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Prognostic value and immune landscapes of anoikis-associated lncRNAs in lung adenocarcinoma

机译:失巢凋亡相关 lncRNAs 在肺腺癌中的预后价值和免疫景观

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摘要

Background: Methods for predicting the outcome of lung adenocarcinoma (LUAD) in the clinic are limited. Anoikis is an important route to programmed cell death in LUAD, and the prognostic value of a model constructed with anoikis-related lncRNAs (ARlncRNAs) in LUAD is unclear.Methods: Transcriptome and basic information for LUAD patients was obtained from the Cancer Genome Atlas. Coexpression and Cox regression analyses were utilized to identify prognostically significant ARlncRNAs and construct a prognostic signature. Furthermore, the signature was combined with clinical characteristics to create a nomogram. Finally, we performed principal component, enrichment, tumor mutation burden (TMB), tumor microenvironment (TME) and drug sensitivity analyses to evaluate the basic research and clinical merit of the signature.Results: The prognostic signature developed with eleven ARlncRNAs can accurately predict that high-risk group patients have a worse prognosis, as proven by the receiver operating characteristic (ROC) curve (AUC: 0.718). Independent prognostic analyses indicated that the risk score is a significant independent prognostic element for LUAD (P<0.001). In the high-risk group, enrichment analysis demonstrated that glucose metabolism and DNA replication were the main enrichment pathways. TMB analysis indicated that the high-risk group had a high TMB (P<0.05). Drug sensitivity analyses can recognize drugs that are sensitive to different risk groups. Finally, 11 ARlncRNAs of this signature were verified by RT-qPCR analysis.Conclusions: A novel prognostic signature developed with 11 ARlncRNAs can accurately predict the OS of LUAD patients and offer clinical guidance value for immunotherapy and chemotherapy treatment.
机译:背景: 在临床上预测肺腺癌 (LUAD) 结果的方法有限。失巢凋亡是 LUAD 中程序性细胞死亡的重要途径,在 LUAD 中用失巢凋亡相关 lncRNA (ARlncRNA) 构建的模型的预后价值尚不清楚。方法: LUAD 患者的转录组和基本信息来自癌症基因组图谱。利用共表达和 Cox 回归分析来识别具有预后意义的 ARlncRNAs 并构建预后特征。此外,将特征与临床特征相结合,创建列线图。最后,我们进行了主成分、富集、肿瘤突变负荷 (TMB) 、肿瘤微环境 (TME) 和药物敏感性分析,以评价特征的基础研究和临床价值。结果: 用 11 个 ARlncRNAs 开发的预后特征可以准确预测高危组患者的预后较差,接受者工作特征 (ROC) 曲线 (AUC: 0.718) 证明。独立预后分析表明,风险评分是 LUAD 的重要独立预后因素 (P<0.001)。在高危组中,富集分析表明葡萄糖代谢和 DNA 复制是主要的富集途径。TMB 分析显示,高危组 TMB 高 (P<0.05)。药物敏感性分析可以识别对不同风险群体敏感的药物。最后,通过 RT-qPCR 分析验证了 11 个具有该特征的 ARlncRNA。结论: 用 11 个 ARlncRNAs 开发的新型预后特征可以准确预测 LUAD 患者的 OS,为免疫治疗和化疗治疗提供临床指导价值。

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