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Identification of an early diagnostic biomarker of lung adenocarcinoma based on co-expression similarity and construction of a diagnostic model

Overview of attention for article published in Journal of Translational Medicine, July 2018
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Title
Identification of an early diagnostic biomarker of lung adenocarcinoma based on co-expression similarity and construction of a diagnostic model
Published in
Journal of Translational Medicine, July 2018
DOI 10.1186/s12967-018-1577-5
Pubmed ID
Authors

Zhirui Fan, Wenhua Xue, Lifeng Li, Chaoqi Zhang, Jingli Lu, Yunkai Zhai, Zhenhe Suo, Jie Zhao

Abstract

The purpose of this study was to achieve early and accurate diagnosis of lung cancer and long-term monitoring of the therapeutic response. We downloaded GSE20189 from GEO database as analysis data. We also downloaded human lung adenocarcinoma RNA-seq transcriptome expression data from the TCGA database as validation data. Finally, the expression of all of the genes underwent z test normalization. We used ANOVA to identify differentially expressed genes specific to each stage, as well as the intersection between them. Two methods, correlation analysis and co-expression network analysis, were used to compare the expression patterns and topological properties of each stage. Using the functional quantification algorithm, we evaluated the functional level of each significantly enriched biological function under different stages. A machine-learning algorithm was used to screen out significant functions as features and to establish an early diagnosis model. Finally, survival analysis was used to verify the correlation between the outcome and the biomarkers that we found. We screened 12 significant biomarkers that could distinguish lung cancer patients with diverse risks. Patients carrying variations in these 12 genes also presented a poor outcome in terms of survival status compared with patients without variations. We propose a new molecular-based noninvasive detection method. According to the expression of the stage-specific gene set in the peripheral blood of patients with lung cancer, the difference in the functional level is quantified to realize the early diagnosis and prediction of lung cancer.

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Mendeley readers

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 %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 19%
Student > Master 5 16%
Student > Ph. D. Student 4 13%
Student > Bachelor 3 9%
Student > Doctoral Student 1 3%
Other 4 13%
Unknown 9 28%
Readers by discipline Count As %
Medicine and Dentistry 10 31%
Biochemistry, Genetics and Molecular Biology 2 6%
Nursing and Health Professions 2 6%
Engineering 2 6%
Psychology 2 6%
Other 4 13%
Unknown 10 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 22 July 2018.
All research outputs
#18,643,992
of 23,096,849 outputs
Outputs from Journal of Translational Medicine
#2,991
of 4,052 outputs
Outputs of similar age
#253,133
of 328,924 outputs
Outputs of similar age from Journal of Translational Medicine
#56
of 93 outputs
Altmetric has tracked 23,096,849 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,052 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.6. This one is in the 17th percentile – i.e., 17% of its peers scored the same or lower than it.
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We're also able to compare this research output to 93 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.