ArticleNature nanotechnology2026
Machine perception liquid biopsy identifies brain tumours via systemic immune and tumour microenvironment signature.
Article in Nature nanotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- A transferrin receptor-targeted liposome for iron metabolism regulation-augmented photodynamic therapy in glioblastoma.Bioactive materials · 2027Article
- Integrating structurally defined DNA-carbon nanotube sensors with machine learning for cancer detection.Science advances · 2026Article
- Inverse design of guanine-defects in carbon nanotubes for high-resolution emission tuning.Science advances · 2026Article
- Liquid Biopsy in Primary CNS Tumors: Bridging Biology, Technology, and Clinical Care.Molecular diagnosis & therapy · 2026Review
- Advances in Brain Tumor Biomarkers: From Molecular Profiling to Liquid Biopsy and AI-Driven Detection.Cancers · 2026Review
- Industrial Adoption of Carbon Nanotubes.Nano letters · 2026Review
- Review
- Machine perception liquid biopsy identifies brain tumours via systemic immune and tumour microenvironment signature.Nature nanotechnology · 2026Article
- Handle-free attachment of small molecules on single-walled carbon nanotubes.Nature synthesis · 2026Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
The detection and identification of intracranial tumours is limited by the lack of accurate biomarkers and requires invasive biopsy procedures. We investigated a machine perception liquid biopsy approach to detect and identify intracranial tumours from peripheral blood and to discover biomarkers responsible for the predictions. Quantum well defect-modified single-walled carbon nanotubes stabilized with single-stranded DNA, interrogating 739 plasma samples from brain tumour patients, were used to train and validate machine-learning models to detect intracranial tumours with 98% accuracy and identify tumour type. The protein corona of the top model-contributing nanosensor was interrogated using quantitative proteomics, resulting in the identification of tumour ecosystem-secreted factors, both previously reported and newly discovered, originating from intracranial tumour cells, the tumour microenvironment and the innate immune system of patients with glioblastoma and meningioma. Newly discovered factors elicited linear nanosensor responses and were elevated in one or both tumour types, matching the original protein corona enrichment. This investigation reveals that a perception-based detection of disease in blood can identify biomarkers responsible for the signal and also amplify cancer detection signals by detecting factors beyond tumour cells, thereby recruiting the entire tumour ecosystem for cancer diagnosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.