Electrochemical Nanobiosensors for Detection of Breast Cancer Biomarkers

A new insight into the early detection of HER2 protein in breast cancer patients with a focus on electrochemical biosensors approaches: A review

Breast cancer is one of the leading causes of death in women and is a prevalent kind of cancerous growth, representing a substantial risk to women's health. Early detection of breast cancer is essential for effective treatment and improved survival rates. Biomarkers, active substances that signal the existence and advancement of a tumor, play a significant role in the early detection of breast cancer. Hence, accurate identification of biomarkers for tumors is crucial for diagnosing and treating breast cancer. However, the primary diagnostic methods used for the detection of breast cancer require specific equipment, skilled professionals, and specialized analysis, leading to elevated detection expenses. Regarding this obstacle, recent studies emphasize electrochemical biosensors as more advanced and sensitive detection tools compared to traditional methods. Electrochemical biosensors are employed to identify biomarkers that act as unique indicators for the onset, recurrence, and monitoring of therapeutic interventions for breast cancer. This study aims to provide a summary of the electrochemical biosensors that have been employed for the detection of breast cancer at an early stage over the past decade. Initially, the text provides concise information about breast cancer and tumor biomarkers. Subsequently, an in-depth analysis is conducted to systematically review the progress of electrochemical biosensors developed for the stable, specific, and sensitive identification of biomarkers associated with breast cancer. Particular emphasis was given to crucial clinical biomarkers, specifically the human epidermal growth factor receptor-2 (HER2). The analysis then explores the limitations and challenges inherent in the design of effective biosensors for diagnosing and treating breast cancer. Ultimately, we provided an overview of future research directions and concluded by outlining the advantages of electrochemical biosensor approaches.

Electrochemical cytosensor utilizing tetrahedral DNA/bimetallic AuPd holothurian-shaped nanoparticles for ultrasensitive non-destructive detection of circulating tumor cells

As a real-time fluid biopsy method, the detection of circulating tumor cells (CTCs) provides important information for the early diagnosis, precise treatment, and prognosis of cancer. However, the low density of CTCs in the peripheral blood hampers their capture and detection with high sensitivity and selectivity using currently available methods. Hence, we designed a sandwich-type electrochemical aptasensor that utilizes holothurian-shaped AuPd nanoparticles (AuPd HSs), tetrahedral DNA nanostructures (TDNs), and CuPdPt nanowire networks (NWs) interwoven with a graphdiyne (GDY) sheet for ultrasensitive non-destructive detection of MCF-7 breast cancer cells. CuPdPt NW-GDY effectively enhanced the electron transfer rate and coupled with the loaded TDNs. The TDNs could capture MCF-7 cells with precision and firmness, and the resulting composite complex was combined with AuPd HSs to form a sandwich-type structure. This novel aptasensor showed a linear range between 10 and 106 cells mL-1 and an ultralow detection limit of 7 cells mL-1. The specificity, stability, and repeatability of the measurements were successfully verified. Moreover, we used benzonase nuclease to achieve non-destructive recovery of cells for further clinical studies. According to the results, our aptasensor was more sensitive measuring the number of CTCs than other approaches because of the employment of TDNs, CuPdPt NW-GDY, and AuPd HSs. We designed a reliable sensor system for the detection of CTCs in the peripheral blood, which could serve as a new approach for cancer diagnosis at an early stage.

A Nanorobotics-Based Approach of Breast Cancer in the Nanotechnology Era

We are living in an era of advanced nanoscience and nanotechnology. Numerous nanomaterials, culminating in nanorobots, have demonstrated ingenious applications in biomedicine, including breast cancer (BC) nano-theranostics. To solve the complicated problem of BC heterogeneity, non-targeted drug distribution, invasive diagnostics or surgery, resistance to classic onco-therapies and real-time monitoring of tumors, nanorobots are designed to perform multiple tasks at a small scale, even at the organelles or molecular level. Over the last few years, most nanorobots have been bioengineered as biomimetic and biocompatible nano(bio)structures, resembling different organisms and cells, such as urchin, spider, octopus, fish, spermatozoon, flagellar bacterium or helicoidal cyanobacterium. In this review, readers will be able to deepen their knowledge of the structure, behavior and role of several types of nanorobots, among other nanomaterials, in BC theranostics. We summarized here the characteristics of many functionalized nanodevices designed to counteract the main neoplastic hallmark features of BC, from sustaining proliferation and evading anti-growth signaling and resisting programmed cell death to inducing angiogenesis, activating invasion and metastasis, preventing genomic instability, avoiding immune destruction and deregulating autophagy. Most of these nanorobots function as targeted and self-propelled smart nano-carriers or nano-drug delivery systems (nano-DDSs), enhancing the efficiency and safety of chemo-, radio- or photodynamic therapy, or the current imagistic techniques used in BC diagnosis. Most of these nanorobots have been tested in vitro, using various BC cell lines, as well as in vivo, mainly based on mice models. We are still waiting for nanorobots that are low-cost, as well as for a wider transition of these favorable effects from laboratory to clinical practice.

MXene-based electrochemical devices applied for healthcare applications

The initial part of the review provides an extensive overview about MXenes as novel and exciting 2D nanomaterials describing their basic physico-chemical features, methods of their synthesis, and possible interfacial modifications and techniques, which could be applied to the characterization of MXenes. Unique physico-chemical parameters of MXenes make them attractive for many practical applications, which are shortly discussed. Use of MXenes for healthcare applications is a hot scientific discipline which is discussed in detail. The article focuses on determination of low molecular weight analytes (metabolites), high molecular weight analytes (DNA/RNA and proteins), or even cells, exosomes, and viruses detected using electrochemical sensors and biosensors. Separate chapters are provided to show the potential of MXene-based devices for determination of cancer biomarkers and as wearable sensors and biosensors for monitoring of a wide range of human activities.

An update on pathogenesis and clinical scenario for Parkinson's disease: diagnosis and treatment

In severe cases, Parkinson's disease causes uncontrolled movements known as motor symptoms such as dystonia, rigidity, bradykinesia, and tremors. Parkinson's disease also causes non-motor symptoms such as insomnia, constipation, depression and hysteria. Disruption of dopaminergic and non-dopaminergic neural networks in the substantia nigra pars compacta is a major cause of motor symptoms in Parkinson's disease. Furthermore, due to the difficulty of clinical diagnosis of Parkinson's disease, it is often misdiagnosed, highlighting the need for better methods of detection. Treatment of Parkinson's disease is also complicated due to the difficulties of medications passing across the blood-brain barrier. Moreover, the conventional methods fail to solve the aforementioned issues. As a result, new methods are needed to detect and treat Parkinson's disease. Improved diagnosis and treatment of Parkinson's disease can help avoid some of its devastating symptoms. This review explores how nanotechnology platforms, such as nanobiosensors and nanomedicine, have improved Parkinson's disease detection and treatment. Nanobiosensors integrate science and engineering principles to detect Parkinson's disease. The main advantages are their low cost, portability, and quick and precise analysis. Moreover, nanotechnology can transport medications in the form of nanoparticles across the blood-brain barrier. However, because nanobiosensors are a novel technology, their use in biological systems is limited. Nanobiosensors have the potential to disrupt cell metabolism and homeostasis, changing cellular molecular profiles and making it difficult to distinguish sensor-induced artifacts from fundamental biological phenomena. In the treatment of Parkinson's disease, nanoparticles, on the other hand, produce neurotoxicity, which is a challenge in the treatment of Parkinson's disease. Techniques must be developed to distinguish sensor-induced artifacts from fundamental biological phenomena and to reduce the neurotoxicity caused by nanoparticles.

Cyclic Voltammetry of Screen-Printed Carbon Electrode Coated with Ag-ZnO Nanoparticles in Chitosan Matrix

In this paper, the authors describe the fabrication of nanocomposite chitosan-based systems of zinc oxide (ZnO), silver (Ag) and Ag-ZnO. Recently, the development of coated screen-printed electrodes using metal and metal oxide nanoparticles (NPs) for the specific detection and monitoring of different cancer tumors has been obtaining important results. Ag, ZnO NPs and Ag-ZnO prepared by the hydrolysis of zinc acetate blended with a chitosan (CS) matrix were used for the surface modification of screen-printed carbon electrodes (SPCEs) in order to analyze the electrochemical behavior of the typical redox system of a 10 mM potassium ferrocyanide-0.1 M buffer solution (BS). The solutions of CS, ZnO/CS, Ag/CS and Ag-ZnO/CS were prepared in order to modify the carbon electrode surface, and were measured at different scan rates from 0.02 V/s to 0.7 V/s by cyclic voltammetry. The cyclic voltammetry (CV) was performed on a house-built potentiostat (HBP). The cyclic voltammetry of the measured electrodes showed the influence of varying the scan rate. The variation of the scan rate has an influence on the intensity of the anodic and cathodic peak. Both values of currents (anodic and cathodic currents) have higher values for 0.1 V/s (Ia = 22 μA and Ic = -25 μA) compared to the values for 0.06 V/s (Ia = 10 μA and Ic = -14 μA). The CS, ZnO/CS, Ag/CS and Ag-ZnO/CS solutions were characterized using a field emission scanning electron microscopy (FE-SEM) with EDX elemental analysis. The modified coated surfaces of screen-printed electrodes were analyzed using optical microscopy (OM). The present coated carbon electrodes showed a different waveform compared to the voltage applied to the working electrode, depending on the scan rate and chemical composition of the modified electrodes.

Electrochemical and Photoelectrochemical Immunosensors for the Detection of Ovarian Cancer Biomarkers

Photoelectrochemical (PEC) sensing is an emerging technological innovation for monitoring small substances/molecules in biological or non-biological systems. In particular, there has been a surge of interest in developing PEC devices for determining molecules of clinical significance. This is especially the case for molecules that are markers for serious and deadly medical conditions. The increased interest in PEC sensors to monitor such biomarkers can be attributed to the many apparent advantages of the PEC system, including an enhanced measurable signal, high potential for miniaturization, rapid testing, and low cost, amongst others. The growing number of published research reports on the subject calls for a comprehensive review of the various findings. This article is a review of studies on electrochemical (EC) and PEC sensors for ovarian cancer biomarkers in the last seven years (2016-2022). EC sensors were included because PEC is an improved EC; and a comparison of both systems has, expectedly, been carried out in many studies. Specific attention was given to the different markers of ovarian cancer and the EC/PEC sensing platforms developed for their detection/quantification. Relevant articles were sourced from the following databases: Scopus, PubMed Central, Web of Science, Science Direct, Academic Search Complete, EBSCO, CORE, Directory of open Access Journals (DOAJ), Public Library of Science (PLOS), BioMed Central (BMC), Semantic Scholar, Research Gate, SciELO, Wiley Online Library, Elsevier and SpringerLink.

Biomedical Applications of an Ultra-Sensitive Surface Plasmon Resonance Biosensor Based on Smart MXene Quantum Dots (SMQDs)

In today’s world, the use of biosensors occupies a special place in a variety of fields such as agriculture and industry. New biosensor technologies can identify biological compounds accurately and quickly. One of these technologies is the phenomenon of surface plasmon resonance (SPR) in the development of biosensors based on their optical properties, which allow for very sensitive and specific measurements of biomolecules without time delay. Therefore, various nanomaterials have been introduced for the development of SPR biosensors to achieve a high degree of selectivity and sensitivity. The diagnosis of deadly diseases such as cancer depends on the use of nanotechnology. Smart MXene quantum dots (SMQDs), a new class of nanomaterials that are developing at a rapid pace, are perfect for the development of SPR biosensors due to their many advantageous properties. Moreover, SMQDs are two-dimensional (2D) inorganic segments with a limited number of atomic layers that exhibit excellent properties such as high conductivity, plasmonic, and optical properties. Therefore, SMQDs, with their unique properties, are promising contenders for biomedicine, including cancer diagnosis/treatment, biological sensing/imaging, antigen detection, etc. In this review, SPR biosensors based on SMQDs applied in biomedical applications are discussed. To achieve this goal, an introduction to SPR, SPR biosensors, and SMQDs (including their structure, surface functional groups, synthesis, and properties) is given first; then, the fabrication of hybrid nanoparticles (NPs) based on SMQDs and the biomedical applications of SMQDs are discussed. In the next step, SPR biosensors based on SMQDs and advanced 2D SMQDs-based nanobiosensors as ultrasensitive detection tools are presented. This review proposes the use of SMQDs for the improvement of SPR biosensors with high selectivity and sensitivity for biomedical applications.

Smartphone-Based Multiplexed Biosensing Tools for Health Monitoring

Many emerging technologies have the potential to improve health care by providing more personalized approaches or early diagnostic methods. In this review, we cover smartphone-based multiplexed sensors as affordable and portable sensing platforms for point-of-care devices. Multiplexing has been gaining attention recently for clinical diagnosis considering certain diseases require analysis of complex biological networks instead of single-marker analysis. Smartphones offer tremendous possibilities for on-site detection analysis due to their portability, high accessibility, fast sample processing, and robust imaging capabilities. Straightforward digital analysis and convenient user interfaces support networked health care systems and individualized health monitoring. Detailed biomarker profiling provides fast and accurate analysis for disease diagnosis for limited sample volume collection. Here, multiplexed smartphone-based assays with optical and electrochemical components are covered. Possible wireless or wired communication actuators and portable and wearable sensing integration for various sensing applications are discussed. The crucial features and the weaknesses of these devices are critically evaluated.

Aptamers as Recognition Elements for Electrochemical Detection of Exosomes

Exosome analysis is emerging as an attractive noninvasive approach for disease diagnosis and treatment monitoring in the field of liquid biopsy. Aptamer is considered as a promising molecular probe for exosomes detection because of the high binding affinity, remarkable specificity, and low cost. Recently, many approaches have been developed to further improve the performance of electrochemical aptamer based(E-AB) sensors with a lower limit of detection. In this review, we focus on the development of using aptamer as a specific recognition element for exosomes detection in electrochemical sensors. We first introduce recent advances in evolving aptamers against exosomes. Then, we review methods of immobilization aptamers on electrode surfaces, followed by a summary of the main strategies of signal amplification. Finally, we present the insights of the challenges and future directions of E-AB sensors for exosomes analysis.

Exosome Processing and Characterization Approaches for Research and Technology Development

Exosomes are extracellular vesicles that share components of their parent cells and are attractive in biotechnology and biomedical research as potential disease biomarkers as well as therapeutic agents. Crucial to realizing this potential is the ability to manufacture high-quality exosomes; however, unlike biologics such as proteins, exosomes lack standardized Good Manufacturing Practices for their processing and characterization. Furthermore, there is a lack of well-characterized reference exosome materials to aid in selection of methods for exosome isolation, purification, and analysis. This review informs exosome research and technology development by comparing exosome processing and characterization methods and recommending exosome workflows. This review also provides a detailed introduction to exosomes, including their physical and chemical properties, roles in normal biological processes and in disease progression, and summarizes some of the on-going clinical trials.

Deep Learning on Histopathology Images for Breast Cancer Classification: A Bibliometric Analysis

Medical imaging is gaining significant attention in healthcare, including breast cancer. Breast cancer is the most common cancer-related death among women worldwide. Currently, histopathology image analysis is the clinical gold standard in cancer diagnosis. However, the manual process of microscopic examination involves laborious work and can be misleading due to human error. Therefore, this study explored the research status and development trends of deep learning on breast cancer image classification using bibliometric analysis. Relevant works of literature were obtained from the Scopus database between 2014 and 2021. The VOSviewer and Bibliometrix tools were used for analysis through various visualization forms. This study is concerned with the annual publication trends, co-authorship networks among countries, authors, and scientific journals. The co-occurrence network of the authors’ keywords was analyzed for potential future directions of the field. Authors started to contribute to publications in 2016, and the research domain has maintained its growth rate since. The United States and China have strong research collaboration strengths. Only a few studies use bibliometric analysis in this research area. This study provides a recent review on this fast-growing field to highlight status and trends using scientific visualization. It is hoped that the findings will assist researchers in identifying and exploring the potential emerging areas in the related field.

On Demand Biosensors for Early Diagnosis of Cancer and Immune Checkpoints Blockade Therapy Monitoring from Liquid Biopsy

Recently, considerable interest has emerged in the development of biosensors to detect biomarkers and immune checkpoints to identify and measure cancer through liquid biopsies. The detection of cancer biomarkers from a small volume of blood is relatively fast compared to the gold standard of tissue biopsies. Traditional immuno-histochemistry (IHC) requires tissue samples obtained using invasive procedures and specific expertise as well as sophisticated instruments. Furthermore, the turnaround for IHC assays is usually several days. To overcome these challenges, on-demand biosensor-based assays were developed to provide more immediate prognostic information for clinicians. Novel rapid, highly precise, and sensitive approaches have been under investigation using physical and biochemical methods to sense biomarkers. Additionally, interest in understanding immune checkpoints has facilitated the rapid detection of cancer prognosis from liquid biopsies. Typically, these devices combine various classes of detectors with digital outputs for the measurement of soluble cancer or immune checkpoint (IC) markers from liquid biopsy samples. These sensor devices have two key advantages: (a) a small volume of blood drawn from the patient is sufficient for analysis, and (b) it could aid physicians in quickly selecting and deciding the appropriate therapy regime for the patients (e.g., immune checkpoint blockade (ICB) therapy). In this review, we will provide updates on potential cancer markers, various biosensors in cancer diagnosis, and the corresponding limits of detection, while focusing on biosensor development for IC marker detection.

Recent advances in biosensing approaches for point-of-care breast cancer diagnostics: challenges and future prospects

Timely and accurate diagnosis of breast cancer is essential for efficient treatment and the best possible survival rates. Biosensors have emerged as a smart diagnostic platform for the detection of biomarkers specific to the onset, recurrence, and therapeutic drug monitoring of breast cancer. There have been exciting recent developments, including significant improvements in the validation, sensitivity, specificity, and integration of sample processing steps to develop point-of-care (POC) integrated micro-total analysis systems for clinical settings. The present review highlights various biosensing modalities (electrical, optical, piezoelectric, mass, and acoustic sensing). It provides deep insights into their design principles, signal amplification strategies, and comparative performance analysis. Finally, this review emphasizes the status of existing integrated micro-total analysis systems (μ-TAS) for personalized breast cancer therapeutics and associated challenges and outlines the approach required to realize their successful translation into clinical settings.

Recent Development of Nanomaterials-Based Cytosensors for the Detection of Circulating Tumor Cells

The accurate analysis of circulating tumor cells (CTCs) holds great promise in early diagnosis and prognosis of cancers. However, the extremely low abundance of CTCs in peripheral blood samples limits the practical utility of the traditional methods for CTCs detection. Thus, novel and powerful strategies have been proposed for sensitive detection of CTCs. In particular, nanomaterials with exceptional physical and chemical properties have been used to fabricate cytosensors for amplifying the signal and enhancing the sensitivity. In this review, we summarize the recent development of nanomaterials-based optical and electrochemical analytical techniques for CTCs detection, including fluorescence, colorimetry, surface-enhanced Raman scattering, chemiluminescence, electrochemistry, electrochemiluminescence, photoelectrochemistry and so on.

Novel Prostate Cancer Biomarkers: Aetiology, Clinical Performance and Sensing Applications

The review initially provides a short introduction to prostate cancer (PCa) incidence, mortality, and diagnostics. Next, the need for novel biomarkers for PCa diagnostics is briefly discussed. The core of the review provides details about PCa aetiology, alternative biomarkers available for PCa diagnostics besides prostate specific antigen and their biosensing. In particular, low molecular mass biomolecules (ions and metabolites) and high molecular mass biomolecules (proteins, RNA, DNA, glycoproteins, enzymes) are discussed, along with clinical performance parameters.

An Ultrasensitive Biosensor for Detection of Femtogram Levels of the Cancer Antigen AGR2 Using Monoclonal Antibody Modified Screen-Printed Gold Electrodes

The detection of cancer antigens is a major aim of cancer research in order to develop better patient management through early disease detection. Many cancers including prostate, lung, and ovarian secrete a protein disulfide isomerase protein named AGR2 that has been previously detected in urine and plasma using mass spectrometry. Here we determine whether a previously developed monoclonal antibody targeting AGR2 can be adapted from an indirect two-site ELISA format into a direct detector using solid-phase printed gold electrodes. The screen-printed gold electrode was surface functionalized with the anti-AGR2 specific monoclonal antibody. The interaction of the recombinant AGR2 protein and the anti-AGR2 monoclonal antibody functionalized electrode changed its electrochemical impedance spectra. Nyquist diagrams were obtained after incubation in an increasing concentration of purified AGR2 protein with a range of concentrations from 0.01 fg/mL to 10 fg/mL. In addition, detection of the AGR2 antigen can be achieved from cell lysates in medium or artificial buffer. These data highlight the utility of an AGR2-specific monoclonal antibody that can be functionalized onto a gold printed electrode for a one-step capture and quantitation of the target antigen. These platforms have the potential for supporting methodologies using more complex bodily fluids including plasma and urine for improved cancer diagnostics.

Spectral-Spatial Features Integrated Convolution Neural Network for Breast Cancer Classification

Cancer identification and classification from histopathological images of the breast depends greatly on experts, and computer-aided diagnosis can play an important role in disagreement of experts. This automatic process has increased the accuracy of the classification at a reduced cost. The advancement in Convolution Neural Network (CNN) structure has outperformed the traditional approaches in biomedical imaging applications. One of the limiting factors of CNN is it uses spatial image features only for classification. The spectral features from the transform domain have equivalent importance in the complex image classification algorithm. This paper proposes a new CNN structure to classify the histopathological cancer images based on integrating the spectral features obtained using a multi-resolution wavelet transform with the spatial features of CNN. In addition, batch normalization process is used after every layer in the convolution network to improve the poor convergence problem of CNN and the deep layers of CNN are trained with spectral-spatial features. The proposed structure is tested on malignant histology images of the breast for both binary and multi-class classification of tissue using the BreaKHis Dataset and the Breast Cancer Classification Challenge 2015 Datasest. Experimental results show that the combination of spectral-spatial features improves classification accuracy of the CNN network and requires less training parameters in comparison with the well known models (i.e., VGG16 and ALEXNET). The proposed structure achieves an average accuracy of 97.58% and 97.45% with 7.6 million training parameters on both datasets, respectively.