Development of a prognostic nomogram based on an eight-gene signature for esophageal squamous cell carcinoma by weighted gene co-expression network analysis (WGCNA)

Jiahong Xie; Pingshan Yang; Hongjian Wei; Peiwen Mai; Xiaoli Yu

doi:10.21037/atm-21-6935

Development of a prognostic nomogram based on an eight-gene signature for esophageal squamous cell carcinoma by weighted gene co-expression network analysis (WGCNA)

Ann Transl Med. 2022 Jan;10(2):88. doi: 10.21037/atm-21-6935.

Authors

Jiahong Xie^#¹, Pingshan Yang^#², Hongjian Wei¹, Peiwen Mai³, Xiaoli Yu¹

Affiliations

¹ Department of Cardiothoracic Surgery, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
² Department of Cardiothoracic Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
³ Guangzhou Panyu District Blood Center, Guangzhou, China.

^# Contributed equally.

Abstract

Background: Esophageal squamous cell carcinoma (ESCC) is a highly aggressive malignant tumor. This study aims to develop a robust prognostic model for ESCC.

Methods: Expression profiles of ESCC were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Co-expressed modules were constructed by weighted gene co-expression network analysis (WGCNA). Differentially expressed genes (DEGs) between ESCC and normal samples were identified with the screening criteria of adjusted P value <0.05 and log |fold change (FC)| >1. After univariate and multivariate Cox regression analysis, an 8-gene module was constructed. A receiver operating characteristic (ROC) curve for overall survival (OS) was used to assess the prediction efficacy of the risk score. A nomogram was developed based on the risk score, age, gender, and stage for 1-, 2- and 3-year survival. The potential biological functions and pathways of the 8 genes were predicted using the Metascape database.

Results: The 2 ESCC-related co-expression modules were built via WGCNA. Among all DEGs, 55 survival-related genes were identified for ESCC. Based on these genes, an 8-gene module was constructed, composed of CFAP53, FCGR2A, FCGR3A, GNGT1, IGF2, LINC01524, MAGEA3, and MAGEA6. The area under the curve (AUC) was 0.961, suggesting that the risk score could effectively predict the OS of patients with ESCC. Furthermore, the nomogram exhibited high accuracy in predicting the survival rate of ESCC patients at 1, 2, and 3 years. These genes were mainly involved in ESCC-related pathways such as extracellular matrix organization, collagen formation, and blood vessel development.

Conclusions: Our nomogram based on the 8-gene risk score could be a reliable prognostic tool for ESCC.

Keywords: Esophageal squamous cell carcinoma (ESCC); nomogram; prognosis; risk score; weighted gene co-expression network analysis (WGCNA).