Mil Med Res. 2026 Jan 6;12(1):94. doi: 10.1186/s40779-025-00679-z.

ABSTRACT

BACKGROUND: Despite the predictive impact of circulating tumor DNA (ctDNA) minimal residual disease (MRD), accurate prediction of failure risk after curative-intent treatments for early-stage or localized non-small cell lung cancer (NSCLC) patients to guide personalized therapy remains challenging. This study aimed to develop and validate an interpretable artificial intelligence-assisted model using global data resources.

METHODS: Liquid biopsy data, blood-based genomic alterations, clinicopathological features, and survival outcomes of stage I-III NSCLC patients who underwent surgery or definitive chemoradiotherapy were collected from 6 cohorts. PRIME (Progression Risk prediction by Interpretable Machine learning on ctDNA-MRD, Mutations, and clinical-therapeutic features) was trained by 6 machine learning algorithms across 4 cohorts and validated in 2 independent cohorts. Model performance was evaluated by the area under the curve (AUC) and interpreted by SHapley Additive exPlanations (SHAP). Whole-exome sequencing (WES) or whole-genome sequencing (WGS) of tumor tissue from 430 stage II-III NSCLC patients and RNA-sequencing (RNA-seq) data from 1149 subjects, sourced from The Cancer Genome Atlas, were used to validate the prognostic effect of mutations identified in peripheral blood and investigate the underlying mechanisms.

RESULTS: A global dataset encompassing 781 blood samples from 493 patients was analyzed. Clinical stage, pre-treatment ctDNA, post-treatment MRD, blood-based Kelch-like ECH-associated protein 1 (KEAP1), serine/threonine kinase 11 (STK11), and cyclin-dependent kinase inhibitor 2A (CDKN2A) mutations, and treatment modality were significantly associated with the risk of disease progression and were thereby included in the model training. WES/WGS and RNA-seq confirmed the poor prognostic effect of KEAP1, STK11, and CDKN2A mutations, which were characterized by the suppressive tumor microenvironment and attenuated humoral immunity. The neural network (NN) model exhibited optimal prediction of treatment failure risk in the training (AUC = 0.85, 95% CI 0.81-0.89) and validation sets (AUC = 0.82, 95% CI 0.74-0.89). SHAP analysis indicated that MRD (+0.306), treatment modality (+0.128), and pre-treatment ctDNA (+0.043) ranked in the top 3 contributions. NN-PRIME outperformed single liquid biopsy biomarkers and clinical-therapeutic signatures, and demonstrated consistent robustness across different clinical scenarios. High-risk patients identified by NN-PRIME had poorer prognoses but derived significant benefits from adjuvant therapy after surgery.

CONCLUSIONS: As an interpretable model integrating readily-accessible and crucial clinical-genomic predictors, PRIME achieves enhanced performance, allowing for early outcome prediction, refined risk stratification, and personalized clinical decision-making.

PMID:41491583 | PMC:PMC12771999 | DOI:10.1186/s40779-025-00679-z

Cell. 2025 Aug 22:S0092-8674(25)00922-5. doi: 10.1016/j.cell.2025.08.008. Online ahead of print.

ABSTRACT

Performing total RNA profiling on formalin-fixed, paraffin-embedded (FFPE) samples, the predominant sample conservation method in clinical practice, remains challenging for current spatial transcriptomics techniques. Here, we introduce Stereo-seq V2, which employs random primers to capture and sequence RNAs in situ on FFPE sections and provides single-cell resolution. The random-priming-based strategy offers unbiased transcript capturing and uniform gene body coverage, which increase the sensitivity to marker genes, the efficiency of non-polyadenylation (poly(A)) RNA profiling, and immune repertoire coverage. We demonstrated the robust performance of Stereo-seq V2 on clinical FFPE samples using triple-negative breast cancer (TNBC) sections and identified tumor-specific alternative splicing events. In a Mycobacterium tuberculosis (Mtb)-infected mouse model, we monitored gene expression dynamics of host and pathogen transcriptomes simultaneously by utilizing Stereo-seq V2. We also assembled immune repertoires and identified Mtb-specific BCR clones, which could also be observed in human tuberculous lung samples. These results highlight Stereo-seq V2's potential in biomedical research and personalized medicine.

PMID:40882628 | DOI:10.1016/j.cell.2025.08.008

Int J Surg. 2025 Jul 22. doi: 10.1097/JS9.0000000000002999. Online ahead of print.

ABSTRACT

BACKGROUND: Breast cancer remains the most prevalent malignancy among women, and patients presenting with both breast and lung cancer pose significant challenges in clinical diagnosis and treatment. Currently, comprehensive multi-omics analyses for such multiple malignancies are lacking.

METHODS: An integrated multi-omics analysis was performed, incorporating quantitative proteomics and radiomics data from patients with single primary breast cancer as well as those with multiple primary tumors (breast and lung cancer).

RESULTS: Quantitative proteomics analysis revealed four distinct molecular signatures (Types I-IV). Patients with single breast cancer exhibited driving pathways primarily linked to cell proliferation (e.g., HER2), whereas those with multiple breast cancers showed enrichment in ER-related and proliferative pathways. In contrast, patients with multiple lung cancers displayed pathways associated with immune response and immune escape. Additionally, immune subtyping identified three distinct immune landscapes (Types I-III). Radiomic analysis demonstrated strong correlations between these molecular/immune subtypes and imaging findings. Patients with high imaging information scores exhibited pronounced tumor heterogeneity and reduced immune infiltration.

CONCLUSIONS: This study provides new insights into the molecular pathogenesis of multiple primary malignancies, particularly breast and lung cancer.

PMID:40694032 | DOI:10.1097/JS9.0000000000002999

Sci Data. 2025 Apr 1;12(1):545. doi: 10.1038/s41597-025-04912-1.

ABSTRACT

Diagnosing lung cancer at a curable stage offers the opportunity for a favorable prognosis. The emerging epigenomics analysis on plasma cell-free DNA (cfDNA), including 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) modifications, has acted as a promising approach facilitating the identification of lung cancer. And, integrating 5mC biomarker with chest computed tomography (CT) image features could optimize the diagnosis of lung cancer, exceeding the performance of models built on single feature. However, the clinical applicability of integrated markers might be limited by the potential risk of overfitting due to small sample size. Hence, we prospectively collected peripheral blood sample and the paired chest CT images of 2032 patients with indeterminate pulmonary nodules across 5 centers, and constructed a large-scale, multi-institutional, multiomics database that encompass CT imaging data and plasma cfDNA fragmentomic in 5mC-, 5hmC-enriched regions. To our best knowledge, this dataset is the first radio-epigenomic dataset with the largest sample size, and provides multi-dimensional insights for early diagnosis of lung cancer, facilitating the individuated management for lung cancer.

PMID:40169596 | PMC:PMC11961589 | DOI:10.1038/s41597-025-04912-1

Animals (Basel). 2025 Jan 13;15(2):200. doi: 10.3390/ani15020200.

ABSTRACT

Asthma has been extensively studied in humans and animals, but the molecular mechanisms underlying asthma in Meishan pigs, a breed with distinct genetic and physiological characteristics, remain elusive. Understanding these mechanisms could provide insights into veterinary medicine and human asthma research. We investigated asthma pathogenesis in Meishan pigs through transcriptomic and metabolomic analyses of blood samples taken during autumn and winter. Asthma in Meishan pigs is related to inflammation, mitochondrial oxidative phosphorylation, and tricarboxylic acid (TCA) cycle disorders. Related genes include CXCL10, CCL8, CCL22, CCL21, OLR1, and ACKR1, while metabolites include succinic acid, riboflavin-5-phosphate, and fumaric acid. Transcriptomic sequencing was performed on panting and normal Meishan pigs, and differentially expressed genes underwent functional enrichment screening. Metabolomic analysis revealed differential metabolites and pathways between groups. Combined analyses indicated that lung inflammation is influenced by genetic, allergenic, and environmental factors disrupting oxidative phosphorylation in lung mitochondria, affecting the TCA cycle. Mitochondrial reactive oxygen species, glutathione S-transferases, arginase 1 and RORC in immune regulation, the Notch pathway, YPEL4 in cell proliferation, and MARCKS in airway mucus secretion play roles in asthma pathogenesis. This study highlights that many cytokines and signaling pathways contribute to asthma. Further studies are needed to elucidate their complex interactions.

PMID:39858200 | PMC:PMC11759154 | DOI:10.3390/ani15020200