Background
The tumor microenvironment (TME) in lung adenocarcinoma (LUAD) influences tumor progression and immunosuppressive phenotypes through cell communication. We aimed to decipher cellular communication and molecular patterns in LUAD.
Conclusion
Our study elucidates molecular patterns and cell communication interactions in LUAD as effective biomarkers and predictors of immunotherapy response. Targeting cell communication interactions offers novel avenues for LUAD immunotherapy and prognostic evaluations, with ITGB1 emerging as a promising therapeutic target.
Methods
We analyzed scRNA-seq data from LUAD patients in multiple cohorts, revealing complex cell communication networks within the TME. Using cell chat analysis and COSmap technology, we inferred LUAD's spatial organization. Employing the NMF algorithm and survival screening, we identified a cell communication interactions (CCIs) model and validated it across various datasets.
Results
We uncovered intricate cell communication interactions within the TME, identifying three LUAD patient subtypes with distinct prognosis, clinical characteristics, mutation status, expression patterns, and immune infiltration. Our CCI model exhibited robust performance in prognosis and immunotherapy response prediction. Several potential therapeutic targets and agents for high CCI score patients with immunosuppressive profiles were identified. Machine learning algorithms pinpointed the novel candidate gene ITGB1 and validated its role in LUAD tumor phenotype in vitro.