Cancer Marker Proteins Research Articles

Esterase-Responsive Self-Immolative Prodrugs for the Sustained Delivery of the Anticancer Drug 5-Fluorouracil with Turn-On Fluorescence.

Stimuli-responsive prodrugs of anticancer drugs are advantageous for the selective delivery of drugs to cancer cells with minimized off-target side effects. In the present study, esterase-activatable fluorogenic prodrugs of the chemotherapeutic drug 5-fluorouracil (5-FU) have been rationally designed and synthesized using multi-step organic synthesis. While 5-FU was connected directly with the fluorophore via a C-N bond in the prodrug BJ-50, an additional self-immolative benzylic spacer with a carbonate linker was incorporated in the prodrug BJ-92. Although absorption and emission spectroscopic studies revealed the activation of both the prodrugs by porcine liver esterase (PLE), reverse-phase HPLC studies confirmed the inability of BJ-50 to release the active drug 5-FU. In contrast, a sustained release of 5-FU and Cou-OH was observed from BJ-92 in the presence of PLE. The endogenous esterase-mediated activation of the prodrug BJ-92 was validated by the turn-on fluorescence in A549 cells and the anti-proliferative activities in A549, and HEK-293 cells. Modulation of the expression of a few cancer marker proteins by BJ-92 and 5-FU was studied to evaluate their anticancer activities. As esterases are overexpressed in cancer cells, the prodrug in the present study would be helpful in selectively delivering 5-FU to cancer cells with reduced off-target side-effects.

Liposomal encapsulated curcumin attenuates lung cancer proliferation, migration, and induces apoptosis

Lung cancer is one of the most diagnosed types of cancer worldwide, accounting to one fifth of cancer-related deaths. The high prevalence of lung cancer (LC) is due to various factors such as environmental pollution or lifestyle factors such as cigarette smoking. Non-small cell lung cancer (NSCLC) is the most diagnosed type of lung cancer. Despite the availability of several lines of treatment for NSCLC, including surgery, chemotherapy, radiotherapy, immunotherapy, and combinations of these, this disease still has very low survival rate, highlighting the urgent need to develop novel therapeutics. Phytoceuticals, or plant-derived bioactives are a promising source of biologically active compounds. Among these, curcumin is particularly relevant due to its wide range of anticancer, antioxidant, and anti-inflammatory activity. However, its poor solubility causes low bioavailability, severely limiting its clinical application. Encapsulation of curcumin in nanoparticle-based delivery systems such as liposomes holds promise to overcome this limitation. In the present study, we demonstrate promising in vitro anticancer affect or curcumin-loaded liposomes (PlexoZome®) on A549 human lung adenocarcinoma cells. The study reveals how liposomal curcumin functionally supresses the proliferation, migration, and colony formation of these cells whilst also drastically reducing the expression of multiple cancer marker proteins. This work provides foundational data for the development of a curcumin-based nano formulation to be used as therapy for NSCLC.

Multitargeted molecular docking and dynamics simulation of thymol-based chalcones against cancer protein markers: Synthesis, characterization, and computational study

Cancer prevention has become a significant public health concern in the 21st century, ranking as the second leading cause of global mortality following cardiovascular diseases. Despite prominent advancements in treatment, addressing cancer remains a substantial public health challenge, necessitating precise interventions tailored to diverse cancer types. The targeting of Epidermal Growth Factor Receptor (EGFR) has emerged as a fundamental strategy in modern cancer therapy due to its critical role in oncogenesis. Chalcones, a subclass of flavonoids, are promising anticancer agents due to their ability to target multiple cellular pathways implicated in cancer progression. The presence of α, β-unsaturated carbonyl moiety is a key structural feature attributed to their biological activity. This study presents an efficient synthesis approach for chalcone derivatives 4a-f designed from natural thymol. The synthesized compounds were further characterized by HRMS, 1H and 13C NMR spectral data, and further evaluated for in silico studies through molecular docking, molecular dynamic simulation, and ADMET screening. All the designed compounds 4a-f displayed favourable physicochemical properties and ADMET profiles. Molecular docking revealed favourable interactions between compounds 4a-f and the EGFR target protein. Compound 4d (p-nitro) and 4b (p-methyl) showed the more favourable docking score with low energy conformation (-8.02 kcal/mol), (-7.42 kcal/mol) respectively compared to the standard drug Gefitinib. Molecular dynamic simulations confirmed the stability of compounds 4d and 4b, exhibiting consistent structural stability in ligand-protein complexes throughout the 100 ns simulation. Acceptable RMSF values for amino acid residues indicated structural integrity and a stable protein-ligand interaction. The study identified 4d and 4b as promising leads for targeting EGFR.

Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer

Gastric cancer (GC) represents a significant burden of cancer-related mortality worldwide, underscoring an urgent need for the development of early detection strategies and precise postoperative interventions. However, the identification of non-invasive biomarkers for early diagnosis and patient risk stratification remains underexplored. Here, we conduct a targeted metabolomics analysis of 702 plasma samples from multi-center participants to elucidate the GC metabolic reprogramming. Our machine learning analysis reveals a 10-metabolite GC diagnostic model, which is validated in an external test set with a sensitivity of 0.905, outperforming conventional methods leveraging cancer protein markers (sensitivity < 0.40). Additionally, our machine learning-derived prognostic model demonstrates superior performance to traditional models utilizing clinical parameters and effectively stratifies patients into different risk groups to guide precision interventions. Collectively, our findings reveal the metabolic landscape of GC and identify two distinct biomarker panels that enable early detection and prognosis prediction respectively, thus facilitating precision medicine in GC.

The detection of multiple cancer types with an extended set of methylation and protein markers.

3040 Background: Cancer is the 2nd leading cause of mortality in the US, with an estimated 1.96M new cases expected in 2023. With guideline recommended screening unavailable for most cancers, multicancer early detection (MCED) tests in development may extend the reach of cancer screening. As part of an ongoing program examining multiple biomarker classes in cancer early detection, we examined the feasibility of combining blood DNA methylation and cancer protein markers for the detection of multiple cancer types. Methods: 236 samples from subjects with cancer (13 tumor organ types) were retrospectively collected and gender/age matched with 146 unscreened non-cancer control samples. Plasma and serum were used for cfDNA methylation analysis of 36 differentially methylated regions and 8 cancer-associated protein markers in a randomized, blinded fashion. Methylated DNA markers (MDMs) were assayed using target enrichment long-probe quantitative-amplified signal assays. Protein markers were assayed with commercial kits. Normalized methylation values/protein concentrations were used to develop a logistic regression model for cancer detection. The model was built with 5 random 0.7/0.3 training/validation splits, keeping case/control, cancer types, gender, and age distributions similar to the original data set. Models for MDMs and protein markers were created, and a combined model including both MDMs and protein markers was also evaluated for performance improvement. Results: A reduced subset of MDM and protein markers was identified to have robust detection of several cancer types. At 99% specificity, overall sensitivity for cancer detection was 68% (95% CI: 62%-74%) for MDMs, 43% (37%-50%) for proteins, and 75% (69%-80%) for MDMs and proteins combined. Conclusions: Building on promising results with mutation and protein biomarker analysis (Science 369:6499, 2020) this pilot study demonstrates the potential of combining DNA methylation and protein biomarkers and contributes to a larger scale effort to develop a multi-biomarker class MCED test (Ann Oncol 2022 Vol. 33:S575). [Table: see text]

Effect of Lactobacillus fermentum ZS40 on the NF-κB signaling pathway in an azomethane-dextran sulfate sodium-induced colon cancer mouse model.

The occurrence of intestinal diseases such as colon cancer is closely related to the intestinal flora. Lactobacillus fermentum is a gut probiotic that plays an important role in chronic intestinal inflammation and colon cancer. In the current study, we investigated the effect of Lactobacillus fermentum ZS40 on NF-κB signaling pathway of azomethane-dextran sulfate sodium (AOM-DSS) -induced colon cancer in mice. Animals were divided into control group (NC), AOM-DSS-induced model group (CRC), AOM-DSS plus high-dose Lactobacillus fermentum ZS40 (ZS40-H), AOM-DSS plus low-dose Lactobacillus fermentum ZS40 (ZS40-L), AOM-DSS plus Lactobacillus bulgaricus (BLA), and AOM-DSS plus sulfasalazine (SD)-treated group. Observation of animal physiological activity (body weight and defecation), biochemical measurements, histopathological examination of colon tissue, qPCR to evaluate the expression of inflammation-related genes, immunohistochemical analysis of CD34 and CD117, and Western blot analysis of NF-κB signaling pathway were performed. Compared with the CRC group, the ZS40-H, ZS40-L, BLA, and SD groups had decreased levels of colon cancer marker proteins CD34 and CD117, and the number of abnormal colonic lesions observed by colon histology decreased, while the ZS40-H group showed excellent results. In addition, all probiotic interventions showed weight loss effects. The expression of inflammatory stimulators TNF-α and IL-1β in the probiotic treatment group decreased; the expression of key proteins IκBα and p65 in the NF-κB signaling pathway also decreased, resulting in a decrease in the expression of the target protein Cox-2. Therefore, administration of Lactobacillus fermentum ZS40 as a probiotic can alleviate intestinal inflammation and prevent colon cancer in mice.

In Silico Study of Anticancer Activity of Red Betel Leaves Bioactive Compounds against Colon Cancer Marker Proteins

Colon cancer or colorectal cancer is one of the major health problems in the world. Previous research has proven that red betel leaves extract has anticancer activity against breast cancer cells. The study aimed to look at the potential of compounds contained in red betel ethyl acetate fraction as anticancer substances for colon cancer by conducting in silico tests through the docking of three colon cancer biomarker receptors against eight red betel leaves compounds. The parameters of binding affinity energy and inhibition constant used in the molecular docking analysis showed that there are several compounds that have the potential as inhibitors against colon cancer marker proteins to inhibit the development of colorectal cancer and have high bioavalibility as oral drugs. Based on in silico test it is known that the compound N-1,N-9-Bis [E-(2-nitophenyl) methylene] non anedihydrazide has the highest inhibition of human leukotriene A4 hydrolase receptor with binding affinity energy of 11.3160 Kcal/mol. The next compound is 4-({4,6-Bis[3R,5S)-3,5-diamino-1-piperydinyl]-1,3,5-traizin-2-yl}amino) benzenosulfon amide which has the highest inhibition in Chek1 with binding affinity energy of 9.7110 Kcal/mol, and the compound 4-(4-methoxy-phenylamino)-2,3-dihydro-1H-4a, 9-diaza-cyclopenta (b) fluorine-10-carbonitrile has the highest inhibition in the Bcl 2 navitoclax analog receptor with binding affinity energy of 8.4470 Kcal/mol.

SARS-CoV-2 Kerala Isolate Spike Protein Induces Cancer Proliferating Markers for Lung and Breast Cancer: An In Silico Approach

Background: Coronavirus disease (COVID 19) has been emerging as a major threat to humans all over the world. Severe Acute Respiratory Syndrome CoronaVirus 2 (nSARSCoV2) is the causative agent for the disease resulting in severe acute respiratory illness. Earlier, it took several years to come up with a vaccine or other sorts of treatments for viral diseases. But now with the advent of biotechnology and development of bio-informatic tools, the process has been accelerated. The WHO reports 39,806,488 affected cases and 1,112,208 deaths till today all over the world (17 Oct 2020). nSARS CoV2 has a greater influence on people with comorbidities mainly cancer. Objective: The study herein attempts to understand the binding affinity of the spike protein of the novel coronavirus with the lung and breast cancer marker proteins by docking and ClusPro analysis. Method: The analysis was conducted in reference to hACE2 (human Angiotensin Converting Enzyme 2), the receptor of nSARS CoV2. Total 22 different marker proteins were analyzed using ClusPro. Results: BRCA1 (Breast Cancer type 1 susceptibility protein) and CXCR4 (a chemokine receptor belonging to the G protein coupled receptor family) were found to exhibit higher binding affinities.-73.82 kcal/mol and -66.45 kcal/mol were the global energies they showed upon binding to S protein respectively. Conclusion: Therefore, novel SARSCoV2 has a higher chance of inducing cancer in non-cancerous individuals and aids in cancer acceleration in cancer patients . This poses a threat to cancer patients and immunocompromised individuals. The study can be exploited to identify the optimal drug delivery system for novel SARS CoV2.

Near‐Infrared Multilayer MoS2 Photoconductivity‐Enabled Ultrasensitive Homogeneous Plasmonic Colorimetric Biosensing (Adv. Mater. Interfaces 24/2021)

Plasmonic Colorimetric Biosensing of Proteins in Biofluids In article number 2101291, Younggeun Park, Xiaogan Liang, Katsuo Kurabayashi, and co-workers demonstrate rapid, “mix-and-read” detection of cancer marker proteins at sub-femtomolar concentrations in unprocessed whole blood using a multilayered molybdenum disulfide photoconductive channel and plasmonic nanoparticles. Minimized carrier scattering within the photoconductive channel allows for detecting subtle near-infrared light transmission shifts accompanying antigen-induced plasmonic nanoparticle aggregation in blood.

Multi-analytical test based on serum miRNAs and proteins quantification for ovarian cancer early detection.

Advanced ovarian cancer is one of the most lethal gynecological tumor, mainly due to late diagnoses and acquired drug resistance. MicroRNAs (miRNAs) are small-non coding RNA acting as tumor suppressor/oncogenes differentially expressed in normal and epithelial ovarian cancer and has been recognized as a new class of tumor early detection biomarkers as they are released in blood fluids since tumor initiation process. Here, we evaluated by droplet digital PCR (ddPCR) circulating miRNAs in serum samples from healthy (N = 105) and untreated ovarian cancer patients (stages I to IV) (N = 72), grouped into a discovery/training and clinical validation set with the goal to identify the best classifier allowing the discrimination between earlier ovarian tumors from health controls women. The selection of 45 candidate miRNAs to be evaluated in the discovery set was based on miRNAs represented in ovarian cancer explorative commercial panels. We found six miRNAs showing increased levels in the blood of early or late-stage ovarian cancer groups compared to healthy controls. The serum levels of miR-320b and miR-141-3p were considered independent markers of malignancy in a multivariate logistic regression analysis. These markers were used to train diagnostic classifiers comprising miRNAs (miR-320b and miR-141-3p) and miRNAs combined with well-established ovarian cancer protein markers (miR-320b, miR-141-3p, CA-125 and HE4). The miRNA-based classifier was able to accurately discriminate early-stage ovarian cancer patients from health-controls in an independent sample set (Sensitivity = 80.0%, Specificity = 70.3%, AUC = 0.789). In addition, the integration of the serum proteins in the model markedly improved the performance (Sensitivity = 88.9%, Specificity = 100%, AUC = 1.000). A cross-study validation was carried out using four data series obtained from Gene Expression Omnibus (GEO), corroborating the performance of the miRNA-based classifier (AUCs ranging from 0.637 to 0.979). The clinical utility of the miRNA model should be validated in a prospective cohort in order to investigate their feasibility as an ovarian cancer early detection tool.

Multiplex assays of bladder cancer protein markers with magnetic structural color hydrogel microcarriers based on microfluidics

Bladder cancer is one of the most common malignant tumors of the urinary system with a significantly increased incidence in recent years. Tumor biomarkers detection with high specificity and sensitivity bears great significance in clinical diagnosis and treatment. Here, we propose a novel microfluidics-based magnetically responsive structural color hydrogel microcarrier as the multiplex detection platform, with the advantages of easy accessibility and high sensitivity. The resin material with the superior mechanical property was applied to prepare the inverse opal as the framework, in whose gaps perfused with a multifunctional hydrogel composed of polyethylene glycol diacrylate (PEGDA), acrylic acid (AA) and Fe3O4 nanoparticles. The composition enabled the microcarrier with high mechanical strength, low non-specific molecular adhesion, and directional magnetic motion ability, which significantly improved the detection efficiency of the platform. The results demonstrated that our detection system had good sensitivity and reliable specificity in the multiplex analysis of bladder cancer-related protein nuclear matrix protein 22, human bladder tumor antigen and fructose-1,6-diphosphate. Furthermore, the microcarriers were proved to have an ideal application prospect in clinical diagnosis and monitoring. Therefore, magnetically responsive structural color hydrogel microcarriers were suitable for the construction of liquid chips for bladder cancer protein markers.

Study of the prostate-specific antigen–aptamer stability in the PSA–aptamer-single wall carbon nanotube assembly by docking and molecular dynamics simulation

ABSTRACT Prostate-specific antigen (PSA) is used to detect prostate cancer. RNA aptamers against this protein have been reported previously. In this work, the stability and interaction mechanism of aptamer with PSA and single-wall carbon nanotube (SWCNT) was studied by computational techniques. Molecular dynamics simulations were used to investigate the interaction of aptamer segments with the surfaces of SWCNT and prostate-specific antigen (PSA) protein in an aqueous solution by two steps. Firstly, the interaction of aptamer and SWCNT was studied by the molecular dynamic simulation method. Our results showed that an aptamer segment can attach to the surface of SWCNT with its helix axis perpendicularly. Our analysis shows that the π–π stacking interactions play an important role in the binding of the aptamer to carbon rings. Secondly, we analysed the interaction of PSA protein with aptamer in the aptamer–SWCNT assemble. We found that the 3D structure of aptamer in the presence of PSA protein and SWCNT was stable. Finally, this study provides evidence about the stability of aptamer as a sensor on the surface of SWCNT (as a fixed surface) for PSA protein as a ligand. This finding can help to improve new biosensors for detecting cancer protein markers.

Designing smarter anticancer T cells

Synthetic Biology Biological signaling systems can exhibit a large, nonlinear—or “ultrasensitive”—response, which would be useful to engineer into therapeutic T cells to allow for better discrimination between cancer cells and normal tissues. Hernandez-Lopez et al. modified human T cells using a two-step mechanism that allowed them to kill cells expressing large amounts of cancer marker protein but not cells expressing a small amount of the same protein. A first synthetic receptor recognized the antigen with low affinity. That receptor signaled to increase expression of a chimeric antigen receptor (CAR) with high affinity for the same antigen. The circuit proved effective in cell culture and mouse cancer models, offering hope of extending the CAR T cell strategy against solid tumors. Science , this issue p. [1166][1] [1]: /lookup/doi/10.1126/science.abc1855

A Novel cascaded-deep learning classifier for diagnosis of Covid19 and pneumonia disease in chest X-Ray

Breast cancer is one of the most frequent female cancer. The currently available therapeutics are induces cancer cell death and also developing side effects in healthy cells. To overcome the chemotherapeutic challenges, plant-based phytochemicals and nutraceuticals are consist efficient anticancer activity. Naringin is one of the plant derived flavonoids, naturally present in citrus fruits with anticancer activity. In this present study, we analyze the frontier molecular orbital (HOMO-LUMO) for chemical potential and stability of molecules and also the molecular docking studies of naringin to find the binding efficiency of ligand and breast cancer marker proteins. The 3D structure of proteins is retrieved from the RCSB protein data bank and the tool autodock vina helps in molecular docking studies. Among the seven target proteins, PR have efficient binding energy (-9.3 Kcal/mol), which confirms the potential to target breast cancer.

Immunohistochemical Biomarkers in Ductal Carcinoma &amp;lt;i&amp;gt;In Situ&amp;lt;/i&amp;gt;

Introduction: Breast ductal carcinoma In Situ (DCIS) can be defined as a malignant epithelial proliferation with growth limited by the basal membrane of the ductal epithelium, with no evidence of stromal invasion. There has been a trend of trying to subcategorize DCIS based on cell proliferation assays (Ki67) and the expression of hormone receptors and the human epidermal growth receptor (HER-2) as detected by immunohistochemistry, similar to invasive breast carcinomas (IBC). The aims were to evaluate the expression of breast cancer marker proteins in DCIS by immunohistochemistry to better categorize it. Methods: 46 biopsies from women with DCIS and IBC Luminal A-like were evaluated by immunohistochemistry staining of proteins already known to be biomarkers in IBC. For controls, normal breast tissue from mammoplasty (n = 3) was used. Results: Our results showed an increase of estrogen receptor (ER) and progesterone receptor (PR) expression relative to that in normal tissue samples (p

Computational methods for recognition of cancer protein markers in saliva.

In recent years, many studies have supported that cancer tissues can make disease-specific changes in some salivary proteins through some mediators in the pathogenesis of systemic diseases. These salivary proteins have the potential to become cancer-specific biomarkers in the early diagnosis stage. How to effectively identify these potential markers is one of the challenging issues. In this paper, we propose novel machine learning methods for recognition cancer biomarkers in saliva by two stages. In the first stage, salivary secreted proteins are recognized which are considered as candidate biomarkers of cancers. We picked up 557 salivary secretory proteins from 20379 human proteins by public databases and published literatures. Then, we present a training set construction strategy to solve the imbalance problem in order to make the classification methods get better accuracy. From all human protein set, the proteins belonging to the same families as salivary secretory proteins are removed. After that, we use SVC-KM method to cluster the remaining proteins, and select negative samples from each cluster in proportion. Next, the features of proteins are calculated by tools. We collect 24 protein properties such as sequence, structure and physicochemical properties, a total of 1087 features. An innovative procedure based on the local samples is proposed for selecting the appropriate features, in order to further improve the performance of SVM classifier. Experimental results show that the average sensitivity, specificity and accuracy of salivary secretory protein recognition using selected 32 features in training set are 97.09%, 98.10%, 97.61%, respectively. The use of these methods can improve the accuracy of recognition by solving the problems of unbalanced sample size and uneven distribution in training set. In the second stage, we apply the best model to dig out the salivary secreted proteins from 58 reported cancer markers, and get a total of 42 proteins which are considered to be used for salivary diagnosis. We analyze the gene expression data of three types of cancer, and predict that 33 genes will appear in saliva after they are translated into proteins. This study provides an important computational tool to help biologists and researchers reduce the number of candidate proteins and the cost of research. So as to further accelerate the discovery of cancer biomarkers in saliva and promote the development of saliva diagnosis.

Two-dimensional oriented growth of Zn-MOF-on-Zr-MOF architecture: A highly sensitive and selective platform for detecting cancer markers

Fabricating novel bimetallic metal organic framework (MOF) architectures and exploiting them as aptasensor scaffolds for detecting diverse analytes, especially cancer markers, have aroused widespread research attention. Herein, we report a novel strategy for obtaining ZnZr bimetallic MOFs via the MOF-on-MOF method and exploit them as an aptasensor platform for detecting the cancer marker protein tyrosine kinase-7 (PTK7). Basic characterizations reveal that the chemical structure, crystalline properties, and surface functionality of bimetallic ZnZr-MOFs can be modulated by changing the order of addition of metal precursors and organic ligands. The Zn-MOF-on-Zr-MOF hybrid exhibits a hierarchically decussated foliace, whereas Zr-MOF-on-Zn-MOF demonstrates a multilayered nanosheet structure. The electrochemical results reveal that Zr-MOF facilitates aptamer strand immobilization, whereas the Zn-MOF stabilizes the G-quadruplex formed by aptamer strands and PTK7. The Zn-MOF-on-Zr-MOF-based aptasensor outperforms the Zr-MOF-on-Zn-MOF-based one, providing ultralow detection limits of 0.84 and 0.66 pg mL−1, as obtained by electrochemical impedance spectroscopy and differential pulse voltammetry, respectively, within the PTK7 concentration range of 1.0 pg mL−1 to 1.0 ng mL−1. The proposed Zn-MOF-on-Zr-MOF-based aptasensor exhibits high selectivity in the presence of various interferences, good stability, reproducibility, and acceptability in human serum. The proposed strategy provides a new approach for fabricating ultrasensitive and selective bimetallic MOFs-based aptasensors and contributes to efforts to broaden their applications in early cancer diagnosis.

Electrochemical ELISA-based platform for bladder cancer protein biomarker detection in urine

A novel fluidic-based electrochemical ELISA platform is descried for estimation of the bladder cancer protein markers nuclear mitotic apparatus protein 1 (NUMA1) and complement factor H-related 1 (CFHR1). The platform uses an off-site chamber for a sandwich immunoassay and performs the electrochemistry on-chip in a separate chamber. The off-site matrices were connected to the sensor chip in a manner that the sensors were exposed only to the final electroactive product for signal detection, thus avoiding interference from other molecules present in the sample. Two off-site matrices using 3D polymethyl methacrylate (PMMA) sheets and 2D polycarbonate (PC) membranes modified with the desired antibodies were investigated. Antibodies for NUMA1 and CFHR1 were utilized for the immunoassay and hair comb structured gold electrodes were used for sensing. Results in 10% synthetic urine reveal that the system can detect NUMA1 and CFHR1 in the 1–100 ng/ml range with high sensitivities of 260 nA/(ng/ml) and 310 nA/(ng/ml), for NUMA1 and CFHR1, respectively; negligible interference from the diluted urine and other molecules has been observed. A fully automated fluidic prototype has also been developed to demonstrate that automation of the process and multiplexing of detection can be achieved in a small footprint benchtop device. The use of off-site matrix-based platforms paves the way towards a new generation of electrochemical immunosensors for biomarker estimation with negligible non-specific interactions and false signals in complex samples.

Label‐free Electrochemical Aptasensor for Jurkat Cells Detection as a Potential Diagnostic Tool for Leukemia

AbstractElectrochemical impedance spectroscopy (EIS) and thickness shear mode acoustic method (TSM) have been used for development of the aptamer‐based biosensor for detection leukemic Jurkat cells. Thiolated DNA aptamers specific to the cancer marker protein tyrosine kinase‐7 (PTK7) have been chemisorbed on a gold surface. Redox probe [Fe(CN)6]3−/4− has been used for monitoring changes in charge transfer resistance, Rct, in EIS experiments. Rct increased with increasing the concentration of Jurkat cells. TSM allowed label‐free detection based on decrease of resonant frequency following addition of the cells. We obtained high sensitivity of Jurkat cells determination with limit of detection (LOD) 105±10 and 463±50 cells/mL for electrochemical and acoustic sensor, respectively. Small non‐specific interactions have been observed for control U266 cells which can be particularly due to the interaction of the aptamers with lipid part of the biomembranes.

An ultrasensitive fluorescent aptasensor for detection of cancer marker proteins based on graphene oxide-ssDNA.

A novel biosensing platform was developed by integrating a new ssDNA aptamer and graphene oxide (GO) for highly sensitive and selective detection of liver cancer biomarkers (alpha-fetoprotein, AFP). The key concept of this biosensing platform is that the fluorescence of dye-modified ssDNA can be effectively quenched by GO after forming the hybrid structure of graphene oxide–ssDNA (GO–ssDNA). The AFP can selectively react with GO–ssDNA and lead to the decomposition of GO–ssDNA, which results in the recovery of fluorescence, and an increase in fluorescence intensity with the increasing concentration of AFP in the range of 0 to 300 pg mL−1. The linear range was obtained from 1 to 150 pg mL−1 and the detection limit was 0.909 pg mL−1. Moreover, this biosensing platform can be applied to serum and cell imaging for the detection of AFP. The results show that the proposed biosensor has great potential application in AFP-related clinical diagnosis and research.