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Immunohistochemical validation of overexpressed genes identified by global expression microarrays in adrenocortical carcinoma reveals potential predictive and prognostic biomarkers.

ABSTRACT: Adrenocortical carcinoma (ACC) is a rare malignancy with a poor prognosis. The aim of this study was to identify novel protein signatures that would predict clinical outcomes in a large cohort of patients with ACC based on data from previous gene expression microarray studies.A tissue microarray was generated from the paraffin tissue blocks of 61 patients with clinical outcomes data. Selected protein biomarkers based on previous gene expression microarray profiling studies were selected, and immunohistochemistry staining was performed. Staining patterns were correlated with clinical outcomes, and a multivariate analysis was undertaken to identify potential biomarkers of prognosis.Median overall survival was 45 months, with a 5-year overall survival rate of 44%. Median disease-free survival was 58 months, with a 5-year disease-free survival rate of 44%. The proliferation marker Ki-67 and DNA topoisomerase TOP2A were associated with significantly poorer overall and disease-free survival. The results also showed strong correlation between the transcriptional repressor EZH2 and TOP2A expression, suggesting a novel role for EZH2 as an additional marker of prognosis. In contrast, increased expression of the BARD1 protein, with its ubiquitin ligase function, was associated with significantly improved overall and disease-free survival, which has yet to be documented for ACC.We present novel biomarkers that assist in determining prognosis for patients with ACC. Ki-67, TOP2A, and EZH2 were all significantly associated with poorer outcomes, whereas BARD1 was associated with improved overall survival. It is hoped that these biomarkers may help tailor additional therapy and be potential targets for directed therapy.

SUBMITTER: Ip JC

PROVIDER: S-EPMC4350804 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature

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Immunohistochemical validation of overexpressed genes identified by global expression microarrays in adrenocortical carcinoma reveals potential predictive and prognostic biomarkers.

Ip Julian C Y JC Pang Tony C Y TC Glover Anthony R AR Soon Patsy P Zhao Jing Ting JT Clarke Stephen S Robinson Bruce G BG Gill Anthony J AJ Sidhu Stan B SB

The oncologist 20150205 3

<h4>Background</h4>Adrenocortical carcinoma (ACC) is a rare malignancy with a poor prognosis. The aim of this study was to identify novel protein signatures that would predict clinical outcomes in a large cohort of patients with ACC based on data from previous gene expression microarray studies.<h4>Materials and methods</h4>A tissue microarray was generated from the paraffin tissue blocks of 61 patients with clinical outcomes data. Selected protein biomarkers based on previous gene expression mi ...[more]

PMID: 25657202

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Project 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.

Dataset Information

Immunohistochemical validation of overexpressed genes identified by global expression microarrays in adrenocortical carcinoma reveals potential predictive and prognostic biomarkers.

Publications

Immunohistochemical validation of overexpressed genes identified by global expression microarrays in adrenocortical carcinoma reveals potential predictive and prognostic biomarkers.

Similar Datasets

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