A prognostic 4-lncRNA expression signature for lung squamous cell carcinoma. (18th August 2018)

• Record Type: Journal Article
• Authors: Luo, Danju; Deng, Bin; Weng, Mixia; Luo, Zhen; Nie, Xiu
• Is Part Of: Artificial cells, nanomedicine, and biotechnology. Volume 46:Number 6(2018)
• Page Start: 1207
• Page End: 1214
• Publication Date: 2018-08-18
• Subjects: Lung squamous cell carcinoma -- TCGA -- RNA-seq -- Kaplan–Meier -- survival analysis; Artificial cells -- Periodicals; Nanotechnology -- Periodicals; Blood substitutes -- Periodicals; Tissue engineering -- Periodicals; Molecules -- Periodicals; Biotechnology -- Periodicals; 615.39
• DOI: 10.1080/21691401.2017.1366334
• Languages: English
• ISSNs: 2169-1401

Abstract

Abstract: The outcomes of Lung squamous cell carcinoma (LUSC) is still challenging to evaluate or predict. We aimed to screen prognostic lncRNAs and to mine their roles in LUSC. RNA-Seq data of primary lung cancer were extracted from the Cancer Genome Atlas. Generally, changed lncRNAs in cancer samples were screened and analyzed in univariate survival analysis for identification of prognostic lncRNAs. Robust likelihood-based survival model was generated and random sampling iterations were performed 1000 times to calculate the frequency of feature key lncRNAs. Clustering and multivariate survival analysis of these lncRNAs was used to evaluate their functions and impacts on prognosis. Finally, the stability and validity of the optimal clustering model were verified. In total, we obtained 5664 generally changed lncRNAs among primary lung cancer samples, including 289 identified relating to prognosis in univariate survival analysis. Robust likelihood-based survival modelling for 1000 iterations generated 11 feature lncRNAs with frequency larger than 300. Their interacting proteins were found participating in DNA repairing and cell proliferation. Among stable assembly of 11 lncRNAs, a 4-lncRNA model was selected finally with high stability and feasibility. The ideal 4-lncRNA model can cluster patient samples with significant difference, providing new avenues for the prognostic predication of LUSC.