Identification of key molecular markers in epithelial ovarian cancer by integrated bioinformatics analysis.

OBJECTIVE: The current research was aimed to identify candidate genes associated with development and progression of epithelial ovarian carcinoma using bioinformatics analysis. MATERIALS AND METHODS: We screened and validated candidate genes associated with carcinogenesis and development of epithelial ovarian carcinoma via bioinformatic analysis in three microarray datasets (GSE14407, GSE29450, and GSE54388) downloaded from the Gene Expression Omnibus (GEO) database. RESULTS: Our bioinformatic analysis identified 514 differentially expressed genes (DEGs) and nine candidate hub genes (CCNB1, CDK1, BUB1, CDC20, CCNA2, BUB1B, AURKA, RRM2, and TTK). Survival analysis using the Kaplan-Meier plotter showed that high expression levels of seven candidate genes (CCNB1, RRM2, BUB1, CCNA2, AURKA, CDK1, and BUB1B) were associated with poor overall survival (OS). Gene Expression Profiling Interactive Analysis (GEPIA) revealed a higher expression level of these seven candidate genes in ovarian carcinoma samples than in normal ovarian samples. Immunostaining results from the Human Protein Atlas (HPA) database suggested that the protein expression levels of CCNB1, CCNA2, AURKA, and CDK1 were increased in ovarian cancer tissues. No difference was observed in RRM2 protein expression level between normal ovarian and ovarian cancer samples. Oncomine analysis revealed an association between the expression patterns of BUB1B, CCNA2, AURKA, CCNB1, CDK1, and BUB1 and patient clinicopathological information. Finally, six genes, namely CCNB1, CCNA2, AURKA, BUB1, BUB1B, and CDK1, were identified as hub genes and a transcription factor (TF)-gene regulatory network was constructed to identify TFs, including POLR2A, ZBTB11, KLF9, and ELF1, that were implicated in regulating these hub genes. CONCLUSION: Six significant hub DEGs associated with a poor prognosis in epithelial ovarian cancer were identified. These could be potential biomarkers for ovarian cancer patients.

Prognostic value of pre-therapy C-reactive protein level in diffuse large B-cell lymphoma: a meta-analysis.

The current meta-analysis was conducted aiming to explore the association between pre-therapy CRP and DLBCL survival outcomes. We retrieved previously published studies available from PubMed, EMBASE, Web of Science, The Cochrane Library, Wanfang databases, and China National Knowledge Infrastructure databases by June 2017. A total of 12 studies composed of 3000 patients were included in this study according to our selection criteria. The pooled HRs of elevated pre-therapy CRP were 2.66 (95% CI = 1.95-3.64, p < .001) for OS while 2.19 (95% CI = 1.72-2.78, p < .001) for PFS. Sensitivity analysis and subgroup analysis indicated that CRP remained a prognostic biomarker for OS and PFS. The pooled HR was 3.01 (95% CI = 2.38-3.81, p < .001) for OS if excluding 3 heterogeneous studies, and the heterogeneity decreased from 59.6% to 11.2%. Our study revealed that pre-therapy CRP can be a potential prognostic marker for patients with DLBCL.