scholarly journals Identification of potential crucial genes and pathways associated with vein graft restenosis based on gene expression analysis in experimental rabbits

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4704 ◽  
Author(s):  
Qiang Liu ◽  
Xiujie Yin ◽  
Mingzhu Li ◽  
Li Wan ◽  
Liqiao Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
Vein Graft ◽  
Molecular Mechanisms ◽  
Rapid Development ◽  
Bypass Graft ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Network Analyses

Occlusive artery disease (CAD) is the leading cause of death worldwide. Bypass graft surgery remains the most prevalently performed treatment for occlusive arterial disease, and veins are the most frequently used conduits for surgical revascularization. However, the clinical efficacy of bypass graft surgery is highly affected by the long-term potency rates of vein grafts, and no optimal treatments are available for the prevention of vein graft restenosis (VGR) at present. Hence, there is an urgent need to improve our understanding of the molecular mechanisms involved in mediating VGR. The past decade has seen the rapid development of genomic technologies, such as genome sequencing and microarray technologies, which will provide novel insights into potential molecular mechanisms involved in the VGR program. Ironically, high throughput data associated with VGR are extremely scarce. The main goal of the current study was to explore potential crucial genes and pathways associated with VGR and to provide valid biological information for further investigation of VGR. A comprehensive bioinformatics analysis was performed using high throughput gene expression data. Differentially expressed genes (DEGs) were identified using the R and Bioconductor packages. After functional enrichment analysis of the DEGs, protein–protein interaction (PPI) network and sub-PPI network analyses were performed. Finally, nine potential hub genes and fourteen pathways were identified. These hub genes may interact with each other and regulate the VGR program by modulating the cell cycle pathway. Future studies focusing on revealing the specific cellular and molecular mechanisms of these key genes and pathways involved in regulating the VGR program may provide novel therapeutic targets for VGR inhibition.

scholarly journals Identifcation of The Ferroptosis-Related Gene Signature In Placenta of Patients With Early-Onset Preeclampsia

2021 ◽  
Author(s):  
Nana Yang ◽  
Qianghua Wang ◽  
Biao Ding ◽  
Yinging Gong ◽  
Yue Wu ◽  
...  
Keyword(s):  
Iron Homeostasis ◽  
Molecular Mechanisms ◽  
Late Onset ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Network Analyses

Abstract Background: The accumulation of ROS resulting from upregulated levels of oxidative stress is commonly implicated in preeclampsia (PE). Ferroptosis is a novel form of iron-dependent cell death instigated by lipid peroxidation likely plays important role in PE pathogenesis. This study aims to investigate expression profiles and functions of the ferroptosis-related genes (FRGs) in early- and late-onset preeclampsia.Methods: The gene expression data and clinical information were downloaded from GEO database. The “limma” R package was used for screening differentially expressed genes. GO(Gene Ontology), Kyoto Encyclopedia of Genes and Genomes(KEGG) and protein protein interaction (PPI) network analyses were conducted to investigate the bioinformatics functions and molecular interactions of significantly different FRGs. Quantitative real-time reverse transcriptase PCR was used to verify the expression of hub FRGs in PE.Results: A total number of 4,215 DEGs were identified between EOPE and preterm cases and 3,356 DEGs were found between EOPE and LOPE subtypes. 20 significantly different FRGs were identified in EOPE, while only 3 in LOPE. Functional enrichment analysis revealed that the differentially expressed FRGs was mainly involved in EOPE and enriched in hypoxia- and iron-related pathways, such as response to hypoxia, iron homeostasis and iron ion binding process. The PPI network analysis and verification by RT-qPCR resulted in the identification of the following six interesting FRGs: FTH1, HIF1A, FTL, IREB2, MAPK8 and PLIN2. Conclusions: EOPE and LOPE owned distinct underlying molecular mechanisms and ferroptosis may be mainly implicated in pathogenesis of EOPE. Further studies are necessary for deeper inquiry into placental ferroptosis and its role in the pathogenesis of EOPE.

Identification of Four Key Biomarkers and Small Molecule Drugs Innasopharyngeal Carcinoma by Weighted Gene Co-expression Network Analysis

2020 ◽  
Author(s):  
Xi Pan ◽  
Jian-Hao Liu
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Small Molecule ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Tumor Stage ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Therapeutic Goals ◽  
Small Molecule Drugs

Abstract Background Nasopharyngeal carcinoma (NPC) is a heterogeneous carcinoma that the underlying molecular mechanisms involved in the tumor initiation, progression, and migration are largely unclear. The purpose of the present study was to identify key biomarkers and small-molecule drugs for NPC screening, diagnosis, and therapy via gene expression profile analysis. Methods Raw microarray data of NPC were retrieved from the Gene Expression Omnibus (GEO) database and analyzed to screen out the potential differentially expressed genes (DEGs). The key modules associated with histology grade and tumor stage was identified by using weighted correlation network analysis (WGCNA). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of genes in the key module were performed to identify potential mechanisms. Candidate hub genes were obtained, which based on the criteria of module membership (MM) and high connectivity. Then we used receiver operating characteristic (ROC) curve to evaluate the diagnostic value of hub genes. The Connectivity map database was further used to screen out small-molecule drugs of hub genes. Results A total of 430 DEGs were identified based on two GEO datasets. The green gene module was considered as key module for the tumor stage of NPC via WGCNA analysis. The results of functional enrichment analysis revealed that genes in the green module were enriched in regulation of cell cycle, p53 signaling pathway, cell part morphogenesis. Furthermore, four DEGs-related hub genes in the green module were considered as the final hub genes. Then ROC revealed that the final four hub genes presented with high areas under the curve, suggesting these hub genes may be diagnostic biomarkers for NPC. Meanwhile, we screened out several small-molecule drugs that have provided potentially therapeutic goals for NPC. Conclusions Our research identified four potential prognostic biomarkers and several candidate small-molecule drugs for NPC, which may contribute to the new insights for NPC therapy.

scholarly journals Identification of Potentially Therapeutic Target Genes of Hepatocellular Carcinoma

2020 ◽  
Vol 17 (3) ◽  
pp. 1053
Author(s):  
Chengzhang Li ◽  
Jiucheng Xu
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Gene Expression Profiles ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Gene Differential Expression ◽  
Oncomine Database

Background: Hepatocellular carcinoma (HCC) is a major threat to public health. However, few effective therapeutic strategies exist. We aimed to identify potentially therapeutic target genes of HCC by analyzing three gene expression profiles. Methods: The gene expression profiles were analyzed with GEO2R, an interactive web tool for gene differential expression analysis, to identify common differentially expressed genes (DEGs). Functional enrichment analyses were then conducted followed by a protein-protein interaction (PPI) network construction with the common DEGs. The PPI network was employed to identify hub genes, and the expression level of the hub genes was validated via data mining the Oncomine database. Survival analysis was carried out to assess the prognosis of hub genes in HCC patients. Results: A total of 51 common up-regulated DEGs and 201 down-regulated DEGs were obtained after gene differential expression analysis of the profiles. Functional enrichment analyses indicated that these common DEGs are linked to a series of cancer events. We finally identified 10 hub genes, six of which (OIP5, ASPM, NUSAP1, UBE2C, CCNA2, and KIF20A) are reported as novel HCC hub genes. Data mining the Oncomine database validated that the hub genes have a significant high level of expression in HCC samples compared normal samples (t-test, p < 0.05). Survival analysis indicated that overexpression of the hub genes is associated with a significant reduction (p < 0.05) in survival time in HCC patients. Conclusions: We identified six novel HCC hub genes that might be therapeutic targets for the development of drugs for some HCC patients.

scholarly journals Identification of Potentially Functional Circular RNAs hsa_circ_0070934 and hsa_circ_0004315 as Prognostic Factor of Hepatocellular Carcinoma by Integrated Bioinformatics Analysis

2021 ◽  
Author(s):  
Pejman Morovat ◽  
Saman Morovat ◽  
Arash M. Ashrafi ◽  
Shahram Teimourian
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Prognostic Factor ◽  
Sequence Data ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Circular Rnas ◽  
Ppi Network ◽  
Hub Genes ◽  
Cerna Network

Abstract Hepatocellular carcinoma (HCC) is one of the most prevalent cancers worldwide, which has a high mortality rate and poor treatment outcomes with yet unknown molecular basis. It seems that gene expression plays a pivotal role in the pathogenesis of the disease. Circular RNAs (circRNAs) can interact with microRNAs (miRNAs) to regulate gene expression in various malignancies by acting as competitive endogenous RNAs (ceRNAs). However, the potential pathogenesis roles of the ceRNA network among circRNA/miRNA/mRNA in HCC are unclear. In this study, first, the HCC circRNA expression data were obtained from three Gene Expression Omnibus microarray datasets (GSE164803, GSE94508, GSE97332), and the differentially expressed circRNAs (DECs) were identified using R limma package. Also, the liver hepatocellular carcinoma (LIHC) miRNA and mRNA sequence data were retrieved from TCGA, and differentially expressed miRNAs (DEMIs) and mRNAs (DEGs) were determined using the R DESeq2 package. Second, CSCD website was used to uncover the binding sites of miRNAs on DECs. The DECs' potential target miRNAs were revealed by conducting an intersection between predicted miRNAs from CSCD and downregulated DEMIs. Third, some related genes were uncovered by intersecting targeted genes predicted by miRWalk and targetscan online tools with upregulated DEGs. The ceRNA network was then built using the Cytoscape software. The functional enrichment and the overall survival time of these potential targeted genes were analyzed, and a PPI network was constructed in the STRING database. Network visualization was performed by Cytoscape, and ten hub genes were detected using the CytoHubba plugin tool. Four DECs (hsa_circ_0000520, hsa_circ_0008616, hsa_circ_0070934, hsa_circ_0004315) were obtained and six miRNAs (hsa-miR-542-5p, hsa-miR-326, hsa-miR-511-5p, hsa-miR-195-5p, hsa-miR-214-3p, and hsa-miR-424-5p) which are regulated by the above DECs were identified. Then 543 overlapped genes regulated by six miRNAs mentioned above were predicted. Functional enrichment analysis showed that these genes are mostly associated with cancer regulation functions. Ten hub genes (TTK،AURKB, KIF20A، KIF23، CEP55، CDC6، DTL، NCAPG، CENPF، PLK4) have been screened from the PPI network of the 204 survival-related genes. KIF20A, NCAPG, TTK, PLK4, and CDC6 were selected for the highest significant p-values. In the end, a circRNA-miRNA-mRNA regulatory axis was established for five final selected hub genes. This study implies the potential pathogenesis of the obtained network and proposes that the two DECs (has_circ_0070934 and has_circ_0004315) may be important prognostic factor for HCC.

scholarly journals Integrated Bioinformatic Analysis of Key Biomarkers and Signaling Pathways in Psoriasis

2021 ◽  
Author(s):  
Suwei Tang ◽  
Ping Xu ◽  
Shaoqiong Xie ◽  
Wencheng Jiang ◽  
Jiajing Lu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signaling Pathways ◽  
Immune Cell ◽  
Cell Types ◽  
Bioinformatic Analysis ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Hub Genes ◽  
Diagnostic Efficacy ◽  
Network Analyses

Abstract Background: Psoriasis is a relatively common autoimmune inflammatory skin disease with a chronic etiology. The present study was designed to detect novel biomarkers and pathways associated with psoriasis incidence. Methods: Differentially expressed genes (DEGs) associated with psoriasis in the Gene Expression Omnibus (GEO) database were identified, and their functional roles and interactions were then annotated and evaluated through GO, KEGG, and gene set variation (GSVA) analyses. In addition, the STRING database was leveraged to construct a protein-protein interaction (PPI) network, and key hub genes from this network were validated as being relevant through receiver operating characteristic (ROC) curve analyses of three additional GEO datasets. The CIBERSORT database was additionally used to assess the relationship between these gene expression-related findings and immune cell infiltration. Results: In total 197 psoriasis-related DEGs were identified and found to primarily be associated with the NOD-like receptor, IL-17, and cytokine-cytokine receptor interaction signaling pathways. GSVA revealed significant differences between normal and lesional groups (P < 0.05), while PPI network analyses identified CXCL10 as the hub gene with the highest degree value, whereas IRF7, IFIT3, OAS1, GBP1, and ISG15 were promising candidate genes for the therapeutic treatment of psoriasis. ROC analyses confirmed that these 6 hub genes exhibited good diagnostic efficacy (AUC > 70%), and were predicted to be associated with increased sensitivity to 10 drugs (P < 0.01). The CIBERSORT database further predicted that these hub genes were associated with infiltration by 22 different immune cell types. Conclusion: These results offer a robust foundation for future studies of the molecular basis for psoriasis, potentially guiding efforts to treat this common and disruptive disease.

scholarly journals Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257343
Author(s):  
Shaoshuo Li ◽  
Baixing Chen ◽  
Hao Chen ◽  
Zhen Hua ◽  
Yang Shao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Network Analysis ◽  
Postmenopausal Osteoporosis ◽  
Molecular Mechanism ◽  
Molecular Mechanisms ◽  
Functional Enrichment ◽  
Support Vector ◽  
Hub Genes ◽  
Genetic Biomarkers

Objectives Smoking is a significant independent risk factor for postmenopausal osteoporosis, leading to genome variations in postmenopausal smokers. This study investigates potential biomarkers and molecular mechanisms of smoking-related postmenopausal osteoporosis (SRPO). Materials and methods The GSE13850 microarray dataset was downloaded from Gene Expression Omnibus (GEO). Gene modules associated with SRPO were identified using weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, and pathway and functional enrichment analyses. Feature genes were selected using two machine learning methods: support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF). The diagnostic efficiency of the selected genes was assessed by gene expression analysis and receiver operating characteristic curve. Results Eight highly conserved modules were detected in the WGCNA network, and the genes in the module that was strongly correlated with SRPO were used for constructing the PPI network. A total of 113 hub genes were identified in the core network using topological network analysis. Enrichment analysis results showed that hub genes were closely associated with the regulation of RNA transcription and translation, ATPase activity, and immune-related signaling. Six genes (HNRNPC, PFDN2, PSMC5, RPS16, TCEB2, and UBE2V2) were selected as genetic biomarkers for SRPO by integrating the feature selection of SVM-RFE and RF. Conclusion The present study identified potential genetic biomarkers and provided a novel insight into the underlying molecular mechanism of SRPO.

scholarly journals Identification of potential biomarkers with colorectal cancer based on bioinformatics analysis and machine learning

2021 ◽  
Vol 18 (6) ◽  
pp. 8997-9015
Author(s):  
Ahmed Hammad ◽  
◽  
Mohamed Elshaer ◽  
Xiuwen Tang ◽  
◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Machine Learning ◽  
Roc Curve ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Hub Genes ◽  
Survival Analyses ◽  
Potential Biomarkers

<abstract> <p>Colorectal cancer (CRC) is one of the most common malignancies worldwide. Biomarker discovery is critical to improve CRC diagnosis, however, machine learning offers a new platform to study the etiology of CRC for this purpose. Therefore, the current study aimed to perform an integrated bioinformatics and machine learning analyses to explore novel biomarkers for CRC prognosis. In this study, we acquired gene expression microarray data from Gene Expression Omnibus (GEO) database. The microarray expressions GSE103512 dataset was downloaded and integrated. Subsequently, differentially expressed genes (DEGs) were identified and functionally analyzed via Gene Ontology (GO) and Kyoto Enrichment of Genes and Genomes (KEGG). Furthermore, protein protein interaction (PPI) network analysis was conducted using the STRING database and Cytoscape software to identify hub genes; however, the hub genes were subjected to Support Vector Machine (SVM), Receiver operating characteristic curve (ROC) and survival analyses to explore their diagnostic values. Meanwhile, TCGA transcriptomics data in Gene Expression Profiling Interactive Analysis (GEPIA) database and the pathology data presented by in the human protein atlas (HPA) database were used to verify our transcriptomic analyses. A total of 105 DEGs were identified in this study. Functional enrichment analysis showed that these genes were significantly enriched in biological processes related to cancer progression. Thereafter, PPI network explored a total of 10 significant hub genes. The ROC curve was used to predict the potential application of biomarkers in CRC diagnosis, with an area under ROC curve (AUC) of these genes exceeding 0.92 suggesting that this risk classifier can discriminate between CRC patients and normal controls. Moreover, the prognostic values of these hub genes were confirmed by survival analyses using different CRC patient cohorts. Our results demonstrated that these 10 differentially expressed hub genes could be used as potential biomarkers for CRC diagnosis.</p> </abstract>

Identification of Potential Biomarkers in Synovial Tissue of Rheumatoid Arthritis via Integrated Microarray Analysis. Identification of Biomarkers of Rheumatoid Arthritis

2021 ◽  
Author(s):  
Li Tao ◽  
ChaoLiang Xiong ◽  
Li Xue
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Pathway Analysis ◽  
Expression Profiles ◽  
Diagnostic Value ◽  
Functional Enrichment ◽  
Venn Diagram ◽  
Ppi Network ◽  
Hub Genes ◽  
Gene Markers

Abstract Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by synovitis and subsequent destruction of cartilage and bone. This study aimed to explore RA-related gene markers and the underlying molecular mechanism.Material and Methods: The expression profiles of GSE77298, GSE55235 and GSE12021 were obtained from the Gene Expression Omnibus database. Then, the differential gene expression analysis was conducted between GSE77298 and GSE55235 datasets. Limma package and a Venn diagram were utilized to screen the overlapping differentially expressed genes (DEGs), and Functional enrichment and pathway analysis were performed by using DAVID database. Subsequently, a protein-protein interaction (PPI) network was established, and candidate hub genes were recognized by using STRING and Cytoscape software. Finally, another dataset (GSE12021) was used for the validation of diagnostic value of the candidate hub genes and to identify real hub genes by using receiver operating characteristic (ROC) curves.Results: A total of 385 DEGs were detected, which include 19 downregulated genes and 366 upregulated genes. GO and KEGG pathway analysis showed that DEGs was mainly enriched in various immune and inflammatory response-related functions and pathways. The PPI network was composed of 374 nodes and 767 edges. A total of 8 real hub genes (HLA-DRA, HLA-DRB1, LCK, VAV1, HLA-DPA1, HLA-DPB1, C3AR1 and CD3D) which displayed an excellent diagnostic value for RA were identified.Conclusion: these findings may provide novel and reliable biomarkers for RA, which have some interesting implications for early diagnosis, prognosis and targeted therapy.

Validation of novel hub genes and molecular mechanisms in acute lung injury using an integrative bioinformatics approach

2021 ◽  
Author(s):  
Qingchun Liang ◽  
Qin Zhou ◽  
Jinhe Li ◽  
Zhugui Chen ◽  
Zhihao Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Lung Injury ◽  
Lung Injury ◽  
Molecular Mechanisms ◽  
Molecular Biomarkers ◽  
Functional Enrichment ◽  
Lung Injury Score ◽  
Control Group ◽  
High Morbidity ◽  
Hub Genes

Abstract Acute lung injury (ALI) is an inflammatory pulmonary disease that can easily develop into serious acute respiratory distress syndrome, which has high morbidity and mortality. However, the molecular mechanism of ALI remains unclear, and few molecular biomarkers for diagnosis and treatment have been identified. In this study, we aimed to identify novel molecular biomarkers using a bioinformatics approach. Gene expression data were obtained from the Gene Expression Omnibus database, co-expressed differentially expressed genes (CoDEGs) were identified using R software, and further functional enrichment analyses were conducted using the online tool Database for Annotation, Visualization, and Integrated Discovery. A protein–protein interaction network was established using the STRING database and Cytoscape software. Lipopolysaccharide (LPS)-induced ALI mouse model was constructed and verified. The hub genes were screened and validated in vivo. The transcription factors (TFs) and miRNAs associated with the hub genes were predicted using the NetworkAnalyst database. In total, 71 CoDEGs were screened and found to be mainly involved in the cytokine–cytokine receptor interactions, and the tumor necrosis factor and malaria signaling pathways. Animal experiments showed that the lung injury score, bronchoalveolar lavage fluid protein concentration, and wet-to-dry weight ratio were higher in the LPS group than those in the control group. Real-time polymerase chain reaction analysis indicated that most of the hub genes such as colony-stimulating factor 2 (Csf2) were overexpressed in the LPS group. A total of 20 TFs including nuclear respiratory factor 1 (NRF1) and two miRNAs were predicted to be regulators of the hub genes. In summary, Csf2 may serve as a novel diagnostic and therapeutic target for ALI. NRF1 and mmu-mir-122-5p may be key regulators in the development of ALI.

Identification Of Putative Gene Signatures Associated With Diagnosis And Prognosis Of Breast Cancer

2021 ◽  
Vol 44 (3) ◽  
pp. E45-54
Author(s):  
Chao Tan ◽  
Fang Zuo ◽  
Mingqian Lu ◽  
Sai Chen ◽  
Zhenzhen Tian ◽  
...  
Keyword(s):  
Risk Model ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Support Vector ◽  
Ppi Network ◽  
Hub Genes ◽  
Putative Gene ◽  
Network Analyses ◽  
Cluster 2

Purpose: This study aimed to identify potential diagnostic and therapeutic biomakers for the development ofbreast cancer (BC). Methods: GSE86374 dataset containing 159 samples was acquired from the Gene Expression Omnibus (GEO) database followed by differentially expressed genes (DEGs) identification and cluster analysis. Corresponding functional enrichment and protein-protein interaction (PPI) network analyses were performed to identify hub genes. Prognostic evaluation using clinical information obtained from TCGA database and hub genes was conducted to screen for crucial indicators for BC progression. The risk model was established and validated. Results: In total, 186 DEGs were identified and grouped into four clusters: 96 in cluster 1; 69 in cluster 2; 16 in  cluster 3; and 5 in cluster 4. Functional enrichment analysis showed that DEGs, including ADH1B in cluster 1,  were dramatically enriched in the tyrosine and drug metabolism pathways, while genes in cluster 2, including  SPP1 and RRM2, played crucial roles in PI3K-Akt and p53 signalling pathway. SPP1 and RRM2 served as hub  genes in the PPI network, resulting in an support vector machine classifier with good accuracy and specificity.Ad ditionally, the results of prognostic analysis suggest that age, metastasis stage, SPP1 and ADH1B were correlated with risk of BC, which was validated by using the established risk model analysis. Conclusion: SPP1, RRM2 and ADH1B appear to play vital roles in the development of BC. Age and TNM stage  were also preferentially associated with risk of developing BC. Evaluation of the risk model based on larger sample size and further experimental validation are required.

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