scholarly journals Identification of Potential miRNA-mRNA Regulatory Network in Tetralogy of Fallot: A Network Biology Approach

2021 ◽  
Author(s):  
Huaming Wang ◽  
Guorong Lyu ◽  
Shaozheng He ◽  
Xi Lin ◽  
Bingtian Dong ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Regulatory Network ◽  
Early Stage ◽  
Ppi Network ◽  
Hub Genes ◽  
Transcription Activity ◽  
Kegg Analysis ◽  
Modular Analysis ◽  
High Connectivity ◽  
Geo Database

Abstract BackgroundTetralogy of Fallot (ToF) is one of the most prevalent and fatal birth defects. Its pathogenesis remains unknown and it’s not easily diagnosed at early stage. Therefore, it’s necessary to explore the critical regulators and molecular mechanism in ToF to find out novel molecular markers for early diagnosis.MethodsThree ToF datasets (GSE35490, GSE40128, GSE36761) were downloaded from the GEO database. The differentially expressed microRNAs (DEMs) and mRNAs (DEGs) were identified between ToF infants and normal infants after data preprocessing, followed by GO and KEGG analysis of DEGs. Then, PPI network and modular analysis were performed to identify the hub genes. Ultimately, potential miRNA-mRNA pathways in ToF were constructed based on the integrated data.Results22 overlapping DEMs were found in the GES35490 and GSE40128. Simultaneous, 181 intersected genes were found and identified as the significant DEGs including 84 down-regulated DEGs and 97 up-regulated DEGs. Further, 20 hub genes ranked by top 10% with high connectivity, including STAT3, FBN1, RUNX2, were found by PPT network and modular analysis. Furthermore, GO and KEGG analysis showed these DEGs were linked to cell cycle, apoptotic process, transcription activity and FOX signaling pathway. Additionally, we establish three potential miRNA-mRNA pathway, including miR-22-STAT3, miR-336/miR486-FBN1, miR-222-RUNX2, which can participate in the formation of ToF by cell cycle and transcription activity.ConclusionsThree miRNA-mRNA pathways were established and indicated cell cycle and transcription activity may be involved in the pathogenesis of ToF. Our study provided a novel bio-marker for early diagnosis of ToF.

scholarly journals Integrated Bioinformatics Analysis of Master Regulators in Anaplastic Thyroid Carcinoma

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Zongfu Pan ◽  
Lu Li ◽  
Qilu Fang ◽  
Yangyang Qian ◽  
Yiwen Zhang ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Thyroid Carcinoma ◽  
Signaling Pathway ◽  
Regulatory Network ◽  
Anaplastic Thyroid Carcinoma ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Hub Genes ◽  
Normal Thyroid ◽  
Master Regulators

Anaplastic thyroid carcinoma (ATC) is one of the most aggressive and rapidly lethal tumors. However, limited advances have been made to prolong the survival and to reduce the mortality over the last decades. Therefore, identifying the master regulators underlying ATC progression is desperately needed. In our present study, three datasets including GSE33630, GSE29265, and GSE65144 were retrieved from Gene Expression Omnibus with a total of 32 ATC samples and 78 normal thyroid tissues. A total of 1804 consistently changed differentially expressed genes (DEGs) were identified from three datasets. KEGG pathways enrichment suggested that upregulated DEGs were mainly enriched in ECM-receptor interaction, cell cycle, PI3K-Akt signaling pathway, focal adhesion, and p53 signaling pathway. Furthermore, key gene modules in PPI network were identified by Cytoscape plugin MCODE and they were mainly associated with DNA replication, cell cycle process, collagen fibril organization, and regulation of leukocyte migration. Additionally, TOP2A, CDK1, CCNB1, VEGFA, BIRC5, MAPK1, CCNA2, MAD2L1, CDC20, and BUB1 were identified as hub genes of the PPI network. Interestingly, module analysis showed that 8 out of 10 hub genes participated in Module 1 network and more than 70% genes of Module 2 consisted of collagen family members. Notably, transcription factors (TFs) regulatory network analysis indicated that E2F7, FOXM1, and NFYB were master regulators of Module 1, while CREB3L1 was the master regulator of Module 2. Experimental validation showed that CREB3L1, E2F7, and FOXM1 were significantly upregulated in ATC tissue and cell line when compared with normal thyroid group. In conclusion, the TFs regulatory network provided a more detail molecular mechanism underlying ATC occurrence and progression. TFs including E2F7, FOXM1, CREB3L1, and NFYB were likely to be master regulators of ATC progression, suggesting their potential role as molecular therapeutic targets in ATC treatment.

scholarly journals Bioinformatics study on genes related to a high-risk postoperative recurrence of lung adenocarcinoma

2021 ◽  
Vol 104 (3) ◽  
pp. 003685042110180
Author(s):  
Xiao Lin ◽  
Meng Zhou ◽  
Zehong Xu ◽  
Yusheng Chen ◽  
Fan Lin
Keyword(s):  
Cell Cycle ◽  
Gene Ontology ◽  
High Risk ◽  
Lung Adenocarcinoma ◽  
Candidate Genes ◽  
Postoperative Recurrence ◽  
High Expression ◽  
Ppi Network ◽  
Hub Genes ◽  
Expression Levels

In this study, we aimed to screen out genes associated with a high risk of postoperative recurrence of lung adenocarcinoma and investigate the possible mechanisms of the involvement of these genes in the recurrence of lung adenocarcinoma. We identify Hub genes and verify the expression levels and prognostic roles of these genes. Datasets of GSE40791, GSE31210, and GSE30219 were obtained from the Gene Expression Omnibus database. Enrichment analysis of gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed for the screened candidate genes using the DAVID database. Then, we performed protein–protein interaction (PPI) network analysis through the database STRING. Hub genes were screened out using Cytoscape software, and their expression levels were determined by the GEPIA database. Finally, we assessed the relationships of Hub genes expression levels and the time of survival. Forty-five candidate genes related to a high-risk of lung adenocarcinoma recurrence were screened out. Gene ontology analysis showed that these genes were enriched in the mitotic spindle assembly checkpoint, mitotic sister chromosome segregation, G2/M-phase transition of the mitotic cell cycle, and ATP binding, etc. KEGG analysis showed that these genes were involved predominantly in the cell cycle, p53 signaling pathway, and oocyte meiosis. We screened out the top ten Hub genes related to high expression of lung adenocarcinoma from the PPI network. The high expression levels of eight genes (TOP2A, HMMR, MELK, MAD2L1, BUB1B, BUB1, RRM2, and CCNA2) were related to short recurrence-free survival and they can be used as biomarkers for high risk of lung adenocarcinoma recurrence. This study screened out eight genes associated with a high risk of lung adenocarcinoma recurrence, which might provide novel insights into researching the recurrence mechanisms of lung adenocarcinoma as well as into the selection of targets in the treatment of the disease.

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

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.

scholarly journals Identification of hub genes and their clinical value for predicting the development of prostate cancer from benign prostate hyperplasia by bioinformatic analysis

2021 ◽  
Author(s):  
Xi Chen ◽  
Junjie Ma ◽  
Chengdang Xu ◽  
Licheng Wang ◽  
Yicong Yao ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Survival Data ◽  
Benign Prostate Hyperplasia ◽  
Disease Free Survival ◽  
Ppi Network ◽  
Hub Genes ◽  
Prostate Hyperplasia ◽  
Hub Gene ◽  
Benign Prostate ◽  
Geo Database

Abstract BackgroundProstate cancer (PCa) and benign prostate hyperplasia (BPH) are commonly encountered diseases in elderly males. The two diseases have some commonalities: both are growth depend on hormone and respond to antiandrogen therapy. Some studies have shown that genetic factors are responsible for the occurrences of both diseases. There may be a correlation between BPH and PCa. MethodsThe GEO database can help determine the differentially expressed genes (DEGs) between BPH and PCa. Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were utilized to find pathways in which the DEGs were enriched. The STRING database can provide a protein–protein interaction (PPI) network, and Cytoscape software can find hub genes in PPI network. GEPIA can be used to analyze expression and survival data for hub genes. R software was used to progress regression analysis, decision curve analysis and built nomograph. UALCAN and The Human Protein Atlas was utilized to test the results. Finally, we made clinical and cell experiments to verify the results.ResultsSixty DEGs, consisting of 15 up-regulated and 45 down-regulated genes, were found based on the GEO database. Using Cytoscape, we found 7 hub gene in the PPI network. The hub gene expression was tested on TCGA database. Except CXCR4, all hub genes expressed differently between tumor and normal samples. Meanwhile, all hub genes exclude CXCR4 has diagnostic value in predicting PCa and their mutations are risk factors leading to PCa. The expression of CSRP1, MYL9 and SNAI2 changed in different tumor stage. CSRP1 and MYH11 could affect the disease-free survival (DFS). The same results reflected in different database. In addition, we also chose three hub gene, MYC, MYL9, and SNAI2, to validate their functions in clinical specimens and cells.ConclusionThese identified hub genes can help us to understand the process and mechanism by which BPH develops into PCa and provide achievable targets for predicting which BPH patients may later develop PCa.

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 Identification of biomarkers, pathways and potential therapeutic targets for heart failure using bioinformatics analysis

2021 ◽  
Author(s):  
Basavaraj Mallikarjunayya Vastrad ◽  
Chanabasayya Mallikarjunayya Vastrad
Keyword(s):  
Heart Failure ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Characteristic Curve ◽  
Topological Analysis ◽  
Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Hub Gene ◽  
Gene Regulatory

Heart failure (HF) is a complex cardiovascular diseases associated with high mortality. To discover key molecular changes in HF, we analyzed next-generation sequencing (NGS) data of HF. In this investigation, differentially expressed genes (DEGs) were analyzed using limma in R package from GSE161472 of the Gene Expression Omnibus (GEO). Then, gene enrichment analysis, protein-protein interaction (PPI) network, miRNA-hub gene regulatory network and TF-hub gene regulatory network construction, and topological analysis were performed on the DEGs by the Gene Ontology (GO), REACTOME pathway, STRING, HiPPIE, miRNet, NetworkAnalyst and Cytoscape. Finally, we performed receiver operating characteristic curve (ROC) analysis of hub genes. A total of 930 DEGs 9464 up regulated genes and 466 down regulated genes) were identified in HF. GO and REACTOME pathway enrichment results showed that DEGs mainly enriched in localization, small molecule metabolic process, SARS-CoV infections and the citric acid (TCA) cycle and respiratory electron transport. Subsequently, the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed, and 10 hub genes in these network were focused on by centrality analysis and module analysis. Furthermore, data showed that HSP90AA1, ARRB2, MYH9, HSP90AB1, FLNA, EGFR, PIK3R1, CUL4A, YEATS4 and KAT2B were good diagnostic values. In summary, this study suggests that HSP90AA1, ARRB2, MYH9, HSP90AB1, FLNA, EGFR, PIK3R1, CUL4A, YEATS4 and KAT2B may act as the key genes in HF.

scholarly journals Bioinformatics analysis of differentially expressed genes in non alcoholic fatty liver disease using next generation sequencing data

2021 ◽  
Author(s):  
Basavaraj Mallikarjunayya Vastrad ◽  
Chanabasayya Mallikarjunayya Vastrad
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Roc Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Alcoholic Fatty Liver ◽  
Hub Gene ◽  
Gene Regulatory ◽  
Generation Sequencing ◽  
Alcoholic Fatty Liver Disease

Non alcoholic fatty liver disease (NAFLD) is the most common metabolic disease in humans, affecting the majority of individuals. In the current investigation, we aim to identify potential key genes linked with NAFLD through bioinformatics analyses of next generation sequencing (NGS) dataset. NGS dataset of GSE135251 from the Gene Expression Omnibus (GEO) database were retrieved. Differentially expressed genes (DEGs) were obtained by DESeq2 package. g:Profiler database was further used to identify the potential gene ontology (GO) and REACTOME pathways. Protein-protein interaction (PPI) network was constructed using the Hippie interactome database. miRNet and NetworkAnalyst databases were used to establish a miRNA-hub gene regulatory network and TF-hub gene regulatory network for the hub genes. Hub genes were verified based on receiver operating characteristic curve (ROC) analysis. Totally, 951 DEGs were identified including 476 up regulated genes and 475 down regulated genes screened in NAFLD and normal control. GO showed that DEGs were significantly enhanced for signaling and regulation of biological quality. REACTOME pathway analysis revealed that DEGs were enriched in signaling by interleukins and extracellular matrix organization. ESR2, JUN, PTN, PTGER3, CEBPB, IKBKG, HSPA8, SFN, CDKN1A and E2F1 were indicated as hub genes from PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network. Furthermore, ROC analysis revealed that ESR2, JUN, PTN, PTGER3, CEBPB, IKBKG, HSPA8, SFN, CDKN1A and E2F1 might serve as diagnostic biomarkers in NAFLD. The current investigation provided insights into the molecular mechanism of NAFLD that might be useful in further investigations.

scholarly journals Identification of hub genes and biological pathways in hepatocellular carcinoma by integrated bioinformatics analysis

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10594
Author(s):  
Qian Zhao ◽  
Yan Zhang ◽  
Shichun Shao ◽  
Yeqing Sun ◽  
Zhengkui Lin
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Correlation Analysis ◽  
Bioinformatics Analysis ◽  
Biological Pathways ◽  
Gene Module ◽  
Ppi Network ◽  
Hub Genes ◽  
Data Set ◽  
Gene Modules

Background Hepatocellular carcinoma (HCC), the main type of liver cancer in human, is one of the most prevalent and deadly malignancies in the world. The present study aimed to identify hub genes and key biological pathways by integrated bioinformatics analysis. Methods A bioinformatics pipeline based on gene co-expression network (GCN) analysis was built to analyze the gene expression profile of HCC. Firstly, differentially expressed genes (DEGs) were identified and a GCN was constructed with Pearson correlation analysis. Then, the gene modules were identified with 3 different community detection algorithms, and the correlation analysis between gene modules and clinical indicators was performed. Moreover, we used the Search Tool for the Retrieval of Interacting Genes (STRING) database to construct a protein protein interaction (PPI) network of the key gene module, and we identified the hub genes using nine topology analysis algorithms based on this PPI network. Further, we used the Oncomine analysis, survival analysis, GEO data set and random forest algorithm to verify the important roles of hub genes in HCC. Lastly, we explored the methylation changes of hub genes using another GEO data (GSE73003). Results Firstly, among the expression profiles, 4,130 up-regulated genes and 471 down-regulated genes were identified. Next, the multi-level algorithm which had the highest modularity divided the GCN into nine gene modules. Also, a key gene module (m1) was identified. The biological processes of GO enrichment of m1 mainly included the processes of mitosis and meiosis and the functions of catalytic and exodeoxyribonuclease activity. Besides, these genes were enriched in the cell cycle and mitotic pathway. Furthermore, we identified 11 hub genes, MCM3, TRMT6, AURKA, CDC20, TOP2A, ECT2, TK1, MCM2, FEN1, NCAPD2 and KPNA2 which played key roles in HCC. The results of multiple verification methods indicated that the 11 hub genes had highly diagnostic efficiencies to distinguish tumors from normal tissues. Lastly, the methylation changes of gene CDC20, TOP2A, TK1, FEN1 in HCC samples had statistical significance (P-value < 0.05). Conclusion MCM3, TRMT6, AURKA, CDC20, TOP2A, ECT2, TK1, MCM2, FEN1, NCAPD2 and KPNA2 could be potential biomarkers or therapeutic targets for HCC. Meanwhile, the metabolic pathway, the cell cycle and mitotic pathway might played vital roles in the progression of HCC.

scholarly journals Identification of Potential miRNA-mRNA Regulatory Network Contributing to Parkinson’s Disease

2021 ◽  
Author(s):  
Xi Yin ◽  
Miao Wang ◽  
Wei Wang ◽  
Tong Chen ◽  
Ge Song ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Regulatory Network ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Rab Protein ◽  
Healthy Donors ◽  
Geo Database

Abstract Parkinson’s disease (PD) is a common neurodegenerative disease and the mechanism underlying PD pathogenesis is incompletely understood. Increasing evidence indicates that microRNA (miRNA) plays critical regulatory role in the pathogenesis of PD. This study aimed to determine the miRNA-mRNA regulatory network for PD. The differentially expressed miRNAs (DEmis) and genes (DEGs) between PD patients and healthy donors were screened from miRNA dataset GSE16658 and mRNA dataset GSE100054 downloaded from the Gene Expression Omnibus (GEO) database. Target genes of the DEmis were selected when predicted by 3 or 4 online databases and overlapped with DEGs from GSE100054. Next, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was conducted by Database for Annotation, Visualization and Integrated Discovery (DAVID) and Metascape analytic tool. The correlation between the screened genes and PD was evaluated by the online tool Comparative Toxicogenomics Database (CTD). The protein-protein interactions (PPI) network was built by STRING platform. Finally, we testify the expression of members of the miRNA-mRNA regulatory network in the blood samples collected from PD patients and healthy donors by using qRT-PCR. 1505 upregulated and 1302 downregulated DEGs, 77 upregulated DEmis and 112 downregulated DEmis were preliminarily screened from GEO database. Through further functional enrichment analysis, 10 PD-related hub genes were selected, including RAC1, IRS2, LEPR, PPARGC1A, CAMKK2, RAB10, RAB13, RAB27B, RAB11A and JAK2, which were mainly involved in Rab protein signaling transduction, AMPK signaling pathway and signaling by Leptin. The miRNA-mRNA regulatory network was constructed with 10 hub genes and their interacting miRNAs overlapped with DEmis, including miR-30e-5p, miR-142-3p, miR-101-3p, miR-32-3p, miR-508-5p, miR-642a-5p, miR-19a-3p and miR-21-5p. Analysis on clinical samples verified significant upregulation of LEPR and downregulation of miR-101-3p in PD patients compared with healthy donors. In the study, the potential miRNA-mRNA regulatory network was constructed in PD, which may provide novel insight into pathogenesis and treatment of PD.

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