Circulating Proteins and Metabolites Panel for Noninvasive Preoperative Diagnosis of Epithelial Ovarian Cancer
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo Sapiens
SUBMITTER: 
Shuzhong Yao
PROVIDER: PXD066792 | iProX | Thu Jul 31 00:00:00 BST 2025
REPOSITORIES: iProX
Similar Datasets
Project description:We aimed to discover and validate a panel of serum biomarkers for high grade serous ovarian cancer (HGSOC) using our lectin-magnetic bead array-coupled proteomics platform. Serum from age-matched women with HGSOC, benign tumours or healthy controls were analysed in discovery (UKCTOCS, n=30 and UKOPS, n=30) and validation (Australian Ovarian Cancer Study, n=95) cohorts using shotgun and targeted proteomics, respectively. A 7-lectin discovery screen shortlisted 60 candidate proteins and 3 lectins for validation, which revealed elevated levels of AAL, SNA or STL-binding FIBB, CO9, ITIH3, HPT, A1AT, AACT in HGSOC, while IBP3, A2MG, PON1, CHLE and ALS were reduced. Multimarker panels were developed using generalized regression with lasso estimation and leave-one-out cross-validation. The best performing panel comprised of 13 peptides from Solanum Tuberosum lectin (STL)-binding proteins with 96.3% area under the receiver operating curve, 97.7% specificity and 78.6% sensitivity for distinguishing HGSOC from benign and healthy groups. The peptides robust in cross-validations were from IBP3, KNG1, CO9, THRB, HPTR, HPT, FINC, FA10, GELS. The validated serum biomarkers show promise for early detection of HGSOC and should be further evaluated.
2023-10-24 | PXD032299 | Pride
Project description:Introduction: Accurate diagnosis of distal cholangiocarcinoma (CCA) and pancreatic ductal adenocarcinoma (PDAC) is a challenge with clinical consequences. Both are lethal malignancies with distinct therapeutic options. This study aimed to identify a circulating microRNA (miRNA) signature to diagnose and discriminate distal CCA from PDAC. Methods: In the discovery phase, microarray profiling of 752 miRNAs was performed on plasma samples of seven patients with distal CCA and seven age- and sex-matched healthy controls. Significant candidate miRNAs were selected for validation based on predefined selection criteria. In the validation phase, these miRNAs were analyzed by RT-qPCR in an independent cohort of healthy controls (n=32), benign periampullary disease (n=20), distal CCA (n=24), and age- sex- and stage-matched PDAC (n=24). Data were normalized to a combination of two reference genes. The optimal diagnostic combination of miRNAs was determined by logistic regression. Sensitivity and specificity were evaluated by ROC curves and AUC values. Results: In the discovery phase, 19 miRNAs were significantly deregulated in patients with distal CCA compared to healthy controls. In the validation phase, 12 candidate miRNAs were selected for validation by RT-qPCR based on pre-defined selection criteria. A two-miRNA panel of downregulated miR-16 and upregulated miR-877 was able accurately distinguish distal CCA from benign disease as well as from PDAC. Conclusion: This is the first study to identify a combined panel of plasma miRNAs which shows promising diagnostic capability to serve as distal CCA signature with the potential to discriminate distal CCA from PDAC.
2019-11-26 | GSE117687 | GEO
Project description:Abstract: Sequencing technologies have enabled in-depth analysis of liquid biopsies in cancer, offering a minimally invasive sample collection. The most widely used material is blood which, next to circulating tumor cells and circulating tumor DNA, is the source of tumor-educated platelets (TEPs). Methods: We developed imPlatelet method which converts RNA-sequenced platelet data to images, additionally implementing biological knowledge from the Kyoto Encyclopedia of Genes and Genomes Pathway. First, we tested imPlatelet method on a cohort of 401 non-small cell lung cancer patients and 62 sarcoma patients. Next, we applied the developed tool to platelets collected from a new, independent cohort of 28 ovarian cancer patients and 30 non-cancer benign gynaecological conditions. Results: imPlatelet provided excellent discrimination between cancer cases and healthy controls, with accuracy equal to 1 in training, validation and independent datasets. When discriminating between ovarian cancer cases and benign conditions, imPlatelet reached 0.91 balanced accuracy, with sensitivity and specificity equal to 0.95 and 0.88, respectively, in an independent test set. ImPlatelet outperformed current state-of-the-art method thromboSeq in the aspects of balanced classification accuracy, the computational power needed, user experience, and execution time. Conclusions: According to our knowledge, this is the first study implementing an image-based deep learning approach combined with biological knowledge to classify human samples. Our results on classification of ovarian cancer considerably outperform previously published methods and our own alternative attempts of discrimination. We show that a deep learning image-based classifier accurately identifies cancer, despite the limited number of samples and even among non-cancer conditions which affect platelet transcriptome making the diagnosis difficult.
2021-10-06 | GSE158508 | GEO
Project description:We aimed to discover and validate a panel of serum biomarkers for high grade serous ovarian cancer (HGSOC) using our lectin-magnetic bead array-coupled proteomics platform. Serum from age-matched women with HGSOC, benign tumours or healthy controls were analysed in discovery (UKCTOCS, n=30 and UKOPS, n=30) and validation (Australian Ovarian Cancer Study, n=95) cohorts using shotgun and targeted proteomics, respectively. A 7-lectin discovery screen shortlisted 60 candidate proteins and 3 lectins for validation, which revealed elevated levels of AAL, SNA or STL-binding FIBB, CO9, ITIH3, HPT, A1AT, AACT in HGSOC, while IBP3, A2MG, PON1, CHLE and ALS were reduced. Multimarker panels were developed using generalized regression with lasso estimation and leave-one-out cross-validation. The best performing panel comprised of 13 peptides from Solanum Tuberosum lectin (STL)-binding proteins with 96.3% area under the receiver operating curve, 97.7% specificity and 78.6% sensitivity for distinguishing HGSOC from benign and healthy groups. The peptides robust in cross-validations were from IBP3, KNG1, CO9, THRB, HPTR, HPT, FINC, FA10, GELS. The validated serum biomarkers show promise for early detection of HGSOC and should be further evaluated.
2024-08-06 | PXD033108 | panorama
Project description:Purpose: Gastric cancer (GC) is one of the most common causes of cancer deaths worldwide; however, reliable and non-invasive screening methods for GC are not established. Therefore, we conducted this study to develop a biomarker for GC detection, consisting of urinary microRNAs (miRNAs). Experimental Design: We matched 306 participants by age and sex (153 pairs consisting of patients with GC and healthy controls [HCs]), then randomly divided them across three groups: (1) the discovery cohort (4 pairs); (2) the training cohort (95 pairs); (3) the validation cohort (54 pairs). Moreover, 64 participants (32 pairs) with serum samples were also enrolled in the serum cohort. Result: There were 22 urinary miRNAs with significantly aberrant expressions between the two groups in the discovery cohort. Upon multivariate analysis of the training cohort, urinary expression levels of miR-6807-5p and miR-6856-5p were significantly independent biomarkers for diagnosis of GC, in addition to Helicobacter pylori (H. pylori) status. A diagnostic panel that combined the aberrant miRNAs and H. pylori status distinguished between HC and GC samples with an area under the curve (AUC) = 0.736. In the validation cohort, urinary miR-6807-5p and miR-6856-5p showed significantly higher expression levels in the GC group, and the combination biomarker panel of miR-6807-5p, miR-6856-5p, and H. pylori status also showed excellent performance (AUC = 0.885). In addition, serum levels of miR-6807-5p and miR-6856-5p were significantly higher in the GC group. Conclusion: This novel biomarker panel enables early and non-invasive detection of GC.
2019-12-22 | GSE128720 | GEO
Project description:We constructed a heterogeneous patient cohort of pre-treatment (prior to either surgery or chemotherapy) blood samples from 180 women enrolled in two independent prospective cohort studies of women presenting with an adnexal mass. One sample (E-1056) was later excluded due to an abnormally high level of mir-122 believed to be due to a recent MI. The remaining 179 samples were subdivided 3:1 into a 135 patient training set and 44 patient validation set. Total serum RNA was extracted, converted into miRNA next generation sequencing libraries, and sequenced. The variables for classification model development were selected using three methods – a significance filter (using a student’s t-test ), a group-stratified fold change filter, and a correlation-based feature selection (CFS). We deployed 11 different machine learning algorithms on the three sets of variables to separate the cases of invasive cancer from the healthy controls or benign/borderline masses. The tools were graded in terms of receiver operating characteristic area under the curve (ROC AUC). The model was validated both by qPCR on the study samples as well as by external validation on an independent publicly available dataset of 454 patients with a range of diagnoses, including ovarian cancer. The qPCR signature was then externally validated on another independent external cohort.
2017-10-13 | GSE94533 | GEO
Project description:Tumors frequently found in dogs include canine oral tumors, either cancerous or noncancerous. The bloodstream is an important route for tumor metastasis, particularly for late-stage oral melanoma (LOM) and late-stage oral squamous cell carcinoma (LOSCC). The present study aimed to investigate serum peptidome-based biomarkers of dogs with early-stage oral melanoma, LOM, LOSCC, benign oral tumors, chronic periodontitis and healthy controls, using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and liquid chromatography tandem mass spectrometry.
2023-09-28 | PXD037051 | JPOST Repository
Project description:We developed a discovery-validation mass-spectrometry based pipeline to identify a set of proteins that are regulated in serum of patients with cervical intraepithelial neoplasia (CIN) and squamous cell cervical cancer using isobaric Tags for Relative and Absolute Quantitation (iTRAQ®), label-free shotgun and targeted mass-spectrometric quantification. At the discovery stage we used a “pooling” strategy for the comparative analysis of immunodepleted serum from patients with early-(CES) and late-stage (CLS) cervical cancer versus healthy controls that revealed 15 up- and 26 down-regulated proteins. The analysis of a new of non-depleted serum samples from patients with CIN, CES, CLS, and healthy controls showed significant changes in abundance of alpha-1-acid glycoprotein 1, alpha-1-antitrypsin, serotransferrin, haptoglobin, alpha-2-HS-glycoprotein and vitamin D-binding protein. To further validate our findings we developed a fast UPLC/MRM method for the simultaneous targeted quantification of these proteins in a new set of 48 CIN, 50 CES, 34 CLS and 48 healthy controls as well as 49 serum samples from patients with ovarian cancer. The panel of six proteins showed 62% sensitivity and 82 % specificity for discrimination of patients with CIN grade 2 or worse from healthy controls, a stage of the disease where current protein-based biomarkers fail to show any discrimination.
2014-10-06 | PXD001034 | Pride
Project description:Background: Liposarcoma constitute about 13% of all soft tissue sarcoma and are associated with a high risk of metastases. As the preoperative differentiation between benign and malign lipomatous tumors is restricted to magnetic resonance imaging, computed tomography and biopsy, we performed a miRNA array to distinguish liposarcoma patients from healthy controls and lipoma patients. Workflow: Blood samples of patients with dedifferentiated liposarcoma, healthy controls and lipoma patients were collected. Whole blood RNA was extracted and samples of patients with dedifferentiated liposarcoma (n=6) and of healthy donors (n=4) were analyzed using an Affymetrix GeneChip miRNA Array v. 4.0. qRT-PCR was carried out to confirm the most differentially expressed miRNA; being further analyzed in an independent cohort of healthy controls as well as in lipoma patients. Results: As shown by the microarray, two miRNAs (miR-4668-5p and miR-3613-3p) were shown to be significantly upregulated (fold change: >2.5; p<0.05) in patients with dedifferentiated liposarcoma (n=6) as compared to healthy controls (n=4). miR-4668-5p was further validated by qRT-PCR to be significantly upregulated in liposarcoma patients compared to an independent cohort of healthy controls (n=3) and lipoma patients (n=6). Conclusion: We identified a specific whole blood miRNA (miR-4668-5p) that may serve to distinguish between lipoma and liosarcoma patients, thus potentially serving as a specific biomarker for dedifferentiated liposarcoma.
2018-06-01 | E-MTAB-5126 | biostudies-arrayexpress
Project description:Background: The identification of new high sensitivity and specificity markers for HCC are essential. We aimed to identify serum microRNAs for diagnosing hepatitis B virus (HBV) –related HCC. Methods: Serum microRNA expression was investigated with four cohorts including 667 participants (261 HCC patients ,233 cirrhosi patients and 173 healthy controls), recruited between August 2010 and June 2013. First, An initial screening of miRNA expression by Illumina sequencing was performed using serum samples pooled from HCC patients and controls,respectively. Quantitative reverse-transcriptase polymerase chain reaction assay was then applied to evaluate the expression of selected microRNAs. A logistic regression model was constructed using a training cohort (n=357) and then validated using a cohort(n=241). The area under the receiver operating characteristic curve (AUC) was used to evaluate diagnostic accuracy. Results: , We identified 8 miRNAs(hsa-miR-206, hsa-miR-141-3p, hsa-miR-433-3p, hsa-miR-1228-5p, hsa-miR-199a-5p, hsa-miR-122-5p, hsa-miR-192-5p and hsa-miR-26a-5p.) formed a miRNA panel that provided a high diagnostic accuracy of HCC (AUC=0.887 and 0.879 for training and validation data set, respectively). The microRNA panel can also differentiate HCC from healthy (AUC =0.894) and cirrhosis (AUC = 0.892), respectively. Conclusions:We found a serum microRNAs panel that has considerable clinical value in diagnosing HCC.
2015-12-20 | GSE59418 | GEO