Characterization of Radiotherapy Sensitivity Genes by Comparative Gene Set Enrichment Analysis

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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.

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Zhu, M., Li, X., Wang, S., Guo, W., & Li, X. (2018). Characterization of Radiotherapy Sensitivity Genes by Comparative Gene Set Enrichment Analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10955 LNCS, pp. 205–216). Springer Verlag. https://doi.org/10.1007/978-3-319-95933-7_25

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