Phenomics. 2025 Dec 15;5(5):469-486. doi: 10.1007/s43657-024-00188-4. eCollection 2025 Oct.

ABSTRACT

Quantifying individual health status from increasingly accumulated omics data is essential for both early prevention and intervention of diseases, which attracts great attention from communities of biology and medicine. Most of the existing approaches mainly classify individuals into different catalogues or classes based on phenotypes and biomarkers. However, an individual's health status from a dynamical systems viewpoint can be viewed as a non-equilibrium steady state, which can generally be characterized by two key features, i.e. (1) homeostatic potential that represents the ability of homeostatic resilience to withstand perturbations or maintain functions at the current state/phenotype of this individual and (2) phenotypic potential that represents the state/phenotype of the individual on the whole process from health to disease. Here, we proposed a health state manifold (HSM) method derived from dynamic network biomarker method and diffusion map theory to quantify individual health status with the characterization of such two features in a robust and accurate manner based on multi-omics data. To verify our method, HSM method was applied to the quantification of diabetes mellitus (rat subjects) and the Roux-en-Y Gastric Bypass (human subjects) for both disease progression process and recovery process, which demonstrated its effectiveness and potential for personalized medicine and preventive medicine.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s43657-024-00188-4.

PMID:41659741 | PMC:PMC12881232 | DOI:10.1007/s43657-024-00188-4

BMC Bioinformatics. 2025 Dec 3;26(1):290. doi: 10.1186/s12859-025-06319-6.

ABSTRACT

Lung cancer is one of the most prevalent malignant tumors with high morbidity and mortality rates worldwide. Extensive multi-omics analyses have revealed significant intratumoral heterogeneity even within the same histopathological subtype. However, a database that systematically integrates multi-omics data for lung cancer research has long been lacking. Here, we developed LOSTdb, a molecular subtype annotation system for lung cancer that integrates multi-omics data and metadata. LOSTdb comprises 295 multi-omics datasets, including bulk RNA-seq, genomic, proteomic, methylation, and scRNA-seq data, with over 10,000 manually curated metadata entries. This resource encompasses high-quality clinical specimens, mouse models, and cell lines, totaling 34,393 samples and more than 1.2 million single cells. Each omics sample was annotated with both literature-based classical subtypes and NMF-derived meta-program (MP) subtypes. The platform supports cross-searching of omics and metadata at the gene and dataset levels, offers multiple visualization and analysis methods, and includes five tool modules, enabling functions such as integrated analysis, significance analysis between metadata as well as between genes and metadata, and target prediction for lung cancer molecular subtypes, serving as an essential tool for lung cancer precision medicine. LOSTdb is a user-friendly interactive database freely accessible at http://lostdbcancer.com:8080 .

PMID:41339793 | DOI:10.1186/s12859-025-06319-6

BMC Bioinformatics. 2025 Dec 3;26(1):290. doi: 10.1186/s12859-025-06319-6.

ABSTRACT

Lung cancer is one of the most prevalent malignant tumors with high morbidity and mortality rates worldwide. Extensive multi-omics analyses have revealed significant intratumoral heterogeneity even within the same histopathological subtype. However, a database that systematically integrates multi-omics data for lung cancer research has long been lacking. Here, we developed LOSTdb, a molecular subtype annotation system for lung cancer that integrates multi-omics data and metadata. LOSTdb comprises 295 multi-omics datasets, including bulk RNA-seq, genomic, proteomic, methylation, and scRNA-seq data, with over 10,000 manually curated metadata entries. This resource encompasses high-quality clinical specimens, mouse models, and cell lines, totaling 34,393 samples and more than 1.2 million single cells. Each omics sample was annotated with both literature-based classical subtypes and NMF-derived meta-program (MP) subtypes. The platform supports cross-searching of omics and metadata at the gene and dataset levels, offers multiple visualization and analysis methods, and includes five tool modules, enabling functions such as integrated analysis, significance analysis between metadata as well as between genes and metadata, and target prediction for lung cancer molecular subtypes, serving as an essential tool for lung cancer precision medicine. LOSTdb is a user-friendly interactive database freely accessible at http://lostdbcancer.com:8080 .

PMID:41339793 | PMC:PMC12676782 | DOI:10.1186/s12859-025-06319-6

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