Project description:Primary outcome(s): Diagnostic ability (sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the curve) for lymph node metastasis
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.
Project description:Primary outcome(s): Accuracy of CRP and PCT to detect IAI, in terms of Area under the curve, determination of threshold value and corresponding sensitivity, specificity values and positive and negative predictive valuesTimepoint: february, 2020
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.
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) â??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. 9 serum samples pooled from 3 healthy control donors and 3 HCC patients, 3 cirrhosi patients treated at The First Affiliated Hospital of Soochow University were subjected to Illumina HiSeq 2000 deep sequencing to identify the miRNAs that were significantly differentially expressed.
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.
Project description:Interventions: -
Primary outcome(s): The accuracy (in terms of sensitivity, specificity and area under the curve) of an electronic nose by means of faecal VOC analysis in detecting colorectal neoplasia (CRC and its precursors) in patients with Lynch syndrome.
Study Design: Non-randomized controlled trial, Open (masking not used), N/A , unknown, Other
Project description:Background Hypertrophic cardiomyopathy (HCM) is defined clinically by pathological left ventricular hypertrophy (LVH). We have previously developed a plasma proteomics biomarker panel that correlates with clinical markers of disease severity and sudden cardiac death (SCD) risk in adult patients with HCM. The aim of this study was to validate the adult biomarkers and perform new discovery proteomics in childhood-onset HCM. Methods Fifty-nine protein biomarkers were identified from an exploratory plasma proteomics screen in children with HCM and augmented into our existing multiplexed targeted liquid chromatography-tandem/mass spectrometry-based assay. The association of these biomarkers with clinical phenotypes and outcomes was prospectively tested in plasma collected from 148 children with HCM and 50 healthy controls. Machine learning techniques were used to develop novel paediatric plasma proteomic biomarker panels.Results Four previously validated adult HCM markers (Aldolase Fructose-Bisphosphate A, Complement C3a, Talin-1 and Thrombospondin 1) and three new markers (Glycogen Phosphorylase B, Lipoprotein A and Profilin 1) were elevated in paediatric HCM. Using supervised machine learning applied to training (n=137) and validation cohorts (n=61), this 7-biomarker panel differentiated HCM from healthy controls with an area under the curve of 1.0 in the training dataset (sensitivity 100% [95% confidence interval: 95–100]; specificity 100% [96–100]) and 0.82 in the validation dataset (sensitivity 75% [59–86]; specificity 88% [75–94]). Reduced circulating levels of 4 other peptides (Apolipoprotein L1-, Complement 5b-, Immunoglobulin Heavy Constant Epsilon- and Serum Amyloid A4-peptide) found in children with high SCD risk provided complete separation from the low and intermediate risk groups, and predicted mortality and adverse cardiovascular outcomes (hazard ratio 1.53 [1.2 –2.0], p = 0.001). ConclusionIn children, a 7-biomarker proteomics panel can distinguish HCM from controls with high sensitivity and specificity, and a second 4-biomarker panel identifies those at high risk of adverse outcomes, including sudden cardiac death.
Project description:Rationale There is a need for new and better biomarkers for hypertrophic cardiomyopathy (HCM) which correlate more closely with disease progression as determined by clinical imaging and biohumoral information. We have used a combination of heart tissue and plasma proteomics to identify potential biomarkers for HCM and developed them into an exploratory targeted proteomic assay. Objective To identify informative staging biomarkers for HCM and develop them into a blood test. The test using 10 µl of plasma, was developed into a 10 min liquid chromatography-tandem/mass spectrometry (LC-MS/MS) assay to analyze multiple candidate biomarkers and evaluate their association with clinical phenotypes in patients with HCM. Methods and Results Myocardial tissue and plasma samples from patients with HCM and healthy volunteers (controls) were screened using a combined gel- and nano-LC quadrupole time of flight MS approach. Twenty-six potential biomarkers were identified from the proteomics screens and developed into a multiplexed targeted proteomic assay. Their association with clinical phenotypes was tested in plasma samples collected from 207 prospectively recruited participants: 110 patients with HCM (50.1 ± 15.0 years, 70% male; 48 [44%] with identified genetic mutations) and 97 controls (49.6 ± 13.4 years, 58% male), randomly split into training (80 HCM, 67 controls) and validation datasets (30 HCM, 30 controls). Six markers (Aldolase Fructose-Bisphosphate A, Complement C3, Glutathione S-Transferase Omega 1, Ras Suppressor Protein 1, Talin 1, and Thrombospondin 1) were significantly increased (P<0.006) in the plasma of HCM patients compared to controls in the training dataset. These markers correlated with left ventricular (LV) wall thickness, LV mass and % myocardial scar on cardiovascular magnetic resonance imaging. Using supervized machine learning (ML) this panel differentiated HCM from controls (area under the curve: 0.89 in the training dataset, sensitivity 96%, 95% confidence interval [CI] 77–93; specificity 87%, 95%CI 77–94; and 0.87 in the validation dataset, sensitivity 97%, 95%CI 83–100; specificity 77%, 95%CI 58–90). Four of the biomarkers as well as the composite ML score of the plasma proteome correlated with the presence of nonsustained ventricular tachycardia and the estimated 5-year risk of sudden cardiac death. Conclusion By developing a high-throughput, multiplex, and targeted proteomic plasma assay we identified 6 biomarkers that correlate with the presence of disease and with clinical risk score for sudden cardiac death.
Project description:Mural granulosa cells (MGC) and cumulus cells (CC) were isolated immediately after oocyte retrieval during IVF treatment from the 16 competent and non competent follicles. mRNA was extracted resulting in 19 MGC and 27 CC samples of sufficient quality to be included in the study and expression profiles were generated on the Human Gene 1.0 ST Affymetrix array . Prediction of live birth after embryo transfer was performed using machine learning algorithms (support vector machines) with performance estimation by leave-one-out cross validation and independent validation on an external data set. We defined a signature of 30 genes expressed in CC predictive of live birth. This live birth prediction model had an accuracy of 81%, a sensitivity of 0.83, a specificity of 0.80, a positive predictive value of 0.77, and a negative predictive value of 0.86. Receiver operating characteristic analysis found an area under the curve of 0.86, significantly greater than random chance. When applied on 3 external data sets with the end-point outcome measure of blastocyst formation, the signature resulted in 62%, 75% and 88% accuracy, respectively.