Abstract
Gastric cancer remains highly lethal, yet how protein S-palmitoylation shapes tumour ecosystems and clinical outcome is unclear. We integrated single-cell RNA sequencing (119,931 cells from 25 gastric tumours) with spatial transcriptomics and bulk cohorts to delineate palmitoylation-linked states across malignant, immune, and stromal compartments. A palmitoylation-high malignant programme partitioned into three metastasis-enriched subclusters with increased fatty-acid metabolism and Ras–MAPK signalling and predicted worse survival. Spatial mapping and ligand–receptor inference revealed co-localised niches where palmitoylation-high tumour cells interacted with immunosuppressive myeloid cells and distinct CAF subsets, with strengthened pro-angiogenic and pro-fibrotic cues. We derived and validated an 87-gene multicellular palmitoylation signature for risk stratification, and higher scores were consistently associated with adverse outcomes in external cohorts. Drug-response modelling highlighted vulnerabilities involving the HSP90–PI3K/MAPK axis. Functional assays and xenografts confirmed SH3BGRL as a key driver within this poor-prognosis programme.
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Data Availability
The datasets generated and/or analysed during the current study are available in the corresponding repositories. RNA sequencing data and associated clinical data for GC samples were retrieved from The Cancer Genome Atlas (TCGA) and can be accessed through the TCGA portal. Single-cell RNA-seq and spatial transcriptomics data are available from the GEO database (accession numbers: GSE167297, GSE184198, OMIX001073). Further inquiries regarding the datasets should be directed to the corresponding authors.
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Acknowledgements
This study was supported by the General Programme of Health Commission of WuXi (No. M202330) and the collaborative custom development of RNA probes at Soochow University (No. P112213323). The authors would like to acknowledge the contributions of the research participants and the technical support from the institutions involved in this project.
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J.X., Y.H., and Q.Q. contributed equally to this work. J.X. conceived and designed the study. Y.H. and Q.Q. performed data acquisition, preprocessing, and integration of the TCGA and GEO datasets. Y.D.L. and F.C. developed and optimised the machine-learning framework and conducted model training and validation. S.S.H. and H.B.Y. performed the single-cell and spatial transcriptomic analyses. J.L. supervised the computational experiments and provided methodological guidance. Y.Q. and S.H.X. coordinated the project, provided critical revisions, and secured funding. J.X., Y.H., and Q.Q. drafted the manuscript. Y.D.L., F.C., S.S.H., and H.B.Y. prepared figures and contributed to data visualisation. J.L., Y.Q., and S.H.X. reviewed and edited the manuscript. All authors discussed the results, approved the final version, and agree to be accountable for all aspects of the work.
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Xu, J., Hu, Y., Qiao, Q. et al. AI-enabled single-cell dissection of the palmitoylation landscape identifies a multicellular prognostic program in gastric cancer. npj Precis. Onc. (2026). https://doi.org/10.1038/s41698-026-01359-4
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DOI: https://doi.org/10.1038/s41698-026-01359-4
