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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Genet. | doi: 10.3389/fgene.2019.00821

Screening and identification of potential prognostic biomarkers in adrenocortical carcinoma

 Wen-Hao Xu1, 2,  Junlong Wu1, 2,  Jun Wang1, 2, Fang-Ning Wan1, 2, Hong-Kai Wang1, 2, Da-Long Cao1, 2,  Yuanyuan Qu1, 2*, Hai-Liang Zhang1, 2* and Dingwei Ye1, 2*
  • 1Fudan University Shanghai Cancer Center, China
  • 2Fudan University Shanghai Medical College, China

Objective: Adrenocortical carcinoma (ACC) is a rare but aggressive malignant cancer that has been attracting growing attention over recent decades. This study aims to integrate protein interaction networks with gene expression profiles to identify potential biomarkers with prognostic value in silico.
Methods: Three microarray data sets were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) according to the normalization annotation information. Enrichment analyses were utilized to describe biological functions. A protein–protein interaction network (PPI) of the DEGs was developed, and the modules were analyzed using STRING and Cytoscape. LASSO Cox regression was used to identify independent prognostic factors. The Kaplan–Meier method for the integrated expression score was applied to analyze survival outcomes. A receiver operating characteristic (ROC) curve was constructed with area under curve (AUC) analysis to determine the diagnostic ability of the candidate biomarkers.
Results: A total of 150 DEGs and 24 significant hub genes with functional enrichment were identified as candidate prognostic biomarkers. LASSO Cox regression suggested that ZWINT, PRC1, CDKN3, CDK1 and CCNA2 were independent prognostic factors in ACC. In multivariate Cox analysis, the integrated expression scores of the modules showed statistical significance in predicting disease-free survival (DFS, P=0.019) and overall survival (OS, P<0.001). Meanwhile, ROC curves were generated to validate the ability of the Cox model to predict prognosis. The AUC index for the integrated genes scores was 0.861 (P<0.0001).
Conclusion: In conclusion, the present study identifies DEGs and hub genes that may be involved in poor prognosis and early recurrence of ACC. The expression levels of ZWINT, PRC1, CDKN3, CDK1 and CCNA2 are of high prognostic value, and may help us understand better the underlying carcinogenesis or progression of ACC. Further studies are required to elucidate molecular pathogenesis and alteration in signaling pathways for these genes in ACC.

Keywords: Adrenocortical Carcinoma, Bioinformatics analysis, biomarker, prognosis, Network module

Received: 21 Dec 2018; Accepted: 08 Aug 2019.

Copyright: © 2019 Xu, Wu, Wang, Wan, Wang, Cao, Qu, Zhang and Ye. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Dr. Yuanyuan Qu, Fudan University Shanghai Cancer Center, Shanghai, China, quyy1987@163.com
Prof. Hai-Liang Zhang, Fudan University Shanghai Cancer Center, Shanghai, China, zhanghl918@163.com
Prof. Dingwei Ye, Fudan University Shanghai Cancer Center, Shanghai, China, dwyeli@163.com