Identification of a Novel Prognostic Signature of Genome Instability-Related LncRNAs in Early Stage Lung Adenocarcinoma

比例危险模型 基因组不稳定性 计算生物学 生物 微卫星不稳定性 基因 基因签名 腺癌 肿瘤科 生物信息学 癌症 基因表达 生存分析 内科学 医学 遗传学 DNA损伤 微卫星 等位基因 DNA
作者
Bo Peng,Huawei Li,Ruisi Na,Tong Lu,Yongchao Li,Jiaying Zhao,Han Zhang,Linyou Zhang
出处
期刊:Frontiers in Cell and Developmental Biology [Frontiers Media]
卷期号:9 被引量:11
标识
DOI:10.3389/fcell.2021.706454
摘要

Increasing evidence has demonstrated that long non-coding RNAs (lncRNAs) play a crucial part in maintaining genomic instability. We therefore identified genome instability-related lncRNAs and constructed a prediction signature for early stage lung adenocarcinoma (LUAD) as well in order for classification of high-risk group of patients and improvement of individualized therapies.Early stage LUAD RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) were randomly divided into training set (n = 177) and testing set (n = 176). A total of 146 genomic instability-associated lncRNAs were identified based on somatic mutation profiles combining lncRNA expression profiles from TCGA by the "limma R" package. We performed Cox regression analysis to develop this predictive indicator. We validated the prognostic signature by an external independent LUAD cohort with microarray platform acquired from the Gene Expression Omnibus (GEO).A genome instability-related six-lncRNA-based gene signature (GILncSig) was established to divide subjects into high-risk and low-risk groups with different outcomes at statistically significant levels. According to the multivariate Cox regression and stratification analysis, the GILncSig was an independent predictive factor. Furthermore, the six-lncRNA signature achieved AUC values of 0.745, 0.659, and 0.708 in the training set, testing set, and TCGA set, respectively. When compared with other prognostic lncRNA signatures, the GILncSig also exhibited better prediction performance.The prognostic lncRNA signature is a potent tool for risk stratification of early stage LUAD patients. Our study also provided new insights for identifying genome instability-related cancer biomarkers.

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