Identification of Modulated MicroRNAs Associated with Breast Cancer, Diet, and Physical Activity

Abstract

Background: Several studies have shown that healthy lifestyles prevent the risk of breast cancer (BC) and are associated with better prognosis. It was hypothesized that lifestyle strategies induce microRNA (miRNA) modulation that, in turn, may lead to important epigenetic modifications. The identification of miRNAs associated with BC, diet, and physical activity may give further insights into the role played by lifestyle interventions and their efficacy for BC patients. To predict which miRNAs may be modulated by diet and physical activity in BC patients, the analyses of different miRNA expression datasets were performed. Methods: The GEO DataSets database was used to select miRNA expression datasets related to BC patients, dietary interventions, and physical exercise. Further bioinformatic approaches were used to establish the value of selected miRNAs in BC development and prognosis. Results: The analysis of datasets allowed the selection of modulated miRNAs associated with BC development, diet, and physical exercise. Seven miRNAs were also associated with the overall survival of BC patients. Conclusions: The identified miRNAs may play a role in the development of BC and may have a prognostic value in patients treated with integrative interventions including diet and physical activity. Validation of such modulated miRNAs on BC patients undergoing lifestyle strategies will be mandatory.

Keywords: bioinformatics; biomarkers; breast cancer; diet; epigenetic; microRNA; physical activity; prognosis.

Figure 1

Differentially expressed miRNAs in BC samples compared to normal breast tissues in at least five out of eight datasets. The results of differential analyses were expressed as log2FC values indicating with red scale boxes the upregulated miRNAs (Panel (A)) and with blue scale boxes the downregulated ones (Panel (B)). Dataset IDs were marked with different colors depending on the platform technology used.

Figure 2

Panel (A): Differentially expressed miRNAs before and after exercise. Panel (B): Differentially expressed miRNAs before and after dietary interventions. miRNAs in common in at least >50% of the selected datasets were selected. The results of the differential analyses were expressed as log2FC values indicating with red scale boxes the upregulated miRNAs and blue scale boxes the downregulated ones. Dataset IDs were marked with different colors depending on the platform technology used. In bold are reported miRNAs also involved in BC.

Figure 3

EMT gene expression levels in breast cancer samples compared to the controls. The GEPIA software performs a four-way ANOVA differential analysis (using sex, age, ethnicity, and disease state (tumor or normal) as variables for calculating differential expression. The p-values were adjusted according to the Benjamini and Hochberg false discovery rate. The p-value threshold was set at 0.01 (* = p < 0.01). The relative expression levels were first log2(TPM+1) transformed and the log2FC was defined as median (Tumor)—Median (Normal), where TPM is the transcript count per million.

Figure 4

mirDIP analysis of BC miRNAs and EMT genes. Panel (A): Interaction between BC upregulated miRNAs and EMT genes. Panel (B): Interaction between BC downregulated miRNAs and EMT gene. The intensity of miRNA–gene interactions is reported as a red color scale ranging from yellow (low interaction levels) and dark red (very high interaction levels). Each EMT gene is reported with the level of interaction with the computationally selected BC miRNAs.

Figure 5

Panel (A): Interaction levels between EMT genes and miRNAs modulated by exercise. Panel (B): Interaction levels between EMT genes and miRNAs modulated by diet. The intensity of miRNA–gene interactions is reported as a red color scale ranging from yellow (low interaction levels) and dark red (very high interaction levels). For each EMT gene is reported the level of interaction with the computationally selected BC miRNAs.

Figure 6

STRING protein interaction network of 61 out of 652 genes targeted by the selected miRNAs and directly involved in the breast cancer KEGG pathway (hsa05224).

Figure 7

STRING interaction network between genes targeted by the diet-modulated miRNAs. In red are the genes involved in the breast cancer KEGG pathway (hsa05224).

Figure 8

STRING and Gene Ontology (GO) enrichment analyses of exercise- and diet-miRNA-modulated genes. (A) Exercise-modulated genes clustered according to biological process; (B) exercise-modulated genes clustered according to molecular function; (C) exercise-modulated genes clustered according to cellular component; (D) diet-modulated genes clustered according to biological process; (E) diet-modulated genes clustered according to molecular function; and (F) diet-modulated genes clustered according to cellular component.

Figure 9

Prognostic value of BC dysregulated miRNAs according to OncoLnc. Panel (A): Upregulated miRNAs statistically associated with the survival of patients. In the red box is reported the upregulated miRNA whose expression is not concordant with survival curves. Panel (B): downregulated BC miRNA statistically associated with the survival of patients.

Figure 10

Prognostic value of exercise-modulated miRNAs according to OncoLnc.