Gurudeeban Selvaraj1, Satyavani Kaliamurthi1, Aman Chandra Kaushik2, Abbas Khan2, Yong-Kai Wei3, William C Cho4, Keren Gu1, Dong-Qing Wei5. 1. Center of Interdisciplinary Sciences-Computational Life Sciences, College of Food Science and Engineering, Henan University of Technology, Zhengzhou, China; College of Chemistry, Chemical Engineering, and Environment, Henan University of Technology, Zhengzhou, China. 2. Department of Bioinformatics, The State Key Laboratory of Microbial Metabolism, College of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China. 3. College of Science, Henan University of Technology, Zhengzhou, China. 4. Department of Clinical Oncology, Queen Elizabeth Hospital, Kowloon, Hong Kong. 5. Center of Interdisciplinary Sciences-Computational Life Sciences, College of Food Science and Engineering, Henan University of Technology, Zhengzhou, China; College of Science, Henan University of Technology, Zhengzhou, China; Department of Bioinformatics, The State Key Laboratory of Microbial Metabolism, College of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China. Electronic address: dqwei@sjtu.edu.cn.
Abstract
BACKGROUND: 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. RESULTS: 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. CONCLUSION: 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.
BACKGROUND: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. RESULTS: 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 NSCLCpatients. 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 NSCLCpatient 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. CONCLUSION: 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 NSCLCpatients.
Authors: Su Yon Jung; Jeanette C Papp; Eric M Sobel; Matteo Pellegrini; Herbert Yu; Zuo-Feng Zhang Journal: Am J Cancer Res Date: 2020-09-01 Impact factor: 6.166
Authors: Yidi Wang; Yaxuan Wang; Kenan Li; Yabing Du; Kang Cui; Pu Yu; Tengfei Zhang; Hong Liu; Wang Ma Journal: Biosci Rep Date: 2020-10-30 Impact factor: 3.840