Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumor-specificity and predictive potential of extracellular vesicles for cell invasion and proliferation - A meta-analysis

Although interest in the role of extracellular vesicles (EV) in oncology is growing, not all potential aspects have been investigated. In this meta-analysis, data regarding (i) the EV proteome and (ii) the invasion and proliferation capacity of the NCI-60 tumor cell lines (60 cell lines from nine di...

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Published in:Cell communication and signaling 2023-11, Vol.21 (1), p.333-333, Article 333
Main Authors: Bukva, Matyas, Dobra, Gabriella, Gyukity-Sebestyen, Edina, Boroczky, Timea, Korsos, Marietta Margareta, Meckes, Jr, David G, Horvath, Peter, Buzas, Krisztina, Harmati, Maria
Format: Article
Language:English
Subjects:
Bioinformatics
Biomarkers
Cancer
Cell Proliferation
Classification
Datasets
Extracellular vesicles
Extracellular Vesicles - metabolism
Gene expression
Humans
Invasion
Learning algorithms
Leukemia
Machine learning
Meta-analysis
Metastasis
NCI-60
Neoplasms - pathology
Prediction
Proliferation
Proteins
Proteome - metabolism
Proteomes
Proteomics
Proteomics - methods
Tumor cell lines
Tumors
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Summary:Although interest in the role of extracellular vesicles (EV) in oncology is growing, not all potential aspects have been investigated. In this meta-analysis, data regarding (i) the EV proteome and (ii) the invasion and proliferation capacity of the NCI-60 tumor cell lines (60 cell lines from nine different tumor types) were analyzed using machine learning methods. On the basis of the entire proteome or the proteins shared by all EV samples, 60 cell lines were classified into the nine tumor types using multiple logistic regression. Then, utilizing the Least Absolute Shrinkage and Selection Operator, we constructed a discriminative protein panel, upon which the samples were reclassified and pathway analyses were performed. These panels were validated using clinical data (n = 4,665) from Human Protein Atlas. Classification models based on the entire proteome, shared proteins, and discriminative protein panel were able to distinguish the nine tumor types with 49.15%, 69.10%, and 91.68% accuracy, respectively. Invasion and proliferation capacity of the 60 cell lines were predicted with R  = 0.68 and R  = 0.62 (p 
ISSN:1478-811X
1478-811X
DOI:10.1186/s12964-023-01344-5