Single-cell analysis of lung cancer metabolism and its clinical implications.

[BACKGROUND] Lung cancer is a highly prevalent and invasive malignancy, characterized by profound metabolic reprogramming as one of its key features. The advent of single-cell RNA sequencing (scRNA-seq) has allowed us to study cellular metabolism in greater detail. In this study, we systematically explore the metabolic pathways of distinct cell types within the lung cancer tumor microenvironment using scRNA-seq data. Moreover, we identify potential biomarkers with diagnostic and prognostic significance.

[METHODS] We leveraged scRNA-seq data from lung cancer to map the metabolic landscape of different cell types in the tumor microenvironment. Malignant cells were classified into three distinct subgroups based on their metabolic activity: high-metabolism, intermediate-metabolism, and low-metabolism.

[RESULTS] Malignant cells exhibit significantly higher metabolic activity compared to non-malignant cell types. The low-metabolism state was strongly associated with immune signaling pathways, with FSCN1 identified as a key marker. This state revealed a distinct population of cells enriched for cancer stem cell (CSC)-like characteristics.

[CONCLUSION] This study provides a comprehensive exploration of the metabolic characteristics of malignant cells in lung cancer at single-cell resolution. Our findings provide insights that could improve prognosis and support more targeted treatments for lung cancer patients.