Research Paper Volume 15, Issue 22 pp 13504—13541

Figure 8. Identification of candidate drugs for high CoCuLncSig risk score patients. (A) The data for our drug prediction comes from the CTRP and PRISM databases, and the Venn diagram shows the compounds they include. (B) The flowchart shows the steps we explored in the drug databases of CTRP and PRISM, respectively, mainly including the Wilcoxon rank sum and the Spearman correlation statistical algorithms. (C) A collection of potential drugs has been discovered in the CTRP drug database. The top portion displays eight drug candidates that were identified through Spearman correlation and differential drug response analyses. The lower section presents the validation of the most encouraging LUAD therapeutics with high CoCuLncSig scores, using evidence from various sources. (D) The PRISM drug database has revealed a set of potential drug candidates. The top section displays eight drug candidates that were identified through Spearman correlation and differential drug response analyses. The lower section showcases the validation process for the most promising LUAD therapeutics with high CoCuLncSig scores, drawing on evidence from various sources. CoCuLncSig: copper homeostasis and cuproptosis regulated lncRNA signature; LUAD: lung adenocarcinoma; FC: fold change; ^#: fold change differences of drug targets between tumor and normal tissue (> 0 represents up-regulated in tumor tissue); ^***P-value < 0.001; In the analysis, a P-value of less than 0.05 was considered statistically significant, denoted by an asterisk.