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Computers in Biology and Medicine Sep 2020Diagnosis of Parkinson's disease (PD) remains a challenge in clinical practice, mostly due to lack of peripheral blood markers. Transcriptomic analysis of blood samples... (Meta-Analysis)
Meta-Analysis
Diagnosis of Parkinson's disease (PD) remains a challenge in clinical practice, mostly due to lack of peripheral blood markers. Transcriptomic analysis of blood samples has emerged as a potential means to identify biomarkers and gene signatures of PD. In this context, classification algorithms can assist in detecting data patterns such as phenotypes and transcriptional signatures with potential diagnostic application. In this study, we performed gene expression meta-analysis of blood transcriptome from PD and control patients in order to identify a gene-set capable of predicting PD using classification algorithms. We examined microarray data from public repositories and, after systematic review, 4 independent cohorts (GSE6613, GSE57475, GSE72267 and GSE99039) comprising 711 samples (388 idiopathic PD and 323 healthy individuals) were selected. Initially, analysis of differentially expressed genes resulted in minimal overlap among datasets. To circumvent this, we carried out meta-analysis of 17,712 genes across datasets, and calculated weighted mean Hedges' g effect sizes. From the top-100- positive and negative gene effect sizes, algorithms of collinearity recognition and recursive feature elimination were used to generate a 59-gene signature of idiopathic PD. This signature was evaluated by 9 classification algorithms and 4 sample size-adjusted training groups to create 36 models. Of these, 33 showed accuracy higher than the non-information rate, and 2 models built on Support Vector Machine Regression bestowed best accuracy to predict PD and healthy control samples. In summary, the gene meta-analysis followed by machine learning methodology employed herein identified a gene-set capable of accurately predicting idiopathic PD in blood samples.
Topics: Algorithms; Gene Expression Profiling; Humans; Parkinson Disease; Support Vector Machine; Transcriptome
PubMed: 32889300
DOI: 10.1016/j.compbiomed.2020.103925 -
Pediatric Allergy and Immunology :... Feb 2020Peptide microarray technology has been proposed as a useful tool for diagnosing food allergy. However, there is considerable heterogeneity in the clinical methods and...
BACKGROUND
Peptide microarray technology has been proposed as a useful tool for diagnosing food allergy. However, there is considerable heterogeneity in the clinical methods and analytical procedures used to assess its diagnostic and prognostic performance. We performed a systematic review of studies that have used B-cell epitopes by peptide microarray in food allergies to identify the clinical utility of this immunologic technique.
METHODS
Studies were screened in PubMed, Web of Science, and Embase according to an established keyword algorithm. Data extraction was performed, and information was collected in an Excel database. Descriptive analysis was carried out using Stata software.
RESULTS
Thirty relevant studies were identified. Most articles were cross-sectional (n = 24), included epitope mapping (n = 9), and assessed diagnostic utility (n = 11). All studies recruited allergic patients, and some included additional patients (sensitized, persistent, and tolerant). The primary microarray variables studied were IgE intensity (n = 29), IgG4 intensity (n = 15), and number of peptides (n = 17). Statistical approaches differed significantly between studies, with the Wilcoxon test being the most frequently used (n = 10).
CONCLUSIONS
Sensitization to particular epitopes of milk, peanut, and shrimp allergens can be used to determine clinical reactivity, persistence, severity, or response to oral immunotherapy; however, important methodological questions need to be addressed before drawing definitive conclusions. More research is needed to address the accuracy and clinical benefits of microarray-based technology. Standards are required to improve consistency and reproducibility, and to allow for better understanding of research findings.
Topics: Allergens; Animals; Epitope Mapping; Epitopes, B-Lymphocyte; Food; Food Hypersensitivity; Humans; Microarray Analysis; Peptides; Reproducibility of Results
PubMed: 31655013
DOI: 10.1111/pai.13141 -
Endocrine, Metabolic & Immune Disorders... 2024Several studies have identified CD163 as a potential mediator of diabetes mellitus through an immune-inflammation. Further study is necessary to identify its specific...
BACKGROUND
Several studies have identified CD163 as a potential mediator of diabetes mellitus through an immune-inflammation. Further study is necessary to identify its specific mechanism.
OBJECTIVES
In this study, we aimed to investigate CD163 as a potential biomarker associated with immune inflammation in diabetes mellitus through a systematic review and bioinformatics analysis.
METHODS
We searched PubMed, Web of Science, the Cochrane Library, and Embase databases with a time limit of September 2, 2022. Furthermore, we conducted a systematic search and review based on PRISMA guidelines. Additionally, diabetic gene expression microarray datasets GSE29221, GSE30528, GSE30529, and GSE20966 were downloaded from the GEO database (http://www.ncbi.nlm.nih.gov/geo) for bioinformatics analysis. The PROSPERO number for this study is CRD420222347160.
RESULTS
Following the inclusion and exclusion criteria, seven articles included 1607 patients, comprising 912 diabetic patients and 695 non-diabetic patients. This systematic review found significantly higher levels of CD163 in diabetic patients compared to non-diabetic patients. People with diabetes had higher levels of CRP expression compared to the control group. Similarly, two of the three papers that used TNF- α as an outcome indicator showed higher expression levels in diabetic patients. Furthermore, IL-6 expression levels were higher in diabetic patients than in the control group. A total of 62 samples were analyzed by bioinformatics (33 case controls and 29 experimental groups), and 85 differential genes were identified containing CD163. According to the immune cell correlation analysis, CD163 was associated with macrophage M2, γδ T lymphocytes, macrophage M1, and other immune cells. Furthermore, to evaluate the diagnostic performance of CD163, we validated it using the GSE20966 dataset. In the validation set, CD163 showed high diagnostic accuracy.
CONCLUSION
This study suggests CD163 participates in the inflammatory immune response associated with diabetes mellitus and its complications by involving several immune cells. Furthermore, the results suggest CD163 may be a potential biomarker reflecting immune inflammation in diabetic mellitus.
Topics: Humans; Biomarkers; Computational Biology; Diabetes Mellitus; Inflammation; Macrophages
PubMed: 37455460
DOI: 10.2174/1871530323666230714162324 -
Frontiers in Endocrinology 2021Molecular tests are being used increasingly as an auxiliary diagnostic tool so as to avoid a diagnostic surgery approach for cytologically indeterminate thyroid nodules... (Comparative Study)
Comparative Study Meta-Analysis
Thyroseq v3, Afirma GSC, and microRNA Panels Versus Previous Molecular Tests in the Preoperative Diagnosis of Indeterminate Thyroid Nodules: A Systematic Review and Meta-Analysis.
BACKGROUND
Molecular tests are being used increasingly as an auxiliary diagnostic tool so as to avoid a diagnostic surgery approach for cytologically indeterminate thyroid nodules (ITNs). Previous test versions, Thyroseq v2 and Afirma Gene Expression Classifier (GEC), have proven shortcomings in malignancy detection performance.
OBJECTIVE
This study aimed to evaluate the diagnostic performance of the established Thyroseq v3, Afirma Gene Sequencing Classifier (GSC), and microRNA-based assays versus prior iterations in ITNs, in light of "rule-in" and "rule-out" concepts. It further analyzed the impact of noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) reclassification and Bethesda cytological subtypes on the performance of molecular tests.
METHODS
Pubmed, Scopus, and Web of Science were the databases used for the present research, a process that lasted until September 2020. A random-effects bivariate model was used to estimate the summary sensitivity, specificity, positive (PLR) and negative likelihood ratios (NLR), and area under the curve (AUC) for each panel. The conducted sensitivity analyses addressed different Bethesda categories and NIFTP thresholds.
RESULTS
A total of 40 eligible studies were included with 7,831 ITNs from 7,565 patients. Thyroseq v3 showed the best overall performance (AUC 0.95; 95% confidence interval: 0.93-0.97), followed by Afirma GSC (AUC 0.90; 0.87-0.92) and Thyroseq v2 (AUC 0.88; 0.85-0.90). In terms of "rule-out" abilities Thyroseq v3 (NLR 0.02; 95%CI: 0.0-2.69) surpassed Afirma GEC (NLR 0.18; 95%CI: 0.10-0.33). Thyroseq v2 (PLR 3.5; 95%CI: 2.2-5.5) and Thyroseq v3 (PLR 2.8; 95%CI: 1.2-6.3) achieved superior "rule-in" properties compared to Afirma GSC (PLR 1.9; 95%CI: 1.3-2.8). Evidence for Thyroseq v3 seems to have higher quality, notwithstanding the paucity of studies. Both Afirma GEC and Thyroseq v2 performance have been affected by NIFTP reclassification. ThyGenNEXT/ThyraMIR and RosettaGX show prominent preliminary results.
CONCLUSION
The newly emerged tests, Thyroseq v3 and Afirma GSC, designed for a "rule-in" purpose, have been proved to outperform in abilities to rule out malignancy, thus surpassing previous tests no longer available, Thyroseq 2 and Afirma GEC. However, Thyroseq v2 still ranks as the best rule-in molecular test.
SYSTEMATIC REVIEW REGISTRATION
http://www.crd.york.ac.uk/PROSPERO, identifier CRD42020212531.
Topics: Area Under Curve; Biopsy, Fine-Needle; Female; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Humans; Male; MicroRNAs; Oligonucleotide Array Sequence Analysis; Preoperative Period; Reproducibility of Results; Sensitivity and Specificity; Thyroid Neoplasms; Thyroid Nodule
PubMed: 34054725
DOI: 10.3389/fendo.2021.649522 -
DNA and Cell Biology Mar 2020Diabetes mellitus (DM) is one of the growing public health threats globally and as one of the common serious microvascular complications of DM, diabetic retinopathy (DR)... (Meta-Analysis)
Meta-Analysis
Diabetes mellitus (DM) is one of the growing public health threats globally and as one of the common serious microvascular complications of DM, diabetic retinopathy (DR) is the leading cause of irreversible visual impairments and blindness. There is growing concern about the role of microRNAs (miRNAs) in the pathogenesis of DR. This meta-analysis was designed to collect those published miRNA expression profiling studies that compared the miRNA expression profiles in the biological samples of DR patients with those in the control group. Eight publications were finally included in the meta-analysis, and a total of 93 differentially expressed miRNAs were reported. Although six miRNAs were reported in at least two studies and with the consistent direction, after stratification by the type of biological samples, miR-320a was consistently reported to be upregulated in two serum sample-based studies and miR-423-5p was consistently reported to be upregulated in two vitreous humor sample-based studies. miR-27b was consistently reported to be downregulated in two serum sample-based studies. In conclusion, the results of this meta-analysis of human DR miRNAs' expression profiling studies might provide some clues of the potential biomarkers of DR. Further investigation of the mechanisms of miRNAs and more external validation studies are warranted with the aim of developing new diagnostic markers for preventing or reversing DR.
Topics: Adult; Aged; Diabetic Retinopathy; Female; Gene Expression Profiling; Humans; Male; MicroRNAs; Microarray Analysis; Middle Aged; Up-Regulation; Vitreous Body
PubMed: 32101049
DOI: 10.1089/dna.2019.4942 -
Annual Review of Biomedical Engineering Jul 2021Modeling immunity in vitro has the potential to be a powerful tool for investigating fundamental biological questions, informing therapeutics and vaccines, and providing...
Modeling immunity in vitro has the potential to be a powerful tool for investigating fundamental biological questions, informing therapeutics and vaccines, and providing new insight into disease progression. There are two major elements to immunity that are necessary to model: primary immune tissues and peripheral tissues with immune components. Here, we systematically review progress made along three strategies to modeling immunity: ex vivo cultures, which preserve native tissue structure; microfluidic devices, which constitute a versatile approach to providing physiologically relevant fluid flow and environmental control; and engineered tissues, which provide precise control of the 3D microenvironment and biophysical cues. While many models focus on disease modeling, more primary immune tissue models are necessary to advance the field. Moving forward, we anticipate that the expansion of patient-specific models may inform why immunity varies from patient to patient and allow for the rapid comprehension and treatment of emerging diseases, such as coronavirus disease 2019.
Topics: Adaptive Immunity; Animals; Biophysics; COVID-19; Humans; Immune System; Immunity, Innate; In Vitro Techniques; Lab-On-A-Chip Devices; Lymphocytes; Macrophages; Mice; Microfluidics; SARS-CoV-2; Thymus Gland; Tissue Array Analysis; Tissue Engineering
PubMed: 33872520
DOI: 10.1146/annurev-bioeng-082420-124920 -
Clinica Chimica Acta; International... Mar 2022Fetalhyperechogenickidneys (HEK)are associated with a wide range of etiologies and prognoses. Prenatal counselling and management can be extremely challenging,...
BACKGROUND
Fetalhyperechogenickidneys (HEK)are associated with a wide range of etiologies and prognoses. Prenatal counselling and management can be extremely challenging, especially for isolated HEK.
METHODS
A total of 28 pregnancies were screened by ultrasonography with HEK from March 1, 2016 to December 31, 2020. Genetic testings for aneuploidy and copy number variations (CNVs) are routine during the investigation for etiologies of fetal HEK in our unit. Trio-whole exome sequencing(WES) was offered to the family when karyotyping and microarray were not diagnostic.A systematic review (SR) was conducted to use the authoritative literature retrieval databases describing genetic testings' results in prenatal HEK cases.
RESULTS
In the 28 HEK fetuses, 2 (7.14%) cases were identified with chromosome abnormalities and 6 (21.43%) cases were detected with pathogenic CNVs. Through trio-WES analysis, pathogenic or likely pathogenic variations were detected in the following genes: PKD1, BBS2, BBS9, HNF1B, PKHD1 and ETFA in another 10 (35.71%) fetuses. And the remaining 10 (35.71%) cases were undiagnosed. The pooled data from all reviewed studies indicate that HNF1B gene heterozygous deletion or mutation are the most common genetic causes associated with HEK.
CONCLUSION
This is the first study to accurately describe the genotype ratio at different levels of genetic testing associated with fetal HEK. Our study has suggested that trio-WES could improve the detection rate and efficiency ofidentification genetic pathologies in fetuseswith isolated HEK. The WES results provide new evidences to guide prenatal counseling and management.
Topics: DNA Copy Number Variations; Female; Fetus; Humans; Kidney; Kidney Diseases; Pregnancy; Prenatal Diagnosis
PubMed: 35065907
DOI: 10.1016/j.cca.2022.01.012 -
Biochemistry and Biophysics Reports Mar 2024Papillary thyroid cancer (PTC) is a prevalent kind of thyroid cancer (TC), with the risk of metastasis increasing faster than any other malignancy. So, understanding the...
The role of MAPK, notch and Wnt signaling pathways in papillary thyroid cancer: Evidence from a systematic review and meta-analyzing microarray datasets employing bioinformatics knowledge and literature.
Papillary thyroid cancer (PTC) is a prevalent kind of thyroid cancer (TC), with the risk of metastasis increasing faster than any other malignancy. So, understanding the role of PTC in pathogenesis requires studying the various gene expressions to find out which particular molecular biomarkers will be helpful. The authors conducted a comprehensive search on the PubMed microarray database and a meta-analysis approach on the remaining ones to determine the differentially expressed genes between PTC and normal tissues, along with the analyses of overall survival (OS) and recurrence-free survival (RFS) rates in patients with PTC. We considered the associated genes with MAPK, Wnt, and Notch signaling pathways. Two GEO datasets have been included in this research, considering inclusion and exclusion criteria. Nineteen genes were found to have higher differences through the meta-analysis procedure. Among them, ten genes were upregulated, and nine genes were downregulated. The expression of 19 genes was examined using the GEPIA2 database, and the Kaplan-Meier plot statistics were used to analyze RFS and the OS rates. We discovered seven significant genes with the validation: PRICKLE1, KIT, RPS6KA5, GADD45B, FGFR2, FGF7, and DTX4. To further explain these findings, it was discovered that the mRNA expression levels of these seven genes and the remaining 12 genes were shown to be substantially linked with the results of the experimental literature investigations on the PTC. Our research found nineteen panels of genes that could be involved in the PTC progression and metastasis and the immune system infiltration of these cancers
PubMed: 38371530
DOI: 10.1016/j.bbrep.2023.101606 -
Infection, Genetics and Evolution :... Jan 2021Tuberculosis (TB) is one of the deadliest diseases since ancient times and is still a global health problem. So, there is a need to develop new approaches for early... (Comparative Study)
Comparative Study Meta-Analysis
Tuberculosis (TB) is one of the deadliest diseases since ancient times and is still a global health problem. So, there is a need to develop new approaches for early detection of TB and understand the host-pathogen relationship. In the present study, we have analyzed microarray data sets and compared the transcriptome profiling of the healthy individual with latent infection (LTBI) and active TB (TB) patients, and identified the differentially expressed genes (DEGs). Next, we performed the systematic network meta-analysis of the DEGs, which identified the seven most influencing hub genes (IL6, IL1B, TNF, NFKB1, STAT1, JAK2, and MAPK8) as the potential therapeutic target in the tuberculosis disease. These target genes are involved in many biological processes like cell cycle control, apoptosis, complement signalling, enhanced cytokine & chemokine signalling, pro-inflammatory responses, and host immune responses. Additionally, we also identified 22 inferred genes that are mainly engaged in the induction of innate immune response, cellular response to interleukin-6, inflammatory response, apoptotic process, I-kappaB-phosphorylation, JAK-STAT signalling pathway, macrophage activation, cell growth, and cell signalling. The proper attention of these inferred genes may open up a new horizon to understand the defensive mechanisms of TB disease. The transcriptome profiling and network approach can enhance the understanding of the molecular pathogenesis of tuberculosis infection and have implications for the plan and execution of mRNA expression tools to support early diagnostics and treatment of Mycobacterium tuberculosis (M.tb).
Topics: Antitubercular Agents; Biomarkers; Gene Expression Profiling; Genes, Bacterial; Genetic Variation; Healthy Volunteers; Humans; Latent Tuberculosis; Mycobacterium tuberculosis; Network Meta-Analysis; Protein Array Analysis; Transcriptome
PubMed: 33271338
DOI: 10.1016/j.meegid.2020.104649 -
Frontiers in Physiology 2022Cancer is one of the top causes of death globally. Recently, microarray gene expression data has been used to aid in cancer's effective and early detection. The use of...
Cancer is one of the top causes of death globally. Recently, microarray gene expression data has been used to aid in cancer's effective and early detection. The use of DNA microarray technology to uncover information from the expression levels of thousands of genes has enormous promise. The DNA microarray technique can determine the levels of thousands of genes simultaneously in a single experiment. The analysis of gene expression is critical in many disciplines of biological study to obtain the necessary information. This study analyses all the research studies focused on optimizing gene selection for cancer detection using artificial intelligence. One of the most challenging issues is figuring out how to extract meaningful information from massive databases. Deep Learning architectures have performed efficiently in numerous sectors and are used to diagnose many other chronic diseases and to assist physicians in making medical decisions. In this study, we have evaluated the results of different optimizers on a RNA sequence dataset. The Deep learning algorithm proposed in the study classifies five different forms of cancer, including kidney renal clear cell carcinoma (KIRC), Breast Invasive Carcinoma (BRCA), lung adenocarcinoma (LUAD), Prostate Adenocarcinoma (PRAD) and Colon Adenocarcinoma (COAD). The performance of different optimizers like Stochastic gradient descent (SGD), Root Mean Squared Propagation (RMSProp), Adaptive Gradient Optimizer (AdaGrad), and Adaptive Momentum (AdaM). The experimental results gathered on the dataset affirm that AdaGrad and Adam. Also, the performance analysis has been done using different learning rates and decay rates. This study discusses current advancements in deep learning-based gene expression data analysis using optimized feature selection methods.
PubMed: 36246115
DOI: 10.3389/fphys.2022.952709