Yi Chuan. 2026 Sep;48(9):931-945. doi: 10.16288/j.yczz.25-275.

ABSTRACT

The abundance of omics data has significantly advanced the development of multi-omics data integration techniques. Non-linear embedding approaches for data integration have gradually become the mainstream in multi-omics research, as these approaches can substantially improve cancer analysis by enhancing the quality of the embeddings. However, current multi-omics data integration methods are typically confined to omics measurements, neglecting domain-specific prior knowledge encompassing biological pathways. In this study, we proposed a multi-omics integrated classification model, PathTransGCN, based on pathway self-attention and graph convolutional networks (GCN). The model integrated biological pathway information into multi-omics data analysis with the aim of enhancing the accuracy of cancer classification. Multi-omics data for breast cancer (BRCA), non-small cell lung cancer (NSCLC), and low-grade glioma (LGG) were obtained from The Cancer Genome Atlas (TCGA) and UCSC Xena databases. These data included gene mutations, DNA methylation, copy number variations, and gene expression, and were used to assess the model's generalizability across different cancers. First, PathTransGCN employed a pathway self-attention module to learn latent representations of samples across different pathways, thereby obtaining multi-omics integration vectors. Concurrently, a patient similarity network (PSN) was constructed using the similarity network fusion (SNF) approach. Second, the integrated vectors and the PSN were jointly fed into a GCN for end-to-end training, enabling precise classification of cancer subtypes. Through multi-omics data analysis of the BRCA dataset, PathTransGCN outperformed several popular algorithms (such as MoGCN and DeePathNet) in the five-class classification of cancer subtypes, achieving an accuracy rate of 87.6% and an F1 score of 86.4%. Moreover, the model demonstrated robust generalization capabilities across both NSCLC and LGG datasets, while effectively identifying key disease-associated biomarkers at the pathway level. Experimental results demonstrate that PathTransGCN exhibits outstanding performance in integrating omics data and delivering interpretable classification outcomes, presenting significant potential for clinical applications.

PMID:42751828 | DOI:10.16288/j.yczz.25-275

Front Immunol. 2026 Jun 18;17:1855906. doi: 10.3389/fimmu.2026.1855906. eCollection 2026.

ABSTRACT

Pulmonary gas exchange and host defense depend on the dynamic coordination of resident and recruited macrophage populations. Historically, macrophage functions have often been interpreted through the classic M1/M2 dichotomy; however, this binary framework does not capture the heterogeneity and context-dependent plasticity of macrophage states within the lung microenvironment. Advances in single-cell RNA sequencing and spatial multi-omics have substantially refined our understanding of this complex macrophage network. Here, we synthesize evidence from human studies and experimental models to summarize macrophage functional states in homeostasis and across chronic obstructive pulmonary disease, asthma, idiopathic pulmonary fibrosis, pulmonary hypertension, acute lung injury/acute respiratory distress syndrome, and lung cancer. We highlight how macrophage transcriptional programs are shaped by ontogeny, tissue niche, and epigenetic-metabolic regulation, and how these programs are linked to disease-specific remodeling of the pulmonary microenvironment. Across diverse respiratory diseases, persistent tissue injury and microenvironmental stress remodel resident macrophage programs and are frequently accompanied by the expansion and context-dependent differentiation of recruited monocyte-derived macrophages. These macrophage states are associated with inflammatory amplification, epithelial and endothelial barrier dysfunction, extracellular matrix remodeling, and tumor immune evasion. Ligand-receptor and spatial analyses further identify candidate communication axes linking macrophages with stromal, epithelial, endothelial, and immune cells, some of which appear partially conserved across disease contexts. Emerging macrophage-targeted strategies are increasingly being explored beyond broad depletion, with growing interest in context-specific reprogramming and niche modulation, including antibody-based, nanocarrier-mediated, and engineered-cell approaches. Decoding the spatiotemporal trajectories and cell-cell communication networks of specific macrophage subsets, while considering tissue context, species differences, and levels of experimental support, may help clarify mechanisms of tissue remodeling, therapeutic resistance, and macrophage-targeted intervention in complex pulmonary diseases.

PMID:42396453 | PMC:PMC13322945 | DOI:10.3389/fimmu.2026.1855906