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Plasma miRNAs in predicting radiosensitivity in non-small cell lung cancer

Overview of attention for article published in Tumor Biology, April 2016
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  • High Attention Score compared to outputs of the same age and source (91st percentile)

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1 X user
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2 patents

Citations

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54 Mendeley
Title
Plasma miRNAs in predicting radiosensitivity in non-small cell lung cancer
Published in
Tumor Biology, April 2016
DOI 10.1007/s13277-016-5052-8
Pubmed ID
Authors

Xu Chen, Yanmei Xu, Xingyun Liao, Rongxia Liao, Luping Zhang, Kai Niu, Tao Li, Dezhi Li, Zhengtang Chen, Yuzhong Duan, Jianguo Sun

Abstract

Radioresistance of thoracic radiotherapy is a major bottleneck in the treatment of non-small cell lung cancer (NSCLC). Until now, there have been no effective biomarkers to predict the radiosensitivity. Based on miRNA profile screened from NSCLC cell lines with different radiosensitivity, this study was conducted to explore the correlation between plasma miRNAs and radiotherapy response in NSCLC patients, and to identify biomarkers of the radiosensitivity in NSCLC. Differentially expressed genes were acquired from time-series gene expression profiles of radioresistant H1299 and radiosensitive H460 lung cancer cells (GSE20549). Potential miRNAs were screened from these differentially expressed genes by combining bioinformatics with GO analysis, pathway analysis, and miRNA prediction. A clinical observational study was performed to explore the correlation between candidate miRNAs and radiotherapy response. Stage IIIa-IV NSCLC patients who received two to four cycles of previous chemotherapy and underwent thoracic radiotherapy alone were included. Total RNA was purified from peripheral blood before radiotherapy, and plasma miRNAs were detected by real-time PCR (qRT-PCR). Then, tumor response, progression-free survival (PFS), and overall survival (OS) were acquired. Four miRNAs significantly different between effective and ineffective groups were further analyzed to obtain cutpoints from receiver operating characteristic (ROC) curves and the predictive value of radiosensitivity. Candidate miRNAs included 14 miRNAs screened from radioresistant genes and five from radiosensitive genes. From Jan., 2013 to Dec., 2014, 54 eligible patients were enrolled with a median follow-up of 15.3 months (range 4.6 to 31.4) by the deadline of Aug. 31, 2015. Totally, there were no case of complete response (CR), 15 of partial response (PR), 35 of stable disease (SD), and 4 of progressive disease (PD). Eight patients had no progression and 19 patients were still alive. The median PFS and OS were 6.6 months (range 2.3 to 29.3) and 15.3 months (range 4.6 to 31.4), respectively. Four miRNAs (hsa-miR-98-5p, hsa-miR-302e, hsa-miR-495-3p, and hsa-miR-613) demonstrated a higher expression in effective group (CR + PR, 15 cases) than in ineffective group (SD + PD, 39 cases). Based on each cutpoint, objective response rate (ORR) was higher in miR-high group than in miR-low group. No miRNA showed correlation with median PFS or OS. Bioinformatical analysis and clinical verification reveal the correlation between plasma miRNAs and radiosensitivity in NSCLC patients. Plasma miRNAs represent novel biomarkers to predict radiotherapy response clinically.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 54 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 20%
Researcher 10 19%
Student > Bachelor 7 13%
Student > Master 7 13%
Student > Doctoral Student 3 6%
Other 6 11%
Unknown 10 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 15 28%
Medicine and Dentistry 12 22%
Engineering 4 7%
Agricultural and Biological Sciences 2 4%
Unspecified 1 2%
Other 7 13%
Unknown 13 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 August 2022.
All research outputs
#6,981,547
of 24,318,236 outputs
Outputs from Tumor Biology
#330
of 2,635 outputs
Outputs of similar age
#94,559
of 305,472 outputs
Outputs of similar age from Tumor Biology
#4
of 48 outputs
Altmetric has tracked 24,318,236 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 2,635 research outputs from this source. They receive a mean Attention Score of 2.4. This one has done well, scoring higher than 86% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 305,472 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 48 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.