Single cell and plasma RNA sequencing
Ontology highlight
ABSTRACT:
PROVIDER: EGAS00001005194 | EGA |
REPOSITORIES: EGA
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Project description:Liver cancer is one of the most lethal cancers worldwide. Liquid biopsy provides a noninvasive approach in detecting and monitoring cancer biomarkers to overcome current limitations associated with tissue biopsies, comprising the analysis of circulating tumor-derived material. In this study, we profiled plasma cell-free RNA-seq to identify recurrently dysregulated RNA biomarkers for the liquid biopsy of cancer.
2020-09-30 | GSE142987 | GEO
Project description:RNA-seq analysis of hepatocellular carcinoma plasma
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Project description:Cell-free RNAs in biofluids provide opportunities to monitor cancer in a non-invasive manner. Although extracellular microRNAs are extensively characterized, fragmented cell-free long RNAs are not well investigated. Here, we developed Detector-seq (depletion-assisted multiplexing cell-free total RNA sequencing) to enable the deciphering of the cell-free transcriptome. After demonstrating the superior performance of detecting fragmented cell-free long RNAs, we applied Detector-seq to compare cell-free RNAs in human plasma and its extracellular vesicle (EV). Distinct human and microbial RNA signatures were revealed. Structured circular RNA, tRNA, and Y RNA were enriched in plasma, while mRNA and srpRNA were enriched in EV. Meanwhile, cell-free RNAs derived from the virus were more enriched in plasma than in EV. We identified RNAs that showed a selective distribution between plasma and EV and uncovered their distinct functional pathways, that is RNA splicing, antimicrobial humoral response enriched in plasma and transcriptional activity, cell migration, and antigen receptor-mediated immune signals enriched in EV. Although distinctive cancer-relevant RNA signals were identified in plasma and EV, a comparable performance of distinguishing cancer patients from normal individuals could be achieved. Compared to human RNAs, microbe-derived RNA features enabled better classification between colorectal and lung cancer. And for these microbial RNAs, plasma RNAs outperformed EV RNAs for the discrimination of cancer types. Overall, our work provides insights into the unexplored difference of cell-free RNA signals between plasma and EV, thus offering practical guidance for proper selection (with/without EV enrichment) when launching an RNA-based liquid biopsy study. Furthermore, with the ability to capture understudied cell-free long RNA fragments, Detector-seq offers new possibilities for transcriptome-wide characterization of cell-free RNAs to facilitate the understanding of extracellular RNA biology and clinical advances of liquid biopsy.
2024-03-27 | GSE216561 | GEO
Project description:Hepatocellular carcinoma (HCC) is currently the third leading cause of death worldwide and the most common type of primary liver cancer, finding noninvasive biomarkers for HCC diagnostic and prognostic are very urging. Previous genomic studies mainly focus on finding miRNA biomarkers for HCC. In this study, we focus on finding long noncoding RNA fragments suitable for serving as hcc biomarkers with plasma and exosomal RNA-seq
2020-06-14 | GSE123972 | GEO
Project description:Circular RNAs (circRNAs) are recently found to be promising kind of biomarkers for tumors. However, plasma exosomal circRNAs in the diagnosis of hepatocellular carcinoma (HCC) is largely unknown. In this study, we report the application of next-generation sequencing technology for high-throughput profiling of plasma exosome general transcriptome between 3 cases of hepatocellular carcinoma patients and 3 cases of healthy control. We obtained exosomes through ultrahigh-speed centrifugation techniques and verified exosomes through immunoblotting, NanoSight NS300(Malvern Panalytical, Malvern, UK) and JEM-1230 transmission electron microscope (TEM) (JEOL, Tokyo, Japan). And then, we find that there are 41 circRNAs that have differential expression. This study discovered the differences between hepatocellular carcinoma and healthy people preliminarily.
2023-09-30 | GSE174179 | GEO
Project description:Longitudinal monitoring of patients with advanced cancers is crucial to evaluate both disease burden and treatment response. Current liquid biopsy approaches mostly rely on the detection of DNA-based biomarkers. However, plasma RNA analysis can unleash tremendous opportunities for tumor state interrogation and molecular subtyping. Through the application of deep learning algorithms to the deconvolved transcriptomes of RNA within plasma extracellular vesicles (evRNA), we successfully predict consensus molecular subtypes in metastatic colorectal cancer patients. We further demonstrate the ability to monitor changes in transcriptomic subtype under treatment selection pressure and identify molecular pathways in evRNA associated with recurrence. Our approach also identified expressed gene fusions and neoepitopes from evRNA. These results demonstrate the feasibility of transcriptomic-based liquid biopsy platforms for precision oncology approaches, spanning from the longitudinal monitoring of tumor subtype changes to identification of expressed fusions and neoantigens as cancer-specific therapeutic targets, sans the need for tissue-based sampling.
2024-02-29 | GSE255773 | GEO
Project description:Longitudinal monitoring of patients with advanced cancers is crucial to evaluate both disease burden and treatment response. Current liquid biopsy approaches mostly rely on the detection of DNA-based biomarkers. However, plasma RNA analysis can unleash tremendous opportunities for tumor state interrogation and molecular subtyping. Through the application of deep learning algorithms to the deconvolved transcriptomes of RNA within plasma extracellular vesicles (evRNA), we successfully predict consensus molecular subtypes in metastatic colorectal cancer patients. We further demonstrate the ability to monitor changes in transcriptomic subtype under treatment selection pressure and identify molecular pathways in evRNA associated with recurrence. Our approach also identified expressed gene fusions and neoepitopes from evRNA. These results demonstrate the feasibility of transcriptomic-based liquid biopsy platforms for precision oncology approaches, spanning from the longitudinal monitoring of tumor subtype changes to identification of expressed fusions and neoantigens as cancer-specific therapeutic targets, sans the need for tissue-based sampling.
2024-02-29 | GSE255774 | GEO
Project description:Using single-cell RNA sequencing, spatial transcriptomic and bulk multi-omics, we elaborated a molecular architecture of 3 PLC types, namely hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC) and combined hepatocellular-cholangiocarcinoma (CHC) from a high-resolution perspective.
2024-01-26 | PXD044778 | Pride
Project description:NGS liquid biopsy sequencing of miRNAs in total plasma and EV-associated miRNAs
2020-03-10 | GSE143231 | GEO
Project description:A total of 180 Hepatocellular carcinoma (HCC) and 125 adjacent normal samples were examined. Genome-wide DNA methylation profiling was done with Illumina HumanMethylation850 Beadchip of approximately 850,000 CpG sites. Aims: To identify HCC-specific methylation based biomarkers which are suitable for liquid biopsy.
2024-12-30 | GSE221256 | GEO