title: Identification of common gene networks responsive to radiotherapy in human cancer cells.
abstract: 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.
authors.affiliation: Department of Radiation Oncology, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
authors.forename: D-L
authors.initials: DL
authors.lastname: Hou
authors.affiliation: Department of Oncology, Teng Zhou Central People's Hospital of Shandong Province, Tengzhou, China.
authors.forename: L
authors.initials: L
authors.lastname: Chen
authors.affiliation: Department of Radiation Oncology, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
authors.forename: B
authors.initials: B
authors.lastname: Liu
authors.affiliation: Department of Radiation Oncology, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
authors.forename: L-N
authors.initials: LN
authors.lastname: Song
authors.affiliation: Department of Radiation Oncology, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
authors.forename: T
authors.initials: T
authors.lastname: Fang
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic / Q000528:radiation effects; D053263:Gene Regulatory Networks; D056726:Genetic Association Studies; D006801:Humans; D058977:Molecular Sequence Annotation; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism / Q000532:radiotherapy; D025941:Protein Interaction Mapping; D060066:Protein Interaction Maps; D011836:Radiation Tolerance / Q000235:genetics*