Research Paper Volume 15, Issue 19 pp 10473—10500

Prognostic value and immune landscapes of cuproptosis-related lncRNAs in esophageal squamous cell carcinoma

Xiang Zhang1, *, , Nan Feng1, *, , Bo Wu1, , Zishun Guo1, , Tiewen Pan2, , Xiandong Tao2, , Hongyang Zheng2, *, , Wenxiong Zhang1, *, ,

  • 1 Department of Thoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang 330006, China
  • 2 Department of Thoracic Surgery, Third Affiliated Hospital of Naval Medical University, Shanghai 201805, China
* Equal contribution and co-first authors

Received: May 18, 2023       Accepted: August 21, 2023       Published: October 6, 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.


Background: Precisely forecasting the prognosis of esophageal squamous cell carcinoma (ESCC) patients is a formidable challenge. Cuproptosis has been implicated in ESCC pathogenesis; however, the prognostic value of cuproptosis-associated long noncoding RNAs (CuRLs) in ESCC is unclear.

Methods: Transcriptomic and clinical data related to ESCC were sourced from The Cancer Genome Atlas (TCGA). Using coexpression and Cox regression analysis to identify prognostically significant CuRLs, a prognostic signature was created. Nomogram models were established by incorporating the risk score and clinical characteristics. Tumor Immune Dysfunction and Rejection (TIDE) scores were derived by conducting an immune landscape analysis and evaluating the tumor mutational burden (TMB). Drug sensitivity analysis was performed to explore the underlying molecular mechanisms and guide clinical dosing.

Results: Our risk score based on 5 CuRLs accurately predicted poorer prognosis in high-risk ESCC patients across almost all subgroups. The nomogram that included the risk score provided more precise prognostic predictions. Immune pathways, such as the B-cell receptor signaling pathway, were enriched in the datasets from high-risk patients. High TMB in high-risk patients indicated a relatively poor prognosis. High-risk patients with lower TIDE scores were found to benefit more from immunotherapy. High-risk patients exhibited greater responsiveness to Nilotinib, BI-2536, P22077, Zoledronate, and Fulvestrant, as revealed by drug sensitivity analysis. Real-time PCR validation demonstrated significant differential expression of four CuRLs between ESCC and normal cell lines.

Conclusions: The above risk score and nomogram can accurately predict prognosis in ESCC patients and provide guidance for chemotherapy and immunotherapy.


AUC: Area under curve; CuRLs: Cuproptosis-related long non-coding RNAs; CI: Confidence interval; C-index: Concordance index; anti-CTLA4: cytotoxic T lymphocyte antigen 4; ESCA: Esophageal carcinoma; ESCC: Esophageal squamous carcinoma; GO: Gene ontology; HR: Hazard ratio; HEEC: Normal human esophageal epithelial cell lines; ICIs: Immune Checkpoint Inhibitors; KM: Kaplan-Meier; KEGG: Genes and genomes enrichment; LASSO: Least absolute shrinkage and selection operator; TCGA: The Cancer Genome Atlas database; lncRNAs: long noncoding RNAs; OS: overall survival; PID: Pathway Interaction Database; PCA: principal component analysis; PD1: programmed cell death protein 1; PDL1: programmed cell death 1 ligand 1; ROC: Receiver operating characteristic curve; TIDE: The tumor immune dysfunction and exclusion; TMB: Tumor mutation burden; TME: Tumor microenvironment; WP: Wiki-Pathways.