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

## TL;DR

This study identifies two inflammation-based subtypes of lung adenocarcinoma and develops a predictive model to improve understanding of tumor environments and patient outcomes.

## Contribution

The novel contribution is the identification of INF-low and INF-high subtypes and the development of an inflammation-related predictive model.

## Key findings

- Two inflammation-based LUAD subtypes (INF-low and INF-high) were identified with distinct clinicopathological and tumor microenvironment features.
- The INF-low subtype is associated with poor prognosis and more oncogenic mutations, while INF-high shows better clinical outcomes.
- An inflammation-related predictive model was developed and validated for LUAD classification.

## 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.