A novel cross-species differential tumor classification method based on exosome-derived microRNA biomarkers established by human-dog lymphoid and mammary tumor cell lines' transcription profiles.
exosome-derived microRNA
meta-analysis
ortholog
support vector machine
tumor
Journal
Veterinary world
ISSN: 0972-8988
Titre abrégé: Vet World
Pays: India
ID NLM: 101504872
Informations de publication
Date de publication:
May 2022
May 2022
Historique:
received:
11
12
2021
accepted:
17
03
2022
entrez:
29
6
2022
pubmed:
30
6
2022
medline:
30
6
2022
Statut:
ppublish
Résumé
Exosome-derived microRNA (miRNA) has been widely studied as a non-invasive candidate biomarker for tumor diagnosis in humans and dogs. Its application, however, was primarily focused on intraspecies usage for individual tumor type diagnosis. This study aimed to gain insight into its application as a cross-species differential tumor diagnostic tool; we demonstrated the process of identifying and using exosome-derived miRNA as biomarkers for the classification of lymphoid and mammary tumor cell lines in humans and dogs. Exosome-derived miRNA sequencing data from B-cell lymphoid tumor cell lines (n=13), mammary tumor cell lines (n=8), and normal mammary epithelium cultures (n=4) were pre-processed in humans and dogs. F-test and rank product (RP) analyses were used to select candidate miRNA orthologs for tumor cell line classification. The classification was carried out using an optimized support vector machine (SVM) with various kernel classifiers, including linear SVM, polynomial SVM, and radial basis function SVM. The receiver operating characteristic and precision-recall curves were used to assess the performance of all models. MIR10B, MIR21, and MIR30E were chosen as the candidate orthologs from a total of 236 human-dog miRNA orthologs (p≤0.01, F-test score ≥10, and RP score ≤10). Their use of polynomial SVM provided the best performance in classifying samples from various tumor cell lines and normal epithelial culture. The study successfully demonstrated a method for identifying and utilizing candidate human-dog exosome-derived miRNA orthologs for differential tumor cell line classification. Such findings shed light on a novel non-invasive tumor diagnostic tool that could be used in both human and veterinary medicine in the future.
Sections du résumé
Background and Aim
UNASSIGNED
Exosome-derived microRNA (miRNA) has been widely studied as a non-invasive candidate biomarker for tumor diagnosis in humans and dogs. Its application, however, was primarily focused on intraspecies usage for individual tumor type diagnosis. This study aimed to gain insight into its application as a cross-species differential tumor diagnostic tool; we demonstrated the process of identifying and using exosome-derived miRNA as biomarkers for the classification of lymphoid and mammary tumor cell lines in humans and dogs.
Materials and Methods
UNASSIGNED
Exosome-derived miRNA sequencing data from B-cell lymphoid tumor cell lines (n=13), mammary tumor cell lines (n=8), and normal mammary epithelium cultures (n=4) were pre-processed in humans and dogs. F-test and rank product (RP) analyses were used to select candidate miRNA orthologs for tumor cell line classification. The classification was carried out using an optimized support vector machine (SVM) with various kernel classifiers, including linear SVM, polynomial SVM, and radial basis function SVM. The receiver operating characteristic and precision-recall curves were used to assess the performance of all models.
Results
UNASSIGNED
MIR10B, MIR21, and MIR30E were chosen as the candidate orthologs from a total of 236 human-dog miRNA orthologs (p≤0.01, F-test score ≥10, and RP score ≤10). Their use of polynomial SVM provided the best performance in classifying samples from various tumor cell lines and normal epithelial culture.
Conclusion
UNASSIGNED
The study successfully demonstrated a method for identifying and utilizing candidate human-dog exosome-derived miRNA orthologs for differential tumor cell line classification. Such findings shed light on a novel non-invasive tumor diagnostic tool that could be used in both human and veterinary medicine in the future.
Identifiants
pubmed: 35765483
doi: 10.14202/vetworld.2022.1163-1170
pii: Vetworld-15-1163
pmc: PMC9210832
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1163-1170Informations de copyright
Copyright: © Chokeshaiusaha, et al.
Déclaration de conflit d'intérêts
The authors declare that they have no competing interests.
Références
Theriogenology. 2020 Oct 15;156:82-89
pubmed: 32682179
Int J Mol Sci. 2013 Jul 09;14(7):14240-69
pubmed: 23839094
Mol Cancer. 2014 Nov 26;13:256
pubmed: 25428807
Vet World. 2018 Sep;11(9):1203-1209
pubmed: 30410222
J Breast Cancer. 2013 Sep;16(3):254-65
pubmed: 24155754
Cell Rep. 2014 Sep 25;8(6):1649-1658
pubmed: 25242326
Sci Rep. 2020 Jun 15;10(1):9637
pubmed: 32541886
Br J Haematol. 2013 Aug;162(3):336-47
pubmed: 23725219
Breast Cancer Res. 2016 Sep 08;18(1):90
pubmed: 27608715
NAR Genom Bioinform. 2020 Sep;2(3):lqaa078
pubmed: 33015620
Biomed Rep. 2015 Jan;3(1):75-77
pubmed: 25469251
BMC Bioinformatics. 2008 Oct 16;9:439
pubmed: 18925941
J Vet Sci. 2018 Mar 31;19(2):162-171
pubmed: 28927253
Bioinformatics. 2017 Sep 01;33(17):2774-2775
pubmed: 28481966
Mol Ther Nucleic Acids. 2020 Mar 6;19:1423-1433
pubmed: 32160711
Biology (Basel). 2012 Dec 14;1(3):895-905
pubmed: 24832523
Epigenomics. 2019 Jan;11(1):35-51
pubmed: 30211623
Science. 2020 Feb 7;367(6478):
pubmed: 32029601
Oncol Rep. 2019 Jun;41(6):3233-3243
pubmed: 31002358
Bioinformatics. 2016 Sep 15;32(18):2847-9
pubmed: 27207943
J Exp Clin Cancer Res. 2020 Apr 16;39(1):67
pubmed: 32299469
Sci Rep. 2020 Nov 23;10(1):20371
pubmed: 33230132
N Engl J Med. 2012 Aug 16;367(7):636-46
pubmed: 22894576
Cancers (Basel). 2018 Jul 19;10(7):
pubmed: 30029522
Breast J. 2015 Mar-Apr;21(2):192-3
pubmed: 25583284
PLoS One. 2019 Apr 29;14(4):e0208567
pubmed: 31034520
Nat Rev Mol Cell Biol. 2019 Jan;20(1):21-37
pubmed: 30108335
Bioinformatics. 2013 Jan 1;29(1):15-21
pubmed: 23104886
BMC Cancer. 2018 Aug 20;18(1):832
pubmed: 30126376
Nat Commun. 2020 Jul 17;11(1):3616
pubmed: 32680987
Bioinformatics. 2017 Sep 15;33(18):2941-2942
pubmed: 28541403