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Next Generation Sequencing for Identification of Transcriptomes from Serum Exosomes

ABSTRACT: Purpose: The purpose of this study is to apply RNA-Seq to identify diffeerentially expressed RNA in hepatocellular-derived serum exosomes compared with non hepatocellular carcinoma derived serum exosomes to evaluate prognosis-related and tumorigenesis-related potential biomarkers. Methods: Genome profiles of serum exosomes isolated from liver cirrohosis subjects, early-stage HCC subjects amd advanced stage HCC subjects respectively were generated by deep sequencing, using Illumina. The sequence reads that passed quality filters were aligned and analyzed using HISAT2 and STRINGTIE. The constructed transcriptome profiles were conveyed to further analysis. Results: With the help of RNA-Seq technology, 58,243 genes and 200,478 transcripts were aligned, and genome and transcriptome RNA-Seq archives were constructed, showcasing the abundance and depth of our sequencing. Then we systematically screened the RNA type, and selected long non-coding RNA (lncRNA) expression profiles. We used DESeq2 algorithm and set |log2 (fold-change)|>1 and P-value <0.05 as criteria. Finally we discovered 2,070 genes, 6,543 transcripts, 942 lncRNAs were differentially expressed in group advanced stage HCC compared with group liver cirrohosis. 1,600 genes, 6,904 transcripts, 1,067 lncRNAs were differentially expressed in group early stage HCC compared with group liver cirrohosis. Conclusions: Our study creatively represented the detailed analysis of serum exosomes genomes and transcriptomes, which were generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that compared with liver cirrohosis serum exosome samples, the early-stage and advanced stage hepatocellular carcinoma groups detected some unique differential mRNAs and miRNAs, which can be potential biomarkers.

ORGANISM(S): Homo sapiens

PROVIDER: GSE199509 | GEO | 2022/03/30

REPOSITORIES: GEO

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Project description:Background/Aims: Recurrence-free survival (RFS) following curative resection of hepatocellular carcinoma (HCC) in subjects with hepatitis C virus (HCV) infection is highly variable. Traditional clinico-pathological endpoints are recognized as weak predictors of RFS. It has been suggested that gene expression profiling of HCC and nontumoral liver tissue may improve prediction of RFS, aid in understanding of the underlying liver disease, and guide individualized therapy. The goal of this study was to create a gene expression predictor of HCC recurrence in subjects with HCV. Methods: Frozen samples of the tumors and nontumoral liver were obtained from 47 subjects with HCV-associated HCC. Additional nontumoral liver samples were obtained from HCV-free subjects with metastatic liver tumors. Gene expression profiling data was used to determine the molecular signature of HCV-associated HCC and to develop a predictor of RFS. Results: The molecular profile of the HCV-associated HCC confirmed central roles for MYC and TGF-beta1 in liver tumor development. Gene expression in tumors was found to have poor predictive power with regards to RFS, but analysis of nontumoral tissues yielded a strong predictor for RFS in late-recurring (>1 year) subjects. Importantly, nontumoral tissue-derived gene expression predictor of RFS was highly significant in both univariable and multivariable Cox proportional hazard model analyses. Conclusions: Microarray analysis of the nontumoral tissues from subjects with HCV-associated HCC delivers novel molecular signatures of RFS, especially among the late-recurrence subjects. The gene expression signature of the predictor gives important insights into the pathobiology of HCC recurrence and used in design of the individualized therapy. 43 tumor (JT) and 44 non-tumor (JNT) liver tissues surgically resected from patients with HCV-associated hepatocellular carcinoma; 8 non-tumor liver tissues (control samples, JC) surgically resected from HCV- or HBV-free patients with metastatic liver tumor. Inter-batch normalization was carried out using Distance Weighted Discrimination procedure. The supplementary file 'GSE17856_Readme.txt' contains a description of the replicates used for normalization. The 'GSE17856_US14702406_2514850*' files are the raw data files for the replicates.

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Next Generation Sequencing for Identification of Transcriptomes from Serum Exosomes

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