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Protein-coding genes combined with long noncoding RNA as a novel transcriptome molecular staging model to predict the survival of patients with esophageal squamous cell carcinoma

Overview of attention for article published in Cancer Communications, April 2018
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Title
Protein-coding genes combined with long noncoding RNA as a novel transcriptome molecular staging model to predict the survival of patients with esophageal squamous cell carcinoma
Published in
Cancer Communications, April 2018
DOI 10.1186/s40880-018-0277-0
Pubmed ID
Authors

Jin-Cheng Guo, Yang Wu, Yang Chen, Feng Pan, Zhi-Yong Wu, Jia-Sheng Zhang, Jian-Yi Wu, Xiu-E Xu, Jian-Mei Zhao, En-Min Li, Yi Zhao, Li-Yan Xu

Abstract

Esophageal squamous cell carcinoma (ESCC) is the predominant subtype of esophageal carcinoma in China. This study was to develop a staging model to predict outcomes of patients with ESCC. Using Cox regression analysis, principal component analysis (PCA), partitioning clustering, Kaplan-Meier analysis, receiver operating characteristic (ROC) curve analysis, and classification and regression tree (CART) analysis, we mined the Gene Expression Omnibus database to determine the expression profiles of genes in 179 patients with ESCC from GSE63624 and GSE63622 dataset. Univariate cox regression analysis of the GSE63624 dataset revealed that 2404 protein-coding genes (PCGs) and 635 long non-coding RNAs (lncRNAs) were associated with the survival of patients with ESCC. PCA categorized these PCGs and lncRNAs into three principal components (PCs), which were used to cluster the patients into three groups. ROC analysis demonstrated that the predictive ability of PCG-lncRNA PCs when applied to new patients was better than that of the tumor-node-metastasis staging (area under ROC curve [AUC]: 0.69 vs. 0.65, P < 0.05). Accordingly, we constructed a molecular disaggregated model comprising one lncRNA and two PCGs, which we designated as the LSB staging model using CART analysis in the GSE63624 dataset. This LSB staging model classified the GSE63622 dataset of patients into three different groups, and its effectiveness was validated by analysis of another cohort of 105 patients. The LSB staging model has clinical significance for the prognosis prediction of patients with ESCC and may serve as a three-gene staging microarray.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 18%
Student > Ph. D. Student 2 18%
Student > Doctoral Student 1 9%
Student > Bachelor 1 9%
Professor 1 9%
Other 2 18%
Unknown 2 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 45%
Agricultural and Biological Sciences 1 9%
Immunology and Microbiology 1 9%
Psychology 1 9%
Social Sciences 1 9%
Other 1 9%
Unknown 1 9%

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 23 May 2018.
All research outputs
#11,535,823
of 12,979,316 outputs
Outputs from Cancer Communications
#32
of 43 outputs
Outputs of similar age
#235,093
of 271,224 outputs
Outputs of similar age from Cancer Communications
#3
of 3 outputs
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