Genome-scale analysis identifies NEK2, DLGAP5 and ECT2 as promising diagnostic and prognostic biomarkers in human lung cancer
Abstract This study aims to identify promising biomarkers for the early detection of lung cancer and evaluate the prognosis of lung cancer patients. Genome-wide mRNA expression data obtained from the Gene Expression Omnibus (GSE19188, GSE18842 and GSE40791), including 231 primary tumor samples and 2...
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| Autores principales: | , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2017
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/0f15c54d952b407e9ccc6da6024cb1a6 |
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| Sumario: | Abstract This study aims to identify promising biomarkers for the early detection of lung cancer and evaluate the prognosis of lung cancer patients. Genome-wide mRNA expression data obtained from the Gene Expression Omnibus (GSE19188, GSE18842 and GSE40791), including 231 primary tumor samples and 210 normal samples, were used to discover differentially expressed genes (DEGs). NEK2, DLGAP5 and ECT2 were found to be highly expressed in tumor samples. These results were experimentally confirmed by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). The elevated expression of the three candidate genes was also validated using the Cancer Genome Atlas (TCGA) datasets, which consist of 349 tumor and 58 normal tissues. Furthermore, we performed receiver operating characteristics (ROC) analysis to assess the diagnostic value of these lung cancer biomarkers, and the results suggested that NEK2, DLGAP5 and ECT2 expression levels could robustly distinguish lung cancer patients from normal subjects. Finally, Kaplan-Meier analysis revealed that elevated NEK2, DLGAP5 and ECT2 expression was negatively correlated with both overall survival (OS) and relapse-free survival (RFS). Taken together, these findings indicate that these three genes might be used as promising biomarkers for the early detection of lung cancer, as well as predicting the prognosis of lung cancer patients. |
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