The extracellular vesicle generation paradox: a bacterial point of view.

All bacteria produce secreted vesicles that carry out a variety of important biological functions. These extracellular vesicles can improve adaptation and survival by relieving bacterial stress and eliminating toxic compounds, as well as by facilitating membrane remodeling and ameliorating inhospitable environments. However, vesicle production comes with a price. It is energetically costly and, in the case of colonizing pathogens, it elicits host immune responses, which reduce bacterial viability. This raises an interesting paradox regarding why bacteria produce vesicles and begs the question as to whether the benefits of producing vesicles outweigh their costs. In this review, we discuss the various advantages and disadvantages associated with Gram-negative and Gram-positive bacterial vesicle production and offer perspective on the ultimate score. We also highlight questions needed to advance the field in determining the role for vesicles in bacterial survival, interkingdom communication, and virulence.

RASSF1C oncogene elicits amoeboid invasion, cancer stemness, and extracellular vesicle release via a SRC/Rho axis.

Cell plasticity is a crucial hallmark leading to cancer metastasis. Upregulation of Rho/ROCK pathway drives actomyosin contractility, protrusive forces, and contributes to the occurrence of highly invasive amoeboid cells in tumors. Cancer stem cells are similarly associated with metastasis, but how these populations arise in tumors is not fully understood. Here, we show that the novel oncogene RASSF1C drives mesenchymal-to-amoeboid transition and stem cell attributes in breast cancer cells. Mechanistically, RASSF1C activates Rho/ROCK via SRC-mediated RhoGDI inhibition, resulting in generation of actomyosin contractility. Moreover, we demonstrate that RASSF1C-induced amoeboid cells display increased expression of cancer stem-like markers such as CD133, ALDH1, and Nanog, and are accompanied by higher invasive potential in vitro and in vivo. Further, RASSF1C-induced amoeboid cells employ extracellular vesicles to transfer the invasive phenotype to target cells and tissue. Importantly, the underlying RASSF1C-driven biological processes concur to explain clinical data: namely, methylation of the RASSF1C promoter correlates with better survival in early-stage breast cancer patients. Therefore, we propose the use of RASSF1 gene promoter methylation status as a biomarker for patient stratification.

Fluorescent, phosphorescent, magnetic resonance contrast and radioactive tracer labelling of extracellular vesicles.

This review focusses on the significance of fluorescent, phosphorescent labelling and tracking of extracellular vesicles (EVs) for unravelling their biology, pathophysiology, and potential diagnostic and therapeutic uses. Various labeling strategies, such as lipid membrane, surface protein, luminal, nucleic acid, radionuclide, quantum dot labels, and metal complex-based stains, are evaluated for visualizing and characterizing EVs. Direct labelling with fluorescent lipophilic dyes is simple but generally lacks specificity, while surface protein labelling offers selectivity but may affect EV-cell interactions. Luminal and nucleic acid labelling strategies have their own advantages and challenges. Each labelling approach has strengths and weaknesses, which require a suitable probe and technique based on research goals, but new tetranuclear polypyridylruthenium(II) complexes as phosphorescent probes have strong phosphorescence, selective staining, and stability. Future research should prioritize the design of novel fluorescent probes and labelling platforms that can significantly enhance the efficiency, accuracy, and specificity of EV labeling, while preserving their composition and functionality. It is crucial to reduce false positive signals and explore the potential of multimodal imaging techniques to gain comprehensive insights into EVs.

Terahertz s-SNOM Imaging of a Single Cell with Nanoscale Resolution.

Terahertz scattering scanning near-field optical microscopy is a robust spectral detection technique with a nanoscale resolution. However, there are still major challenges in investigating the heterogeneity of cell membrane components in individual cells. Here, we present a novel and comprehensive analytical approach for detecting and investigating heterogeneity in cell membrane components at the single-cell level. In comparison to the resolution of the topographical atomic force microscopy image, the spatial resolution of the terahertz near-field amplitude image is 3 times that of the former. This ultrafine resolution enables the compositional distribution in the cell membrane, such as the distribution of extracellular vesicles, to be finely characterized. Furthermore, via extraction of the near-field absorption images at specific frequencies, the visualization and compositional difference analysis of cell membrane components can be presented in detail. These findings have significant implications for the intuitive and visual analysis of cell development and disease evolutionary pathways.

Delineating Bovine Milk Derived Microvesicles from Exosomes Using Proteomics.

In the work presented herein, a simple serial-pelleting purification strategy combined with a mass spectrometry-based proteomics analysis was developed as a means of discerning differences in extracellular vesicle (EV) populations found in bovine milk samples. A sequence of ultracentrifugation speeds was used to generate changes in the abundances of EV populations, allowing for the identification of associated proteins. A metric was developed to determine the relative abundances of proteins in large EVs (>200 nm) and small EVs (<200 nm). Of the 476 proteins consistently found in this study, 340 are associated with vesicular components. Of these, 156 were heavily enriched in large EVs, 155 shared between large and small EVs, and 29 heavily enriched in small EVs. Additionally, out of 68 proteins annotated as exosome proteins, 32 were enriched in large EVs, 27 shared between large and small EVs, 5 enriched in small EVs, and 7 were found to be nonvesicular contaminant proteins. The top correlated proteins in the small EV group were predominantly membrane-bound proteins, whereas the top correlated proteins in the large EV group were mostly cytosolic enzymes for molecular processing. This method provides a means of assessing the origins of vesicle components and provides new potential marker proteins within discrete vesicle populations.

Free-Standing DNA Origami Superlattice to Facilitate Cryo-EM Visualization of Membrane Vesicles.

Technological breakthroughs in cryo-electron microscopy (cryo-EM) methods open new perspectives for highly detailed structural characterizations of extracellular vesicles (EVs) and synthetic liposome-protein assemblies. Structural characterizations of these vesicles in solution under a nearly native hydrated state are of great importance to decipher cell-to-cell communication and to improve EVs' application as markers in diagnosis and as drug carriers in disease therapy. However, difficulties in preparing holey carbon cryo-EM grids with low vesicle heterogeneities, at low concentration and with kinetic control of the chemical reactions or assembly processes, have limited cryo-EM use in the EV study. We report a straightforward membrane vesicle cryo-EM sample preparation method that assists in circumventing these limitations by using a free-standing DNA-affinity superlattice for covering holey carbon cryo-EM grids. Our approach uses DNA origami to self-assemble to a solution-stable and micrometer-sized ordered molecular template in which structure and functional properties can be rationally controlled. We engineered the template with cholesterol-binding sites to specifically trap membrane vesicles. The advantages of this DNA-cholesterol-affinity lattice (DCAL) include (1) local enrichment of artificial and biological vesicles at low concentration and (2) isolation of heterogeneous cell-derived membrane vesicles (exosomes) from a prepurified pellet of cell culture conditioned medium on the grid.

Programmable RNA Loading of Extracellular Vesicles with Toehold-Release Purification.

Synthetic nanoparticles as lipid nanoparticles (LNPs) are widely used as drug delivery vesicles. However, they hold several drawbacks, including low biocompatibility and unfavorable immune responses. Naturally occurring extracellular vesicles (EVs) hold the potential as native, safe, and multifunctional nanovesicle carriers. However, loading of EVs with large biomolecules remains a challenge. Here, we present a controlled loading methodology using DNA-mediated and programmed fusion between EVs and messenger RNA (mRNA)-loaded liposomes. The fusion efficiency is characterized at the single-particle level by real-time microscopy through EV surface immobilization via lipidated biotin-DNA handles. Subsequently, fused EV-liposome particles (EVLs) can be collected by employing a DNA strand-replacement reaction. Transferring the fusion reaction to magnetic beads enables us to scale up the production of EVLs one million times. Finally, we demonstrated encapsulation of mCherry mRNA, transfection, and improved translation using the EVLs compared to liposomes or LNPs in HEK293-H cells. We envision this as an important tool for the EV-mediated delivery of RNA therapeutics.

Label-Free Light Scattering Imaging with Purified Brownian Motion Differentiates Small Extracellular Vesicles in Cell Microenvironments.

Small extracellular vesicles (sEVs) are heterogeneous biological nanoparticles (NPs) with wide biomedicine applications. Tracking individual nanoscale sEVs can reveal information that conventional microscopic methods may lack, especially in cellular microenvironments. This usually requires biolabeling to identify single sEVs. Here, we developed a light scattering imaging method based on dark-field technology for label-free nanoparticle diffusion analysis (NDA). Compared with nanoparticle tracking analysis (NTA), our method was shown to determine the diffusion probabilities of a single NP. It was demonstrated that accurate size determination of NPs of 41 and 120 nm in diameter is achieved by purified Brownian motion (pBM), without or within the cell microenvironments. Our pBM method was also shown to obtain a consistent size estimation of the normal and cancerous plasma-derived sEVs without and within cell microenvironments, while cancerous plasma-derived sEVs are statistically smaller than normal ones. Moreover, we showed that the velocity and diffusion coefficient are key parameters for determining the diffusion types of the NPs and sEVs in a cancerous cell microenvironment. Our light scattering-based NDA and pBM methods can be used for size determination of NPs, even in cell microenvironments, and also provide a tool that may be used to analyze sEVs for many biomedical applications.

Magnetic Nanoparticle-Based Microfluidic Platform for Automated Enrichment of High-Purity Extracellular Vesicles.

Extracellular vesicles (EVs) are available in various biological fluids and have highly heterogeneous sizes, origins, contents, and functions. Rapid enrichment of high-purity EVs remains crucial for enhancing research on EVs in tumors. In this work, we present a magnetic nanoparticle-based microfluidic platform (ExoCPR) for on-chip isolation, purification, and mild recovery of EVs from cell culture supernatant and plasma within 29 min. The ExoCPR chip integrates bubble-driven micromixers and immiscible filtration assisted by surface tension (IFAST) technology. The bubble-driven micromixer enhances the mixing between immunomagnetic beads and EVs, eliminating the need for manual pipetting or off-chip oscillatory incubation. The high-purity EVs were obtained after passing through the immiscible phase interface where hydrophilic or hydrophobic impurities nonspecifically bound to SIMI were removed. The ExoCPR chip had a capture efficiency of 75.8% and a release efficiency of 62.7% for model EVs. We also demonstrated the powerful performance of the ExoCPR in isolating EVs from biological samples (>90% purity). This chip was further employed in clinical plasma samples and showed that the number of GPC3-positive EVs isolated from hepatocellular carcinoma patients was significantly higher than that of healthy individuals. This ExoCPR chip may provide a promising tool for EV-based liquid biopsy and other fundamental research.

High-Performance Gel-Free and Label-Free Size Fractionation of Extracellular Vesicles with Two-Dimensional Electrophoresis in a Microfluidic Artificial Sieve.

Extracellular vesicles (EVs) are cell-derived particles that exhibit diverse sizes, molecular contents, and clinical implications for various diseases depending on their specific subpopulations. However, fractionation of EV subpopulations with high resolution, efficiency, purity, and yield remains an elusive goal due to their diminutive sizes. In this study, we introduce a novel strategy that effectively separates EV subpopulations in a gel-free and label-free manner, using two-dimensional (2D) electrophoresis in a microfluidic artificial sieve. The microfabricated artificial sieve consists of periodically arranged micro-slit-well structures in a 2D array and generates an anisotropic electric field pattern to size fractionate EVs into discrete streams and steer the subpopulations into designated outlets for collection within a minute. Along with fractionating EV subpopulations, contaminants such as free proteins and short nucleic acids can be simultaneously directed to waste outlets, thus accomplishing both size fractionation and purification of EVs with high performance. Our platform offers a simple, rapid, and versatile solution for EV subpopulation isolation, which can potentially facilitate the discovery of biomarkers for specific EV subtypes and the development of EV-based therapeutics.

Molecular Stratification and Treatment Monitoring of Lung Cancer Using a Small Extracellular Vesicle-Activated Nanocavity Architecture.

Development of molecular diagnostics for lung cancer stratification and monitoring is crucial for the rational planning and timely adjustment of treatments to improve clinical outcomes. In this regard, we propose a nanocavity architecture to sensitively profile the protein signature on small extracellular vesicles (sEVs) to enable accurate, noninvasive staging and treatment monitoring of lung cancer. The nanocavity architecture is formed by molecular recognition through the binding of sEVs with the nanobox-based core-shell surface-enhanced Raman scattering (SERS) barcodes and mirrorlike, asymmetric gold microelectrodes. By imposing an alternating current on the gold microelectrodes, a nanofluidic shear force was stimulated that supported the binding of sEVs and the efficient assembly of the nanoboxes. The binding of sEVs further induced a nanocavity between the nanobox and the gold microelectrode that significantly amplified the electromagnetic field to enable the simultaneous enhancement of Raman signals from four SERS barcodes and generate patient-specific molecular sEV signatures. Importantly, evaluated on a cohort of clinical samples (n = 76) on the nanocavity architecture, the acquired patient-specific sEV molecular signatures achieved accurate identification, stratification, and treatment monitoring of lung cancer patients, highlighting its potential for transition to clinical utility.

Endogenously Gated DNA Walking Machine for Prescreened MicroRNA Detection in Extracellular Vesicles.

Tumor-derived extracellular vesicle (T-EV) microRNAs have been investigated as promising biomarkers in clinical diagnosis as well as disease progression monitoring. However, the expression profiles of microRNA in different tissues vary widely, the precise monitoring of microRNA levels in EVs originating from diseased tissues is susceptible to background interference, thus remains a challenge. Conventional assays require extensive processing, such as EV isolation and even sample lysis, which is both slow and laborious, and the cumbersome pretreatment could spoil the downstream analysis. To address this issue, we developed a generalizable strategy for T-EVs-selective activation and therefore specific amplified microRNA imaging. The conditional signal amplification is established by integrating a traditional DNA walker system with endogenously activated motif to achieve sensitized microRNA imaging in T-EVs. The preorganized endogenous activation with additional sensing criteria narrowed the scope against the complex specimens, and the amplified sensing with reduced off-target signals was supposed to be sensitive to monitor the tiny changes of microRNA expression during the disease course, which holds great potential for accurate diagnosis and prognosis.

Codetection of Proteins and RNAs on Extracellular Vesicles for Pancreatic Cancer Early Diagnosis.

Tumor-derived extracellular vesicles (EVs) carry tumor-specific proteins and RNAs, thus becoming prevalent targets for early cancer diagnosis. However, low expression of EV cargos and insufficient diagnostic power of individual biomarkers hindered EVs application in clinical practice. Herein, we propose a multiplex Codetection platform of proteins and RNAs (Co-PAR) for EVs. Co-PAR adopted a pair of antibody-DNA probes to recognize the same target protein, which in turn formed a double-stranded DNA. Thus, the target protein could be quantified by detecting the double-stranded DNA via qPCR. Meanwhile, qRT-PCR simultaneously quantified the target RNAs. Thus, with a regular qPCR instrument, Co-PAR enabled the codetection of multiplex proteins and RNAs, with the sensitivity of 102 EVs/µL (targeting CD63) and 1 EV/µL (targeting snRNA U6). We analyzed the coexpressions of three protein markers (CD63, GPC-1, HER2) and three RNA markers (snRNA U6, GPC-1 mRNA, miR-10b) on EVs from three pancreatic cell lines and 30 human plasma samples using Co-PAR. The diagnostic accuracy of the 6-biomarker combination reached 92.9%, which was at least 6.2% higher than that of 3-biomarker combinations and at least 13.5% higher than that of 6 single biomarkers. Co-PAR, as a multiparameter detection platform for EVs, has great potential in early disease diagnosis.

Heterogeneous Analysis of Extracellular Vesicles for Osteosarcoma Diagnosis.

Osteosarcoma (OS) is the most prevalent primary tumor of bones, often diagnosed late with a poor prognosis. Currently, few effective biomarkers or diagnostic methods have been developed for early OS detection with high confidence, especially for metastatic OS. Tumor-derived extracellular vesicles (EVs) are emerging as promising biomarkers for early cancer diagnosis through liquid biopsy. Here, we report a plasmonic imaging-based biosensing technique, termed subpopulation protein analysis by single EV counting (SPASEC), for size-dependent EV subpopulation analysis. In our SPASEC platform, EVs are accurately sized and counted on plasmonic sensor chips coated with OS-specific antibodies. Subsequently, EVs are categorized into distinct subpopulations based on their sizes, and the membrane proteins of each size-dependent subpopulation are profiled. We measured the heterogeneous expression levels of the EV markers (CD63, BMP2, GD2, and N-cadherin) in each of the EV subsets from both OS cell lines and clinical plasma samples. Using the linear discriminant analysis (LDA) model, the combination of four markers is applied to classify the healthy donors (n = 37), nonmetastatic OS patients (n = 13), and metastatic patients (n = 12) with an area under the curve of 0.95, 0.92, and 0.99, respectively. SPASEC provides accurate EV sensing technology for early OS diagnosis.

Applications of Data Characteristic AI-Assisted Raman Spectroscopy in Pathological Classification.

Raman spectroscopy has been widely used for label-free biomolecular analysis of cells and tissues for pathological diagnosis in vitro and in vivo. AI technology facilitates disease diagnosis based on Raman spectroscopy, including machine learning (PCA and SVM), manifold learning (UMAP), and deep learning (ResNet and AlexNet). However, it is not clear how to optimize the appropriate AI classification model for different types of Raman spectral data. Here, we selected five representative Raman spectral data sets, including endometrial carcinoma, hepatoma extracellular vesicles, bacteria, melanoma cell, diabetic skin, with different characteristics regarding sample size, spectral data size, Raman shift range, tissue sites, Kullback-Leibler (KL) divergence, and significant Raman shifts (i.e., wavenumbers with significant differences between groups), to explore the performance of different AI models (e.g., PCA-SVM, SVM, UMAP-SVM, ResNet or AlexNet). For data set of large spectral data size, Resnet performed better than PCA-SVM and UMAP. By building data characteristic-assisted AI classification model, we optimized the network parameters (e.g., principal components, activation function, and loss function) of AI model based on data size and KL divergence etc. The accuracy improved from 85.1 to 94.6% for endometrial carcinoma grading, from 77.1 to 90.7% for hepatoma extracellular vesicles detection, from 89.3 to 99.7% for melanoma cell detection, from 88.1 to 97.9% for bacterial identification, from 53.7 to 85.5% for diabetic skin screening, and mean time expense of 5 s.

Multispectral 3D DNA Machine Combined with Multimodal Machine Learning for Noninvasive Precise Diagnosis of Bladder Cancer.

Extracellular vesicle (EV) molecular phenotyping offers enormous opportunities for cancer diagnostics. However, the majority of the associated studies adopted biomarker-based unimodal analysis to achieve cancer diagnosis, which has high false positives and low precision. Herein, we report a multimodal platform for the high-precision diagnosis of bladder cancer (BCa) through a multispectral 3D DNA machine in combination with a multimodal machine learning (ML) algorithm. The DNA machine was constructed using magnetic microparticles (MNPs) functionalized with aptamers that specifically identify the target of interest, i.e., five protein markers on bladder-cancer-derived urinary EVs (uEVs). The aptamers were hybridized with DNA-stabilized silver nanoclusters (DNA/AgNCs) and a G-quadruplex/hemin complex to form a sensing module. Such a DNA machine ensured multispectral detection of protein markers by fluorescence (FL), inductively coupled plasma mass spectrometry (ICP-MS), and UV-vis absorption (Abs). The obtained data sets then underwent uni- or multimodal ML for BCa diagnosis to compare the analytical performance. In this study, urine samples were obtained from our prospective cohort (n = 45). Our analytical results showed that the 3D DNA machine provided a detection limit of 9.2 × 103 particles mL-1 with a linear range of 4 × 104 to 5 × 107 particles mL-1 for uEVs. Moreover, the multimodal data fusion model exhibited an accuracy of 95.0%, a precision of 93.1%, and a recall rate of 93.2% on average, while those of the three types of unimodal models were no more than 91%. The elevated diagnosis precision by using the present fusion platform offers a perspective approach to diminishing the rate of misdiagnosis and overtreatment of BCa.

Dual-Recognition-Mediated Autocatalytic Amplification Assay for the Subpopulations of PD-L1 Positive Extracellular Vesicle.

The PD-L1 protein on extracellular vesicles (EVs) is a promising biomarker for tumor immunotherapy. However, PD-L1+ EVs have various cell origins, so further analysis of the subpopulations is essential to help understand better their relationship with tumor immunotherapy. Different from the previous work which focus on the level of total PD-L1+ EVs expression, we, herein, report a dual-recognition mediated autocatalytic amplification (DRMAA) assay to detect the PD-L1 derived from tumors (EpCAM+), immune T cells (CD3+), and total (Lipids) EVs, respectively. The DRMAA assay employed proximity hybridization to construct a complete trigger sequence and then catalyzed the cross-hybridization of three hairpin probes, producing a three-way DNA junction (3-WJ) structure carrying the newly exposed trigger sequence. The 3-WJ complex subsequently initiated an autocatalytic amplification reaction and higher sensitivity than the traditional catalytic hairpin assembly assay was obtained. It was found that the EpCAM+ and PD-L1+ EVs were more effective than others in distinguishing lung cancer patients from healthy people. Surprisingly, the CD3+ and PD-L1+ EVs in lung cancer patients were also upregulated, indicating that immune cell-derived PD-L1+ EVs are also non-negligible marker in a tumor microenvironment. Our results suggested that the DRMAA assay would improve the study of subpopulations of PD-L1+ EVs to provide new insights for cancer immunotherapies.

Chemically-Induced Lipoprotein Breakdown for Improved Extracellular Vesicle Purification.

Extracellular vesicles (EVs) are nanosized biomolecular packages involved in intercellular communication. EVs are released by all cells, making them broadly applicable as therapeutic, diagnostic, and mechanistic components in (patho)physiology. Sample purity is critical for correctly attributing observed effects to EVs and for maximizing therapeutic and diagnostic performance. Lipoprotein contaminants represent a major challenge for sample purity. Lipoproteins are approximately six orders of magnitude more abundant in the blood circulation and overlap in size, shape, and density with EVs. This study represents the first example of an EV purification method based on the chemically-induced breakdown of lipoproteins. Specifically, a styrene-maleic acid (SMA) copolymer is used to selectively breakdown lipoproteins, enabling subsequent size-based separation of the breakdown products from plasma EVs. The use of the polymer followed by tangential flow filtration or size-exclusion chromatography results in improved EV yield, preservation of EV morphology, increased EV markers, and reduced contaminant markers. SMA-based EV purification enables improved fluorescent labeling, reduces interactions with macrophages, and enhances accuracy, sensitivity, and specificity to detect EV biomarkers, indicating benefits for various downstream applications. In conclusion, SMA is a simple and effective method to improve the purity and yield of plasma-derived EVs, which favorably impacts downstream applications.

The power of imaging to understand extracellular vesicle biology in vivo.

Extracellular vesicles (EVs) are nano-sized lipid bilayer vesicles released by virtually every cell type. EVs have diverse biological activities, ranging from roles in development and homeostasis to cancer progression, which has spurred the development of EVs as disease biomarkers and drug nanovehicles. Owing to the small size of EVs, however, most studies have relied on isolation and biochemical analysis of bulk EVs separated from biofluids. Although informative, these approaches do not capture the dynamics of EV release, biodistribution, and other contributions to pathophysiology. Recent advances in live and high-resolution microscopy techniques, combined with innovative EV labeling strategies and reporter systems, provide new tools to study EVs in vivo in their physiological environment and at the single-vesicle level. Here we critically review the latest advances and challenges in EV imaging, and identify urgent, outstanding questions in our quest to unravel EV biology and therapeutic applications.

HCC EV ECG score: An extracellular vesicle-based protein assay for detection of early-stage hepatocellular carcinoma.

BACKGROUND AND AIMS: The sensitivity of current surveillance methods for detecting early-stage hepatocellular carcinoma (HCC) is suboptimal. Extracellular vesicles (EVs) are promising circulating biomarkers for early cancer detection. In this study, we aim to develop an HCC EV-based surface protein assay for early detection of HCC. APPROACH AND RESULTS: Tissue microarray was used to evaluate four potential HCC-associated protein markers. An HCC EV surface protein assay, composed of covalent chemistry-mediated HCC EV purification and real-time immuno-polymerase chain reaction readouts, was developed and optimized for quantifying subpopulations of EVs. An HCC EV ECG score, calculated from the readouts of three HCC EV subpopulations ( E pCAM + CD63 + , C D147 + CD63 + , and G PC3 + CD63 + HCC EVs), was established for detecting early-stage HCC. A phase 2 biomarker study was conducted to evaluate the performance of ECG score in a training cohort ( n  = 106) and an independent validation cohort ( n  = 72).Overall, 99.7% of tissue microarray stained positive for at least one of the four HCC-associated protein markers (EpCAM, CD147, GPC3, and ASGPR1) that were subsequently validated in HCC EVs. In the training cohort, HCC EV ECG score demonstrated an area under the receiver operating curve (AUROC) of 0.95 (95% confidence interval [CI], 0.90-0.99) for distinguishing early-stage HCC from cirrhosis with a sensitivity of 91% and a specificity of 90%. The AUROCs of the HCC EV ECG score remained excellent in the validation cohort (0.93; 95% CI, 0.87-0.99) and in the subgroups by etiology (viral: 0.95; 95% CI, 0.90-1.00; nonviral: 0.94; 95% CI, 0.88-0.99). CONCLUSION: HCC EV ECG score demonstrated great potential for detecting early-stage HCC. It could augment current surveillance methods and improve patients' outcomes.