Dermatology Journal of Cosmetic and Laser Therapy

Background: Cuproptosis is a recently discovered method of copper-induced cell death that serves an essential part in the progression and spread of stomach adenocarcinoma (STAD). Multiple studies have found that lncRNAs, or long non-coding RNAs, are strongly correlated with the outcome for STAD patients. However, the nature of the connection between cuproptosis and lncRNAs in STAD is still not completely understood. Our study set out to create a predictive hallmark of STAD based on lncRNAs associated with cuproptosis, with the hope that this would allow for more accurate prediction of STAD outcomes. Methods: We retrieved the transcriptional profile of STAD as well as clinical information from The Cancer Genome Atlas (TCGA). The cuproptosis-related genes (CRGs) were gathered through the highest level of original research and complemented with information from the available literature. We constructed a risk model using co-expression network analysis, Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) analysis to identify lncRNAs associated with cuproptosis, and then validated its performance in a validation set. Survival study, progression-free survival analysis (PFS), receiver operating characteristic (ROC) curve analysis, Cox regression analysis, nomograms, clinicopathological characteristic correlation analysis, and principal components analysis were used to evaluate the signature's prognostic utility. Additionally, ssGSEA algorithms, KEGG, and GO were employed to assess biological functions. The tumor mutational burden (TMB) and tumor immune dysfunction and rejection (TIDE) scores were utilized in order to evaluate the effectiveness of the immunotherapy. Results: In order to construct predictive models, nine distinct lncRNAs (AC087521.1, AP003498.2, AC069234.5, LINC01094, AC019080.1, BX890604.1, AC005041.3, DPP4-DT, AL356489.2, AL139147.1) were identified. The Kaplan-Meier and ROC curves, which were applied to both the training and testing sets of the TCGA, provided evidence that the signature contained a sufficient amount of predictive potential. The signature was shown to contain risk indicators that were independent of the other clinical variables, as demonstrated by the findings of a Cox regression and a stratified survival analysis. The ssGSEA study provided additional evidence that predictive variables were highly connected with the immunological condition of STAD patients. Surprisingly, the combination of high risk and high TMB reduced survival time for patients. A worse prognosis for the immune checkpoint blockade response was also suggested by the fact that patients in the high-risk group had higher TIDE scores. Conclusion: The potential clinical uses of the identified risk profiles for the 10 cuproptosis-related lncRNAs include the assessment of the prognosis and molecular profile of STAD patients and the creation of more targeted therapy strategies.