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pmid:34432380
Key radioresistance regulation models and marker genes identified by integrated transcriptome analysis in nasopharyngeal carcinoma.
Zhuang Sun · Xiaohui Wang · Jingyun Wang · Jing Wang · Xiao Liu · Runda Huang · Chunyan Chen · Meiling Deng · Hanyu Wang · Fei Han
Cancer medicine · 2021 doi:10.1002/cam4.4228
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Experiment
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Data
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Analysis
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Execution
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Complete file
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Radiotherapy is the usual treatment, however, radioresistance remains a major reason for failure. This study aimed to find key radioresistance regulation models and marker genes of NPC and clarify the mechanism of NPC radioresistance by RNA sequencing and bioinformatics analysis of the differences in gene expression profiles between radioresistant and radiosensitive NPC tissues. A total of 21 NPC biopsy specimens with different radiosensitivity were analyzed by RNA sequencing. Differentially expressed genes in RNA sequencing data were identified using R software. The differentially expressed gene data derived from RNA sequencing as well as prior knowledge in the form of pathway databases were integrated to find sub-networks of related genes. The data of RNA sequencing with the GSE48501 data from the GEO database were combined to further search for more reliable genes associated with radioresistance of NPC. Survival analyses using the Kaplan-Meier method based on the expression of the genes were conducted to facilitate the understanding of the clinical significance of the differentially expressed genes. RT-qPCR was performed to validate the expression levels of the differentially expressed genes. We identified 1182 differentially expressed genes between radioresistant and radiosensitive NPC tissue samples. Compared to the radiosensitive group, 22 genes were significantly upregulated and 1160 genes were downregulated in the radioresistant group. In addition, 10 major NPC radiation resistance network models were identified through integration analysis with known NPC radiation resistance-associated genes and mechanisms. Furthermore, we identified three core genes, DOCK4, MCM9, and POPDC3 among 12 common downregulated genes in the two datasets, which were validated by RT-qPCR. 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