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

Sci China Life Sci. 2026 Sep 3. doi: 10.1007/s11427-026-3438-4. Online ahead of print.

ABSTRACT

Oncogenic KRAS mutations define a distinct molecular subset of gastric adenocarcinoma (GA), yet their impact on the tumor immune microenvironment remains incompletely understood. In this study, we established a genetically faithful and immunocompetent KRAS^G12D-driven mouse model of GA, together with matched organoids and cell lines, to investigate how oncogenic KRAS shapes tumor-immune interactions. KRAS-mutant tumors consistently developed an immunosuppressive microenvironment characterized by enrichment of regulatory T cells (Tregs), accompanied by reduced cytotoxic lymphocyte infiltration and intrinsic resistance to PD-1 blockade. Although pharmacologic targeting of KRAS effectively suppressed tumor growth and increased immune cell infiltration, functional immune analyses revealed persistent Treg-mediated immunosuppression that limited effective antitumor immunity. Mechanistically, TGF-β signaling was required to maintain Treg dominance and suppress effector T cell function in KRAS-driven tumors. Importantly, disruption of this suppressive axis through combined KRAS inhibition and CTLA-4 blockade attenuated TGF-β activity, impaired Treg function, and enhanced antitumor immune responses in vivo. Collectively, these findings identify oncogenic KRAS as a key regulator of TGF-β-dependent immune suppression in GA and provide mechanistic insight into immune evasion within this molecular subtype.

PMID:42714795 | DOI:10.1007/s11427-026-3438-4

Front Cell Infect Microbiol. 2026 Mar 16;16:1793213. doi: 10.3389/fcimb.2026.1793213. eCollection 2026.

ABSTRACT

The progression from cirrhosis to hepatocellular carcinoma (HCC) is a key outcome in the management of chronic liver disease. This process has a long incubation period and significant individual differences, making early warning still difficult. Clinical follow-up mainly relies on imaging examinations and alpha fetoprotein, but the ability to identify high risk precancerous states is limited. The imbalance of gut microbiota and its metabolites may occur earlier than the visible stage of tumors. They can affect barrier integrity, chronic inflammation, immune surveillance, and metabolic homeostasis through the gut liver axis, and participate in the formation of a pro tumor microenvironment. Therefore, such changes may provide more upstream risk stratification clues for the population with cirrhosis. This article summarizes previous research evidence and summarizes the common microbiome and metabolite characteristics of cirrhosis and high-risk populations, including a decrease in short chain fatty acid (SCFA) related symbiotic bacteria, an increase in inflammation related bacteria, bile acid spectrum shift, and other intestinal derived metabolite abnormalities. This article also outlines the key mechanisms that these features may correspond to, such as barrier damage and microbial translocation, immune suppression, etc. There are still significant uncertainties at present. The effect of SCFA is context dependent. Different etiologies, diets, medications, and complications can lead to significant confounding and affect cross cohort consistency. Subsequent research requires longitudinal cohort validation and the promotion of multi omics integration and the construction of interpretable predictive models to support clinical translation.

PMID:41918873 | PMC:PMC13033666 | DOI:10.3389/fcimb.2026.1793213