Cancer Stem Cell Derived Extracellular Vesicles with Self-Functionalized 3D Nanosensor for Real-Time Cancer Diagnosis: Eliminating the Roadblocks in Liquid Biopsy

Advances of machine learning-assisted small extracellular vesicles detection strategy

Detection of extracellular vesicles (EVs), particularly small EVs (sEVs), is of great significance in exploring their physiological characteristics and clinical applications. The heterogeneity of sEVs plays a crucial role in distinguishing different types of cells and diseases. Machine learning, with its exceptional data processing capabilities, offers a solution to overcome the limitations of conventional detection methods for accurately classifying sEV subtypes and sources. Principal component analysis, linear discriminant analysis, partial least squares discriminant analysis, XGBoost, support vector machine, k-nearest neighbor, and deep learning, along with some combined methods such as principal component-linear discriminant analysis, have been successfully applied in the detection and identification of sEVs. This review focuses on machine learning-assisted detection strategies for cell identification and disease prediction via sEVs, and summarizes the integration of these strategies with surface-enhanced Raman scattering, electrochemistry, inductively coupled plasma mass spectrometry and fluorescence. The performance of different machine learning-based detection strategies is compared, and the advantages and limitations of various machine learning models are also evaluated. Finally, we discuss the merits and limitations of the current approaches and briefly outline the perspective of potential research directions in the field of sEV analysis based on machine learning.

Label-Free Multiplex Profiling of Exosomal Proteins with a Deep Learning-Driven 3D Surround-Enhancing SERS Platform for Early Cancer Diagnosis

Identification of protein profiling on plasma exosomes by SERS can be a promising strategy for early cancer diagnosis. However, it is still challenging to detect multiple exosomal proteins simultaneously by SERS since the Raman signals of exosomes detected by conventional colloidal nanocrystals or two-dimensional SERS substrates are incomplete and complex. Herein, we develop a novel three-dimensional (3D) surround-enhancing SERS platform, named 3D se-SERS, for the multiplex detection of exosomal proteins. In this 3D se-SERS, proteins and exosomes are covered with "hotspots" generated by the gold nanoparticles, which surround the analytes densely and three-dimensionally, providing sensitive and comprehensive SERS signals. Combining this 3D se-SERS with a deep learning model, we successfully quantitatively profiled seven proteins including CD63, CD81, CD9, CD151, CD171, TSPAN8, and PD-L1 on the surface of plasma exosomes from patients, which can predict the occurrence and advancement of lung cancer. This 3D se-SERS integrating deep learning technique benefits from high sensitivity and significant multiplexing ability for comprehensive analysis of proteins and exosomes, demonstrating the potential of deep learning-driven 3D se-SERS technology for plasma exosome-based early cancer diagnosis.

Surface-enhanced Raman spectroscopy of the filtrate portions of the blood serum samples of breast cancer patients obtained by using 30 kDa filtration device

Raman spectroscopy is reliable tool for analyzing and exploring early disease diagnosis related to body fluids, such as blood serum, which contain low molecular weight fraction (LMWF) and high molecular weight fraction (HMWF) proteins. The disease biomarkers consist of LMWF which are dominated by HMWF hence their analysis is difficult. In this study, in order to overcome this issue, centrifugal filter devices of 30 kDa were used to obtain filtrate and residue portions obtained from whole blood serum samples of control and breast cancer diagnosed patients. The filtrate portions obtained in this way are expected to contain the marker proteins of breast cancer of the size below this filter size. These may include prolactin, Microphage migration inhabitation factor (MIF), γ-Synuclein, BCSG1, Leptin, MUC1, RS/DJ-1 present in the centrifuged blood serum (filtrate portions) which are then analyzed by the SERS technique to recognize the SERS spectral characteristics associated with the progression of breast cancer in the samples of different stages as compared to the healthy ones. The key intention of this study is to achieve early-stage breast cancer diagnosis through the utilization of Surface Enhanced Raman Spectroscopy (SERS) after the centrifugation of healthy and breast cancer serum samples with Amicon ultra-filter devices of 30 kDa. The silver nanoparticles with high plasmon resonance are used as a substrate for SERS analysis. Principal Component Analysis (PCA) and Partial Least Discriminant Analysis (PLS-DA) models are utilized as spectral classification tools to assess and predict rapid, reliable, and non-destructive SERS-based analysis. Notably, they were particularly effective in distinguishing between different SERS spectral groups of the cancerous and non-cancerous samples. By comparing all these spectral data sets to each other PLSDA shows the 79 % accuracy, 76 % specificity, and 81 % sensitivity in samples with AUC value of AUC = 0.774 SERS has proven to be a valuable technique for the rapid identification of the SERS spectral features of blood serum and its filtrate fractions from both healthy individuals and those with breast cancer, aiding in disease diagnosis.

Nanobiotechnology augmented cancer stem cell guided management of cancer: liquid-biopsy, imaging, and treatment

Cancer stem cells (CSCs) represent both a key driving force and therapeutic target of tumoral carcinogenesis, tumor evolution, progression, and recurrence. CSC-guided tumor diagnosis, treatment, and surveillance are strategically significant in improving cancer patients' overall survival. Due to the heterogeneity and plasticity of CSCs, high sensitivity, specificity, and outstanding targeting are demanded for CSC detection and targeting. Nanobiotechnologies, including biosensors, nano-probes, contrast enhancers, and drug delivery systems, share identical features required. Implementing these techniques may facilitate the overall performance of CSC detection and targeting. In this review, we focus on some of the most recent advances in how nanobiotechnologies leverage the characteristics of CSC to optimize cancer diagnosis and treatment in liquid biopsy, clinical imaging, and CSC-guided nano-treatment. Specifically, how nanobiotechnologies leverage the attributes of CSC to maximize the detection of circulating tumor DNA, circulating tumor cells, and exosomes, to improve positron emission computed tomography and magnetic resonance imaging, and to enhance the therapeutic effects of cytotoxic therapy, photodynamic therapy, immunotherapy therapy, and radioimmunotherapy are reviewed.

A Panorama of Extracellular Vesicle Applications: From Biomarker Detection to Therapeutics

Extracellular vesicles (EVs) secreted by all cell types are involved in the cell-to-cell transfer of regulatory factors that influence cell and tissue phenotypes in normal and diseased tissues. EVs are thus a rich source of biomarker targets for assays that analyze blood and urinary EVs for disease diagnosis. Sensitive biomarker detection in EVs derived from specific cell populations is a key major hurdle when analyzing complex biological samples, but innovative approaches surveyed in this Perspective can streamline EV isolation and enhance the sensitivity of EV detection procedures required for clinical application of EV-based diagnostics and therapeutics, including nanotechnology and microfluidics, to achieve EV characterizations. Finally, this Perspective also outlines opportunities and challenges remaining for clinical translation of EV-based assays.

Identifying tumor cell-released extracellular vesicles as biomarkers for breast cancer diagnosis by a three-dimensional hydrogel-based electrochemical immunosensor

Tumor cell-released LC3+ extracellular vesicles (LC3+ EVs) participate in immunosuppression during autophagy and contribute to the occurrence and development of breast cancer. In view of the strong association between the LC3+ EVs and breast cancer, developing an effective strategy for the quantitative detection of LC3+ EVs levels with high sensitivity to identify LC3+ EVs as new biomarkers for accurate diagnosis of breast cancer is crucial, but yet not been reported. Herein, an ultrasensitive electrochemical immunosensor is presented for the quantitative determination of LC3+ EVs using a three-dimensional graphene oxide hydrogel-methylene blue composite as a redox probe, showing a low detection limit and a wide linear range. With this immunosensor, the expression levels of LC3+ EVs in various practical sample groups including different cancer cell lines, the peripheral blood of tumor-bearing mice before and after immunotherapy, and the peripheral blood from breast cancer patients with different subtypes and stages were clearly distinguished. This study demonstrated that LC3+ EVs were superior as biomarkers for the accurate diagnosis of breast cancer compared to traditional biomarkers, particularly for cancer subtype discrimination. This work would provide a new noninvasive detection tool for the early diagnosis and prognosis assessment of breast cancer in clinics.

Application of microfluidic technology based on surface-enhanced Raman scattering in cancer biomarker detection: A review

With the continuous discovery and research of predictive cancer-related biomarkers, liquid biopsy shows great potential in cancer diagnosis. Surface-enhanced Raman scattering (SERS) and microfluidic technology have received much attention among the various cancer biomarker detection methods. The former has ultrahigh detection sensitivity and can provide a unique fingerprint. In contrast, the latter has the characteristics of miniaturization and integration, which can realize accurate control of the detection samples and high-throughput detection through design. Both have the potential for point-of-care testing (POCT), and their combination (lab-on-a-chip SERS (LoC-SERS)) shows good compatibility. In this paper, the basic situation of circulating proteins, circulating tumor cells, exosomes, circulating tumor DNA (ctDNA), and microRNA (miRNA) in the diagnosis of various cancers is reviewed, and the detection research of these biomarkers by the LoC-SERS platform in recent years is described in detail. At the same time, the challenges and future development of the platform are discussed at the end of the review. Summarizing the current technology is expected to provide a reference for scholars engaged in related work and interested in this field.

Self-Functionalized Superlattice Nanosensor Enables Glioblastoma Diagnosis Using Liquid Biopsy

Glioblastoma (GBM), the most aggressive and lethal brain cancer, is detected only in the advanced stage, resulting in a median survival rate of 15 months. Therefore, there is an urgent need to establish GBM diagnosis tools to identify the tumor accurately. The clinical relevance of the current liquid biopsy techniques for GBM diagnosis remains mostly undetermined, owing to the challenges posed by the blood-brain barrier (BBB) that restricts biomarkers entering the circulation, resulting in the unavailability of clinically validated circulating GBM markers. GBM-specific liquid biopsy for diagnosis and prognosis of GBM has not yet been developed. Here, we introduce extracellular vesicles of GBM cancer stem cells (GBM CSC-EVs) as a previously unattempted, stand-alone GBM diagnosis modality. As GBM CSCs are fundamental building blocks of tumor initiation and recurrence, it is desirable to investigate these reliable signals of malignancy in circulation for accurate GBM diagnosis. So far, there are no clinically validated circulating biomarkers available for GBM. Therefore, a marker-free approach was essential since conventional liquid biopsy relying on isolation methodology was not viable. Additionally, a mechanism capable of trace-level detection was crucial to detecting the rare GBM CSC-EVs from the complex environment in circulation. To break these barriers, we applied an ultrasensitive superlattice sensor, self-functionalized for surface-enhanced Raman scattering (SERS), to obtain holistic molecular profiling of GBM CSC-EVs with a marker-free approach. The superlattice sensor exhibited substantial SERS enhancement and ultralow limit of detection (LOD of attomolar 10-18 M concentration) essential for trace-level detection of invisible GBM CSC-EVs directly from patient serum (without isolation). We detected as low as 5 EVs in 5 μL of solution, achieving the lowest LOD compared to existing SERS-based studies. We have experimentally demonstrated the crucial role of the signals of GBM CSC-EVs in the precise detection of glioblastoma. This was evident from the unique molecular profiles of GBM CSC-EVs demonstrating significant variation compared to noncancer EVs and EVs of GBM cancer cells, thus adding more clarity to the current understanding of GBM CSC-EVs. Preliminary validation of our approach was undertaken with a small amount of peripheral blood (5 μL) derived from GBM patients with 100% sensitivity and 97% specificity. Identification of the signals of GBM CSC-EV in clinical sera specimens demonstrated that our technology could be used for accurate GBM detection. Our technology has the potential to improve GBM liquid biopsy, including real-time surveillance of GBM evolution in patients upon clinical validation. This demonstration of liquid biopsy with GBM CSC-EV provides an opportunity to introduce a paradigm potentially impacting the current landscape of GBM diagnosis.

Emerging role of exosome-shuttled noncoding RNAs in gastrointestinal cancers: From intercellular crosstalk to clinical utility

Gastrointestinal cancer remains a significant global health burden. The pursuit of advancing the comprehension of tumorigenesis, along with the identification of reliable biomarkers and the development of precise therapeutic strategies, represents imperative objectives in this field. Exosomes, small membranous vesicles released by most cells, commonly carry functional biomolecules, including noncoding RNAs (ncRNAs), which are specifically sorted and encapsulated by exosomes. Exosome-mediated communication involves the release of exosomes from tumor or stromal cells and the uptake by nearby or remote recipient cells. The bioactive cargoes contained within these exosomes exert profound effects on the recipient cells, resulting in significant modifications in the tumor microenvironment (TME) and distinct alterations in gastrointestinal tumor behaviors. Due to the feasibility of isolating exosomes from various bodily fluids, exosomal ncRNAs have shown great potential as liquid biopsy-based indicators for different gastrointestinal cancers, using blood, ascites, saliva, or bile samples. Moreover, exosomes are increasingly recognized as natural delivery vehicles for ncRNA-based therapeutic interventions. In this review, we elucidate the processes of ncRNA-enriched exosome biogenesis and uptake, examine the regulatory and functional roles of exosomal ncRNA-mediated intercellular crosstalk in gastrointestinal TME and tumor behaviors, and explore their potential clinical utility in diagnostics, prognostics, and therapeutics.

Recent Advances in Nanomaterials-Based Targeted Drug Delivery for Preclinical Cancer Diagnosis and Therapeutics

Nano-oncology is a branch of biomedical research and engineering that focuses on using nanotechnology in cancer diagnosis and treatment. Nanomaterials are extensively employed in the field of oncology because of their minute size and ultra-specificity. A wide range of nanocarriers, such as dendrimers, micelles, PEGylated liposomes, and polymeric nanoparticles are used to facilitate the efficient transport of anti-cancer drugs at the target tumor site. Real-time labeling and monitoring of cancer cells using quantum dots is essential for determining the level of therapy needed for treatment. The drug is targeted to the tumor site either by passive or active means. Passive targeting makes use of the tumor microenvironment and enhanced permeability and retention effect, while active targeting involves the use of ligand-coated nanoparticles. Nanotechnology is being used to diagnose the early stage of cancer by detecting cancer-specific biomarkers using tumor imaging. The implication of nanotechnology in cancer therapy employs photoinduced nanosensitizers, reverse multidrug resistance, and enabling efficient delivery of CRISPR/Cas9 and RNA molecules for therapeutic applications. However, despite recent advancements in nano-oncology, there is a need to delve deeper into the domain of designing and applying nanoparticles for improved cancer diagnostics.

Machine Learning-Based Label-Free SERS Profiling of Exosomes for Accurate Fuzzy Diagnosis of Cancer and Dynamic Monitoring of Drug Therapeutic Processes

Exosomes are a class of extracellular vesicles secreted by cells, which can be used as promising noninvasive biomarkers for the early diagnosis and treatment of diseases, especially cancer. However, due to the heterogeneity of exosomes, it remains a grand challenge to distinguish accurately and reliably exosomes from clinical samples. Herein, we achieve accurate fuzzy discrimination of exosomes from human serum samples for accurate diagnosis of breast cancer and cervical cancer through machine learning-based label-free surface-enhanced Raman spectroscopy (SERS), by using "hot spot" rich 3D plasmonic AuNPs nanomembranes as substrates. Due to the existence of some weak distinguishable SERS fingerprint signals and the high sensitivity of the method, the machine learning-based SERS analysis can precisely identify three (normal and cancerous) cell lines, two of which are different types of cancer cells, without specific labeling of biomarkers. The prediction accuracy based on the machine learning algorithm was up to 91.1% for the discrimination of different cell lines (H8, HeLa, and MCF-7 cell)-derived exosomes. Our model trained with SERS spectra of cell-derived exosomes could reach 93.3% prediction accuracy for clinical samples. Furthermore, the action mechanism of the chemotherapeutic process of MCF-7 cells can be revealed by dynamic monitoring of SERS profiling of the exosomes secreted. The method would be useful for noninvasive and accurate diagnosis and postoperative assessment of cancer or other diseases in the future.

Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends

Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and microbiology. Although SERS is often limited by the lack of selectivity in the analysis of samples with complex matrices, the use of multivariate statistics and mathematical tools has been demonstrated to be an efficient strategy to circumvent this issue. Importantly, since the rapid development of artificial intelligence has been promoting the implementation of a wide variety of advanced multivariate methods in SERS, a discussion about the extent of their synergy and possible standardization becomes necessary. This critical review comprises the principles, advantages, and limitations of coupling SERS with chemometrics and machine learning for both qualitative and quantitative analytical applications. Recent advances and trends in combining SERS with uncommonly used but powerful data analysis tools are also discussed. Finally, a section on benchmarking and tips for selecting the suitable chemometric/machine learning method is included. We believe this will help to move SERS from an alternative detection strategy to a general analytical technique for real-life applications.

Dual-Aptamer-Assisted Ratiometric SERS Biosensor for Ultrasensitive and Precise Identification of Breast Cancer Exosomes

Due to the heterogeneity of breast cancer, its early accurate diagnosis remains a challenge. Exosomes carry abundant genetic materials and proteins and are ideal biomarkers for early cancer detection. Herein, a ratiometric surface-enhanced Raman scattering (SERS) biosensor for exosome detection was constructed using a regularly arranged Au@Ag nanoparticles/graphene oxide (Au@Ag NPs/GO) substrate with 4-nitrothiophenol (4-NTP) molecules as an internal standard. Aptamers of two overexpressed proteins (epithelial cell adhesion molecule and human epidermal growth factor receptor 2) were linked by a short complementary DNA with rhodamine X modified at the 3'-terminal to form V-shaped double-stranded DNA, which attached to the surface of Au@Ag NPs/GO substrate for the selective recognition of breast cancer cell-derived exosomes. In the presence of exosomes, a competitive reaction occurred, resulting in the formation of the V-shaped double-stranded DNA/exosomes complex, and the V-shaped double-stranded DNA separated from the SERS substrate. The SERS signal of rhodamine X on the V-shaped double-stranded DNA decreased with the concentration of exosomes increasing, whereas the SERS signal of 4-NTP on the substrate remained stable. The ratiometric SERS strategy provides huge electromagnetic enhancement and abundant DNA adsorbing sites on the GO layer, achieving a wide detection range of 2.7 × 10² to 2.7 × 10⁸ particles/mL and an ultralow limit of detection down to 1.5 × 10² particles/mL, without the requirement of any nucleic acid amplification. Particularly, the proposed method has significant applications in early cancer diagnosis as it can accurately identify breast cancer cell-derived exosomes in clinical serum samples and can differentiate pancreatic cancer patients and healthy individuals.

Extracellular Vesicles in Colorectal Cancer: From Tumor Growth and Metastasis to Biomarkers and Nanomedications

Colorectal cancer (CRC) is a leading public health concern due to its incidence and high mortality rates, highlighting the requirement of an early diagnosis. Evaluation of circulating extracellular vesicles (EVs) might constitute a noninvasive and reliable approach for CRC detection and for patient follow-up because EVs display the molecular features of the cells they originate. EVs are released by almost all cell types and are mainly categorized as exosomes originating from exocytosis of intraluminal vesicles from multivesicular bodies, ectosomes resulting from outward budding of the plasma membrane and apoptotic bodies' ensuing cell shrinkage. These vesicles play a critical role in intercellular communications during physiological and pathological processes. They facilitate CRC progression and premetastatic niche formation, and they enable transfer of chemotherapy resistance to sensitive cells through the local or remote delivery of their lipid, nucleic acid and protein content. On another note, their stability in the bloodstream, their permeation in tissues and their sheltering of packaged material make engineered EVs suitable vectors for efficient delivery of tracers and therapeutic agents for tumor imaging or treatment. Here, we focus on the physiopathological role of EVs in CRCs, their value in the diagnosis and prognosis and ongoing investigations into therapeutic approaches.