生物
长非编码RNA
核糖核酸
免疫系统
免疫疗法
腺癌
计算生物学
DNA测序
临床意义
生物信息学
癌症研究
免疫学
遗传学
基因
癌症
内科学
医学
作者
Yuqing Ren,Ruhao Wu,Chunwei Li,Long Liu,Lifeng Li,Siyuan Weng,Hui Xu,Zhe Xing,Yuyuan Zhang,Libo Wang,Xinwei Han,Xinwei Han
出处
期刊:BMC Biology
[Springer Nature]
日期:2024-03-22
卷期号:22 (1)
标识
DOI:10.1186/s12915-024-01866-5
摘要
Recently, long non-coding RNAs (lncRNAs) have been demonstrated as essential roles in tumor immune microenvironments (TIME). Nevertheless, researches on the clinical significance of TIME-related lncRNAs are limited in lung adenocarcinoma (LUAD).Single-cell RNA sequencing and bulk RNA sequencing data are integrated to identify TIME-related lncRNAs. A total of 1368 LUAD patients are enrolled from 6 independent datasets. An integrative machine learning framework is introduced to develop a TIME-related lncRNA signature (TRLS).This study identified TIME-related lncRNAs from integrated analysis of single‑cell and bulk RNA sequencing data. According to these lncRNAs, a TIME-related lncRNA signature was developed and validated from an integrative procedure in six independent cohorts. TRLS exhibited a robust and reliable performance in predicting overall survival. Superior prediction performance barged TRLS to the forefront from comparison with general clinical features, molecular characters, and published signatures. Moreover, patients with low TRLS displayed abundant immune cell infiltration and active lipid metabolism, while patients with high TRLS harbored significant genomic alterations, high PD-L1 expression, and elevated DNA damage repair (DDR) relevance. Notably, subclass mapping analysis of nine immunotherapeutic cohorts demonstrated that patients with high TRLS were more sensitive to immunotherapy.This study developed a promising tool based on TIME-related lncRNAs, which might contribute to tailored treatment and prognosis management of LUAD patients.
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