兰州大学学报（医学版）2026，Vol.52Issue(1)：59-70,12.DOI:10.13885/j.issn.2097-681X.M20241021
基于双硫死亡相关lncRNA构建肺腺癌预后模型及ceRNA网络
Identification of a disulfidptosis related lncRNA prognostic model and ceRNA network in lung adenocarcinoma
摘要
Abstract
Objective To screen disulfidptosis-related lncRNAs for prognostic biomarkers for lung adenocarci-noma(LUAD)diagnosis and treatment.Methods RNA-seq data and clinical information were obtained from The Cancer Genome Atlas(TCGA)database.LASSO regression analysis was used to construct a prognostic model based on disulfidptosis-related lncRNA.Patients were divided into high/low risk groups based on their risk-scores.Differences in overall survival,functional enrichment,tumor immune cell infiltration and drug sensitivity were further explored between risk groups.Finally,the ceRNA network was constructed and the ex-pression of three lncRNAs in LUAD was verified by external experiments.Results Developing a prognostic model consisting of three disulfidptosis lncRNA(AL365181.2,AC012615.1,AL606489.1),the survival rate of the low-risk group was significantly better than that of the high-risk group.There were significant differenc-es in OS,immune cell infiltration,immune checkpoint expression and immunotherapy response among pa-tients in different risk groups and in building ceRNA networks with model-associated lncRNA.Experimental validation showed that three lncRNA were expressed higher in LUAD tumor tissues than in normal tissues.Conclusion The present prognostic model was constructed using 3 disulfidptosis-related lncRNA and dem-onstrated that the model can independently predict survival in LUAD patients,providing new biomarkers for disulfidptosis in LUAD therapy.关键词
肺腺癌/双硫死亡/长链非编码RNA/预后模型/内源竞争RNA网络Key words
lung adenocarcinoma/disulfidptosis/long non-coding RNA/prognostic model/competing endog-enous RNA分类
医药卫生引用本文复制引用
张言鹏,王泓懿,杨大海,张广健,孙敬阳,高志远,蒋荣轩,林亦翰,王志英,董欢欢,董妞妞,李明..基于双硫死亡相关lncRNA构建肺腺癌预后模型及ceRNA网络[J].兰州大学学报（医学版）,2026,52(1):59-70,12.基金项目
国家自然科学基金资助项目(82103467) （82103467）
西安交通大学基础-临床融合创新项目基金资助项目(YXJLRH2022033) （YXJLRH2022033）
