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一种用于肺腺癌的新型免疫相关四lncRNA特征的鉴定与验证

Identification and Validation of a Novel Immune-Related Four-lncRNA Signature for Lung Adenocarcinoma.

作者信息

Wang Jixin, Yin Xiangjun, Zhang Yin-Qiang, Ji Xuming

机构信息

Zhejiang University-University of Edinburgh Institute, Zhejiang University, Zhejiang, China.

School of Basic Medical Science, Zhejiang Chinese Medical University, Zhejiang, China.

出版信息

Front Genet. 2021 Feb 23;12:639254. doi: 10.3389/fgene.2021.639254. eCollection 2021.

Abstract

Lung adenocarcinoma (LUAD) is a major subtype of lung cancer, the prognosis of patients with which is associated with both lncRNAs and cancer immunity. In this study, we collected gene expression data of 585 LUAD patients from The Cancer Genome Atlas (TCGA) database and 605 subjects from the Gene Expression Omnibus (GEO) database. LUAD patients were divided into high and low immune-cell-infiltrated groups according to the single sample gene set enrichment analysis (ssGSEA) algorithm to identify differentially expressed genes (DEGs). Based on the 49 immune-related DE lncRNAs, a four-lncRNA prognostic signature was constructed by applying least absolute shrinkage and selection operator (LASSO) regression, univariate Cox regression, and stepwise multivariate Cox regression in sequence. Kaplan-Meier curve, ROC analysis, and the testing GEO datasets verified the effectiveness of the signature in predicting overall survival (OS). Univariate Cox regression and multivariate Cox regression suggested that the signature was an independent prognostic factor. The correlation analysis revealed that the infiltration immune cell subtypes were related to these lncRNAs.

摘要

肺腺癌(LUAD)是肺癌的一种主要亚型,其患者的预后与长链非编码RNA(lncRNAs)和癌症免疫均相关。在本研究中,我们从癌症基因组图谱(TCGA)数据库收集了585例LUAD患者的基因表达数据,并从基因表达综合数据库(GEO)收集了605名受试者的数据。根据单样本基因集富集分析(ssGSEA)算法,将LUAD患者分为免疫细胞高浸润组和低浸润组,以鉴定差异表达基因(DEGs)。基于49个免疫相关的差异表达lncRNAs,通过依次应用最小绝对收缩和选择算子(LASSO)回归、单变量Cox回归和逐步多变量Cox回归,构建了一个四lncRNA预后特征。Kaplan-Meier曲线、ROC分析以及测试GEO数据集验证了该特征在预测总生存期(OS)方面的有效性。单变量Cox回归和多变量Cox回归表明该特征是一个独立的预后因素。相关性分析显示浸润性免疫细胞亚型与这些lncRNAs相关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e150/7940686/5e226d5cd64b/fgene-12-639254-g001.jpg

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