ArticleFrontiers in pharmacology2025
Spatial deciphering of the transcriptomic heterogeneity of tumor spread through air spaces in lung cancer.
Article in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Ensemble machine learning classifiers based on computed tomography radiomics for predicting spread through air spaces in lung adenocarcinoma: a multicenter retrospective cohort study with transcriptomic interpretation.Quantitative imaging in medicine and surgery · 2026Article
- Preoperative Prediction of Spread Through Air Spaces in Lung Cancer UsingDiagnostics (Basel, Switzerland) · 2026Article
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9 authors.
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Abstract
Background: Spread through air spaces (STAS) represents a novel invasion mechanism in adenocarcinoma that considerably influences lung cancer clinical outcomes; however, studies of its mechanisms at the spatial level are lacking. Methods: We used the NanoString GeoMx digital spatial profiling (DSP) technology to conduct a spatial transcriptomic analysis of surgically resected tissues from non-small-cell lung cancer (NSCLC) patients with or without STAS. Results: Compared with tumor nests in non-STAS patients, Conclusion: This study is the first to use DSP to analyze spatial transcriptomic profiles of NSCLC tumor nests and air space tumors, and it identifies potential module features that may be used for STAS identification and prognosis.
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