Evaluation of Clinically Significant miRNAs Level by Machine Learning Approaches Utilizing Total Transcriptome Data.
gradient boosting
machine learning
miRNA
transcriptome
Journal
Doklady. Biochemistry and biophysics
ISSN: 1608-3091
Titre abrégé: Dokl Biochem Biophys
Pays: United States
ID NLM: 101126895
Informations de publication
Date de publication:
27 Mar 2024
27 Mar 2024
Historique:
received:
28
12
2023
accepted:
03
02
2024
revised:
25
01
2024
medline:
28
3
2024
pubmed:
28
3
2024
entrez:
28
3
2024
Statut:
aheadofprint
Résumé
Analysis of the mechanisms underlying the occurrence and progression of cancer represents a key objective in contemporary clinical bioinformatics and molecular biology. Utilizing omics data, particularly transcriptomes, enables a detailed characterization of expression patterns and post-transcriptional regulation across various RNA types relative to the entire transcriptome. Here, we assembled a dataset comprising transcriptomic data from approximately 16 000 patients encompassing over 160 types of cancer. We employed state-of-the-art gradient boosting algorithms to discern intricate correlations in the expression levels of four clinically significant microRNAs, specifically, hsa-mir-21, hsa-let-7a-1, hsa-let-7b, and hsa-let-7i, with the expression levels of the remaining 60 660 unique RNAs. Our analysis revealed a dependence of the expression levels of the studied microRNAs on the concentrations of several small nucleolar RNAs and regulatory long noncoding RNAs. Notably, the roles of these RNAs in the development of specific cancer types had been previously established through experimental evidence. Subsequent evaluation of the created database will facilitate the identification of a broader spectrum of overarching dependencies related to changes in the expression levels of various RNA classes in diverse cancers. In future, it will make possible to discover unique alterations specific to certain types of malignant transformations.
Identifiants
pubmed: 38539010
doi: 10.1134/S1607672924700790
pii: 10.1134/S1607672924700790
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. Pleiades Publishing, Ltd.
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