Identification of lactylation-related biomarkers in HNSC by integrating machine learning and spatial transcriptomics analysis

Objective: Head and neck squamous cell carcinoma (HNSC) is a common malignant tumor with a 5-year survival rate less than 60% and lactylation modification plays a key role in its occurrence and progression. This study aims to identify key lactylation-related biomarkers in HNSC and their prognostic impact by combining machine learning methods and spatial transcriptomics analysis. Methods: This study obtained high-throughput gene expression data of HNSC from the TCGA database. The “Limma” R package was used to screen differentially expressed genes (DEGs), and their biological significance was investigated through GO and KEGG functional enrichment analyses. Subsequently, weighted gene co-expression network analysis (WGCNA) identified disease trait-associated modules. We used 12 machine learning methods to screen for key genes and validated them in the GSE6631 dataset. Survival curves for TCGA-HNSC were plotted to evaluate the prognostic ability of key genes. Transcriptional regulation analysis via “RcisTarget” package identified transcription factors associated with motifs exceeding NES>3.5. Single-cell and spatial transcriptomic analysis was performed using Seurat packages, which involved dimensionality reduction ,clustering via UMAP .cell type annotation and plotted spatial expression distribution maps of key genes. Finally, for immune infiltration analysis, the CIBERSORT algorithm was used to calculate the proportions of 22 types of immune cells, and analyzed correlations between key genes and immune-related genes. Results: This study obtained high-throughput gene expression data of HNSC from the TCGA database. The “Limma” R package was used to screen differentially expressed genes (DEGs), and their biological significance was investigated through GO and KEGG functional enrichment analyses. Subsequently, weighted gene co-expression network analysis (WGCNA) identified disease trait-associated modules. We used 12 machine learning methods to screen for key genes and validated them in the GSE6631 dataset. Survival curves for TCGA-HNSC were plotted to evaluate the prognostic ability of key genes. Transcriptional regulation analysis via “RcisTarget” package identified transcription factors associated with motifs exceeding NES>3.5. Single-cell and spatial transcriptomic analysis was performed using Seurat packages, which involved dimensionality reduction ,clustering via UMAP .cell type annotation and plotted spatial expression distribution maps of key genes. Finally, for immune infiltration analysis, the CIBERSORT algorithm was used to calculate the proportions of 22 types of immune cells, and analyzed correlations between key genes and immune-related genes. Conclusion: MSN, KIF2C and RFC4 can be used as potential biomarkers for diagnosing and predicting the prognosis of HNSC, providing a new insight into diagnosis and targeted therapy