The N6-Methyladenosine Epitranscriptomic Landscape of Lung Adenocarcinoma

Basic Medical Sciences Environmental and Public Health Oncology Medicine
Lung adenocarcinoma Epitranscriptome m6A RNA modification Tumor metastasis EML4 Invasion

The N6-Methyladenosine Epitranscriptomic Profiling of Lung Adenocarcinoma

The Epitranscriptomic Landscape of N6-Methyladenosine Modification in Lung Adenocarcinoma

Background

Lung cancer is one of the most common cancers globally and a leading cause of cancer-related deaths. Non-small cell lung cancer (NSCLC) accounts for about 80%-85% of all lung cancer cases, among which lung adenocarcinoma (LUAD) is of particular concern due to its low survival rate. Therefore, there is an urgent need for a better understanding of the molecular mechanisms and therapeutic strategies for LUAD. Despite advancements in genomics, transcriptomics, epigenomics, and proteomics that have unveiled some genetic and genomic variations leading to the high lethality of LUAD, the post-transcriptional mechanisms significantly contributing to tumor biology remain underexplored. Epitranscriptomics, often referred to as the “epigenetics” of RNA, includes a series of modifications, with N6-methyladenosine (m6A) being the most common modification in mammalian RNA. This modification extensively influences mRNA output, splicing, translation, and degradation processes. Numerous studies have confirmed the significant impact of m6A modification on tumorigenesis and metastasis, but systematic studies on the m6A modification landscape of major tumors are still lacking.

Source of the Study

This paper was authored by Shiyan Wang, Yong Zeng, Lin Zhu, and others from various research institutions including the Department of Thoracic Surgery and the Institute of Precision Medicine at the First Affiliated Hospital of Sun Yat-sen University, the State Key Laboratory of Environmental and Biological Analysis at Hong Kong Baptist University, and the University of Toronto. The paper was published in the 2024 issue of the journal Cancer Discovery.

Detailed Description of the Study

Research Process

  1. Sample Collection and Processing:

    • Total RNA samples were extracted from 10 non-tumorous lung tissues (NL) and 51 LUAD tumor tissues, with 2 micrograms of total RNA from each sample.
    • NL tissue samples were taken from normal lung tissues during lung cancer resection surgeries, while tumor samples were derived from the Princess Margaret Cancer Center’s patient-derived lung cancer model (UHN-PLCME) project.

  2. m6A Modification Detection Method:

    • The optimized m6A immunoprecipitation coupled with high-throughput sequencing (m6A MeRIP-seq) method was used to detect m6A modifications in RNA samples.
    • Using the cutting-edge RNA of Escherichia coli K-12 for library size normalization, more than 80% of the MeRIP IP reads could be uniquely mapped per sample, with 85.8% originating from protein-coding genes and 10.5% from long non-coding RNA (lncRNA) genes.

  3. Data Analysis:

    • Principal component analysis (PCA) was employed to analyze read counts, distinguishing NL from tumor samples.
    • A total of 11,409 genes with m6A modifications were identified, including 6,140 non-methylated genes and 8,030 methylated genes. The m6A levels of each gene were further quantified using three methods.

Research Results

  1. Association between m6A Levels and Regulatory Factors:

    • Associating mRNA abundance of m6A regulatory factors in samples with the m6A levels of the 8,030 methylated genes revealed three groups (G1, G2, and G3) based on their m6A levels and association with regulatory factors.
    • G1 genes had significantly lower m6A levels and the highest mRNA abundance, primarily enriched in the 3’-UTR and near the termination codon, whereas G2 and G3 genes were mainly distributed in the CDS region.

  2. Association between m6A Modification Patterns and Clinical Outcomes:

    • Using consensus clustering methods, patients were classified into subtypes based on the m6A levels of the top 20% methylated genes. It was observed that the P2 subtype with the lowest m6A levels had significantly higher survival rates than the other groups.
    • Further analysis revealed that the P2 subtype was associated with low RNA and protein abundance of the core writer WTAP and high protein levels of the eraser ALKBH5.

  3. Common Hypomethylation Phenomenon in Tumor Samples:

    • Compared to NL tissues, tumor samples exhibited overall lower and more variable m6A levels. Specific methylated genes found in tumor samples were significantly enriched in immune-related pathways, such as immunoglobulin receptor binding and antigen binding.

  4. Function of Differentially Methylated Genes:

    • Among the 430 hypomethylated genes, EML4 had a significantly increased m6A level in tumors, promoting its translation and leading to its overexpression in primary tumors. EML4 regulated cytoskeleton dynamics through interaction with ARPC1A, enhancing pseudopodia formation, cell migration, local invasion, and metastatic capacity.
    • Small molecule inhibitors of METTL3 significantly reduced EML4’s m6A and protein levels, effectively inhibiting lung metastasis in vivo.

Research Conclusion

This study reveals the dynamic and functional epitranscriptomic landscape of LUAD, providing an important resource for further research. By identifying hypermethylation of EML4 associated with tumor metastasis, the study proposes a new therapeutic strategy to prevent LUAD metastasis by inhibiting EML4. These findings not only enrich our understanding of the regulatory dynamics of m6A modification but also provide scientific evidence for developing new treatment methods.

Research Highlights

  • Key Findings: EML4 is a key driver of LUAD metastasis, with hypermethylation promoting its protein translation and extensive overexpression in primary tumors.
  • Novelty: Identification of tumor-specific methylated genes and their critical roles in tumor metastasis.
  • Application Value: Proposes a novel therapeutic strategy targeting m6A modification of EML4 to effectively prevent or reduce LUAD metastasis.

Summary

Using a low-input m6A MeRIP-seq method, this paper generated an epitranscriptomic landscape of 10 NL tissues and 51 LUAD tumors, providing important data for understanding the dynamic regulation of m6A modifications in clinical oncology. The study revealed common hypomethylation in LUAD tumors and discovered that the m6A level of BLVRA is independently associated with patient survival by correlating multi-omics data and survival data. This research not only holds significant scientific value but also provides deep insights for developing new therapeutic strategies in the future.