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Journal of Ovarian Research
Published in: Journal of Ovarian Research 1/2017

Open Access 01-12-2017 | Research

Bioinformatics analysis to screen the key prognostic genes in ovarian cancer

Authors: Li Li, Shengyun Cai, Shengnan Liu, Hao Feng, Junjie Zhang

Published in: Journal of Ovarian Research | Issue 1/2017

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Abstract

Background

Ovarian cancer (OC) is a gynecological oncology that has a poor prognosis and high mortality. This study is conducted to identify the key genes implicated in the prognosis of OC by bioinformatic analysis.

Methods

Gene expression data (including 568 primary OC tissues, 17 recurrent OC tissues, and 8 adjacent normal tissues) and the relevant clinical information of OC patients were downloaded from The Cancer Genome Atlas database. After data preprocessing, cluster analysis was conducted using the ConsensusClusterPlus package in R. Using the limma package in R, differential analysis was performed to identify feature genes. Based on Kaplan-Meier (KM) survival analysis, prognostic seed genes were selected from the feature genes. After key prognostic genes were further screened by cluster analysis and KM survival analysis, they were performed functional enrichment analysis and multivariate survival analysis. Using the survival package in R, cox regression analysis was conducted for the microarray data of GSE17260 to validate the key prognostic genes.

Results

A total of 3668 feature genes were obtained, among which 75 genes were identified as prognostic seed genes. Then, 25 key prognostic genes were screened, including AXL, FOS, KLF6, WDR77, DUSP1, GADD45B, and SLIT3. Especially, AXL and SLIT3 were enriched in ovulation cycle. Multivariate survival analysis showed that the key prognostic genes could effectively differentiate the samples and were significantly associated with prognosis. Additionally, GSE17260 confirmed that the key prognostic genes were associated with the prognosis of OC.

Conclusion

AXL, FOS, KLF6, WDR77, DUSP1, GADD45B, and SLIT3 might affect the prognosis of OC.
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Metadata
Title
Bioinformatics analysis to screen the key prognostic genes in ovarian cancer
Authors
Li Li
Shengyun Cai
Shengnan Liu
Hao Feng
Junjie Zhang
Publication date
01-12-2017
Publisher
BioMed Central
Published in
Journal of Ovarian Research / Issue 1/2017
Electronic ISSN: 1757-2215
DOI
https://doi.org/10.1186/s13048-017-0323-6

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