Molecular subtyping for lung adenocarcinoma and a novel prognostic model based on ligand-receptor pairs

腺癌 亚型 肺癌 医学 阿卡克信息准则 肿瘤科 内科学 比例危险模型 Lasso(编程语言) 癌症 生物信息学 生物 机器学习 万维网 计算机科学 程序设计语言
作者
Dong Li,Xu-chen Ma,Song-lei Ou
出处
期刊:Advances in Medical Sciences [Elsevier BV]
卷期号:67 (2): 316-327
标识
DOI:10.1016/j.advms.2022.08.004
摘要

Lung adenocarcinoma (LUAD) is a leading cause of cancer death worldwide. Ligands and receptors play important roles in cell communication. This study aimed to demonstrate the importance of ligand-receptor (LR) pairs in LUAD development through constructing molecular subtypes and a prognostic model based on LR pairs.A total of 1110 LUAD samples with clinical and expression data were obtained from public databases. Unsupervised consensus clustering was applied to construct molecular subtypes based on LR pairs. Least absolute shrinkage and selection operator (LASSO) Cox regression and stepwise Akaike information criterion (stepAIC) were conducted to build a prognostic model.Three molecular subtypes (C1, C2 and C3) were constructed based on 17 prognosis-related LR pairs. C1 subtype had the worst prognosis, while C3 subtype had the optimal prognosis. Oncogenic pathways such as epithelial-mesenchymal transition (EMT) were activated in C1 subtype. A prognostic model was built based on 8 LR pairs, and could classify samples into high- and low-LR score groups. Two groups had distinct overall survival and tumor microenvironment (TME). High-LR score group was more sensitive to chemotherapeutic drugs, while low-LR score group could benefit much from anti-PD-1/PD-L1 therapy.The study showed that LR pairs played critical roles in LUAD development. The prognostic model could predict prognosis and guide personalized therapy for LUAD patients.
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