Screening and Identification of Potential Prognostic Biomarkers in Adrenocortical Carcinoma
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...
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Frontiers Media S.A.
2019-09-01
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Series: | Frontiers in Genetics |
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Online Access: | https://www.frontiersin.org/article/10.3389/fgene.2019.00821/full |
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record_format |
Article |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wen-Hao Xu Wen-Hao Xu Junlong Wu Junlong Wu Jun Wang Jun Wang Fang-Ning Wan Fang-Ning Wan Hong-Kai Wang Hong-Kai Wang Da-Long Cao Da-Long Cao Yuan-Yuan Qu Yuan-Yuan Qu Hai-Liang Zhang Hai-Liang Zhang Ding-Wei Ye Ding-Wei Ye |
spellingShingle |
Wen-Hao Xu Wen-Hao Xu Junlong Wu Junlong Wu Jun Wang Jun Wang Fang-Ning Wan Fang-Ning Wan Hong-Kai Wang Hong-Kai Wang Da-Long Cao Da-Long Cao Yuan-Yuan Qu Yuan-Yuan Qu Hai-Liang Zhang Hai-Liang Zhang Ding-Wei Ye Ding-Wei Ye Screening and Identification of Potential Prognostic Biomarkers in Adrenocortical Carcinoma Frontiers in Genetics adrenocortical carcinoma bioinformatics analysis biomarker prognosis network module |
author_facet |
Wen-Hao Xu Wen-Hao Xu Junlong Wu Junlong Wu Jun Wang Jun Wang Fang-Ning Wan Fang-Ning Wan Hong-Kai Wang Hong-Kai Wang Da-Long Cao Da-Long Cao Yuan-Yuan Qu Yuan-Yuan Qu Hai-Liang Zhang Hai-Liang Zhang Ding-Wei Ye Ding-Wei Ye |
author_sort |
Wen-Hao Xu |
title |
Screening and Identification of Potential Prognostic Biomarkers in Adrenocortical Carcinoma |
title_short |
Screening and Identification of Potential Prognostic Biomarkers in Adrenocortical Carcinoma |
title_full |
Screening and Identification of Potential Prognostic Biomarkers in Adrenocortical Carcinoma |
title_fullStr |
Screening and Identification of Potential Prognostic Biomarkers in Adrenocortical Carcinoma |
title_full_unstemmed |
Screening and Identification of Potential Prognostic Biomarkers in Adrenocortical Carcinoma |
title_sort |
screening and identification of potential prognostic biomarkers in adrenocortical carcinoma |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Genetics |
issn |
1664-8021 |
publishDate |
2019-09-01 |
description |
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. |
topic |
adrenocortical carcinoma bioinformatics analysis biomarker prognosis network module |
url |
https://www.frontiersin.org/article/10.3389/fgene.2019.00821/full |
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doaj-896b01e19672472287dd9d2708ac3fdd2020-11-25T02:15:22ZengFrontiers Media S.A.Frontiers in Genetics1664-80212019-09-011010.3389/fgene.2019.00821444622Screening and Identification of Potential Prognostic Biomarkers in Adrenocortical CarcinomaWen-Hao Xu0Wen-Hao Xu1Junlong Wu2Junlong Wu3Jun Wang4Jun Wang5Fang-Ning Wan6Fang-Ning Wan7Hong-Kai Wang8Hong-Kai Wang9Da-Long Cao10Da-Long Cao11Yuan-Yuan Qu12Yuan-Yuan Qu13Hai-Liang Zhang14Hai-Liang Zhang15Ding-Wei Ye16Ding-Wei Ye17Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, ChinaDepartment of Oncology, Shanghai Medical College, Fudan University, Shanghai, ChinaDepartment of Urology, Fudan University Shanghai Cancer Center, Shanghai, ChinaDepartment of Oncology, Shanghai Medical College, Fudan University, Shanghai, ChinaDepartment of Urology, Fudan University Shanghai Cancer Center, Shanghai, ChinaDepartment of Oncology, Shanghai Medical College, Fudan University, Shanghai, ChinaDepartment of Urology, Fudan University Shanghai Cancer Center, Shanghai, ChinaDepartment of Oncology, Shanghai Medical College, Fudan University, Shanghai, ChinaDepartment of Urology, Fudan University Shanghai Cancer Center, Shanghai, ChinaDepartment of Oncology, Shanghai Medical College, Fudan University, Shanghai, ChinaDepartment of Urology, Fudan University Shanghai Cancer Center, Shanghai, ChinaDepartment of Oncology, Shanghai Medical College, Fudan University, Shanghai, ChinaDepartment of Urology, Fudan University Shanghai Cancer Center, Shanghai, ChinaDepartment of Oncology, Shanghai Medical College, Fudan University, Shanghai, ChinaDepartment of Urology, Fudan University Shanghai Cancer Center, Shanghai, ChinaDepartment of Oncology, Shanghai Medical College, Fudan University, Shanghai, ChinaDepartment of Urology, Fudan University Shanghai Cancer Center, Shanghai, ChinaDepartment of Oncology, Shanghai Medical College, Fudan University, Shanghai, ChinaObjective: 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.https://www.frontiersin.org/article/10.3389/fgene.2019.00821/fulladrenocortical carcinomabioinformatics analysisbiomarkerprognosisnetwork module |