مقالات پذیرفته شده در ششمین کنگره بین المللی زیست پزشکی
Identification of Colorectal Cancer Highly Associated CircRNAs Through Integrative Bioinformatics and ceRNA Networks
Identification of Colorectal Cancer Highly Associated CircRNAs Through Integrative Bioinformatics and ceRNA Networks
Liora Yesharim,1,*
1. Department of Medical Genetics, School of Medicine, Iran university of Medical Sciences, Tehran, Iran
Introduction: Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide and is the second leading cause of cancer-related mortality. Despite all advances, the overall survival and therapeutic efficiency of CRC is still poor, and there is a great need to identify key pathways involved in CRC pathogenesis, novel biomarkers and drug targets. Circular RNAs (circRNAs) are covalently closed RNAs that play critical roles in the cell, including regulating gene expression, which is mainly through sponging miRNAs. Numerous studies have shown that circRNAs are involved in tumorigenesis. Due to their high stability and specific cellular functions, they may be ideal biomarkers and drug targets for cancer. To identify dysregulated circRNAs in CRC with high statistical significance, a meta-analysis of microarray datasets was performed and the major circRNAs with regulatory effects on hub genes were investigated.
Methods: A total of 40 circRNA microarray samples from three GEO (www.ncbi.nlm.nih.gov/geo) datasets (GSE126094, GSE138589, GSE142837) were selected for this study. All datasets were from the same platform (GPL19978) and included cancer and paired normal tissues. First, quality control was performed. Samples of poor quality were removed. Log2 transformation and quantile normalization (using the Limma package in the R software) were performed when necessary. The batch effect was removed using the Combat function of the R package called "SVA". Meta-analysis of the three datasets was performed using two approaches: direct merging (DM) and random-effects model (REM). Differentially expressed circRNAs (DECs) were screened using the cutoff value of 0.05 Benjamini-Hochberg's adjusted P-value and |log2FC| > 1. CircRNAs identified by both the DM and REM methods were considered for further analysis. MiRNA targets of DECs were identified using the CircMine (www.biomedical-web.com/circmine) database. The most likely mRNA targets of the miRNAs were identified using the miRDB (score cut-off 90) web tool (http://mirdb.org/). The CircRNA-miRNA mRNA network, also known as ceRNA network, was constructed (Figure 1) using Cytoscape (v.3.8.9). CytoHubba, a Cytoscape plugin, was used to identify the top 10 hub genes of the ceRNA network. Then, the circRNAs interacting with these hub genes were analyzed as "crucial circRNAs." To identify the pathways involving crucial circRNAs, KEGG pathway enrichment analysis was performed.
Results: REM Meta-analysis of microarray datasets of circRNAs revealed 338 dysregulated circRNAs and DM revealed 125 dysregulated circRNAs. A total of 54 DECs, including 10 upregulated and 44 downregulated circRNAs, were identified by both the DM and REM meta-analysis approaches and used for further investigation. 193 miRNAs were identified as targets for DECs sponges and 567 genes were identified as targets for miRNAs. Based on the Maximal Clique Centrality (MCC) algorithm of CytoHubba, PTEN, AGO1, AGO3, AGO4, MAPK8, MECP2, NRAS, CCND2, ETS1, and ITGAV were identified as the major hub genes. Examination of the circRNA-miRNA hub gene network (Figure 2) revealed that seven circRNAs (hsa_circ_0000512, hsa_circ_0072387, hsa_circ_0001022, hsa_circ_0001525, hsa_circ_0041555, hsa_circ_0049356, hsa_circ_0061817) can potentially regulate the expression of key hub genes in CRC. The mTOR and FoxO signaling pathways, microRNAs in cancer, and EGFR tyrosine kinase inhibitor resistance were among the enriched KEGG pathways of the seven hub circRNAs.
Conclusion: In summary, the present study aimed to prioritize the most likely circRNA candidates that play a role in colorectal cancer and could be used as future biomarkers or drug targets. Two different approaches of the meta-analysis were performed to identify DECs with high statistical significance. In addition, seven circRNAs were identified as potential modulators of hub genes in CRC. Furthermore, this study provides a deeper understanding of circRNA-related competing endogenous RNA regulatory mechanisms in CRC pathogenesis and the major signaling pathways involved in CRC tumorigenesis.