Int J Biol Macromol. 2025 Sep 5;327(Pt 2):147473. doi: 10.1016/j.ijbiomac.2025.147473. Online ahead of print.

ABSTRACT

Osteosarcoma (OS), the most prevalent primary bone malignancy in adolescents, is characterized by aggressive progression and early metastasis. However, the epigenetic drivers of its metastatic heterogeneity remain poorly understood. Herein, we integrated bulk DNA methylation profiling and single-cell RNA sequencing (scRNA-seq) to elucidate the epigenetic mechanisms driving OS metastatic heterogeneity. Consensus clustering identified two methylation subtypes (K = 2) with distinct survival outcomes, where hypermethylated (MSO-high) tumors exhibited poor prognosis. Weighted gene co-expression network analysis (WGCNA) revealed methylation-associated modules enriched in metabolic and immune pathways, pinpointing key genes such as CAMK1G and SLC11A1. Single-cell profiling uncovered MSO-high myeloid cells associated with inflammatory and oxidative phosphorylation pathways, while MSO-high OS cells displayed transdifferentiation toward fibroblasts via pseudotime trajectories, remodeling the extracellular matrix (ECM) to facilitate lung metastasis. Conversely, MSO-low tumors activated HLA-B-mediated neutrophil-CD8+ T cell interactions, promoting lymphatic metastasis via CXCR4/CXCL12 signaling. Furthermore, functional validation using the DNA demethylating agent decitabine demonstrated reduced fibroblastic transdifferentiation and suppressed invasive capacity in MSO-high osteosarcoma cells, supporting the therapeutic potential of targeting methylation dysregulation. These findings establish a model where DNA methylation dictates metastatic phenotypes through differential tumor-stromal crosstalk, providing novel targets for epigenetic therapy to disrupt fibrotic-immune networks and metastatic colonization.

PMID:40915448 | DOI:10.1016/j.ijbiomac.2025.147473

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

Discov Oncol. 2025 May 21;16(1):847. doi: 10.1007/s12672-025-02692-z.

ABSTRACT

Lung cancer remains one of the leading causes of cancer-related mortality, with non-small cell lung cancer (NSCLC) accounting for 85% of cases worldwide. NSCLC pathogenesis and progression are intricately linked to inflammatory stimuli, immune evasion, and metabolic reprogramming. In this study, the impact of inflammation, immunity, and metabolism on NSCLC was investigated by a Mendelian randomization analysis taking 91 inflammatory factors, 731 immune cells, and 1400 metabolites as exposures, and the FinnGen database NSCLC cohort (ncases = 5315, ncontrol = 314,193) was the outcome. A number of metabolites, inflammatory proteins, and immune cells were identified as potentially associated with NSCLC based on mendelian randomization analysis. Validation in the UK Biobank database lung cancer cohort (ncases = 2671, ncontrols = 372,016) further confirmed the inhibitory role of the metabolite N-acetyl-aspartyl-glutamate (NAAG) on lung cancer. Subsequently, single-cell and protein-protein interaction analyses identified inflammatory protein expression patterns in NSCLC, distribution ratios of immune cells in NSCLC. Subsequent multi-omics network analysis showed key interaction nodes between NAAG and inflammatory proteins. These findings enhance the understanding of the roles of inflammation, immunity, and metabolism in NSCLC occurrence and progression, offering potential targets and strategies for further research on its treatment and management.

PMID:40397292 | PMC:PMC12095725 | DOI:10.1007/s12672-025-02692-z