AI and Cancer Research ── Lung ── 2026-10-17
Lung cancer: treatment, trials and AI
- Abstract
Background: Chronic inflammation and metabolic dysregulation are increasingly recognized as interconnected drivers of tumorigenesis and immune evasion, yet their combined prognostic and mechanistic relevance in lung adenocarcinoma (LUAD) remains poorly defined.
Methods: A curated 117-gene set representing systemic inflammation-metabolism dysregulation was obtained from MSigDB. Core prognostic genes were identified through differential expression analysis and univariate Cox regression. Using TCGA-LUAD as the training cohort and six independent GEO cohorts for external validation, 101 machine learning algorithm combinations were systematically evaluated to construct a robust prognostic model. Model performance was assessed by C-index, time-dependent ROC, and PCA. Immune characteristics were analyzed using TCIA, ESTIMATE, and TIMER-based deconvolution. ATP2A2, identified as the gene most strongly correlated with risk score, underwent multi-cohort transcriptomic and immunohistochemical validation, in vitro functional characterization, and mechanistic investigation via IOBR-integrated immune profiling. Single-cell RNA sequencing and multiplex immunofluorescence provided cellular-resolution verification of microenvironmental associations.
Results: The RSF + SuperPC model achieved optimal performance and effectively stratified patients into high- and low-risk groups with significantly divergent survival across all seven cohorts. High-risk tumors showed reduced immune/stromal infiltration and elevated tumor purity. ATP2A2 was consistently overexpressed in tumor tissue, correlated with poor differentiation, advanced stage, and shorter survival, and predicted immunotherapy resistance. Mechanistically, high ATP2A2 expression was associated with suppressed immune cell infiltration, attenuated chemokine signaling, impaired cancer-immunity cycle activity, and an “immune-desert” phenotype confirmed by single-cell and spatial analyses.
Conclusion: This study establishes a validated inflammation-metabolism-based prognostic signature in LUAD and identifies ATP2A2 as a key driver linking tumor aggressiveness to immunosuppressive microenvironment remodeling, positioning it as a promising prognostic biomarker and potential therapeutic target for overcoming immunotherapy resistance.
Journal IF-equivalent: 4.8 (OpenAlex 2-year mean citedness, value as of 2026-10-15, retrieved 2026-10-16; not the official Clarivate IF)Reference: Hu X, Zhang G, Zhang Y, Zhao Y, Zhou F, Li G. An AI-derived inflammation-metabolism ecosystem signature reveals ATP2A2 as a microenvironment-remodeling driver of immune evasion in lung adenocarcinoma. Front Cell Dev Biol. 2026 Oct 14;14:1971779. doi:10.3389/fcell.2026.1971779.Checked: Abstract only - Abstract
BACKGROUND: Distinct treatment strategies for small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC) brain metastases require accurate noninvasive subtyping. PURPOSE: To develop and externally validate a biparametric MRI-based 2.5D multi-instance learning (MIL) deep learning (DL) model for discriminating SCLC from NSCLC brain metastases, with performance compared against clinical, radiomics, and 3D-DL models. STUDY TYPE: Retrospective. POPULATION: This study included 366 patients from four institutions. Center 1 provided the training set (n = 199; mean age, 63.88 ± 10.79 years; 72.36% male) and an internal tuning set (n = 85; mean age, 64.73 ± 9.53 years; 67.06% male). Centers 2-4 were pooled into a single external test set (n = 82; mean age, 62.26 ± 10.60 years; 63.41% male). FIELD STRENGTH/SEQUENCE: 1.5 T and 3.0 T; contrast-enhanced T1-weighted imaging and fluid-attenuated inversion-recovery imaging. ASSESSMENT: The 2.5D-MIL model was benchmarked against clinical, radiomics, and 3D-DL models and evaluated on internal tuning and pooled external test sets. STATISTICAL TESTS: Shapiro-Wilk, Student's t-test, Mann-Whitney U, chi-square, univariable and multivariable logistic regression, and DeLong tests were used. Performance measures included the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals and classification and calibration metrics. Statistical significance was defined as two-sided p < 0.05. RESULTS: In the internal tuning set, AUCs were 0.842 (2.5D-MIL), 0.740 (clinical), 0.710 (radiomics), and 0.747 (3D-DL). In the external test set, AUCs were 0.826 (2.5D-MIL), 0.826 (clinical), 0.696 (radiomics), and 0.664 (3D-DL). Differences between 2.5D-MIL and the clinical (p = 1.000), radiomics (p = 0.139), and 3D-DL (p = 0.119) models were not significant. DATA CONCLUSION: The MRI-based 2.5D-MIL model showed potential for noninvasive differentiation of SCLC from NSCLC brain metastases. Larger prospective studies are needed before clinical use. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.
Journal IF-equivalent: 2.4 (OpenAlex 2-year mean citedness, value as of 2026-10-15, retrieved 2026-10-16; not the official Clarivate IF)Reference: Zhu J, Liu C, Zou L, Xu R, Xie X, Tian Y, et al. MRI‐Based 2.5D Multi‐Instance Deep Learning for Differentiating Pathological Subtypes of Brain Metastases From Lung Cancer. Magnetic Resonance Imaging. 2026 Oct 13 [Epub ahead of print]. doi:10.1002/jmri.70588.Checked: Abstract only