Constructing a Prognostic Model for Non-Small-Cell Lung Cancer Risk Based on Genes Characterising the Differentiation of Myeloid-Derived Suppressor Cells

Cancer is the most common malignancy, with over 2 million new cases and nearly 1.8 million deaths worldwide annually. Non-small-cell lung cancer (NSCLC) is the predominant subtype, accounting for the majority of cases. Myeloid-derived suppressor cells (MDSCs), which originate from monocytes and typically differentiate into macrophages and granulocytes, possess potent immunosuppressive capabilities. MDSCs regulate immune responses in various pathological conditions and are strongly associated with poor prognosis in cancer patients. This study aims to elucidate the complex interplay between MDSCs, immune cells, and tumours in the NSCLC tumour microenvironment (TME). By integrating single-cell RNA sequencing (scRNA-seq) data with bulk RNA sequencing (Bulk RNA-seq) data, we identified MDSCs as the target cell population and used Monocle software (v2.22.0) to infer their developmental trajectories. We identified key genes associated with MDSCs differentiation processes and classified MDSCs into seven distinct states based on their functional roles. Furthermore, we constructed a prognostic risk model based on the impact of MDSCs differentiation on NSCLC prognosis, utilizing Elastic Net regression and multivariate Cox regression analysis of Bulk RNA-seq data. The model's performance and accuracy were validated using both internal and external validation sets. Additionally, we compared risk scores with clinical pathological features and the relationship between risk scores and key immune cells in the immune microenvironment, demonstrating the model's clinical predictive value. We also explored how prognostic genes contribute to poor prognosis in NSCLC. Moreover, small molecule compounds targeting these prognostic genes were screened, and their anti-tumour effects were evaluated as potential therapeutic strategies for NSCLC treatment. This study not only reveals the complex regulatory mechanisms of MDSCs in the NSCLC immune microenvironment but also successfully constructs a prognostic risk model based on MDSCs differentiation states. The model demonstrates excellent clinical performance in predicting patient prognosis, effectively identifying high-risk patients and providing robust support for individualized treatment and immunotherapy decisions. Through association analyses with key immune cells in the immune microenvironment and clinical pathological features, our model can assist clinicians in formulating more precise treatment plans based on patients' immune status and tumour characteristics. Furthermore, we identified small molecule compounds targeting these prognostic genes, providing novel and promising therapeutic targets for NSCLC, which could further enhance treatment efficacy and improve patients' survival quality.