Common and specific gene signatures among three different endometriosis subtypes

Aims To identify the common and specific molecular mechanisms of three well-defined subtypes of endometriosis (EMs): ovarian endometriosis (OE), peritoneal endometriosis (PE), and deep infiltrating endometriosis (DIE). Methods Four microarray datasets: GSE7305 and GSE7307 for OE, E-MTAB-694 for PE, and GSE25628 for DIE were downloaded from public databases and conducted to compare ectopic lesions (EC) with eutopic endometrium (EU) from EMs patients. Differentially expressed genes (DEGs) identified by limma package were divided into two parts: common DEGs among three subtypes and specific DEGs in each subtype, both of which were subsequently performed with the Kyoto Encyclopedia of Genes (KEGG) pathway enrichment analysis. The protein-protein interaction (PPI) network was constructed by common DEGs and five hub genes were screened out from the PPI network. Besides, these five hub genes together with selected interested pathway-related genes were further validated in an independent OE RNA-sequencing dataset GSE105764. Results A total of 54 EC samples from three EMs subtypes (OE, PE, DIE) and 58 EU samples were analyzed, from which we obtained 148 common DEGs among three subtypes, and 729 specific DEGs in OE, 777 specific DEGs in PE and 36 specific DEGs in DIE. The most enriched pathway of 148 shared DEGs was arachidonic acid (AA) metabolism, in which most genes were up-regulated in EC, indicating inflammation was the most common pathogenesis of three subtypes. Besides, five hub genes AURKB, RRM2, DTL, CCNB1, CCNB2 identified from the PPI network constructed by 148 shared DEGs were all associated with cell cycle and mitosis, and down-regulated in EC, suggesting a slow and controlled proliferation in ectopic lesions. The KEGG pathway analysis of specific DEGs in each subtype revealed that abnormal ovarian steroidogenesis was a prominent feature in OE; OE and DIE seems to be at more risk of malignant development since both of their specific DEGs were enriched in the pathways in cancer, though enriched genes were different, while PE tended to be more associated with dysregulated peritoneal immune and inflammatory microenvironment. Conclusion By integrated bioinformatic analysis, we explored common and specific molecular signatures among different subtypes of endometriosis: activated arachidonic acid (AA) metabolism-related inflammatory process and a slow and controlled proliferation in ectopic lesions were common features in OE, PE and DIE; OE and DIE seemed to be at more risk of malignant development while PE tended to be more associated with dysregulated peritoneal immune and inflammatory microenvironment, all of which could deepen our perception of endometriosis.

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Bioinformatics Analysis of Candidate Genes and Pathways Related to Hepatocellular Carcinoma in China: A Study Based on Public Databases

Pathology & Oncology Research ◽

10.3389/pore.2021.588532 ◽

2021 ◽

Vol 27 ◽

Author(s):

Peng Zhang ◽

Jing Feng ◽

Xue Wu ◽

Weike Chu ◽

Yilian Zhang ◽

...

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Candidate Genes ◽

Molecular Mechanisms ◽

Kegg Pathway ◽

Reduction Process ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Chronic Hepatitis B Virus

Background and Objective: Hepatocellular carcinoma (HCC) is a highly aggressive malignant tumor of the digestive system worldwide. Chronic hepatitis B virus (HBV) infection and aflatoxin exposure are predominant causes of HCC in China, whereas hepatitis C virus (HCV) infection and alcohol intake are likely the main risk factors in other countries. It is an unmet need to recognize the underlying molecular mechanisms of HCC in China.Methods: In this study, microarray datasets (GSE84005, GSE84402, GSE101685, and GSE115018) derived from Gene Expression Omnibus (GEO) database were analyzed to obtain the common differentially expressed genes (DEGs) by R software. Moreover, the gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by using Database for Annotation, Visualization and Integrated Discovery (DAVID). Furthermore, the protein-protein interaction (PPI) network was constructed, and hub genes were identified by the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape, respectively. The hub genes were verified using Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, and Kaplan-Meier Plotter online databases were performed on the TCGA HCC dataset. Moreover, the Human Protein Atlas (HPA) database was used to verify candidate genes’ protein expression levels.Results: A total of 293 common DEGs were screened, including 103 up-regulated genes and 190 down-regulated genes. Moreover, GO analysis implied that common DEGs were mainly involved in the oxidation-reduction process, cytosol, and protein binding. KEGG pathway enrichment analysis presented that common DEGs were mainly enriched in metabolic pathways, complement and coagulation cascades, cell cycle, p53 signaling pathway, and tryptophan metabolism. In the PPI network, three subnetworks with high scores were detected using the Molecular Complex Detection (MCODE) plugin. The top 10 hub genes identified were CDK1, CCNB1, AURKA, CCNA2, KIF11, BUB1B, TOP2A, TPX2, HMMR and CDC45. The other public databases confirmed that high expression of the aforementioned genes related to poor overall survival among patients with HCC.Conclusion: This study primarily identified candidate genes and pathways involved in the underlying mechanisms of Chinese HCC, which is supposed to provide new targets for the diagnosis and treatment of HCC in China.

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

10.1101/2020.12.21.20248688 ◽

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.

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miR-130b Acts as an Oncogene and Prognostic Biomarker of Breast Cancer: A Bioinformatic Analysis

10.21203/rs.3.rs-132700/v1 ◽

2020 ◽

Author(s):

Xinyue Chen ◽

Lijun Hao

Keyword(s):

Breast Cancer ◽

Thyroid Hormone ◽

Signaling Pathway ◽

Target Genes ◽

Kegg Pathway ◽

Prognostic Biomarker ◽

Pathway Enrichment Analysis ◽

Hormone Signaling ◽

Ppi Network ◽

Hub Genes

Abstract Background: Breast cancer (BC) is the most prevalent cancer among females globally. microRNAs (miRNAs) could regulate the expression levels of cancer-related genes through binding with target mRNAs. In various cancers, the abnormal expression of miR-130b has been detected. We aims to investigate the molecular mechanism and biological function of miR130b in breast cancer.Methods: We obtained two microRNA expression profiles from the Gene Expression Omnibus (GEO) database, including GSE45666 and GSE26659. We identified differentially expressed miRNAs (DE-miRNAs) between BC tissue and normal breast tissue based on the GEO2R web tool. DE-miRNAs were filtered by significant prognostic value resulting from Kaplan–Meier plotter. We used the JASPAR database to explore upstream regulators of miR-130b. The potential molecular mechanisms of miR-130b correlation genes were revealed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis in WebGestalt. Protein–protein interaction (PPI) network of miR-130b target genes was constructed by STRING. Cytoscape software was used to visualize the PPI network and hub genes.Results: miR-130b was highly expressed in breast cancer tissues, which positively correlates with poor prognostic. JASPAR revealed THAP11 might be the upstream regulator of miR-130b. In addition, GO, and KEGG pathway revealed that miR-130b positively regulated PFKP, STAT1, SRC, and NOTCH2, participating in the Thyroid hormone signaling pathway. The PPI network further identified that AR, KIT, and ESR1 as hub genes in BC development.Conclusion: miR-130b, which is regulated by THAP11, acts as an oncogene and prognostic biomarker in BC by mediating the Thyroid hormone signaling pathway and potential target genes. miR-130b might be a novel therapeutic target for BC treatment.

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In silico Identification of 10 Hub Genes and an miRNA–mRNA Regulatory Network in Acute Kawasaki Disease

Frontiers in Genetics ◽

10.3389/fgene.2021.585058 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jin Ma ◽

Huan Gui ◽

Yunjia Tang ◽

Yueyue Ding ◽

Guanghui Qian ◽

...

Keyword(s):

Kawasaki Disease ◽

Signaling Pathway ◽

Regulatory Network ◽

Molecular Mechanisms ◽

Target Genes ◽

Unknown Etiology ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Ppi Networks

Kawasaki disease (KD) causes acute systemic vasculitis and has unknown etiology. Since the acute stage of KD is the most relevant, the aim of the present study was to identify hub genes in acute KD by bioinformatics analysis. We also aimed at constructing microRNA (miRNA)–messenger RNA (mRNA) regulatory networks associated with acute KD based on previously identified differentially expressed miRNAs (DE-miRNAs). DE-mRNAs in acute KD patients were screened using the mRNA expression profile data of GSE18606 from the Gene Expression Omnibus. The functional and pathway enrichment analysis of DE-mRNAs were performed with the DAVID database. Target genes of DE-miRNAs were predicted using the miRWalk database and their intersection with DE-mRNAs was obtained. From a protein–protein interaction (PPI) network established by the STRING database, Cytoscape software identified hub genes with the two topological analysis methods maximal clique centrality and Degree algorithm to construct a miRNA-hub gene network. A total of 1,063 DE-mRNAs were identified between acute KD and healthy individuals, 472 upregulated and 591 downregulated. The constructed PPI network with these DE-mRNAs identified 38 hub genes mostly enriched in pathways related to systemic lupus erythematosus, alcoholism, viral carcinogenesis, osteoclast differentiation, adipocytokine signaling pathway and tumor necrosis factor signaling pathway. Target genes were predicted for the up-regulated and down-regulated DE-miRNAs, 10,203, and 5,310, respectively. Subsequently, 355, and 130 overlapping target DE-mRNAs were obtained for upregulated and downregulated DE-miRNAs, respectively. PPI networks with these target DE-mRNAs produced 15 hub genes, six down-regulated and nine upregulated hub genes. Among these, ten genes (ATM, MDC1, CD59, CD177, TRPM2, FCAR, TSPAN14, LILRB2, SIRPA, and STAT3) were identified as hub genes in the PPI network of DE-mRNAs. Finally, we constructed the regulatory network of DE-miRNAs and hub genes, which suggested potential modulation of most hub genes by hsa-miR-4443 and hsa-miR-6510-5p. SP1 was predicted to potentially regulate most of DE-miRNAs. In conclusion, several hub genes are associated with acute KD. An miRNA–mRNA regulatory network potentially relevant for acute KD pathogenesis provides new insights into the underlying molecular mechanisms of acute KD. The latter may contribute to the diagnosis and treatment of acute KD.

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Comprehensive Analyses of miRNA-mRNA Network and Potential Drugs in Idiopathic Pulmonary Arterial Hypertension

BioMed Research International ◽

10.1155/2020/5156304 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Chan Li ◽

Zeyu Zhang ◽

Qian Xu ◽

Ruizheng Shi

Keyword(s):

Pulmonary Arterial Hypertension ◽

Arterial Hypertension ◽

Kegg Pathway ◽

Enrichment Analysis ◽

Idiopathic Pulmonary Arterial Hypertension ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Pulmonary Arterial

Introduction. Idiopathic pulmonary arterial hypertension (IPAH) is a severe cardiopulmonary disease with a relatively low survival rate. Moreover, the pathogenesis of IPAH has not been fully recognized. Thus, comprehensive analyses of miRNA-mRNA network and potential drugs in IPAH are urgent requirements. Methods. Microarray datasets of mRNA and microRNA (miRNA) in IPAH were searched and downloaded from Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMIs) were identified. Then, the DEMI-DEG network was conducted with associated comprehensive analyses including Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) network analysis, while potential drugs targeting hub genes were investigated using L1000 platform. Results. 30 DEGs and 6 DEMIs were identified in the lung tissue of IPAH. GO and KEGG pathway analyses revealed that these DEGs were mostly enriched in antimicrobial humoral response and African trypanosomiasis, respectively. The DEMI-DEG network was conducted subsequently with 4 DEMIs (hsa-miR-34b-5p, hsa-miR-26b-5p, hsa-miR-205-5p, and hsa-miR-199a-3p) and 16 DEGs, among which 5 DEGs (AQP9, SPP1, END1, VCAM1, and SAA1) were included in the top 10 hub genes of the PPI network. Nimodipine was identified with the highest CMap connectivity score in L1000 platform. Conclusion. Our study conducted a miRNA-mRNA network and identified 4 miRNAs as well as 5 mRNAs which may play important roles in the pathogenesis of IPAH. Moreover, we provided a new insight for future therapies by predicting potential drugs targeting hub genes.

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POS0743 GENE EXPRESSION MICROARRAY IN LUPUS NEPHRITIS BY BIOINFORMATIC ANALYSIS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.2062 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 623.1-623

Author(s):

C. Wang ◽

S. X. Zhang ◽

S. Song ◽

J. Qiao ◽

R. Zhao ◽

...

Keyword(s):

Lupus Nephritis ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Differential Expression Analysis ◽

Deep Understanding ◽

Enrichment Analysis ◽

Shanxi Province ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Hub Genes

Background:Nephritis is one of the predominant causes of morbidity and mortality in patients with lupus1 2.The lack of understanding regarding the molecular mechanisms of lupus nephritis(LN) hinders the development of specific targeted therapy for this progressive disease3.Objectives:In this study, we use bioinformatics method to analyze the genes involved in regulating the potential pathogenesis of LN.Methods:The expression profile of LN(GSE104948 and GSE32591) was obtained from the GEO database.GSE104948 was a memory chip, which included 32 LN glomerular biopsy tissues and 3 glomerular tissues from living donors.GSE32591 dataset included 32 LN glomerular biopsy tissues and 15 glomerular tissues from living donors. The Oligo package was used to process the data to obtain the expression matrix files of all the related genes.P<0.05 and |log2(FC)|>2 were setted as cut-off criteria for the DEGs.Ggplot2, heatmap packages were used to DEGs visualization. Metascape online tool was used to annotating DEGs for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis performed.We used STRING online database to construct protein-protein interaction (PPI) network. Hub genes were identified by Cytoscape.Results:In differential expression analysis,357 DEGs were identified,including 248 up-regulated genes and 109 down-regulated genes (Figure 1A,B).GO enrichment showed that these DEGs were primarily enriched in biological pathways, cell localization and molecular function and revealed that LN-related genes mainly involved in immune response.KEGG pathway annotation enrichment analysis revealed these DEGs were closely associated with Staphylococcus aureus infection,Complement and coagulation cascades (Figure 1D). Fourteen hub genes(IFT3,IRF7,OAS3,GBP2,RSAD2,MX1,IFIT2,IFI6,MX2,ISF15,IFIT1,QAS2,OASL,OAS1) were identified from PPI network (Figure 1C,E).Conclusion:Illuminating the molecular mechanisms of LN was help for deep understanding of LN.References:[1]Song J, Zhao L, Li Y. Comprehensive bioinformatics analysis of mRNA expression profiles and identification of a miRNA-mRNA network associated with lupus nephritis. Lupus 2020;29(8):854-61. doi: 10.1177/0961203320925155 [published Online First: 2020/05/22].[2]Yao F, Sun L, Fang W, et al. HsamiR3715p inhibits human mesangial cell proliferation and promotes apoptosis in lupus nephritis by directly targeting hypoxiainducible factor 1alpha. Mol Med Rep 2016;14(6):5693-98. doi: 10.3892/mmr.2016.5939 [published Online First: 2016/11/24].[3]Dall’Era M. Treatment of lupus nephritis: current paradigms and emerging strategies. Curr Opin Rheumatol 2017;29(3):241-47. doi: 10.1097/BOR.0000000000000381 [published Online First: 2017/02/17].Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

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MYLK, CNN1, TAGLN and LMOD1 identified as potential prognostic biomarkers for bladder cancer using bioinformatics analysis

10.21203/rs.3.rs-379852/v1 ◽

2021 ◽

Author(s):

zhiyong tan ◽

Xuhua Qiao ◽

Shi Fu ◽

Xianzhong Duan ◽

Yigang Zuo ◽

...

Keyword(s):

Gene Expression ◽

Extracellular Matrix ◽

Bladder Cancer ◽

Signaling Pathway ◽

Kegg Pathway ◽

Mapk Signaling ◽

Prognostic Biomarkers ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Hub Genes

Abstract Background: Bladder cancer (BCa) is a challenge carcinoma that occurs on the bladder mucosa, which is the most common malignant neoplasm of the urinary system. Great efforts have been made to elucidate its pathogenesis. However, the molecular mechanisms involved in BCa remain unclear. Therefore, there is an urgent need to identify effective biomarkers to accurately predict the progression and prognosis of BCa.Material and methods: To investigate potential prognostic biomarkers of BCa, we download the GSE23732 expression profile from Gene Expression Omnibus (GEO) database. The GEO2R analysis tool was performed to identify the DEGs between BCa and normal bladder mucosae tissue. Gene Ontology (GO) functional annotation analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed for the screened DEGs by the Database for Annotation, Visualization, and Integrated Discovery (DAVID) online tool. We employed the Search Tool for the Retrieval of Interacting Genes (STRING) database to construct the protein-protein interaction (PPI) network of DEGs. Subsequently, the PPI network’s information was visualized by Cytoscape software. The Gene Expression Profiling Interactive Analysis (GEPIA) resource was used to describe the OS and DFS outcomes in bladder cancer patients based on the hub genes expression levels.Results: A total of 396 DEGs comprising 344 upregulated genes and 52 downregulated genes were screened. The results of the GO analysis showed that DEG was mainly enriched in proteinaceous extracellular matrix, extracellular matrix, heparin binding and extracellular matrix organization. In addition, KEGG pathway analysis showed that DEGs were mainly enriched in PI3K-Akt signaling pathway, Focal adhesion, MAPK signaling pathway. A PPI network was constructed using the 396 DEGs, 10 hub genes were selected and 4 of them including MYLK, CNN1, TAGLN and LMOD1 were associated with overall survival and disease-free survival.Conclusion: MYLK, CNN1, TAGLN and LMOD1 may represent promising prognostic biomarkers and potential therapeutic option for BCa.

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In silico analyses for potential key genes associated with gastric cancer

PeerJ ◽

10.7717/peerj.6092 ◽

2018 ◽

Vol 6 ◽

pp. e6092 ◽

Cited By ~ 14

Author(s):

Ping Yan ◽

Yingchun He ◽

Kexin Xie ◽

Shan Kong ◽

Weidong Zhao

Keyword(s):

Gastric Cancer ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Therapeutic Targets ◽

Receptor Interaction ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Kaplan Meier ◽

Kaplan Meier Plotter

Background Understanding hub genes involved in gastric cancer (GC) metastasis could lead to effective approaches to diagnose and treat cancer. In this study, we aim to identify the hub genes and investigate the underlying molecular mechanisms of GC. Methods To explore potential therapeutic targets for GC,three expression profiles (GSE54129, GSE33651, GSE81948) of the genes were extracted from the Gene Expression Omnibus (GEO) database. The GEO2R online tool was applied to screen out differentially expressed genes (DEGs) between GC and normal gastric samples. Database for Annotation, Visualization and Integrated Discovery was applied to perform Gene Ontology (GO) and KEGG pathway enrichment analysis. The protein-protein interaction (PPI) network of these DEGs was constructed using a STRING online software. The hub genes were identified by the CytoHubba plugin of Cytoscape software. Then, the prognostic value of these identified genes was verified by gastric cancer database derived from Kaplan-Meier plotter platform. Results A total of 85 overlapped upregulated genes and 44 downregulated genes were identified. The majority of the DEGs were enriched in extracellular matrix organization, endodermal cell differentiation, and endoderm formation. Moreover, five KEGG pathways were significantly enriched, including ECM-receptor interaction, amoebiasis, AGE-RAGE signaling pathway in diabetic complications, focal adhesion, protein digestion and absorption. By combining the results of PPI network and CytoHubba, a total of nine hub genes including COL1A1, THBS1, MMP2, CXCL8, FN1, TIMP1, SPARC, COL4A1, and ITGA5 were selected. The Kaplan-Meier plotter database confirmed that overexpression levels of these genes were associated with reduced overall survival, except for THBS1 and CXCL8. Conclusions Our study suggests that COL1A1, MMP2, FN1, TIMP1, SPARC, COL4A1, and ITGA5 may be potential biomarkers and therapeutic targets for GC. Further study is needed to assess the effect of THBS1 and CXCL8 on GC.

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Bioinformatics analysis of differentially expressed genes and identification of an miRNA–mRNA network associated with entorhinal cortex and hippocampus in Alzheimer’s disease

Hereditas ◽

10.1186/s41065-021-00190-0 ◽

2021 ◽

Vol 158 (1) ◽

Author(s):

Haoming Li ◽

Linqing Zou ◽

Jinhong Shi ◽

Xiao Han

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Differentially Expressed Genes ◽

Entorhinal Cortex ◽

Molecular Mechanisms ◽

Kegg Pathway ◽

Neurodegenerative Disorder ◽

Early Stage ◽

Differentially Expressed ◽

Hub Genes

Abstract Background Alzheimer’s disease (AD) is a fatal neurodegenerative disorder, and the lesions originate in the entorhinal cortex (EC) and hippocampus (HIP) at the early stage of AD progression. Gaining insight into the molecular mechanisms underlying AD is critical for the diagnosis and treatment of this disorder. Recent discoveries have uncovered the essential roles of microRNAs (miRNAs) in aging and have identified the potential of miRNAs serving as biomarkers in AD diagnosis. Methods We sought to apply bioinformatics tools to investigate microarray profiles and characterize differentially expressed genes (DEGs) in both EC and HIP and identify specific candidate genes and pathways that might be implicated in AD for further analysis. Furthermore, we considered that DEGs might be dysregulated by miRNAs. Therefore, we investigated patients with AD and healthy controls by studying the gene profiling of their brain and blood samples to identify AD-related DEGs, differentially expressed miRNAs (DEmiRNAs), along with gene ontology (GO) analysis, KEGG pathway analysis, and construction of an AD-specific miRNA–mRNA interaction network. Results Our analysis identified 10 key hub genes in the EC and HIP of patients with AD, and these hub genes were focused on energy metabolism, suggesting that metabolic dyshomeostasis contributed to the progression of the early AD pathology. Moreover, after the construction of an miRNA–mRNA network, we identified 9 blood-related DEmiRNAs, which regulated 10 target genes in the KEGG pathway. Conclusions Our findings indicated these DEmiRNAs having the potential to act as diagnostic biomarkers at an early stage of AD.

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Identification of Novel Hub Genes Through Expression Profiles Analysis in Adrenocortical Carcinoma

10.21203/rs.3.rs-38768/v1 ◽

2020 ◽

Author(s):

Chenhe Yao ◽

Xiaoling Zhao ◽

Xuemeng Shang ◽

Binghan Jia ◽

Shuaijie Dou ◽

...

Keyword(s):

Adrenocortical Carcinoma ◽

Expression Profiling ◽

Molecular Mechanisms ◽

Cross Validation ◽

Expression Profiles ◽

Ppi Network ◽

Hub Genes ◽

Tumor Association ◽

Prediction Of Prognosis ◽

Geo Database

Abstract Background: Adrenocortical carcinoma (ACC) is a heterogeneous and rare malignant tumor associated with a poor prognosis. The molecular mechanisms of ACC remain elusive and more accurate biomarkers for the prediction of prognosis are needed.Methods: In this study, integrative profiling analyses were performed to identify novel hub genes in ACC to provide promising targets for future investigation. Three gene expression profiling datasets in the GEO database were used for the identification of overlapped differentially expressed genes (DEGs) following the criteria of adj.P.Value<0.05 and |log2 FC|>0.5 in ACC. Novel hub genes were screened out following a series of processes: the retrieval of DEGs with no known associations with ACC on Pubmed, then the cross-validation of expression values and significant associations with overall survival in the GEPIA2 and starBase databases, and finally the prediction of gene-tumor association in the GeneCards database.Results: Four novel hub genes were identified and two of them, TPX2 and RACGAP1, were positively correlated with the staging. Interestingly, co-expression analysis revealed that the association between TPX2 and RACGAP1 was the strongest and that the expression of HOXA5 was almost completely independent of that of RACGAP1 and TPX2. Furthermore, the PPI network consisting of four novel genes and seed genes in ACC revealed that HOXA5, TPX2, and RACGAP1 were all associated with TP53. Conclusions: This study identified four novel hub genes (TPX2, RACHAP1, HXOA5 and FMO2) that may play crucial roles in the tumorigenesis and the prediction of prognosis of ACC.

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