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Updated: Aug 14, 2025
A Method for Measuring RNA N6-methyladenosine Modifications in Cells and Tissues
Published on: December 5, 2016
Characterization of ligamentum flavum hypertrophy based on m6A RNA methylation modification and the immune
Zhongyuan He1,2, Zhengya Zhu1, Tao Tang1,2
1Innovation Platform of Regeneration and Repair of Spinal Cord and Nerve Injury, Department of Orthopaedic Surgery, The Seventh Affiliated Hospital, Sun Yat-sen University Shenzhen, Guangdong, China.
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.
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.
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.
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.
Insights
N6-methyladenosine (m6A) modification influences the immune microenvironment in ligamentum flavum hyperplasia (LFH). This study identified key m6A regulators and developed a diagnostic model for LFH, aiding in predicting disease progression.
Area of Science:
- Epigenetics
- Immunology
- Molecular Biology
Background:
- N6-methyladenosine (m6A) modification is increasingly recognized for its role in disease pathogenesis.
- The interplay between epigenetic regulation, specifically RNA m6A modification, and immune responses is an emerging area of research.
- The specific impact of m6A-related genes on the immune microenvironment in ligamentum flavum hyperplasia (LFH) remains largely unexplored.
Purpose of the Study:
- To investigate the effect of m6A modification on the immune microenvironment in ligamentum flavum hyperplasia (LFH).
- To identify m6A regulators associated with LFH and their correlation with immune cell infiltration.
- To develop a prognostic signature based on m6A-related genes for LFH diagnosis and prognosis.
Main Methods:
- Analysis of the GSE113212 dataset from the Gene Expression Omnibus (GEO) database.
- Utilized Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and protein-protein interaction (PPI) networks.
- Employed Least Absolute Shrinkage and Selection Operator (Lasso) regression for prognostic signature development and validated findings with Real-Time quantitative PCR (RT-qPCR) and western blotting (WB).
Main Results:
- Identified 1259 differentially expressed genes, with METTL16, PCIF1, and FTO showing significant differences.
- Enrichment analyses suggested a key role for immune factors in LFH.
- Developed a 6-gene LFH immune-related prediction model, with METTL16, PCIF1, FTO, and ALKBH5 demonstrating superior diagnostic efficiency.
Conclusions:
- m6A modification significantly influences the diversity and complexity of the LFH immune microenvironment.
- The identified m6A-related genes and the developed diagnostic model offer strong evidence for predicting LFH diagnosis and prognosis.
- This research provides valuable insights into the epigenetic regulation of immunity in LFH.
