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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 |
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| creator | Bukva, Matyas Dobra, Gabriella Gyukity-Sebestyen, Edina Boroczky, Timea Korsos, Marietta Margareta Meckes, Jr, David G Horvath, Peter Buzas, Krisztina Harmati, Maria |
| description | 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 |
| doi_str_mv | 10.1186/s12964-023-01344-5 |
| format | article |
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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 < 0.0001). The results of the Reactome pathway analysis of the discriminative protein panel suggest that the molecular content of EVs might be indicative of tumor-specific biological processes.
Integrating in vitro EV proteomic data, cell physiological characteristics, and clinical data of various tumor types illuminates the diagnostic, prognostic, and therapeutic potential of EVs. Video Abstract.</description><identifier>ISSN: 1478-811X</identifier><identifier>EISSN: 1478-811X</identifier><identifier>DOI: 10.1186/s12964-023-01344-5</identifier><identifier>PMID: 37986165</identifier><language>eng</language><publisher>England: BioMed Central</publisher><subject>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</subject><ispartof>Cell communication and signaling, 2023-11, Vol.21 (1), p.333-333, Article 333</ispartof><rights>2023. The Author(s).</rights><rights>2023. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c441t-9fb5d3d1cbdb66636cb15c22d85cde0d2189c15fe49751a18d30ddac16c566133</citedby><cites>FETCH-LOGICAL-c441t-9fb5d3d1cbdb66636cb15c22d85cde0d2189c15fe49751a18d30ddac16c566133</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2902126364?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25753,27924,27925,37012,37013,44590</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37986165$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bukva, Matyas</creatorcontrib><creatorcontrib>Dobra, Gabriella</creatorcontrib><creatorcontrib>Gyukity-Sebestyen, Edina</creatorcontrib><creatorcontrib>Boroczky, Timea</creatorcontrib><creatorcontrib>Korsos, Marietta Margareta</creatorcontrib><creatorcontrib>Meckes, Jr, David G</creatorcontrib><creatorcontrib>Horvath, Peter</creatorcontrib><creatorcontrib>Buzas, Krisztina</creatorcontrib><creatorcontrib>Harmati, Maria</creatorcontrib><title>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</title><title>Cell communication and signaling</title><addtitle>Cell Commun Signal</addtitle><description>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 < 0.0001). The results of the Reactome pathway analysis of the discriminative protein panel suggest that the molecular content of EVs might be indicative of tumor-specific biological processes.
Integrating in vitro EV proteomic data, cell physiological characteristics, and clinical data of various tumor types illuminates the diagnostic, prognostic, and therapeutic potential of EVs. 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Academic</collection><collection>DOAJ</collection><jtitle>Cell communication and signaling</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bukva, Matyas</au><au>Dobra, Gabriella</au><au>Gyukity-Sebestyen, Edina</au><au>Boroczky, Timea</au><au>Korsos, Marietta Margareta</au><au>Meckes, Jr, David G</au><au>Horvath, Peter</au><au>Buzas, Krisztina</au><au>Harmati, Maria</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>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</atitle><jtitle>Cell communication and signaling</jtitle><addtitle>Cell Commun Signal</addtitle><date>2023-11-20</date><risdate>2023</risdate><volume>21</volume><issue>1</issue><spage>333</spage><epage>333</epage><pages>333-333</pages><artnum>333</artnum><issn>1478-811X</issn><eissn>1478-811X</eissn><abstract>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 < 0.0001). The results of the Reactome pathway analysis of the discriminative protein panel suggest that the molecular content of EVs might be indicative of tumor-specific biological processes.
Integrating in vitro EV proteomic data, cell physiological characteristics, and clinical data of various tumor types illuminates the diagnostic, prognostic, and therapeutic potential of EVs. Video Abstract.</abstract><cop>England</cop><pub>BioMed Central</pub><pmid>37986165</pmid><doi>10.1186/s12964-023-01344-5</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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| source | Publicly Available Content Database; PubMed Central |
| 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 |
| title | 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 |
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