Background
Non-small cell lung cancer (NSCLC) is a prevalent form of lung cancer characterized by a significant death rate. Anoikis (ANO), refers to a distinct kind of programmed cell death that is strongly linked to the body's immune response to cancer. Nevertheless, the precise function of ANO in NSCLC is still not well understood.
Methods
ANO-related genes were analysed using multiple methods, including AUCell, UCell, single-sample gene set enrichment analysis (ssGSEA), Singscore, AddModuleScore, GSVA and weighted gene co-expression network analysis (WGCNA). We have developed an innovative machine learning framework that combines 10 different machine learning algorithms and 101 possible combinations of these algorithms. The goal of this framework is to build a reliable signature, known as the Anoikis-related signature (ARS), which is related to the phenomenon of anoikis. The performance of ARS was evaluated in both the training and validation sets. Column line graphs using ARS were developed as a quantitative technique to predict prognosis in clinical settings. Multi-omics studies, including genomic and bulk transcriptomic, were performed to gain more in-depth knowledge of prognostic features. We analysed the responsiveness of risk groups to immunotherapy and searched for tailored drugs to target specific risk categories.
Results
We discovered 103 genes associated with ANO at both single cell and bulk transcriptome levels. A computational framework using machine learning and 101 combinations was used to generate the consensus ARS. This framework showed exceptional performance in accurately predicting prognosis and clinical change, and the ARS can also be used to predict the initiation, progression and spread of NSCLC. Statistical studies have shown that it is an independent prognostic determinant of (OS) and disease-specific survival (DSS) in NSCLC. The integrated column line graphs of the ARS provide an accurate and quantitative tool for clinical practice. We also identified distinct metabolic processes, patterns of genetic mutations and the presence of immune cells in the tumour microenvironment that differed between the high-risk and low-risk groups. Significantly, there were significant changes in the immunophenotype score (IPS) between the risk groups, suggesting that the high-risk group is likely to have a more favourable response to immunotherapy. In addition, potential drugs targeting specific at-risk populations were identified.
Conclusion
The purpose of our work is to create a signature associated with immunogenic cell death. This signature has the potential to be a useful tool for predicting the prognosis of NSCLC, as well as for targeted prevention and personalised therapy. We are also providing new insights into the molecular pathways involved in the growth and progression of NSCLC through the use of mass transcriptomics and genomics research.
