Dataset Information

Single Gene Prognostic Biomarkers in Ovarian Cancer: A Meta-Analysis.

ABSTRACT:

Purpose

To discover novel prognostic biomarkers in ovarian serous carcinomas.

Methods

A meta-analysis of all single genes probes in the TCGA and HAS ovarian cohorts was performed to identify possible biomarkers using Cox regression as a continuous variable for overall survival. Genes were ranked by p-value using Stouffer's method and selected for statistical significance with a false discovery rate (FDR) <.05 using the Benjamini-Hochberg method.

Results

Twelve genes with high mRNA expression were prognostic of poor outcome with an FDR <.05 (AXL, APC, RAB11FIP5, C19orf2, CYBRD1, PINK1, LRRN3, AQP1, DES, XRCC4, BCHE, and ASAP3). Twenty genes with low mRNA expression were prognostic of poor outcome with an FDR <.05 (LRIG1, SLC33A1, NUCB2, POLD3, ESR2, GOLPH3, XBP1, PAXIP1, CYB561, POLA2, CDH1, GMNN, SLC37A4, FAM174B, AGR2, SDR39U1, MAGT1, GJB1, SDF2L1, and C9orf82).

Conclusion

A meta-analysis of all single genes identified thirty-two candidate biomarkers for their possible role in ovarian serous carcinoma. These genes can provide insight into the drivers or regulators of ovarian cancer and should be evaluated in future studies. Genes with high expression indicating poor outcome are possible therapeutic targets with known antagonists or inhibitors. Additionally, the genes could be combined into a prognostic multi-gene signature and tested in future ovarian cohorts.

SUBMITTER: Willis S

PROVIDER: S-EPMC4757072 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Single Gene Prognostic Biomarkers in Ovarian Cancer: A Meta-Analysis.

Willis Scooter S Villalobos Victor M VM Gevaert Olivier O Abramovitz Mark M Williams Casey C Sikic Branimir I BI Leyland-Jones Brian B

PloS one 20160217 2

<h4>Purpose</h4>To discover novel prognostic biomarkers in ovarian serous carcinomas.<h4>Methods</h4>A meta-analysis of all single genes probes in the TCGA and HAS ovarian cohorts was performed to identify possible biomarkers using Cox regression as a continuous variable for overall survival. Genes were ranked by p-value using Stouffer's method and selected for statistical significance with a false discovery rate (FDR) <.05 using the Benjamini-Hochberg method.<h4>Results</h4>Twelve genes with hi ...[more]

PMID: 26886260

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Project description:The association of several inflammation-based biomarkers [lymphocyte-to-monocyte, neutrophil-to-lymphocyte, and platelet-to-lymphocyte ratios (LMR, NLR, and PLR, respectively)] with the survival of epithelial ovarian cancer (EOC) patients has been extensively investigated in several systematic reviews and meta-analyses (MAs) of observational studies. The aim of this umbrella systematic review is to appraise all available results in published MAs that explored the association between these biomarkers and EOC outcomes. An umbrella systematic review of the current evidence for systemic inflammatory biomarkers in the peripheral blood of EOC patients was performed by searching several databases including PubMed/Medline and Web of Science. The quality of the MAs was appraised using the AMSTAR-2 tool as well as other qualitative criteria. The evidence was graded from convincing (Class I) to weak (Class IV). Our umbrella review appraised 17 MAs of retrospective studies (range: 7-16) with a number of enrolled patients ranging from 1,636 to 4,910 patients in each MA. All these MAs demonstrated that pretreatment high NLR and PLR, as well as low LMR, were independent predictors of poor overall survival and progression-free survival in EOC. Nearly all published MAs were conducted by Chinese researchers (16/17) and were redundant in their character. Another issue in these MAs is the absence of prior PROSPERO database registration as well as the earlier exclusion of the gray literature. On the other hand, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Meta-analyses Of Observational Studies in Epidemiology (MOOSE)-based reporting guidelines were used in nine out of the 17 MAs. A good number of MAs have transparently provided funding acknowledgment. The AMSTAR-2-based assessment showed low quality in 11 out of the 17 reviewed MAs. This negative rating was largely due to the absence of critical domains. Finally, all evaluated MAs were rated as Class III or IV (suggestive and weak, respectively). Despite the power of MAs in increasing sampling and precision, the quality of the current non-randomized evidence on this topic is still weak.Systematic review registrationPROSPERO, identifier CRD42020201493.

Dataset Information

Single Gene Prognostic Biomarkers in Ovarian Cancer: A Meta-Analysis.

Purpose

Methods

Results

Conclusion

Publications

Single Gene Prognostic Biomarkers in Ovarian Cancer: A Meta-Analysis.

Similar Datasets

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