Identification and Validation of Immune-Related LncRNA Prognostic Signature for Lung Adenocarcinoma - Wu, Guomin; Wang, Qihao; Zhu, Ting; Fu, Linhai; Li, Zhupeng; Wu, Yuanlin; Zhang, Chu (Frontiers in Genetics, 2021)
To dig a prognostic signature of lung adenocarcinoma (LUAD) and establish a prognostic risk model, this study firstly divided 535 LUAD samples in TCGA-LUAD into high-, medium- and low-immune infiltration groups by consensus clustering analysis according to the results of immunological competence assessment by single-sample gene set enrichment analysis (ssGSEA). Profile of long non-coding RNAs (lncRNAs) in normal samples and LUAD samples in TCGA was used for a differential expression analysis, and meanwhile a differential expression analysis was performed on lncRNA profile in the high- and low-immune infiltration groups. 1,570 immune-related differential lncRNAs in LUAD were obtained by intersecting the above results. Afterwards, univariate COX regression analysis and multivariate stepwise COX regression analysis were conducted to screen prognosis-related lncRNAs, and an 8 immune-related lncRNA prognostic signature was finally acquired (AL365181.2, AC012213.4, DRAIC, MRGPRG-AS1, AP002478.1, AC092168.2, FAM30A, LINC02412). Kaplan-Meier analysis and ROC analysis indicated that the 8 lncRNA-based risk score model was accurate to predict the prognosis of LUAD patients. Simultaneously, univariate COX regression analysis and multivariate COX regression analysis were undertaken on clinical features and risk scores, and it was illustrated that the risk score was a prognostic factor independent from clinical features. Moreover, immune data of LUAD in TIMER database were analyzed, and it was uncovered that the 8 immune-related lncRNA prognostic signature was related to the infiltration of B cells, CD4+ T-cells and dendritic cells. The objective of this study is to construct an immune-related lncRNA-based prognostic model, which helps to treat LUAD patients and explore molecules related to LUAD immune infiltration to deeply understand the specific mechanism.