Inflammation-based lung adenocarcinoma molecular subtype identification and construction of an inflammation-related signature with bulk and single-cell RNA-seq data

• Published in: Aging (Albany, NY.) Vol. 16; no. 10; pp. 8822 - 8842
• Main Authors: Gu, Yan, Bian, Chengyu, Wang, Hongchang, Fu, Chenghao, Xue, Wentao, Zhang, Wenhao, Mu, Guang, Xia, Yang, Wei, Ke, Wang, Jun
• Format: Journal Article
• Language: English
• Published: United States Impact Journals 20.05.2024
• Subjects: Adenocarcinoma of Lung - genetics; Adenocarcinoma of Lung - immunology; Adenocarcinoma of Lung - pathology; Aged; Female; Gene Expression Regulation, Neoplastic; Humans; Inflammation - genetics; Lung Neoplasms - genetics; Lung Neoplasms - immunology; Lung Neoplasms - pathology; Male; Middle Aged; Prognosis; Research Paper; RNA-Seq; Single-Cell Analysis; Single-Cell Gene Expression Analysis; Tumor Microenvironment - genetics; Tumor Microenvironment - immunology; molecular subtypes; immune; tumor microenvironment; inflammation; lung adenocarcinoma
• DOI: 10.18632/aging.205840
• ISSN: 1945-4589

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.