Conclusion
A new cuproptosis-related signature for survival prediction was constructed and validated by machine learning algorithm and in vitro experiments to reflect tumor immune infiltration in LUAD patients. The purpose of this article was to provide a potential diagnostic and therapeutic strategy for LUAD.
Methods
An integrated set of bioinformatics tools was utilized to analyze the expression and prognostic significance of cuproptosis-related genes. Meanwhile, a robust risk signature was developed using machine learning based on prognostic cuproptosis-related genes and explored the value of prognostic cuproptosis-related signature for clinical applications, functional enrichment and immune landscape. Lastly, the dysregulation of the cuproptosis-related genes in LUAD was validated by in vitro experiment.
Results
In this study, first, cuproptosis-related genes were found to be differentially expressed in LUAD patients of public databases, and nine of them had prognostic value. Next, a cuproptosis-related model with five features (DLTA, MTF1, GLS, PDHB and PDHA1) was constructed to separate the patients into high- and low-risk groups based on median risk score. Internal validation set and external validation set were used for model validation and evaluation. What's more, Enrichment analysis of differential genes and the WGCNA identified that cuproptosis-related signatures affected tumor prognosis by influencing tumor immunity. Small molecule compounds were predicted based on differential expressed genes to improve poor prognosis in the high-risk group and a nomogram was constructed to further advance clinical applications. In closing, our data showed that FDX1 affected the prognosis of lung cancer by altering the expression of cuproptosis-related signature.