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Identification of Common Gene Networks Responsive To Cancer Gene Therapy (2014) 21, 542–548 © 2014 Nature America, Inc. All rights reserved 0929-1903/14 www.nature.com/cgt ORIGINAL ARTICLE Identification of common gene networks responsive to radiotherapy in human cancer cells D-L Hou1, L Chen2, B Liu1, L-N Song1 and T Fang1 Identification of the genes that are differentially expressed between radiosensitive and radioresistant cancers by global gene analysis may help to elucidate the mechanisms underlying tumor radioresistance and improve the efficacy of radiotherapy. An integrated analysis was conducted using publicly available GEO datasets to detect differentially expressed genes (DEGs) between cancer cells exhibiting radioresistance and cancer cells exhibiting radiosensitivity. Gene Ontology (GO) enrichment analyses, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and protein–protein interaction (PPI) networks analysis were also performed. Five GEO datasets including 16 samples of radiosensitive cancers and radioresistant cancers were obtained. A total of 688 DEGs across these studies were identified, of which 374 were upregulated and 314 were downregulated in radioresistant cancer cell. The most significantly enriched GO terms were regulation of transcription, DNA-dependent (GO: 0006355, P = 7.00E-09) for biological processes, while those for molecular functions was protein binding (GO: 0005515, P = 1.01E-28), and those for cellular component was cytoplasm (GO: 0005737, P = 2.81E-26). The most significantly enriched pathway in our KEGG analysis was Pathways in cancer (P = 4.20E-07). PPI network analysis showed that IFIH1 (Degree = 33) was selected as the most significant hub protein. This integrated analysis may help to predict responses to radiotherapy and may also provide insights into the development of individualized therapies and novel therapeutic targets. Cancer Gene Therapy (2014) 21, 542–548; doi:10.1038/cgt.2014.62; published online 21 November 2014 INTRODUCTION and analytic methods were totally different from that of our study. Radiotherapy is a major modality of cancer therapy, but certain The objective of this study was to identify common genes asso- evidence reveals that radiotherapy (as monotherapy or in com- ciated with radioresistance and relevant biological processes bination with other types of treatments) is effective in 52% of by systematic integration of gene expression data from differ- cancer patients, despite of more accurate tumor localization by ent microarray platforms which is capable of increasing the computed tomography and better radiotherapy techniques.1 The statistical power to understand the mechanism of cancer cell major reason for such low effectiveness of radiotherapy may be radioresistance. due to intrinsic tumor cell radioresistance.2 Radioresistance of tumor cells is a multifactorial characteristic, mainly depending on the repair capacity of radiation-induced DNA lesions3 and other MATERIALS AND METHODS factors including hypoxia,4 differential gene expression,5 growth Identification of eligible radioresistance gene expression datasets factor receptors, mutations in proto-oncogenes and tumor sup- Radioresistance expression profiling studies were identified by searching pressor genes,6 expression of receptor tyrosine kinases7 and the Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih. adhesion of cells to extracellular matrix molecules.8 Although such gov/geo).15 The following key words and their combinations were used: discoveries have significantly advanced the understanding of ‘radioresistance, cancer, gene expression, microarray’. Those datasets were molecular mechanisms responsible for radiosensitivity of tumor obtained from original experimental articles, which make a comparison cells which are essential for developing the personalized therapy between radiosensitive and radioresistant cancer cells on the gene expres- sion profiling. Non-human studies, review articles and integrated analysis and improving the patient prognosis, the entire process remains of expression profiles were excluded. to be uncovered. Hence, it is urgently needed to discover new insights into the radioresistance mechanisms. As a high-throughput technology, microarray analysis allows Data preprocessing the simultaneous analysis of thousands of genes at the transcript Normalization is important for an accurate comparison of microarray expression level in a single experiment. The microarray have been datasets from multiple platforms. The heterogeneity of different microarray datasets is caused by different platforms, different gene nomenclature and widely used to detect the gene expression difference between fi radiosensitive and radioresistant tumors9 to assess genes involved different cancer type, so it is dif cult to compare the microarray datasets directly. Inappropriate normalization may lead to skewing comparison in radioresistance in a number of cancer cell types, including cervical, 10–13 results, and would reduce the credibility of measurements of changes in pancreatic, oral, lung, esophageal, head and neck cancers. the expression of individual genes. In this study, the expression values for 14 Kim et al. performed an integration of four radiosensitivity each gene were transformed to the z-score for global normalization. We profiling data applied to NCI-60, of which the selected datasets investigated the global shifts of gene expression between radiosensitive 1Department of Radiation Oncology, Beijing Shijitan Hospital, Capital Medical University, Beijing, China and 2Department of Oncology, Teng Zhou Central People’s Hospital of Shandong Province, Tengzhou, China. Correspondence: Dr T Fang, Department of Radiation Oncology, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China. E-mail: [email protected] Received 3 September 2014; revised 18 October 2014; accepted 23 October 2014; published online 21 November 2014 Radiotherapy-responsive gene networks in cancer D-L Hou et al 543 and radioresistant cancer cell by using the assembled expression chemistry pathways.18 In this work, the KEGG database was applied to compendium. Z-scores were calculated for each probe as following: investigate the enrichment analysis of the DEGs to find the biochemistry - pathways which might be involved in the tumor radioresistance. ¼ xi x Z score δ PPI network construction where xi indicates raw intensity data for each gene; x indicates the average intensity of gene within a single experiment and δ indicates standard The protein–protein interactions (PPIs) research is of pivotal role to reveal deviation (s.d.) of all measured intensities. the functions of proteins at the molecular level and discover the rules underlying cellular activities including growth, development, metabolism, 19 fi Statistical analysis differentiation and apoptosis. The identi cation of protein interactions in a genome-wide scale is important to interpret the cellular control mech- fi The Signi cance Analysis of Microarray software was then used to identify anisms.20 In this study, we attempted to construct a PPI network based on differentially expressed genes (DEGs) between radiosensitive and radio- Biological General Repository for Interaction Datasets (BioGRID) (http:// resistant cancer cell by carrying out gene-specific t-tests, with a ‘relative thebiogrid.org/) and the data in the constructed PPI network on the top 10 difference’ score for each gene. The definition of D value was the average up- and downregulated DEGs were visualized with Cytoscape software gene expression change from different expression levels to the standard 21 deviation of measurements for that gene. Genes with a fold-change 41.5 (http://cytoscape.org/). and false discovery rate ⩽ 0.05 were considered as DEGs.16 The heat map of the top 50 DEGs was constructed by R statistical software. RESULTS Functional classification of DEGs Short overview of the studies included The biological functions of the DEGs were interpreted by Gene Ontology A total of five expression profiling studies met inclusion criteria 17 (GO) enrichment analysis using the web-based software GENECODIS. GO and were included. Across these studies, 16 samples of radio- provides a common descriptive framework and functional annotation and sensitive and radioresistant cancer cell was obtained. The general classification for analyzing gene sets. GO categories are organized into three groups: biological process, cellular component and molecular func- information of these studies is detailed in Table 1. In these, studies tion. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database on several types of cancer were included, such as head and neck is a recognized and comprehensive database including all kinds of bio- cancer, nasopharyngeal carcinoma and lung cancer. Table 1. Characteristics of the individual studies GEO ID Platform Tumor type Samples (sensitive: Country Time resistant) GSE9712 GPL96 [HG-U133A] Affymetrix Human Genome U133A Array Head and neck cancer 3 : 3 USA 2007 GSE9713 GPL96 [HG-U133A] Affymetrix Human Genome U133A Array Head and neck cancer 3 : 3 USA 2007 GSE9714 GPL96 [HG-U133A] Affymetrix Human Genome U133A Array Head and neck cancer 2 : 2 USA 2007 GSE48503 GPL570 [HG-U133_Plus_2] Affymetrix Human Nasopharyngeal carcinoma 2 : 2 China 2014 Genome U133 Plus 2.0 Array GSE20549 GPL6244 [HuGene-1_0-st] Affymetrix Human Lung cancer 6 : 6 Korea 2010 Gene 1.0 ST Array [transcript (gene) version] Abbreviation: GEO, Gene Expression Omnibus database. Table 2. The top 10 most significantly up- or downregulated DEGs
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