Single-cell and Multi-omics Analysis Confirmed the Signature and Potential Targets of Cuproptosis in Colorectal...

Background: Cuproptosis, a form of copper-mediated programmed cell death, has recently garnered significant attention. However, the mechanisms by which CRGs affect the progression of CRC remain unclear. Methods: Bioinformatics approaches were employed to analyze transcriptomic datasets and clinical data from 630 CRC patients, focusing on copy number variations, prognostic implications, and immune infiltration characteristics associated with CRGs. Key CRG-related genes impacting prognosis were identified using LASSO and Cox regression methods. A prognostic model incorporating various molecular markers and clinical parameters was constructed with a training cohort and validated with a separate validation cohort. This model was used to explore clinical indicators, immune infiltration, and tumor microenvironment characteristics in CRC patients. Additionally, single-cell analysis was performed to investigate the biological roles of critical genes, and expression patterns of these genes were assessed via qRT-PCR and WB. Results: A prognostic scoring model was established based on three pivotal genes associated with CRC prognosis. This model, an independent prognostic indicator, outperformed traditional clinicopathological features in predicting patient outcomes. Kaplan-Meier survival curves demonstrated superior prognostic outcomes for individuals in the low-risk group compared to those in the high-risk group. Model stability and reliability were confirmed through ROC analysis and univariate and multivariate Cox regression analyses. Further analysis revealed significant correlations between prognostic scores and the presence of M0 macrophages and memory CD4⁺ T cells. Differences in the expression of CDKN2A, PLCB4, and NXPE4 across various CRC tissues and cells were characterized using WB, IHC and qRT-PCR. Conclusion: This study not only highlights the diverse omics profiles of CRGs in CRC but also introduces a novel model for accurate prognostic forecasting.