Cancer cells undergo phenotypic changes or mutations during treatment, making detecting protein-based or gene-based biomarkers challenging. Here, we used algorithmic analysis combined with patient-derived tumor models to derive an early prediction tool using patient-derived cell clusters from liquid biopsy (LIQBP) for cancer prognosis in a label-free manner. The LIQBP platform incorporated a customized microfluidic biochip that mimicked the tumor microenvironment to establish patient clusters, and extracted physical parameters from images of each sample, including size, thickness, roughness, and thickness per area (n = 31). Samples from healthy volunteers (n = 5) and cancer patients (pretreatment; n = 4) could be easily distinguished with high sensitivity (91.16 ± 1.56%) and specificity (71.01 ± 9.95%). Furthermore, we demonstrated that the multiple unique quantitative parameters reflected patient responses. Among these, the ratio of normalized gray value to cluster size (RGVS) was the most significant parameter correlated with cancer stage and treatment duration. Overall, our work presented a novel and less invasive approach for the label-free prediction of disease prognosis to identify patients who require adjustments to their treatment regime. We envisioned that such efforts would promote the management of personalized patient care conveniently and cost effectively.

Cancer spatial and temporal heterogeneity fuels resistance to therapies. To realize the routine assessment of cancer prognosis and treatment, we demonstrate the development of an Intelligent Disease Detection Tool (IDDT), a microfluidic-based tumor model integrated with deep learning-assisted algorithmic analysis. IDDT was clinically validated with liquid blood biopsy samples (n = 71) from patients with various types of cancers (e.g., breast, gastric, and lung cancer) and healthy donors, requiring low sample volume (∼200 μl) and a high-throughput 3D tumor culturing system (∼300 tumor clusters). To support automated algorithmic analysis, intelligent decision-making, and precise segmentation, we designed and developed an integrative deep neural network, which includes Mask Region-Based Convolutional Neural Network (Mask R-CNN), vision transformer, and Segment Anything Model (SAM). Our approach significantly reduces the manual labeling time by up to 90% with a high mean Intersection Over Union (mIoU) of 0.902 and immediate results (<2 s per image) for clinical cohort classification. The IDDT can accurately stratify healthy donors (n = 12) and cancer patients (n = 55) within their respective treatment cycle and cancer stage, resulting in high precision (∼99.3%) and high sensitivity (∼98%). We envision that our patient-centric IDDT provides an intelligent, label-free, and cost-effective approach to help clinicians make precise medical decisions and tailor treatment strategies for each patient.

Tumor-derived circulating cell-free DNA (ctDNA) has emerged as a pivotal biomarker for non-invasive cancer diagnosis, treatment monitoring, and prognostic evaluation. However, its inherently low abundance, high fragmentation, and rapid degradation impose stringent requirements on assay sensitivity, specificity, and analytical robustness. Rapid advances in nanotechnology have significantly accelerated progress in ctDNA detection. This review summarizes recent nanotechnology-assisted strategies for ctDNA analysis, including surface-engineered nanomaterials for selective enrichment, nano-enabled signal amplification modalities, and integrated platforms such as CRISPR-based detection, microfluidics and nanopore technologies. We further highlight nanostructure-based approaches for decoding methylation, fragmentation profiles, and multi-omics signatures, focusing on their potential to enhance early cancer detection and real-time therapeutic assessment. Moreover, increasing incorporation of artificial intelligence (AI) which spans nanostructure characterization, aptamer and probe design, multi-omics data integration, and algorithm development is reshaping the landscape of nano-assisted liquid biopsy. Finally, current challenges and future perspectives concerning the clinical translation of nanotechnology-assisted ctDNA detection are presented, emphasizing standardization, biocompatibility, automation, and regulatory readiness. Overall, this review provides a comprehensive outlook on how converging nanotechnology and AI innovations are advancing ctDNA-based precision oncology.

The immunosuppressive tumor microenvironment remains a major barrier to effective immunotherapy in gastric cancer. In this study, we identified the E3 ubiquitin ligase BFAR as a critical regulator of neutrophil-mediated immune evasion through the S100A8/A9–BFAR–PRP19–YBX1 signaling axis. Multiomics analyses revealed that BFAR is overexpressed in gastric cancer and correlates with poor prognosis. Functional studies demonstrated that BFAR knockdown suppressed tumor growth by reducing neutrophil infiltration and immunosuppressive reprogramming to restore CD8+ T-cell function. Mechanistically, BFAR mediated K48-linked ubiquitination and degradation of PRP19, leading to stabilization of the oncoprotein YBX1, which transcriptionally upregulated neutrophil-recruiting chemokines CXCL1/CXCL3. Infiltrating neutrophils secreted S100A8/A9, which activated NF-κB to induce BFAR expression in tumor cells and created a feed-forward loop that sustains an immunosuppressive tumor microenvironment. Furthermore, BFAR promoted neutrophil PD-L1 expression via GM-CSF, reinforcing T-cell exhaustion. Clinically, BFAR expression correlated with neutrophil infiltration and poor response to anti–PD-1 therapy, whereas its inhibition synergizes with immune checkpoint blockade in preclinical models. Our work unveils BFAR as a central orchestrator of neutrophil-driven immunosuppression and proposes targeting this axis to enhance immunotherapy efficacy in gastric cancer.

Supplementary Figure 4. Bfar knockdown restrains the neutrophil chemokines expressions