Research Paper Volume 16, Issue 10 pp 8822—8842
Inflammation-based lung adenocarcinoma molecular subtype identification and construction of an inflammation-related signature with bulk and single-cell RNA-seq data
- 1 Department of Thoracic Surgery, Jiangsu Province Hospital and the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
- 2 Department of Thoracic Surgery, The First People’s Hospital of Changzhou and The Third Affiliated Hospital of Soochow University, Changzhou 213004, Jiangsu, China
Received: December 11, 2023 Accepted: April 15, 2024 Published: May 20, 2024
https://doi.org/10.18632/aging.205840How to Cite
Copyright: © 2024 Gu 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
The role of inflammation is increasingly understood to have a central influence on therapeutic outcomes and prognosis in lung adenocarcinoma (LUAD). However, the detailed molecular divisions involved in inflammatory responses are yet to be fully elucidated. Our study identified two main inflammation-oriented LUAD grades: the inflammation-low (INF-low) and the inflammation-high (INF-high) subtypes. Both presented with unique clinicopathological features, implications for prognosis, and distinctive tumor microenvironment profiles. Broadly, the INF-low grade, marked by its dominant immunosuppressive tumor microenvironment, was accompanied by less favorable prognostic outcomes and a heightened prevalence of oncogenic mutations. In contrast, the INF-high grade exhibited more optimistic clinical trajectories, underscored by its immune-active environment. In addition, our efforts led to the conceptualization and empirical validation of an inflammation-centric predictive model with considerable predictive potency. Our study paves the way for a refined inflammation-centric LUAD classification and fosters a deeper understanding of tumor microenvironment intricacies.
Abbreviations
AUC: area under the curve; CCL: cancer cell lines; CNV: copy number variation; CR: complete response; CTRP: cancer therapeutics response portal; FRAP: fluorescence recovery after photo-bleaching; GEO: Gene Expression Omnibus; IC50: half-maximal inhibitory concentration; ICI: immune checkpoint inhibitor; IDR: intrinsically disordered region; INF-high: inflammation-high; INF-low: inflammation-low; LLPS: liquid-liquid phase separation; LUAD: lung adenocarcinoma; OS: overall survival; PCA: principal component analysis; PD: progressive disease; PFS: progression-free survival; PR: partial response; PRISM: profiling of relative inhibition simultaneously in mixtures; qRT-PCR: quantitative real-time PCR; RELA-OE: RELA overexpression; RELA-sh: RELA short hairpin RNA; SD: stable disease; siRNA: small interfering RNA; SNV: single nucleotide variant; TCGA: The Cancer Genome Atlas; TME: tumor microenvironment.