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Transcriptomic analysis and fusion gene identification of Exos, MPs, MBRs

ABSTRACT: Transcriptomic analysis and fusion gene identification of Exos, MPs, MBRs

PROVIDER: PRJNA1023206 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR26261332_1.fastq.gz	Fastqsanger.gz
	SRR26261332_2.fastq.gz	Fastqsanger.gz
	SRR26261333_1.fastq.gz	Fastqsanger.gz
	SRR26261333_2.fastq.gz	Fastqsanger.gz
	SRR26261334_1.fastq.gz	Fastqsanger.gz

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Transcriptomic analysis and fusion gene identification of Exos, MPs, MBRs

Project description:Previously, we reported that human primary (SW480) and metastatic (SW620) colorectal (CRC) cells secrete (shed) midbody remnants (MBRs), exosomes (Exos), and microparticles (MPs) are molecularly distinct at the protein level. To gain further biochemical insights into MBRs, Exos, and MPs and their emerging role in CRC, we performed, and report here for the first time, a comprehensive transcriptome and long noncoding RNA sequencing analysis and fusion gene identification of these three EV classes using the next-generation RNA sequencing technique. Differential transcript expression analysis revealed that MBRs have a distinct transcriptomic profile compared to Exos and MPs with a high enrichment of mitochondrial transcripts lncRNA/pseudogene transcripts that are predicted to bind to ribonucleoprotein complexes, spliceosome and RNA/stress granule proteins. A salient finding from this study is a high enrichment of several fusion genes in MBRs compared to Exos, MPs and cell lysates from their parental cells such as MSH2 (gene encoded DNA mismatch repair protein MSH2). This suggests potential EV-liquid biopsy targets for cancer detection. Importantly, the expression of cancer progression-related transcripts found in EV classes derived from SW480 (EGFR) and SW620 (MET and MACCA1) cell lines reflects their parental cell types. Our study is the first discovery of RNA and fusion gene compositions within MBRs (including Exos and MPs) that could have an impact on EV functionality in cancer progression and detection using EV-based RNA/ fusion gene candidates for cancer biomarkers

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