Integrative Analysis of Cuproptosis-Related Mitochondrial Depolarisation Genes for Prognostic Prediction in Non-Small Cell Lung Cancer.

In this research, we conducted an in-depth analysis of differentially expressed genes associated with mitochondrial depolarisation in non-small cell lung cancer (NSCLC) using single-cell sequencing. By combining our findings with cuproptosis-related genes, we identified 10 significant risk genes: DCN, PTHLH, CRYAB, HMGCS1, DSG3, ZFP36L2, SCAND1, NUDT4, NDUFA4L2 and RPL36A, using univariate Cox regression analysis and machine learning methods. These genes form the core of our prognosis risk prediction model, which demonstrated high specificity and accuracy in predicting patient outcomes, as evidenced by ROC curve analysis. Kaplan-Meier curves further confirmed that patients in the low-risk group had significantly better survival rates compared to those in the high-risk group. Our models also provided valuable insights into the tumour microenvironment, immunotherapy sensitivity and chemotherapy response. To facilitate the quantification of the probability of patient survival, we incorporated clinical data into a nomogram. We comprehensively analysed the mutation status and expression patterns of the 10 risk genes using bulk transcriptomic, single-cell and spatial transcriptomic datasets. Drug target predictions highlighted DSG3, PTHLH, ZFP36L2, DCN and NDUFA4L2 as promising therapeutic targets. Notably, RPL36A emerged as a potential tumour marker for NSCLC, with its expression validated in lung cancer cell lines through qPCR. This study has established a predictive models based on mitochondrial depolarisation genes associated with cuproptosis, aiding clinicians in forecasting overall survival and guiding personalised treatment strategies. The identification of novel tumour markers has paved the way for targeted therapies, and therapeutic targets are critical for advancing the treatment of NSCLC.