Open AccessSystematic analysis identifies three-lncRNA signature as a potentially prognostic biomarker for lung squamous cell carcinoma using bioinformatics strategy
Author(s) -
Jing Hu,
Lutong Xu,
Tao Shou,
Qiang Chen
Publication year - 2019
Publication title -
translational lung cancer research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.474
H-Index - 41
eISSN - 2226-4477
pISSN - 2218-6751
DOI - 10.21037/tlcr.2019.09.13
Subject(s) - competing endogenous rna , microrna , proportional hazards model , survival analysis , lung cancer , univariate , oncology , computational biology , gene , gene expression profiling , bioinformatics , medicine , biology , gene expression , multivariate statistics , long non coding rna , rna , computer science , genetics , machine learning
Lung squamous cell carcinoma (LUSC) is the second most common histological subtype of lung cancer (LC), and the prognoses of most LUSC patients are so far still very poor. The present study aimed at integrating lncRNA, miRNA and mRNA expression data to identify lncRNA signature in competitive endogenous RNA (ceRNA) network as a potentially prognostic biomarker for LUSC patients.