2018
DOI: 10.1038/s41598-018-27307-2
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A seven-lncRNA signature predicts overall survival in esophageal squamous cell carcinoma

Abstract: Esophageal squamous cell carcinoma (ESCC) is one of the most common types of cancer and the leading causes of cancer-related mortality worldwide, especially in Eastern Asia. Here, we downloaded the microarray data of lncRNA expression profiles of ESCC patients from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) data sets and divided into training, validation and test set. The random survival forest (RSF) algorithm and Cox regression analysis were applied to identify a seven-lncRNA signature. … Show more

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Cited by 64 publications
(61 citation statements)
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“…As the sixth leading cause of cancer‐associated death, esophageal cancer results in approximately 400 000 mortalities globally every year . In Asian countries, ESCC remains the predominant histologic type of esophageal cancer . Despite incremental advancement in diagnostics and therapeutics, its prognosis remains poor with a 5‐year survival rate of 15%‐25% .…”
Section: Introductionmentioning
confidence: 99%
“…As the sixth leading cause of cancer‐associated death, esophageal cancer results in approximately 400 000 mortalities globally every year . In Asian countries, ESCC remains the predominant histologic type of esophageal cancer . Despite incremental advancement in diagnostics and therapeutics, its prognosis remains poor with a 5‐year survival rate of 15%‐25% .…”
Section: Introductionmentioning
confidence: 99%
“…Long non-coding RNAs (lncRNAs) are closely associated with tumor development and influence the prognosis of patients with tumors (19)(20)(21). The previous studies have indicated the predictive ability of lncRNA signatures for the prognosis of ESCC including our previous study (22)(23)(24). In addition, some studies also revealed that lncRNA was associated with the MetS or related metabolism disorder (25,26).…”
Section: Introductionmentioning
confidence: 85%
“…Genes associated with survival in the univariate Cox and KM analyses were collected to perform the least absolute shrinkage and selection operator (LASSO) regression using R package GLMNET (https://CRAN.R-project.org/package=glmnet). LASSO regression aids in selection of appropriate variables to simplify the final signature and avoid over‐fitting. The penalty regularization parameter λ, which control the complexity of the signature, was obtained at minimum partial likelihood deviance .…”
Section: Methodsmentioning
confidence: 99%
“… LASSO regression aids in selection of appropriate variables to simplify the final signature and avoid over‐fitting. The penalty regularization parameter λ, which control the complexity of the signature, was obtained at minimum partial likelihood deviance . Further, R package Survminer (https://CRAN.R-project.org/package=survminer) was used to construct the final multivariate Cox survival risk model .…”
Section: Methodsmentioning
confidence: 99%