Conclusion
Diversity and complexity of LFH's immune microenvironment are influenced by M6A modification, and our study provides strong evidence for predicting the diagnosis and prognosis of LFH.
Methods
The GSE113212 dataset was downloaded from the Gene Expression Omnibus (GEO) database. We systematically analyzed m6A regulators in eight patient samples and the corresponding clinical information of the samples. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA) and protein-protein interactions (PPIs) were used to explore the correlation of m6A clusters with the immune microenvironment in LFH. A least absolute shrinkage and selection operator (Lasso) regression was then used to further explore the m6A prognostic signature in LFH. The relative abundance of immune cell types was quantified using a single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm. We explored the relationship between hub genes and small molecule drug sensitivity by clustering hub gene-based samples. In addition, Real-Time quantitative PCR (RT-qPCR) as well as western blotting (WB) were used to validate the gene expression of the differentially expressed genes.
Objective
N6-methyladenosine (m6A) has been implicated in the progression of several diseases, and the role of epigenetic regulation in immunity is emerging, particularly for RNA m6A modification. However, it is unclear how m6A-related genes affect the immune microenvironment of ligamentum flavum hyperplasia (LFH). Therefore, we aimed to investigate the effect of m6A modification on the LFH immune microenvironment.
Results
A total of 1259 differentially expressed genes were identified, of which 471 were upregulated and 788 were downregulated. A total of three genes showed significant differences (METTL16, PCIF1, and FTO). According to the enrichment analysis, immune factors may play a key role in LFH. ssGSEA was used to cluster the immune infiltration score, construct the hub gene diagnosis model, and screen a total of 6 LFH immune-related prediction model genes. The predictive diagnostic model of LFH was further constructed, revealing that METTL16, PCIF1, FTO and ALKBH5 had superior diagnostic efficiency. RT-qPCR results showed that 6 genes (METTL16, PCIF1, POSTN, TNNC1, MMP1 and ACTA1; P < 0.05) exhibited expression consistent with the results of the bioinformatics analysis of the mRNA microarray. Up-regulated METTL16, PCIF1, and ALKBH5 levels in LFH were validated by western blotting.