Development and Validation of a Prognostic Model for Esophageal Adenocarcinoma Based on Necroptosis-Related Genes

Suhong Zhang; Shuang Liu; Zheng Lin; Juwei Zhang; Zhifeng Lin; Haiyin Fang; Zhijian Hu

doi:10.3390/genes13122243

Development and Validation of a Prognostic Model for Esophageal Adenocarcinoma Based on Necroptosis-Related Genes

Genes (Basel). 2022 Nov 29;13(12):2243. doi: 10.3390/genes13122243.

Authors

Suhong Zhang¹, Shuang Liu¹, Zheng Lin¹, Juwei Zhang¹, Zhifeng Lin¹, Haiyin Fang¹, Zhijian Hu^{1

2}

Affiliations

¹ Department of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, Fuzhou 350108, China.
² Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou 350108, China.

Abstract

Necroptosis is a newly developed cell death pathway that differs from necrosis and apoptosis; however, the potential mechanism of necroptosis-related genes in EAC and whether they are associated with the prognosis of EAC patients remain unclear. We obtained 159 NRGs from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and performed differential expression analysis of the NRGs in 9 normal samples and 78 EAC tumor samples derived from The Cancer Genome Atlas (TCGA). Finally, we screened 38 differentially expressed NRGs (DE-NRGs). The results of the GO and KEGG analyses indicated that the DE-NRGs were mainly enriched in the functions and pathways associated with necroptosis. Protein interaction network (PPI) analysis revealed that TNF, CASP1, and IL-1B were the core genes of the network. A risk score model based on four DE-NRGs was constructed by Least Absolute Shrinkage and Selection Operator (LASSO) regression, and the results showed that the higher the risk score, the worse the survival. The model achieved more efficient diagnosis compared with the clinicopathological variables, with an area under the receiver operating characteristic (ROC) curve of 0.885. The prognostic value of this model was further validated using Gene Expression Omnibus (GEO) datasets. Gene set enrichment analyses (GSEA) demonstrated that several metabolism-related pathways were activated in the high-risk population. Single-sample GSEA (ssGSEA) provided further confirmation that this prognostic model was remarkably associated with the immune status of EAC patients. Finally, the nomogram map exhibited a certain prognostic prediction efficiency, with a C-index of 0.792 and good consistency. Thus, the prognostic model based on four NRGs could better predict the prognosis of EAC and help to elucidate the mechanism of necroptosis-related genes in EAC, which can provide guidance for the target prediction and clinical treatment of EAC patients.

Keywords: TCGA; bioinformatics analysis; esophageal adenocarcinoma; necroptosis-related genes; prognostic model.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adenocarcinoma* / genetics
Esophageal Neoplasms* / genetics
Humans
Necroptosis / genetics
Prognosis

Supplementary concepts

Adenocarcinoma Of Esophagus

Grants and funding

This work was supported by a grant from the Scientific and Technological Innovation Joint Capital Projects of Fujian Province (No. 2020Y9018), the Natural Science Foundation of Fujian Province (No. 2021J01726), the Natural Science Foundation of Fujian Province (No. 2021J01733), a central government-led local science and technology development special project (No. 2020L3009), and the Grant of Science and Technology of Fujian, China (No. 2019L3006). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.