Identification of Key Genes in Colorectal Cancer Regulated by miR-34a

Abstract

BACKGROUND The aim of this study was to screen the molecular targets of miR-34a in colorectal cancer (CRC) and construct the regulatory network, to gain more insights to the pathogenesis of CRC. MATERIAL AND METHODS The microarray data of CRC samples and normal samples (GSE4988), as well as CRC samples transformed with miR-34a and non-transfected CRC samples (GSE7754), were downloaded from the Gene Expression Omnibus (GEO) database. The differently expressed genes (DEGs) were identified via the LIMMA package in R language. The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to identify significant Gene Ontology (GO) terms and pathways in DEGs. The targets of miR-34a were obtained via the miRWalk database, and then the overlaps between them were selected out to construct the regulatory network of miR-34a in CRC using the Cytoscape software. RESULTS A total of 392 DEGs were identified in CRC samples compared with normal samples, including 239 upregulated genes and 153 downregulated ones. These DEGs were enriched in 75 GO terms and one Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. At the same time, 332 DEGs (188 upregulated and 144 downregulated) were screened out between miR-34a transformed CRC and miR-34a non-transfected CRC samples and they were enriched in 20 GO terms and eight KEGG pathways. Six overlapped genes were identified in two DEGs groups. There were 1,668 targets of miR-34a obtained via the miRWalk database, among which 21 were identified differently expressed in miR-34a transformed CRC samples compared with miR-34a non-transfected CRC samples. Two regulatory networks of miR-34a in CRC within these two groups of overlapped genes were constructed respectively. CONCLUSIONS Pathways related to cell cycle, DNA replication, oocyte meiosis, and pyrimidine metabolism might play critical roles in the progression of CRC. Several genes such as SERPINE1, KLF4, SEMA4B, PPARG, CDC45, and KIAA0101 might be the targets of miR-34a and the potential therapeutic targets of CRC.

Figure 1

The DEGs change trend (A, B) and hierarchical cluster analysis of samples (C, D) in the data set of GSE4988 (A, C) and GSE7754 (B, D). Colors in the heat map represent for different gene expression level. Based on DEGs-1, CRC samples and normal samples were classified to different clusters (C). Similarly, miR-34a transformed CRC samples and non-transfected CRC samples were classified to different clusters according to DEGs-2 (D).

Figure 2

The top 10 GO terms for which the DEGs-1 (A) and the DEGs-2 (B) were enriched.

Figure 3

Regulatory network between miR-34a and the overlapped DEGs. Genes in A were the overlaps between DEGs-2 and the targets of miR-34a, well genes in B were the overlaps between DEGs-1 and DEGs-2.

Figure 4

The mRNA expression levels of ERPINE1, KLF4, SEMA4B, PPARG, CDC45 and KIAA0101 in miR-34a group and control group. * meant p<0.05, *** meant p<0.001.