scholarly journals Comprehensive analysis of IgA nephropathy expression profiles: identification of potential biomarkers and therapeutic agents

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Alieh Gholaminejad ◽  
Yousof Gheisari ◽  
Sedigheh Jalali ◽  
Amir Roointan
Keyword(s):  
Iga Nephropathy ◽  
Regulatory Network ◽  
Target Genes ◽  
Expression Profiles ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
Integrative Approach ◽  
Potential Factors ◽  
Ngf Signaling ◽  
Underlying Mechanisms

Abstract Background IgA nephropathy (IgAN) is a kidney disease recognized by the presence of IgA antibody depositions in kidneys. The underlying mechanisms of this complicated disease are remained to be explored and still, there is an urgent need for the discovery of noninvasive biomarkers for its diagnosis. In this investigation, an integrative approach was applied to mRNA and miRNA expression profiles in PBMCs to discover a gene signature and novel potential targets/biomarkers in IgAN. Methods Datasets were selected from gene expression omnibus database. After quality control checking, two datasets were analyzed by Limma to identify differentially expressed genes/miRNAs (DEGs and DEmiRs). Following identification of DEmiR-target genes and data integration, intersecting mRNAs were subjected to different bioinformatic analyses. The intersecting mRNAs, DEmiRs, related transcription factors (from TRRUST database), and long-non coding RNAs (from LncTarD database) were used for the construction of a multilayer regulatory network via Cytoscape. Result “GSE25590” (miRNA) and “GSE73953” (mRNA) datasets were analyzed and after integration, 628 intersecting mRNAs were identified. The mRNAs were mainly associated with “Innate immune system”, “Apoptosis”, as well as “NGF signaling” pathways. A multilayer regulatory network was constructed and several hub-DEGs (Tp53, STAT3, Jun, etc.), DEmiRs (miR-124, let-7b, etc.), TFs (NF-kB, etc.), and lncRNAs (HOTAIR, etc.) were introduced as potential factors in the pathogenesis of IgAN. Conclusion Integration of two different expression datasets and construction of a multilayer regulatory network not only provided a deeper insight into the pathogenesis of IgAN, but also introduced several key molecules as potential therapeutic target/non-invasive biomarkers.

Comprehensive Analysis of IgA Nephropathy Expression Profiles: Identification of Potential Biomarkers and Therapeutic Agents

2020 ◽  
Author(s):  
Alieh Gholaminejad ◽  
Yousof Gheisari ◽  
Sedigheh Jalali ◽  
Amir Roointan
Keyword(s):  
Regulatory Network ◽  
Target Genes ◽  
Expression Profiles ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
Integrative Approach ◽  
Potential Factors ◽  
Ngf Signaling ◽  
Underlying Mechanisms ◽  
Noninvasive Biomarkers

Abstract Background: IgA nephropathy (IgAN) is a kidney disease recognized by the presence of IgA antibody depositions in kidneys. The underlying mechanisms of this complicated disease is remained to be more explored and still there is an urgent need for discovery of noninvasive biomarkers for its diagnosis. In this investigation, an integrative approach was applied on mRNA and miRNA expression profiles in PBMCs to discover a gene signature and novel potential targets/biomarkers in IgAN.Methods: Datasets were selected from gene expression omnibus database. After quality control checking, two datasets were analyzed by Limma to identify differentially expressed genes/miRNAs (DEGs and DEmiRs). Following identification of DEmiR-target genes and data integration, intersecting mRNAs were subjected to different bioinformatic analyses. The intersecting mRNAs, DEmiRs, related transcription factors (from TRRUST database) and long-non coding RNAs (from LncTarD database) were used for construction of a multilayer regulatory network via Cytoscape.Result: “GSE25590” (miRNA) and “GSE73953” (mRNA) datasets were analyzed and after integration, 628 intersecting mRNAs were identified. The mRNAs were mainly associated with “Innate immune system”, “Apoptosis”, as well as “NGF signaling” pathways. A multilayer regulatory network was constructed and several hub-DEGs (Tp53, STAT3, Jun, etc.), DEmiRs (miR-124, let-7b, etc.), TFs (NF-kB, etc.), and lncRNAs (HOTAIR, etc.) were introduced as potential factors in the pathogenesis of IgAN.Conclusion: Integration of two different expression datasets and construction of a multilayer regulatory network not only provided a deeper insight into the pathogenesis of IgAN, but also introduced several key molecules as potential therapeutic target/non-invasive biomarkers.

scholarly journals Identification of miRNA-mRNA network and immune-related gene signatures in IgA nephropathy by integrated bioinformatics analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Shi-Yao Wei ◽  
Shuang Guo ◽  
Bei Feng ◽  
Shang-Wei Ning ◽  
Xuan-Yi Du
Keyword(s):  
T Cells ◽  
Iga Nephropathy ◽  
Immune Cells ◽  
Regulatory Network ◽  
Target Genes ◽  
Immune Cell ◽  
Expression Profiles ◽  
Specific Treatment ◽  
Hub Genes ◽  
Ppi Networks

Abstract Background IgA nephropathy (IgAN) is the most common form of primary glomerulonephritis worldwide, and its diagnosis depends mainly on renal biopsy. However, there is no specific treatment for IgAN. Moreover, its causes and underlying molecular events require further exploration. Methods The expression profiles of GSE64306 and GSE93798 were downloaded from the Gene Expression Omnibus (GEO) database and used to identify the differential expression of miRNAs and genes, respectively. The StarBase and TransmiR databases were employed to predict target genes and transcription factors of the differentially expressed miRNAs (DE-miRNAs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to predict biological functions. A comprehensive analysis of the miRNA-mRNA regulatory network was constructed, and protein–protein interaction (PPI) networks and hub genes were identified. CIBERSORT was used to examine the immune cells in IgAN, and correlation analyses were performed between the hub genes and infiltrating immune cells. Results Four downregulated miRNAs and 16 upregulated miRNAs were identified. Forty-five and twelve target genes were identified for the upregulated and downregulated DE-miRNAs, respectively. CDKN1A, CDC23, EGR1, HIF1A, and TRIM28 were the hub genes with the highest degrees of connectivity. CIBERSORT revealed increases in the numbers of activated NK cells, M1 and M2 macrophages, CD4 naive T cells, and regulatory T cells in IgAN. Additionally, HIF1A, CDC23, TRIM28, and CDKN1A in IgAN patients were associated with immune cell infiltration. Conclusions A potential miRNA-mRNA regulatory network contributing to IgAN onset and progression was successfully established. The results of the present study may facilitate the diagnosis and treatment of IgAN by targeting established miRNA-mRNA interaction networks. Infiltrating immune cells may play significant roles in IgAN pathogenesis. Future studies on these immune cells may help guide immunotherapy for IgAN patients.

scholarly journals Identification of Potential miRNA-mRNA Regulatory Network Contributing to Hypertrophic Cardiomyopathy (HCM)

2021 ◽  
Vol 8 ◽  
Author(s):  
Lin Wang ◽  
Fengmin Lu ◽  
Jing Xu
Keyword(s):  
Logistic Regression ◽  
Hypertrophic Cardiomyopathy ◽  
Regression Model ◽  
Regulatory Network ◽  
Logistic Regression Model ◽  
Target Genes ◽  
Expression Profiles ◽  
Gene Expression Omnibus ◽  
Diagnostic Methods ◽  
Go Annotation

Background: Hypertrophic cardiomyopathy (HCM) is a myocardial disease with unidentified pathogenesis. Increasing evidence indicated the potential role of microRNA (miRNA)-mRNA regulatory network in disease development. This study aimed to explore the miRNA-mRNA axis in HCM.Methods: The miRNA and mRNA expression profiles obtained from the Gene Expression Omnibus (GEO) database were used to identify differentially expressed miRNAs (DEMs) and genes (DEGs) between HCM and normal samples. Target genes of DEMs were determined by miRTarBase. Gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to identify biological functions of the DEGs and DEMs. miRNA-mRNA regulatory network was constructed to identify the hub genes and miRNAs. Logistic regression model for HCM prediction was established basing on the network.Results: A total of 224 upregulated and 366 downregulated DEGs and 10 upregulated and 14 downregulated DEMs were determined. We identified 384 DEM-targeted genes, and 20 of them were overlapped with the DEGs. The enriched functions include extracellular structure organization, organ growth, and phagosome and melanoma pathways. The four miRNAs and three mRNAs, including hsa-miR-373, hsa-miR-371-3p, hsa-miR-34b, hsa-miR-452, ARHGDIA, SEC61A1, and MYC, were identified through miRNA-mRNA regulatory network to construct the logistic regression model. The area under curve (AUC) values over 0.9 suggested the good performance of the model.Conclusion: The potential miRNA-mRNA regulatory network and established logistic regression model in our study may provide promising diagnostic methods for HCM.

scholarly journals Screening Driving Transcription Factors in the Processing of Gastric Cancer

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Guangzhong Xu ◽  
Kai Li ◽  
Nengwei Zhang ◽  
Bin Zhu ◽  
Guosheng Feng
Keyword(s):  
Gastric Cancer ◽  
Transcription Factors ◽  
Regulatory Network ◽  
Target Genes ◽  
Transcriptional Regulatory Network ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Regulatory Mechanisms ◽  
Integrated Analysis ◽  
Transcriptional Regulatory

Background. Construction of the transcriptional regulatory network can provide additional clues on the regulatory mechanisms and therapeutic applications in gastric cancer.Methods. Gene expression profiles of gastric cancer were downloaded from GEO database for integrated analysis. All of DEGs were analyzed by GO enrichment and KEGG pathway enrichment. Transcription factors were further identified and then a global transcriptional regulatory network was constructed.Results. By integrated analysis of the six eligible datasets (340 cases and 43 controls), a bunch of 2327 DEGs were identified, including 2100 upregulated and 227 downregulated DEGs. Functional enrichment analysis of DEGs showed that digestion was a significantly enriched GO term for biological process. Moreover, there were two important enriched KEGG pathways: cell cycle and homologous recombination. Furthermore, a total of 70 differentially expressed TFs were identified and the transcriptional regulatory network was constructed, which consisted of 566 TF-target interactions. The top ten TFs regulating most downstream target genes were BRCA1, ARID3A, EHF, SOX10, ZNF263, FOXL1, FEV, GATA3, FOXC1, and FOXD1. Most of them were involved in the carcinogenesis of gastric cancer.Conclusion. The transcriptional regulatory network can help researchers to further clarify the underlying regulatory mechanisms of gastric cancer tumorigenesis.

Microarray Analyses in a Case-Control Cohort of T-ALL Samples Identifies Gene Signature of Potential Prognostic Significance.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 1448-1448
Author(s):  
Stuart S. Winter ◽  
Hadya Khawaja ◽  
Zeyu Jiang ◽  
Charles Kooperberg ◽  
Adolfo Ferrando ◽  
...  
Keyword(s):  
Microarray Analysis ◽  
Target Genes ◽  
Expression Profiles ◽  
Prognostic Significance ◽  
Gene Signature ◽  
Case Control ◽  
Response To Therapy ◽  
Prognostic Indicators ◽  
Specific Gene ◽  
High Dose

Abstract Risk stratification remains limited for patients being treated for T-ALL due to a lack of biologic predictors of outcome. As a consequence, treatment assignment on modern protocols has been largely achieved through random assignment. Recent observations have suggested that overexpression of specific gene(s) may provide a reliable means to risk stratify patients. We hypothesized that microarray analysis may identify gene sets that distinguish both therapeutic response and patient outcome in T-ALL. We analyzed the gene expression profiles of 45 primary T-ALL samples (24 CCR, 21 relapse) from a matched, case control study with sufficient cRNA for microarray analysis (COG #8704). We performed oligonucleotide microarray analysis using Affymetrix U133Av.2 genechips which have approximately 54,000 target genes and ESTs. Following heirarchical clustering in dChip and R Language analyses (Chiaretti et al. Blood, 2004), but using RMA normalization, we identified 37 genes that serve as reliable predictors of CCR or relapse. Leave-one-out least discriminant analysis cross-over validation further constrained our prognostic gene identifiers to 21 genes of robust significance. These 21 genes predict 87 % of CCR and 82% of relapse accurately (p<0.0001, two-tailed Fisher’s exact test). These results were verified by qRT-PCR. Transcriptional factors previously described as having prognostic significance were not identified in our study. Twenty-six of the 45 cases received high dose L-asparaginase (16 CCR, 10 relapse) on the companion study. As a result, we examined whether a distinct signature could be also identified that distinguishes response to dose-intensified asparaginase treatment for patients with T-ALL. Using the same approach, a 27-member gene signature was identified that accurately predicted response in 24 of the 26 cases (92 %; 2 cases of relapse were misclassified). These results have identified two sets of genes that may be further pursued as prognostic indicators in T-ALL or as predictors of response to therapy.

scholarly journals An Integrated Analysis of mRNAs and miRNAs Microarray Profiles to Screen miRNA Signatures Involved in Nasopharyngeal Carcinoma

2020 ◽  
Vol 19 ◽  
pp. 153303382095699
Author(s):  
Lei Liu ◽  
Hailing Wang ◽  
Chaohui Yan ◽  
Shudong Tao
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
Target Genes ◽  
Expression Profiles ◽  
Area Under The Curve ◽  
Gene Expression Omnibus ◽  
Integrated Analysis ◽  
Messenger Rnas ◽  
Screening Criteria ◽  
Go Annotation ◽  
Microarray Datasets

Objective: We aim to identify several microRNAs (miRNAs/miRs)-messenger RNAs (mRNAs) biomarkers correlated to nasopharyngeal carcinoma (NPC) based on an integrated analysis of miRNA and mRNAs microarray expression profiles. Methods: The available mRNA and miRNA microarray datasets were retrieved from Gene Expression Omnibus (GEO) database according to pre-determined screening criteria. Differentially expressed miRNA and mRNAs (DEmiRNAs and DEmRNAs) were extracted between NPC and noncancerous nasopharyngeal tissues. The target genes of DEmiRNAs were predicted with miRTarBase followed by the construction of DEmiRNAs-target DEmRNAs network, and functional analyses were performed. The DEmiRNAs expressions were validated and the performance of these DEmiRNAs was assessed by the area under the curve (AUC) values. Finally, the correlations between DEmiRNAs and specific clinical factors were analyzed. Results: There were 1140 interaction pairs (including let-7d/f- MYC/ HMGA2 and miR-452- ITGA9) in DEmiRNAs-target DEmRNAs network. The GO annotation analysis showed that several genes such as MYC, HMGA2 and ITGA9 primarily participated in cellular process. KEGG analysis showed that these targets were associated with cell cycle and cancer-related pathways. Down-regulated let-7(-d and –f) and up-regulated miR-452 were verified in datasets. The AUC values of these 3 DEmiRNAs (let-7d, let-7-f and miR-452) was 0.803, 0.835 and 0.735, respectively. Besides, miR-452 was significantly related to survival rate of NPC patients. Conclusion: The findings implied let-7d/f- MYC/ HMGA2 and miR-452- ITGA9 might be promising targets for the detection and treatment of NPC.

scholarly journals Construction of potential periodontitis-related miRNA-mRNA regulatory network

2020 ◽  
Author(s):  
Zheng Zhang ◽  
Youli Zheng ◽  
Xiaowei Bian ◽  
Mingguang Jin
Keyword(s):  
Candidate Genes ◽  
Regulatory Network ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Expression Profiles ◽  
Multivariate Logistic Regression Analysis ◽  
Gene Expression Profiles ◽  
Mirna Expression Profile ◽  
Differentially Expressed ◽  
B Cell Differentiation

Abstract Background MicroRNAs (miRNAs) are found to be involved in the pathogenesis of periodontitis, a major cause of tooth loss in adults. However, a comprehensive miRNA-mRNA regulatory network has still not been established. Methods One miRNA expression profile and 2 gene expression profiles were downloaded from the GEO database and analyzed using GEO2R. Candidate genes commonly appeared in differentially expressed mRNAs (DE-mRNAs) and target genes of differentially expressed miRNAs (DE-miRNAs) were selected for functional and pathway enrichment analyses using Enrichr database. Multivariate Logistic regression analysis was used to screen independent variables among candidate genes. The diagnostic values of screened genes were determined by the area under the receiver operating characteristic (ROC) curve (AUC). Results A total of 5 DE-miRNAs (4 upregulated and 1 downregulated) and 11 candidate genes (3 upregulated and 8 downregulated) were screened. After the construction of miRNA-mRNA regulatory network, 12 miRNA-mRNA pairs were identified. In the network, the upregulated genes were significantly enriched in cellular triglyceride homeostasis and positive regulation of B cell differentiation, whereas the downregulated genes were enriched in vesicle organization, negative regulation of lymphocyte and leukocyte migration. EPCAM and RAB30 were screened as risk factors of periodontitis. The combined AUC of these 2 genes was 0.896 (GSE10334) and 0.916 (GSE16134). Conclusion In this study, we established a potential periodontitis-related miRNA-mRNA regulatory network, which brings new insights into the molecular mechanisms and provides key clues in seeking novel therapeutic targets for periodontitis. In the future, more experiments need to be carried out to validate our current findings.

scholarly journals Comprehensive Annotation and Functional Exploration of MicroRNAs in Lettuce

2021 ◽  
Vol 12 ◽  
Author(s):  
Yang Deng ◽  
Yajuan Qin ◽  
Pan Yang ◽  
Jianjun Du ◽  
Zheng Kuang ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Regulatory Networks ◽  
Target Genes ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Computational Prediction ◽  
Regulatory Relationship ◽  
Specific Expression ◽  
Economic Significance ◽  
Specific Expression Pattern

MicroRNA (miRNA) is an important endogenous post-transcriptional regulator, while lettuce (Lactuca sativa) is a leafy vegetable of global economic significance. However, there are few studies on miRNAs in lettuce, and research on miRNA regulatory network in lettuce is absent. In this study, through deep sequencing of small RNAs in different tissues, together with a reference genome, 157 high-confidence miRNA loci in lettuce were comprehensively identified, and their expression patterns were determined. Using a combination of computational prediction and high-throughput experimental verification, a set of reliable lettuce miRNA targets were obtained. Furthermore, through RNA-Seq, the expression profiles of these targets and a comprehensive view of the negative regulatory relationship between miRNAs and their targets was acquired based on a correlation analysis. To further understand miRNA functions, a miRNA regulatory network was constructed, with miRNAs at the core and combining transcription factors and miRNA target genes. This regulatory network, mainly composed of feed forward loop motifs, greatly increases understanding of the potential functions of miRNAs, and many unknown potential regulatory links were discovered. Finally, considering its specific expression pattern, Lsa-MIR408 as a hub gene was employed to illustrate the function of the regulatory network, and genetic experiments revealed its ability to increase the fresh weight and achene size of lettuce. In short, this work lays a solid foundation for the study of miRNA functions and regulatory networks in lettuce.

scholarly journals Distinct Molecular Mechanisms Analysis of Obesity Based on Gene Expression Profiles

2020 ◽  
Author(s):  
Harish Joshi ◽  
Basavaraj Mallikarjunayya Vastrad ◽  
Nidhi Joshi ◽  
Chanabasayya Mallikarjunayya Vastrad
Keyword(s):  
Gene Expression ◽  
Regulatory Network ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Expression Profiles ◽  
Therapeutic Targets ◽  
Rank Aggregation ◽  
Receiver Operating Curve ◽  
Progression Rate ◽  
Hub Genes

Abstract Background Obesity is the most common metabolic disorder worldwide. Its progression rate has remained high in recent years. ObjectivesTherefore, the aim of this study was to diagnose important differentially expressed genes (DEGs) associated in its development, which may be used as novel biomarkers or potential therapeutic targets for obesity. MethodsThe gene expression profile of E-MTAB-6728 was downloaded from the database. After screening DEGs in each ArrayExpress dataset, we further used the robust rank aggregation method to diagnose 876 significant DEGs including 438 up regulated and 438 down regulated genes. Pathway enrichment analyses and Gene Ontology (GO) were performed by online tool ToppCluster. These DEGs were shown to be significantly enriched in different obesity related pathways and GO functions. Then, the mentha, miRNet and NetworkAnalyst databases were used to construct the protein–protein interaction network, target genes - miRNA regulatory network and target genes - TF regulatory network. The module analysis was performed by the PEWCC1 plug‐in of Cytoscape based on the whole PPI network.Results We finally filtered out HSPA8, ESR1, YWHAH, RPL14, SOD2, BTG2, LYZ and EFNA1 hub genes. Hub genes were validated by ICH analysis, Receiver operating curve (ROC) analysis and RT-PCR. The robust DEGs linked with the development of obesity were screened through the ArrayExpress database, and integrated bioinformatics analysis was conducted. ConclusionsOur study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for obesity.

scholarly journals Identification of Potential Serum Exosomal microRNAs Involved in Acinar-Ductal Metaplasia That is A Precursor of Pancreatic Cancer Associated with Chronic Pancreatitis

2020 ◽  
Author(s):  
Li-Ping Sheng ◽  
Chao-Qun Han ◽  
Chi Nie ◽  
Tao Xu ◽  
Kun Zhang ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Chronic Pancreatitis ◽  
Early Diagnosis ◽  
Regulatory Network ◽  
Target Genes ◽  
Software Tool ◽  
Gene Expression Omnibus ◽  
String Database ◽  
Protein Protein Interaction ◽  
Pathway Analyses

Abstract Backgrounds Due to difficulty in early diagnosis of chronic pancreatitis (CP), it is urgent to find novel biomarkers to detect CP. Exosomal microRNAs (Exo-miRNAs) located in the serum may be potential diagnostic and therapeutic targets for CP. In our study, we performed a bioinformatics analysis to identify differentially expressed Exo-miRNAs (DE-Exo-miRNAs) in the serum of CP patients. Methods The dataset GSE128508 was downloaded from the Gene Expression Omnibus (GEO) database. The analysis was carried out using BRB-ArrayTools and Significance Analysis of Microarrays (SAM). The target genes of DE-S-Exo-miRNAs were predicted by miRWalk databases. Further gene ontology (GO) term and Kyoto Encyclopedia of Genomes (KEGG) pathway analyses were performed with plug-in ClueGO in Cytoscape software 3.7.0. Subsequently, the interaction regulatory network between encoded proteins of target genes was performed with the Search Tool for the Retrieval of Interacting Genes (STRING) database and analyzed using plug-in MCODE and cytoHubba in Cytoscape software 3.7.0. Results We identified 227 DE-Exo-miRNAs in the serum. Further analysis using the miRWalk database identified 5164 target genes of these miRNAs. The protein-protein interaction (PPI) regulatory network of 1912 potential target genes for hub 10 up-regulated miRNAs with high degrees and one down-regulated miRNAs were constructed using the STRING database and Cytoscape software. The functional analysis using Cytoscape software tool highlighted that target genes involved in pancreatic cancer. Acinar-ductal metaplasia (ADM) in the inflammatory environment of CP is a precursor of pancreatic cancer. Subsequently, we constructed a network of target genes associated with ADM and their miRNAs. Conclusions Exo-miRNAs in the serum as well as their target genes may be promising targets for the early diagnosis and treatment of CP. In addition, we identified potential Exo-miRNAs involved in ADM that is a precursor of pancreatic cancer associated with CP.

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