aceIntegrating network toxicology, machine learning, and single-cell sequencing systems to analyze autophagy core genes in lung adenocarcinoma
The heterogeneity and complex tumor microenvironment of lung adenocarcinoma lead to poor prognosis. Autophagy, as a key cellular process, interacts with tumor immune infiltration and jointly affects the progression of lung adenocarcinoma, but its core regulatory genes and mechanisms are still unclear.This study integrated three lung adenocarcinoma transcriptome datasets from the GEO database and performed cross-analysis with the human autophagy gene set to screen for differentially expressed autophagy-related ge…
Using Cox regression, SHAP analysis and 100-algorithm validation, researchers selected five autophagy-related core genes and built a prognostic model for lung adenocarcinoma that showed AUC > 0.9 in independent cohort GSE68465.
Evidence
- Peer-reviewedNaunyn-Schmiedeberg's Archives of Pharmacology2026-08-02
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Truvace Impact Record TRV-2026-0625, v1: “Integrating network toxicology, machine learning, and single-cell sequencing systems to analyze autophagy core genes in lung adenocarcinoma.” Truvace, 2026-08-03. /record/TRV-2026-0625 (accessed at citation time). sha256 aa535aba62288704…
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