors: Ziling Fang; Bo Cao; Jun-Ming Liao; Jun Deng; Kevin D Plummer; Peng Liao; Tao Liu; Wensheng Zhang; Kun Zhang; Li Li; David Margolin; Shelya X Zeng; Jianping Xiong; Hua Lu Journal: Elife Date: 2018-03-16 Impact factor: 8.140