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…
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Researchers combined three lung adenocarcinoma GEO transcriptome datasets with a human autophagy gene set, identified 276 shared differentially expressed autophagy genes, and used protein interaction networks plus machine learning to select five core prognostic genes ENG, CDH1, KLF4, IL6 and MMP9, validating a model based on them in independent cohort GSE68465 with AUC > 0.9 by August 2026.
The work matters because it links autophagy regulation to tumor immune microenvironment changes and smoking risk via nicotine binding, suggesting new prognostic and therapeutic targets for lung adenocarcinoma, while it remains uncertain how the five-gene signature performs prospectively in clinical care or across diverse patient populations.
- Study integrated three GEO lung adenocarcinoma transcriptome datasets with human autophagy gene set and found 276 shared autophagy-related differentially expressed genes.
- Enrichment analysis showed core genes significantly enriched in cellular senescence, autophagy, and FoxO signaling pathways.
- Immune infiltration analysis found M1 macrophages and naefve B cells significantly upregulated in tumor tissues whereas resting dendritic cells were downregulated.
- Single-cell analysis identified specific expression of CDH1 and TGFB1 in type II alveolar cells and immune cells.
- Network toxicology and molecular docking showed nicotine exhibits high-affinity binding to CDH1, HIF1A, KLF4, TGFB1, and BCL2.
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.
The rundown
By publication date 2026-08-02, authors reported integrating three GEO transcriptome datasets, constructing protein interaction networks, applying Cox regression and SHAP analysis, and validating with independent dataset GSE68465 using a combination of 100 algorithms.
Additional analyses reported included enrichment analysis, immune infiltration assessment, single-cell transcriptome analysis identifying CDH1 and TGFB1 in type II alveolar cells, and network toxicology with molecular docking for nicotine binding.
Sources
- Peer-reviewedNaunyn-Schmiedeberg's Archives of Pharmacology2026-08-02
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