Development of a Novel Autophagy-related Prognostic Signature for Serous Ovarian Cancer

An, Yuanyuan; Bi, Fangfang; You, Yue; Liu, Xinhui; Yang, Qing

doi:10.7150/jca.25587

International Journal of Biological Sciences

International Journal of Medical Sciences

Theranostics

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J Cancer 2018; 9(21):4058-4071. doi:10.7150/jca.25587 This issue Cite

Research Paper

Development of a Novel Autophagy-related Prognostic Signature for Serous Ovarian Cancer

Yuanyuan An, Fangfang Bi, Yue You, Xinhui Liu, Qing Yang^✉

Department of Gynecology and Obstetrics, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, P.R. China

Citation:

An Y, Bi F, You Y, Liu X, Yang Q. Development of a Novel Autophagy-related Prognostic Signature for Serous Ovarian Cancer. J Cancer 2018; 9(21):4058-4071. doi:10.7150/jca.25587. https://www.jcancer.org/v09p4058.htm

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Abstract

Purpose: Considerable evidence suggests that autophagy plays a crucial role in the biological processes of ovarian cancer. The aim of this study was to develop a novel autophagy-related prognostic signature for serous ovarian cancer.

Methods: A univariate Cox proportional regression model was used to analyze mRNA microarray and clinical data in The Cancer Genome Atlas (TCGA) for the purpose of selecting autophagy-related prognostic genes. A multivariate Cox proportional regression model and the survival analysis were used to develop an eight-gene prognostic signature. The multivariate Cox and stratification analysis suggested that this signature was an independent prognostic factor for serous ovarian cancer patients. Bioinformatics functions were investigated by a principal components analysis and gene set enrichment analysis (GSEA). Finally, the correlation between the prognostic signature and gene mutation status was further analyzed in serous ovarian cancer, and especially with regard to the mutation status of BRCA1 and BRCA2 (BRCA1/2) genes.

Results: Distinctly different autophagy-related gene expression profiles were identified in normal ovarian tissues and serous ovarian cancer tissues. We profiled an autophagy-related gene set and identified eight genes with significant prognostic values for serous ovarian cancer. Subsequently, an autophagy-related ovarian cancer risk signature was constructed, and patients at a high-risk or low-risk for poor prognosis were identified based on their signature. High-risk patients had significantly shorter overall survival (OS) and disease-free survival (DFS) times than low-risk patients. GSEA results suggested an enhanced intensity of autophagy regulation in high-risk patients when compared with low-risk patients. When studied as an independent prognostic factor for serous ovarian cancer, the significant prognostic value of this signature could be seen in the stratified cohorts. For clinical use, we developed a nomogram that included the prognostic classifier and seven clinical risk factors. Additionally, we identified the 10 most frequently mutated genes found in serous ovarian cancer patients, and analyzed them for their differences in high-risk and low-risk patients. Among 293 patients, 62 had BRCA1/2 gene mutations, and this result was significantly correlated with the autophagy-related prognostic signature.

Conclusions: Our findings suggest that the eight-gene autophagy-related signature could serve as an independent prognostic indicator for cases of serous ovarian cancer.

Keywords: Autophagy, Serous ovarian cancer, Prognostic signature, TCGA

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APA

An, Y., Bi, F., You, Y., Liu, X., Yang, Q. (2018). Development of a Novel Autophagy-related Prognostic Signature for Serous Ovarian Cancer. Journal of Cancer, 9(21), 4058-4071. https://doi.org/10.7150/jca.25587.

ACS

An, Y.; Bi, F.; You, Y.; Liu, X.; Yang, Q. Development of a Novel Autophagy-related Prognostic Signature for Serous Ovarian Cancer. J. Cancer 2018, 9 (21), 4058-4071. DOI: 10.7150/jca.25587.

NLM

CSE

An Y, Bi F, You Y, Liu X, Yang Q. 2018. Development of a Novel Autophagy-related Prognostic Signature for Serous Ovarian Cancer. J Cancer. 9(21):4058-4071.

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