The Role of MicroRNAs in the Regulation of Gastric Cancer Stem Cells: A Meta-Analysis of the Current Status

Abstract

Gastric cancer (GC) remains one of the major causes of cancer-related mortality worldwide. As for other types of cancers, several limitations to the success of current therapeutic GC treatments may be due to cancer drug resistance that leads to tumor recurrence and metastasis. Increasing evidence suggests that cancer stem cells (CSCs) are among the major causative factors of cancer treatment failure. The research of molecular CSC mechanisms and the regulation of their properties have been intensively studied. To date, molecular gastric cancer stem cell (GCSC) characterization remains largely incomplete. Among the GCSC-targeting approaches to overcome tumor progression, recent studies have focused their attention on microRNA (miRNA). The miRNAs are short non-coding RNAs which play an important role in the regulation of numerous cellular processes through the modulation of their target gene expression. In this review, we summarize and discuss recent findings on the role of miRNAs in GCSC regulation. In addition, we perform a meta-analysis aimed to identify novel miRNAs involved in GCSC homeostasis.

Keywords: gastric cancer; gastric cancer stem cells; meta-analysis; miRNAs; self-renewal.

Figure 1

Heatmap of significant predicted downregulated miRNAs (both 5’ and 3’ arm) and relative KEGG activated pathways. The color in the heatmap represents the significance levels (p-values) between each miRNA and every pathway. A merged p-value is extracted by combining the previously calculated significance levels, using Fisher’s meta-analysis method. Thus, the resulting merged p-values signify if a particular pathway is targeted by at least one miRNA out of the initially selected group. T-CDS: microRNA target coding sequences; TGF: transforming growth factor.

Figure 2

Heatmap of significant predicted upregulated miRNAs (both 5’ and 3’ arm) and relative KEGG activated pathways. The color in the heatmap represents the significance levels (p-values) between each miRNA and every pathway. A merged p-value is extracted by combining the previously calculated significance levels, using Fisher’s meta-analysis method. Thus, the resulting merged p-values signify if a particular pathway is targeted by at least one miRNA out of the initially selected group.

Figure 3

The transforming growth factor-β (TGF-β) signaling pathway with marked expression levels of deregulated genes. Arbitrary signal intensity acquired from microarray analysis is represented by colors (red, higher; blue, lower expression).

Figure 4

Hippo Signaling pathway with marked expression levels of deregulated genes. Arbitrary signal intensity acquired from microarray analysis is represented by colors (red, higher; blue, lower expression).