Research Paper Volume 15, Issue 12 pp 5304—5338

Construction and validation of a model based on immunogenic cell death-associated lncRNAs to predict prognosis and direct therapy for kidney renal clear cell carcinoma

Chenxi Cai1,2, , Kexin Shu2,3, , Wanying Chen2,3, , Jiatong Ding2,3, , Zishun Guo1, , Yiping Wei1, &, , Wenxiong Zhang1, &, ,

  • 1 Department of Thoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang 330006, China
  • 2 Jiangxi Medical College, Nanchang University, Nanchang 330006, China
  • 3 Department of Urinary Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang 330006, China

Received: January 31, 2023       Accepted: May 9, 2023       Published: June 27, 2023
How to Cite

Copyright: © 2023 Cai 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: Immunogenic cell death (ICD) is an important part of the antitumor effect, yet the role played by long noncoding RNAs (lncRNAs) remains unclear. We explored the value of ICD-related lncRNAs in tumor prognosis assessment in kidney renal clear cell carcinoma (KIRC) patients to provide a basis for answering the above questions.

Methods: Data on KIRC patients were obtained from The Cancer Genome Atlas (TCGA) database, prognostic markers were identified, and their accuracy was verified. An application-validated nomogram was developed based on this information. Furthermore, we performed enrichment analysis, tumor mutational burden (TMB) analysis, tumor microenvironment (TME) analysis, and drug sensitivity prediction to explore the mechanism of action and clinical application value of the model. RT-qPCR was performed to detect the expression of lncRNAs.

Results: The risk assessment model constructed using eight ICD-related lncRNAs provided insight into patient prognoses. Kaplan-Meier (K-M) survival curves showed a more unfavorable outcome in high-risk patients (p<0.001). The model had good predictive value for different clinical subgroups, and the nomogram constructed based on this model worked well (risk score AUC=0.765). Enrichment analysis revealed that mitochondrial function-related pathways were enriched in the low-risk group. The adverse prognosis of the higher-risk cohort might correspond to a higher TMB. The TME analysis revealed a higher resistance to immunotherapy in the increased-risk subgroup. Drug sensitivity analysis can guide the selection and application of antitumor drugs in different risk groups.

Conclusions: This prognostic signature based on eight ICD-associated lncRNAs has significant implications for prognostic assessment and treatment selection in KIRC.


ATP: Adenosine triphosphate; CCND1: Recombinant Cyclin D1; DCA: Decision curve analysis; FDR: False discovery rate; GSEA: Gene set enrichment analysis; KEGG: Kyoto Encyclopedia of Genes and Genomes; H: high; HR: Hazard ratios; IC50: half maximal inhibitory concentration; ICD: immunogenic cell death; KIRC: kidney renal clear cell carcinoma; K-M survival analysis: Kaplan-Meier survival analysis; L: low; LASSO: Least absolute shrinkage and selection operator; lncRNAs: Long non-coding RNA; OS: Overall survival; PCA: Principal component analysis; PPI: protein-protein interaction; ROC: receiver operating characteristic; TCGA: The Cancer Genome Atlas; TMB: Tumor Mutation Burden; TME: tumor microenvironment; TAM: tumor-associated macrophage.