Dataset Information

Early detection of hepatocellular carcinoma via liquid biopsy: panel of small extracellular vesicle-derived long noncoding RNAs identified as markers.

ABSTRACT: This study investigated the diagnostic potential of serum small extracellular vesicle-derived long noncoding RNAs (EV-lncRNAs) for hepatocellular carcinoma (HCC). Driver oncogenic lncRNA candidates were selected by a comparative analysis of lncRNA expression profiles from two whole transcriptome human HCC datasets (Catholic_LIHC and TCGA_LIHC). Expression of selected lncRNAs in serum and small EVs was evaluated using quantitative reverse transcription PCR. Diagnostic power of serum EV-lncRNAs for HCC was determined in the test (n = 44) and validation (n = 139) cohorts. Of the six promising driver onco-lncRNAs, DLEU2, HOTTIP, MALAT1, and SNHG1 exhibited favorable performance in the test cohort. In the validation cohort, serum EV-MALAT1 displayed excellent discriminant ability, while EV-DLEU2, EV-HOTTIP, and EV-SNHG1 showed good discriminant ability between HCC and non-HCC. Furthermore, a panel combining EV-MALAT1 and EV-SNHG1 achieved the best area under the curve (AUC; 0.899, 95% CI = 0.816-0.982) for very early HCC, whereas a panel with EV-DLEU2 and alpha-fetoprotein exhibited the best positivity (96%) in very early HCC. Serum small EV-MALAT1, EV-DLEU2, EV-HOTTIP, and EV-SNHG1 may represent promising diagnostic markers for very early-stage HCC.

SUBMITTER: Kim SS

PROVIDER: S-EPMC8486572 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Early detection of hepatocellular carcinoma via liquid biopsy: panel of small extracellular vesicle-derived long noncoding RNAs identified as markers.

Kim Soon Sun SS Baek Geum Ok GO Son Ju A JA Ahn Hye Ri HR Yoon Moon Kyung MK Cho Hyo Jung HJ Yoon Jung Hwan JH Nam Suk Woo SW Cheong Jae Youn JY Eun Jung Woo JW

Molecular oncology 20210712 10

This study investigated the diagnostic potential of serum small extracellular vesicle-derived long noncoding RNAs (EV-lncRNAs) for hepatocellular carcinoma (HCC). Driver oncogenic lncRNA candidates were selected by a comparative analysis of lncRNA expression profiles from two whole transcriptome human HCC datasets (Catholic_LIHC and TCGA_LIHC). Expression of selected lncRNAs in serum and small EVs was evaluated using quantitative reverse transcription PCR. Diagnostic power of serum EV-lncRNAs fo ...[more]

PMID: 34185961

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Project description:Rationale: Long extracellular RNAs (exRNAs) in plasma can be profiled by new sequencing technologies, even with low abundance. However, cancer-related exRNAs and their variations remain understudied. Methods: We investigated different variations (i.e. differential expression, alternative splicing, alternative polyadenylation, and differential editing) in diverse long exRNA species (e.g. long noncoding RNAs and circular RNAs) using 79 plasma exosomal RNA-seq (exoRNA-seq) datasets of multiple cancer types. We then integrated 53 exoRNA-seq datasets and 65 self-profiled cell-free RNA-seq (cfRNA-seq) datasets to identify recurrent variations in liver cancer patients. We further combined TCGA tissue RNA-seq datasets and validated biomarker candidates by RT-qPCR in an individual cohort of more than 100 plasma samples. Finally, we used machine learning models to identify a signature of 3 noncoding RNAs for the detection of liver cancer. Results: We found that different types of RNA variations identified from exoRNA-seq data were enriched in pathways related to tumorigenesis and metastasis, immune, and metabolism, suggesting that cancer signals can be detected from long exRNAs. Subsequently, we identified more than 100 recurrent variations in plasma from liver cancer patients by integrating exoRNA-seq and cfRNA-seq datasets. From these datasets, 5 significantly up-regulated long exRNAs were confirmed by TCGA data and validated by RT-qPCR in an independent cohort. When using machine learning models to combine two of these validated circular and structured RNAs (SNORD3B-1, circ-0080695) with a miRNA (miR-122) as a panel to classify liver cancer patients from healthy donors, the average AUROC of the cross-validation was 89.4%. The selected 3-RNA panel successfully detected 79.2% AFP-negative samples and 77.1% early-stage liver cancer samples in the testing and validation sets. Conclusions: Our study revealed that different types of RNA variations related to cancer can be detected in plasma and identified a 3-RNA detection panel for liver cancer, especially for AFP-negative and early-stage patients.

Project description:While the field of precision oncology is rapidly expanding and more targeted options are revolutionizing cancer treatment paradigms, therapeutic resistance particularly to immunotherapy remains a pressing challenge. This can be largely attributed to the dynamic tumor-stroma interactions that continuously alter the microenvironment. While to date most advancements have been made through examining the clinical utility of tissue-based biomarkers, their invasive nature and lack of a holistic representation of the evolving disease in a real-time manner could result in suboptimal treatment decisions. Thus, using minimally-invasive approaches to identify biomarkers that predict and monitor treatment response as well as alert to the emergence of recurrences is of a critical need. Currently, research efforts are shifting towards developing liquid biopsy-based biomarkers obtained from patients over the course of disease. Liquid biopsy represents a unique opportunity to monitor intercellular communication within the tumor microenvironment which could occur through the exchange of extracellular vesicles (EVs). EVs are lipid bilayer membrane nanoscale vesicles which transfer a plethora of biomolecules that mediate intercellular crosstalk, shape the tumor microenvironment, and modify drug response. The capture of EVs using innovative approaches, such as microfluidics, magnetic beads, and aptamers, allow their analysis via high throughput multi-omics techniques and facilitate their use for biomarker discovery. Artificial intelligence, using machine and deep learning algorithms, is advancing multi-omics analyses to uncover candidate biomarkers and predictive signatures that are key for translation into clinical trials. With the increasing recognition of the role of EVs in mediating immune evasion and as a valuable biomarker source, these real-time snapshots of cellular communication are promising to become an important tool in the field of precision oncology and spur the recognition of strategies to block resistance to immunotherapy. In this review, we discuss the emerging role of EVs in biomarker research describing current advances in their isolation and analysis techniques as well as their function as mediators in the tumor microenvironment. We also highlight recent lung cancer and melanoma studies that point towards their application as predictive biomarkers for immunotherapy and their potential clinical use in precision immuno-oncology.

Dataset Information

Early detection of hepatocellular carcinoma via liquid biopsy: panel of small extracellular vesicle-derived long noncoding RNAs identified as markers.

Publications

Early detection of hepatocellular carcinoma via liquid biopsy: panel of small extracellular vesicle-derived long noncoding RNAs identified as markers.

Similar Datasets

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