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Identification of four serum miRNAs as potential markers to screen for thirteen cancer types

INTRODUCTION: Cancer consistently remains one of the top causes of death in the United States every year, with many cancer deaths preventable if detected early. Circulating serum miRNAs are a promising, minimally invasive supplement or even an alternative to many current screening procedures. Many s...

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Autores principales: Chen, Joe W., Dhahbi, Joseph
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9187062/
https://www.ncbi.nlm.nih.gov/pubmed/35687572
http://dx.doi.org/10.1371/journal.pone.0269554
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author Chen, Joe W.
Dhahbi, Joseph
author_facet Chen, Joe W.
Dhahbi, Joseph
author_sort Chen, Joe W.
collection PubMed
description INTRODUCTION: Cancer consistently remains one of the top causes of death in the United States every year, with many cancer deaths preventable if detected early. Circulating serum miRNAs are a promising, minimally invasive supplement or even an alternative to many current screening procedures. Many studies have shown that different serum miRNAs can discriminate healthy individuals from those with certain types of cancer. Although many of those miRNAs are often reported to be significant in one cancer type, they are also altered in other cancer types. Currently, very few studies have investigated serum miRNA biomarkers for multiple cancer types for general cancer screening purposes. METHOD: To identify serum miRNAs that would be useful in screening multiple types of cancers, microarray cancer datasets were curated, yielding 13 different types of cancer with a total of 3352 cancer samples and 2809 non-cancer samples. The samples were divided into training and validation sets. One hundred random forest models were built using the training set to select candidate miRNAs. The selected miRNAs were then used in the validation set to see how well they differentiate cancer from normal samples in an independent dataset. Furthermore, the interactions between these miRNAs and their target mRNAs were investigated. RESULT: The random forest models achieved an average of 97% accuracy in the training set with 95% bootstrap confidence interval of 0.9544 to 0.9778. The selected miRNAs were hsa-miR-663a, hsa-miR-6802-5p, hsa-miR-6784-5p, hsa-miR-3184-5p, and hsa-miR-8073. Each miRNA exhibited high area under the curve (AUC) value using receiver operating characteristic analysis. Moreover, the combination of four out of five miRNAs achieved the highest AUC value of 0.9815 with high sensitivity of 0.9773, indicating that these miRNAs have a high potential for cancer screening. miRNA-mRNA and protein-protein interaction analysis provided insights into how these miRNAs play a role in cancer.
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spelling pubmed-91870622022-06-11 Identification of four serum miRNAs as potential markers to screen for thirteen cancer types Chen, Joe W. Dhahbi, Joseph PLoS One Research Article INTRODUCTION: Cancer consistently remains one of the top causes of death in the United States every year, with many cancer deaths preventable if detected early. Circulating serum miRNAs are a promising, minimally invasive supplement or even an alternative to many current screening procedures. Many studies have shown that different serum miRNAs can discriminate healthy individuals from those with certain types of cancer. Although many of those miRNAs are often reported to be significant in one cancer type, they are also altered in other cancer types. Currently, very few studies have investigated serum miRNA biomarkers for multiple cancer types for general cancer screening purposes. METHOD: To identify serum miRNAs that would be useful in screening multiple types of cancers, microarray cancer datasets were curated, yielding 13 different types of cancer with a total of 3352 cancer samples and 2809 non-cancer samples. The samples were divided into training and validation sets. One hundred random forest models were built using the training set to select candidate miRNAs. The selected miRNAs were then used in the validation set to see how well they differentiate cancer from normal samples in an independent dataset. Furthermore, the interactions between these miRNAs and their target mRNAs were investigated. RESULT: The random forest models achieved an average of 97% accuracy in the training set with 95% bootstrap confidence interval of 0.9544 to 0.9778. The selected miRNAs were hsa-miR-663a, hsa-miR-6802-5p, hsa-miR-6784-5p, hsa-miR-3184-5p, and hsa-miR-8073. Each miRNA exhibited high area under the curve (AUC) value using receiver operating characteristic analysis. Moreover, the combination of four out of five miRNAs achieved the highest AUC value of 0.9815 with high sensitivity of 0.9773, indicating that these miRNAs have a high potential for cancer screening. miRNA-mRNA and protein-protein interaction analysis provided insights into how these miRNAs play a role in cancer. Public Library of Science 2022-06-10 /pmc/articles/PMC9187062/ /pubmed/35687572 http://dx.doi.org/10.1371/journal.pone.0269554 Text en © 2022 Chen, Dhahbi https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Chen, Joe W.
Dhahbi, Joseph
Identification of four serum miRNAs as potential markers to screen for thirteen cancer types
title Identification of four serum miRNAs as potential markers to screen for thirteen cancer types
title_full Identification of four serum miRNAs as potential markers to screen for thirteen cancer types
title_fullStr Identification of four serum miRNAs as potential markers to screen for thirteen cancer types
title_full_unstemmed Identification of four serum miRNAs as potential markers to screen for thirteen cancer types
title_short Identification of four serum miRNAs as potential markers to screen for thirteen cancer types
title_sort identification of four serum mirnas as potential markers to screen for thirteen cancer types
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9187062/
https://www.ncbi.nlm.nih.gov/pubmed/35687572
http://dx.doi.org/10.1371/journal.pone.0269554
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