Early detection of hepatocellular carcinoma via liquid biopsy: panel of small extracellular vesicle-derived long noncoding RNAs identified as markers.
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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.
Project description:Extracellular vesicles (EVs) are emerging as a new source of biomarkers in liquid biopsy because of their wide presence in most body fluids and their ability to load cargoes from disease-related cells. Owing to the crucial role of EVs in disease diagnosis and treatment, significant efforts have been made to isolate, detect, and analyze EVs with high efficiency. A recent overview of advanced EV detection nanotechnologies is discussed here. First, several key challenges in EV-based liquid biopsies are introduced. Then, the related pivotal advances in nanotechnologies for EV isolation based on physical features, chemical affinity, and the combination of nanostructures and chemical affinity are summarized. Next, a summary of high-sensitivity sensors for EV detection and advanced approaches for single EV detection are provided. Later, EV analysis is introduced in practical clinical scenarios, and the application of machine learning in this field is highlighted. Finally, future opportunities for the development of next-generation nanotechnologies for EV detection are presented.
Project description:The prevalence of a high-energy diet and a sedentary lifestyle has increased the incidence of type 2 diabetes (T2D). T2D is a chronic disease characterized by high blood glucose levels and insulin resistance in peripheral tissues. The pathological mechanism of this disease is not fully clear. Accumulated evidence has shown that noncoding RNAs have an essential regulatory role in the progression of diabetes and its complications. The roles of small noncoding RNAs, such as miRNAs, in T2D, have been extensively investigated, while the function of long noncoding RNAs (lncRNAs) in T2D has been unstudied. It has been reported that lncRNAs in T2D play roles in the regulation of pancreatic function, peripheral glucose homeostasis and vascular inflammation. In addition, lncRNAs carried by small extracellular vesicles (sEV) were shown to mediate communication between organs and participate in diabetes progression. Some sEV lncRNAs derived from stem cells are being developed as potential therapeutic agents for diabetic complications. In this review, we summarize the current knowledge relating to lncRNA biogenesis, the mechanisms of lncRNA sorting into sEV and the regulatory roles of lncRNAs and sEV lncRNAs in diabetes. Knowledge of lncRNAs and sEV lncRNAs in diabetes will aid in the development of new therapeutic drugs for T2D in the future.
Project description:The translation of discoveries on extracellular vesicle (EV) based cancer biomarkers to personalised precision oncology requires the development of robust, sensitive and specific assays that are amenable to adoption in the clinical laboratory. Whilst a variety of elegant approaches for EV liquid biopsy have been developed, most of them remain as research prototypes due to the requirement of a high level of microfabrication and/or sophisticated instruments. Hence, this study is set to develop a simple DNA aptamer-enabled and fluorescence polarisation-based homogenous assay that eliminates the need to separate unbound detection ligands from the bound species for EV detection. High specificity is achieved by immobilising EVs with one set of antibodies and subsequently detecting them with a DNA aptamer targeting a distinct EV biomarker. This two-pronged strategy ensures the removal of most, if not all, non-EV substances in the input biofluids, including soluble proteins, protein aggregates or non-vesicular particles, prior to quantifying biomarker-positive EVs. A limit of detection of 5.0 × 106 EVs/mL was achieved with a linear quantification range of 5.0 × 108 to 2.0 × 1010 EVs/mL. Facilitated by a multiple parametric analysis strategy, this aptamer-guided fluorescence polarisation assay was capable of distinguishing EVs from three different types of solid cancer cells based on quantitative differences in the levels of the same sets of biomarkers on EVs. Given the simplicity of the method and its ease of implementation in automated clinical biochemistry analysers, this assay could be exploited for future EV-based continuous and real-time monitoring of the emergence of new macro- or micro-metastasis, cancer progression as well as the response to treatment throughout different stages of cancer management in the clinic.
Project description:Extracellular vesicles are cell-derived membranous vesicles that are secreted into biofluids. Emerging evidence suggests that EVs play an essential role in the pathogenesis of many diseases by transferring proteins, genetic material, and small signaling molecules between cells. Among these molecules, microRNAs (miRNAs), a type of small noncoding RNA, are one of the most important signals and are involved in various biological processes. Lung cancer is one of the leading causes of cancer-related deaths worldwide. Early diagnosis of lung cancer may help to reduce mortality and increase the 5 years survival rate and thereby reduce the associated socioeconomic burden. In the past, EV-miRNAs have been recognized as biomarkers of several cancers to assist in diagnosis or prognosis. In this review, we discuss recent findings and clinical practice for EV-miRNAs of lung cancer in several biofluids, including blood, bronchoalveolar lavage fluid (BALF), and pleural lavage.
Project description:Cancer screening and diagnosis can be achieved by analyzing specific molecules within serum-derived extracellular vesicles (EVs). This study sought to profile EV-derived proteins to identify potential lung cancer biomarkers. EVs were isolated from 80 serum samples from healthy individuals and cancer patients via polyethylene glycol (PEG)-based precipitation and immunoaffinity separation using antibodies against CD9, CD63, CD81, and EpCAM. Proteomic analysis was performed using 2-D gel electrophoresis and matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS). The expression of proteins that were differentially upregulated in the EVs or tissue of lung cancer samples was validated by Western blotting. The area under the curve (AUC) was calculated to assess the predictability of each differentially expressed protein (DEP) for lung cancer. A total of 55 upregulated protein spots were selected, seven of which (CD5L, CLEC3B, ITIH4, SERFINF1, SAA4, SERFINC1, and C20ORF3) were found to be expressed at high levels in patient-derived EVs by Western blotting. Meanwhile, only the expression of EV CD5L correlated with that in cancer tissues. CD5L also demonstrated the highest AUC value (0.943) and was found to be the core regulator in a pathway related to cell dysfunction. Cumulatively, these results show that EV-derived CD5L may represent a potential biomarker-detected via a liquid biopsy-for the noninvasive diagnosis of lung cancer.
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.
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:BackgroundExtracellular vesicles (EVs), released by most cell types, provide an excellent source of biomarkers in biological fluids. However, in order to perform validation studies and screenings of patient samples, it is still necessary to develop general techniques permitting rapid handling of small amounts of biological samples from large numbers of donors.ResultsHere we describe a method that, using just a few microliters of patient's plasma, identifies tumour markers exposed on EVs. Studying physico-chemical properties of EVs in solution, we demonstrate that they behave as stable colloidal suspensions and therefore, in immunocapture assays, many of them are unable to interact with a stationary functionalised surface. Using flocculation methods, like those used to destabilize colloids, we demonstrate that cationic polymers increase EV ζ-potential, diameter, and sedimentation coefficient and thus, allow a more efficient capture on antibody-coated surfaces by both ELISA and bead-assisted flow cytometry. These findings led to optimization of a protocol in microtiter plates allowing effective immunocapture of EVs, directly in plasma without previous ultracentrifugation or other EV enrichment. The method, easily adaptable to any laboratory, has been validated using plasma from lung cancer patients in which the epithelial cell marker EpCAM has been detected on EVs.ConclusionsThis optimized high throughput, easy to automate, technology allows screening of large numbers of patients to phenotype tumour markers in circulating EVs, breaking barriers for the validation of proposed EV biomarkers and the discovery of new ones.
Project description:As the second most common malignant tumor in the world, lung cancer is a great threat to human health. In the past several decades, the role and mechanism of ncRNAs in lung cancer as a class of regulatory RNAs have been studied intensively. In particular, ncRNAs in body fluids have attracted increasing attention as biomarkers for lung cancer diagnosis and prognosis and for the evaluation of lung cancer treatment due to their low invasiveness and accessibility. As emerging tumor biomarkers in lung cancer, circulating ncRNAs are easy to obtain, independent of tissue specimens, and can well reflect the occurrence and progression of tumors due to their correlation with some biological processes in tumors. Circulating ncRNAs have a very high potential to serve as biomarkers and hold promise for the development of ncRNA-based therapeutics. In the current study, there has been extensive evidence that circulating ncRNA has clinical significance and value as a biomarker. In this review, we summarize how ncRNAs are generated and enter the circulation, remaining stable for subsequent detection. The feasibility of circulating ncRNAs as biomarkers in the diagnosis and prognosis of non-small cell lung cancer is also summarized. In the current systematic treatment of non-small cell lung cancer, circulating ncRNAs can also predict drug resistance, adverse reactions, and other events in targeted therapy, chemotherapy, immunotherapy, and radiotherapy and have promising potential to guide the systematic treatment of non-small cell lung cancer.
Project description:The last two decades of cancer research have been devoted in two directions: (1) understanding the mechanism of carcinogenesis for an effective treatment, and (2) improving cancer prevention and screening for early detection of the disease. This last aspect has been developed, especially for certain types of cancers, thanks also to the introduction of new concepts such as liquid biopsies and precision medicine. In this context, there is a growing interest in the application of alternative and noninvasive methodologies to search for cancer biomarkers. The new frontiers of the research lead to a search for RNA molecules circulating in body fluids. Searching for biomarkers in extracellular body fluids represents a better option for patients because they are easier to access, less painful, and potentially more economical. Moreover, the possibility for these types of samples to be taken repeatedly, allows a better monitoring of the disease progression or treatment efficacy for a better intervention and dynamic treatment of the patient, which is the fundamental basis of personalized medicine. RNA molecules, freely circulating in body fluids or packed in microvesicles, have all the characteristics of the ideal biomarkers owing to their high stability under storage and handling conditions and being able to be sampled several times for monitoring. Moreover, as demonstrated for many cancers, their plasma/serum levels mirror those in the primary tumor. There are a large variety of RNA species noncoding for proteins that could be used as cancer biomarkers in liquid biopsies. Among them, the most studied are microRNAs, but recently the attention of the researcher has been also directed towards Piwi-interacting RNAs, circular RNAs, and other small noncoding RNAs. Another class of RNA species, the long noncoding RNAs, is larger than microRNAs and represents a very versatile and promising group of molecules which, apart from their use as biomarkers, have also a possible therapeutic role. In this review, we will give an overview of the most common noncoding RNA species detectable in extracellular fluids and will provide an update concerning the situation of the research on these molecules as cancer biomarkers.