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Discovery of prognostic biomarkers for predicting lung cancer metastasis using microarray and survival data

Overview of attention for article published in BMC Bioinformatics, February 2015
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Mentioned by

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2 tweeters
facebook
1 Facebook page

Citations

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19 Dimensions

Readers on

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32 Mendeley
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Title
Discovery of prognostic biomarkers for predicting lung cancer metastasis using microarray and survival data
Published in
BMC Bioinformatics, February 2015
DOI 10.1186/s12859-015-0463-x
Pubmed ID
Authors

Hui-Ling Huang, Yu-Chung Wu, Li-Jen Su, Yun-Ju Huang, Phasit Charoenkwan, Wen-Liang Chen, Hua-Chin Lee, William Cheng-Chung Chu, Shinn-Ying Ho

Abstract

Few studies have investigated prognostic biomarkers of distant metastases of lung cancer. One of the central difficulties in identifying biomarkers from microarray data is the availability of only a small number of samples, which results overtraining. Recently obtained evidence reveals that epithelial-mesenchymal transition (EMT) of tumor cells causes metastasis, which is detrimental to patients' survival. This work proposes a novel optimization approach to discovering EMT-related prognostic biomarkers to predict the distant metastasis of lung cancer using both microarray and survival data. This weighted objective function maximizes both the accuracy of prediction of distant metastasis and the area between the disease-free survival curves of the non-distant and distant metastases. Seventy-eight patients with lung cancer and a follow-up time of 120 months are used to identify a set of gene markers and an independent cohort of 26 patients is used to evaluate the identified biomarkers. The medical records of the 78 patients show a significant difference between the disease-free survival times of the 37 non-distant- and the 41 distant-metastasis patients. The experimental results thus obtained are as follows. 1) The use of disease-free survival curves can compensate for the shortcoming of insufficient samples and greatly increase the test accuracy by 11.10%; and 2) the support vector machine with a set of 17 transcripts, such as CCL16 and CDKN2AIP, can yield a leave-one-out cross-validation accuracy of 93.59%, a test accuracy of 76.92%, a large disease-free survival area of 74.81%, and a mean survival prediction error of 3.99 months. The identified putative biomarkers are examined using related studies and signaling pathways to reveal the potential effectiveness of the biomarkers in prospective confirmatory studies. The proposed new optimization approach to identifying prognostic biomarkers by combining multiple sources of data (microarray and survival) can facilitate the accurate selection of biomarkers that are most relevant to the disease while solving the problem of insufficient samples.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Unknown 31 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 25%
Researcher 5 16%
Other 3 9%
Student > Bachelor 3 9%
Professor > Associate Professor 3 9%
Other 4 13%
Unknown 6 19%
Readers by discipline Count As %
Computer Science 8 25%
Biochemistry, Genetics and Molecular Biology 5 16%
Agricultural and Biological Sciences 5 16%
Medicine and Dentistry 2 6%
Engineering 2 6%
Other 3 9%
Unknown 7 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 01 April 2015.
All research outputs
#2,278,446
of 4,946,937 outputs
Outputs from BMC Bioinformatics
#1,736
of 2,859 outputs
Outputs of similar age
#69,218
of 149,977 outputs
Outputs of similar age from BMC Bioinformatics
#74
of 120 outputs
Altmetric has tracked 4,946,937 research outputs across all sources so far. This one has received more attention than most of these and is in the 51st percentile.
So far Altmetric has tracked 2,859 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 34th percentile – i.e., 34% of its peers scored the same or lower than it.
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 149,977 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 50% of its contemporaries.
We're also able to compare this research output to 120 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.