{
  "abstract": "Tumor-derived exosomes carry multidimensional molecular cargo, including surface proteins, microRNAs, and lipids that encode tumor identity and disease dynamics. These features support their application as biomarkers for liquid biopsy-based cancer diagnostics. Circulating tumor DNA undergoes rapid nuclease-mediated degradation, whereas exosomes retain stable molecular information that reflects the proteomic, transcriptomic, and metabolic states of parent tumor cells. However, clinical translation of exosome-based sensing remains limited by variability in isolation, biological heterogeneity, and the analytical difficulty of detecting low-abundance biomarkers in clinical samples. In this review, we examine cancer-specific exosomal signatures across breast, lung, colorectal, and gastric cancers and evaluate biosensing platforms for exosomal biomarker profiling. We integrate engineering principles, clinical performance metrics, and AI-assisted analysis across complementary biosensing modalities to establish a cross-platform analytical framework. We compare optical platforms based on surface plasmon resonance, localized surface plasmon resonance, and surface-enhanced Raman scattering with photoluminescence- and electrochemical-based platforms in terms of sensitivity, clinical compatibility, and translational potential. Furthermore, we examine artificial intelligence (AI)-assisted biosensing frameworks, including classical machine learning classifiers, deep convolutional networks, ensemble models, explainable AI methods, and large language model interfaces. We evaluate how each framework addresses high-dimensional spectral complexity, nonlinear relationships among signals, and inter-patient variability in exosomal data. Finally, we identify remaining challenges, such as the lack of standardized isolation protocols and the absence of large-scale clinical validation. We further highlight minimal residual disease monitoring and early-stage cancer detection as important and underexplored directions for AI-integrated exosomal biosensing in precision oncology.",
  "abstract_shown": true,
  "affiliations": [
    "Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea",
    "Samsung Precision Genome Medicine Institute, Samsung Medical Center, Seoul, 06351, Republic of Korea; Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea",
    "Samsung Precision Genome Medicine Institute, Samsung Medical Center, Seoul, 06351, Republic of Korea; Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea. Electronic address: jyun.lee@samsung.com",
    "Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea; Division of Bio-Medical Science and Technology, KIST School, University of Science and Technology (UST), Seoul, 02792, Republic of Korea. Electronic address: hongnam.kim@kist.re.kr"
  ],
  "article_type": "review",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Sojin Song"
    },
    {
      "affs": [
        1
      ],
      "name": "Sujin Hyung",
      "orcid": "0000-0003-1192-0972"
    },
    {
      "affs": [
        2
      ],
      "name": "Jeeyun Lee",
      "orcid": "0000-0002-4911-6165"
    },
    {
      "affs": [
        3
      ],
      "name": "Hong Nam Kim",
      "orcid": "0000-0002-0329-0029"
    },
    {
      "name": "Nakwon Choi",
      "orcid": "0000-0003-2993-9233"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4rrnedcmnnees.bib",
  "cited_by": 7,
  "doi": "10.1016/j.bios.2026.118809",
  "fulltext_url": null,
  "id": "4rrnedcmnnees",
  "issns": [
    "0956-5663",
    "1873-4235"
  ],
  "landing_url": "https://doi.org/10.1016/j.bios.2026.118809",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Extracellular vesicles in disease"
    ]
  ],
  "openalex": "W7161257516",
  "pdf_url": null,
  "pmcid": null,
  "pmid": "42161118",
  "published": "2026-05-15",
  "publisher": "Elsevier BV",
  "ris": "https://research.draconitic.com/paper/4rrnedcmnnees.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:26:48Z",
      "native_id": "W7161257516",
      "source": "openalex",
      "url": "https://doi.org/10.1016/j.bios.2026.118809"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Extracellular vesicles in disease",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Recent advances in biosensing platforms utilizing exosomal biomarker profiling for cancer diagnosis",
  "title_text": "Recent advances in biosensing platforms utilizing exosomal biomarker profiling for cancer diagnosis",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T13:20:30Z",
  "url": "https://research.draconitic.com/paper/4rrnedcmnnees",
  "venue": "Biosensors and Bioelectronics",
  "versions": []
}