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Identification of 10 Important Genes with Poor Prognosis in Non-Small Cell Lung Cancer Through Bioinformatical Analysis

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Journal of ISSN: 2581-7388 Inno Biomedical Research and Reviews Volume 3: 2 J Biomed Res Rev 2020 Identification of 10 Important Genes with Poor Prognosis in Non-Small Cell Lung Cancer through Bioinformatical Analysis Wenbin Wu†1 1Experiment Animal Center, Experiment Center for Science and Technology, Shanghai University of Traditional Chinese Medicine, PR China †2,3 Cheng Fang 2Center for Traditional Chinese Medicine and Immunology Research, Shanghai University of Traditional Chinese Medicine, PR China Chaochao Zhang1 3Department of Immunology and Pathogenic Biology, School of Basic Medical Nanhua Hu*4 Sciences, Shanghai University of Traditional Chinese Medicine, PR China 4Department of Hepatopancreatobiliary and Traditional Chinese Medicine, Minhang *2,3 Lixin Wang Branch, Fudan University Shanghai Cancer Center, PR China † These authors contributed equally to this work Abstract Article Information Objective: The lung cancer has become the most lethal cause of Article Type: Research Article cancer-related death in China and is responsible for more than 1 million Article Number: JBRR-143 deaths all over the world every year, especially non-small cell lung cancer Received Date: 16 October, 2020 (NSCLC). Although great advance in pharmaceutical therapies for lung Accepted Date: 11 November, 2020 Published Date: 18 November, 2020 the effective biomarkers in order to improve and predict the prognosis ofcancer lung patients, cancer patients.the overall The survival integrated is still bioinformaticalpoor. It is necessary analysis, to find as out a *Corresponding author: Lixin Wang, Center for useful tool to dig up the valuable clues, can be applied to search new Traditional Chinese Medicine and Immunology Research; effective therapeutic targets. Methods: In this work, we utilized four School of Basic Medical Sciences, Shanghai University of Traditional Chinese Medicine, Shanghai, 1200 Cai Lun NSCLC datasets (GSE18842, GSE31210, GSE33532 and GSE101929) Rd. Shanghai 201203, PR China. Tel: (+86) 21-5132-2148; from Gene Expression Omnibus (GEO) to analyze. We totally found E-mail: [email protected] that there were 162 differentially expressed genes (DEGs) in these four datasets, including 41 up-regulated genes and 121 down-regulated genes in NSCLC tissues. The analysis of gene ontology (GO) enrichment Nanhua Hu, Department of Hepatopancreatobiliary and Traditional Chinese Medicine, Minhang Branch, Fudan and Kyoto encyclopedia of genes and genomes (KEGG) pathway was University Shanghai Cancer Center, 106 Rui Li Rd. done by Database for Annotation, Visualization and Integrated Discovery Shanghai 200240, Shanghai, PR China. Tel: (+86) 21-6462- 9290; E-mail: [email protected] protein-protein interaction (PPI) network. Last, we further analyzed the (DAVID) software. Then, we identified 10 core oncogenes by constructing 10 core oncogenes through Kaplan Meier plotter online database and Citation: Wu W, Fang C, Zhang C, Hu N, Wang L (2020) Identification of 10 Important Genes with Poor Prognosis Results: We discovered 10 key oncogenes which were associated with in Non-Small Cell Lung Cancer through Bioinformatical theGene progression Expression and Profiling poor prognosis Interactive for NSCLC,Analysis including (GEPIA) ANLN, respectively. CCNA2, Analysis. J Biomed Res Rev Vol: 3, Issu: 2 (41-50). CDCA7, DEPDC1, DLGAP5, HMMR, KIAA0101, RRM2, TOP2A, and UBE2T. Conclusion: These 10 genes can be served as the therapeutic targets Copyright: © 2020 Wang L. This is an open-access article and useful prognostic biomarkers for NSCLC treatment. distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, Keywords: Non-small cell lung cancer; Bioinformatical analysis; distribution, and reproduction in any medium, provided the Prognostic biomarkers; Differentially expressed genes; Therapeutic original author and source are credited. targets. Introduction The lung cancer, which is the leading cause of cancer-related death in China and is responsible for more than 1 million deaths all over the world every year [1], can be divided two classes: non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). NSCLC accounts for approximately 85% of all lung cancer cases, including adenocarcinoma, www.innovationinfo.org squamous cell carcinoma and large cell carcinoma. Nowadays, incorporated 32 NSCLC tissues and 34 normal lung tissues. although great advance in pharmaceutical therapies for lung cancer patients, the overall survival is still poor. NSCLC has Screening of differentially expressed genes (DEGs) become the most lethal human cancer. Hence, it is necessary The DEGs between NSCLC tissues and normal lung tissues were screened by using the GEO2R online tools. The improve the prognosis of lung cancer patients. fold change value (FC) obtained for each genes was indicated to find out the effective therapeutic targets in order to as logFC in order to normalize the data derived from the Gene chip, as a proven technique, could make many same microarray platform [3]. We considered DEGs as slice data be produced and stored in public databases [2]. |logFC| >2 and adjust value < 0.05. Venn software online Therefore, we can explore a large number of valuable clues P (http://bioinformatics.psb.ugent.be/webtools/Venn/) was via these data. Meanwhile, the integrated bioinformatic used to analyze the DEGs among the above four datasets via results also can help us to further study and discover the checking the raw data in TXT format. In the present study, potential mechanism. In order to further discover the novel the DEGs with log FC > 2 was considered as an up-regulated targets for treating NSCLC, we applied the public gene chip gene, and the DEGs with log FC < -2 was regarded as a down- databases to carry on data mining. Through this study, we regulated gene. NSCLC, also it is useful for improving the survival and the DEGs gene ontology (GO) enrichment and Kyoto qualitycould find of life. out some new candidate therapeutic targets for encyclopedia of genes and genomes (KEGG) pathway analyses 4 databases related with non-small cell lung cancer from After screening the DEGs from the above four datasets, In the present study, as shown in figure 1, we chose Gene Expression Omnibus (GEO), including GSE18842, we performed the GO enrichment and KEGG pathway GSE31210, GSE33532 and GSE101929. First, we found that analyses using the Database for Annotation, Visualization there were 162 differentially expressed genes (DEGs) in and Integrated Discovery (DAVID) (https://david.ncifcrf. these four databases above, including 41 up-regulated genes gov/tools.jsp), which is designed to identify a huge number and 121 down-regulated genes in NSCLC tissues. Then, we of genes or proteins function [4]. GO analysis is used to integrate annotation data and provide tools access to core genes by establishing protein-protein interaction (PPI) all the data provided by the study, and identify unique did some other bioinformatic analyses and identified 10 biological properties of these datasets [5]. KEGG can core genes in NSCLC, we further analyzed the survival curve integrate the currently known protein interaction network network. In order to confirm the important role of these 10 and the DEGs expression between NSCLC tissues and normal information, including metabolism, genetic information lung tissues through Kaplan Meier plotter online database processing, environmental information related processes, and cell physiological process, etc [6]. We used DAVID to respectively. Taken above, these 10 DEGs were all related perform biological analyses of DEGs and visualize the DEGs and Gene Expression Profiling Interactive Analysis (GEPIA) with the prognosis of NSCLC. In conclusion, our bioinformatic enrichment of biological processes (BP), molecular functions study provides some additional useful biomarkers for NSCLC (MF), cellular components (CC) and pathways. P<0.05 was patients. These biomarkers can be considered as candidate therapeutic targets for NSCLC, and the results also supply some ideas for our further study. consideredProtein-protein as significant interaction difference. (PPI) network analysis Materials and Methods DEGs by using Search Tool for the Retrieval of Interacting Data source and preprocessing GenesPPI (STRING) network (https://string-db.org/),analysis was performed whichfor the is identifiedan online NCBI-GEO (https://www.ncbi.nlm.nih.gov/geo/) was software of interactions of genes and proteins. The PPI selected for our research, which is a free public database of network could be visualized by Cytoscape in order to examine the potential correlation between the DEGs (maximum microarray/gene profile. We used the key words (‘non-small Besides, the Molecular Complex Detection (MCODE) app cell lung cancer’ [All Fields] OR ‘lung adenocarcinomas’ innumber Cytoscape of interactors=0 was used to andanalyze confidence the modules score of≥0.4) the PPI[7]. [All Fields]) AND (‘human’ [Organism]) AND (‘Expression GSE18842,profiling by array’GSE31210, [Filter]) GSE33532to select related and datasets.GSE101929) Next, node score cutoff=0.2) [8]. we screened four gene expression profiles (including network (degree cutoff=2, max. Depth=100, κ-core=2, and according to the following inclusion criteria: a. Human Analyzing overall survival and RNA sequencing NSCLC tissues, not cell lines; b. Normal lung tissues used expression of core genes as controls; c. The total sample numbers, containing tumor tissues and normal tissues, are over 50;
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