摘要
出身背景
肺腺癌（LUAD）是非小细胞肺癌中最常见的组织类型。Anoikis是一种程序性细胞死亡的替代形式，在癌症的侵袭和转移中起着关键作用，阻止分离的癌症细胞重新粘附到其他基质上进行异常增殖。本研究的目的是对失巢相关基因（ARGs）在LUAD中的预后影响进行全面分析。
方法
利用差异表达分析从癌症基因组图谱（TCGA）数据库和基因卡数据集中选择ARG。使用单变量Cox回归分析和LASSO回归分析确定合并ARGs的特征。此外，通过单变量和多变量Cox回归分析，开发了包含特征和临床信息的列线图。应用Kaplan–Meier生存分析和受试者操作特征（ROC）曲线来评估这些风险模型的预测有效性。最后，还对选定的ARGs进行了签名功能分析和免疫景观分析。
后果
整合16个基因特征，将LUAD患者分为不同的生存风险组。根据特征和TNM分期产生的预后风险评分被确定为独立的预后因素，并用于制定列线图。签名和列线图在预测LUAD患者的总生存期（OS）方面都显示出令人满意的预测性能。ARGs在多种生物功能和信号通路中富集。最后，根据特征对高风险组和低风险组的免疫景观进行了分层调查。
结论
这项研究揭示了ARGs与LUAD预后之间的潜在关系。本研究中确定的预后预测因子可作为临床应用的潜在生物标志物。
Abstract
Background
Lung adenocarcinoma (LUAD) is the most prevalent histotype of non-small cell lung cancer. Anoikis, an alternative form of programmed cell death, plays a pivotal role in cancer invasion and metastasis, preventing the detached cancer cells from readhering to other substrates for abnormal proliferation. The aim of this study was to conduct a comprehensive analyses of the prognostic implications of anoikis-related genes (ARGs) in LUAD.

Methods
ARGs were selected from The Cancer Genome Atlas (TCGA) database and Genecards dataset using differential expression analysis. The signature incorporating ARGs was identified using univariate Cox regression analysis and LASSO regression analysis. Furthermore, a nomogram containing the signature and clinical information was developed through univariate and multivariate Cox regression analysis. Kaplan–Meier survival analysis and receiver operating characteristic (ROC) curves were applied to evaluate the predictive validity of these risk models. Finally, functional analysis of the selected ARGs in signature and analysis of immune landscape were also conducted.

Results
A 16-gene signature was integrated to stratify LUAD patients into different survival risk groups. The prognostic risk score generated from the signature and TNM stage were identified as independent prognostic factors and utilized to develop a nomogram. Both the signature and the nomogram showed satisfactory prediction performance in predicting overall survival (OS) of LUAD patients. The ARGs were enriched in several biological functions and signaling pathways. Finally, differences of immune landscape were investigated among the high- and low-risk groups stratified by the signature.

Conclusions
This study revealed potential relationships between ARGs and prognosis of LUAD. The prognostic predictors identified in present study could be utilized as potential biomarkers for clinical applications.

https://onlinelibrary.wiley.com/doi/full/10.1111/1759-7714.14766