Dataset Information

An exosome-based one-step high-throughput microfluidic platform for epithelial ovarian cancer diagnosis

ABSTRACT: Exosomes, reflecting the live cellular state, hold promise for non-invasive disease detection. However, the significance of exosome cargos has been underestimated, particularly in the clinic, mainly due to the scarcity of exosome. Here, we present a "one-step" high-throughput microfluidic platform integrating exosome capture, in situ lysis, and protein biomarker detection, which was further designed for epithelial ovarian cancer (EOC) detection in a prospective cohort with 2–8 μL of serum samples. Through proteomic analysis of exosomes from patients' serum, combined with cell lines, we identified total 1818 differential expressed protein (DEP) and determined DEPs to develop the EOC detection microfluidic platform via multi-step screening. Based on the results of the platform's examination of the prospective cohort, an EOC detection model (P9) was developed and validated, whose specificities reached 98.8% (95% CI, 93.6%–99.8%) in the training set and 100.0% (95% CI, 91.6%–100.0%) in the validation set. The combination of P9 and CA125 exhibited 100.0% specificity in both training and validation sets, indicating a promising non-invasive modality for EOC detection that could reduce the risks associated with false-positive results. Our study also suggests that this high-throughput microfluidic platform can be customized to target a variety of exosome cargos for diverse clinical applications.

ORGANISM(S): Homo Sapiens

SUBMITTER: Mo Li

PROVIDER: PXD053690 | iProX | Thu Jul 04 00:00:00 BST 2024

REPOSITORIES: iProX

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Small extracellular vesicle-based one-step high-throughput microfluidic platform for epithelial ovarian cancer diagnosis.

Wu Yu Y Wang Chao C Guo Yuhan Y Zhang Yunhong Y Zhang Xue X Wang Pan P Yue Wei W Zhu Xin X Liu Zhaofei Z Zhang Yu Y Guo Hongyan H Han Lin L Li Mo M

Journal of nanobiotechnology 20250407 1

<h4>Background</h4>Ovarian cancer (OC) is diagnosed at advanced stages, resulting in limited treatment options for patients. While early detection of OC has been investigated, the invasiveness of approaches, high sample requirements, or false-positive rates undermined its benefits. Here, we present a "one-step" high-throughput microfluidic platform for epithelial ovarian cancer (EOC) detection that integrates small extracellular vesicle (sEV) capture, in situ lysis, and protein biomarker detecti ...[more]

PMID: 40189497

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Project description:Abstract: Background: With amyloid-β (Aβ) therapies for Alzheimer’s disease (AD) under active debate, there is a need for diagnostic tools that reflect disease biology beyond Aβ and tau for early detection and for patient stratification. The indicator cell assay platform (iCAP) is a tool for blood-based diagnostics that uses standardized cells as biosensors to transduce complex circulating signals into gene-expression readouts, which are then used to train multivariate disease classifiers for precision medicine. We developed an iCAP for early detection of Alzheimer’s disease (AD-iCAP). Methods: In a retrospective study, AD-iCAP was developed by incubating banked plasma samples from patients with early-stage AD (mild cognitive impairment or mild dementia) and age-matched controls with standardized neurons; whole-transcriptome responses were measured and used train disease classifiers by machine learning. The assay was optimized and analytically validated. Patient AD-iCAP data were separated into a training set, external validation set and an independent test set and used to parameterize and test models. To minimize bias, modeling features were selected from a predefined (a priori) 84-gene panel derived from an independent AD-iCAP dataset generated using plasma from 5xFAD mice. Results: We developed AD-iCAP using 191 banked plasma samples across three cohorts. Performance was assessed in two held-out sets: an external validation set (n=82; AUC 0.64, 95% CI 0.51–0.78) and an independent test set (n=23, AUC 0.77, 95% CI 0.57–0.96). Systems biology analyses of differential response profiles showed concordance with postmortem AD brain transcriptomes and enrichment of AD-relevant pathways beyond amyloid, including cholesterol biosynthesis, synaptic structure/neurotransmission and NGF/TrkA signaling (FDR < 0.05). The final model’s features included AD-linked genes AKT3, GPR50, PALLD and RGMA, related to neuronal signaling and cytoskeletal/axon-guidance processes. Conclusions: AD-iCAP is a blood-based diagnostic for early-stage detection of AD. It outputs cell-based transcriptional profiles with disease-relevance that are induced by circulating factors. In retrospective, multi-cohort testing, it showed modest-to-good discrimination, supporting prospective confirmation in larger cohorts. Because its readout captures biology beyond amyloid and tau, it may provide complementary information for combined testing. The multivariate readout supports further development for patient stratification as Aβ-targeted therapies and alternatives evolve.

Project description:Pancreatic cancer is the fourth leading cause of cancer death. Lack of early detection technology for pancreatic cancer invariably leads to a typical clinical presentation of incurable disease at initial diagnosis. Oral fluid (saliva) meets the demand for non-invasive, accessible, and highly efficient diagnostic medium. The level of salivary analytes, such as mRNA and microflora, vary upon disease onset; thus possess valuable signatures for early detection and screening. In this study, we evaluated the performance and translational utilities of the salivary transcriptomic and microbial biomarkers for non-invasive detection of early pancreatic cancer. Two biomarker discovery technologies were used to profile transcriptome in saliva supernatant and microflora in saliva pellet. The Affymetrix Human Genome U133 Plus 2.0 Array was used to discover altered gene expression in saliva supernatant. The Human Oral Microbe Identification Microarray (HOMIM) was used to investigate microflora shift in saliva pellet. Biomarkers selected from both studies were subjected to an independent clinical validation using a cohort of 30 early pancreatic cancer, 30 chronic pancreatitis and 30 healthy matched-control saliva samples. Two panels of salivary biomarkers, including eleven mRNA biomarkers and two microbial biomarkers were discovered and validated for pancreatic cancer detection. The logistic regression model with the combination of three mRNA biomarkers (ACRV1, DMXL2 and DPM1) yielded a ROC-plot AUC value of 0.974 (95% CI, 0.896 to 0.997; P < 0.0001) with 93.3% sensitivity and 90% specificity in distinguishing pancreatic cancer patients from healthy subjects. The logistic regression model with the combination of two bacterial biomarkers (Neisseria elongata and Streptococcus mitis) yielded a ROC-plot AUC value of 0.895 (95% CI, 0.784 to 0.961; P < 0.0001) with 96.4% sensitivity and 82.1% specificity in distinguishing pancreatic cancer patients from healthy subjects. Importantly, the logistic regression model with the combination of four biomarkers (mRNA biomarkers, ACRV1, DMXL2 and DPM1; bacterial biomarker, S. mitis) could differentiate pancreatic cancer patients from all non-cancer subjects (chronic pancreatitis and healthy control), yielding a ROC-plot AUC value of 0.949 (95% CI, 0.877 to 0.985; P < 0.0001) with 92.9% sensitivity and 85.5% specificity. This study comprehensively compared the salivary transcriptome and microflora between pancreatic cancer and control subjects. We have discovered and validated eleven mRNA biomarkers and two microbial biomarkers for early detection of pancreatic cancer in saliva. The logistic regression model with four salivary biomarkers can detect pancreatic cancer specifically without the complication of chronic pancreatitis. This is the first report demonstrating the value of multiplex salivary biomarkers for the non-invasive detection of a high impact systemic cancer. Keywords: Salivary biomarker, pancreatic cancer, early detection, salivary transcriptome, salivary microflora

Dataset Information

An exosome-based one-step high-throughput microfluidic platform for epithelial ovarian cancer diagnosis

Publications

Small extracellular vesicle-based one-step high-throughput microfluidic platform for epithelial ovarian cancer diagnosis.

Similar Datasets

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