Digital gene expression profiling analysis and its application in the identification of genes associated with improved response to neoadjuvant chemotherapy in breast cancer
Abstract Background This study aimed to screen sensitive biomarkers for the efficacy evaluation of neoadjuvant chemotherapy in breast cancer. Methods In this study, Illumina digital gene expression sequencing technology was applied and differentially expressed genes (DEGs) between patients presentin...
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doaj-2165d65ed8c34a68b2bd105d72d2a8f22020-11-25T01:01:34ZengBMCWorld Journal of Surgical Oncology1477-78192018-04-011611810.1186/s12957-018-1380-zDigital gene expression profiling analysis and its application in the identification of genes associated with improved response to neoadjuvant chemotherapy in breast cancerXiaozhen Liu0Gan Jin1Jiacheng Qian2Hongjian Yang3Hongchao Tang4Xuli Meng5Yongfeng Li6Pathology Department, Zhejiang Cancer HospitalThe 2nd Clinical Medical College, Zhejiang Chinese Medical UniversityThe 2nd Clinical Medical College, Zhejiang Chinese Medical UniversityDepartment of Breast Surgery, Zhejiang Cancer HospitalThe 2nd Clinical Medical College, Zhejiang Chinese Medical UniversityDepartment of Breast Surgery, Zhejiang Cancer HospitalDepartment of Breast Surgery, Zhejiang Cancer HospitalAbstract Background This study aimed to screen sensitive biomarkers for the efficacy evaluation of neoadjuvant chemotherapy in breast cancer. Methods In this study, Illumina digital gene expression sequencing technology was applied and differentially expressed genes (DEGs) between patients presenting pathological complete response (pCR) and non-pathological complete response (NpCR) were identified. Further, gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were then performed. The genes in significant enriched pathways were finally quantified by quantitative real-time PCR (qRT-PCR) to confirm that they were differentially expressed. Additionally, GSE23988 from Gene Expression Omnibus database was used as the validation dataset to confirm the DEGs. Results After removing the low-quality reads, 715 DEGs were finally detected. After mapping to KEGG pathways, 10 DEGs belonging to the ubiquitin proteasome pathway (HECTD3, PSMB10, UBD, UBE2C, and UBE2S) and cytokine–cytokine receptor interactions (CCL2, CCR1, CXCL10, CXCL11, and IL2RG) were selected for further analysis. These 10 genes were finally quantified by qRT-PCR to confirm that they were differentially expressed (the log2 fold changes of selected genes were − 5.34, 7.81, 6.88, 5.74, 3.11, 19.58, 8.73, 8.88, 7.42, and 34.61 for HECTD3, PSMB10, UBD, UBE2C, UBE2S, CCL2, CCR1, CXCL10, CXCL11, and IL2RG, respectively). Moreover, 53 common genes were confirmed by the validation dataset, including downregulated UBE2C and UBE2S. Conclusion Our results suggested that these 10 genes belonging to these two pathways might be useful as sensitive biomarkers for the efficacy evaluation of neoadjuvant chemotherapy in breast cancer.http://link.springer.com/article/10.1186/s12957-018-1380-zBreast cancerDigital gene expressionNeoadjuvant chemotherapyUbiquitin proteasomeCytokine–cytokine receptor interactions |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiaozhen Liu Gan Jin Jiacheng Qian Hongjian Yang Hongchao Tang Xuli Meng Yongfeng Li |
spellingShingle |
Xiaozhen Liu Gan Jin Jiacheng Qian Hongjian Yang Hongchao Tang Xuli Meng Yongfeng Li Digital gene expression profiling analysis and its application in the identification of genes associated with improved response to neoadjuvant chemotherapy in breast cancer World Journal of Surgical Oncology Breast cancer Digital gene expression Neoadjuvant chemotherapy Ubiquitin proteasome Cytokine–cytokine receptor interactions |
author_facet |
Xiaozhen Liu Gan Jin Jiacheng Qian Hongjian Yang Hongchao Tang Xuli Meng Yongfeng Li |
author_sort |
Xiaozhen Liu |
title |
Digital gene expression profiling analysis and its application in the identification of genes associated with improved response to neoadjuvant chemotherapy in breast cancer |
title_short |
Digital gene expression profiling analysis and its application in the identification of genes associated with improved response to neoadjuvant chemotherapy in breast cancer |
title_full |
Digital gene expression profiling analysis and its application in the identification of genes associated with improved response to neoadjuvant chemotherapy in breast cancer |
title_fullStr |
Digital gene expression profiling analysis and its application in the identification of genes associated with improved response to neoadjuvant chemotherapy in breast cancer |
title_full_unstemmed |
Digital gene expression profiling analysis and its application in the identification of genes associated with improved response to neoadjuvant chemotherapy in breast cancer |
title_sort |
digital gene expression profiling analysis and its application in the identification of genes associated with improved response to neoadjuvant chemotherapy in breast cancer |
publisher |
BMC |
series |
World Journal of Surgical Oncology |
issn |
1477-7819 |
publishDate |
2018-04-01 |
description |
Abstract Background This study aimed to screen sensitive biomarkers for the efficacy evaluation of neoadjuvant chemotherapy in breast cancer. Methods In this study, Illumina digital gene expression sequencing technology was applied and differentially expressed genes (DEGs) between patients presenting pathological complete response (pCR) and non-pathological complete response (NpCR) were identified. Further, gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were then performed. The genes in significant enriched pathways were finally quantified by quantitative real-time PCR (qRT-PCR) to confirm that they were differentially expressed. Additionally, GSE23988 from Gene Expression Omnibus database was used as the validation dataset to confirm the DEGs. Results After removing the low-quality reads, 715 DEGs were finally detected. After mapping to KEGG pathways, 10 DEGs belonging to the ubiquitin proteasome pathway (HECTD3, PSMB10, UBD, UBE2C, and UBE2S) and cytokine–cytokine receptor interactions (CCL2, CCR1, CXCL10, CXCL11, and IL2RG) were selected for further analysis. These 10 genes were finally quantified by qRT-PCR to confirm that they were differentially expressed (the log2 fold changes of selected genes were − 5.34, 7.81, 6.88, 5.74, 3.11, 19.58, 8.73, 8.88, 7.42, and 34.61 for HECTD3, PSMB10, UBD, UBE2C, UBE2S, CCL2, CCR1, CXCL10, CXCL11, and IL2RG, respectively). Moreover, 53 common genes were confirmed by the validation dataset, including downregulated UBE2C and UBE2S. Conclusion Our results suggested that these 10 genes belonging to these two pathways might be useful as sensitive biomarkers for the efficacy evaluation of neoadjuvant chemotherapy in breast cancer. |
topic |
Breast cancer Digital gene expression Neoadjuvant chemotherapy Ubiquitin proteasome Cytokine–cytokine receptor interactions |
url |
http://link.springer.com/article/10.1186/s12957-018-1380-z |
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