Dataset Information

Screening and identification of key biomarkers in adrenocortical carcinoma based on bioinformatics analysis.

ABSTRACT: Adrenocortical carcinoma (ACC) is a rare malignancy with a poor prognosis. The presently available understanding of the pathogenesis of ACC is incomplete and the treatment options for patients with ACC are limited. Gene marker identification is required for accurate and timely diagnosis of the disease. In order to identify novel candidate genes associated with the occurrence and progression of ACC, the microarray datasets, GSE12368 and GSE19750, were obtained from Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified, and functional enrichment analysis was performed. A protein-protein interaction network (PPI) was constructed to identify significantly altered modules, and module analysis was performed using Search Tool for the Retrieval of Interacting Genes and Cytoscape. A total of 228 DEGs were screened, consisting of 29 up and 199 downregulated genes. The enriched functions and pathways of the DEGs primarily included 'cell division', 'regulation of transcription involved in G1/S transition of mitotic cell cycle', 'G1/S transition of mitotic cell cycle', 'p53 signaling pathway' and 'oocyte meiosis'. A total of 14 hub genes were identified, and biological process analysis revealed that these genes were significantly enriched in cell division and mitotic cell cycle. Furthermore, survival analysis revealed that AURKA, TYMS, GINS1, RACGAP1, RRM2, EZH2, ZWINT, CDK1, CCNB1, NCAPG and TPX2 may be involved in the tumorigenesis, progression or prognosis of ACC. In conclusion, the 14 hub genes identified in the present study may aid researchers in elucidating the molecular mechanisms associated with the tumorigenesis and progression of ACC, and may be powerful and promising candidate biomarkers for the diagnosis and treatment of ACC.

SUBMITTER: Xing Z

PROVIDER: S-EPMC6781718 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Screening and identification of key biomarkers in adrenocortical carcinoma based on bioinformatics analysis.

Xing Zengmiao Z Luo Zuojie Z Yang Haiyan H Huang Zhenxing Z Liang Xinghuan X

Oncology letters 20190906 5

Adrenocortical carcinoma (ACC) is a rare malignancy with a poor prognosis. The presently available understanding of the pathogenesis of ACC is incomplete and the treatment options for patients with ACC are limited. Gene marker identification is required for accurate and timely diagnosis of the disease. In order to identify novel candidate genes associated with the occurrence and progression of ACC, the microarray datasets, GSE12368 and GSE19750, were obtained from Gene Expression Omnibus. Differ ...[more]

PMID: 31611976

Similar Datasets

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.

Project description:BackgroundPapillary renal cell carcinoma (PRCC) is the most common type of renal cell carcinoma after clear cell renal cell carcinoma (ccRCC). Its pathological classification is controversial, and its molecular mechanism is poorly understood. Therefore, the identification of key genes and their biological pathways is of great significance to elucidate the molecular mechanisms of PRCC occurrence and progression.MethodsThe PRCC-related datasets GSE7023, GSE48352 and GSE15641 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, and gene ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Cytoscape and STRING were used to construct the protein-protein interaction network (PPI) and perform module analysis to identify hub genes and key pathways. A heatmap of hub genes was constructed using the UCSC cancer genomics browser. Overall survival and recurrence-free survival of patients stratified by the expression levels of hub genes were analysed using Kaplan-Meier Plotter. The online database UALCAN was applied to analyse gene expression based on tissue type, stage, subtype and race.ResultsA total of 214 DEGs, specifically, 205 downregulated genes and 9 upregulated genes, were identified. The DEGs were mainly enriched in angiogenesis, kidney development, oxidation-reduction process, metabolic pathways, etc. The 17 hub genes identified were mainly enriched in the biological processes of angiogenesis, cell adhesion, platelet degranulation, and leukocyte transendothelial migration. Survival analysis showed that EGF, KDR, CXCL12, REN, PECAM1, CDH5, THY1, WT1, PLAU and DCN might be related to the carcinogenesis, metastasis or recurrence of PRCC. UALCAN analysis showed that low expression of PECAM1 and PLAU in PRCC tissues was related to stage, subtype and race.ConclusionsThe DEGs and hub genes identified in the present study provide insight into the specific molecular mechanisms of PRCC occurrence and development and may be potential molecular markers and therapeutic targets for the accurate classification and efficient diagnosis and treatment of PRCC.

Dataset Information

Screening and identification of key biomarkers in adrenocortical carcinoma based on bioinformatics analysis.

Publications

Screening and identification of key biomarkers in adrenocortical carcinoma based on bioinformatics analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets