Gene expression network analysis of lymph node involvement in colon cancer identifies AHSA2, CDK10, and CWC22 as possible prognostic markers

Sung Won Han,Young Seon Noh,Seong Ji Choi,Ji Young Ahn,Hee Chan Jung,Eun Sun Kim,Min Hyung Lee,Hae Jun Park,Ji-Yun Lee,Soobin Lee,Hoon Jai Chun

doi:10.1038/s41598-020-63806-x

Abstract

Colon cancer has been well studied using a variety of molecular techniques, including whole genome sequencing. However, genetic markers that could be used to predict lymph node (LN) involvement, which is the most important prognostic factor for colon cancer, have not been identified. In the present study, we compared LN(+) and LN(−) colon cancer patients using differential gene expression and network analysis. Colon cancer gene expression data were obtained from the Cancer Genome Atlas and divided into two groups, LN(+) and LN(−). Gene expression networks were constructed using LASSO (Least Absolute Shrinkage and Selection Operator) regression. We identified hub genes, such as APBB1, AHSA2, ZNF767, and JAK2, that were highly differentially expressed. Survival analysis using selected hub genes, such as AHSA2, CDK10, and CWC22, showed that their expression levels were significantly associated with the survival rate of colon cancer patients, which indicates their possible use as prognostic markers. In addition, protein-protein interaction network, GO enrichment, and KEGG pathway analysis were performed with selected hub genes from each group to investigate the regulatory relationships between hub genes and LN involvement in colon cancer; these analyses revealed differences between the LN(−) and LN(+) groups. Our network analysis may help narrow down the search for novel candidate genes for the treatment of colon cancer, in addition to improving our understanding of the biological processes underlying LN involvement. All R implementation codes are available at journal website as Supplementary Materials.

Highlights

Colon cancer has been well studied using a variety of molecular techniques, including whole genome sequencing
The genetic basis of the development of colon cancer is well understood, prognostic factors related to the lymph node (LN) involvement are still under investigation
We have attempted to understand the pathophysiology of colon cancer and how the gene changes from LN(−) to LN(+) using a network analysis and by comparing differential expression of genes (DEG) in LN(+) and LN(−) groups of colon cancer patients using the the Cancer Genome Atlas (TCGA) data set

Summary

Introduction

Colon cancer has been well studied using a variety of molecular techniques, including whole genome sequencing. Sometimes patients are under-staged because of an inadequate number of LNs retrieved during surgery; these under-staged patients lose their opportunity for adjuvant chemotherapy resulting in a higher risk of tumor recurrence[7] This makes the prediction or diagnosis of lymph node involvement extremely important for patient care. A DEG analysis has the evident limitation of being unable to identify interactions between multiple genes, and the inability to ensure the involvement of the most significantly differentially expressed genes with the disease[8,9] To overcome these limitations, we combined a network analysis referred to as the degree of centrality method with the DEG analysis[10]. It is possible to identify very important hub genes or connector genes in terms of degree on the network by a degree centrality analysis, which detects how far genes are located from the center or genes acting as connectors or hubs in a network

Methods

Results

Conclusion