Nature, Published online: 02 July 2025; doi:10.1038/s41586-025-09096-7
The Somatic Mosaicism across Human Tissues Network aims to create a reference catalogue of somatic mosaicism across different tissues and cells within individuals.
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
Front Pharmacol. 2025 Feb 19;16:1540477. doi: 10.3389/fphar.2025.1540477. eCollection 2025.
ABSTRACT
BACKGROUND: Lung adenocarcinoma (LUAD) remains a major cause of cancer-related mortality worldwide, with high heterogeneity and poor prognosis. Epigenetic dysregulation plays a crucial role in LUAD progression, yet its potential in molecular classification and therapeutic prediction remains largely unexplored.
METHODS: We performed an integrated multi-omics analysis of 432 LUAD patients from TCGA and 398 patients from GEO datasets. Using consensus clustering and random survival forest (RSF) algorithms, we established an epigenetic-based molecular classification system and constructed a prognostic model. The model's performance was validated in multiple independent cohorts, and its biological implications were investigated through comprehensive functional analyses.
RESULTS: We identified two distinct molecular subtypes (CS1 and CS2) with significant differences in epigenetic modification patterns, immune microenvironment, and clinical outcomes (P = 0.005). The RSF-based prognostic model demonstrated robust performance in both training (TCGA-LUAD) and validation (GSE72094) cohorts, with time-dependent AUC values ranging from 0.625 to 0.694. Low-risk patients exhibited enhanced immune cell infiltration, particularly CD8+ T cells and M1 macrophages, and showed better responses to immune checkpoint inhibitors. Drug sensitivity analysis revealed subtype-specific therapeutic vulnerabilities, with low-risk patients showing higher sensitivity to conventional chemotherapy and targeted therapy.
CONCLUSION: Our study establishes a novel epigenetic-based classification system and predictive model for LUAD, providing valuable insights into patient stratification and personalized treatment selection. The model's ability to predict immunotherapy response and drug sensitivity offers practical guidance for clinical decision-making, potentially improving patient outcomes through precision medicine approaches.
PMID:40046740 | PMC:PMC11879945 | DOI:10.3389/fphar.2025.1540477
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