Self-aggregated photo-heterostructures enhancing fluorescence for cancer diagnosis and treatment monitoring
- 1Guangxi Key Laboratory of Pharmaceutical Precision Detection and Screening, State Key Laboratory of Targeting Oncology, School of Pharmacy, Guangxi Medical University, Nanning, China
- 2Department of Hepatobiliary Surgery, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
- 3Department of Clinical Laboratory, the Affiliated Tumor Hospital of Guangxi Medical University, Nanning, China
Abstract
Semiconductor zinc oxide (ZnO) nanostructures as classic photonic materials have sparked the development of low-cost, high-performance fluorescence sensors; however, their scalable clinical applications remain hampered by inadequate sensitivity. Here, we engineer a high-curvature photo-heterostructure aggregate-enhanced fluorescence (PAEF) sensor based on bubble-scaffolded ZnO nanorods. Leveraging synergistic evanescent field coupling and self-aggregation of photo-heterostructures, PAEF sensor streamlines efficient isolation (~90%) and ultrasensitive profiling of tumor extracellular vesicles (EVs) in a single assay, achieving sensitivity <10 EVs/μL (104-fold better than ELISA) without additional amplification, outperforming most state-of-the-art EV fluorescence assays. This self-sufficient sensor enables accurate monitoring of targeted therapeutic efficacy in mouse xenograft models via simultaneous dual-marker readout. We further show its clinical utility in hepatocellular carcinoma diagnosis with ≥96% accuracy (≥98% specificity) in two cohorts (n = 146), improving dynamic monitoring of surgical treatment responses, particularly in alpha-fetoprotein-negative patients. This work provides a cost-effective ultrasensitive liquid biopsy tool advancing non-invasive cancer diagnosis.
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BibTeX
@article{mo2026selfaggregated,
title = {{Self-aggregated photo-heterostructures enhancing fluorescence for cancer diagnosis and treatment monitoring}},
author = {Xifeng Mo and Xiaojie Qin and Linfeng Mao and Yuming Tang and You Li and Hao Lu and Binqi Wei and Yu Yang and Yuyuan Zhang and Min Fang and Xinchun Li and Fan Yang},
journal = {Nature Communications},
year = {2026},
month = sep,
doi = {10.1038/s41467-026-78181-w},
url = {https://doi.org/10.1038/s41467-026-78181-w},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Self-aggregated photo-heterostructures enhancing fluorescence for cancer diagnosis and treatment monitoring
AU - Xifeng Mo
AU - Xiaojie Qin
AU - Linfeng Mao
AU - Yuming Tang
AU - You Li
AU - Hao Lu
AU - Binqi Wei
AU - Yu Yang
AU - Yuyuan Zhang
AU - Min Fang
AU - Xinchun Li
AU - Fan Yang
PY - 2026
DA - 2026/09/25
JO - Nature Communications
DO - 10.1038/s41467-026-78181-w
UR - https://doi.org/10.1038/s41467-026-78181-w
AB - Semiconductor zinc oxide (ZnO) nanostructures as classic photonic materials have sparked the development of low-cost, high-performance fluorescence sensors; however, their scalable clinical applications remain hampered by inadequate sensitivity. Here, we engineer a high-curvature photo-heterostructure aggregate-enhanced fluorescence (PAEF) sensor based on bubble-scaffolded ZnO nanorods. Leveraging synergistic evanescent field coupling and self-aggregation of photo-heterostructures, PAEF sensor streamlines efficient isolation (~90%) and ultrasensitive profiling of tumor extracellular vesicles (EVs) in a single assay, achieving sensitivity <10 EVs/μL (104-fold better than ELISA) without additional amplification, outperforming most state-of-the-art EV fluorescence assays. This self-sufficient sensor enables accurate monitoring of targeted therapeutic efficacy in mouse xenograft models via simultaneous dual-marker readout. We further show its clinical utility in hepatocellular carcinoma diagnosis with ≥96% accuracy (≥98% specificity) in two cohorts (n = 146), improving dynamic monitoring of surgical treatment responses, particularly in alpha-fetoprotein-negative patients. This work provides a cost-effective ultrasensitive liquid biopsy tool advancing non-invasive cancer diagnosis.
SN - 2041-1723
ER -