Eur J Cancer. 2026 Mar 24;239:116699. doi: 10.1016/j.ejca.2026.116699. Online ahead of print.

ABSTRACT

INTRODUCTION: The latest generation of liquid biopsies incorporates multi-omic features, including genomics, methylomics, and fragmentomics. Machine learning (ML) approaches have been proposed to synthesize these complex biological data for the development of diagnostic classifiers. This study aims to evaluate the integration of ML with circulating cell-free DNA (cfDNA) analysis for early cancer detection.

METHODS: Medline, Embase, Cochrane, and Web of Science were searched in July 2025. Eligible studies combined ML and cfDNA features to distinguish cancer patients (stages I-III) from non-cancer controls. Summary diagnostic performance metrics and their 95% confidence intervals (CI) were calculated.

RESULTS: The study included 109 articles permitting analyses for lung (n = 34), liver (n = 29), colorectal (n = 28), pancreatic (n = 16), breast (n = 17), esophageal (n = 12), ovarian (n = 13), gastric (n = 9), head and neck (n = 4), and mixed (n = 27) cancer types. Specificity was consistently high across all tumor types and stages (94%-99%). Sensitivity ranged from 72% to 92% for stage I-III, 44-91% for stage I, 71-98% for stage II and 83-99% for stage III. In the pooled study population, neural networks (90%, 95% CI: 81%-95%), random forest (86%, 95% CI: 77%-92%) and heterogeneous ensemble learning (85%, 95% CI: 79%-89%) demonstrated the highest sensitivity. The stratified analysis by classifier feature revealed 86% (95% CI: 80%-90%) sensitivity for fragmentation and 81% (95% CI: 76%-85%) for methylation, with 92%-96% specificity.

CONCLUSION: ML and cfDNA profiling show potential for early cancer detection, with ensemble methods, neural networks and random forests achieving the best overall performance. Fragmentomic features provide the highest sensitivity.

PMID:41930854 | DOI:10.1016/j.ejca.2026.116699

Front Immunol. 2026 Mar 18;17:1791707. doi: 10.3389/fimmu.2026.1791707. eCollection 2026.

ABSTRACT

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with a complex tumor ecosystem that contributes to its progression. Deubiquitinases (DUBs) are vital regulators in cancer. However, the overall activity of DUBs and their role in driving PDAC progression within immune microenvironment remain largely unknown.

METHODS: We employed an integrative multi-omics strategy combining machine learning (ML) on bulk transcriptomic data, single-cell RNA sequencing and spatial transcriptomic profiling. We applied Coxnet and Fuzzy SVM for prognostic modeling, inferCNV for malignant cell identification, SCENIC for transcription factor regulon analysis, LIANA⁺ for inferring inter-cellular communication networks and cell2location for spatial deconvolution. USP54 expression was detected by real-time quantitative PCR, western blotting and immunohistochemistry. USP54 function was validated through in vitro and in vivo assays.

RESULTS: ML-based pathway analysis revealed post-translational modification as a major prognostic category, within which elevated DUBs activity emerged as an independent adverse prognostic factor. At the single-cell level, USP54 was upregulated along the trajectory of malignant ductal cells and correlated with an inflamed tumor microenvironment. Cell-cell communication analysis predicted signaling from monocytes/macrophages to tumor cells via the THBS1-integrin ligand-receptor pair. This immune-derived signaling potentially converged on KLF5-positive tumor cells, with KLF5 identified as a putative transcriptional activator of USP54. Spatial transcriptomics validated the co-localization of USP54 expression, elevated DUB activity, and KRAS signaling within specific tumor niches adjacent to THBS1-enriched immune regions. High USP54 expression was frequently observed in PDAC tissues and associated with poor patient survival. More importantly, in both BxPC-3 and PANC-1 cell lines, USP54 knockdown suppressed cell proliferation and metastasis, whereas its overexpression enhanced these malignant phenotypes. Subcutaneous xenograft growth and tail vein injection experiments validated these findings in vivo.

CONCLUSIONS: Our comprehensive multi-omics analysis and experimental validation identify the deubiquitinase USP54 as a novel promoter of PDAC progression within a spatially organized tumor-immune microenvironment. These findings suggest USP54 as both a candidate prognostic biomarker and a potential therapeutic target for this lethal malignancy.

PMID:41929495 | PMC:PMC13038871 | DOI:10.3389/fimmu.2026.1791707