scholarly journals Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in diabetic nephropathy

2020 ◽  
Author(s):  
Harish Joshi ◽  
Basavaraj Vastrad ◽  
Nidhi Joshi ◽  
Anandkumar Tengli ◽  
Chanabasayya Vastrad ◽  
...  
Keyword(s):  
Small Molecules ◽  
Regulatory Network ◽  
High Throughput Sequencing ◽  
Docking Studies ◽  
Curve Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Roc Curve Analysis ◽  
Protein Protein Interaction ◽  
Diagnostic Values

Abstract The underlying molecular mechanisms of diabetic nephropathy (DN) have yet not been investigated clearly. In this investigation, we aimed to identify key genes involved in the pathogenesis and prognosis of DN. We selected expression profiling by high throughput sequencing dataset GSE142025 from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between DN and normal control samples were analyzed with limma package. Gene ontology (GO) and REACTOME enrichment analysis were performed using ToppGene. Then we established the protein-protein interaction (PPI) network, miRNA-DEG regulatory network and TF-DEG regulatory network. The diagnostic values of hub genes were performed through receiver operating characteristic (ROC) curve analysis. Finally, the candidate small molecules as potential drugs to treat DM were predicted using molecular docking studies. Through expression profiling by high throughput sequencing dataset, a total of 549 DEGs were detected including 275 up regulated and 274 down regulated genes. Biological process analysis of functional enrichment showed these DEGs were mainly enriched in cell activation, response to hormone, cell surface, integral component of plasma membrane, signaling receptor binding, lipid binding, immunoregulatory interactions between a lymphoid and a non-lymphoid cell and biological oxidations. DEGs with high degree of connectivity (MDFI, LCK, BTK, IRF4, PRKCB, EGR1, JUN, FOS, ALB and NR4A1) were selected as hub genes from protein-protein interaction (PPI) network, miRNA-DEG regulatory network and TF-DEG regulatory network. The ROC curve analysis confirmed that hub genes were high diagnostic values. Finally, the significant small molecules were obtained based on molecular docking studies. Our results indicated that MDFI, LCK, BTK, IRF4, PRKCB, EGR1, JUN, FOS, ALB and NR4A1 could be the potential novel biomarkers for GC diagnosis prognosis and the promising therapeutic targets. The present study may be crucial to understanding the molecular mechanism of DN initiation and progression.

scholarly journals Screening and identification of key biomarkers in clear cell renal cell carcinoma based on bioinformatics analysis

2020 ◽  
Author(s):  
Basavaraj Vastrad ◽  
Chanabasayya Vastrad ◽  
Iranna Kotturshetti
Keyword(s):  
Regulatory Network ◽  
Target Gene ◽  
Node Degree ◽  
Rt Pcr ◽  
Hub Genes ◽  
Roc Curve Analysis ◽  
Histochemical Analysis ◽  
Cell Renal Cell Carcinoma ◽  
Protein Protein Interaction ◽  
Stage Analysis

AbstractClear cell renal cell carcinoma (ccRCC) is one of the most common types of malignancy of the urinary system. The pathogenesis and effective diagnosis of ccRCC have become popular topics for research in the previous decade. In the current study, an integrated bioinformatics analysis was performed to identify core genes associated in ccRCC. An expression dataset (GSE105261) was downloaded from the Gene Expression Omnibus database, and included 26 ccRCC and 9 normal kideny samples. Assessment of the microarray dataset led to the recognition of differentially expressed genes (DEGs), which was subsequently used for pathway and gene ontology (GO) enrichment analysis. This data was utilized in the construction of the protein-protein interaction network and module analysis was conducted using Human Integrated Protein-Protein Interaction rEference (HIPPIE) and Cytoscape software. In addation, target gene - miRNA regulatory network and target gene - TF regulatory network were constructed and analysed. Finally, hub genes were validated by survival analysis, expression analysis, stage analysis, mutation analysis, immune histochemical analysis, receiver operating characteristic (ROC) curve analysis, RT-PCR and immune infiltration analysis. The results of these analyses led to the identification of a total of 930 DEGs, including 469 up regulated and 461 down regulated genes. The pathwayes and GO found to be enriched in the DEGs (up and down regulated genes) were dTMP de novo biosynthesis, glycolysis, 4-hydroxyproline degradation, fatty acid beta-oxidation (peroxisome), cytokine, defense response, renal system development and organic acid metabolic process. Hub genes were identified from PPI network according to the node degree, betweenness centrality, stress centrality, closeness centrality and clustering coefficient. Similarly, targate genes were identified from target gene - miRNA regulatory network and target gene - TF regulatory network according to the node degree. Furthermore, survival analysis, expression analysis, stage analysis, mutation analysis, immune histochemical analysis, ROC curve analysis, RT-PCR and immune infiltration analysis revealed that CANX, SHMT2, IFI16, P4HB, CALU, CDH1, ERBB2, NEDD4L, TFAP2A and SORT1 may be associated in the tumorigenesis, advancement or prognosis of ccRCC. In conclusion, the 10 hub genes diagonised in the current study may help researchers in exemplify the molecular mechanisms linked with the tumorigenesis and advancement of ccRCC, and may be powerful and favorable candidate biomarkers for the prognosis, diagnosis and treatment of ccRCC.

scholarly journals Analysis of Potential Hub Genes and Related Transcription Factors in Rosacea Patients using Bioinformatics Analysis

2021 ◽  
Author(s):  
Xin Wang ◽  
Wenfang Dong ◽  
Huan Wang ◽  
Jianjun You ◽  
Ruobing Zheng ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Regulatory Network ◽  
Bioinformatics Analysis ◽  
Biological Functions ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
String Databases ◽  
Cell Signaling Pathways

Abstract Objective The aim of this study is to discover the adipocyte genes and pathways involved in rosacea using bioinformatics analysis.Methods The GSE65914 gene expression profile was obtained. The GEO2R tool was used to screen out differentially expressed genes (DEGs). It was further analyzed with Gene Ontology (GO) to explore functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) to explore cell signaling pathways. Protein-protein interaction (PPI) networks among the DEGs were found by STRING databases and visualized in Cytoscape software. The related transcription factors regulatory network of the DEGs were also constructed.Results A total of 254 DEGs, including 72 up-regulated genes and 182 down-regulated genes, were obtained in rosacea samples. The biological functions of DEGs are mainly involved in the inflammatory response and chemokine activity. A PPI network consisting of 217 nodes and 710 edges was constructed using STRING, and ten hub genes were identified with Cytoscape software. Some transcriptional factors were also found to interact with these hub DEGs.Conclusion In this study, we obtained ten hub genes, including CXCL8, CCR5, CXCR4, CXCL10, MMP9, CD2, CCL19, CXCL9, CCL5, CD3D, which play an essential role in the pathology of rosacea, and these genes may provide a basis for the screening of treatment biomarkers for rosacea in the future.

scholarly journals SNX10 and PTGDS are Associated with the Progression and Prognosis of Cervical Squamous Cell Carcinoma

2020 ◽  
Author(s):  
Pinping Jiang ◽  
Ying Cao ◽  
Feng Gao ◽  
Wei Sun ◽  
Jinhui Liu ◽  
...  
Keyword(s):  
Functional Analysis ◽  
Cox Regression ◽  
Expression Profiles ◽  
Curve Analysis ◽  
Ppi Network ◽  
Methylation Analysis ◽  
Hub Genes ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis ◽  
Tumor Tissues

Abstract Background: Cervical cancer (CC) is an important cause of death in women. This study sought to investigate the potential mechanism and prognostic genes of CC. Methods: We downloaded four gene expression profiles from GEO. The RRA method was used to integrate and screen differentially expressed genes (DEGs) between CC and normal samples. Functional analysis was performed by clusterprofiler. We built PPI network by Search Tool for the Retrieval of Interacting Genes Database (STRING) and selected hub modules via Molecular COmplex Detection (MCODE). CMap database was used to find molecules with therapeutic potential for CC. The hub genes were validated in GEO datasets, Gene Expession Profiling Interactive Analysis (GEPIA), immunohistochemistry, Cox regression analysis, TCGA methylation analysis and ONCOMINE were carried out. ROC curve analysis and GSEA were also performed to describe the prognostic significance of hub genes.Results: Functional analysis revealed that 147 DEGs were significantly enriched in binding, cell proliferation, transcriptional activity and cell cycle regulation. PPI network screened 30 hub genes, with CDK1 having the strongest connectivity with CC. Cmap showed that apigenin, thioguanine and trichostatin A might be used to treat CC(P<0.05). Eight genes (APOD, CXCL8, MMP1, MMP3, PLOD2, PTGDS, SNX10 and SPP1) were screened out through GEPIA. Of them, only PTGDS and SNX10 had not appeared in previous studies about CC. The validation in GEO showed that PTGDS showed low expression in tumor tissues while SNX10 showed high expression in tumor tissues. Their expression profiles were consistent with the results in immunohistochemistry. ROC curve analysis indicated that the model had a good diagnostic efficiency(AUC= 0.738). GSEA showed that the two genes were associated with the chemokine signaling pathway(P<0.05). TCGA methylation analysis showed that patients with lowly-expressed and highly-methylated PTGDS had a worse prognosis than those with highly-expressed and lowly-methylated PTGDS (p=0.037). Cox regression analysis showed that SNX10 (P=0.007;HR=1.424;95%CI:1.103-1.838) and PTGDS (P=0.003;HR=0.802;95%CI:0.693-0.928) were independent prognostic indicators for OS among CC patients. Conclusions: PTGDS and SNX10 showed abnormal expression and methylation in CC. Both genes might have high prognostic value of CC patients..

scholarly journals SNX10 and PTGDS are Associated with the Progression and Prognosis of Cervical Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Pinping Jiang ◽  
Ying Cao ◽  
Feng Gao ◽  
Wei Sun ◽  
Jinhui Liu ◽  
...  
Keyword(s):  
Functional Analysis ◽  
Cox Regression ◽  
Expression Profiles ◽  
Curve Analysis ◽  
Ppi Network ◽  
Methylation Analysis ◽  
Hub Genes ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis ◽  
Tumor Tissues

Abstract Background: Cervical cancer (CC) is the primary cause of death in women. This study sought to investigate the potential mechanism and prognostic genes of CC. Methods: We downloaded four gene expression profiles from GEO. The RRA method was used to integrate and screen differentially expressed genes (DEGs) between CC and normal samples. Functional analysis was performed by clusterprofiler. We built PPI network by Search Tool for the Retrieval of Interacting Genes Database (STRING) and selected hub modules via Molecular COmplex Detection (MCODE). CMap database was used to find molecules with therapeutic potential for CC. The hub genes were validated in GEO datasets, Gene Expession Profiling Interactive Analysis (GEPIA), immunohistochemistry, Cox regression analysis, TCGA methylation analysis and ONCOMINE were carried out. ROC curve analysis and GSEA were also performed to describe the prognostic significance of hub genes. Results: Functional analysis revealed that 147 DEGs were significantly enriched in binding, cell proliferation, transcriptional activity and cell cycle regulation. PPI network screened 30 hub genes, with CDK1 having the strongest connectivity with CC. Cmap showed that apigenin, thioguanine and trichostatin A might be used to treat CC(P<0.05). Eight genes (APOD, CXCL8, MMP1, MMP3, PLOD2, PTGDS, SNX10 and SPP1) were screened out through GEPIA. Of them, only PTGDS and SNX10 had not appeared in previous studies about CC. The validation in GEO showed that PTGDS showed low expression in tumor tissues while SNX10 showed high expression in tumor tissues. Their expression profiles were consistent with the results in immunohistochemistry. ROC curve analysis indicated that the model had a good diagnostic efficiency(AUC=0.738). GSEA showed that the two genes were associated with the chemokine signaling pathway(P<0.05). TCGA methylation analysis showed that patients with lowly-expressed and highly-methylated PTGDS had a worse prognosis than those with highly-expressed and lowly-methylated PTGDS (p=0.037). Cox regression analysis showed that SNX10 and PTGDS were independent prognostic indicators for OS among CC patients(P=0.007 and 0.003). Conclusions: PTGDS and SNX10 showed abnormal expression and methylation in CC. Both genes might have high prognostic value of CC patients.

scholarly journals SNX10 and PTGDS are Associated with the Progression and Prognosis of Cervical Squamous Cell Carcinoma

2020 ◽  
Author(s):  
Pinping Jiang ◽  
Ying Cao ◽  
Feng Gao ◽  
Wei Sun ◽  
Jinhui Liu ◽  
...  
Keyword(s):  
Functional Analysis ◽  
Cox Regression ◽  
Expression Profiles ◽  
Curve Analysis ◽  
Ppi Network ◽  
Methylation Analysis ◽  
Hub Genes ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis ◽  
Tumor Tissues

Abstract Background: Cervical cancer (CC) is the primary cause of death in women. This study sought to investigate the potential mechanism and prognostic genes of CC. Methods: We downloaded four gene expression profiles from GEO. The RRA method was used to integrate and screen differentially expressed genes (DEGs) between CC and normal samples. Functional analysis was performed by clusterprofiler. We built PPI network by Search Tool for the Retrieval of Interacting Genes Database (STRING) and selected hub modules via Molecular COmplex Detection (MCODE). CMap database was used to find molecules with therapeutic potential for CC. The hub genes were validated in GEO datasets, Gene Expession Profiling Interactive Analysis (GEPIA), immunohistochemistry, Cox regression analysis, TCGA methylation analysis and ONCOMINE were carried out. ROC curve analysis and GSEA were also performed to describe the prognostic significance of hub genes. Results: Functional analysis revealed that 147 DEGs were significantly enriched in binding, cell proliferation, transcriptional activity and cell cycle regulation. PPI network screened 30 hub genes, with CDK1 having the strongest connectivity with CC. Cmap showed that apigenin, thioguanine and trichostatin A might be used to treat CC(P<0.05). Eight genes (APOD, CXCL8, MMP1, MMP3, PLOD2, PTGDS, SNX10 and SPP1) were screened out through GEPIA. Of them, only PTGDS and SNX10 had not appeared in previous studies about CC. The validation in GEO showed that PTGDS showed low expression in tumor tissues while SNX10 showed high expression in tumor tissues. Their expression profiles were consistent with the results in immunohistochemistry. ROC curve analysis indicated that the model had a good diagnostic efficiency(AUC= 0.738). GSEA showed that the two genes were associated with the chemokine signaling pathway(P<0.05). TCGA methylation analysis showed that patients with lowly-expressed and highly-methylated PTGDS had a worse prognosis than those with highly-expressed and lowly-methylated PTGDS (p=0.037). Cox regression analysis showed that SNX10 and PTGDS were independent prognostic indicators for OS among CC patients(P=0.007 and 0.003). Conclusions: PTGDS and SNX10 showed abnormal expression and methylation in CC. Both genes might have high prognostic value of CC patients..

scholarly journals SNX10 and PTGDS are associated with the progression and prognosis of cervical squamous cell carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pinping Jiang ◽  
Ying Cao ◽  
Feng Gao ◽  
Wei Sun ◽  
Jinhui Liu ◽  
...  
Keyword(s):  
Functional Analysis ◽  
Regression Analysis ◽  
Cox Regression ◽  
Expression Profiles ◽  
Curve Analysis ◽  
Ppi Network ◽  
Methylation Analysis ◽  
Hub Genes ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis

Abstract Background Cervical cancer (CC) is the primary cause of death in women. This study sought to investigate the potential mechanism and prognostic genes of CC. Methods We downloaded four gene expression profiles from GEO. The RRA method was used to integrate and screen differentially expressed genes (DEGs) between CC and normal samples. Functional analysis was performed by clusterprofiler. We built PPI network by Search Tool for the Retrieval of Interacting Genes Database (STRING) and selected hub modules via Molecular COmplex Detection (MCODE). CMap database was used to find molecules with therapeutic potential for CC. The hub genes were validated in GEO datasets, Gene Expession Profiling Interactive Analysis (GEPIA), immunohistochemistry, Cox regression analysis, TCGA methylation analysis and ONCOMINE were carried out. ROC curve analysis and GSEA were also performed to describe the prognostic significance of hub genes. Results Functional analysis revealed that 147 DEGs were significantly enriched in binding, cell proliferation, transcriptional activity and cell cycle regulation. PPI network screened 30 hub genes, with CDK1 having the strongest connectivity with CC. Cmap showed that apigenin, thioguanine and trichostatin A might be used to treat CC(P < 0.05). Eight genes (APOD, CXCL8, MMP1, MMP3, PLOD2, PTGDS, SNX10 and SPP1) were screened out through GEPIA. Of them, only PTGDS and SNX10 had not appeared in previous studies about CC. The validation in GEO showed that PTGDS showed low expression while SNX10 presented high expression in tumor tissues. Their expression profiles were consistent with the results in immunohistochemistry. ROC curve analysis indicated that the model had a good diagnostic efficiency (AUC = 0.738). GSEA analysis demonstrated that the two genes were correlated with the chemokine signaling pathway (P < 0.05). TCGA methylation analysis showed that patients with lowly-expressed and highly-methylated PTGDS had a worse prognosis than those with highly-expressed and lowly-methylated PTGDS (p = 0.037). Cox regression analysis showed that SNX10 and PTGDS were independent prognostic indicators for OS among CC patients (P = 0.007 and 0.003). Conclusions PTGDS and SNX10 showed abnormal expression and methylation in CC. Both genes might have high prognostic value of CC patients.

scholarly journals Differential Expression of mRNAs in Peripheral Blood Related to Prodrome and Progression of Alzheimer’s Disease

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Weishuang Xue ◽  
Jinwei Li ◽  
Kailei Fu ◽  
Weiyu Teng
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Peripheral Blood ◽  
Bioinformatics Analysis ◽  
Gene Expression Omnibus ◽  
Venn Diagram ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks

Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease that affects the quality of life of elderly individuals, while the pathogenesis of AD is still unclear. Based on the bioinformatics analysis of differentially expressed genes (DEGs) in peripheral blood samples, we investigated genes related to mild cognitive impairment (MCI), AD, and late-stage AD that might be used for predicting the conversions. Methods. We obtained the DEGs in MCI, AD, and advanced AD patients from the Gene Expression Omnibus (GEO) database. A Venn diagram was used to identify the intersecting genes. Gene Ontology (GO) and Kyoto Gene and Genomic Encyclopedia (KEGG) were used to analyze the functions and pathways of the intersecting genes. Protein-protein interaction (PPI) networks were constructed to visualize the network of the proteins coded by the related genes. Hub genes were selected based on the PPI network. Results. Bioinformatics analysis indicated that there were 61 DEGs in both the MCI and AD groups and 27 the same DEGs among the three groups. Using GO and KEGG analyses, we found that these genes were related to the function of mitochondria and ribosome. Hub genes were determined by bioinformatics software based on the PPI network. Conclusions. Mitochondrial and ribosomal dysfunction in peripheral blood may be early signs in AD patients and related to the disease progression. The identified hub genes may provide the possibility for predicting AD progression or be the possible targets for treatments.

The Screening and Identification of Key Biomarkers in Adrenocortical Carcinoma: Evidence from a Bioinformatics Analysis

2022 ◽  
Vol 12 (3) ◽  
pp. 523-532
Author(s):  
Xin Yan ◽  
Chunfeng Liang ◽  
Xinghuan Liang ◽  
Li Li ◽  
Zhenxing Huang ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Gene Ontology ◽  
Protein Interaction ◽  
Adrenocortical Carcinoma ◽  
Gene Expression Omnibus ◽  
Heparin Binding ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Upregulated Genes

<sec> <title>Objective:</title> This study aimed to identify the potential key genes associated with the progression and prognosis of adrenocortical carcinoma (ACC). </sec> <sec> <title>Methods:</title> Differentially expressed genes (DEGs) in ACC cells and normal adrenocortical cells were assessed by microarray from the Gene Expression Omnibus database. The biological functions of the classified DEGs were examined by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses and a protein–protein interaction (PPI) network was mapped using Cytoscape software. MCODE software was also used for the module analysis and then 4 algorithms of cytohubba software were used to screen hub genes. The overall survival (OS) examination of the hub genes was then performed by the ualcan online tool. </sec> <sec> <title>Results:</title> Two GSEs (GSE12368, GSE33371) were downloaded from GEO including 18 and 43 cases, respectively. One hundred and sixty-nine DEGs were identified, including 57 upregulated genes and 112 downregulated genes. The Gene Ontology (GO) analyses showed that the upregulated genes were significantly enriched in the mitotic cytokines is, nucleus and ATP binding, while the downregulated genes were involved in the positive regulation of cardiac muscle contraction, extracellular space, and heparin-binding (P < 0.05). The Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) pathway examination showed significant pathways including the cell cycle and the complement and coagulation cascades. The protein– protein interaction (PPI) network consisted of 162 nodes and 847 edges, including mitotic nuclear division, cytoplasmic, protein kinase binding, and cell cycle. All 4 identified hub genes (FOXM1, UBE2C, KIF11, and NDC80) were associated with the prognosis of adrenocortical carcinoma (ACC) by survival analysis. </sec> <sec> <title>Conclusions:</title> The present study offered insights into the molecular mechanism of adrenocortical carcinoma (ACC) that may be beneficial in further analyses. </sec>

scholarly journals CircRNA-miRNA-mRNA Regulatory Network in Colorectal Cancer

2021 ◽  
Author(s):  
Liyuan Liu ◽  
Shan Wu ◽  
Dan Jiang ◽  
Yuliang Qu ◽  
Hongxia Wang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Regulatory Network ◽  
Research Direction ◽  
Differentially Expressed ◽  
Circular Rnas ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Qrt Pcr ◽  
Cancer Tissues ◽  
Chip Analysis

Abstract Background: Abnormal expression of Circular RNAs (circRNAs) occurs in the occurrence and progression of colorectal cancer (CRC) and plays an important role in the pathogenesis of tumors. We combined bioinformatics and laboratory-validated methods to search for key circRNAs and possible potential mechanisms. Methods: Colorectal cancer tissues and normal paracancerous tissues were detected by microarray analysis and qRT-PCR validation, and differentially expressed circRNAs were screened and identified. The circRNA-miRNA-mRNA regulatory network (cirReNET) was constructed, Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to ascertain the functions of circRNAs in CRCs. In addition, a protein-protein interaction (PPI) network of hub genes which acquired by string and plugin app CytoHubba in cytoscape was established. Validation of expression of hub genes was identified by GEPIA database. Results: 564 differentially expressed circRNAs which include 207 up-regulated and 357 down-regulated circRNAs were detected. The top 3 up-regulated circRNAs (hsa_circRNA_100833, hsa_circRNA_103828, hsa_circRNA_103831) and the top 3 down-regulated circRNAs (hsa_circRNA_103752, hsa_circRNA_071106, hsa_circRNA_102293) in chip analysis were chosen to be verified in 33 pairs of CRCs by qRT-PCR. The cirReNET include of 6 circRNAs, 19 miRNAs and 210 mRNA. And the targeted mRNAs were associated with cellular metabolic process, cell cycle and glandular epithelial cell differentiation and so on. 12 and 10 target hub genes were shown separately in upregulated circRNA-downregulated miRNA-upregulated mRNA (UcDiUm-RNA) group and downregulated circRNA-upregulated miRNA-downregulated mRNA (DcUiDm-RNA) group. Finally, we may have predicted and discovered several critical circRNA-miRNA-mRNA regulatory axes (cirReAXEs) which may play important roles in colorectal cancer. Conclusion: We constructed a cirReNET including 6 candidate circRNAs, which were crucial in CRCs, may become potential diagnostic markers and predictive indicators of CRCs, and we may provide a research direction for the pathogenesis of colorectal cancer.

scholarly journals Weighted gene co-expression network analysis to define pivotal modules and genes in diabetic heart failure

2020 ◽  
Vol 40 (7) ◽  
Author(s):  
Weiwei Liang ◽  
FangFang Sun
Keyword(s):  
Heart Failure ◽  
Kegg Pathway ◽  
Diabetic Heart ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Light Yellow ◽  
Go Enrichment ◽  
Key Genes ◽  
Functional Analyses

Abstract This research was carried out to reveal specific hub genes involved in diabetic heart failure, as well as remarkable pathways that hub genes locate. The GSE26887 dataset from the GEO website was downloaded. The gene co-expression network was generated and central modules were analyzed to identify key genes using the WGCNA method. Functional analyses were conducted on genes of the clinical interest modules via Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene ontology (GO) enrichment, associated with protein–protein interaction (PPI) network construction in a sequence. Centrality parameters of the PPI network were determined using the CentiScape plugin in Cytoscape. Key genes, defined as genes in the ≥95% percentile of the degree distribution of significantly perturbed networks, were identified. Twenty gene co-expression modules were detected by WGCNA analysis. The module marked in light yellow exhibited the most significant association with diabetes (P=0.08). Genes involved in this module were primarily located in immune response, plasma membrane and receptor binding, as shown by the GO analysis. These genes were primarily assembled in endocytosis and phagosomes for KEGG pathway enrichment. Three key genes, STK39, HLA-DPB1 and RAB5C, which may be key genes for diabetic heart failure, were identified. To our knowledge, our study is the first to have constructed the co-expression network involved in diabetic heart failure using the WGCNA method. The results of the present study have provided better understanding the molecular mechanism of diabetic heart failure.

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