ArticleCell reports. Medicine2024
Machine learning-based analysis identifies and validates serum exosomal proteomic signatures for the diagnosis of colorectal cancer.
Article in Cell reports. Medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
55 citing papers in PubMed.
- Artificial intelligence and extracellular vesicles in oncology: towards tumor diagnosis, prediction, and therapy.Drug delivery · 2026Review
- Machine learning-assisted detection of canine mammary tumors using serum autoantibody signatures.The veterinary quarterly · 2026Article
- Synthetically engineered probiotic suppresses colorectal cancer via inhibition of Wnt/β-catenin signaling.Cell reports. Medicine · 2026Article
- Extracellular vesicle and particle biomarkers in cancer: a machine learning blueprint for liquid biopsy.Journal of nanobiotechnology · 2026Review
- Identification and validation of serum SLIT1 as a biomarker for predicting anti-PD-1/PD-L1 resistance in non-small cell lung cancer.Biological procedures online · 2026Article
- MicroRNA profiles in colostrum and transition milk of non-dairy goats fed with or without resveratrol.Scientific data · 2026Article
- Origin dictates function: The dual roles of exosomes derived from diverse origins in the onset and progression of colorectal cancer (Review).Oncology reports · 2026Review
- Variation in antiviral immunity and inflammation pathways precedes HIV-1 infection in a high-risk African cohort.The Journal of clinical investigation · 2026Article
- Integrative genomic profiling of iron homeostasis predicts clinical outcomes and identifies HAMP/hepcidin as a therapeutic target in colorectal cancer.Cell & bioscience · 2026Article
- Recent advances in machine learning-enhanced extracellular vesicle omics for oncology.Journal of nanobiotechnology · 2026Review
- Article
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- Review
- Review
- ColoLDB: a machine learning-based predictive model for colorectal cancer using routine laboratory parameters.Journal of gastrointestinal oncology · 2026Article
- Exosomal bioactive molecules: molecular regulation, functional heterogeneity, and translational implications in disease diagnostics and therapeutics.Clinical and experimental medicine · 2026Review
- Extracellular vesicles in colorectal cancer: immunomodulation, diagnostics, and therapeutic perspectives.Medical oncology (Northwood, London, England) · 2026Review
- Advancing Extracellular Vesicle Research: A Review of Systems Biology and Multiomics Perspectives.Proteomics · 2026Review
- Machine learning for extracellular vesicles enables diagnostic and therapeutic nanobiotechnology.Journal of nanobiotechnology · 2026Review
- Emerging insights into exosomal protein functions in colorectal cancer.Discover oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The potential of serum extracellular vesicles (EVs) as non-invasive biomarkers for diagnosing colorectal cancer (CRC) remains elusive. We employed an in-depth 4D-DIA proteomics and machine learning (ML) pipeline to identify key proteins, PF4 and AACT, for CRC diagnosis in serum EV samples from a discovery cohort of 37 cases. PF4 and AACT outperform traditional biomarkers, CEA and CA19-9, detected by ELISA in 912 individuals. Furthermore, we developed an EV-related random forest (RF) model with the highest diagnostic efficiency, achieving AUC values of 0.960 and 0.963 in the train and test sets, respectively. Notably, this model demonstrated reliable diagnostic performance for early-stage CRC and distinguishing CRC from benign colorectal diseases. Additionally, multi-omics approaches were employed to predict the functions and potential sources of serum EV-derived proteins. Collectively, our study identified the crucial proteomic signatures in serum EVs and established a promising EV-related RF model for CRC diagnosis in the clinic.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.