Long-Term Exercise Alters the Profiles of Circulating Micro-RNAs in the Plasma of Young Women

Abstract

Objective: The objective of this paper was to study the effects of long-term exercise on circulating microRNAs (miRNAs) in human plasma. Methods: Whole blood was collected from 10 female elite athletes with at least 5 years of training experience in a Synchronized Swimming Group (S group) and 15 female college students without regular exercise training (C group). Plasma miRNAs were then isolated, sequenced, and semi-quantified by the second-generation sequencing technology, and the results were analyzed by bioinformatics methods. Results: We found 380 differentially expressed miRNAs in the S group compared with the C group, among which 238 miRNAs were upregulated and 142 were downregulated. The top five abundant miRNAs in the 380 miRNAs of the S group are hsa-miR-451a, hsa-miR-486, hsa-miR-21-5p, hsa-miR-423-5p, and hsa-let-7b-5p. Muscle-specific/enriched miRNAs were not significantly different, except for miR-206 and miR-486. According to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, a large proportion of the differentially expressed miRNAs are targeted in cancer-related pathways, including proteoglycans in cancer and miRNAs in cancer and basal cell carcinoma. As the levels of circulating miRNAs (ci-miRNAs) are commonly known to be significantly deregulated in cancer patients, we further compared the levels of some well-studied miRNAs in different types of cancer patients with those in the S group and found that long-term exercise regulates the level of ci-miRNAs in an opposite direction to those in cancer patients. Conclusion: Long-term exercise significantly alters the profiles of plasma miRNAs in healthy young women. It may reduce the risk of certain types of cancers by regulating plasma miRNA levels.

Keywords: circulating miRNAs; human plasma; long-term exercise; risk of cancer; women.

FIGURE 1

Heat maps of the Pearson’s correlation coefficients for different samples. When the linear relationship between the two variables is enhanced, the correlation coefficient tends to be 1 or –1. A positive correlation tends to be 1, while a negative correlation tends to be –1.

FIGURE 2

(A) Volcano plot of the differentially expressed miRNAs. The plot shows the log₂ fold change on the X-axis vs. the adjusted P values (on the log10 scale) on the Y-axis. Red dots indicate upregulated miRNAs, green dots are downregulated miRNAs, and blue dots are miRNAs with no significant difference. (B) Venn diagram of the differentially expressed miRNAs. Each circle represents a comparison group. The overlapping part is the common miRNAs among the comparison groups.

FIGURE 3

KEGG pathway enrichment bubble plot of the differentially expressed miRNAs. X-axis label represents rich factor (rich factor = amount of differentially expressed genes enriched in the pathway/amount of all genes in the background gene set) and Y-axis label represents pathway. The size and color of the bubble represent the amount of differentially expressed genes enriched in the pathway and the enrichment significance (Q_ value is the P value corrected after multiple hypothesis testing), respectively.