Commun Biol. 2025 Aug 6;8(1):1160. doi: 10.1038/s42003-025-08533-7.

ABSTRACT

The rapid advancement of single-cell technologies has led to the development of various analysis methods, each with trade-offs between predictive power and interpretability particularly for multimodal data integration. Complex machine learning models achieve high accuracy, but they often lack transparency, while simpler models are more interpretable but less effective for prediction. In this manuscript, we introduce an innovative method for single-cell analysis using Multiple Kernel Learning (scMKL), that merges the predictive capabilities of complex models with the interpretability of linear approaches, aimed at providing actionable insights from single-cell multiomics data. scMKL excels at classifying healthy and cancerous cell populations across multiple cancer types, utilizing data from single-cell RNA sequencing, ATAC sequencing, and 10x Multiome. It outperforms existing methods while delivering interpretable results that identify key transcriptomic and epigenetic features, as well as multimodal pathways- that existing methods have failed to achieve, in breast, lymphatic, prostate, and lung cancers. Leveraging insights from one dataset to inform analysis in a new dataset, scMKL uncovers biological pathways that distinguish treatment responses in breast cancer, low-grade from high-grade prostate tumors, and subtypes in lung cancer, thereby enhancing our understanding of cancer biology and tumor progression.

PMID:40770488 | PMC:PMC12328712 | DOI:10.1038/s42003-025-08533-7

Int J Med Sci. 2025 Jul 10;22(13):3268-3276. doi: 10.7150/ijms.117603. eCollection 2025.

ABSTRACT

Hepatocellular carcinoma (HCC) is a genetically heterogeneous malignancy in which single-gene mutations serve as critical drivers of tumor initiation, progression, and therapeutic resistance. Advances in high-throughput genomics and liquid biopsy technologies have highlighted the clinical utility of mutations in genes such as TP53, CTNNB1, and TERT as diagnostic, prognostic, and predictive biomarkers. These mutations disrupt key oncogenic pathways, modulate the tumor immune microenvironment, and contribute to intratumoral heterogeneity, complicating disease management. Mutation-guided precision medicine, including telomerase inhibitors, Wnt/β-catenin pathway modulators, and immune checkpoint blockade, offers promising avenues for individualized treatment in HCC. However, challenges persist in translating these findings into clinical practice due to mutation complexity, resistance mechanisms, and limitations in biomarker standardization. Emerging strategies such as multi-omics integration, artificial intelligence, and gene editing technologies hold potential to overcome these barriers and facilitate the development of personalized therapeutic regimens. This review summarizes the molecular mechanisms, clinical applications, and translational challenges of single-gene mutations in HCC, with the aim of guiding future research and precision oncology.

PMID:40765562 | PMC:PMC12320797 | DOI:10.7150/ijms.117603

Nat Commun. 2025 Aug 5;16(1):7178. doi: 10.1038/s41467-025-62593-1.

ABSTRACT

Spatial transcriptomics links gene expression with tissue morphology, however, current tools often prioritize genomic analysis, lacking integrated image interpretation. To address this, we present Thor, a comprehensive platform for cell-level analysis of spatial transcriptomics and histological images. Thor employs an anti-shrinking Markov diffusion method to infer single-cell spatial transcriptome from spot-level data, effectively combining gene expression and cell morphology. The platform includes 10 modular tools for genomic and image-based analysis, and is paired with Mjolnir, a web-based interface for interactive exploration of gigapixel images. Thor is validated on simulated data and multiple spatial platforms (ISH, MERFISH, Xenium, Stereo-seq). Thor characterizes regenerative signatures in heart failure, screens breast cancer hallmarks, resolves fine layers in mouse olfactory bulb, and annotates fibrotic heart tissue. In high-resolution Visium HD data, it enhances spatial gene patterns aligned with histology. By bridging transcriptomic and histological analysis, Thor enables holistic tissue interpretation in spatial biology.

PMID:40764306 | PMC:PMC12325965 | DOI:10.1038/s41467-025-62593-1