PDX summary data for gene fusions as detected by STAR-Fusion using RNA-seq and stranded RNA-seq data from patient-derived xenografts in NSG mice
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens (human)
SUBMITTER:
PROVIDER: S-BSST896 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description:BackgroundDNA methylation is an important regulator of gene expression and chromatin structure. Methylated DNA immunoprecipitation sequencing (MeDIP-Seq) is commonly used to identify regions of DNA methylation in eukaryotic genomes. Within MeDIP-Seq libraries, methylated cytosines can be found in both double-stranded (symmetric) and single-stranded (asymmetric) genomic contexts. While symmetric CG methylation has been relatively well-studied, asymmetric methylation in any dinucleotide context has received less attention. Importantly, no currently available software for processing MeDIP-Seq reads is able to resolve these strand-specific DNA methylation signals. Here we introduce DISMISS, a new software package that detects strand-associated DNA methylation from existing MeDIP-Seq analyses.ResultsUsing MeDIP-Seq datasets derived from Apis mellifera (honeybee), an invertebrate species that contains more asymmetric- than symmetric- DNA methylation, we demonstrate that DISMISS can identify strand-specific DNA methylation signals with similar accuracy as bisulfite sequencing (BS-Seq; single nucleotide resolution methodology). Specifically, DISMISS is able to confidently predict where DNA methylation predominates (plus or minus DNA strands - asymmetric DNA methylation; plus and minus DNA stands - symmetric DNA methylation) in MeDIP-Seq datasets derived from A. mellifera samples. When compared to DNA methylation data derived from BS-Seq analysis of A. mellifera worker larva, DISMISS-mediated identification of strand-specific methylated cytosines is 80 % accurate. Furthermore, DISMISS can correctly (p <0.0001) detect the origin (sense vs antisense DNA strands) of DNA methylation at splice site junctions in A. mellifera MeDIP-Seq datasets with a precision close to BS-Seq analysis. Finally, DISMISS-mediated identification of DNA methylation signals associated with upstream, exonic, intronic and downstream genomic loci from A. mellifera MeDIP-Seq datasets outperforms MACS2 (Model-based Analysis of ChIP-Seq2; a commonly used MeDIP-Seq analysis software) and closely approaches the results achieved by BS-Seq.ConclusionsWhile asymmetric DNA methylation is increasingly being found in growing numbers of eukaryotic species and is the predominant pattern observed in some invertebrate genomes, it has been difficult to detect in MeDIP-Seq datasets using existing software. DISMISS now enables more sensitive examinations of MeDIP-Seq datasets and will be especially useful for the study of genomes containing either low levels of DNA methylation or for genomes containing relatively high amounts of asymmetric methylation.
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Project description:BackgroundRNA 5-methylcytosine (m5C) methyltransferases NSUN1 is a member of the NOP2/SUN (NSUN) RNA methyltransferase family. Studies have found that the expression of NSUN1 is elevated in breast and colon cancer and can predict poor prognosis. However, the NSUN1 gene has only been studied in a few tumors.MethodsSingle-cell RNA sequencing (scRNA-seq) and Bulk RNA-seq data were used for comprehensive analysis of NSUN1 in cancers. The Human Protein Atlas (HPA) database was used to identify the gene location. Immunofluorescence staining was used to detect NSUN1 subcellular distribution within the nucleus, endoplasmic reticulum (ER), and microtubules of A-431, U-2, U-251 cells. The cBioPortal tool was used to analyze the alteration frequency and mutation type. The epigenetic profile of NSUN1 also was analyzed by using the University of Alabama at Birmingham CANcer data analysis Portal (UCLCAN). Tumor mutation burden (TMB), microsatellite instability (MSI), and immune checkpoint expression in cancers were analyzed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to perform enrichment and visualization. The study was based on online resources and public databases.ResultsElevated NSUN1 expression had been observed in most human cancers. Analysis of scRNA-seq data showed that NSUN1 was highly expressed in immune cells such as T cells, B cells, and dendritic (DC) cells. High NSUN1 expression indicated poor overall survival (OS) and disease-free survival (DFS). The characteristics of genetic alteration, methylation and phosphorylation of NSUN1 were analyzed and higher levels of phosphorylation in tumor tissues were found. In addition, the expression of NSUN1 was closely related to tumor-infiltrating immune cells. At the same time, the expression of NSUN1 was positively correlated with the expression of multiple immune checkpoints.ConclusionsThe gene expression profile, survival status, genetic alteration, methylation, phosphorylation and infiltrating immune cells of NSUN1 in human cancers were comprehensively analyzed. The results herein implied that NSUN1 may be an effective biomarker for early cancer diagnosis, prognosis and therapy.
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