Potential diagnostic value of serum microRNAs for 19 cancer types: a meta-analysis of bioinformatics data

Abstract

Cancer is the second most common cause of mortality worldwide, accounting for almost 10 million deaths in 2020. These deaths were partly due to delayed diagnosis that led to deferred treatment. Therefore, new diagnostic methods are necessary to enhance the accuracy of noninvasive cancer detection. The present study developed a microRNA (miRNA)-based serum diagnostic marker for detecting a wide range of cancers. The study involved 61,019 serum samples from 19 different cancer types. A miRNA prediction model was established through bioinformatics analysis of serum samples from various cancer pathologies and qRT-PCR results from studies in PubMed aligned to the analysis criteria. R software v.4.1.1 with the limma data analysis package was used for single gene expression series data series, and batchNormalize and robustRankAggreg were used to predict the changes in miRNA expression in multiple datasets. GO and KEGG analyses showed that these miRNAs play a role in cancer-related biological signaling pathways. Finally, the diagnostic capability of these miRNA biomarkers was assessed using area under the curve analysis. The study predicted that 7 miRNAs were upregulated and 10 miRNAs were downregulated in 19 different types of cancer. Some miRNAs showed significant differential expression in a specific cancer type. Additionally, downstream genes regulated by miRNAs focused on many cancer-related molecular signaling pathways. In this review, we summarize the current understanding of miRNAs in various cancers, with a particular focus on their potential as future noninvasive diagnostic biomarkers. The emphasis is on their capacity for achieving high accuracy and cost savings compared to conventional biomarkers.

Communicated by Ramaswamy H. Sarma

Keywords:

• Cancer
• miRNA
• network
• diagnostic

Acknowledgments

The authors wish to thank the Center for Molecular Biology - DuyTan University for providing the data of their study.

Author contributions

CMTT interpreted data, performed the statistical analyses and designed and drafted the manuscript. PSD designed the study, revised the manuscript and supervised the whole work. All authors read and approved the final manuscript.

Disclosure statement

The authors declare that they have no conflict of interests.

Data availability statement

All relevant data supporting the findings of this study are available within the article.

Ethics statement

GEO belong to public datasets. The contributors to the database have obtained ethical approval. Thus, our research has no ethical issues.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.