Phosphoproteomics of extracellular vesicles integrated with multiomics analysis reveals novel kinase networks for lung cancer

Phosphorylation regulates the functions of proteins and aberrant phosphorylation often leads to a variety of diseases, including cancers. Extracellular vesicles (EVs) are important messengers in the microenvironment and their proteome contributes to cancer genesis and metastasis, while the kinases that driving EVs proteins' phosphorylation are less known. Clinical tissue samples from 13 patients with non-small-cell lung cancer (NSCLC) were utilized to isolate cancer EVs and adjacent normal EVs. Through quantitative phosphoproteomics analysis, 2473 phosphorylation sites on 1567 proteins were successfully identified and quantified. Accordingly, 152 kinases were identified, and 25 of them were differentially expressed. Based on Tied Diffusion through Interacting Events (TieDIE) algorithm, we integrated genomic and transcriptomic data sets of NSCLC from TCGA with our phosphoproteome data set to construct signaling networks. Through database integration and multiomics enrichment analysis, a compact network of 234 nodes with 1599 edges was constructed, which consisted of 34 transcription factors, 33 kinases, 63 aberrant genes, and 172 linking proteins. Rarely studied phosphorylation sites were specifically enriched. Key phosphoproteins of network nodes were validated in patients' EVs, including MAPK6(S189), IKBKES172, SRCY530, CDK7(S164), and CDK1(T14). These networks depict intrinsic signal-regulation derived from EVs' phosphoproteins, providing a comprehensive and pathway-based strategy for in-depth lung cancer research.

Automated summary

Phosphorylation regulates protein functions, and abnormal phosphorylation is associated with diseases such as cancer. In this study, clinical samples from lung cancer patients were used to isolate cancer EVs and normal EVs, and quantitative phosphoproteomics analysis revealed differentially expressed kinases. By integrating genomic, transcriptomic, and phosphoproteome data, a signaling network of lung cancer was constructed, providing a comprehensive strategy for in-depth research on lung cancer.