scholarly journals MiR-574-5P, miR-1827, And miR-4429 As Potential Biomarkers For Schizophrenia

2021 ◽  
Author(s):  
Omran Davarinejad ◽  
Sajad Najafi ◽  
Hossein Zhaleh ◽  
Farzaneh Golmohammadi ◽  
Farnaz Radmehr ◽  
...  
Keyword(s):  
Mrna Expression ◽  
Meta Analysis ◽  
Interaction Network ◽  
Receiver Operating Curve ◽  
Healthy Controls ◽  
Protein Protein Interaction ◽  
Mirna Array ◽  
Expression Arrays ◽  
Limma Package ◽  
Novel Biomarkers

Abstract Schizophrenia is a severe chronic debilitating disorder with millions of affected individuals. Lack of a reliable mollecular diagnostic invokes the identification of novel biomarkers. To elucidate the molecular basis of the disease, two mRNA expression arrays including GSE93987 and GSE38485, and one miRNA array, GSE54914, were downloaded from GEO, and meta-analysis was performed for mRNA expression arrays by employment of metaDE package. By WGCNA package, we performed network analysis for both mRNA expression arrays separately. Then, we made protein-protein interaction network for significant modules. Limma package was employed to analyze the miRNA array and dysregulated miRNAs (DEMs) were identified. Using genes of significant modules and DEMs, a mRNA-miRNA network was constructed and hub genes and miRNAs were identified. To confirm the dysregulation of genes, expression values were evaluated by available datasets including GEO series GSE62333, GSE93987, and GSE38485. The ability of the detected hub miRNAs to discriminate Schizophrenia from healthy controls was evaluated by assessing the receiver-operating curve. Finally, by performing Real-Time PCR, the expression level of genes and miRNAs were evaluated in 40 Schizophrenia patients compared with healthy controls. The results confirmed dysregulation of hsa-miR-574-5P, hsa-miR-1827, hsa-miR-4429, CREBRF, ARPP19, TGFBR2, and YWHAZ in blood samples of schizophrenia patients.

scholarly journals The Study of Angptl4-Modulated Podocyte Injury in IgA Nephropathy

2021 ◽  
Vol 11 ◽  
Author(s):  
Sha Jia ◽  
Xiaofeng Peng ◽  
Ludan Liang ◽  
Ying Zhang ◽  
Meng Li ◽  
...  
Keyword(s):  
Iga Nephropathy ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Healthy Controls ◽  
Podocyte Injury ◽  
Protein Protein Interaction ◽  
Promising Tool ◽  
Significant Difference ◽  
Limma Package

BackgroundIncreasing evidence shows that Angptl4 affects proteinuria in podocytes injured kidney disease, however, whether there is a relationship between Angptl4 and IgA nephropathy (IgAN) has not been studied yet.MethodsPlasma and urine samples were obtained from 71 patients with IgAN and 61 healthy controls. Glomeruli from six renal biopsy specimens (three IgAN patients and three healthy controls) were separated by RNA-Seq. Differentially expressed genes (DEGs) related to podocytes and Angptl4 between IgAN patients and healthy controls were performed using the Limma package. Gene set enrichment analysis was used to determine whether there was a statistically significant difference between the two groups. STRING was used to create a protein-protein interaction network of DEGs. Association analysis between Angptl4 levels and clinical features of IgAN was performed.ResultsThirty-three podocyte-related and twenty-three Angpt4-related DEGs were found between IgAN patients and healthy controls. By overlapping the genes, FOS and G6PC were found to be upregulated in IgAN patients, while MMP9 was downregulated in IgAN patients. Plasma and urine Angptl4 levels were closely related to the degree of podocyte injury and urine protein, but not to the protein-creatine ratio.ConclusionOur findings show that Angptl4 levels in plasma and urine are related to podocyte damage and, therefore, may be a promising tool for assessing the severity of IgAN patients to identify and reverse the progression to ESRD.

scholarly journals Downregulation of KIF2C and TEKT2 is Associated with Male Infertility and Testicular Carcinoma

2021 ◽  
Author(s):  
Haiming Cao ◽  
Weiqiang Xu ◽  
Fei Wang ◽  
Xiaofeng LI ◽  
Jianquan Hou
Keyword(s):  
Target Gene ◽  
Interaction Network ◽  
Validation Dataset ◽  
Diagnostic Biomarkers ◽  
Protein Protein Interaction ◽  
Infertile Men ◽  
Potential Biomarker ◽  
Testicular Carcinoma ◽  
Limma Package ◽  
Protein Protein Interaction Network

Abstract Background: Genes have an important role in spermatogenesis and the maintenance of fertility, and may act as a potential biomarker for the clinical diagnosis of infertility. However, a comprehensive understanding of how these biological processes of infertility are regulated at the molecular level remains to be illustrated. Methods: In the present study, we sought to identify associated genes by reanalyzing separate studies from GEO datasets (GSE45885, GSE45887, and GSE9210) and validation dataset (GSE4797). DEGs were used the limma package. GO and KEGG pathway enrichment analyses were performed using the clusterprofier package. The STRING database was used construct a protein-protein interaction network. The interaction between mRNA and TF was predicted by using miRWalk. At last, the expression levels of hub genes were determined by TCGA data in GEPIA. Results: The results showed that several shared genes significantly associated with azoospermia. Finally, we effectively screen out two genes (KIF2C and TEKT2) for validation by GSE4797 in spermatozoa of infertile men with Johnsen score. Among these two genes, KIF2C and TEKT2 significantly down-regulated in spermatozoa of infertile men. The regulatory network of TF‐miRNA‐target gene was established, we found KIF2C-miRNAs(has-miR-3154,6075,6760-5p,1251-5p,186-sp)-TFs(EP300,SP1) might work in spermatozoa of infertile men.Conclusions: Our study might help to improve our understanding of the mechanisms in azoospermia and provide diagnostic biomarkers and therapeutics targets.

scholarly journals Whole Blood mRNA Expression Pattern Differentiates AD Patients and Healthy Controls Through Bioinformatics Analysis

2019 ◽  
Vol 10 (2) ◽  
pp. 46
Author(s):  
Mengjia Zhu ◽  
Liqun Wang
Keyword(s):  
Mrna Expression ◽  
Expression Pattern ◽  
Whole Blood ◽  
Rna Binding ◽  
Maximal Clique ◽  
Healthy Controls ◽  
Mrna Expression Pattern ◽  
Protein Protein Interaction ◽  
Potential Biomarker ◽  
Wide Range

Background: Gene chip has a wide range of applications in screening disease markers.Methods: GSE63063 dataset including 238 healthy controls and 285 patients with Alzheimer’s disease (AD) was downloaded to investigate the whole blood mRNA expression pattern. Lumi and LIMMA packages of R software were used to screening differential-expressed genes (DEGs). We functionally annotate DEGs through DAVID database. Then STRING database and Cytoscape software were used to construct protein-protein interaction models for hub genes.Results: Our results indicated that 51 DEGs altered in AD patients compared with healthy controls. These DEGs was associated with transcription (BP), RNA binding (MF) and ribosome (CC) terms and the ribosome signaling pathway. In addition, Ribosomal protein S17 (RPS17) was identified as the top 1 in hub genes using maximal clique centrality. RPS17 mutations reduced erythrocyte production and impaired brain development. Finally, the expression levels of the three genes (NDUFA1, RPL36AL, and NDUFS5) showed a good predictive effect.Conclusion: In conclusion, we explored the expression of genes in the AD blood and NDUFA1 may be a potential biomarker for predicting AD.

scholarly journals Glycoproteomic Analysis Reveals Aberrant Expression of Complement C9 and Fibronectin in the Plasma of Patients with Colorectal Cancer

Proteomes ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 26
Author(s):  
Juthamard Chantaraamporn ◽  
Voraratt Champattanachai ◽  
Amnart Khongmanee ◽  
Chris Verathamjamras ◽  
Naiyarat Prasongsook ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Interaction Analysis ◽  
Study Cohort ◽  
Healthy Controls ◽  
Label Free ◽  
Aberrant Expression ◽  
Protein Protein Interaction ◽  
Acetylneuraminic Acid ◽  
Novel Biomarkers ◽  
Complement C9

Colorectal cancer (CRC) is a major cause of cancer mortality. Currently used CRC biomarkers provide insufficient sensitivity and specificity; therefore, novel biomarkers are needed to improve the CRC detection. Label-free quantitative proteomics were used to identify and compare glycoproteins, enriched by wheat germ agglutinin, from plasma of CRC patients and age-matched healthy controls. Among 189 identified glycoproteins, the levels of 7 and 15 glycoproteins were significantly altered in the non-metastatic and metastatic CRC groups, respectively. Protein-protein interaction analysis revealed that they were predominantly involved in immune responses, complement pathways, wound healing and coagulation. Of these, the levels of complement C9 (C9) was increased and fibronectin (FN1) was decreased in both CRC states in comparison to those of the healthy controls. Moreover, their levels detected by immunoblotting were validated in another independent cohort and the results were consistent with in the study cohort. Combination of CEA, a commercial CRC biomarker, with C9 and FN1 showed better diagnostic performance. Interestingly, predominant glycoforms associated with acetylneuraminic acid were obviously detected in alpha-2 macroglobulin, haptoglobin, alpha-1-acid glycoprotein 1, and complement C4-A of CRC patient groups. This glycoproteomic approach provides invaluable information of plasma proteome profiles of CRC patients and identification of CRC biomarker candidates.

FYN and CD247: key Genes for Septic Shock Based on Bioinformatics and Meta-Analysis

2021 ◽  
Vol 24 ◽  
Author(s):  
Yue Jiang ◽  
Qian Miao ◽  
Lin Hu ◽  
Tingyan Zhou ◽  
Yingchun Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Septic Shock ◽  
Survival Analysis ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Key Genes ◽  
Protein Protein Interaction Network ◽  
Geo Database

Background: Septic shock is sepsis accompanied by hemodynamic instability and high clinical mortality. Material and Methods: GSE95233, GSE57065, GSE131761 gene-expression profiles of healthy control subjects and septic shock patients were downloaded from the Gene-Expression Omnibus (GEO) database, and differences of expression profiles and their intersection were analysed using GEO2R. Function and pathway enrichment analysis was performed on common differentially expressed genes (DEG), and key genes for septic shock were screened using a protein-protein interaction network created with STRING. Also, data from the GEO database were used for survival analysis for key genes, and a meta-analysis was used to explore expression trends of core genes. Finally, high-throughput sequencing using the blood of a murine sepsis model was performed to analyse the expression of CD247 and FYN in mice. Results: A total of 539 DEGs were obtained (p < 0.05). Gene ontology analysis showed that key genes were enriched in functions, such as immune response and T cell activity, and DEGs were enriched in signal pathways, such as T cell receptors. FYN and CD247 are in the centre of the protein-protein interaction network, and survival analysis found that they are positively correlated with survival from sepsis. Further, meta-analysis results showed that FYN could be useful for the prognosis of patients, and CD247 might distinguish between sepsis and systemic inflammatory response syndrome patients. Finally, RNA sequencing using a mouse septic shock model showed low expression of CD247 and FYN in this model. Conclusion: FYN and CD247 are expected to become new biomarkers of septic shock.

scholarly journals Investigating the association of CD36 gene polymorphisms (rs1761667 and rs1527483) with T2DM and dyslipidemia: Statistical analysis, machine learning based prediction, and meta-analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257857
Author(s):  
Ma’mon M. Hatmal ◽  
Walhan Alshaer ◽  
Ismail S. Mahmoud ◽  
Mohammad A. I. Al-Hatamleh ◽  
Hamzeh J. Al-Ameer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Statistical Analysis ◽  
Prediction Models ◽  
Wide Spectrum ◽  
Meta Analysis ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Jordanian Population ◽  
Prognostic Prediction ◽  
Cluster Of Differentiation

CD36 (cluster of differentiation 36) is a membrane protein involved in lipid metabolism and has been linked to pathological conditions associated with metabolic disorders, such as diabetes and dyslipidemia. A case-control study was conducted and included 177 patients with type-2 diabetes mellitus (T2DM) and 173 control subjects to study the involvement of CD36 gene rs1761667 (G>A) and rs1527483 (C>T) polymorphisms in the pathogenesis of T2DM and dyslipidemia among Jordanian population. Lipid profile, blood sugar, gender and age were measured and recorded. Also, genotyping analysis for both polymorphisms was performed. Following statistical analysis, 10 different neural networks and machine learning (ML) tools were used to predict subjects with diabetes or dyslipidemia. Towards further understanding of the role of CD36 protein and gene in T2DM and dyslipidemia, a protein-protein interaction network and meta-analysis were carried out. For both polymorphisms, the genotypic frequencies were not significantly different between the two groups (p > 0.05). On the other hand, some ML tools like multilayer perceptron gave high prediction accuracy (≥ 0.75) and Cohen’s kappa (κ) (≥ 0.5). Interestingly, in K-star tool, the accuracy and Cohen’s κ values were enhanced by including the genotyping results as inputs (0.73 and 0.46, respectively, compared to 0.67 and 0.34 without including them). This study confirmed, for the first time, that there is no association between CD36 polymorphisms and T2DM or dyslipidemia among Jordanian population. Prediction of T2DM and dyslipidemia, using these extensive ML tools and based on such input data, is a promising approach for developing diagnostic and prognostic prediction models for a wide spectrum of diseases, especially based on large medical databases.

scholarly journals Bioinformatics analysis of high-throughput data to validate potential novel biomarkers and small molecule drugs for glioblastoma multiforme

2020 ◽  
Vol 48 (7) ◽  
pp. 030006052092454
Author(s):  
Fuwei Qi ◽  
Qing Li ◽  
Xiaojun Lu ◽  
Zhihua Chen
Keyword(s):  
Gene Expression ◽  
Glioblastoma Multiforme ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Protein Protein Interaction ◽  
Diagnosis And Prognosis ◽  
Novel Biomarkers ◽  
Patient Prognosis ◽  
Microarray Datasets

Objective There have been no recent improvements in the glioblastoma multiforme (GBM) outcome, with median survival remaining 15 months. Consequently, the need to identify novel biomarkers for GBM diagnosis and prognosis, and to develop targeted therapies is high. This study aimed to establish biomarkers for GBM pathogenesis and prognosis. Methods In total, 220 overlapping differentially expressed genes (DEGs) were obtained by integrating four microarray datasets from the Gene Expression Omnibus database (GSE4290, GSE12657, GSE15824, and GSE68848). Then a 140-node protein–protein interaction network with 343 interactions was constructed. Results The immune response and cell adhesion molecules were the most significantly enriched functions and pathways, respectively, among DEGs. The designated hub genes ITGB5 and RGS4, which have a high degree of connectivity, were closely correlated with patient prognosis, and GEPIA database mining further confirmed their differential expression in GBM versus normal tissue. We also determined the 20 most appropriate small molecules that could potentially reverse GBM gene expression, Prestwick-1080 was the most promising and had the highest negative scores. Conclusions This study identified ITGB5 and RGS4 as potential biomarkers for GBM diagnosis and prognosis. Insights into molecular mechanisms governing GBM occurrence and progression will help identify alternative biomarkers for clinical practice.

scholarly journals LAPTM5 Plays a Key Role in the Diagnosis and Prognosis of Testicular Germ Cell Tumors

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Xiunan Li ◽  
Yu Su ◽  
Jiayao Zhang ◽  
Ye Zhu ◽  
Yingkun Xu ◽  
...  
Keyword(s):  
Germ Cell ◽  
Germ Cell Tumors ◽  
Interaction Network ◽  
Testicular Germ Cell Tumors ◽  
Testicular Germ Cell ◽  
Hub Gene ◽  
Protein Protein Interaction ◽  
Diagnosis And Prognosis ◽  
Potential Biomarker ◽  
Novel Biomarkers

Objective. Testicular germ cell tumors (TGCT) are a serious malignant tumor with low early diagnosis rates and high mortality. Methods. To investigate novel biomarkers to predict the diagnosis and prognosis of this cancer, bioinformatics analysis was used as an accurate, efficient, and economical method. Results. Our study detected 39 upregulated and 589 downregulated differentially expressed genes (DEGs) using the GEO and TCGA databases. To identify the function of DEGs, GO functional analysis, three pathway analysis (KEGG, REACTOME, and PANTHER), and protein-protein interaction network were performed using the KOBAS website, as well as the String database. After a series of analyses in GEPIA and TIMER, including differential expression, we found one candidate gene related to the prognosis and diagnosis of TGCT. LAPTM5 was also associated with CD8+ T cell and PDCD1 expression, which suggests that it may affect immune infiltration. Conclusions. LAPTM5 was identified as a hub gene, which could be used as a potential biomarker for TGCT diagnosis and prognosis.

scholarly journals Lysosome and Cytoskeleton Pathways Are Robustly Enriched in the Blood of Septic Patients: A Meta-Analysis of Transcriptomic Data

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Jie Ma ◽  
Chuanxi Chen ◽  
Andreas S. Barth ◽  
Chris Cheadle ◽  
Xiangdong Guan ◽  
...  
Keyword(s):  
Mrna Expression ◽  
Messenger Rna ◽  
Meta Analysis ◽  
Therapeutic Interventions ◽  
Acid Hydrolases ◽  
Mrna Expression Data ◽  
Human Sepsis ◽  
Protein Protein Interaction ◽  
Genome Wide ◽  
Microarray Studies

Background. Sepsis is a leading cause of mortality in intensive care units worldwide. A better understanding of the blood systems response to sepsis should expedite the identification of biomarkers for early diagnosis and therapeutic interventions.Methods. We analyzed microarray studies whose data is available from the GEO repository and which were performed on the whole blood of septic patients and normal controls.Results. We identified 6 cohorts consisting of 450 individuals (sepsis = 323, control = 127) providing genome-wide messenger RNA (mRNA) expression data. Through meta-analysis we found the “Lysosome” and “Cytoskeleton” pathways were upregulated in human sepsis patients relative to controls, in addition to previously known signaling pathways (including MAPK, TLR). The key regulatory genes in the “Lysosome” pathway include lysosomal acid hydrolases (e.g., protease cathepsin A, D) as well as the major (LAMP1, 2) and minor (SORT1, LAPTM4B) membrane proteins. In contrast, pathways related to “Ribosome”, “Spliceosome” and “Cell adhesion molecules” were found to be downregulated, along with known pathways for immune dysfunction. Overall, our study revealed distinct mRNA activation profiles and protein-protein interaction networks in blood of human sepsis.Conclusions. Our findings suggest that aberrant mRNA expression in the lysosome and cytoskeleton pathways may play a pivotal role in the molecular pathobiology of human sepsis.

Construction of a lncRNA–miRNA–mRNA network to determine the key regulators of the Th1/Th2 imbalance in multiple sclerosis

Epigenomics ◽  
2021 ◽  
Author(s):  
Hanieh Azari ◽  
Elham Karimi ◽  
Mohammad Shekari ◽  
Ahmad Tahmasebi ◽  
Amin Reza Nikpoor ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Bioinformatics Analysis ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
T Helper ◽  
Protein Protein Interaction ◽  
Novel Biomarkers ◽  
Ifn Γ ◽  
Protein Protein Interaction Network ◽  
Competitive Endogenous Rna

Aim: The exact epigenetic mechanisms that determine the balance of T helper cells 1 and 2 (Th1/Th2) and autoimmune responses in multiple sclerosis (MS) remain unclear. We aim to clarify these. Methods: A combination of bioinformatics analysis and molecular evaluations was utilized to identify master hub genes. Results: A competitive endogenous RNA network containing six long noncoding RNAs (lncRNAs), 21 miRNAs and 86 mRNAs was provided through enrichment analysis and a protein–protein interaction network. NEAT1 and MALAT1 were found as differentially expressed lncRNAs using GEO (GSE21942). Quantitative real-time PCR results demonstrate dysregulation in the RUNX3 (a regulator of Th1/Th2 balance), GATA3 and TBX21, as well as miR-544a and miR-210-3p (which directly target RUNX3). ELISA also confirmed an imbalance in IFN-γ (Th1)/IL-4 (Th2) in MS patients. Conclusion: Our findings introduce novel biomarkers leading to Th1/Th2 imbalance in MS.

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