MicroRNA expression pattern as an ancillary prognostic signature for radiotherapy 11 Medical and Health Sciences 1112 Oncology and Carcinogenesis

An Lun Li, Tao Sang Chung, Yao Ning Chan, Chien Lung Chen, Shih-Chieh Lin, Yun Ru Chiang, Chen Huan Lin, Chi Ching Chen, Nianhan Ma

Research output: Contribution to journalArticle

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Abstract

Background: In view of the limited knowledge of plasma biomarkers relating to cancer resistance to radiotherapy, we have set up screening, training and testing stages to investigate the microRNAs (miRNAs) expression profile in plasma to predict between the poor responsive and responsive groups after 6 months of radiotherapy. Methods: Plasma was collected prior to and after radiotherapy, and the microRNA profiles were analyzed by quantitative reverse transcription polymerase chain reaction (qRT-PCR) arrays. Candidate miRNAs were validated by single qRT-PCR assays from the training and testing set. The classifier for ancillary prognosis was developed by multiple logistic regression analysis to correlate the ratios of miRNAs expression levels with clinical data. Results: We revealed that eight miRNAs expressions had significant changes after radiotherapy and the expression levels of miR-374a-5p, miR-342-5p and miR-519d-3p showed significant differences between the responsive and poor responsive groups in the pre-radiotherapy samples. The Kaplan-Meier curve analysis also showed that low miR-342-5p and miR-519d-3p expressions were associated with worse prognosis. Our results revealed two miRNA classifiers from the pre- and post-radiotherapy samples to predict radiotherapy response with area under curve values of 0.8923 and 0.9405. Conclusions: The expression levels of miR-374a-5p, miR-342-5p and miR-519d-3p in plasma are associated with radiotherapy responses. Two miRNA classifiers could be developed as a potential non-invasive ancillary tool for predicting patient response to radiotherapy.

Original languageEnglish
Article number341
JournalJournal of Translational Medicine
Volume16
Issue number1
DOIs
Publication statusPublished - 2018 Dec 5

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Oncology
Radiotherapy
MicroRNAs
Carcinogenesis
Health
Plasmas
Classifiers
Polymerase chain reaction
Transcription
Reverse Transcription
Polymerase Chain Reaction
Kaplan-Meier Estimate
Testing
Biomarkers
Regression analysis
Area Under Curve
Logistics
Assays
Screening
Logistic Models

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)

Cite this

Li, An Lun ; Chung, Tao Sang ; Chan, Yao Ning ; Chen, Chien Lung ; Lin, Shih-Chieh ; Chiang, Yun Ru ; Lin, Chen Huan ; Chen, Chi Ching ; Ma, Nianhan. / MicroRNA expression pattern as an ancillary prognostic signature for radiotherapy 11 Medical and Health Sciences 1112 Oncology and Carcinogenesis. In: Journal of Translational Medicine. 2018 ; Vol. 16, No. 1.
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abstract = "Background: In view of the limited knowledge of plasma biomarkers relating to cancer resistance to radiotherapy, we have set up screening, training and testing stages to investigate the microRNAs (miRNAs) expression profile in plasma to predict between the poor responsive and responsive groups after 6 months of radiotherapy. Methods: Plasma was collected prior to and after radiotherapy, and the microRNA profiles were analyzed by quantitative reverse transcription polymerase chain reaction (qRT-PCR) arrays. Candidate miRNAs were validated by single qRT-PCR assays from the training and testing set. The classifier for ancillary prognosis was developed by multiple logistic regression analysis to correlate the ratios of miRNAs expression levels with clinical data. Results: We revealed that eight miRNAs expressions had significant changes after radiotherapy and the expression levels of miR-374a-5p, miR-342-5p and miR-519d-3p showed significant differences between the responsive and poor responsive groups in the pre-radiotherapy samples. The Kaplan-Meier curve analysis also showed that low miR-342-5p and miR-519d-3p expressions were associated with worse prognosis. Our results revealed two miRNA classifiers from the pre- and post-radiotherapy samples to predict radiotherapy response with area under curve values of 0.8923 and 0.9405. Conclusions: The expression levels of miR-374a-5p, miR-342-5p and miR-519d-3p in plasma are associated with radiotherapy responses. Two miRNA classifiers could be developed as a potential non-invasive ancillary tool for predicting patient response to radiotherapy.",
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MicroRNA expression pattern as an ancillary prognostic signature for radiotherapy 11 Medical and Health Sciences 1112 Oncology and Carcinogenesis. / Li, An Lun; Chung, Tao Sang; Chan, Yao Ning; Chen, Chien Lung; Lin, Shih-Chieh; Chiang, Yun Ru; Lin, Chen Huan; Chen, Chi Ching; Ma, Nianhan.

In: Journal of Translational Medicine, Vol. 16, No. 1, 341, 05.12.2018.

Research output: Contribution to journalArticle

TY - JOUR

T1 - MicroRNA expression pattern as an ancillary prognostic signature for radiotherapy 11 Medical and Health Sciences 1112 Oncology and Carcinogenesis

AU - Li, An Lun

AU - Chung, Tao Sang

AU - Chan, Yao Ning

AU - Chen, Chien Lung

AU - Lin, Shih-Chieh

AU - Chiang, Yun Ru

AU - Lin, Chen Huan

AU - Chen, Chi Ching

AU - Ma, Nianhan

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Y1 - 2018/12/5

N2 - Background: In view of the limited knowledge of plasma biomarkers relating to cancer resistance to radiotherapy, we have set up screening, training and testing stages to investigate the microRNAs (miRNAs) expression profile in plasma to predict between the poor responsive and responsive groups after 6 months of radiotherapy. Methods: Plasma was collected prior to and after radiotherapy, and the microRNA profiles were analyzed by quantitative reverse transcription polymerase chain reaction (qRT-PCR) arrays. Candidate miRNAs were validated by single qRT-PCR assays from the training and testing set. The classifier for ancillary prognosis was developed by multiple logistic regression analysis to correlate the ratios of miRNAs expression levels with clinical data. Results: We revealed that eight miRNAs expressions had significant changes after radiotherapy and the expression levels of miR-374a-5p, miR-342-5p and miR-519d-3p showed significant differences between the responsive and poor responsive groups in the pre-radiotherapy samples. The Kaplan-Meier curve analysis also showed that low miR-342-5p and miR-519d-3p expressions were associated with worse prognosis. Our results revealed two miRNA classifiers from the pre- and post-radiotherapy samples to predict radiotherapy response with area under curve values of 0.8923 and 0.9405. Conclusions: The expression levels of miR-374a-5p, miR-342-5p and miR-519d-3p in plasma are associated with radiotherapy responses. Two miRNA classifiers could be developed as a potential non-invasive ancillary tool for predicting patient response to radiotherapy.

AB - Background: In view of the limited knowledge of plasma biomarkers relating to cancer resistance to radiotherapy, we have set up screening, training and testing stages to investigate the microRNAs (miRNAs) expression profile in plasma to predict between the poor responsive and responsive groups after 6 months of radiotherapy. Methods: Plasma was collected prior to and after radiotherapy, and the microRNA profiles were analyzed by quantitative reverse transcription polymerase chain reaction (qRT-PCR) arrays. Candidate miRNAs were validated by single qRT-PCR assays from the training and testing set. The classifier for ancillary prognosis was developed by multiple logistic regression analysis to correlate the ratios of miRNAs expression levels with clinical data. Results: We revealed that eight miRNAs expressions had significant changes after radiotherapy and the expression levels of miR-374a-5p, miR-342-5p and miR-519d-3p showed significant differences between the responsive and poor responsive groups in the pre-radiotherapy samples. The Kaplan-Meier curve analysis also showed that low miR-342-5p and miR-519d-3p expressions were associated with worse prognosis. Our results revealed two miRNA classifiers from the pre- and post-radiotherapy samples to predict radiotherapy response with area under curve values of 0.8923 and 0.9405. Conclusions: The expression levels of miR-374a-5p, miR-342-5p and miR-519d-3p in plasma are associated with radiotherapy responses. Two miRNA classifiers could be developed as a potential non-invasive ancillary tool for predicting patient response to radiotherapy.

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