A perception-based nanosensor platform to detect cancer biomarkers.
Ontology highlight
ABSTRACT:
SUBMITTER: Yaari Z
PROVIDER: S-EPMC8604403 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The clinical course of prostate cancer (PCa) is highly variable, demanding an individualized approach to therapy and robust prognostic markers for treatment decisions. We here present a random forest-based classification model to predict aggressive behaviour of PCa. DNA methylation changes between PCa cases with good or poor prognosis (discovery cohort with n=78) were used as input. The model was validated with data from two independent PCa cohorts from ICGC and TCGA. Ranking of cancer progression-related DNA methylation changes allowed selection of candidate genes for additional validation by immunohistochemistry. We identified loss of ZIC2 protein expression as a promising novel prognostic biomarker for PCa in >12,000 tissue micro-array tumors. The prognostic value of ZIC2 proved to be independent from established clinico-pathological variables including Gleason, stage, nodal stage and PSA. In summary, we have developed a PCa classification model which either directly or via expression analyses of the identified top ranked candidate genes might help in decision making related to the treatment of prostate cancer patients.
2019-10-29 | GSE127985 | GEO
Project description:BackgroundThe clinical course of prostate cancer (PCa) is highly variable, demanding an individualized approach to therapy. Overtreatment of indolent PCa cases, which likely do not progress to aggressive stages, may be associated with severe side effects and considerable costs. These could be avoided by utilizing robust prognostic markers to guide treatment decisions.ResultsWe present a random forest-based classification model to predict aggressive behaviour of prostate cancer. DNA methylation changes between PCa cases with good or poor prognosis (discovery cohort with n = 70) were used as input. DNA was extracted from formalin-fixed tumour tissue, and genome-wide DNA methylation differences between both groups were assessed using Illumina HumanMethylation450 arrays. For the random forest-based modelling, the discovery cohort was randomly split into a training (80%) and a test set (20%). Our methylation-based classifier demonstrated excellent performance in discriminating prognosis subgroups in the test set (Kaplan-Meier survival analyses with log-rank p value < 0.0001). The area under the receiver operating characteristic curve (AUC) for the sensitivity analysis was 95%. Using the ICGC cohort of early- and late-onset prostate cancer (n = 222) and the TCGA PRAD cohort (n = 477) for external validation, AUCs for sensitivity analyses were 77.1% and 68.7%, respectively. Cancer progression-related DNA hypomethylation was frequently located in 'partially methylated domains' (PMDs)-large-scale genomic areas with progressive loss of DNA methylation linked to mitotic cell division. We selected several candidate genes with differential methylation in gene promoter regions for additional validation at the protein expression level by immunohistochemistry in > 12,000 tissue micro-arrayed PCa cases. Loss of ZIC2 protein expression was associated with poor prognosis and correlated with significantly shorter time to biochemical recurrence. The prognostic value of ZIC2 proved to be independent from established clinicopathological variables including Gleason grade, tumour stage, nodal stage and prostate-specific-antigen.ConclusionsOur results highlight the prognostic relevance of methylation loss in PMD regions, as well as of several candidate genes not previously associated with PCa progression. Our robust and externally validated PCa classification model either directly or via protein expression analyses of the identified top-ranked candidate genes will support the clinical management of prostate cancer.
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Project description:In the last few years, quantum dot (QD) nanoparticles have been employed for bioimaging and sensing due to their excellent optical features. Most studies have used photoluminescence (PL) intensity-based techniques, which have some drawbacks, especially when working with nanoparticles in intracellular media, such as fluctuations in the excitation power, fluorophore concentration dependence, or interference from cell autofluorescence. Some of those limitations can be overcome with the use of time-resolved spectroscopy and fluorescence lifetime imaging microscopy (FLIM) techniques. In this work, CdSe/ZnS QDs with long decay times were modified with aminophenylboronic acid (APBA) to achieve QD-APBA conjugates, which can act as glucose nanosensors. The attachment of the boronic acid moiety on the surface of the nanoparticle quenched the PL average lifetime of the QDs. When glucose bonded to the boronic acid, the PL was recovered and its lifetime was enhanced. The nanosensors were satisfactorily applied to the detection of glucose into MDA-MB-231 cells with FLIM. The long PL lifetimes of the QD nanoparticles made them easily discernible from cell autofluorescence, thereby improving selectivity in their sensing applications. Since the intracellular levels of glucose are related to the metabolic status of cancer cells, the proposed nanosensors could potentially be used in cancer diagnosis.
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Project description:This paper presents an aptameric graphene nanosensor for detection of small-molecule biomarkers. To address difficulties in direct detection of small molecules associated with their low molecular weight and electrical charge, we incorporate an aptamer-based competitive affinity assay in a graphene field effect transistor (FET), and demonstrate the utility of the nanosensor with dehydroepiandrosterone sulfate (DHEA-S), a small-molecule steroid hormone, as the target analyte. In the competitive affinity assay, DHEA-S specifically binds to aptamer molecules pre-hybridized to their complementary DNA anchor molecules immobilized on the graphene surface. This results in the competitive release of the strongly charged aptamer from the DNA anchor and hence a change in electrical properties of the graphene, which can be measured to achieve the detection of DHEA-S. We present experimental data on the label-free, specific and quantitative detection of DHEA-S at clinically appropriate concentrations with an estimated detection limit of 44.7 nM, and analyze the trend observed in the experiments using molecular binding kinetics theory. These results demonstrate the potential of our nanosensor in the detection of DHEA-S and other small molecules in biomedical applications.
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Project description:Currently, traumatic brain injury (TBI) is detected by medical imaging; however, medical imaging requires expensive capital equipment, is time- and resource-intensive, and is poor at predicting patient prognosis. To date, direct measurement of elevated protease activity has yet to be utilized to detect TBI. In this work, we engineered an activity-based nanosensor for TBI (TBI-ABN) that responds to increased protease activity initiated after brain injury. We establish that a calcium-sensitive protease, calpain-1, is active in the injured brain hours within injury. We then optimize the molecular weight of a nanoscale polymeric carrier to infiltrate into the injured brain tissue with minimal renal filtration. A calpain-1 substrate that generates a fluorescent signal upon cleavage was attached to this nanoscale polymeric carrier to generate an engineered TBI-ABN. When applied intravenously to a mouse model of TBI, our engineered sensor is observed to locally activate in the injured brain tissue. This TBI-ABN is the first demonstration of a sensor that responds to protease activity to detect TBI.
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Project description:BACKGROUND:Currently, there are no FDA approved screening tools for detecting early stage ovarian cancer in the general population. Development of a biomarker-based assay for early detection would significantly improve the survival of ovarian cancer patients. METHODS:We used a multiplex approach to identify protein biomarkers for detecting early stage ovarian cancer. This new technology (Proseek® Multiplex Oncology Plates) can simultaneously measure the expression of 92 proteins in serum based on a proximity extension assay. We analyzed serum samples from 81 women representing healthy, benign pathology, early, and advanced stage serous ovarian cancer patients. RESULTS:Principle component analysis and unsupervised hierarchical clustering separated patients into cancer versus non-cancer subgroups. Data from the Proseek® plate for CA125 levels exhibited a strong correlation with current clinical assays for CA125 (correlation coefficient of 0.89, 95% CI 0.83, 0.93). CA125 and HE4 were present at very low levels in healthy controls and benign cases, while higher levels were found in early stage cases, with highest levels found in the advanced stage cases. Overall, significant trends were observed for 38 of the 92 proteins (p < 0.001), many of which are novel candidate serum biomarkers for ovarian cancer. The area under the ROC curve (AUC) for CA125 was 0.98 and the AUC for HE4 was 0.85 when comparing early stage ovarian cancer versus healthy controls. In total, 23 proteins had an estimated AUC of 0.7 or greater. Using a naïve Bayes classifier that combined 12 proteins, we improved the sensitivity corresponding to 95% specificity from 93 to 95% when compared to CA125 alone. Although small, a 2% increase would have a significant effect on the number of women correctly identified when screening a large population. CONCLUSIONS:These data demonstrate that the Proseek® technology can replicate the results established by conventional clinical assays for known biomarkers, identify new candidate biomarkers, and improve the sensitivity and specificity of CA125 alone. Additional studies using a larger cohort of patients will allow for validation of these biomarkers and lead to the development of a screening tool for detecting early stage ovarian cancer in the general population.
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