Identification of target gene and prognostic evaluation for lung adenocarcinoma using gene expression meta-analysis, network analysis and neural network algorithms

Gurudeeban Selvaraj, Satyavani Kaliamurthi, Aman Chandra Kaushik, Abbas Khan, Yong-Kai Wei, William C. Cho, Keren Gu, Dong-Qing Wei
  • Journal of Biomedical Informatics, October 2018, Elsevier
  • DOI: 10.1016/j.jbi.2018.09.004

Identification of target gene and prognostic evaluation for LUAD

What is it about?

Lung adenocarcinoma (LUAD) is a heterogeneous disease with poor survival in the advanced stage and a high incidence rate in the world. Novel drug targets are urgently required to improve patient treatment. Therefore, we aimed to identify therapeutic targets for LUAD based on protein-protein and protein-drug interaction network analysis with neural network algorithms using mRNA expression profiles.

Why is it important?

A comprehensive meta-analysis of selective non-small cell lung cancer (NSCLC) mRNA expression profile datasets from Gene Expression Omnibus were used to identify potential biomarkers and the molecular mechanisms related to the prognosis of NSCLC patients. Using the Network Analyst tool, based on combined effect size (ES) methods, we recognized 6566 differentially expressed genes (DEGs), which included 3036 downregulated and 3530 upregulated genes linked to NSCLC patient survival. ClueGO, a Cytoscape plugin, was exploited to complete the function and pathway enrichment analysis, which disclosed “regulated exocytosis”, “purine nucleotide binding”, “pathways in cancer”, and “cell cycle” between exceptionally supplemented terms. Enrichr, a web tool examination, demonstrated “early growth response protein 1 (EGR-1)”, “hepatocyte nuclear factor 4α (HNF4A)”, “mitogen-activated protein kinase 14 (MAP3K14)”, and “cyclin-dependent kinase 1 (CDK1)” to be among the most prevalent TFs and kinases associated with NSCLC. Our meta-analysis identified that MAPK1 and aurora kinase (AURKA) are the most obvious class of hub nodes. Furthermore, protein-drug interaction network and neural network algorithms identified candidate drugs such as phosphothreonine and 4-(4-methylpiperazin-1-yl)-n-[5-(2-thienylacetyl)-1,5-dihydropyrrolo[3,4-c]pyrazol-3-yl] benzamide and for the targets MAPK1 and AURKA, respectively.

Perspectives

Dr Gurudeeban Selvaraj
Concordia University

Our study has identified novel candidate biomarkers, pathways, transcription factors (TFs), and kinases associated with NSCLC prognosis, as well as drug candidates, which may assist treatment strategy for NSCLC patients.

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http://dx.doi.org/10.1016/j.jbi.2018.09.004

The following have contributed to this page: Dr Gurudeeban Selvaraj and Dr Satyavani Kaliamurthi