OBJECTIVES: Lung cancer is one of the most common malignant tumors, but the etiology is not yet clear. Our study aims to deepen the understandings about the mechanisms of lung cancer via screening relevant key genes and functional pathways.
MATERIALS AND METHODS: Microarray data set was collected and differentially expressed genes (DEGs) were selected out. KEGG pathway analysis and Gene Ontology (GO) enrichment analysis were performed for the DEGs. Interaction networks were constructed for the lung cancer-related DEGs with information from Human Protein Reference Database (HRPD) to screen out potential biomarkers.
RESULTS: Functional annotation revealed that cell cycle, DNA replication, immune system, and signal molecules and interactions were significantly over-represented in all the DEGs, suggesting their close involvement in the development of lung cancer. 40 genes with high degree, betweenness and clustering coefficient were identified from the interaction network. 26 out of them are known cancer genes according to the database F-census. Besides, 4 biomarkers were revealed through analyzing their interactions with oncogenes.
CONCLUSIONS: Our study not only advances the understandings about the molecular mechanisms of lung cancer, but also provides several potential biomarkers for clinical use.Free PDF Download
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
To cite this article
Q.-p. Wu, J.-x. Min, L. Jiang, J.-m. Li, K. Yao
Screening of biomarkers for lung cancer with gene expression profiling data
Eur Rev Med Pharmacol Sci
Vol. 17 - N. 23