Transcriptome and Network Dissection of Microsatellite Stable and Highly Instable Colorectal Cancer.

Vahid Akbari,Abolfazl Movafagh,Marzieh Kallhor,Behrouz Mollashahi

doi:10.31557/apjcp.2019.20.8.2445

Abstract

Background:Colorectal cancer (CRC) is one of the most common cancers worldwide with high number of mortality every year. Microsatellite instability (MSI) is a considerable feature of CRC which affects prognosis and treatment. High level of MSI or MSI-high (MSI-H) colorectal cancer has better prognosis and immunotherapy response, while microsatellite stable (MSS) CRC has better response to 5-fluorouracil (5-FU)-based chemotherapy. More studies are needed, specifically on MSS CRC which has worse prognosis, to further reveal biological differences and similarities between MSS and MSI colorectal cancer, which may equip us with the knowledge to develop more promising therapeutic approaches to target both types or be more effective for each type. Methods:We aimed to find affected biological processes and their regulators in both type, MSS and MSI-H, of CRC; as well as reveal specific ones in each type. We applied meta- and network analysis on freely available transcriptome data in MSS and MSI-H colorectal cancer from gene expression omnibus (GEO) database to detect common differentially expressed genes (DEGs) and critical biological processes and predict their most significant regulators. Results:Our results demonstrate considerable up and downregulation in cell cycle and lipid catabolism processes, respectively; and introduced MYC and FOXM1 as two central and up-stream regulators of DEGs in both type of CRC. Chemokine-mediated processes displayed up-regulation in MSI-H type, while metastasis-related processes showed more activation in MSS CRC. Additionally, DACH1 and TP53 were detected as two important transcription factors that differentially expressed just in MSS and MSI-H, respectively. Conclusion:Our results can explain why MSI and MSS CRC display different immunotherapy response, prognosis, and metastasis feature. Moreover, our predicted up-stream regulators in the regulatory networks may be promising therapeutic targets.

Highlights

Colorectal cancer is one of the major causes of cancer mortality in the world with an estimation of more than 1.8 million new colorectal cancer cases and 881,000 deaths in 2018 (Bray et al, 2018)
Among all found Differential Expression (DE)-TFs in both microsatellite stable (MSS) and Microsatellite instability (MSI)-H Colorectal cancer (CRC), MYC and FOXM1 were common in both type based on our criteria of differentially expressed genes (DEGs) selection, and had high number of targets and were among the most up-stream differentially expressed transcription factors (DE-TFs) based on centrality and core regulatory network analysis
They showed a high number of interactions in Protein-protein interaction (PPI) networks in comparison with the other DE-TFs

Summary

Introduction

Colorectal cancer is one of the major causes of cancer mortality in the world with an estimation of more than 1.8 million new colorectal cancer cases and 881,000 deaths in 2018 (Bray et al, 2018). 12%–17% of whole colorectal tumors have microsatellite instability and majority of them are sporadic (Ward et al, 2001; Popat et al, 2005). High level of MSI or MSI-high (MSI-H) colorectal cancer has better prognosis and immunotherapy response, while microsatellite stable (MSS) CRC has better response to 5-fluorouracil (5-FU)-based chemotherapy. On MSS CRC which has worse prognosis, to further reveal biological differences and similarities between MSS and MSI colorectal cancer, which may equip us with the knowledge to develop more promising therapeutic approaches to target both types or be more effective for each type. We applied meta- and network analysis on freely available transcriptome data in MSS and MSI-H colorectal cancer from gene expression omnibus (GEO) database to detect common differentially expressed genes (DEGs) and critical biological processes and predict their most significant regulators. Our predicted up-stream regulators in the regulatory networks may be promising therapeutic targets

Objectives

Methods

Results

Conclusion