scholarly journals A Novel Classifier Based on Urinary Proteomics for Distinguishing Between Benign and Malignant Ovarian Tumors

2021 ◽  
Vol 9 ◽  
Author(s):  
Maowei Ni ◽  
Jie Zhou ◽  
Zhihui Zhu ◽  
Jingtao Yuan ◽  
Wangang Gong ◽  
...  
Keyword(s):  
Early Stage ◽  
Ovarian Tumors ◽  
Urine Samples ◽  
Training Dataset ◽  
Validation Dataset ◽  
Non Invasive ◽  
Data Independent Acquisition ◽  
Orbitrap Mass Spectrometer ◽  
Serum Ca125 ◽  
Tumor Types

BackgroundPreoperative differentiation of benign and malignant tumor types is critical for providing individualized treatment interventions to improve prognosis of patients with ovarian cancer. High-throughput proteomics analysis of urine samples was performed to identify reliable and non-invasive biomarkers that could effectively discriminate between the two ovarian tumor types.MethodsIn total, 132 urine samples from 73 malignant and 59 benign cases of ovarian carcinoma were divided into C1 (training and test datasets) and C2 (validation dataset) cohorts. Mass spectrometry (MS) data of all samples were acquired in data-independent acquisition (DIA) mode with an Orbitrap mass spectrometer and analyzed using DIA-NN software. The generated classifier was trained with Random Forest algorithm from the training dataset and validated in the test and validation datasets. Serum CA125 and HE4 levels were additionally determined in all patients. Finally, classification accuracy of the classifier, serum CA125 and serum HE4 in all samples were evaluated and plotted via receiver operating characteristic (ROC) analysis.ResultsIn total, 2,199 proteins were quantified and 69 identified with differential expression in benign and malignant groups of the C1 cohort. A classifier incorporating five proteins (WFDC2, PTMA, PVRL4, FIBA, and PVRL2) was trained and validated in this study. Evaluation of the performance of the classifier revealed AUC values of 0.970 and 0.952 in the test and validation datasets, respectively. In all 132 patients, AUCs of 0.966, 0.947, and 0.979 were achieved with the classifier, serum CA125, and serum HE4, respectively. Among eight patients with early stage malignancy, 7, 6, and 4 were accurately diagnosed based on classifier, serum CA125, and serum HE4, respectively.ConclusionThe novel classifier incorporating a urinary protein panel presents a promising non-invasive diagnostic biomarker for classifying benign and malignant ovarian tumors.

scholarly journals Urine biomarker: novel approach to hepatocellular carcinoma screening

2020 ◽  
Author(s):  
Amy K Kim ◽  
James P. Hamilton ◽  
Selena Y. Lin ◽  
Ting-Tsung Chang ◽  
Hie-Won Hann ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cross Validation ◽  
Learning Algorithm ◽  
Early Stage ◽  
High Risk Patient ◽  
Circulating Tumor Dna ◽  
Urine Samples ◽  
Detection Rates ◽  
Non Invasive ◽  
Fold Cross Validation

ABSTRACTBackground & AimsContinued limitations in hepatocellular carcinoma (HCC) screening have led to late diagnosis with poor survival, despite well-defined high-risk patient populations. Our aim is to develop a non-invasive urine circulating tumor DNA (ctDNA) biomarker panel for HCC screening to aid in early detection.MethodsCandidate ctDNA biomarkers was prescreened in urine samples obtained from HCC, cirrhosis, and hepatitis patients. Then, 609 patient urine samples with HCC, cirrhosis, or chronic hepatitis B were collected from five academic medical centers and evaluated by serum alpha feto-protein (AFP) and urine ctDNA panel using logistic regression, a Two-Step machine learning algorithm, and iterated 10-fold cross-validation.ResultsMutated TP53, and methylated RASSF1a and GSTP1, were selected for the urine ctDNA panel. The sensitivity of AFP-alone (9.8 ng/mL cut-off) to detect HCC was 71% by Two-Step. The combination of ctDNA and AFP increased the sensitivity to 81% at a specificity of 90%. The AUROC for the combination of ctDNA and AFP vs. AFP-alone were 0.925 (95% CI, 0.924-0.925) and 0.877 (95% CI, 0.876-0.877), respectively. Notably, among the patients with AFP <20 ng/mL, the combination panel correctly identified 64% of HCC cases. The panel performed superiorly to AFP-alone in early-stage HCC (BCLC A) with 80% sensitivity and 90% specificity. In an iterated 10-fold cross-validation analysis, the AUROC for the combination panel was 0.898 (95% CI, 0.895-0.901).ConclusionsThe combination of urine ctDNA and serum AFP can increase HCC detection rates including in those patients with low-AFP. Given the ease of collection, a urine ctDNA panel could be a potential non-invasive HCC screening test.

Borderline Ovarian Tumors

2021 ◽  
pp. 223-239
Author(s):  
George Pados ◽  
Dimitrios Zouzoulas
Keyword(s):  
Ovarian Cancer ◽  
Gold Standard ◽  
Early Stage ◽  
Ovarian Tumors ◽  
Microscopic Appearance ◽  
Borderline Tumors ◽  
Non Invasive ◽  
Borderline Ovarian Tumors ◽  
Specific Subgroup ◽  
Serous Tumors

Borderline ovarian tumors (BOTs) are a specific subgroup of ovarian tumors and are characterized by cell proliferation and nuclear atypia without invasion or stromal invasion. They are usually more present in younger people than the invasive ovarian cancer and are diagnosed at an early stage and thus have a better prognosis. Histologically, borderline tumors are divided into serous (50%), mucosal (46%), and mixed (4%). The serous tumors are bilateral in 30% of the cases and are accompanied by infiltrations outside the ovary in 35% of the cases. These infiltrations may be non-invasive or invasive depending on their microscopic appearance and may affect treatment. Surgery is the approach of choice, and laparoscopic surgery, with the undeniable advantages it offers today, is the “gold standard.” All the surgical steps required to properly treat borderline tumors, at both diagnostic and therapeutic levels, can be safely and successfully be applied laparoscopically. Manipulations during surgery should be limited, and biopsies for rapid biopsy should be done within an endoscopic bag.

scholarly journals A radiomics-based nomogram for preoperative T staging prediction of rectal cancer

2021 ◽  
Author(s):  
Xue Lin ◽  
Sheng Zhao ◽  
Huijie Jiang ◽  
Fucang Jia ◽  
Guisheng Wang ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Goodness Of Fit ◽  
Clinical Decision Making ◽  
Early Stage ◽  
Locally Advanced ◽  
Area Under The Curve ◽  
Training Dataset ◽  
Validation Dataset ◽  
Prospective Analysis ◽  
Clinical Value

Abstract Purpose To investigate the value of a radiomics-based nomogram in predicting preoperative T staging of rectal cancer. Methods A total of 268 eligible rectal cancer patients from August 2012 to December 2018 were enrolled and allocated into two datasets: training (n = 188) and validation datasets (n = 80). Another set of 32 patients from January 2019 to July 2019 was included in a prospective analysis. Pretreatment T2-weighted images were used to radiomics features extraction. Feature selection and radiomics score (Rad-score) construction were performed through a least absolute shrinkage and selection operator regression analysis. The nomogram, which included Rad-scores and clinical factors, was built using multivariate logistic regression. Discrimination, calibration, and clinical utility were used to evaluate the performance of the nomogram. Results The Rad-score containing nine selected features was significantly related to T staging. Patients who had locally advanced rectal cancer (LARC) generally had higher Rad-scores than patients with early-stage rectal cancer. The nomogram incorporated Rad-scores and carcinoembryonic antigen levels and showed good discrimination, with an area under the curve (AUC) of 0.882 (95% confidence interval [CI] 0.835–0.930) in the training dataset and 0.846 (95% CI 0.757–0.936) in the validation dataset. The calibration curves confirmed high goodness of fit, and the decision curve analysis revealed the clinical value. A prospective analysis demonstrated that the AUC of the nomogram to predict LARC was 0.859 (95% CI 0.730–0.987). Conclusion A radiomics-based nomogram is a novel method for predicting LARC and can provide support in clinical decision making.

Sensitive detection of renal cell carcinoma using plasma and urine cell-free DNA methylomes.

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 728-728 ◽  
Author(s):  
Pier Vitale Nuzzo ◽  
Jacob E Berchuck ◽  
Sandor Spisak ◽  
Keegan Korthauer ◽  
Amin Nassar ◽  
...  
Keyword(s):  
Cancer Detection ◽  
Early Stage ◽  
Urine Samples ◽  
Plasma Samples ◽  
Early Stage Disease ◽  
Stage Disease ◽  
Tumor Types ◽  
And Control ◽  
Control Samples

728 Background: Improving early cancer detection has the potential to significantly reduce cancer-related mortality. Cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMedDIP-seq) is a highly sensitive, low-input, cost-efficient and bisulfite-free assay capable of detecting and classifying various tumor types. We tested the feasibility of cfMeDIP-seq to detect RCC in plasma samples and, for the first time, in urine cell-free DNA (cfDNA), with an emphasis on early-stage disease. Methods: We performed cfMeDIP-seq on 117 samples (72 plasma and 45 urine samples): 68 stage I-IV RCC cases pre-nephrectomy, 21 stage IV urothelial bladder cancer (UBC) plasma samples from 15 patients, and 28 healthy cancer-free controls. 60.5% of plasma samples and 66.7% of urine samples came from patients with TNM Stage I/II disease. cfDNA was immunoprecipitated and enriched using an antibody targeting 5-methylcytosine and amplified to create a sequence-ready library. The top differentially methylated regions (DMRs) which partitioned RCC and control samples or UBC were used to train a regularized binomial generalized linear model using 80% of the samples as a training set. The 20% of withheld test samples were then assigned a probability of being RCC or control. This process was repeated 100 times. This was performed using both plasma and urine cfDNA samples. Results: We identified 89,799 DMRs in plasma samples and 38,462 DMRs in urine samples. Iterative training and classification of held out samples, using the 300 DMRs which partitioned RCC and control samples, resulted in a mean AUROC of 0.990 (95% CI 0.984-0.997) in plasma samples and 0.791 (95% CI 0.759-0.823) in urine samples. Classification performance between tumor types was evaluated comparing plasma cfDNA from patients with RCC and UBC, resulting in a mean AUROC of 0.954 (95% CI 0.940-0.969). Conclusions: cfMeDIP-seq is a powerful tool for genome-wide discovery of non-invasive DNA methylation biomarkers. This is the first independent validation of plasma cfMeDIP-seq, demonstrating near-perfect classification of RCC in a cohort enriched for patients with early-stage disease and the potential of urine cfDNA methylome-based biomarkers for cancer detection.

DeepSSPred: A Deep Learning Based Sulfenylation site predictor via a novel n-segmented optimize federated feature encoder

2020 ◽  
Vol 27 ◽  
Author(s):  
Zaheer Ullah Khan ◽  
Dechang Pi
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
Research Work ◽  
Training Data ◽  
Training Dataset ◽  
Validation Dataset ◽  
Cytokine Signaling ◽  
Minority Class ◽  
Independent Dataset ◽  
Feature Encoding

Background: S-sulfenylation (S-sulphenylation, or sulfenic acid) proteins, are special kinds of post-translation modification, which plays an important role in various physiological and pathological processes such as cytokine signaling, transcriptional regulation, and apoptosis. Despite these aforementioned significances, and by complementing existing wet methods, several computational models have been developed for sulfenylation cysteine sites prediction. However, the performance of these models was not satisfactory due to inefficient feature schemes, severe imbalance issues, and lack of an intelligent learning engine. Objective: In this study, our motivation is to establish a strong and novel computational predictor for discrimination of sulfenylation and non-sulfenylation sites. Methods: In this study, we report an innovative bioinformatics feature encoding tool, named DeepSSPred, in which, resulting encoded features is obtained via n-segmented hybrid feature, and then the resampling technique called synthetic minority oversampling was employed to cope with the severe imbalance issue between SC-sites (minority class) and non-SC sites (majority class). State of the art 2DConvolutional Neural Network was employed over rigorous 10-fold jackknife cross-validation technique for model validation and authentication. Results: Following the proposed framework, with a strong discrete presentation of feature space, machine learning engine, and unbiased presentation of the underline training data yielded into an excellent model that outperforms with all existing established studies. The proposed approach is 6% higher in terms of MCC from the first best. On an independent dataset, the existing first best study failed to provide sufficient details. The model obtained an increase of 7.5% in accuracy, 1.22% in Sn, 12.91% in Sp and 13.12% in MCC on the training data and12.13% of ACC, 27.25% in Sn, 2.25% in Sp, and 30.37% in MCC on an independent dataset in comparison with 2nd best method. These empirical analyses show the superlative performance of the proposed model over both training and Independent dataset in comparison with existing literature studies. Conclusion : In this research, we have developed a novel sequence-based automated predictor for SC-sites, called DeepSSPred. The empirical simulations outcomes with a training dataset and independent validation dataset have revealed the efficacy of the proposed theoretical model. The good performance of DeepSSPred is due to several reasons, such as novel discriminative feature encoding schemes, SMOTE technique, and careful construction of the prediction model through the tuned 2D-CNN classifier. We believe that our research work will provide a potential insight into a further prediction of S-sulfenylation characteristics and functionalities. Thus, we hope that our developed predictor will significantly helpful for large scale discrimination of unknown SC-sites in particular and designing new pharmaceutical drugs in general.

Review of Various Tasks Performed in the Preprocessing Phase of a Diabetic Retinopathy Diagnosis System

2020 ◽  
Vol 16 (4) ◽  
pp. 397-426
Author(s):  
Muhammad Nadeem Ashraf ◽  
Muhammad Hussain ◽  
Zulfiqar Habib
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Early Stage ◽  
Spherical Shape ◽  
Cost Effective ◽  
Lesion Detection ◽  
Diabetic Patients ◽  
Cad System ◽  
Non Invasive ◽  
Optic Discs

Diabetic Retinopathy (DR) is a major cause of blindness in diabetic patients. The increasing population of diabetic patients and difficulty to diagnose it at an early stage are limiting the screening capabilities of manual diagnosis by ophthalmologists. Color fundus images are widely used to detect DR lesions due to their comfortable, cost-effective and non-invasive acquisition procedure. Computer Aided Diagnosis (CAD) of DR based on these images can assist ophthalmologists and help in saving many sight years of diabetic patients. In a CAD system, preprocessing is a crucial phase, which significantly affects its performance. Commonly used preprocessing operations are the enhancement of poor contrast, balancing the illumination imbalance due to the spherical shape of a retina, noise reduction, image resizing to support multi-resolution, color normalization, extraction of a field of view (FOV), etc. Also, the presence of blood vessels and optic discs makes the lesion detection more challenging because these two artifacts exhibit specific attributes, which are similar to those of DR lesions. Preprocessing operations can be broadly divided into three categories: 1) fixing the native defects, 2) segmentation of blood vessels, and 3) localization and segmentation of optic discs. This paper presents a review of the state-of-the-art preprocessing techniques related to three categories of operations, highlighting their significant aspects and limitations. The survey is concluded with the most effective preprocessing methods, which have been shown to improve the accuracy and efficiency of the CAD systems.

scholarly journals Increasing Electric Vehicles Reliability by Non-Invasive Diagnosis of Motor Winding Faults

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2510
Author(s):  
Konrad Górny ◽  
Piotr Kuwałek ◽  
Wojciech Pietrowski
Keyword(s):  
Electric Vehicles ◽  
Diagnostic Method ◽  
Induction Motors ◽  
Early Stage ◽  
Short Circuit ◽  
Damage Classification ◽  
Non Invasive ◽  
Signal Features ◽  
Empirical Wavelet Transform ◽  
Simulation Results

The article proposes a proprietary approach to the diagnosis of induction motors allowing increasing the reliability of electric vehicles. This approach makes it possible to detect damage in the form of an inter-turn short-circuit at an early stage of its occurrence. The authors of the article describe an effective diagnostic method using the extraction of diagnostic signal features using an Enhanced Empirical Wavelet Transform and an algorithm based on the method of Ensemble Bagged Trees. The article describes in detail the methodology of the carried out research, presents the method of extracting features from the diagnostic signal and describes the conclusions resulting from the research. Phase current waveforms obtained from a real object as well as simulation results based on the field-circuit model of an induction motor were used as a diagnostic signal in the research. In order to determine the accuracy of the damage classification, simple metrics such as accuracy, sensitivity, selectivity, precision as well as complex metrics weight F1 and macro F1 were used.

scholarly journals Longitudinal stability of urinary extracellular vesicle protein patterns within and between individuals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Leyla A. Erozenci ◽  
Sander R. Piersma ◽  
Thang V. Pham ◽  
Irene V. Bijnsdorp ◽  
Connie R. Jimenez
Keyword(s):  
Expression Patterns ◽  
Interaction Network ◽  
Extracellular Vesicle ◽  
Protein Signaling ◽  
Protein Patterns ◽  
Specific Expression ◽  
Non Invasive ◽  
Data Independent Acquisition ◽  
Time Period ◽  
Using Data

AbstractThe protein content of urinary extracellular vesicles (EVs) is considered to be an attractive non-invasive biomarker source. However, little is known about the consistency and variability of urinary EV proteins within and between individuals over a longer time-period. Here, we evaluated the stability of the urinary EV proteomes of 8 healthy individuals at 9 timepoints over 6 months using data-independent-acquisition mass spectrometry. The 1802 identified proteins had a high correlation amongst all samples, with 40% of the proteome detected in every sample and 90% detected in more than 1 individual at all timepoints. Unsupervised analysis of top 10% most variable proteins yielded person-specific profiles. The core EV-protein-interaction network of 516 proteins detected in all measured samples revealed sub-clusters involved in the biological processes of G-protein signaling, cytoskeletal transport, cellular energy metabolism and immunity. Furthermore, gender-specific expression patterns were detected in the urinary EV proteome. Our findings indicate that the urinary EV proteome is stable in longitudinal samples of healthy subjects over a prolonged time-period, further underscoring its potential for reliable non-invasive diagnostic/prognostic biomarkers.

scholarly journals Screening of gene markers related to the prognosis of metastatic skin cutaneous melanoma based on Logit regression and survival analysis

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Guoliang Jia ◽  
Zheyu Song ◽  
Zhonghang Xu ◽  
Youmao Tao ◽  
Yuanyu Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cutaneous Melanoma ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Training Dataset ◽  
Validation Dataset ◽  
Survival Prognosis ◽  
Gene Markers ◽  
Clinical Prognosis ◽  
Logit Regression

Abstract Background Bioinformatics was used to analyze the skin cutaneous melanoma (SKCM) gene expression profile to provide a theoretical basis for further studying the mechanism underlying metastatic SKCM and the clinical prognosis. Methods We downloaded the gene expression profiles of 358 metastatic and 102 primary (nonmetastatic) CM samples from The Cancer Genome Atlas (TCGA) database as a training dataset and the GSE65904 dataset from the National Center for Biotechnology Information database as a validation dataset. Differentially expressed genes (DEGs) were screened using the limma package of R3.4.1, and prognosis-related feature DEGs were screened using Logit regression (LR) and survival analyses. We also used the STRING online database, Cytoscape software, and Database for Annotation, Visualization and Integrated Discovery software for protein–protein interaction network, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses based on the screened DEGs. Results Of the 876 DEGs selected, 11 (ZNF750, NLRP6, TGM3, KRTDAP, CAMSAP3, KRT6C, CALML5, SPRR2E, CD3G, RTP5, and FAM83C) were screened using LR analysis. The survival prognosis of nonmetastatic group was better compared to the metastatic group between the TCGA training and validation datasets. The 11 DEGs were involved in 9 KEGG signaling pathways, and of these 11 DEGs, CALML5 was a feature DEG involved in the melanogenesis pathway, 12 targets of which were collected. Conclusion The feature DEGs screened, such as CALML5, are related to the prognosis of metastatic CM according to LR. Our results provide new ideas for exploring the molecular mechanism underlying CM metastasis and finding new diagnostic prognostic markers.

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