Exploring the mechanism of prognostic genes associated with neuroendocrine differentiation and immunotherapy resistance in non-small cell lung cancer based on the bulk transcriptome and single cell RNA sequencing

Background: The challenge of immunotherapy resistance has become a current research hotspot. Previous studies showed that neuroendocrine differentiation (NED) might contribute significantly to the initiation and development of non-small cell lung cancer (NSCLC). However, the interplay between immunotherapy resistance and NED of NSCLC remains unclear. This article mainly explored the mechanism of NED-related genes (NEDRGs) and immunotherapy resistance related genes in NSCLC. Methods: The NSCLC and control samples, and the NSCLC samples with PD-1 blockade were selected from the public databases to obtain differentially expressed genes (DEGs). Then, univariate Cox regression analysis and the proportional hazards (PH) assumption test were adopted to obtain prognostic genes based on the DEGs and NEDRGs, and a risk model was built and validated. Then, the nomogram was established. Subsequently, gene set enrichment analysis (GSEA), immune analysis and drug sensitivity were adopted. The key cells were ascertained in a single cell RNA sequencing (scRNA-seq) dataset. And we collected specimens of six patients from Tianjin Medical University General Hospital for validation using Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) and Immunohistochemistry (IHC). Results: A total of 3 prognostic genes including RRM2, WDR76 and PLEKHH2 were obtained and the risk model could predict the survival outcomes of NSCLC patients. The nomogram had good predictive ability of survival rate for NSCLC. the GSEA results revealed that notable pathways in the 2 risk groups included cell cycle, and prognostic genes were all enriched in this pathway. Furthermore, activated memory CD4 T cells might contribute significantly while 109 drugs such as AZD6738 might be the candidate drugs of NSCLC. Epithelials were identified as the key population, wherein the expression of prognostic genes altered significantly across different epithelials developmental stages. PCR and IHC validation confirmed that RRM2 and WDR76 expression increased in cancer versus normal tissues and with advancing stage, while PLEKHH2 showed opposite trends. Conclusion: This study identified 3 prognostic genes (RRM2, WDR76 and PLEKHH2) to build risk model, and the risk model exhibited superior predictive accuracy of NSCLC. These results might provide new ideas for investigating novel therapeutic targets in NSCLC.