Effectiveness of Cuproptosis-related long non-coding RNAs in predicting prognosis and immune response in patients with lung squamous cell carcinoma

Abstract

Abstract Background Thirty percent of non-small cell lung malignancies is lung squamous cell carcinomas (LUSC). However, its prognostic indicators are not universally accepted as standard. Long non-coding RNAs (lncRNAs), which plays complex and precise regulatory roles in gene expression, are involved in various biological processes including tumor proliferation, apoptosis, invasion, and metastasis, and are often widely studied as prognostic indicators. During mitochondrial respiration, cuproptosis, a novel form of cell death occurs when tricarboxylic acid cycle’s (TCA) lipid acylated components bind directly to copper ions. Cuproptosis causes proteotoxic stress due to aggregated lipid acylated proteins and the downregulation of iron-sulfur cluster proteins, eventually causing cell death. This research dealt with exploring the cuproptosis-related lncRNAs function in predicting clinical prognosis and immunotherapy in patients with LUSC. Methods Clinical, genomic, and mutational data of LUSC patients were accessed at the Cancer Genome Atlas (TCGA). Subsequently, the mRNA-lncRNA co-expression network was visualized to screen cuproptosis-related lncRNAs. LASSO and Cox regression analysis was executed to establish lncRNA risk models to assess the LUSC patients’ prognostic risk. The stratification of patients into high-risk and low-risk groups was performed. In addition, ROC, survival, risk curves, nomogram, C-Index, independent prognostic analysis, and clinical subgroup model validation were used to assess prognostic value. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, immune-related functional analysis, and tumor mutation burden (TMB) analysis were performed. Subsequently, the impact of immune escape and immunotherapy in high- and low-risk groups was judged by the TIDE score. Finally, potential drugs for LUSC were identified, and their sensitivities were calculated. Results The resulting data identified five cuproptosis-related lncRNAs as being capable of independently predicting the prognosis (AC010328.1, LINC01740, AL358613.2, MIR3945HG, and AC002467.1). In addition, the patient’s risk scores were quantified. The two risk groups depicted significant differences in OS and PFS with a better prognosis for the lower-risk patients in contrast with the high-risk patients. Prognostic analysis by Kaplan-Meier survival analysis, ROC curve, nomogram, and C-index indicated the capacity of lncRNAs that were linked to cuproptosis in accurately predicting the patient’s prognosis. Heatmap and nomogram depicted clear distribution of cuproptosis-related lncRNAs in high- and low-risk groups. Enrichment analysis indicated that the lncRNAs’ biological functions are involved in the metabolism of antitumor drugs. Additionally, the vast majority of immune functions were significantly active in the high-risk group, whereas the median survival time of high-TMB and low-risk patients was considerably longer than the other groups. According to TIDE analysis, the high-risk group patients had a high risk of immune escape and worse immunotherapy outcomes. Several drugs with higher sensitivity for high-risk LUSC patients were screened as well. Conclusion A model based on five cuproptosis-related genes was established to predict LUSC patients’ prognoses. The model’s reliability was evaluated in various aspects such as immune response. Overall, the findings of this study may offer new perspectives into the clinical management and immunotherapy of LUSC.