Identification of potential microRNA diagnostic panels and uncovering regulatory mechanisms in breast cancer pathogenesis

Abstract

Early diagnosis of breast cancer (BC), as the most common cancer among women, increases the survival rate and effectiveness of treatment. MicroRNAs (miRNAs) control various cell behaviors, and their dysregulation is widely involved in pathophysiological processes such as BC development and progress. In this study, we aimed to identify potential miRNA biomarkers for early diagnosis of BC. We also proposed a consensus-based strategy to analyze the miRNA expression data to gain a deeper insight into the regulatory roles of miRNAs in BC initiation. Two microarray datasets (GSE106817 and GSE113486) were analyzed to explore the differentially expressed miRNAs (DEMs) in serum of BC patients and healthy controls. Utilizing multiple bioinformatics tools, six serum-based miRNA biomarkers (miR-92a-3p, miR-23b-3p, miR-191-5p, miR-141-3p, miR-590-5p and miR-190a-5p) were identified for BC diagnosis. We applied our consensus and integration approach to construct a comprehensive BC-specific miRNA-TF co-regulatory network. Using different combination of these miRNA biomarkers, two novel diagnostic models, consisting of miR-92a-3p, miR-23b-3p, miR-191-5p (model 1) and miR-92a-3p, miR-23b-3p, miR-141-3p, and miR-590-5p (model 2), were obtained from bioinformatics analysis. Validation analysis was carried out for the considered models on two microarray datasets (GSE73002 and GSE41922). The model based on similar network topology features, comprising miR-92a-3p, miR-23b-3p and miR-191-5p was the most promising model in the diagnosis of BC patients from healthy controls with 0.89 sensitivity, 0.96 specificity and area under the curve (AUC) of 0.98. These findings elucidate the regulatory mechanisms underlying BC and represent novel biomarkers for early BC diagnosis.

Figure 1

Workflow and study design of the present study. BC breast cancer, DEMs differentially expressed miRNAs, AUC area under the curve, DE differentially expressed, PPI protein–protein interaction, TF transcription factor, miRNA microRNA, TCGA the cancer genome Atlas.

Figure 2

(A) The differential expression meta-profiling heatmap of the eight candidate biomarkers, in blood samples of cancer patients and healthy controls, across multiple cancer types. MiR-23b-3p, miR-92a-3p and miR-191-5p show significant expression differences in blood samples of BC patients and healthy controls. (B) The differential expression meta-profiling heatmap of the eight candidate biomarkers, in tissue samples of cancer patients and healthy controls, across multiple cancer types. MiR-141-3p, miR-590-5p and miR-190a-5p show significant expression differences in tissue samples of BC patients and healthy controls. BRCA breast cancer (C) The ROC curves for the 8 predicted miRNA biomarkers.

Figure 3

(A) The miRNA-gene interaction network of the eight predicted miRNA biomarkers (B) miRNA-disease interaction network of the eight candidate biomarkers. (C) Genes involved in cancer-related pathways, extracted from miRNA-gene interaction network.

Figure 4

(A) The miRNA-target gene interaction network of the six final selected miRNA biomarkers and their computationally predicted/ validated target genes. (B) Genes targeted by three out of the six miRNA biomarkers, extracted from miRNA-target gen network.

Figure 5

The three top modules extracted from the PPI network and their associations with the six miRNA biomarkers. The pink circles show the validated and predicted target genes. Blue diamonds indicate the six final miRNA biomarkers.

Figure 6

The miRNA-TF co-regulatory network. Blue diamonds represent miRNAs, green squares represent transcription factors (TFs), and red circles represent target genes. The size of the node correlates to the degree of the nodes.

Figure 7

Expression levels of the hub molecules (TFs and genes) in TCGA normal (n = 114) and BC tumor (n = 1097) using UALCAN database. *p < 0.05, **p < 0.01, ***p < 0.001, *** *p < 0.0001.

Figure 8

Gene Ontology (GO) and KEGG pathway enrichment analysis. (A) Top 10 enriched GO terms for the TFs and target genes of the miRNA-TF co-regulatory network (B) Top 20 enriched KEGG pathways for the TFs and target genes of the miRNA-TF co-regulatory network. BP biological process, MF molecular function, CC cellular component.

Figure 9

Diagnostic performance of model 1 on the validation dataset (GSE73002). The orange line represents the combinatorial ROC curve of miR-23b-3p, miR-191-5p and miR-92a-3p in BC patients versus healthy controls. The blue, black and green lines represent the ROC curves of each biomarker individually in BC patients versus healthy controls. AUC area under the curve, SE sensitivity, SP specificity.

Figure 10

Diagnostic performance of model 2 on the validation dataset (GSE41922). ROC curve analysis of (A–D) miR-23b-3p, miR-590-5p, miR-141-3p and miR-92a-3p in BC patients versus healthy controls. (E) the combination of the four miRNAs in BC patients versus healthy controls.

Figure 11

Expression level of the 5 miRNA biomarkers in the diagnostic models in TCGA normal (n = 104) and BC tumor (n = 1078) using CancerMIRNome database.