Abstract
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.
Highlights
Adrenocortical carcinoma (ACC) is a rare endocrine malignancy with an annual incidence of 0.7–2.0 per million people, accounting for an estimated 0.02% of all cancers (Wajchenberg et al, 2000; Kebebew et al, 2006; Kerkhofs et al, 2013)
We assessed differential expressed genes (DEGs) using three datasets hosted on the GEO platform
LASSO Cox regression suggested that ZW10 interactor (ZWINT), Protein Regulator of cytokinesis 1 (PRC1), Cyclin-dependent kinase inhibitor 3 (CDKN3), Cyclin dependent kinase 1 (CDK1) and CCNA2 are significant weighted prognostic factors, and that an integrated gene panel may serve as an independent penal in ACC samples
Summary
Adrenocortical carcinoma (ACC) is a rare endocrine malignancy with an annual incidence of 0.7–2.0 per million people, accounting for an estimated 0.02% of all cancers (Wajchenberg et al, 2000; Kebebew et al, 2006; Kerkhofs et al, 2013). ACC patients often face aggressive progression, with merely less than 35% of patients surviving 5 years after initial diagnosis (Else et al, 2014). The preferred treatment regimen for ACC is surgical resection of the primary tumor (Fassnacht et al, 2013). Almost half of ACC patients have disseminated metastasis, and approximately one-third of patients have locoregional metastases after surgery (Else et al, 2014). The first–line treatment, and the only ACCspecific medical therapy approved by the US Food and Drug Administration, is Mitotane, which is regularly used as an adjuvant agent in these patients (Else et al, 2014). A major concern of the therapeutic management with Mitotane is the risk of toxicity, which may lead to severe adrenal insufficiency (Paragliola et al, 2018)
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