Abstract Lung cancer is a highly malignant disease, posing a significant threat to global health. The presence of tumor heterogeneity results in substantial variations in prognosis and therapeutic responses among patients. Advances in bulk RNA sequencing and single-cell RNA sequencing have facilitated the identification of driver gene mutations and the exploration of cellular diversity within tumors. However, tumors are complex ecosystems comprising both tumor cells and their microenvironment, where interactions among different cell types give rise to specific functional structural units that collectively drive tumorigenesis and progression. The emergence of spatial omics technologies has allowed for the analysis of tumor ecosystems, providing unprecedented insights into tumor heterogeneity. This review aims to present updates on spatial omics technologies and data analysis algorithms, discuss current technical limitations, and explore potential future developments. Furthermore, we summarize the latest applications of spatial omics in elucidating lung cancer heterogeneity, investigating mechanisms of lung cancer progression and drug resistance, and identifying novel biomarkers. Based on these findings, we propose strategies for integrating spatial omics into lung cancer research, offering new perspectives for precision medicine.
He et al. (2026) studied this question.