@article{TLCR32606,
author = {Jing Hu and Lutong Xu and Tao Shou and Qiang Chen},
title = {Systematic analysis identifies three-lncRNA signature as a potentially prognostic biomarker for lung squamous cell carcinoma using bioinformatics strategy},
journal = {Translational Lung Cancer Research},
volume = {8},
number = {5},
year = {2019},
keywords = {},
abstract = {Background: 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.
Methods: Gene expression data and clinical characteristics of LUSC patients were retrieved from The Cancer Genome Atlas (TCGA) database, and were integratedly analyzed using bioinformatics methods including Differentially Expressed Gene Analysis (DEGA), Weighted Gene Co-expression Network Analysis (WGCNA), Protein and Protein Interaction (PPI) network analysis and ceRNA network construction. Subsequently, univariate and multivariate Cox regression analyses of differentially expressed lncRNAs (DElncRNAs) in ceRNA network were performed to predict the overall survival (OS) in LUSC patients. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of multivariate Cox regression model. Gene expression profiling interactive analysis (GEPIA) was used to validate key genes.
Results: WGCNA showed that turquoise module including 1,694 DElncRNAs, 2,654 DEmRNAs as well as 113 DEmiRNAs was identified as the most significant modules (cor=0.99, P},
issn = {2226-4477}, url = {https://tlcr.amegroups.org/article/view/32606}
}