Research Paper Volume 16, Issue 3 pp 2273—2298
Prognostic value and immune landscapes of anoikis-associated lncRNAs in lung adenocarcinoma
- 1 Department of Thoracic Surgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, China
- 2 Department of Thoracic Surgery, Baoding No.1 Central Hospital, Baoding 071000, China
Received: May 9, 2023 Accepted: December 19, 2023 Published: February 5, 2024
https://doi.org/10.18632/aging.205481How to Cite
Copyright: © 2024 Wu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
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.
Abbreviations
AUC: Area under curve; ARlncRNAs: Anoikis-related lncRNAs; CI: Confidence interval; C-index: Concordance index; DCA: Decision curve analysis; ECM: The extracellular matrix; GSEA: Gene set enrichment analysis; HPA: The Human Protein Atlas; HR: Hazard ratio; LASSO: Least absolute shrinkage and selection operator; LC: Lung cancer; TCGA: The Cancer Genome Atlas database; LncRNAs: Long noncoding RNAs; LUAD: Lung adenocarcinoma; OS: Overall survival; PCA: Principal component analysis; ROC: Receiver operating characteristic curve; TIDE: The tumor immune dysfunction and exclusion; TMB: Tumor mutation burden; TME: Tumor microenvironment.