AI and Cancer Research ── Lung ── 2026-10-22

2 Lung papers. Lead: Integrative transcriptomic and machine-learning analysis of the STAT1–GOLM1 axis in lung adenocarcinoma and small-molecule inhibitor discovery

Journals covered and how papers are chosen: see the index

Lung cancer: treatment, trials and AI

Integrative transcriptomic and machine-learning analysis of the STAT1–GOLM1 axis in lung adenocarcinoma and small-molecule inhibitor discovery
npj Precision Oncology · 2026-10-20 · Evidence: Preclinical (bioinformatics analysis and in vitro experiments)
Abstract
Tumor invasion and distant metastasis remain major causes of treatment failure in lung adenocarcinoma (LUAD), thereby elucidating the molecular mechanisms is critical for precise treatment of LUAD. Using bulk RNA-Seq and single cell transcriptome data, combining with multi-machine learning methods, we identified GOLM1 as a core gene associated with poor prognosis in LUAD, which is highly expressed in malignant epithelial cells characterized by increased extracellular matrix (ECM)-integrin signaling, glycosylation remodelling and invasion-related transcriptional programs. Mechanistically, we validated that STAT1 is an upstream transcription activator of GOLM1 that forms a STAT1-GOLM1 axis that triggers LUAD invasion. Candidate compounds were prioritized by computational molecular docking against STAT1 and subsequently evaluated in A549 cells. Experimental validation revealed that Compound 1 exhibited the strongest inhibitory effect on LUAD cells by regulating PI3K/AKT signaling, cell-cycle and apoptosis. Collectively, our results elucidated the significant role of STAT1-GOLM1 axis in LUAD and provided a promising AI-directed inhibitor for LUAD.
Journal IF-equivalent: 8.9 (OpenAlex 2-year mean citedness, value as of 2026-10-04, retrieved 2026-10-05; not the official Clarivate IF)
Reference: Yang Q, Zeng Y, Ba L, Zhu Z, Bie Y, Zhao N, et al. Integrative transcriptomic and machine-learning analysis of the STAT1–GOLM1 axis in lung adenocarcinoma and small-molecule inhibitor discovery. npj Precis Onc. 2026 Oct 20 [Epub ahead of print]. doi:10.1038/s41698-026-01735-0.
Checked: Abstract only
MO27-6 Prediction of EGFR mutations in non-small cell lung cancer: a patholomics random forest approach
Annals of Oncology · · Evidence: Prediction model development study (conference abstract)
Abstract
Epidermal growth factor receptor (EGFR) mutations play a critical role in guiding targeted therapies for non-small cell lung cancer (NSCLC), yet current diagnostic methods rely heavily on molecular testing. This study proposes a novel patholomics-based approach to predict EGFR mutation status using histopathological (H&E) image features and machine learning.
Journal IF-equivalent: 1.7 (OpenAlex 2-year mean citedness, value as of 2026-10-20, retrieved 2026-10-21; not the official Clarivate IF)
Reference: Cheung E, Li A, Mok F, Kwong V, Cheung A, Tsang E, et al. MO27-6 Prediction of EGFR mutations in non-small cell lung cancer: a patholomics random forest approach. Annals of Oncology. 2026 Nov;37:S299. doi:10.1016/j.annonc.2026.07.158.
Checked: Abstract only

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