Integrative analysis of miRNA expression data reveals a minimal signature for tumour cells classification
MicroRNAs (miRNAs) are pivotal biomarkers for cancer screening. Identifying distinctive expression patterns of miRNAs in specific cancer types can serve as an effective strategy for classification and characterization. However, the development of a minimal signature of miRNAs for accurate cancer cla...
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Elsevier
2025-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037024004458 |
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| author | Sabrina Napoletano David Dannhauser Paolo Antonio Netti Filippo Causa |
| author_facet | Sabrina Napoletano David Dannhauser Paolo Antonio Netti Filippo Causa |
| author_sort | Sabrina Napoletano |
| collection | DOAJ |
| description | MicroRNAs (miRNAs) are pivotal biomarkers for cancer screening. Identifying distinctive expression patterns of miRNAs in specific cancer types can serve as an effective strategy for classification and characterization. However, the development of a minimal signature of miRNAs for accurate cancer classification remains challenging, hindered by the lack of integrated approaches that systematically analyse miRNA expression levels of miRNAs alongside their associated biological pathways. In this study, we present a comprehensive integrative approach that utilizes transcriptomic data from lung, breast, and melanoma cancer cell lines to identify specific expression patterns. By combining bioinformatics, dimensionality reduction techniques, machine learning, and experimental validation, we pinpoint miRNAs linked to critical biological pathways. Our results demonstrate a highly significant differentiation of cancer types, achieving 100 % classification accuracy with minimal training time using a streamlined miRNA signature. Validation of the miRNA profile confirms that each of the three identified miRNAs regulates distinct biological pathways with minimal overlap. This specificity highlights their unique roles in tumour biology and set the stage for further exploration of miRNAs interactions and their contributions to tumourigenesis across diverse cancer types. Our work paves the way for multi-cancer classification, emphasizing the transformative potential of miRNA research in oncology. Beyond advancing the understanding of tumour biology, our step-by-step guide offers a robust tool for a wide range of users to investigate precise diagnostics and promising therapeutic strategies. |
| format | Article |
| id | doaj-art-a613df3fcb5a4edaaf4ad363a09f10d2 |
| institution | Kabale University |
| issn | 2001-0370 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-a613df3fcb5a4edaaf4ad363a09f10d22025-01-08T04:52:23ZengElsevierComputational and Structural Biotechnology Journal2001-03702025-01-0127233242Integrative analysis of miRNA expression data reveals a minimal signature for tumour cells classificationSabrina Napoletano0David Dannhauser1Paolo Antonio Netti2Filippo Causa3Interdisciplinary Research Centre on Biomaterials (CRIB), Università degli Studi di Napoli ''Federico II'', Piazzale Tecchio 80, Naples 80125, Italy; Center for Advanced Biomaterials for Healthcare@CRIB, Istituto Italiano di Tecnologia (IIT), Largo Barsanti e Matteucci 53, Naples 80125, ItalyInterdisciplinary Research Centre on Biomaterials (CRIB), Università degli Studi di Napoli ''Federico II'', Piazzale Tecchio 80, Naples 80125, Italy; Dipartimento di Ingegneria Chimica del Materiali e della Produzione Industriale (DICMAPI), University ''Federico II'', Piazzale Tecchio 80, Naples 80125, Italy; Center for Advanced Biomaterials for Healthcare@CRIB, Istituto Italiano di Tecnologia (IIT), Largo Barsanti e Matteucci 53, Naples 80125, ItalyInterdisciplinary Research Centre on Biomaterials (CRIB), Università degli Studi di Napoli ''Federico II'', Piazzale Tecchio 80, Naples 80125, Italy; Dipartimento di Ingegneria Chimica del Materiali e della Produzione Industriale (DICMAPI), University ''Federico II'', Piazzale Tecchio 80, Naples 80125, Italy; Center for Advanced Biomaterials for Healthcare@CRIB, Istituto Italiano di Tecnologia (IIT), Largo Barsanti e Matteucci 53, Naples 80125, ItalyInterdisciplinary Research Centre on Biomaterials (CRIB), Università degli Studi di Napoli ''Federico II'', Piazzale Tecchio 80, Naples 80125, Italy; Dipartimento di Ingegneria Chimica del Materiali e della Produzione Industriale (DICMAPI), University ''Federico II'', Piazzale Tecchio 80, Naples 80125, Italy; Center for Advanced Biomaterials for Healthcare@CRIB, Istituto Italiano di Tecnologia (IIT), Largo Barsanti e Matteucci 53, Naples 80125, Italy; Corresponding author at: Interdisciplinary Research Centre on Biomaterials (CRIB), Università degli Studi di Napoli ''Federico II'', Piazzale Tecchio 80, Naples 80125, Italy.MicroRNAs (miRNAs) are pivotal biomarkers for cancer screening. Identifying distinctive expression patterns of miRNAs in specific cancer types can serve as an effective strategy for classification and characterization. However, the development of a minimal signature of miRNAs for accurate cancer classification remains challenging, hindered by the lack of integrated approaches that systematically analyse miRNA expression levels of miRNAs alongside their associated biological pathways. In this study, we present a comprehensive integrative approach that utilizes transcriptomic data from lung, breast, and melanoma cancer cell lines to identify specific expression patterns. By combining bioinformatics, dimensionality reduction techniques, machine learning, and experimental validation, we pinpoint miRNAs linked to critical biological pathways. Our results demonstrate a highly significant differentiation of cancer types, achieving 100 % classification accuracy with minimal training time using a streamlined miRNA signature. Validation of the miRNA profile confirms that each of the three identified miRNAs regulates distinct biological pathways with minimal overlap. This specificity highlights their unique roles in tumour biology and set the stage for further exploration of miRNAs interactions and their contributions to tumourigenesis across diverse cancer types. Our work paves the way for multi-cancer classification, emphasizing the transformative potential of miRNA research in oncology. Beyond advancing the understanding of tumour biology, our step-by-step guide offers a robust tool for a wide range of users to investigate precise diagnostics and promising therapeutic strategies.http://www.sciencedirect.com/science/article/pii/S2001037024004458MicroRNAOmicsRNA-seqMachine learning classifiersCancer cells |
| spellingShingle | Sabrina Napoletano David Dannhauser Paolo Antonio Netti Filippo Causa Integrative analysis of miRNA expression data reveals a minimal signature for tumour cells classification Computational and Structural Biotechnology Journal MicroRNA Omics RNA-seq Machine learning classifiers Cancer cells |
| title | Integrative analysis of miRNA expression data reveals a minimal signature for tumour cells classification |
| title_full | Integrative analysis of miRNA expression data reveals a minimal signature for tumour cells classification |
| title_fullStr | Integrative analysis of miRNA expression data reveals a minimal signature for tumour cells classification |
| title_full_unstemmed | Integrative analysis of miRNA expression data reveals a minimal signature for tumour cells classification |
| title_short | Integrative analysis of miRNA expression data reveals a minimal signature for tumour cells classification |
| title_sort | integrative analysis of mirna expression data reveals a minimal signature for tumour cells classification |
| topic | MicroRNA Omics RNA-seq Machine learning classifiers Cancer cells |
| url | http://www.sciencedirect.com/science/article/pii/S2001037024004458 |
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