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Characterization of Radiotherapy Sensitivity Genes by Comparative Gene Set Enrichment Analysis

  • Min Zhu
  • Xiaolai Li
  • Shujie Wang
  • Wei Guo
  • Xueling Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)

Abstract

Postoperative and preoperative radiotherapy has been widely applied to kill the local cancer cells, prevent metastasis and lessen cancer burden in the treatment of cancers. However, the response to radiotherapy varies among cancer patients. In this study we mine and characterize the radiotherapy efficacy associated genes, radiosensitivity genes, in rectal cancer gene expression profile (GSE3493) from a previous study by gene set enrichment analysis. 381 genes were identified by comparing the gene expression profiles of responder and nonresponder rectal cancer patients who underwent preoperative radiotherapy. The top radiotherapy sensitive genes include MCF2, WHAMMP2, PCDHGA8, SHOX2, FAS, X81001, HAVCR1, PLXDC2, OPRM1 and PWAR5. We performed enrichment analysis of transcription factor, chromosome position, and gene sets reported in literatures by comparing this gene set with reported functional or structural gene sets. We find that the gene set has significant overlap with radiotherapy response, irradiation response, inflammation, XRCC3, ATM and BRCA1 related gene sets in different cancers from previous reports. Enriched chromosome positions include 16q13 and 17q21. The top enriched transcription factors with most number of radiotherapy response target genes include FOXP1, TP63, AR, STAT3, SOX2, SMAD4, BACH1, SMAD2, SMAD3, ZNF217 and RELA. The present study suggested the potential molecular mechanism behind the radiotherapy responders and non-responders, where both inflammatory and immune response and DNA damage response are very likely to control the radiotherapy sensitivity. The results may provide insights into the development of novel therapeutic approaches.

Keywords

Radiotherapy sensitivity genes Radiogenomics Gene set enrichment analysis Transcription factors Bioinformatics 

Notes

Acknowledgements

This study was supported by the National Science Foundation of China, (Grant Nos. 31371340, 61673369, 61273324, and 81572948, and was also supported by a start-up program funded by Center of Medical Physics and Technology & Cancer Hospital, Chinese Academy of Sciences, Hefei, Program No. Y6BF0Q1391.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Anhui Province Key Laboratory of Medical Physics and Technology, Center of Medical Physics and Technology, Hefei Institutes of Physical ScienceChinese Academy of SciencesHefeiPeople’s Republic of China
  2. 2.Cancer HospitalChinese Academy of SciencesHefeiPeople’s Republic of China
  3. 3.Hefei Institute of Intelligent MachinesChinese Academy of SciencesHefeiPeople’s Republic of China

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