scholarly journals Selecting Hub Genes and Predicting Target Genes of microRNAs in Tuberculosis via the Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Siqi Deng ◽  
Shijie Shen ◽  
Saeed El-Ashram ◽  
Huan Lu ◽  
Dan Luo ◽  
...  
Keyword(s):  
Expression Profile ◽  
Messenger Rna ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Mirna Expression Profile ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Kegg Pathways

Tuberculosis (TB) is the world's most prevalently infectious disease. Molecular mechanisms behind tuberculosis remain unknown. microRNA (miRNA) is involved in a wide variety of diseases. To validate the significant genes and miRNAs in the current sample, two messenger RNA (mRNA) expression profile datasets and three miRNA expression profile datasets were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed (DE) genes (DEGs) and miRNAs (DE miRNAs) between healthy and TB patients were filtered out. Enrichment analysis was executed, and a protein-protein interaction (PPI) network was developed to understand the enrich pathways and hub genes of TB. Additionally, the target genes of miRNA were predicted and overlapping target genes were identified. We studied a total of 181 DEGs (135 downregulated and 46 upregulated genes) and two DE miRNAs (2 downregulated miRNAs) from two gene profile datasets and three miRNA profile datasets, respectively. 10 hub genes were defined based on high degree of connectivity. A PPI network's top module was constructed. The 23 DEGs identified have a significant relationship with miRNAs. 25 critically significant Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were discovered. The detailed study revealed that, in tuberculosis, the DE miRNA and DEGs form an interaction network. The identification of novel target genes and main pathways would aid with our understanding of miRNA's function in tuberculosis progression.

scholarly journals Identification of Potential Key Genes and Molecular Mechanisms of Medulloblastoma Based on Integrated Bioinformatics Approach

2022 ◽  
Vol 2022 ◽  
pp. 1-17
Author(s):  
Md. Rakibul Islam ◽  
Lway Faisal Abdulrazak ◽  
Mohammad Khursheed Alam ◽  
Bikash Kumar Paul ◽  
Kawsar Ahmed ◽  
...  
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Clinical Information ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
New Treatments

Background. Medulloblastoma (MB) is the most occurring brain cancer that mostly happens in childhood age. This cancer starts in the cerebellum part of the brain. This study is designed to screen novel and significant biomarkers, which may perform as potential prognostic biomarkers and therapeutic targets in MB. Methods. A total of 103 MB-related samples from three gene expression profiles of GSE22139, GSE37418, and GSE86574 were downloaded from the Gene Expression Omnibus (GEO). Applying the limma package, all three datasets were analyzed, and 1065 mutual DEGs were identified including 408 overexpressed and 657 underexpressed with the minimum cut-off criteria of ∣ log   fold   change ∣ > 1 and P < 0.05 . The Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and WikiPathways enrichment analyses were executed to discover the internal functions of the mutual DEGs. The outcomes of enrichment analysis showed that the common DEGs were significantly connected with MB progression and development. The Search Tool for Retrieval of Interacting Genes (STRING) database was used to construct the interaction network, and the network was displayed using the Cytoscape tool and applying connectivity and stress value methods of cytoHubba plugin 35 hub genes were identified from the whole network. Results. Four key clusters were identified using the PEWCC 1.0 method. Additionally, the survival analysis of hub genes was brought out based on clinical information of 612 MB patients. This bioinformatics analysis may help to define the pathogenesis and originate new treatments for MB.

scholarly journals Integrative Bioinformatics Analysis Identifies Noninvasive miRNA Biomarkers for Lung Cancer

2021 ◽  
Author(s):  
Andrew Gao
Keyword(s):  
Lung Cancer ◽  
Messenger Rna ◽  
Target Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Nsclc Patients ◽  
Prognostic Power

AbstractNon-small cell lung cancer (NSCLC), a subtype of lung cancer, affects millions of people. While chemotherapy and other treatments have improved, the 5 year survival rate of NSCLC patients is still only 21%. Early diagnosis is essential for increasing survival as treatments have higher effectiveness at earlier stages of NSCLC. Noninvasive blood-based liquid biopsy tests for NSCLC may be useful for diagnosis and prognosis. MicroRNA (miRNA) and messenger RNA present in blood can serve as biomarkers for such tests. The present study identified 13 miRNAs that are underexpressed in the tissue and blood of NSCLC patients using Gene Expression Omnibus data. Following Kaplan Meier analysis, miR-140-3p, miR-29c, and miR-199a were selected as candidate biomarkers and demonstrated statistically significant prognostic power. An ROC analysis of miR-140-3p expression between NSCLC patients and controls had an area under curve value of 0.85. Functional enrichment analysis of the miRNA target genes revealed several overrepresented pathways relevant to cancer. Eight target genes were hub genes of the protein protein interaction network and possessed significant prognostic power. A combination of IL6, SNAI1, and CDK6 achieved a hazard ratio of 1.4 with p < 0.001. These biomarkers are especially valuable because they can be identified in blood and reflect the tumor state. Since all miRNAs were underexpressed in both tissue and blood, detecting expression of a biomarker miRNA in blood provides information on its expression in tissue as well. These miRNAs may be useful biomarkers for NSCLC prognostic and diagnostic tests and should be further studied.

Predicting functional circular RNA-based competitive endogenous RNA network in gastric carcinoma using novel bioinformatics analysis

2021 ◽  
pp. 153537022110487
Author(s):  
Zirui Zhu ◽  
Rui Huang ◽  
Baojun Huang
Keyword(s):  
Messenger Rna ◽  
Expression Profiles ◽  
Clinical Stage ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Rank Aggregation ◽  
Circular Rnas ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Competitive Endogenous Rna

Gastric cancer (GC) remains one of the most prevalent types of malignancies worldwide, and also one of the most reported lethal tumor-related diseases. Circular RNAs (circRNAs) have been certified to be trapped in multiple aspects of GC pathogenesis. Yet, the mechanism of this regulation is mostly undefined. This research is designed to discover the vital circRNA-microRNA (miRNA)-messenger RNA (mRNA) regulatory network in GC. Expression profiles with diverse levels including circRNAs, miRNAs, and mRNAs were all determined using microarray public datasets from Gene Expression Ominous (GEO). The differential circRNAs expressions were recognized against the published robust rank aggregation algorithm. Besides, a circRNA-based competitive endogenous RNA (ceRNA) interaction network was visualized via Cytoscape software (version 3.8.0). Functional and pathway enrichment analysis associated with differentially expressed targeted mRNAs were conducted using Cytoscape and an online bioinformatics database. Furthermore, an interconnected protein–protein interaction association network which consisted of 51 mRNAs was predicted, and hub genes were screened using STRING and CytoHubba. Then, several hub genes were chosen to explore their expression associated with survival rate and clinical stage in GEPIA and Kaplan-Meier Plotter databases. Finally, a carefully designed circRNA-related ceRNA regulatory subnetwork including four circRNAs, six miRNAs, and eight key hub genes was structured using the online bioinformatics tool.

scholarly journals A Network Pharmacology-Based Study on the Anti-Lung Cancer Effect of Dipsaci Radix

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Jiayan Wu ◽  
Shengkun Hong ◽  
Xiankuan Xie ◽  
Wangmi Liu
Keyword(s):  
Lung Cancer ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Interaction Network ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Active Compounds

Objective. Dipsaci Radix (DR) has been used to treat fracture and osteoporosis. Recent reports have shown that myeloid cells from bone marrow can promote the proliferation of lung cancer. However, the action and mechanism of DR has not been well defined in lung cancer. The aim of the present study was to define molecular mechanisms of DR as a potential therapeutic approach to treat lung cancer. Methods. Active compounds of DR with oral bioavailability ≥30% and drug-likeness index ≥0.18 were obtained from the traditional Chinese medicine systems pharmacology database and analysis platform. The potential target genes of the active compounds and bone were identified by PharmMapper and GeneCards, respectively. The compound-target network and protein-protein interaction network were built by Cytoscape software and Search Tool for the Retrieval of Interacting Genes webserver, respectively. GO analysis and pathway enrichment analysis were performed using R software. Results. Our study demonstrated that DR had 6 active compounds, including gentisin, sitosterol, Sylvestroside III, 3,5-Di-O-caffeoylquinic acid, cauloside A, and japonine. There were 254 target genes related to these active compounds as well as to bone. SRC, AKT1, and GRB2 were the top 3 hub genes. Metabolisms and signaling pathways associated with these hub genes were significantly enriched. Conclusions. This study indicated that DR could exhibit the anti-lung cancer effect by affecting multiple targets and multiple pathways. It reflects the traditional Chinese medicine characterized by multicomponents and multitargets. DR could be considered as a candidate for clinical anticancer therapy by regulating bone physiological functions.

scholarly journals Identification of potential key genes and pathway linked with sporadic Creutzfeldt-Jakob disease based on integrated bioinformatics analyses

2020 ◽  
Author(s):  
Basavaraj Vastrad ◽  
Chanabasayya Vastrad ◽  
Iranna Kotturshetti
Keyword(s):  
Regulatory Network ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Bioinformatics Analyses ◽  
Go Enrichment ◽  
Jakob Disease

AbstractSporadic Creutzfeldt-Jakob disease (sCJD) is neurodegenerative disease also called prion disease linked with poor prognosis. The aim of the current study was to illuminate the underlying molecular mechanisms of sCJD. The mRNA microarray dataset GSE124571 was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened. Pathway and GO enrichment analyses of DEGs were performed. Furthermore, the protein-protein interaction (PPI) network was predicted using the IntAct Molecular Interaction Database and visualized with Cytoscape software. In addition, hub genes and important modules were selected based on the network. Finally, we constructed target genes - miRNA regulatory network and target genes - TF regulatory network. Hub genes were validated. A total of 891 DEGs 448 of these DEGs presented significant up regulated, and the remaining 443 down regulated were obtained. Pathway enrichment analysis indicated that up regulated genes were mainly linked with glutamine degradation/glutamate biosynthesis, while the down regulated genes were involved in melatonin degradation. GO enrichment analyses indicated that up regulated genes were mainly linked with chemical synaptic transmission, while the down regulated genes were involved in regulation of immune system process. hub and target genes were selected from the PPI network, modules, and target genes - miRNA regulatory network and target genes - TF regulatory network namely YWHAZ, GABARAPL1, EZR, CEBPA, HSPB8, TUBB2A and CDK14. The current study sheds light on the molecular mechanisms of sCJD and may provide molecular targets and diagnostic biomarkers for sCJD.

scholarly journals Exosomal miR-199a-5p Inhibits Tumorigenesis and Angiogenesis by Targeting VEGFA in Osteosarcoma

2021 ◽  
Author(s):  
Lu Zhang ◽  
Hongxin Cao ◽  
Guanghui Gu ◽  
Dehui Hou ◽  
Yunhao You ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Luciferase Reporter ◽  
Pathway Enrichment Analysis ◽  
Osteosarcoma Cell ◽  
Plasma Samples ◽  
Osteosarcoma Cells ◽  
Hub Genes

Abstract Background: Osteosarcoma (OS) is the most common primary bone malignancy in children and adolescents. microRNAs have been found to play a vital role in tumor angiogenesis. Here, we investigated the effects of miR-199a-5p on tumor growth and angiogenesis in osteosarcoma. Furthermore, the underlying molecular mechanisms and signaling pathways were explored.Methods: The datasets were extracted from the Gene Expression Omnibus and the differentially expressed miRNAs (DEmiRNAs) were screened out by the GEO2R online platform. The potential target genes were predicted using the miRTarBase database. The predicted target genes were further analyzed by Gene Ontology and pathway enrichment analysis and a regulatory network of DEmiRNAs and their target genes was constructed. In addition, the effects of osteosarcoma cell derived exosomal miR-199a-5p on the proliferation, migration and neovascularization of HUVECs were evaluated by conducting EdU assays, Transwell experiments and tube formation assays. A dual-luciferase reporter assay was performed to detect whether VEGFA was the direct target of miR-199a-5p. Furthermore, in vivo xenograft models were established to further investigate the intrinsic role of miR-199a-5p in osteosarcoma tumorigenesis and angiogenesis. Results: A total of 149 DE-miRNAs were screened out, including 136 upregulated miRNAs and 13 downregulated miRNAs in human osteosarcoma plasma samples compared with normal plasma samples. A total of 1313 target genes of the top three upregulated and downregulated miRNAs were predicted. In the PPI network, the top 10 hub nodes with higher degrees were identified as hub genes, such as TP53 and VEGFA. By constructing the miRNA-hub gene network, we found that most of hub genes could be potentially modulated by miR-663a, miR-199a-5p and miR-223-3p. In addition, we found that the expression level of miR-199a-5p in exosomes derived from osteosarcoma cells was remarkably higher than the osteosarcoma cells, and the exosomes derived from osteosarcoma cells were transported to HUVECs. Overexpression of miR-199a-5p could significantly inhibited HUVEC proliferation, migration and neovascularization, whereas downregulation of miR-199a-5p expression exerted the opposite effect. Moreover, the in vivo results verified that overexpression of miR-199a-5p in osteosarcoma cells could suppress the growth and angiogenesis of tumors. Conclusion: Our results demonstrated that miR-199a-5p could be transported from osteosarcoma cells to HUVECs through exosomes, subsequently targeting VEGFA and inhibiting the growth and angiogenesis of osteosarcoma. Therefore, miR-199a-5p may act as a biomarker in the diagnosis and treatment of osteosarcoma.

scholarly journals Gene Co-expression Network Analysis for Identifying Modules and Functionally Enriched Pathways in Vitiligo Disease: A Systems Biology Study

2020 ◽  
Author(s):  
Afshin Derakhshani ◽  
Homa Mollaei ◽  
Negin Parsamanesh ◽  
Mohammad Fereidouni ◽  
Ebrahim Miri-Moghaddam ◽  
...  
Keyword(s):  
Systems Biology ◽  
Network Analysis ◽  
Messenger Rna ◽  
Molecular Mechanisms ◽  
Biological Activities ◽  
Neutrophil Migration ◽  
Enrichment Analysis ◽  
Autoimmune Disorder ◽  
Hub Genes ◽  
Novel Biomarkers

Vitiligo is the most common cause of skin, hair, and oral depigmentation which is known as an autoimmune disorder. Genetic and environmental factors have important roles in the progression of the disease. Dysregulation of gene expression, like microRNAs (miRNA), may serve as major relevant factors. Several biological processes are involved in vitiligo disease and developing a comprehensive approach helps us to better understand the molecular mechanisms of disease. In this research, we describe how a weighted gene co-expression network analysis as a systems biology approach assists to define the primary gene modules, hub genes, and messenger RNA (mRNA)-miRNA regulatory network in vitiligo disease as the novel biomarkers. The results demonstrated a module with a high correlation with vitiligo state. Moreover, gene enrichment analysis showed that this module's genes were mostly involved in some biological activities including G protein-coupled receptors signaling pathway, lymphocyte chemotaxis, chemokine activity, neutrophil migration, granulocyte chemotaxis, etc. The co-expression network was constructed using top hub genes of the correlated module which are named as CXCL10, ARL9, AKR1B10, COX7B, RPL26, SPA17, NDUFAF2, RPF2, DAPL1, RPL34, CWC15, NDUFB3, RPL26L1, ACOT13, HSPB11, and NSA2. MicroRNAs prediction tool (miRWalk) revealed top miRNAs correlated with the interested module. Finally, a drug-target network was constructed which indicated interactions of some food and drug administration (FDA) approved drugs with hub genes. Our findings specified one important module and main hub genes which can be considered as novel biomarkers for vitiligo therapeutic purposes.   

scholarly journals Identification of key genes and their microRNAs in glioma: Evidence from bioinformatic analysis

2020 ◽  
Author(s):  
Xichao Wen ◽  
Meijuan Fu ◽  
Wensong Wu ◽  
Zhaomu Zeng ◽  
Kebin Zheng
Keyword(s):  
Survival Analysis ◽  
Molecular Mechanisms ◽  
Process Analysis ◽  
Interaction Network ◽  
Tumor Grade ◽  
Bioinformatic Analysis ◽  
Sh3 Domain ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Condensed Chromosome

Abstract Background Glioma is one of the most common primary intracranial tumors. Although a lot of studies have been conducted to elucidate the pathogeny of glioma, the molecular mechanisms are still unclear because of its complex biological functions. Methods To identify the candidate genes in the carcinogenesis and progression of glioma, microarray datasets GSE4290, GSE122498 and GSE2223 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and performed function enrichment of DEGs by Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The protein-protein interaction network (PPI) was constructed using STRING and Cytoscape to find hub genes. Survival analysis and GEPIA database was conducted to screen and validate critical genes. Analysis of miRNA and genetic alteration was used to explore and predict the molecule mechanism. Results A total of 150 DEGs were identified, consisting of 54 downregulated genes and 96 upregulated genes. The enriched functions and pathways of the DEGs include regulation of transportation, synaptic transmission and SH3 domain binding. Fifteen hub genes were identified and biological process analysis revealed that these genes were mainly enriched in SH3 domain binding, neuron projection terminus, mitotic nuclear division, condensed chromosome and affected the brain development. Survival analysis showed that VAMP2, PPP3CA, DLGAP5, KIF14, REPS2, CENPU, KNTC1 and SMC4, may be involved in the carcinogenesis, invasion or recurrence of glioma. These 8 hub genes, which were related miRNAs and genetic changes were commonly involved in the development of glioma, were closely associated with tumor grade. Conclusion DEGs and hub genes identified in the present study help us understand the molecular mechanisms of carcinogenesis and progression of glioma, and provide candidate targets for diagnosis and treatment of glioma.

scholarly journals A Comprehensive Network Pharmacology-Based Strategy to Investigate Multiple Mechanisms of HeChan Tablet on Lung Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Zhenjie Zhuang ◽  
Qianying Chen ◽  
Cihui Huang ◽  
Junmao Wen ◽  
Haifu Huang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Therapeutic Target ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Network Pharmacology ◽  
Hub Genes ◽  
Lung Cancer Patients ◽  
Ppi Networks

Background. HeChan tablet (HCT) is a traditional Chinese medicine preparation extensively prescribed to treat lung cancer in China. However, the pharmacological mechanisms of HCT on lung cancer remain to be elucidated. Methods. A comprehensive network pharmacology-based strategy was conducted to explore underlying mechanisms of HCT on lung cancer. Putative targets and compounds of HCT were retrieved from TCMSP and BATMAN-TCM databases; related genes of lung cancer were retrieved from OMIM and DisGeNET databases; known therapeutic target genes of lung cancer were retrieved from TTD and DrugBank databases; PPI networks among target genes were constructed to filter hub genes by STRING. Furthermore, the pathway and GO enrichment analysis of hub genes was performed by clusterProfiler, and the clinical significance of hub genes was identified by The Cancer Genome Atlas. Result. A total of 206 compounds and 2,433 target genes of HCT were obtained. 5,317 related genes of lung cancer and 77 known therapeutic target genes of lung cancer were identified. 507 unique target genes were identified among HCT-related genes of lung cancer and 34 unique target genes were identified among HCT-known therapeutic target genes of lung cancer. By PPI networks, 11 target genes AKT1, TP53, MAPK8, JUN, EGFR, TNF, INS, IL-6, MYC, VEGFA, and MAPK1 were identified as major hub genes. IL-6, JUN, EGFR, and MYC were shown to associate with the survival of lung cancer patients. Five compounds of HCT， quercetin, luteolin, kaempferol, beta-sitosterol, and baicalein were recognized as key compounds of HCT on lung cancer. The gene enrichment analysis implied that HCT probably benefitted patients with lung cancer by modulating the MAPK and PI3K-Akt pathways. Conclusion. This study predicted pharmacological and molecular mechanisms of HCT against lung cancer and could pave the way for further experimental research and clinical application of HCT.

Prognostic Values and Prospective Pathway Signaling of miR-30a in Ovarian Cancer: A Study Based on Gene Expression Omnibus and Bioinformatics Analysis

2020 ◽  
Author(s):  
Weijia Lu ◽  
Yunyu Wu ◽  
CanXiong Lu ◽  
Ting Zhu ◽  
ZhongLu Ren ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Target Genes ◽  
Ovarian Tissue ◽  
Differentially Expressed Gene ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Public Data

Abstract Objective MicroRNAs (MiRNAs) is considered to play an important role in the occurrence and development of ovarian cancer(OC). Although miRNAs has been widely recognized in ovarian cancer, the role of hsa-miR-30a-5p (miR-30a) in OC has not been fully elucidated. Methods Through the analysis of public data sets in Gene Expression Omnibus (GEO) database and literature review, the significance of miR-30a expression in OC is evaluated. Three mRNA datasets of OC and normal ovarian tissue, GSE14407, GSE18520 and GSE36668, were downloaded from GEO to find the differentially expressed gene (DEG). Then the target genes of hsa-miR-30a-5p were predicted by miRWALK3.0 and TargetScan. Then, the gene overlap between DEG and the predicted target genes of miR-30a in OC was analyzed by Gene Ontology (GO) enrichment analysis. Protein-protein interaction (PPI) network was constructed by STRING and Cytoscape, and the effect of HUB gene on the prognosis of OC was analyzed. Results A common pattern of up-regulation of miR-30a in OC was found. A total of 225 DEG, were identified, both OC-related and miR-30a-related. Many DEG are enriched in the interactions of intracellular matrix tissue, ion binding and biological process regulation. Among the 10 major Hub genes analyzed by PPI, five Hub genes were significantly related to the overall poor survival of OC patients, in which the low expression of ESR1 ,MAPK10, Tp53 and the high expression of YKT ,NSF were related to poor prognosis of OC.

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