Weighted Gene Co-expression Network Analysis Identified a Novel Thirteen-Gene Signature Associated With Progression, Prognosis, and Immune Microenvironment of Colon Adenocarcinoma Patients.

Cangang Zhang,Shukun Yao,Haibo Liu,Zhe Zhao,Dongyan Zhao

doi:10.3389/fgene.2021.657658

Abstract

Colon adenocarcinoma (COAD) is one of the most common malignant tumors and has high migration and invasion capacity. In this study, we attempted to establish a multigene signature for predicting the prognosis of COAD patients. Weighted gene co-expression network analysis and differential gene expression analysis methods were first applied to identify differentially co-expressed genes between COAD tissues and normal tissues from the Cancer Genome Atlas (TCGA)-COAD dataset and GSE39582 dataset, and a total of 309 overlapping genes were screened out. Then, our study employed TCGA-COAD cohort as the training dataset and an independent cohort by merging the GES39582 and GSE17536 datasets as the testing dataset. After univariate and multivariate Cox regression analyses were performed for these overlapping genes and overall survival (OS) of COAD patients in the training dataset, a 13-gene signature was constructed to divide COAD patients into high- and low-risk subgroups with significantly different OS. The testing dataset exhibited the same results utilizing the same predictive signature. The area under the curve of receiver operating characteristic analysis for predicting OS in the training and testing datasets were 0.789 and 0.868, respectively, which revealed the enhanced predictive power of the signature. Multivariate Cox regression analysis further suggested that the 13-gene signature could independently predict OS. Among the 13 prognostic genes, NAT1 and NAT2 were downregulated with deep deletions in tumor tissues in multiple COAD cohorts and exhibited significant correlations with poorer OS based on the GEPIA database. Notably, NAT1 and NAT2 expression levels were positively correlated with infiltrating levels of CD8+ T cells and dendritic cells, exhibiting a foundation for further research investigating the antitumor immune roles played by NAT1 and NAT2 in COAD. Taken together, the results of our study showed that the 13-gene signature could efficiently predict OS and that NAT1 and NAT2 could function as biomarkers for prognosis and the immune response in COAD.

Highlights

Due to a number of factors including environmental exposure to carcinogens and genetic predisposition, the morbidity and mortality rates of colorectal cancer are increasing rapidly, and more than 2.2 million new cases are expected to be diagnosed, accounting for 1.1 million cancer-related deaths by 2030 (Arnold et al, 2017; Islami et al, 2018)
Using the system biology method of Weighted gene co-expression network analysis (WGCNA), co-expression modules in Colon adenocarcinoma (COAD) patients were identified by constructing the co-expression networks from the The Cancer Genome Atlas Cancer Genome (TCGA)-COAD and GSE39582 datasets
The molecular pathogenesis of COAD is multifaceted in nature and characterized by a variety of genomic instabilities, FIGURE 7 | Risk score analysis, Kaplan–Meier survival curves and receiver operating characteristic (ROC) curves for the 13-gene signature in COAD

Summary

Introduction

Due to a number of factors including environmental exposure to carcinogens and genetic predisposition, the morbidity and mortality rates of colorectal cancer are increasing rapidly, and more than 2.2 million new cases are expected to be diagnosed, accounting for 1.1 million cancer-related deaths by 2030 (Arnold et al, 2017; Islami et al, 2018). Colon adenocarcinoma (COAD) is the most frequently diagnosed histological subtype of colorectal cancer, ranking fourth in terms of incidence and mortality among all kinds of malignant tumors in 2018 (Bray et al, 2018). Conventional methods utilizing the American Joint Committee on Cancer (AJCC) tumor node metastasis (TNM) classification system, vascular invasion and other parameters are widely employed to predict prognosis and guide treatment in COAD. Considering the high genetic heterogeneity of COAD, disease metastasis, progression and clinical outcomes cannot be accurately predicted based on conventional staging methods (Weiser et al, 2011; Cancer Genome Atlas Network, 2012; Guinney et al, 2015). It is highly important to identify accurate prognostic biomarkers to understand the pathogenesis, predict clinical outcomes and devise personalized therapies in COAD

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