Non-invasive Cancer Diagnosis Research Articles

Comprehensive Urinary Proteome Profiling Analysis Identifies Diagnosis and Relapse Surveillance Biomarkers for Bladder Cancer.

Bladder cancer (BCa) is the predominant malignancy of the urinary system. Herein, a comprehensive urine proteomic feature was initially established for the noninvasive diagnosis and recurrence monitoring of bladder cancer. 279 cases (63 primary BCa, 87 nontumor controls (NT), 73 relapsed BCa (BCR), and 56 nonrelapsed BCa (BCNR)) were collected to screen urinary protein biomarkers. 4761 and 3668 proteins were qualified and quantified by DDA and sequential window acquisition of all theoretical mass spectra (SWATH-MS) analysis in two discovery sets, respectively. Upregulated proteins were validated by multiple reaction monitoring (MRM) in two independent combined sets. Using the multi-support vector machine-recursive feature elimination (mSVM-RFE) algorithm, a model comprising 13 proteins exhibited good performance between BCa and NT with an AUC of 0.821 (95% CI: 0.675-0.967), 90.9% sensitivity (95% CI: 72.7-100%), and 73.3% specificity (95% CI: 53.3-93.3%) in the diagnosis test set. Meanwhile, an 11-marker classifier significantly distinguished BCR from BCNR with 75.0% sensitivity (95% CI: 50.0-100%), 81.8% specificity (95% CI: 54.5-100%), and an AUC of 0.784 (95% CI: 0.609-0.959) in the test cohort for relapse surveillance. Notably, six proteins (SPR, AK1, CD2AP, ADGRF1, GMPS, and C8A) of 24 markers were newly reported. This paper reveals novel urinary protein biomarkers for BCa and offers new theoretical insights into the pathogenesis of bladder cancer (data identifier PXD044896).

Buoyant Metal-Organic Framework Corona-Driven Fast Isolation and Ultrasensitive Profiling of Circulating Extracellular Vesicles.

Accurately assaying tumor-derived circulating extracellular vesicles (EVs) is fundamental in noninvasive cancer diagnosis and therapeutic monitoring but limited by challenges in efficient EV isolation and profiling. Here, we report a bioinspired buoyancy-driven metal-organic framework (MOF) corona that leverages on-bubble coordination and dual-encoded surface-enhanced Raman scattering (SERS) nanotags to streamline rapid isolation and ultrasensitive profiling of plasma EVs in a single assay for cancer diagnostics. This integrated bubble-MOF-SERS EV assay (IBMsv) allows barnacle-like high-density adhesion of MOFs on a self-floating bubble surface to enable fast isolation (2 min, near 90% capture efficiency) of tumor EVs via enhanced EV-MOF binding. Also, IBMsv harnesses four-plexed SERS nanotags to profile the captured EV surface protein markers at a single-particle level. Such a sensitive assay allows multiplexed profiling of EVs across five cancer types, revealing heterogeneous EV surface expression patterns. Furthermore, the IBMsv assay enables cancer diagnosis in a pilot clinical cohort (n = 55) with accuracies >95%, improves discrimination between cancer and noncancer patients via an algorithm, and monitors the surgical treatment response from hepatocellular carcinoma patients. This assay provides a fast, sensitive, streamlined, multiplexed, and portable blood test tool to enable cancer diagnosis and response monitoring in clinical settings.

Fibronectin-Targeting Dual-Modal MR/NIRF Imaging Contrast Agents for Diagnosis of Gastric Cancer and Peritoneal Metastasis.

The prevalence and fatality rates of gastric cancer (GC) remain elevated, with advanced stages presenting a grim prognosis. Noninvasive diagnosis of GC cancer often proves challenging until the disease has progressed to an advanced stage or metastasized. Initially, the level of fibronectin (FN) in cancer-associated fibroblasts (CAFs) of GC was at least 3.7 times higher than that in normal fibroblasts. Herein, two FN-targeting magnetic resonance/near-infrared fluorescence (MR/NIRF) imaging contrast agents were developed to detect GC and peritoneal metastasis noninvasively. The probes CREKA-Cy7-(Gd-DOTA) and CREKA-Cy7-(Gd-DOTA)3 demonstrated significant FN-targeting capability (with dissociation constants of 1.0 and 2.1 mM) and effective MR imaging performance (with proton relaxivity values of 9.66 and 27.44 mM-1 s-1 at 9.4 T, 37 °C). In vivo imaging revealed a high signal-to-noise ratio and successful visualization of GC metastasis using NIRF imaging as well as successful tumor detection in MR imaging. Therefore, this study highlights the potential of FN-targeting probes for GC diagnosis and aids in the advancement of new diagnostic strategies for the clinical detection of GC.

A non-invasive approach to skin cancer diagnosis via graphene electrical tattoos and electrical impedance tomography

Objective. Making up one of the largest shares of diagnosed cancers worldwide, skin cancer is also one of the most treatable. However, this is contingent upon early diagnosis and correct skin cancer-type differentiation. Currently, methods for early detection that are accurate, rapid, and non-invasive are limited. However, literature demonstrating the impedance differences between benign and malignant skin cancers, as well as between different types of skin cancer, show that methods based on impedance differentiation may be promising. Approach. In this work, we propose a novel approach to rapid and non-invasive skin cancer diagnosis that leverages the technologies of difference-based electrical impedance tomography (EIT) and graphene electronic tattoos (GETs). Main results. We demonstrate the feasibility of this first-of-its-kind system using both computational numerical and experimental skin phantom models. We considered variations in skin cancer lesion impedance, size, shape, and position relative to the electrodes and evaluated the impact of using individual and multi-electrode GET (mGET) arrays. The results demonstrate that this approach has the potential to differentiate based on lesion impedance, size, and position, but additional techniques are needed to determine shape. Significance. In this way, the system proposed in this work, which combines both EIT and GET technology, exhibits potential as an entirely non-invasive and rapid approach to skin cancer diagnosis.

Early automated detection system for skin cancer diagnosis using artificial intelligent techniques

Recently, skin cancer is one of the spread and dangerous cancers around the world. Early detection of skin cancer can reduce mortality. Traditional methods for skin cancer detection are painful, time-consuming, expensive, and may cause the disease to spread out. Dermoscopy is used for noninvasive diagnosis of skin cancer. Artificial Intelligence (AI) plays a vital role in diseases’ diagnosis especially in biomedical engineering field. The automated detection systems based on AI reduce the complications in the traditional methods and can improve skin cancer’s diagnosis rate. In this paper, automated early detection system for skin cancer dermoscopic images using artificial intelligent is presented. Adaptive snake (AS) and region growing (RG) algorithms are used for automated segmentation and compared with each other. The results show that AS is accurate and efficient (accuracy = 96%) more than RG algorithm (accuracy = 90%). Artificial Neural networks (ANN) and support vector machine (SVM) algorithms are used for automated classification compared with each other. The proposed system with ANN algorithm shows high accuracy (94%), precision (96%), specificity (95.83%), sensitivity (recall) (92.30%), and F1-score (0.94). The proposed system is easy to use, time consuming, enables patients to make early detection for skin cancer and has high efficiency.

Circulating tumor DNA in liquid biopsy: Current diagnostic limitation.

With the rapid development of science and technology, cell-free DNA (cfDNA) is rapidly becoming an important biomarker for tumor diagnosis, monitoring and prognosis, and this cfDNA-based liquid biopsy technology has great potential to become an important part of precision medicine. cfDNA is the total amount of free DNA in the systemic circulation, including DNA fragments derived from tumor cells and all other somatic cells. Tumor cells release fragments of DNA into the bloodstream, and this source of cfDNA is called circulating tumor DNA (ctDNA). cfDNA detection has become a major focus in the field of tumor research in recent years, which provides a new opportunity for non-invasive diagnosis and prognosis of cancer. In this paper, we discuss the limitations of the study on the origin and dynamics analysis of ctDNA, and how to solve these problems in the future. Although the future faces major challenges, it also contains great potential.

Potential application of body fluids autofluorescence in the non-invasive diagnosis of endometrial cancer.

Endometrial carcinoma (EC) is the most common cancer of the female reproductive tract in developed countries. The prognosis and 5-year survival rates are closely tied to the stage diagnosis. Current routine diagnostic methods of EC are either lacking specificity or are uncomfortable, invasive and painful for the patient. As of now, the gold diagnostic standard is endometrial biopsy. Early and non-invasive diagnosis of EC requires the identification of new biomarkers of disease and a screening test applicable to routine laboratory diagnostics. The application of untargeted metabolomics combined with artificial intelligence and biostatistics tools has the potential to qualitatively and quantitatively represent the metabolome, but its introduction into routine diagnostics is currently unrealistic due to the financial, time and interpretation challenges. Fluorescence spectral analysis of body fluids utilizes autofluorescence of certain metabolites to define the composition of the metabolome under physiological conditions. This review highlights the potential of fluorescence spectroscopy in the early detection of EC. Data obtained by three-dimensional fluorescence spectroscopy define the quantitative and qualitative composition of the complex fluorescent metabolome and are useful for identifying biochemical metabolic changes associated with endometrial carcinogenesis. Autofluorescence of biological fluids has the prospect of providing new molecular markers of EC. By integrating machine learning and artificial intelligence algorithms in the data analysis of the fluorescent metabolome, this technique has great potential to be implemented in routine laboratory diagnostics.

Available medical imaging modalities for melanoma screening

The prevalence of melanoma of the skin has seen a significant rise in recent decades, constituting approximately one-third of all diagnosed cancer cases. Melanoma, the most fatal variant among cutaneous malignancies, exhibits a 4% probability of occurrence over an individual’s lifetime. The increasing incidence and mortality rates of skin cancer impose a substantial burden on healthcare resources and the economy. In recent years, several optical modalities, including dermoscopy, reflectance confocal microscopy (RCM), optical coherence tomography, multiphoton excited fluorescence imaging, and dermatofluorescence, have been extensively studied and utilized to improve the non-invasive diagnosis of skin cancer. This review article provides an analysis of the approach employed in the recently developed optical non-invasive diagnostic technologies. It explores the clinical uses of these techniques, while also examining their respective advantages and disadvantages. Furthermore, the paper explores the possibility for additional advancements in these technologies in the future.

Electrical Properties of Human Lung Nodules In Vitro From 100 Hz to 100 MHz.

The incidence of pulmonary nodules has been increasing over the past 30 years. Different types of nodules are associated with varying degrees of malignancy, and they engender inconsistent treatment approaches. Therefore, correct distinction is essential for the optimal treatment and recovery of the patients. The commonly-used medical imaging methods have limitations in distinguishing lung nodules to date. A new approach to this problem may be provided by electrical properties of lung nodules. Nevertheless, difference identification is the basis of correct distinction. So, this paper aims to investigate the differences in electrical properties between various lung nodules. At variance with existing studies, benign samples were included for analysis. A total of 252 specimens were collected, including 126 normal tissues, 15 benign nodules, 76 adenocarcinomas, and 35 squamous cell carcinomas. The dispersion properties of each tissue were measured over a frequency range of 100 Hz to 100 MHz. And the relaxation mechanism was analyzed by fitting the Cole-Cole plot. The corresponding equivalent circuit was estimated accordingly. Results validated the significant differences between malignant and normal tissue. Significant differences between benign and malignant lesions were observed in conductivity and relative permittivity. Adenocarcinomas and squamous cell carcinomas are significantly different in conductivity, first-order, second-order differences of conductivity, α-band Cole-Cole plot parameters and capacitance of equivalent circuit. The combination of the different features increased the tissue groups' differences measured by Euclidean distance up to 94.7%. In conclusion, the four tissue groups reveal dissimilarity in electrical properties. This characteristic potentially lends itself to future diagnosis of non-invasive lung cancer.

A novel machine learning algorithm selects proteome signature to specifically identify cancer exosomes

Non-invasive early cancer diagnosis remains challenging due to the low sensitivity and specificity of current diagnostic approaches. Exosomes are membrane-bound nanovesicles secreted by all cells that contain DNA, RNA, and proteins that are representative of the parent cells. This property, along with the abundance of exosomes in biological fluids makes them compelling candidates as biomarkers. However, a rapid and flexible exosome-based diagnostic method to distinguish human cancers across cancer types in diverse biological fluids is yet to be defined. Here, we describe a novel machine learning-based computational method to distinguish cancers using a panel of proteins associated with exosomes. Employing datasets of exosome proteins from human cell lines, tissue, plasma, serum, and urine samples from a variety of cancers, we identify Clathrin Heavy Chain (CLTC), Ezrin, (EZR), Talin-1 (TLN1), Adenylyl cyclase-associated protein 1 (CAP1), and Moesin (MSN) as highly abundant universal biomarkers for exosomes and define three panels of pan-cancer exosome proteins that distinguish cancer exosomes from other exosomes and aid in classifying cancer subtypes employing random forest models. All the models using proteins from plasma, serum, or urine-derived exosomes yield AUROC scores higher than 0.91 and demonstrate superior performance compared to Support Vector Machine, K Nearest Neighbor Classifier and Gaussian Naive Bayes. This study provides a reliable protein biomarker signature associated with cancer exosomes with scalable machine learning capability for a sensitive and specific non-invasive method of cancer diagnosis.

A novel machine learning algorithm selects proteome signature to specifically identify cancer exosomes.

Non-invasive early cancer diagnosis remains challenging due to the low sensitivity and specificity of current diagnostic approaches. Exosomes are membrane-bound nanovesicles secreted by all cells that contain DNA, RNA, and proteins that are representative of the parent cells. This property, along with the abundance of exosomes in biological fluids makes them compelling candidates as biomarkers. However, a rapid and flexible exosome-based diagnostic method to distinguish human cancers across cancer types in diverse biological fluids is yet to be defined. Here, we describe a novel machine learning-based computational method to distinguish cancers using a panel of proteins associated with exosomes. Employing datasets of exosome proteins from human cell lines, tissue, plasma, serum, and urine samples from a variety of cancers, we identify Clathrin Heavy Chain (CLTC), Ezrin, (EZR), Talin-1 (TLN1), Adenylyl cyclase-associated protein 1 (CAP1), and Moesin (MSN) as highly abundant universal biomarkers for exosomes and define three panels of pan-cancer exosome proteins that distinguish cancer exosomes from other exosomes and aid in classifying cancer subtypes employing random forest models. All the models using proteins from plasma, serum, or urine-derived exosomes yield AUROC scores higher than 0.91 and demonstrate superior performance compared to Support Vector Machine, K Nearest Neighbor Classifier and Gaussian Naive Bayes. This study provides a reliable protein biomarker signature associated with cancer exosomes with scalable machine learning capability for a sensitive and specific non-invasive method of cancer diagnosis.

Abstract 4950: Applying fragmentomics profiles of plasma cell-free DNA for breast cancer detection

Abstract The liquid biopsy approach focusing on DNA molecules supports the non-invasive diagnosis of breast cancer. Low-pass whole genome sequencing(lpWGS) for breast cancer detection has been validated for accuracy and cost-effectiveness, especially with copy number variant (CNV) analysis. However, detecting breast cancer with cell-free DNA (cfDNA) CNV was limited by tumor fraction. Utilizing cell-free DNA fragmentation characteristics for cancer detection has shown potential in plasma with high precision and applicability with low tumor fraction. In this study, 217 patients with breast cancer and 10 healthy individuals were enrolled. The cfDNA was extracted and performed using PredicineSCORE, a low-coverage whole genome sequencing assay to identify tumor-specific features. The cfDNA fragmentomics profiles including the coverage at specific regions, length and end-motif of fragment were implemented. We also investigated the copy number variation and the inferred tumor fraction which was adjusted by the Expectation-Maximum (EM) algorithm. Combining, an ensembled machine learning model was constructed for the classification of breast cancer and normal control. We observed a high correlation between tumor fraction and features. The absolute correlation coefficient scores between tumor fraction with the LYL1, MAF, FOXA1, and GRHL2 bonding sites' relative depth were over 0.82, 0.71,0.89,0.87. The insert size ratio of short reads and the CCCA end motif were over 0.77 and 0.72, respectively. We then diluted the tumor samples of tumor fraction at 0.1% to 50% by manually mixing reads from tumor samples into normal samples. By applying the XGBoost algorithm, the performance (F1 score > 0.95) showed a promising ability to distinguish tumors from normal samples. The limit of detection (LOD) was less than 1% which is more sensitive than previously proposed models. Those samples detected mutations with high through panel which can confirm the accuracy of the assay. Our results discovered that profiling the cell-free DNA fragment could facilitate the way of non-invasively detection of breast cancer with high precisions. Citation Format: Yaxin Liu, Shuang Gan, Hang Dong, Haoran Tang, Nan Wang, Xinyu Gui, Anjie Zhu, Ruyan Zhang, Pan Du, Shidong Jia. Applying fragmentomics profiles of plasma cell-free DNA for breast cancer detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4950.

Abstract 2334: Computational methods for optimizing marker selection, clonal lineage reconstruction, and longitudinal tracking of clonal dynamics via circulating tumor DNA (ctDNA)

Abstract Background: Technologies for blood-based profiling of tumor DNA (“liquid biopsy”) have offered great prospects for non-invasive early cancer diagnosis, treatment monitoring, and clinical guidance, but further advances in computational models and data analysis to optimize monitoring protocols and develop liquid biopsy as a robust quantitative assay of tumor clonal evolution. Methods: We propose new computational methods to improve our ability to characterize tumor clonal dynamics from circulating tumor DNA (ctDNA). Our methods address how to apply ctDNA assays most effectively to two distinct questions in profiling tumor clonal dynamics: 1) How to apply longitudinal ctDNA data to refine phylogenetic tree models of clonal evolution, and 2) how to most effectively characterize changes in clonal frequencies that may be indicative of treatment response or tumor progression. We pose these questions computationally in terms of a probabilistic framework for optimally identifying maximum likelihood markers for the preceding tasks and applying measured marker concentrations to their solution. We apply this framework by first evaluating the distribution of plausible clonal lineage models using bootstrap samples over pre-treatment tissue-based genetic variation data, then refining these lineage models and the clonal frequencies we can infer from them over successive longitudinal samples of selected markers. Results: We tested our method on synthetic data under various model assumptions and showed the method to be effective at refining distribution tree models and clonal frequencies over longitudinal samples so as to minimize measures of tree distance relative to the ground truth. We further apply the methods to a real ovarian cancer case and show their ability to develop and refine a clonal lineage model and assess clonal frequencies. Conclusion: Our methods show the power of ctDNA assays in conjunction with computational optimization to lead to improved marker selection, clonal lineage reconstruction, and tracking of clonal dynamics, with potential for more precise and quantitative profiling of tumor progression enabling improved clinical decision-making. Citation Format: Xuecong Fu, Yueqian Deng, Zhicheng Luo, William Laframboise, David Bartlett, Russell Schwartz. Computational methods for optimizing marker selection, clonal lineage reconstruction, and longitudinal tracking of clonal dynamics via circulating tumor DNA (ctDNA) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2334.

Insights into the enigma of oral streptococci in carcinogenesis.

SUMMARYThe genus Streptococcus consists of a taxonomically diverse group of Gram-positive bacteria that have earned significant scientific interest due to their physiological and pathogenic characteristics. Within the genus Streptococcus, viridans group streptococci (VGS) play a significant role in the oral ecosystem, constituting approximately 80% of the oral biofilm. Their primary role as pioneering colonizers in the oral cavity with multifaceted interactions like adherence, metabolic signaling, and quorum sensing contributes significantly to the complex dynamics of the oral biofilm, thus shaping oral health and disease outcomes. Perturbations in oral streptococci composition drive oral dysbiosis and therefore impact host-pathogen interactions, resulting in oral inflammation and representing VGS as an opportunistic pathogen. The association of oral streptococci in tumors across distant organs, spanning the esophagus, stomach, pancreas, and colon, illuminates a potential association between oral streptococci, inflammation, and tumorigenesis. This finding emphasizes the need for further investigations into the role of oral streptococci in mucosal homeostasis and their involvement in carcinogenesis. Hence, here, we review the significance of oral streptococci in biofilm dynamics and how the perturbation may impact mucosal immunopathogenesis in the context of cancer, with a vision of exploiting oral streptococci for cancer intervention and for the development of non-invasive cancer diagnosis.

Laser-induced graphene (LIG)-based electrochemical microfluidic chip for simultaneous analysis of multiplex microRNAs

Because a cancer process is always associated with several microRNAs (miRNAs), therefore, sensing devices that can simultaneously detect multiplex tumor-signature miRNAs are urgently needed for improving the accuracy of the early cancer diagnosis. Herein, a high-throughput electrochemical microfluidic chip (EMC) is devised for simultaneous analysis of multiplex miRNAs with high sensitivity and selectivity. Specifically, laser-induced graphene (LIG) electrode array was directly wrote on a polyimide (PI) film in a flexible fashion and further integrated with microfluidic channel to construct a EMC sensing platform. Moreover, enzyme-assisted target recycling amplification was employed to amplify the output of assistant DNA for capturing more nanoprobes to ensure the sensitivity and specificity. The feasibility and high performance of the EMS sensing platform were demonstrated by the precise multiplexed quantification of miR-21, miR-1246, and miR-155 at concentrations ranging from 0.5 to 1000 pM in a single 18 μL sample with detection limits down to 0.17 pM, 0.24 pM, and 0.11 pM, respectively. For practical application, the expression of multiplex miRNAs in exosomes and clinical serum samples can be assessed in a single-pot assay with low sample requirement. The results showed that the expression level of three miRNAs had a high accuracy for the discrimination of breast cancer patients and healthy donors, demonstrating the great potential of EMC in miRNA-based early cancer diagnosis. Therefore, the proposed EMC sensor is an efficient lab-on-chip device for multiplex miRNAs assay, which opens new avenue for preparing EMC sensing chip for non-invasive early cancer diagnosis.

Plasma Circulating mRNA Profile for the Non-Invasive Diagnosis of Colorectal Cancer Using NanoString Technologies.

Colorectal cancer (CRC) is one of the most prevalent cancers and the second leading cause of cancer deaths in developed countries. Early CRC may have no symptoms and symptoms usually appear with more advanced diseases. Regular screening can identify people who are at increased risk of CRC in order to offer earlier treatment. A cost-effective non-invasive platform for the screening and monitoring of CRC patients allows early detection and appropriate treatment of the disease, and the timely application of adjuvant therapy after surgical operation is needed. In this study, a cohort of 71 plasma samples that include 48 colonoscopy- and histopathology-confirmed CRC patients with TNM stages I to IV were recruited between 2017 and 2019. Plasma mRNA profiling was performed in CRC patients using NanoString nCounter. Normalized data were analyzed using a Mann-Whitney U test to determine statistically significant differences between samples from CRC patients and healthy subjects. A multiple-group comparison of clinical phenotypes was performed using the Kruskal-Wallis H test for statistically significant differences between multiple groups. Among the 27 selected circulating mRNA markers, all of them were found to be overexpressed (gene expression fold change > 2) in the plasma of patients from two or more CRC stages. In conclusion, NanoString-based targeted plasma CRC-associated mRNAs circulating the marker panel that can significantly distinguish CRC patients from a healthy population were developed for the non-invasive diagnosis of CRC using peripheral blood samples.

Self-consuming DNA nanogear retrieval exosomes for grading analysis of gliomas

Direct and accurate analysis of low concentrations of tumor-derived exosomes in complex biological samples is essential for non-invasive cancer diagnosis, but this remains challenging due to the lack of convenient and highly sensitive methods. Herein, we have proposed a novel strategy based on super spherical nucleic acids (SSNAs) combined with nicking enzyme triggering signal amplification for the direct detection of exosomes derived from gliomas. SSNAs utilize AuNPs as carriers, loaded with multifunctional DNA probes. And exosomes are simultaneously labeled by two aptamers. When SSNAs accurately recognize the labeled exosomes, the two make relative gear movement. During the gear movement, the probes act as an exosome-specific retrieval on the one hand, and serves as a fuel that is continuously consumed by the exosomes on the other hand, thus outputting a high-gain fluorescent signal. This strategy exhibits excellent specificity and ultra-high sensitivity for the detection of target exosomes as low as 20 particles/μL. The practicability of this method is further verified by the detection of clinical samples, which retains potential for early glioma diagnosis and grading analysis.

Polydopamine-assisted aptamer-carrying tetrahedral DNA microelectrode sensor for ultrasensitive electrochemical detection of exosomes

BackgroundExosomes are nanoscale extracellular vesicles (30–160 nm) with endosome origin secreted by almost all types of cells, which are considered to be messengers of intercellular communication. Cancerous exosomes serve as a rich source of biomarkers for monitoring changes in cancer-related physiological status, because they carry a large number of biological macromolecules derived from parental tumors. The ultrasensitive quantification of trace amounts of cancerous exosomes is highly valuable for non-invasive early cancer diagnosis, yet it remains challenging. Herein, we developed an aptamer-carrying tetrahedral DNA (Apt-TDNA) microelectrode sensor, assisted by a polydopamine (PDA) coating with semiconducting properties, for the ultrasensitive electrochemical detection of cancer-derived exosomes.ResultsThe stable rigid structure and orientation of Apt-TDNA ensured efficient capture of suspended exosomes. Without PDA coating signal amplification strategy, the sensor has a linear working range of 102–107 particles mL−1, with LOD of ~ 69 exosomes and ~ 42 exosomes for EIS and DPV, respectively. With PDA coating, the electrochemical signal of the microelectrode is further amplified, achieving single particle level sensitivity (~ 14 exosomes by EIS and ~ 6 exosomes by DPV).ConclusionsThe proposed PDA-assisted Apt-TDNA microelectrode sensor, which integrates efficient exosome capture, sensitive electrochemical signal feedback with PDA coating signal amplification, provides a new avenue for the development of simple and sensitive electrochemical sensing techniques in non-invasive cancer diagnosis and monitoring treatment response.Graphical

Artificial intelligence assisted patient blood and urine droplet pattern analysis for non-invasive and accurate diagnosis of bladder cancer

Bladder cancer is one of the most common cancer types in the urinary system. Yet, current bladder cancer diagnosis and follow-up techniques are time-consuming, expensive, and invasive. In the clinical practice, the gold standard for diagnosis remains invasive biopsy followed by histopathological analysis. In recent years, costly diagnostic tests involving the use of bladder cancer biomarkers have been developed, however these tests have high false-positive and false-negative rates limiting their reliability. Hence, there is an urgent need for the development of cost-effective, and non-invasive novel diagnosis methods. To address this gap, here we propose a quick, cheap, and reliable diagnostic method. Our approach relies on an artificial intelligence (AI) model to analyze droplet patterns of blood and urine samples obtained from patients and comparing them to cancer-free control subjects. The AI-assisted model in this study uses a deep neural network, a ResNet network, pre-trained on ImageNet datasets. Recognition and classification of complex patterns formed by dried urine or blood droplets under different conditions resulted in cancer diagnosis with a high specificity and sensitivity. Our approach can be systematically applied across droplets, enabling comparisons to reveal shared spatial behaviors and underlying morphological patterns. Our results support the fact that AI-based models have a great potential for non-invasive and accurate diagnosis of malignancies, including bladder cancer.

Enantiomer-Specific Colorimetric Tandem Assays for Salivary d-Alanine Associated with Gastric Cancer.

Salivary d-alanine (d-Ala) and d-proline (d-Pro) are of concern for their potential in the noninvasive diagnosis of gastric cancer (GC). Most reports have succeeded in determining the total concentration of d-Ala and d-Pro. However, for personalized diagnosis and better elucidation of the underlying specific correlation of d-Ala (or d-Pro) with GC, it is desirable to determine the specific concentration of d-Ala or d-Pro. Herein, we propose an enantiomer-specific tandem assay of d-Ala based on the colorimetric reaction between 2,4-dinitrophenylhydrazine and pyruvic acid generated from the deamination of d-Ala catalyzed by d-amino acid oxidase, which is easily distinguished from l-form amino acids, d-Pro, and many other species. A linear concentration range is established from 20 to 400 μmol/L with a limit of detection of 1.01 μmol/L. Real saliva sample tests reveal that the levels of d-Ala in GC cases are remarkably higher than those in healthy individuals, which offers a simple and low-cost strategy for GC diagnosis. Simultaneously, the total concentrations of d-Ala and d-Pro in saliva are determined. Hence, the concentration of d-Pro and the proportion of d-Ala could be calculated, which further provides more molecule- and individual-specific information. This research may offer a convenient method for noninvasive diagnosis of GC and pave a new route to explore the potentials of rare d-form amino acids in disease diagnosis and treatment.