Nat Cancer. 2026 Jan 16. doi: 10.1038/s43018-025-01107-w. Online ahead of print.

ABSTRACT

The concept of cellular neighborhoods, defined as recurring structures within the tissue with characteristic cell compositions and interactions, has transformed our understanding of the complexity and dynamics of tumor ecosystems. Recent advances in spatial omics and computational modeling have enabled high-resolution mapping of these neighborhoods, providing unprecedented insights into their roles in shaping tumor heterogeneity, evolution and therapeutic responses. Despite these advances, a unified framework for interpreting cellular neighborhoods remains lacking. This Perspective synthesizes emerging concepts and insights, focusing on the definition and classification of cellular neighborhoods in cancer, computational methods for identifying and comparing them, and their clinical relevance.

PMID:41545713 | DOI:10.1038/s43018-025-01107-w

Atherosclerosis. 2026 Jan 8;413:120637. doi: 10.1016/j.atherosclerosis.2026.120637. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: Commercial high-throughput proteomics platforms, such as Olink and SomaLogic, enable large-scale epidemiological studies with integrated multi-omics measurements. While these proteomics approaches have been widely applied in biobanks, issues of data quality remain underappreciated. In this review, we discuss these limitations and outline a way forward for realizing the clinical translation of proteomics as a comprehensive 'liquid health check'.

METHODS: We reviewed the recent literature for artificial intelligence (AI) and multi-omics, particularly proteomics in atherosclerotic cardiovascular disease (ASCVD).

RESULTS: AI-driven multi-omics analyses have the potential to advance our understanding of multifactorial causes of ASCVD, including aging. Emerging concepts such as "ageotypes" suggest the potential for personalized intervention to slow aging processes. Commercial proteomics platforms have accelerated biomarker discovery in ASCVD, but challenges remain in clinical translation. Limited correlation between Olink and SomaLogic necessitates orthogonal validation of findings. Platform-specific issues, such as epitope effects and cross-reactivity, can yield divergent protein quantitative trait loci for the same protein, complicating causal inference. While tissue proteomics provides complementary insights to plasma proteomics, reliance on autopsy samples raises concerns about protein degradation and measurement reliability. Increasingly, single-cell and spatial proteomics are being explored to better capture plaque heterogeneity, complementing bulk proteomics in larger cohorts.

CONCLUSION: Beyond risk prediction, proteomics offers opportunities to elucidate disease mechanisms and enable drug repurposing. To realize the clinical potential of plasma proteomics, absolute or reliably recalibratable relative quantification will be required to guide patient care. Ultimately, the clinical value of proteomics will be determined by the quality rather than the quantity of protein measurements.

PMID:41539063 | DOI:10.1016/j.atherosclerosis.2026.120637

Nat Med. 2026 Jan 14. doi: 10.1038/s41591-025-04105-8. Online ahead of print.

ABSTRACT

The human metabolome reflects complex metabolic states affected by genetic and environmental factors. However, metabolites associated with type 2 diabetes (T2D) risk and their determinants remain insufficiently characterized. Here we integrated blood metabolomic, genomic and lifestyle data from up to 23,634 initially T2D-free participants from ten cohorts. Of 469 metabolites examined, 235 were associated with incident T2D during up to 26 years of follow-up, including 67 associations not previously reported across bile acid, lipid, carnitine, urea cycle and arginine/proline, glycine and histidine pathways. Further genetic analyses linked these metabolites to signaling pathways and clinical traits central to T2D pathophysiology, including insulin resistance, glucose/insulin response, ectopic fat deposition, energy/lipid regulation and liver function. Lifestyle factors-particularly physical activity, obesity and diet-explained greater variations in T2D-associated versus non-associated metabolites, with specific metabolites revealed as potential mediators. Finally, a 44-metabolite signature improved T2D risk prediction beyond conventional factors. These findings provide a foundation for understanding T2D mechanisms and may inform precision prevention targeting specific metabolic pathways.

PMID:41535386 | DOI:10.1038/s41591-025-04105-8

Eur Respir Rev. 2026 Jan 14;35(179):240286. doi: 10.1183/16000617.0286-2024. Print 2026 Jan.

ABSTRACT

Despite recent advances, the underlying mechanisms of the development and progression of many chronic respiratory diseases remain to be elucidated. Factors such as heterogeneity and complexity of human diseases and difficulty interpreting large datasets hinder research into chronic respiratory diseases. Omics assesses the changes in specific biological entities, such as mRNA expression, epigenetics/epigenomics, genomics, proteomics, metagenomics and metabolomics, and provides valuable insights into the roles of these processes in chronic respiratory diseases. High-throughput omics at bulk, single-cell and spatial levels empower the exploration of disease-related changes through untargeted data-driven statistical methods. Multi-omics is the exploration and integration of multiple biological processes, which compared to a single-omics, can provide a substantially greater and more holistic overview of the pathogenic mechanisms that underpin complex diseases. Multi-omics analysis can comprehensively characterise the mechanisms that drive chronic respiratory diseases, capturing unique biological signatures and cellular interactions at different omics levels. Use of these methods has begun to identify key factors and biomarkers in chronic respiratory diseases. Here, we review current omics approaches and highlight recent advances in respiratory research achieved using multi-omics and integrative methods. Our review provides a valuable resource for researchers and clinicians in this area.

PMID:41534886 | DOI:10.1183/16000617.0286-2024

Gut. 2026 Jan 13:gutjnl-2025-335683. doi: 10.1136/gutjnl-2025-335683. Online ahead of print.

ABSTRACT

BACKGROUND: Accumulating evidence has demonstrated that distinct tumour-promoting and tumour-restraining cancer-associated fibroblast (CAF) subtypes coexist in pancreatic ductal adenocarcinoma.

OBJECTIVE: To develop targeted CAF therapeutic strategies by reprogramming tumour-promoting CAF subtypes.

DESIGN: We leveraged multiomics technologies to systematically identify and characterise CAF subtypes transcriptionally, epigenetically and spatially and correlate them with clinicopathological features.

RESULTS: We found that complement-secreting CAFs (csCAFs), initially identified by our group and inflammatory CAFs (iCAFs) share significant overlap in their transcriptional profiles and chromatin accessibility. iCAFs specifically express transcription factors from the heme and oxidative homeostasis pathway and the activator protein 1 family, which are both involved in cellular response to oxidative stress. Notably, the composition of csCAFs among all CAFs declined during pancreatic carcinogenesis, while trajectory analysis showed that csCAFs could potentially differentiate into iCAFs. Spatially resolved analysis indicated that tumour regions with a higher csCAF composition were associated with lower levels of TGF-β ligands, fewer M2 tumour-associated macrophages and increased levels of lipid mediators. Additionally, we identified a spatially defined CXCL12-CXCR4 ligand-receptor interaction between csCAFs and T cells, but in distinct patterns between different metastatic organs. Patients with a higher composition of csCAFs have significantly longer overall survival and recurrence-free survival through multiplex immunohistochemistry and bulk RNA-seq deconvolution.

CONCLUSION: Our study demonstrates that csCAFs may represent an early-stage iCAF subtype and suggests a promising strategy for reprogramming iCAFs into csCAFs.

PMID:41534892 | DOI:10.1136/gutjnl-2025-335683

Clin Exp Gastroenterol. 2026 Jan 7;19:1-11. doi: 10.2147/CEG.S495588. eCollection 2026.

ABSTRACT

Helicobacter pylori (H. pylori) is a human bacterial pathogen that causes one of the most common chronic bacterial infections worldwide. The microorganism has been classified by the International Agency for Research on Cancer as a Group I carcinogen. While the etiological link to gastric cancer is well established, the precise molecular and cellular mechanisms driving this transformation are highly complex and incompletely understood. Fundamentally, the infection results from the chronic presence of acute on chronic gastric mucosal inflammation. H. pylori pathogenicity is increased by bacterial virulence factors including the cytotoxin-associated gene A (CagA) and Vacuolating cytotoxin A (VacA) which may interfere with the host's cell communication and create a pro-tumorigenic microenvironment. Host microRNAs (miRNAs) may amplify these effects by modulating immune responses, enhancing oncogenic signalling. Despite the proven benefits of H. pylori eradication in reducing cancer risk, especially in high-incidence regions, rising antibiotic resistance and host-related variables impede its global implementation. Recent advances in genomics and multi-omics profiling potentially offer new opportunities for targeted prevention. Moreover, emerging evidence suggests H. pylori may also negatively influence immunotherapy outcomes, underscoring its broader relevance in cancer treatment planning. By synthesizing molecular insights, epidemiological trends, and clinical data, this narrative review examines the multifaceted pathways through which H. pylori contributes to gastric carcinogenesis, integrating current knowledge on microbial virulence, host signalling disruption, immune modulation, and epigenetic remodelling.

PMID:41531650 | PMC:PMC12791163 | DOI:10.2147/CEG.S495588