Identification of Key Candidate Genes and Pathways in Colorectal Cancer by Integrated Bioinformatical Analysis.

Yongchen Guo,Yonghua Bao,Wancai Yang,Ming Ma

doi:10.3390/ijms18040722

Abstract

Colorectal cancer (CRC) is one of the most common malignant diseases worldwide, but the involved signaling pathways and driven-genes are largely unclear. This study integrated four cohorts profile datasets to elucidate the potential key candidate genes and pathways in CRC. Expression profiles GSE28000, GSE21815, GSE44076 and GSE75970, including 319 CRC and 103 normal mucosa, were integrated and deeply analyzed. Differentially expressed genes (DEGs) were sorted and candidate genes and pathways enrichment were analyzed. DEGs-associated protein–protein interaction network (PPI) was performed. Firstly, 292 shared DEGs (165 up-regulated and 127 down-regulated) were identified from the four GSE datasets. Secondly, the DEGs were clustered based on functions and signaling pathways with significant enrichment analysis. Thirdly, 180 nodes/DEGs were identified from DEGs PPI network complex. Lastly, the most significant 2 modules were filtered from PPI, 31 central node genes were identified and most of the corresponding genes are involved in cell cycle process, chemokines and G protein-coupled receptor signaling pathways. Taken above, using integrated bioinformatical analysis, we have identified DEGs candidate genes and pathways in CRC, which could improve our understanding of the cause and underlying molecular events, and these candidate genes and pathways could be therapeutic targets for CRC.

Highlights

Colorectal cancer (CRC) is one of the most common malignancies in the world, there were estimated more than 777,000 of new cases in 2015 in the developed countries [1,2], and in China alone, there were about 376,000 of new CRC cases and 191,000 of death CRC cases in 2015, accounting for the fifth of malignant tumor incidence and mortality [3]
The 292 EDGs were classified into three groups by Gene ontology (GO) terms using multiple approaches.In the third step, the up- and down-regulated differentially expressed genes (DEGs) were further clustered based on functions and signaling pathways with significant enrichment analysis.In the fourth step, DEGs protein–protein interaction (PPI) network complex was developed and 180 nodes/DEGs were identified with 518 edges, and the most significant 2 modules were filtered from the protein–protein interaction network (PPI) network complex, among them, 31 central node genes were identified and most of the corresponding genes were associated with cell cycle process, Chemokines and G protein-coupled receptor signaling pathways
Both Wnt/β-catenin [19,20] and inflammatory signaling pathway activation [21] lead to intestinal epithelial disruption ofhomeostasis, for instance, proliferation is increased, differentiation and apoptosis are decreased, in the intestinal tract.Through integrated bioinformatical analysis, we have identified 31 central node/genes, among them, the first module or cluster (Figure 4B) consisting of 17 genes, including CDK1, CCNB1, CENPE, KIF20A, CCNA2, MAD2L1, were listed at the top of the most changed genes, and their biological functions are involved in cell cycle control and mitotic regulation [22]

Summary

Introduction

Colorectal cancer (CRC) is one of the most common malignancies in the world, there were estimated more than 777,000 of new cases in 2015 in the developed countries [1,2], and in China alone, there were about 376,000 of new CRC cases and 191,000 of death CRC cases in 2015, accounting for the fifth of malignant tumor incidence and mortality [3]. The occurrence and progression of colon cancer are correlated with multiple factors from the point of view of science and research, for instance, gene aberrations, cellular and environmental factors [4]. Due to high morbidity and mortality in colorectal cancer, revealing the causes and the underlying molecular mechanisms, discovering molecular biomarkers for early diagnosis, prevention and personalized therapy, is critically important and highly demanded. Gene chip or gene profile is a gene detection technique that has been used for more than ten years, using gene chips can quickly detect all the genes within the same sample time-point expression information, which is suitable for differentially expressed genes screening [5]. Many microarray data profiling studies have been carried out on CRC in recently years [6], and hundreds of differentially expressed genes (DEGs) have been obtained. The integrated bioinformatics methods combining with expression profiling techniques will be innovative and might solve the disadvantages

Methods

Results

Discussion

Conclusion