Single test-based diagnosis of multiple cancer types using Exosome-SERS-AI for early stage cancers

A cavity induced mode hybridization plasmonic sensor for portable detection of exosomes

Exosomes have been considered as promising biomarkers for cancer diagnosis due to their abundant information from originating cells. However, sensitive and reliable detection of exosomes is still facing technically challenges due to the lack of a sensing platform with high sensitivity and reproducibility. To address the challenges, here we propose a portable surface plasmon resonance (SPR) sensing of exosomes with a three-layer Au mirror/SiO2 spacer/Au nanohole sensor, fabricated by an economical polystyrene nanosphere self-assembly method. The SiO2 spacer can act as an optical cavity and induce mode hybridization, leading to excellent optimization of both sensitivity and full width at half maximum compared with normal single layer Au nanohole sensors. When modified with CD63 or EpCAM aptamers, a detection of limit (LOD) of as low as 600 particles/μL was achieved. The sensors showed good capability to distinguish between non-tumor derived L02 exosomes and tumor derived HepG2 exosomes. Additionally, high reproducibility was also achieved in detection of artificial serum samples with RSD as low as 2%, making it feasible for clinical applications. This mode hybridization plasmonic sensor provides an effective approach to optimize the detection sensitivity of exosomes, pushing SPR sensing one step further towards cancer diagnosis.

High-throughput enrichment of portal venous circulating tumor cells for highly sensitive diagnosis of CA19-9-negative pancreatic cancer patients using inertial microfluidics

The carbohydrate antigen 19-9 (CA19-9) is commonly used as a representative biomarker for pancreatic cancer (PC); however, it lacks sensitivity and specificity for early-stage PC diagnosis. Furthermore, some patients with PC are negative for CA19-9 (<37 U/mL), which introduces additional limitations to their accurate diagnosis and treatment. Hence, improved methods to accurately detect PC stages in CA19-9-negative patients are warranted. In this study, tumor-proximal liquid biopsy and inertial microfluidics were coupled to enable high-throughput enrichment of portal venous circulating tumor cells (CTCs) and support the effective diagnosis of patients with early-stage PC. The proposed inertial microfluidic system was shown to provide size-based enrichment of CTCs using inertial focusing and Dean flow effects in slanted spiral channels. Notably, portal venous blood samples were found to have twice the yield of CTCs (21.4 cells per 5 mL) compared with peripheral blood (10.9 CTCs per 5 mL). A combination of peripheral and portal CTC data along with CA19-9 results showed to greatly improve the average accuracy of CA19-9-negative PC patients from 47.1% with regular CA19-9 tests up to 87.1%. Hence, portal venous CTC-based microfluidic biopsy can be used with high sensitivity and specificity for the diagnosis of early-stage PC, particularly in CA19-9-negative patients.

Lectin microarray based glycan profiling of exosomes for dynamic monitoring of colorectal cancer progression

BACKGROUND

Exosomes, as emerging biomarkers in liquid biopsies in recent years, offer profound insights into cancer diagnostics due to their unique molecular signatures. The glycosylation profiles of exosomes have emerged as potential biomarkers, offering a novel and less invasive method for cancer diagnosis and monitoring. Colorectal cancer (CRC) represents a substantial global health challenge and burden. Thus there is a great need for the aberrant glycosylation patterns on the surface of CRC cell-derived exosomes, proposing them as potential biomarkers for tumor characterization.

RESULTS

The interactions of 27 lectins with exosomes from three CRC cell lines (SW480, SW620, HCT116) and one normal colon epithelial cell line (NCM460) have been analyzed by the lectin microarray. The result indicates that Ulex Europaeus Agglutinin I (UEA-I) exhibits high affinity and specificity towards exosomes derived from SW480 cells. The expression of glycosylation related genes within cells has been analyzed by high-throughput quantitative polymerase chain reaction (HT-qPCR). The experimental result of HT-qPCR is consistent with that of lectin microarray. Moreover, the limit of detection (LOD) of UEA-I microarray is calculated to be as low as 2.7 × 105 extracellular vehicles (EVs) mL-1 (three times standard deviation (3σ) of blank sample). The UEA-I microarray has been successfully utilized to dynamically monitor the progression of tumors in mice-bearing SW480 CRC subtype, applicable in tumor sizes ranging from 2 mm to 20 mm in diameter.

SIGNIFICANCE

The results reveal that glycan expression pattern of exosome is linked to specific CRC subtypes, and regulated by glycosyltransferase and glycosidase genes of mother cells. Our findings illuminate the potential of glycosylation molecules on the surface of exosomes as reliable biomarkers for diagnosis of tumor at early stage and monitoring of cancer progression.

Self-Referenced Au Nanoparticles-Coated Glass Wafers for In Situ SERS Monitoring of Cell Secretion

Surface-enhanced Raman spectroscopy (SERS) is a powerful technique for discrimination of bimolecules in complex systems. However, its practical applications face challenges such as complicated manufacturing procedures and limited scalability of SERS substrates, as well as poor reproducibility during detection which compromises the reliability of SERS-based analysis. In this study, we developed a convenient method for simultaneous fabrication of massive SERS substrates with an internal standard to eliminate the substrate-to-substrate differences. We first synthesized Au@CN@Au nanoparticles (NPs) which contain embedded internal standard molecules with a single characteristic peak in the Raman-silent region, and then deposited the NPs on 6 mm glass wafers in a 96-well plate simply by centrifugation for 3 min. The one-time obtained 96 SERS substrates have excellent intrasubstrate uniformity and intersubstrate repeatability for SERS detection by using the internal standard (relative standard deviation = 10.47%), and were able to detect both charged and neutral molecules (crystal violet and triphenylphosphine) at a concentration of 10-9 M. Importantly, cells can be directly cultured on glass wafers in the 96-well plate, enabling real time monitoring of the secretes and metabolism change in response to external stimulation. We found that the release of nucleic acids, amino acids and lipids by MDA-MB-231 cells significantly increased under hypoxic conditions. Overall, our approach enables fast and large-scale production of Au@CN@Au NPs-coated glass wafers as SERS substrates, which are homogeneous and highly sensitive for monitoring trace changes of biomolecules.

CRISPR-Enhanced Photocurrent Polarity Switching for Dual-lncRNA Detection Combining Deep Learning for Cancer Diagnosis

Abnormal expression in long noncoding RNAs (lncRNAs) is closely associated with cancers. Herein, a novel CRISPR/Cas13a-enhanced photocurrent-polarity-switching photoelectrochemical (PEC) biosensor was engineered for the joint detection of dual lncRNAs, using deep learning (DL) to assist in cancer diagnosis. After target lncRNA-activated CRISPR/Cas13a cleaves to induce DNAzyme bidirectional walkers with the help of cofactor Mg2+, nitrogen-doped carbon-Cu/Cu2O octahedra are introduced into the biosensor, producing a photocurrent in the opposite direction of CdS quantum dots (QDs). The developed PEC biosensor shows high specificity and sensitivity with limits of detection down to 25.5 aM for lncRNA HOTAIR and 53.1 aM for lncRNA MALAT1. More importantly, this platform for the lncRNA joint assay in whole blood can successfully differentiate cancers from healthy people. Furthermore, the DL model is applied to explore the potential pattern hidden in data of the established technology, and the accuracy of DL cancer diagnosis can acquire 93.3%. Consequently, the developed platform offers a new avenue for lncRNA joint detection and early intelligent diagnosis of cancer.

An updated review on the development of a nanomaterial-based field-effect transistor-type biosensors to detect exosomes for cancer diagnosis

Cancer, a life-threatening genetic disease caused by abnormalities in normal cell growth regulatory functions, poses a significant challenge that current medical technologies cannot fully overcome. The current desired breakthrough is to diagnose cancer as early as possible and increase survival rates through treatments tailored to the prognosis and appropriate follow-up. From a perspective that reflects this contemporary paradigm of cancer diagnostics, exosomes are emerging as promising biomarkers. Exosomes, serving as mobile biological information repositories of cancer cells, have been known to create a microtumor environment in surrounding cells, and significant insight into the clinical significance of cancer diagnosis targeting them has been reported. Therefore, there are growing interests in constructing a system that enables continuous screening with a focus on patient-friendly and flexible diagnosis, aiming to improve cancer screening rates through exosome detection. This review focuses on a proposed exosome-embedded biological information-detecting platform employing a field-effect transistor (FET)-based biosensor that leverages portability, cost-effectiveness, and rapidity to minimize the stages of sacrifice attributable to cancer. The FET-applied biosensing technique, stemming from variations in an electric field, is considered an early detection system, offering high sensitivity and a prompt response frequency for the qualitative and quantitative analysis of biomolecules. Hence, an in-depth discussion was conducted on the understanding of various exosome-based cancer biomarkers and the clinical significance of recent studies on FET-based biosensors applying them.

Simultaneous Protein Colorful Imaging via Raman Signal Classification

Protein imaging aids diagnosis and drug development by revealing protein-drug interactions or protein levels. However, the challenges of imaging multiple proteins, reduced sensitivity, and high reliance on specific protein properties such as Raman peaks or refractive index hinder the understanding. Here, we introduce multiprotein colorful imaging through Raman signal classification. Our method utilized machine learning-assisted classification of Raman signals, which are the distinctive features of label-free proteins. As a result, three types of proteins could be imaged simultaneously. In addition, we could quantify individual proteins from a mixture of multiple proteins over a wide detection range (10 fg/mL-1 μg/mL). These results showed a 1000-fold improvement in sensitivity and a 30-fold increase in the upper limit of detection compared to existing methods. These advances will enhance our understanding of biology and facilitate the development of disease diagnoses and treatments.

Artificial intelligence-based plasma exosome label-free SERS profiling strategy for early lung cancer detection

As a lung cancer biomarker, exosomes were utilized for in vitro diagnosis to overcome the lack of sensitivity of conventional imaging and the potential harm caused by tissue biopsy. However, given the inherent heterogeneity of exosomes, the challenge of accurately and reliably recognizing subtle differences in the composition of exosomes from clinical samples remains significant. Herein, we report an artificial intelligence-assisted surface-enhanced Raman spectroscopy (SERS) strategy for label-free profiling of plasma exosomes for accurate diagnosis of early-stage lung cancer. Specifically, we build a deep learning model using exosome spectral data from lung cancer cell lines and normal cell lines. Then, we extracted the features of cellular exosomes by training a convolutional neural network (CNN) model on the spectral data of cellular exosomes and used them as inputs to a support vector machine (SVM) model. Eventually, the spectral features of plasma exosomes were combined to effectively distinguish adenocarcinoma in situ (AIS) from healthy controls (HC). Notably, the approach demonstrated significant performance in distinguishing AIS from HC samples, with an area under the curve (AUC) of 0.84, sensitivity of 83.3%, and specificity of 83.3%. Together, the results demonstrate the utility of exosomes as a biomarker for the early diagnosis of lung cancer and provide a new approach to prescreening techniques for lung cancer.

A Dual-Function AgNW@COF SERS Membrane for Organic Pollutant Removal and Simultaneous Concentration Determination

Surface enhanced Raman spectroscopy (SERS) is a highly sensitive analytical detection method commonly employed in biochemical and environmental analysis. Nevertheless, the rapid movement of analytes and interfering components in flow systems can impact the real-time, online detection capability of Raman spectroscopy. To address this issue, we developed an innovative approach utilizing covalent organic framework (COF), a robust porous material with excellent water stability, to coat the surface of Ag nanowire (AgNW) for synthesizing AgNW@COF hybrid. The regular pores of the COF serve to effectively eliminate large interfering molecules while facilitating the efficient transport of specific analytes to SERS hot spots. Additionally, the fluid flow-induced scouring effect aids in excluding interfering molecules from the surface of AgNW. By incorporating AgNW@COF into a bifunctional filter membrane with simultaneous filtration and sensing capabilities, we had achieved efficient purification of organic pollutants as well as real-time identification of pollutant species and concentration.

miR-493-5p Silenced by DNA Methylation Promotes Angiogenesis via Exosomes and VEGF-A-Mediated Intracellular Cross-Talk Between ESCC Cells and HUVECs

Background

Exosomal microRNAs (miRNAs) in the tumor microenvironment play crucial roles in tumorigenesis and tumor progression by participating in intercellular cross-talk. However, the functions of exosomal miRNAs and the mechanisms by which they regulate esophageal squamous cell carcinoma (ESCC) progression are unclear.

Methods

RNA sequencing and GEO analysis were conducted to identify candidate exosomal miRNAs involved in ESCC development. Receiver operating characteristic curve analysis was performed to assess the diagnostic value of plasma exosomal miR-493-5p. EdU, tube formation and Transwell assays were used to investigate the effects of exosomal miR-493-5p on human umbilical vein endothelial cells (HUVECs). A subcutaneous xenograft model was used to evaluate the antitumor effects of miR-493-5p and decitabine (a DNA methyltransferase inhibitor). The relationship between miR-493-5p and SP1/SP3 was revealed via a dual-luciferase reporter assay. A series of rescue assays were subsequently performed to investigate whether SP1/SP3 participate in exosomal miR-493-5p-mediated ESCC angiogenesis.

Results

We found that miR-493-5p expression was notably reduced in the plasma exosomes of ESCC patients, which showed the high potential value in early ESCC diagnosis. Additionally, miR-493-5p, as a candidate tumor suppressor, inhibited the proliferation, migration and tube formation of HUVECs by suppressing the expression of VEGFA and exerted its angiostatic effect via exosomes. Moreover, we found that SP1/SP3 are direct targets of miR-493-5p and that re-expression of SP1/SP3 could reverse the inhibitory effects of miR-493-5p. Further investigation revealed that miR-493-5p expression could be regulated by DNA methyltransferase 3A (DNMT3A) and DNMT3B, and either miR-493-5p overexpression or restoration of miR-493-5p expression with decitabine increased the antitumor effects of bevacizumab.

Conclusion

Exosomal miR-493-5p is a highly valuable ESCC diagnosis marker and inhibits ESCC-associated angiogenesis. miR-493-5p can be silenced via DNA methylation, and restoration of miR-493-5p expression with decitabine increases the antitumor effects of bevacizumab, suggesting its potential as a therapeutic target for ESCC treatment.

Exosomes: a review of biologic function, diagnostic and targeted therapy applications, and clinical trials

Exosomes are extracellular vesicles generated by all cells and they carry nucleic acids, proteins, lipids, and metabolites. They mediate the exchange of substances between cells,thereby affecting biological properties and activities of recipient cells. In this review, we briefly discuss the composition of exocomes and exosome isolation. We also review the clinical applications of exosomes in cancer biology as well as strategies in exosome-mediated targeted drug delivery systems. Finally, the application of exosomes in the context of cancer therapeutics both in practice and literature are discussed.

Recent progress in Surface-Enhanced Raman Spectroscopy detection of biomarkers in liquid biopsy for breast cancer

Breast cancer is the most commonly diagnosed cancer in women globally and a leading cause of cancer-related mortality. However, current detection methods, such as X-rays, ultrasound, CT scans, MRI, and mammography, have their limitations. Recently, with the advancements in precision medicine and technologies like artificial intelligence, liquid biopsy, specifically utilizing Surface-Enhanced Raman Spectroscopy (SERS), has emerged as a promising approach to detect breast cancer. Liquid biopsy, as a minimally invasive technique, can provide a temporal reflection of breast cancer occurrence and progression, along with a spatial representation of overall tumor information. SERS has been extensively employed for biomarker detection, owing to its numerous advantages such as high sensitivity, minimal sample requirements, strong multi-detection ability, and controllable background interference. This paper presents a comprehensive review of the latest research on the application of SERS in the detection of breast cancer biomarkers, including exosomes, circulating tumor cells (CTCs), miRNA, proteins and others. The aim of this review is to provide valuable insights into the potential of SERS technology for early breast cancer diagnosis.

Tumor-Derived Extracellular Vesicles as Liquid Biopsy for Diagnosis and Prognosis of Solid Tumors: Their Clinical Utility and Reliability as Tumor Biomarkers

Early cancer detection and accurate monitoring are crucial to ensure increased patient survival. Recent research has focused on developing non-invasive biomarkers to diagnose cancer early and monitor disease progression at low cost and risk. Extracellular vesicles (EVs), nanosized particles secreted into extracellular spaces by most cell types, are gaining immense popularity as novel biomarker candidates for liquid cancer biopsy, as they can transport bioactive cargo to distant sites and facilitate intercellular communications. A literature search was conducted to discuss the current approaches for EV isolation and the advances in using EV-associated proteins, miRNA, mRNA, DNA, and lipids as liquid biopsies. We discussed the advantages and challenges of using these vesicles in clinical applications. Moreover, recent advancements in machine learning as a novel tool for tumor marker discovery are also highlighted.

Ultrasensitive and Wash-Free Detection of Tumor Extracellular Vesicles by Aptamer-Proximity-Ligation-Activated Rolling Circle Amplification Coupled to Single Particle ICP-MS

Tumor-derived extracellular vesicles (TEVs) are rich in cellular information and hold great promise as a biomarker for noninvasive cancer diagnosis. However, accurate measurement of TEVs presents challenges due to their low abundance and potential interference from a high number of EVs derived from normal cells. Herein, an aptamer-proximity-ligation-activated rolling circle amplification (RCA) method for EV membrane recognition, coupled with single particle inductively coupled plasma mass spectrometry (sp-ICP-MS) for the quantification of TEVs, is developed. When DNA-labeled ultrasmall gold nanoparticle (AuNP) probes bind to the long chains formed by RCA, they aggregate to form large particles. Notably, small AuNPs scarcely produce pulse signals in sp-ICP-MS, thereby detecting TEVs in a wash-free manner. By leveraging the strong binding affinity of aptamers, dual aptamers for EpCAM and PD-L1 recognition, and the sp-ICP-MS technique, this method offers remarkable sensitivity and selectivity in tracing TEVs. Under optimized conditions, the present method shows a favorable linear relationship between the pulse signal frequency of sp-ICP-MS and TEV concentration within the range of 105-107 particles/mL, along with a detection limit of 1.1 × 104 particles/mL. The pulse signals from sp-ICP-MS combined with machine learning algorithms are used to discriminate cancer patients from healthy donors with 100% accuracy. Due to its simple and fast operation and excellent sensitivity and accuracy, this approach holds significant potential for diverse applications in life sciences and personalized medicine.

"Three-in-One" Plasmonic Au@PtOs Nanocluster Driven Lateral Flow Assay for Multimodal Cancer Exosome Biosensing

Exploiting the multiple properties of nanozymes for the multimode lateral flow assay (LFA) is urgently required to improve the accuracy and versatility. Herein, we developed a novel plasmonic Au nanostar@PtOs nanocluster (Au@PtOs) as a multimode signal tag for LFA detection. Based on the PtOs bimetallic nanocluster doping strategy, Au@PtOs can indicate both excellent SERS enhancement and nanozyme catalytic activity. Meanwhile, Au@PtOs displays a better photothermal effect than that of Au nanostars. Therefore, catalytic colorimetric/SERS/temperature three-mode signals can be read out based on the Au@PtOs nanocomposite. The Au@PtOs was combined with LFA and applied for breast cancer exosome detection. The detection limit for the colorimetric/SERS/temperature mode was 2.6 × 103/4.1 × 101/4.6 × 102 exosomes/μL, respectively, which was much superior to the common Au nanoparticles LFA (∼105 exosomes/μL). Moreover, based on the fingerprint molecular recognition ability of the SERS mode, exosome phenotypes derived from different breast cancer cell lines can be discriminated easily. Based on the convenient visual colorimetric mode and sensitive SERS/temperature quantitative modes, Au@PtOs driven LFA can satisfy the requirements of accurate and flexible multimodal sensing in different application scenarios.

Revisiting the advances and challenges in the clinical applications of extracellular vesicles in cancer

Extracellular vesicles (EVs) have been the subject of an exponentially growing number of studies covering their biogenesis mechanisms, isolation and analysis techniques, physiological and pathological roles, and clinical applications, such as biomarker and therapeutic uses. Nevertheless, the heterogeneity of EVs both challenges our understanding of them and presents new opportunities for their potential application. Recently, the EV field experienced a wide range of advances. However, the challenges also remain huge. This review focuses on the recent progress and difficulties encountered in the practical use of EVs in clinical settings. In addition, we also explored the concept of EV heterogeneity to acquire a more thorough understanding of EVs and their involvement in cancer, specifically focusing on the fundamental nature of EVs.

Canine Cancer Diagnostics by X-ray Diffraction of Claws

We report the results of X-ray diffraction (XRD) measurements of the dogs' claws and show the feasibility of using this approach for early, non-invasive cancer detection. The obtained two-dimensional XRD patterns can be described by Fourier coefficients, which were calculated for the radial and circular (angular) directions. We analyzed these coefficients using the supervised learning algorithm, which implies optimization of the random forest classifier by using samples from the training group and following the calculation of mean cancer probability per patient for the blind dataset. The proposed algorithm achieved a balanced accuracy of 85% and ROC-AUC of 0.91 for a blind group of 68 dogs. The transition from samples to patients additionally improved the ROC-AUC by 10%. The best specificity and sensitivity values for 68 patients were 97.4% and 72.4%, respectively. We also found that the structural parameter (biomarker) most important for the diagnostics is the intermolecular distance.

Extracellular Vesicle Preparation and Analysis: A State-of-the-Art Review

In recent decades, research on Extracellular Vesicles (EVs) has gained prominence in the life sciences due to their critical roles in both health and disease states, offering promising applications in disease diagnosis, drug delivery, and therapy. However, their inherent heterogeneity and complex origins pose significant challenges to their preparation, analysis, and subsequent clinical application. This review is structured to provide an overview of the biogenesis, composition, and various sources of EVs, thereby laying the groundwork for a detailed discussion of contemporary techniques for their preparation and analysis. Particular focus is given to state-of-the-art technologies that employ both microfluidic and non-microfluidic platforms for EV processing. Furthermore, this discourse extends into innovative approaches that incorporate artificial intelligence and cutting-edge electrochemical sensors, with a particular emphasis on single EV analysis. This review proposes current challenges and outlines prospective avenues for future research. The objective is to motivate researchers to innovate and expand methods for the preparation and analysis of EVs, fully unlocking their biomedical potential.

SERS-based AI diagnosis of lung and gastric cancer via exhaled breath

Distinct diagnosis between Lung cancer (LC) and gastric cancer (GC) according to the same biomarkers (e.g. aldehydes) in exhaled breath based on surface-enhanced Raman spectroscopy (SERS) remains a challenge in current studies. Here, an accurate diagnosis of LC and GC is demonstrated, using artificial intelligence technologies (AI) based on SERS spectrum of exhaled breath in plasmonic metal organic frameworks nanoparticle (PMN) film. In the PMN film with optimal structure parameters, 1780 SERS spectra are collected, in which 940 spectra come from healthy people (n = 49), another 440 come from LC patients (n = 22) and the rest 400 come from GC patients (n = 8). The SERS spectra are trained through artificial neural network (ANN) model with the deep learning (DL) algorithm, and the result exhibits a good identification accuracy of LC and GC with an accuracy over 89 %. Furthermore, combined with information of SERS peaks, the data mining in ANN model is successfully employed to explore the subtle compositional difference in exhaled breath from healthy people (H) and L/GC patients. This work achieves excellent noninvasive diagnosis of multiple cancer diseases in breath analysis and provides a new avenue to explore the feature of disease based on SERS spectrum.

Surface-Enhanced Raman Spectroscopy: Current Understanding, Challenges, and Opportunities

While surface-enhanced Raman spectroscopy (SERS) has experienced substantial advancements since its discovery in the 1970s, it is an opportunity to celebrate achievements, consider ongoing endeavors, and anticipate the future trajectory of SERS. In this perspective, we encapsulate the latest breakthroughs in comprehending the electromagnetic enhancement mechanisms of SERS, and revisit CT mechanisms of semiconductors. We then summarize the strategies to improve sensitivity, selectivity, and reliability. After addressing experimental advancements, we comprehensively survey the progress on spectrum-structure correlation of SERS showcasing their important role in promoting SERS development. Finally, we anticipate forthcoming directions and opportunities, especially in deepening our insights into chemical or biological processes and establishing a clear spectrum-structure correlation.

Liquid biopsy: An arsenal for tumour screening and early diagnosis

Cancer has become the second leading cause of death in the world, and more than 50% of cancer patients are diagnosed at an advanced stage. Early diagnosis of tumours is the key to improving patient quality of life and survival time and reducing the socioeconomic burden. However, there is still a lack of reliable early diagnosis methods in clinical practice. In recent years, liquid biopsy technology has developed rapidly. It has the advantages of noninvasiveness, easy access to sample sources, and reproducibility. It has become the main focus of research on the early diagnosis methods of tumours. This review summarises the research progress of existing liquid biopsy markers, such as circulating tumour DNA, circulating viral DNA, DNA methylation, circulating tumour cells, circulating RNA, exosomes, and tumour education platelets in early diagnosis of tumours, and analyses the current advantages and limitations of various markers, providing a direction for the application and transformation of liquid biopsy research in early diagnosis of clinical tumours.

Effective enrichment of trace exosomes for the label-free SERS detection via low-cost thermophoretic profiling

Exosome-based liquid biopsies possess great potential in monitoring cancer development However, current exosome detection biosensors require large exosome volumes, showing the weak detection sensitivity. Besides, these methods pay little attention to in situ analysis of exosomes, hence limiting the provision of more accurate clinically-relevant information. Herein, we develop an innovative label-free biosensor combining the low-cost thermophoretic enrichment method with the surface-enhanced Raman spectroscopy (SERS) detection. Based on the thermophoretic enrichment strategy, exosomes and gold nanoparticles can be enriched together into a small area with a scale of 500 μm within 10 min. The Raman signals of various exosomes derived from normal, cancerous cell lines and human serum are dynamically monitored in situ, with the limit of detection of 102-103 particles per microliter, presenting higher sensitivity compared with the similar label-free SERS detection. The spectral data set of different exosomes is applied to train for multivariate classification of cell types and to estimate how the normal exosome data resemble cancer cell exosome. The reliable classification and identification of different exosomes can be realized. The current biosensor is convenient, low-cost and requires small exosome volumes (∼3 μL), and if validated in larger cohorts may contribute to the tumor prediction and diagnosis.

SERS biosensors for liquid biopsy towards cancer diagnosis by detection of various circulating biomarkers: current progress and perspectives

Liquid biopsy has emerged as a promising non-invasive strategy for cancer diagnosis, enabling the detection of various circulating biomarkers, including circulating tumor cells (CTCs), circulating tumor nucleic acids (ctNAs), circulating tumor-derived small extracellular vesicles (sEVs), and circulating proteins. Surface-enhanced Raman scattering (SERS) biosensors have revolutionized liquid biopsy by offering sensitive and specific detection methodologies for these biomarkers. This review comprehensively examines the application of SERS-based biosensors for identification and analysis of various circulating biomarkers including CTCs, ctNAs, sEVs and proteins in liquid biopsy for cancer diagnosis. The discussion encompasses a diverse range of SERS biosensor platforms, including label-free SERS assay, magnetic bead-based SERS assay, microfluidic device-based SERS system, and paper-based SERS assay, each demonstrating unique capabilities in enhancing the sensitivity and specificity for detection of liquid biopsy cancer biomarkers. This review critically assesses the strengths, limitations, and future directions of SERS biosensors in liquid biopsy for cancer diagnosis.

Kidney Cancer and Potential Use of Urinary Extracellular Vesicles

Kidney cancer is the 14th most common cancer globally. The 5-year relative survival rate of kidney cancer at a localized stage is 92.9% and it declines to 17.4% in metastatic stage. Currently, the most accurate method of its diagnosis is tissue biopsy. However, the invasive and costly nature of biopsies makes it undesirable in many patients. Therefore, novel biomarkers for diagnosis and prognosis should be explored. Urinary extracellular vesicles (uEVs) are small vesicles (50-200 nm) in urine carrying nucleic acids, proteins and lipids as their cargos. These uEVs' cargos can provide non-invasive alternative to monitor kidney health. In this review, we have summarized recent studies investigating potential use of uEVs' cargos as biomarkers in kidney cancer for diagnosis, prognosis and therapeutic intervention.

Deep Learning-Assisted Spectrum-Structure Correlation: State-of-the-Art and Perspectives

Spectrum-structure correlation is playing an increasingly crucial role in spectral analysis and has undergone significant development in recent decades. With the advancement of spectrometers, the high-throughput detection triggers the explosive growth of spectral data, and the research extension from small molecules to biomolecules accompanies massive chemical space. Facing the evolving landscape of spectrum-structure correlation, conventional chemometrics becomes ill-equipped, and deep learning assisted chemometrics rapidly emerges as a flourishing approach with superior ability of extracting latent features and making precise predictions. In this review, the molecular and spectral representations and fundamental knowledge of deep learning are first introduced. We then summarize the development of how deep learning assist to establish the correlation between spectrum and molecular structure in the recent 5 years, by empowering spectral prediction (i.e., forward structure-spectrum correlation) and further enabling library matching and de novo molecular generation (i.e., inverse spectrum-structure correlation). Finally, we highlight the most important open issues persisted with corresponding potential solutions. With the fast development of deep learning, it is expected to see ultimate solution of establishing spectrum-structure correlation soon, which would trigger substantial development of various disciplines.

Decoding the secrets of small extracellular vesicle communications: exploring the inhibition of vesicle-associated pathways and interception strategies for cancer treatment

Cancer disease is the second leading cause of death worldwide. In 2023, about 2 million new cancer cases and 609,820 cancer deaths are projected to occur in the United States. The driving forces of cancer progression and metastasis are widely varied and comprise multifactorial events. Although there is significant success in treating cancer, patients still present with tumors at advanced stages. Therefore, the discovery of novel oncologic pathways has been widely developed. Tumor cells communicate with each other through small extracellular vesicles (sEVs), which contribute to tumor-stromal interaction and promote tumor growth and metastasis. sEV-specific inhibitors are being investigated as a next-generation cancer therapy. A literature search was conducted to discuss different options for targeting sEV pathways in cancer cells. However, there are some challenges that need to be addressed in targeting sEVs: i) specificity and toxicity of sEV inhibitor, ii) targeted delivery of sEV inhibitors, iii) combination of sEV inhibitors with current standard chemotherapy to improve patients' clinical outcomes, and iv) data reproducibility and applicability at distinct levels of the disease. Despite these challenges, sEV inhibitors have immense potential for effectively treating cancer patients.

Advanced Nanoencapsulation-Enabled Ultrasensitive Analysis: Unraveling Tumor Extracellular Vesicle Subpopulations for Differential Diagnosis of Hepatocellular Carcinoma via DNA Cascade Reactions

Tumor-derived extracellular vesicles (tEVs) hold immense promise as potential biomarkers for the precise diagnosis of hepatocellular carcinoma (HCC). However, their clinical translation is hampered by their inherent characteristics, such as small size and high heterogeneity and complex environment, including non-EV particles and normal cell-derived EVs, which prolong separation procedures and compromise detection accuracy. In this study, we devised a DNA cascade reaction-triggered individual EV nanoencapsulation (DCR-IEVN) strategy to achieve the ultrasensitive and specific detection of tEV subpopulations via routine flow cytometry in a one-pot, one-step fashion. DCR-IEVN enables the direct and selective packaging of multiple tEV subpopulations in clinical serum samples into flower-like particles exceeding 600 nm. This approach bypasses the need for EV isolation, effectively reducing interference from non-EV particles and nontumor EVs. Compared with conventional analytical technologies, DCR-IEVN exhibits superior efficacy in diagnosing HCC owing to its high selectivity for tEVs. Integration of machine learning algorithms with DCR-IEVN resulted in differential diagnosis accuracy of 96.7% for the training cohort (n = 120) and 93.3% for the validation cohort (n = 30), effectively distinguishing HCC, cirrhosis, and healthy donors. This strategy offers a streamlined workflow and rapid assay completion and requires only small-volume serum samples and routine clinical devices, facilitating the clinical translation of tEV-based tumor diagnosis.

Cancer diagnosis using label-free SERS-based exosome analysis

Exosomes, carrying distinctive biomolecules reflective of their parent cell's status and origin, show promise as liquid biopsy biomarkers for cancer diagnosis. However, their clinical translation remains challenging due to their relatively low concentration in body fluids. Surface-Enhanced Raman spectroscopy (SERS) has recently gained significant attention as a label-free and sensitive technique for exosome analysis. This review explores label-free SERS for exosome detection, covering exosome isolation and characterization methods, advancements in SERS substrates, and fingerprint analysis techniques using machine learning. Furthermore, we emphasize the challenges and offer insights into the future prospects of SERS-based exosome analysis to enhance cancer diagnosis.

3D Superclusters with Hybrid Bioinks for Early Detection in Breast Cancer

The surface-enhanced Raman scattering (SERS) technique has garnered significant interest due to its ultrahigh sensitivity, making it suitable for addressing the growing demand for disease diagnosis. In addition to its sensitivity and uniformity, an ideal SERS platform should possess characteristics such as simplicity in manufacturing and low analyte consumption, enabling practical applications in complex diagnoses including cancer. Furthermore, the integration of machine learning algorithms with SERS can enhance the practical usability of sensing devices by effectively classifying the subtle vibrational fingerprints produced by molecules such as those found in human blood. In this study, we demonstrate an approach for early detection of breast cancer using a bottom-up strategy to construct a flexible and simple three-dimensional (3D) plasmonic cluster SERS platform integrated with a deep learning algorithm. With these advantages of the 3D plasmonic cluster, we demonstrate that the 3D plasmonic cluster (3D-PC) exhibits a significantly enhanced Raman intensity through detection limit down to 10-6 M (femtomole-(10-17 mol)) for p-nitrophenol (PNP) molecules. Afterward, the plasma of cancer subjects and healthy subjects was used to fabricate the bioink to build 3D-PC structures. The collected SERS successfully classified into two clusters of cancer subjects and healthy subjects with high accuracy of up to 93%. These results highlight the potential of the 3D plasmonic cluster SERS platform for early breast cancer detection and open promising avenues for future research in this field.

Patch-Based Convolutional Encoder: A Deep Learning Algorithm for Spectral Classification Balancing the Local and Global Information

Molecular vibrational spectroscopies, including infrared absorption and Raman scattering, provide molecular fingerprint information and are powerful tools for qualitative and quantitative analysis. They benefit from the recent development of deep-learning-based algorithms to improve the spectral, spatial, and temporal resolutions. Although a variety of deep-learning-based algorithms, including those to simultaneously extract the global and local spectral features, have been developed for spectral classification, the classification accuracy is still far from satisfactory when the difference becomes very subtle. Here, we developed a lightweight algorithm named patch-based convolutional encoder (PACE), which effectively improved the accuracy of spectral classification by extracting spectral features while balancing local and global information. The local information was captured well by segmenting the spectrum into patches with an appropriate patch size. The global information was extracted by constructing the correlation between different patches with depthwise separable convolutions. In the five open-source spectral data sets, PACE achieved a state-of-the-art performance. The more difficult the classification, the better the performance of PACE, compared with that of residual neural network (ResNet), vision transformer (ViT), and other commonly used deep learning algorithms. PACE helped improve the accuracy to 92.1% in Raman identification of pathogen-derived extracellular vesicles at different physiological states, which is much better than those of ResNet (85.1%) and ViT (86.0%). In general, the precise recognition and extraction of subtle differences offered by PACE are expected to facilitate vibrational spectroscopy to be a powerful tool toward revealing the relevant chemical reaction mechanisms in surface science or realizing the early diagnosis in life science.

3D plasmonic hexaplex paper sensor for label-free human saliva sensing and machine learning-assisted early-stage lung cancer screening

A label-free detection method for noninvasive biofluids enables rapid on-site disease screening and early-stage cancer diagnosis by analyzing metabolic alterations. Herein, we develop three-dimensional plasmonic hexaplex nanostructures coated on a paper substrate (3D-PHP). This flexible and highly absorptive 3D-PHP sensor is integrated with commercial saliva collection tube to create an efficient on-site sensing platform for lung cancer screening via surface-enhanced Raman scattering (SERS) measurement of human saliva. The multispike hexaplex-shaped gold nanostructure enhances contact with saliva viscosity, enabling effective sampling and SERS enhancement. Through testing patient salivary samples, the 3D-PHP sensor demonstrates successful lung cancer detection and diagnosis. A logistic regression-based machine learning model successfully classifies benign and malignant patients, exhibiting high clinical sensitivity and specificity. Additionally, important Raman peak positions related to different lung cancer stages are investigated, suggesting insights for early-stage cancer diagnosis. Integrating 3D-PHP senor with the conventional saliva collection tube platform is expected to offer promising practicality for rapid on-site disease screening and diagnosis, and significant advancements in cancer detection and patient care.

Extracellular Vesicles as Liquid Biopsy Biomarkers across the Cancer Journey: From Early Detection to Recurrence

BACKGROUND

Cancer is a dynamic process and thus requires highly informative and reliable biomarkers to help guide patient care. Liquid-based biopsies have emerged as a clinical tool for tracking cancer dynamics. Extracellular vesicles (EVs), lipid bilayer delimited particles secreted by cells, are a new class of liquid-based biomarkers. EVs are rich in selectively sorted biomolecule cargos, which provide a spatiotemporal fingerprint of the cell of origin, including cancer cells.

CONTENT

This review summarizes the performance characteristics of EV-based biomarkers at different stages of cancer progression, from early malignancy to recurrence, while emphasizing their potential as diagnostic, prognostic, and screening biomarkers. We discuss the characteristics of effective biomarkers, consider challenges associated with the EV biomarker field, and report guidelines based on the biomarker discovery pipeline.

SUMMARY

Basic science and clinical trial studies have shown the potential of EVs as precision-based biomarkers for tracking cancer status, with promising applications for diagnosing disease, predicting response to therapy, and tracking disease burden. The multi-analyte cargos of EVs enhance the performance characteristics of biomarkers. Recent technological advances in ultrasensitive detection of EVs have shown promise with high specificity and sensitivity to differentiate early-cancer cases vs healthy individuals, potentially outperforming current gold-standard imaging-based cancer diagnosis. Ultimately, clinical translation will be dictated by how these new EV biomarker-based platforms perform in larger sample cohorts. Applying ultrasensitive, scalable, and reproducible EV detection platforms with better design considerations based upon the biomarker discovery pipeline should guide the field towards clinically useful liquid biopsy biomarkers.

Artificial Intelligence for Surface-Enhanced Raman Spectroscopy

Surface-enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever-sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high-level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above-mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.

Methylene Blue-Stained Single-Stranded DNA Aptamers as a Highly Efficient Electronic Switch for Quasi-Reagentless Exosomes Detection: An Old Dog with New Tricks

Improving the convenience, sensitivity, and cost-effectiveness of electrochemical biosensors is crucial for advancing their clinical diagnostic applications. Herein, we presented an elegant approach to construct electrochemical aptasensors for tumor-derived exosome detection by harnessing the alterable interaction between methylene blue (MB) and DNA aptamer. In detail, the anti-EpCAM aptamer, named SYL3C, was found to exhibit a strong affinity toward MB due to the specific interaction between MB and unbound guanine bases. Thereby, SYL3C could be stained with MB to arouse a strong electrochemical signal on a gold electrode (AuE). Upon binding to EpCAM-positive exosomes, SYL3C underwent a conformational transformation. The resulting conformation, or exosomes-SYL3C complex, not only reduced the accumulation of MB on SYL3C by obstructing the accessibility of guanines to MB but also impeded the transfer of electrons from the bound MB to AuE, leading to a notable decrease in the electrochemical signal. Using MB-stained SYL3C as an electronic switch, an electrochemical aptasensor was readily established for the detection of EpCAM-positive exosome detection. Without the need for signal amplification strategies, expensive auxiliary reagents, and complex operation, this unique signal transduction mechanism alone could endow the aptasensor with ultrahigh sensitivity. A limit of detection (LOD) of 234 particles mL-1 was achieved, surpassing the performance of most reported methods. As a proof of concept, the aptasensor was applied to analyze clinical serum samples and effectively distinguish non-small-cell lung cancer (NSCLC) patients from healthy individuals. As EpCAM exhibits broad expression in exosomes derived from different tumor sources, the developed aptasensor holds promise for diagnosing other tumor types.

Label-free approaches for extracellular vesicle detection

Extracellular vesicles (EVs) represent pivotal mediators in cell-to-cell communication. They are lipid-membranous carriers of several biomolecules, which can be produced by almost all cells. In the current Era of precision medicine, EVs gained growing attention thanks to their potential in both biomarker discovery and nanotherapeutics applications. However, current technical limitations in isolating and/or detecting EVs restrain their standard use in clinics. This review explores all the state-of-the-art analytical technologies which are currently overcoming these issues. On one end, several innovative optical-, electrical-, and spectroscopy-based detection methods represent advantageous label-free methodologies for faster EV detection. On the other end, microfluidics-based lab-on-a-chip tools support EV purification from low-concentrated samples. Altogether, these technologies will strengthen the routine application of EVs in clinics.

Physiological oxygen measurements in vitro-Schrödinger's cat in 3D cell biology

After the development of 3D cell culture methods in the middle of the last century and the plethora of data generated with this culture configuration up to date, it could be shown that a three-dimensional arrangement of cells in most of the cases leads to a more physiological behavior of the generated tissue. However, a major determinant for an organotypic function, namely, the dissolved oxygen concentration in the used in vitro-system, has been neglected in most of the studies. This is due to the fact that the oxygen measurement in the beginning was simply not feasible and, if so, disturbed the measurement and/or the in vitro-system itself. This is especially true for the meanwhile more widespread use of 3D culture systems. Therefore, the tissues analyzed by these techniques can be considered as the Schrödinger's cat in 3D cell biology. In this perspective paper we will outline how the measurement and, moreover, the regulation of the dissolved oxygen concentration in vitro-3D culture systems could be established at all and how it may be possible to determine the oxygen concentration in organoid cultures and the respiratory capacity via mito stress tests, especially in spheroids in the size range of a few hundred micrometers, under physiological culture conditions, without disturbances or stress induction in the system and in a high-throughput fashion. By this, such systems will help to more efficiently translate tissue engineering approaches into new in vitro-platforms for fundamental and applied research as well as preclinical safety testing and clinical applications.

Engineered Exosomes as Theranostic Platforms for Cancer Treatment

Tremendous progress in nanotechnology and nanomedicine has made a significant positive effect on cancer treatment by integrating multicomponents into a single multifunctional nanosized delivery system for combinatorial therapies. Although numerous nanocarriers developed so far have achieved excellent therapeutic performance in mouse models via elegant integration of chemotherapy, photothermal therapy, photodynamic therapy, sonodynamic therapy, and immunotherapy, their synthetic origin may still cause systemic toxicity, immunogenicity, and preferential detection or elimination by the immune system. Exosomes, endogenous nanosized particles secreted by multiple biological cells, could be absorbed by recipient cells to facilitate intercellular communication and content delivery. Therefore, exosomes have emerged as novel cargo delivery tools and attracted considerable attention for cancer diagnosis and treatment due to their innate stability, biological compatibility, and biomembrane penetration capacity. Exosome-related properties and functions have been well-documented; however, there are few reviews, to our knowledge, with a focus on the combination of exosomes and nanotechnology for the development of exosome-based theranostic platforms. To make a timely review on this hot subject of research, we summarize the basic information, isolation and functionalization methodologies, diagnostic and therapeutic potential of exosomes in various cancers with an emphasis on the description of exosome-related nanomedicine for cancer theranostics. The existing appealing challenges and outlook in exosome clinical translation are finally introduced. Advanced biotechnology and nanotechnology will definitely not only promote the integration of intrinsic advantages of natural nanosized exosomes with traditional synthetic nanomaterials for modulated precise cancer treatment but also contribute to the clinical translations of exosome-based nanomedicine as theranostic nanoplatforms.

Direct isolation of small extracellular vesicles from human blood using viscoelastic microfluidics

Small extracellular vesicles (sEVs; <200 nm) that contain lipids, nucleic acids, and proteins are considered promising biomarkers for a wide variety of diseases. Conventional methods for sEV isolation from blood are incompatible with routine clinical workflows, significantly hampering the utilization of blood-derived sEVs in clinical settings. Here, we present a simple, viscoelastic-based microfluidic platform for label-free isolation of sEVs from human blood. The separation performance of the device is assessed by isolating fluorescent sEVs from whole blood, demonstrating purities and recovery rates of over 97 and 87%, respectively. Significantly, our viscoelastic-based microfluidic method also provides for a remarkable increase in sEV yield compared to gold-standard ultracentrifugation, with proteomic profiles of blood-derived sEVs purified by both methods showing similar protein compositions. To demonstrate the clinical utility of the approach, we isolate sEVs from blood samples of 20 patients with cancer and 20 healthy donors, demonstrating that elevated sEV concentrations can be observed in blood derived from patients with cancer.

Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives

Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.

Early-stage oral cancer diagnosis by artificial intelligence-based SERS using Ag NWs@ZIF core-shell nanochains

Surface-enhanced Raman spectroscopy (SERS) has great potential in the early diagnosis of diseases by detecting the changes of volatile biomarkers in exhaled breath, because of its high sensitivity, rich chemical molecular fingerprint information, and immunity to humidity. Here, an accurate diagnosis of oral cancer (OC) is demonstrated using artificial intelligence (AI)-based SERS of exhaled breath in plasmonic-metal organic framework (MOF) nanoparticles. These plasmonic-MOF nanoparticles were prepared using a zeolitic imidazolate framework coated on Ag nanowires (Ag NWs@ZIF), which offers Raman enhancement from the plasmonic nanowires and gas enrichment from the ZIF shells. Then, the core-shell nanochains of Ag NWs@ZIF prepared with 0.5 mL Ag NWs were selected to capture gaseous methanethiol, which is a tumor biomarker, from the exhalation of OC patients. The substrate was used to collect a total of 400 SERS spectra of exhaled breath of simulated healthy people and simulated OC patients. The artificial neural network (ANN) model in the AI algorithm was trained with these SERS spectra and could classify them with an accuracy of 99%. Notably, the model predicted OC with an area under the curve (AUC) of 0.996 for the simulated OC breath samples. This work suggests the great potential of the combination of breath analysis and AI as a method for the early-stage diagnosis of oral cancer.

Nanoplasmonic biosensors for precision medicine

Nanoplasmonic biosensors have a huge boost for precision medicine, which allows doctors to better understand diseases at the molecular level and to improve the earlier diagnosis and develop treatment programs. Unlike traditional biosensors, nanoplasmonic biosensors meet the global health industry's need for low-cost, rapid and portable aspects, while offering multiplexing, high sensitivity and real-time detection. In this review, we describe the common detection schemes used based on localized plasmon resonance (LSPR) and highlight three sensing classes based on LSPR. Then, we present the recent applications of nanoplasmonic in other sensing methods such as isothermal amplification, CRISPR/Cas systems, lab on a chip and enzyme-linked immunosorbent assay. The advantages of nanoplasmonic-based integrated sensing for multiple methods are discussed. Finally, we review the current applications of nanoplasmonic biosensors in precision medicine, such as DNA mutation, vaccine evaluation and drug delivery. The obstacles faced by nanoplasmonic biosensors and the current countermeasures are discussed.