Research Paper Volume 15, Issue 17 pp 8993—9021

Identification and validation of a prognostic signature of cuproptosis-related genes for esophageal squamous cell carcinoma

Yiping Zhang1, *, , Kebing Chen2, *, , Liyan Wang1, , Juhui Chen1, , Zhizhong Lin1, , Yuanmei Chen3, , Junqiang Chen1, , Yu Lin1, , Yuanji Xu1, , Haiyan Peng4, ,

  • 1 Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou 350014, China
  • 2 The First Clinical Medical College, Xuzhou Medical University, Xuzhou 221004, China
  • 3 Department of Thoracic Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou 350014, China
  • 4 Department of Clinical Laboratory, The School of Clinical Medicine, Fujian Medical University, The First Hospital of Putian, Putian 351199, China
* Equal contribution

Received: April 18, 2023       Accepted: August 21, 2023       Published: September 2, 2023
How to Cite

Copyright: © 2023 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Esophageal squamous cell carcinoma (ESCC) is a highly lethal form of cancer. Cuproptosis is a recently discovered form of regulated cell death. However, its significance in ESCC remains largely unknown. In this study, we observed significant expression differences in most of the 12 cuproptosis-related genes (CRGs) in the TCGA-ESCC dataset, which was validated using GSE20347, GSE38129, and individual ESCC datasets. We were able to divide patients in the TCGA-ESCC cohort into two subgroups based on disease, and found significant differences in survivor outcomes and biological functions between these subgroups. Additionally, we identified 11 prognosis-related genes from the 12 CRGs using LASSO COX regression analysis and constructed a CRGs signature for ESCC. Patients were categorized into high- and low-risk subgroups based on their median risk score, with those in the high-risk subgroup having significantly worse overall survival than those in the low-risk subgroup. The CRGs signature was also highly accurate in predicting prognosis and survival outcomes. Univariate and multivariate Cox regression analyses revealed that 8 of the 11 CRGs were independent prognostic factors for predicting survival in ESCC patients. Furthermore, our nomogram performed well and could serve as a useful tool for predicting prognosis. Finally, our risk model was found to be relevant to the sensitivity of targeted agents and immune infiltration. Functional enrichment analysis demonstrated that the risk model was associated with biological pathways of tumor migration and invasion. In summary, our study may provide a promising prognostic signature based on CRGs and offers potential targets for personalized therapy.


EC: Esophageal cancer; ESCC: Esophageal squamous cell carcinoma; CRGs: Cuproptosis-related genes; TCA: The tricarboxylic acid; CuAS: Cuproptosis Activation Scoring; ATP7B: ATPase copper transporting beta; CDKN2A: cyclin dependent kinase inhibitor 2A; DLAT: dihydrolipoamide S-acetyltransferase; DLD: dihydrolipoamide dehydrogenase; FDX1: ferredoxin1; GLS: glutaminase; LIAS: lipoic acid synthetase; LIPT1: the lipoyltransferase 1; MTF1: metal regulatory transcription factor 1; PDHA1: yruvate dehydrogenase E1 subunit alpha 1; PDHB: pyruvate dehydrogenase E1; SLC31A1: solute carrier family 31 member 1; TCGA: The Cancer Genome Atlas database; GEO: Gene Expression Omnibus; TPM: Transcripts per Million; FPKM: Fragments per kilobase million; GSVA: Gene Set Variation Analysis; ssGSEA: Single-sample gene-set enrichment analysis; CPs: Cuproptosis score; LASSO: Least absolute shrinkage and selection operator; OS: Overall survival; KM: Kaplan–Meier; ROC: Receiver operating characteristic; AUC: Area under the curve; DCA: Decision curve analysis; GDSC: Genomics of Drug Sensitivity in Cancer; GO: Gene Ontology; BP: Biological process; MF: Molecular function; CC: Cellular component; DEGs: Differentially expressed genes; GSEA: Gene Set Enrichment Analysis; SNPs: Single nucleotide polymorphisms; PCA: Principal component analysis; ECM: Extracellular matrix.