Objectives: Nasopharyngeal carcinoma (NPC) is an aggressive malignancy with high rates of morbidity and mortality, largely because of its late diagnosis and metastatic potential. Lactate metabolism and protein lactylation are thought to play roles in NPC pathogenesis by modulating the tumor microenvironment and immune evasion. However, research specifically linking lactate-related mechanisms to NPC remains limited. This study aimed to identify lactate-associated biomarkers in NPC and explore their underlying mechanisms, with a particular focus on immune modulation and tumor progression.
Methods: To achieve these objectives, we utilized a bioinformatics approach in which publicly available gene expression datasets related to NPC were analysed. Differential expression analysis revealed differentially expressed genes (DEGs) between NPC and normal tissues. We performed weighted gene coexpression network analysis (WGCNA) to identify module genes significantly associated with NPC. Overlaps among DEGs, key module genes and lactate-related genes (LRGs) were analysed to derive lactate-related differentially expressed genes (LR-DEGs). Machine learning algorithms can be used to predict potential biomarkers, and immune infiltration analysis can be used to examine the relationships between identified biomarkers and immune cell types, particularly M0 macrophages and B cells.
Results: A total of 1,058 DEGs were identified between the NPC and normal tissue groups. From this set, 372 key module genes associated with NPC were isolated. By intersecting the DEGs, key module genes and lactate-related genes (LRGs), 17 lactate-related DEGs (LR-DEGs) were identified. Using three machine learning algorithms, this list was further refined, resulting in three primary lactate-related biomarkers: TPPP3, MUC4 and CLIC6. These biomarkers were significantly enriched in pathways related to "immune cell activation" and the "extracellular matrix environment". Additionally, M0 and B macrophages were found to be closely associated with these biomarkers, suggesting their involvement in shaping the NPC immune microenvironment.
Conclusion: In summary, this study identified TPPP3, MUC4 and CLIC6 as lactate-associated clinical modelling indicators linked to NPC, providing a foundation for advancing diagnostic and therapeutic strategies for this malignancy.
Keywords: GEO; Infiltrating immunocytes; Lactylation-related genes; Machine learning algorithms; Nasopharyngeal carcinoma.
© 2024. The Author(s).