Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Utilizing Liquid Biopsy-Derived Extracellular Vesicle Protein Biomarkers for Diagnosis and Prognostic Assessment in Lung Squamous Cell Carcinoma

ABSTRACT: In the context of pulmonary nodules detected in imaging, there is currently a lack of highly effective and non-invasive methods for both diagnosing and prognosticating lung squamous cell carcinoma (LUSC). Therefore, in this study, we explored the potential of plasma-derived extracellular vesicles (EVs) with a new nanomaterial as a source of candidate biomarkers to distinguish between benign and malignant pulmonary nodules and predict the prognosis of LUSC patients.

ORGANISM(S): Homo Sapiens

SUBMITTER: Shujun Cheng

PROVIDER: PXD047893 | iProX | Mon Dec 18 00:00:00 GMT 2023

REPOSITORIES: iProX

ACCESS DATA

Json Xml

Publications

Liquid biopsy-derived extracellular vesicle protein biomarkers for diagnosis and prognostic assessment of lung squamous cell carcinoma.

Ma Sheng S Zhao Na N Dong Xin X Wang Yaru Y Song Lei L Zheng Ruiqi R Zhi Xiaochen X Ma Congcong C Cheng Shujun S Li Jie J Liu Yutao Y Xiao Ting T

Cancer cell international 20250424 1

<h4>Background</h4>For patients with nodules detected in imaging that are indeterminate for malignancy, achieving accurate, early, and non-invasive diagnosis of Lung Squamous Cell Carcinoma (LUSC) remains a significant challenge. Therefore, we aimed to establish diagnostic and prognostic models by identifying plasma extracellular vesicles (EVs) associated protein biomarkers specific to LUSC.<h4>Methods</h4>This study employed a novel nanomaterial, NaY, for the enrichment of EVs from plasma. Vali ...[more]

PMID: 40275246

Similar Datasets

Serum microRNA panel to differentiate malignant pulmonary nodules from benign pulmonary nodules

Project description:A serum miRNA combination could be a favourable classifier to differentiate malignant pulmonary nodules from benign pulmonary nodules.

2023-08-27 | GSE157074 | GEO

Serum microRNA panel to differentiate malignant pulmonary nodules from benign pulmonary nodules

Project description:Serum microRNA panel to differentiate malignant pulmonary nodules from benign pulmonary nodules

| PRJNA660019 | ENA

Next generation sequencing identifies a highly accurate microRNA panel that distinguishes well-differentiated thyroid cancer from benign thyroid nodules

Project description:Background: Fine needle aspiration biopsy (FNAB) is the gold-standard procedure for diagnosing malignant thyroid nodules. Indeterminate cytology is identified in 10-40% of cases and molecular testing may guide management in this setting. Current commercial options are expensive, and are either sensitive or specific. The aim of this study was to utilize next generation sequencing (NGS) technology to identify informative diversities in the microRNA (miRNA) expression profile of benign versus malignant thyroid nodules. Methods: Ex-vivo FNAB samples were obtained from thyroid specimens of patients that underwent thyroidectomy at a referral center. miRNA levels were determined using NGS and multiplexing technologies. Statistical analyses identified differences between normal and malignant samples and miRNA expression profiles that associate with malignancy were established. The accuracy of the miRNA signature in predicting histological malignancy was validated using a group of patient specimens with indeterminate cytology results. Results: 274 samples were obtained from 102 patients undergoing thyroidectomy. Of these samples, 71% were benign and 29% were malignant. Nineteen miRNAs were identified as statistically different between benign and malignant samples and were used to classify 35 additional nodules with indeterminate cytology (validation). The miRNA panel’s sensitivity, specificity, negative and positive predictive values and overall accuracy were 91%, 100%, 87%, 100% and 94%, respectively. Conclusion: Using NGS technology we identified a panel of 19 miRNAs that may be utilized to distinguish benign from malignant thyroid nodules with indeterminate cytology. Impact: Our panel may classify indeterminate thyroid nodules at higher accuracy than commercially available molecular tests.

2018-12-21 | GSE116196 | GEO

Unlocking the diagnostic power of plasma extracellular vesicle miR-200 family in pancreatic ductal adenocarcinoma

Project description:Background Distinguishing benign from malignant pancreaticobiliary disease is challenging because of the absence of reliable biomarkers. Circulating extracellular vesicles (EVs) have emerged as functional mediators between cells. Their cargos, including microRNAs (miRNAs), are increasingly acknowledged as an important source of potential biomarkers. This multicentric, prospective study aimed to establish a diagnostic plasma EV-derived miRNA signature to discriminate pancreatic ductal adenocarcinoma (PDAC) from benign pancreaticobiliary disease. Methods Plasma EVs were isolated using size exclusion chromatography (SEC) and characterised using nanoparticle tracking analysis, electron microscopy and Western blotting. EV-RNAs underwent small RNA sequencing to discover differentially expressed markers for PDAC (n = 10 benign vs. 10 PDAC). Candidate EV-miRNAs were then validated in a cohort of 61 patients (n = 31 benign vs. 30 PDAC) by RT-qPCR. Logistic regression and optimal thresholds (Youden Index) were used to develop an EV-miR-200 family model to detect cancer. This model was tested in an independent cohort of 95 patients (n = 30 benign, 33 PDAC, and 32 cholangiocarcinoma). Results Small RNA sequencing and RT-qPCR showed that EV-miR-200 family members were significantly overexpressed in PDAC vs. benign disease. Combined expression of the EV-miR-200 family showed an AUC of 0.823. In an independent validation cohort, application of this model showed a sensitivity, specificity and AUC of 100%, 88%, and 0.97, respectively, for diagnosing PDAC.

2025-07-31 | GSE303476 | GEO

Identification of Tissue Specific DNA Methylation Signatures for Thyroid Nodule Diagnostics

Project description:PURPOSE: Thyroid cancer is frequently difficult to diagnose due to an overlap of cytological features between malignant and benign nodules. This overlap leads to unnecessary removal of the thyroid in patients without cancer. While providing some improvement over cytopathologic diagnostics, molecular methods frequently fail to provide a correct diagnosis for thyroid nodules. These approaches are based on the difference between malignant nodules and normal adjacent thyroid tissue and assume that normal thyroid tissues are the same as benign nodules. However, in contrast to normal thyroid tissues, benign thyroid nodules can contain genetic alterations that can be found in cancerous nodules. PATIENTS AND METHODS: For the development of a new molecular diagnostic test for thyroid cancer, we evaluated DNA methylation in 109 thyroid tissues by using genome wide single base resolution DNA methylation analysis (Reduced Representation Bisulfite Sequencing). The test was validated in the retrospective cohort containing 64 thyroid nodules. RESULTS: By conducting Reduced Representation Bisulfite Sequencing in 109 thyroid specimens, we found significant differences between normal tissue, benign nodules, and cancer. Based on tissue-specific epigenetic signatures for benign and malignant nodules, we developed a new epigenetic approach for thyroid diagnostics. According to the validation cohort, our test has an estimated specificity of 97% (95% CI, 80 to 100), sensitivity of 100% (95% CI, 86 to 100), PPV of 97% (95% CI, 82-100), NPV of 100% (95%, 85 to 100). CONCLUSION: These data show that epigenetic testing can provide outstanding diagnostic accuracy for thyroid nodules by evaluating tissue specific DNA methylation.

2018-11-30 | GSE107738 | GEO

Multiomic approaches for thyroid cancer diagnosis

Project description:Thyroid cancer is a common endocrine malignancy; however, its diagnosis is not straightforward. The current gold standard for diagnosing thyroid cancer is fine needle aspiration biopsy (FNAB), but when cytological analysis does not provide viable results or samples belong to less defined categories of diagnosis, patients are often referred to diagnostic surgery. Given that not all these nodules require removal, nor all of them are malignant, patients would not necessarily require surgery had the initial FNAB diagnosis been more conclusive. There is therefore a lack of reliable and specific biomarkers for thyroid cancer malignancy, that can complement and improve the current diagnosing methods. “Omics” approaches have gained much attention in the last decade in the field of biomarker discovery for diagnostic and prognostic characterization of various pathophysiological conditions. In this project, proteomics and metabolomics approaches were applied to the same thyroid nodules from patients with benign and malignant lesions. Tissue analysis provided several interesting biomarkers by both proteomics and metabolomics. The combination of these results demonstrated the high energetic and biomass demand of cancer cells, as well as a biomarker panel including 2 free peptides and 2 proteins with high sensitivity and specificity. Together, these results have contributed to increasing the knowledge of thyroid cancer phenotype and corresponding biochemical profiles, as well as providing potential biomarkers for malignancy, and improving diagnostic methodologies.

2023-10-24 | PXD035583 | Pride

Screening and Modeling of LUAD-Associated Predictive and Prognostic Genes Based on Multi-Omics Data

Project description:LUAD is widespread worldwide with onset mainly in the low-grade stage due to early screen. However, the ambiguity surrounding small-sized nodules often leads to hesitation in deciding between surgery and surveillance. Liquid biopsy which leverages the cell-free RNA (cfRNA) shows excellent classification performance among disease groups in addition to the non-invasive feature. This research utilizes liquid biopsy to construct an effective screening system to aid with the further differentiation of benign diseases from lung cancers.

2025-12-04 | GSE278695 | GEO

Non-Small Cell Lung Cancer, Peripheral Immunity, Extending the Tumor Macroenvironment

Project description:We recently established that gene expression in PAXgene stabilized blood RNA distinguishes benign (BN) from malignant (MN) pulmonary nodules in high risk candidates with an AUC of 0.84. We now expand our studies to include incidental nodules identified in routine clinical settings using data from 603 patients analyzed on Illumina microarrays. We identify 300 gene probes achieving an AUC of 0.84 for Indeterminate Pulmonary Nodules (IPN) from 6-25 mm and 0.824 for IPN from 8-20 mm, outperforming 3 prominent clinical models that achieve AUCs of 0.60-0.689. We address the basis for these differences by in silico flow cytometry using CIBERSORT and identify significant differences between MN and BN patients including proportions of T-cells, M0 macrophages, NK cells, B cells and exhausted CD8 T-cells. We identify major increases in expression of genes promoting cell death and strong decreases in functions promoting transcription, lymphogenesis and cell viability. A preferential use of oxidative phosphorylation and a signature of mitochondrial dysfunction are also associated with the presence of a MN.

2020-08-02 | GSE135304 | GEO

Liquid Biopsy in Organ Damage: small extracellular vesicle chip-based assessment of polytrauma.

Project description:BackgroundDespite major advances in medicine, blood-borne biomarkers are urgently needed to support decision-making, including polytrauma. Here, we assessed serum-derived extracellular vesicles (EVs) as potential markers of decision-making in polytrauma.ObjectiveOur Liquid Biopsy in Organ Damage (LiBOD) study aimed to differentiate polytrauma with organ injury from polytrauma without organ injury. We analysed of blood-borne small EVs at the individual level using a combination of immunocapture and high-resolution imaging.MethodsTo this end, we isolated, purified, and characterized small EVs according to the latest Minimal Information for Studies of Extracellular Vesicles (MISEV) guidelines from human blood collected within 24 h post-trauma and validated our results using a porcine polytrauma model.ResultsWe found that small EVs derived from monocytes CD14+ and CD14+CD61+ were significantly elevated in polytrauma with organ damage. To be precise, our findings revealed that CD9+CD14+ and CD14+CD61+ small EVs exhibited superior performance compared to CD9+CD61+ small EVs in accurately indicating polytrauma with organ damage, reaching a sensitivity and a specificity of 0.81% and 0.97%, respectively. The results in humans were confirmed in an independent porcine model of polytrauma.ConclusionThese findings suggest that these specific types of small EVs may serve as valuable, non-invasive, and objective biomarkers for assessing and monitoring the severity of polytrauma and associated organ damage.

| S-EPMC10684673 | biostudies-literature

Integrative Multianalytical Model Based on Novel Plasma Protein Biomarkers for Distinguishing Lung Adenocarcinoma and Benign Pulmonary Nodules

Project description:Given the pressing clinical problem of making decision in diagnosis for subjects with pulmonary nodules, we aimed to discover novel plasma protein biomarkers for lung adenocarcinoma (LUAD) and benign pulmonary nodules (BPN), then develop an integrative multianalytical model to guide the clinical management of LUAD and BPN patients. Through label-free quantitative plasma proteomic analysis, twelve differentially expressed proteins (DEPs) in LUAD and BPN were screened. The diagnostic abilities of the DEPs were validated in two independent validation cohorts. The results showed that the level of three candidate proteins (PRDX2, PON1, APOC3) were lower in the plasma of LUAD than BPN. The three candidate proteins were combined with three promising computed tomography (CT) indicators (Spiculation, Vascular Notch Sign, Lobulation) and three traditional markers (CEA, CA125, CYFRA21-1) to construct an integrative multianalytical model, which was effective in distinguishing LUAD from BPN, with AUC of 0.904, sensitivity of 81.44% and specificity of 90.14%. Moreover, the model possessed impressive diagnostic performance between early LUADs and BPNs, with the AUC, sensitivity, specificity and accuracy of 0.868, 65.63%, 90.14% and 82.52%, respectively. This model maybe a useful auxiliary diagnostic tool for LUAD and BPN by achieving a better balance of sensitivity and specificity.

2024-01-26 | PXD046731 | Pride

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data