Metabolic Pathway Activation and Immune Microenvironment Features in Non-Small Cell Lung Cancer: Insights from Single-Cell Transcriptomics

Yanru Liu ^1,2,3 Hanmin Liu ^1,2* Ying Xiong ^1,2*

• ¹ Department of Pediatric Pulmonology and Immunology, West China Second University Hospital, Sichuan University, Chengdu, China, Chengdu, China
• ² Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China, Chengdu, Sichuan Province, China
• ³ Department of Pediatric Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China, Chengdu, Sichuan Province, China

This research offers a thorough examination of tumor microenvironment (TME) and its metabolic characteristics through single-cell RNA sequencing (scRNA-seq) data of non-small cell lung cancer (NSCLC) obtained from public databases. After quality control, we retained a total of 29,053 cells and employed Principal Component Analysis (PCA) alongside Uniform Manifold Approximation and Projection (UMAP) techniques to distinguish 13 primary cell subpopulations. Within the malignant cell subpopulations, we successfully identified four metabolic pathways that were found to be highly activated. We further subdivided the malignant cell subpopulations into seven distinct subgroups, among which significant differences in differentiation potential and metabolic activity emerged when compared. Differential analysis of enrichment and analysis of expression indicated that differentially expressed genes (DEGs) within tumor and normal tissues were predominantly enriched in immune response and cell adhesion pathways. Additionally, to characterize the gene modules and hub genes that are significantly linked to the four metabolic pathways, we utilized Weighted Gene Co-expression Network Analysis (WGCNA). Subsequently, using machine learning algorithms, we were able to construct a risk signature that exhibited strong predictive capabilities across multiple independent cohorts. Finally, our comprehensive examination of the model revealed substantial variations in both clinical and pathological characteristics within high-risk and low-risk groups, enriched pathways, cancer hallmarks, and immune infiltration scores. We also validated the role of the model gene KRT6B in NSCLC through wet lab experiments. It was demonstrated by the results that the expression of KRT6B is elevated in NSCLC and it stimulates the proliferation of cancer cells. These observations not only enhance our understanding of metabolic reprogramming and its biological functions in NSCLC but also provide new perspectives for early detection, prognostic evaluation, and targeted therapy.