A Novel Cuprotosis-Related lncRNA Signature Effectively Predicts Prognosis in Glioma Patients

Abstract

Cuprotosis is a novel and different cell death mechanism from the existing known ones that can be used to explore new approaches to treating cancer. Just like ferroptosis and pyroptosis, cuprotosis-related genes regulate various types of tumorigenesis, invasion and metastasis. However, the relationship between cuprotosis related long non-coding RNA (cuprotosis-related lncRNA) in glioma development and prognosis has not been investigated. We obtained relevant data from the Genotype-Tissue Expression (GTEx), Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA) and published articles.First, we identi ed 365 cuprotosis-related lncRNAs based on 10 cuprotosis-related differential genes (|R 2 |>0.4, p < 0.001). Then using lasso and Cox regression analysis methods, 12 prognostic cuprotosisrelated lncRNAs were obtained and constructed the CuLncSigi risk score formula. Our next step was to divide the tumor gliomas into two groups (high-risk and low-risk) based on the median risk score, and we found that patients in the high-risk group had a signi cantly worse prognosis. We used internal and external validation methods to simultaneously analyze and validate that the risk score model has good predictive power for patients with glioma. Next, we also performed enrichment analyses such as GSEA and aaGSEA and evaluated the relationship between immune-related drugs and tumor treatment. In conclusion, we successfully constructed a formula of cuprotosis-related lncRNAs with a powerful predictive function. More importantly, our study paves the way for exploring cuprotosis mechanisms in glioma occurrence and development, and helps to nd new relevant biomarkers for glioma early identi cation and diagnosis and to investigate new therapeutic approaches.

A novel signature of cuproptosis-related lncRNAs predicts prognosis in glioma: Evidence from bioinformatic analysis and experiments

“…Recently, researchers have devised a CRLs-based risk model to forecast treatment outcomes for glioma patients, which indicates the potential significance of CRLs in future glioma therapy ( Wang et al, 2022 ). To illustrate, certain scholars have constructed and confirmed a new risk model using CRLs to determine the therapy response and prognosis of glioma patients ( Zhang et al, 2022c ; Wu et al, 2023 ). However, no specific CRLs biomarkers have been identified for glioma therapy and prognosis.…”