Abstract
BackgroundN6-methyladenosine (m6A) is the most abundant modification of RNA in eukaryotic cells and play critical roles in cancer. While most related studies focus on m6A modifications in linear RNA, transcriptome-wide profiling and exploration of m6A modification in circular RNAs in cancer is still lacking.MethodsFor the detection of m6A modification in circRNAs, we developed a new bioinformatics tools called Circm6A and applied it to the m6A-seq data of 77 tissue samples from 58 individuals with pancreatic ductal adenocarcinoma (PDAC).ResultsCircm6A performs better than the existing circRNA identification tools, which achieved highest F1 score among these tools in the detection of circRNAs with m6A modifications. By using Circm6A, we identified a total of 8807 m6A-circRNAs from our m6A-seq data. The m6A-circRNAs tend to be hypermethylated in PDAC tumor tissues compared with normal tissues. The hypermethylated m6A-circRNAs were associated with a significant gain of circRNA-mRNA coexpression network, leading to the dysregulation of many important cancer-related pathways. Moreover, we found the cues that hypermethylated m6A-circRNAs may promote the circularization and translation of circRNAs.ConclusionsThese comprehensive findings further bridged the knowledge gaps between m6A modification and circRNAs fields by depicting the m6A-circRNAs genomic landscape of PDAC patients and revealed the emerging roles played by m6A-circRNAs in pancreatic cancer. Circm6A is available at https://github.com/canceromics/circm6a.
Highlights
N6-methyladenosine (m6A) is the most abundant modification of RNA in eukaryotic cells and play critical roles in cancer
We found that approximately 23.1% of Circular circRNA (circRNA) in pancreatic ductal adenocarcinoma (PDAC) tissues harbored m6A modifications, and they tended to be hypermethylated in PDAC tumor tissues compared to adjacent normal tissues
For the detection of circRNAs with m6A modification (m6A-circRNAs), Circm6A will examine whether the circRNAs are significantly enriched in IP samples compared to INPUT samples in the MeRIP-seq data
Summary
For the detection of m6A modification in circRNAs, we developed a new bioinformatics tools called Circm6A and applied it to the m6A-seq data of 77 tissue samples from 58 individuals with pancreatic ductal adenocarcinoma (PDAC). Construction of Circm6A Circm6A (https://github.com/canceromics/circm6a) is a de novo algorithm for the detection of circRNAs and their m6A modifications from MeRIP-seq data. Circm6A will scan the sequencing alignment files from both MeRIP-seq IP (the IP library represents the RNA fragments captured by m6A-antibody pull-down) and INPUT (the paired INPUT library is derived from initial fragmented RNAs before immunoprecipitation) samples [4]. Circm6A checks whether AG and GT dinucleotides and exon boundaries flank the back-splicing junction (BSJ) sites of candidate circRNAs. The identified circRNAs are annotated according to the GTF file downloaded from the GENCODE database (https://www.gencodegenes.org/). If only RNA-seq data is provided (parameter: -input input_sample.bam), Circm6A will only detect circRNAs from files
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