scholarly journals Identification of key genes, pathways and potential therapeutic agents for liver fibrosis using an integrated bioinformatics analysis

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6645 ◽  
Author(s):  
Zhu Zhan ◽  
Yuhe Chen ◽  
Yuanqin Duan ◽  
Lin Li ◽  
Kenley Mew ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Liver Fibrosis ◽  
Inflammatory Response ◽  
Bioinformatics Analysis ◽  
Cell Model ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Gene Target ◽  
Key Genes

BackgroundLiver fibrosis is often a consequence of chronic liver injury, and has the potential to progress to cirrhosis and liver cancer. Despite being an important human disease, there are currently no approved anti-fibrotic drugs. In this study, we aim to identify the key genes and pathways governing the pathophysiological processes of liver fibrosis, and to screen therapeutic anti-fibrotic agents.MethodsExpression profiles were downloaded from the Gene Expression Omnibus (GEO), and differentially expressed genes (DEGs) were identified by R packages (Affy and limma). Gene functional enrichments of each dataset were performed on the DAVID database. Protein–protein interaction (PPI) network was constructed by STRING database and visualized in Cytoscape software. The hub genes were explored by the CytoHubba plugin app and validated in another GEO dataset and in a liver fibrosis cell model by quantitative real-time PCR assay. The Connectivity Map L1000 platform was used to identify potential anti-fibrotic agents.ResultsWe integrated three fibrosis datasets of different disease etiologies, incorporating a total of 70 severe (F3–F4) and 116 mild (F0–F1) fibrotic tissue samples. Gene functional enrichment analyses revealed that cell cycle was a pathway uniquely enriched in a dataset from those patients infected by hepatitis B virus (HBV), while the immune-inflammatory response was enriched in both the HBV and hepatitis C virus (HCV) datasets, but not in the nonalcoholic fatty liver disease (NAFLD) dataset. There was overlap between these three datasets; 185 total shared DEGs that were enriched for pathways associated with extracellular matrix constitution, platelet-derived growth-factor binding, protein digestion and absorption, focal adhesion, and PI3K-Akt signaling. In the PPI network, 25 hub genes were extracted and deemed to be essential genes for fibrogenesis, and the expression trends were consistent withGSE14323(an additional dataset) and liver fibrosis cell model, confirming the relevance of our findings. Among the 10 best matching anti-fibrotic agents, Zosuquidar and its corresponding gene target ABCB1 might be a novel anti-fibrotic agent or therapeutic target, but further work will be needed to verify its utility.ConclusionsThrough this bioinformatics analysis, we identified that cell cycle is a pathway uniquely enriched in HBV related dataset and immune-inflammatory response is clearly enriched in the virus-related datasets. Zosuquidar and ABCB1 might be a novel anti-fibrotic agent or target.

scholarly journals Bioinformatics analysis reveals novel hub gene pathways associated with IgA nephropathy

2020 ◽  
Vol 25 (1) ◽  
Author(s):  
Xue Jiang ◽  
Zhijie Xu ◽  
Yuanyuan Du ◽  
Hongyu Chen
Keyword(s):  
Gene Ontology ◽  
Inflammatory Response ◽  
Molecular Mechanism ◽  
Bioinformatics Analysis ◽  
Functional Enrichment ◽  
Control Group ◽  
Integrin Subunit ◽  
Ppi Network ◽  
Hub Genes ◽  
Ppi Networks

Abstract Background Immunoglobulin A nephropathy (IgAN) is the most common primary glomerulopathy worldwide. However, the molecular events underlying IgAN remain to be fully elucidated. This study aimed to identify novel biomarkers of IgAN through bioinformatics analysis and elucidate the possible molecular mechanism. Methods Based on the microarray datasets GSE93798 and GSE37460 downloaded from the Gene Expression Omnibus database, the differentially expressed genes (DEGs) between IgAN samples and normal controls were identified. Using the DEGs, we further performed a series of functional enrichment analyses. Protein–protein interaction (PPI) networks of the DEGs were constructed using the STRING online search tool and were visualized using Cytoscape. Next, hub genes were identified and the most important module among the DEGs, Biological Networks Gene Ontology tool (BiNGO), was used to elucidate the molecular mechanism of IgAN. Results In total, 148 DEGs were identified, comprising 53 upregulated genes and 95 downregulated genes. Gene Ontology (GO) analysis indicated that the DEGs for IgAN were mainly enriched in extracellular exosome, region and space, fibroblast growth factor stimulus, inflammatory response, and innate immunity. Module analysis showed that genes in the top 1 significant module of the PPI network were mainly associated with innate immune response, integrin-mediated signaling pathway and inflammatory response. The top 10 hub genes were constructed in the PPI network, which could well distinguish the IgAN and control group in monocyte and tissue samples. We finally identified the integrin subunit beta 2 (ITGB2) and Fc fragment of IgE receptor Ig (FCER1G) genes that may play important roles in the development of IgAN. Conclusions We identified key genes along with the pathways that were most closely related to IgAN initiation and progression. Our results provide a more detailed molecular mechanism for the development of IgAN and novel candidate gene targets of IgAN.

scholarly journals Bioinformatics Analysis Reveals Novel Hub Gene Pathways Associated With IgA Nephropathy

2020 ◽  
Author(s):  
xue Jiang ◽  
Zhijie Xu ◽  
Yuanyuan Du ◽  
Hongyu Chen
Keyword(s):  
Inflammatory Response ◽  
Molecular Mechanism ◽  
Bioinformatics Analysis ◽  
Functional Enrichment ◽  
Control Group ◽  
Integrin Subunit ◽  
Ppi Network ◽  
Hub Genes ◽  
Tissue Samples ◽  
Ppi Networks

Abstract Background: Immunoglobulin A nephropathy (IgAN) is the most common primary glomerulopathy worldwide. However, the molecular events underlying IgAN remain to be fully elucidated. This study aimed to identify novel biomarkers of IgAN through bioinformatics analysis and elucidate the possible molecular mechanism. Methods: Based on the microarray datasets GSE93798 and GSE37460 downloaded from the Gene Expression Omnibus database, the differentially expressed genes (DEGs) between IgAN samples and normal controls were identified. Using the DEGs, we further performed a series of functional enrichment analyses. Protein-protein interaction (PPI) networks of the DEGs were constructed using the STRING online search tool and were visualized using Cytoscape. Next, hub genes were identified and the most important module among the DEGs, Biological Networks Gene Oncology tool (BiNGO), was used to elucidate the molecular mechanism of IgAN.Results: In total, 148 DEGs were identified, comprising 53 upregulated genes and 95 downregulated genes. Gene Oncology (GO) analysis indicated that the DEGs for IgAN were mainly enriched in extracellular exosome, region and space, fibroblast growth factor stimulus, inflammatory response, and innate immunity. Module analysis showed that genes in the top 1 significant module of the PPI network were mainly associated with innate immune response, integrin-mediated signaling pathway and inflammatory response. The top 10 hub genes were constructed in the PPI network, which could well distinguish the IgAN and control group in monocyte and tissue samples. We finally identified the integrin subunit beta 2 (ITGB2) and Fc fragment of IgE receptor Ig (FCER1G) genes that may play important roles in the development of IgAN.Conclusions: We identified key genes along with the pathways that were most closely related to IgAN initiation and progression. Our results provide a more detailed molecular mechanism for the development of IgAN and novel candidate gene targets of IgAN.

scholarly journals Bioinformatics Analysis Reveals Novel Hub Gene Pathways Associated With IgA Nephropathy

2020 ◽  
Author(s):  
xue Jiang ◽  
Zhijie Xu ◽  
Yuanyuan Du ◽  
Hongyu Chen
Keyword(s):  
Inflammatory Response ◽  
Molecular Mechanism ◽  
Bioinformatics Analysis ◽  
Functional Enrichment ◽  
Control Group ◽  
Integrin Subunit ◽  
Ppi Network ◽  
Hub Genes ◽  
Tissue Samples ◽  
Ppi Networks

Abstract Background:Immunoglobulin A nephropathy (IgAN) is the most common primary glomerulopathy worldwide. However, the molecular events underlying IgAN remain to be fully elucidated. This study aimed to identify novel biomarkers of IgAN through bioinformatics analysis and elucidate the possible molecular mechanism. Methods:Based on the microarray datasets GSE93798 and GSE37460 downloaded from the Gene Expression Omnibus database, the differentially expressed genes (DEGs) between IgAN samples and normal controls were identified. Using the DEGs, we further performed a series of functional enrichment analyses. Protein-protein interaction (PPI) networks of the DEGs were constructed using the STRING online search tool and were visualized using Cytoscape. Next, hub genes were identified and the most important module among the DEGs, Biological Networks Gene Ontology tool (BiNGO), was used to elucidate the molecular mechanism of IgAN.Results:In total, 148 DEGs were identified, comprising 53 upregulated genes and 95 downregulated genes. Gene Oncology (GO) analysis indicated that the DEGs for IgAN were mainly enriched in extracellular exosome, region and space, fibroblast growth factor stimulus, inflammatory response, and innate immunity. Module analysis showed that genes in the top 1 significant module of the PPI network were mainly associated with innate immune response, integrin-mediated signaling pathway and inflammatory response. The top 10 hub genes were constructed in the PPI network, which could well distinguish the IgAN and control group in monocyte and tissue samples. We finally identified the integrin subunit beta 2 (ITGB2) and Fc fragment of IgE receptor Ig (FCER1G) genes that may play important roles in the development of IgAN.Conclusions: We identified key genes along with the pathways that were most closely related to IgAN initiation and progression. Our results provide a more detailed molecular mechanism for the development of IgAN and novel candidate gene targets of IgAN.

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 Key Pathways and Genes in Dementia via Integrated Bioinformatics Analysis

2021 ◽  
Author(s):  
Basavaraj Mallikarjunayya Vastrad ◽  
Chanabasayya Mallikarjunayya Vastrad
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Bioinformatics Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Hub Gene ◽  
Network Modules ◽  
Gene Regulatory ◽  
Key Pathways

To provide a better understanding of dementia at the molecular level, this study aimed to identify the genes and key pathways associated with dementia by using integrated bioinformatics analysis. Based on the expression profiling by high throughput sequencing dataset GSE153960 derived from the Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) between patients with dementia and healthy controls were identified. With DEGs, we performed a series of functional enrichment analyses. Then, a protein protein interaction (PPI) network, modules, miRNA hub gene regulatory network and TF hub gene regulatory network was constructed, analyzed and visualized, with which the hub genes miRNAs and TFs nodes were screened out. Finally, validation of hub genes was performed by using receiver operating characteristic curve (ROC) analysis and RT PCR. A total of 948 DEGs were screened out, among which 475 genes were up regulated; while 473 were down regulated. Functional enrichment analyses indicated that DEGs were mainly involved in defense response, ion transport, neutrophil degranulation and neuronal system. The hub genes (CDK1, TOP2A, MAD2L1, RSL24D1, CDKN1A, NOTCH3, MYB, PWP2, WNT7B and HSPA12B) were identified from PPI network, modules, miRNA hub gene regulatory network and TF hub gene regulatory network. We identified a series of key genes along with the pathways that were most closely related with dementia initiation and progression. Our results provide a more detailed molecular mechanism for the advancement of dementia, shedding light on the potential biomarkers and therapeutic targets.

scholarly journals Identification of Hub Genes Associated with Immune Cells in Dilated Cardiomyopathy via Weighted Gene Co-expression Network Analysis

2021 ◽  
Author(s):  
Jielin Deng ◽  
Yunqiu Jiang ◽  
Changjin Deng ◽  
hong jiang
Keyword(s):  
Network Analysis ◽  
Dilated Cardiomyopathy ◽  
Immune Cells ◽  
Bioinformatics Analysis ◽  
Immune Cell ◽  
Gene Profiling ◽  
Functional Enrichment ◽  
Pathophysiological Mechanism ◽  
Hub Genes ◽  
Key Genes

Abstract Background: Dilated cardiomyopathy (DCM) is the most common cardiomyopathy which account for a majority of heart failure. Although massive clinic experiments and gene profiling analyses on DCM have been conducted, the molecular mechanism of DCM associated with immune cells has not been fully elucidated. This study was designed to discover the immune mechanism of DCM using integrative bioinformatics analysis and provide new insights into the pathophysiology of DCM. Methods: The GSE29819 dataset was downloaded, and Cibersort was applied to estimate the relative expression of 22 kinds of immune cells based on 14 samples of 7 DCM patients. Weighted gene co-expression network analysis (WGCNA) was performed to cluster the 2500 genes with the highest average expression into different modules and explore relationships between modules and immune cell types. Functional enrichment analysis was performed on key genes in significant modules identified by WGCNA and Cibersort. Key genes were then applied to Cytoscape to construct protein-protein interaction (PPI) network. Differentially expressed genes (DEGs) were identified based on DCM and normal controls in GSE29819 through R language. Hub genes were selected based on the DEGs and the genes identified by PPI and then verified via public GEO databases. Results: The yellow and tan modules with 163 genes were identified as the key modules based on top 2500 DCM microarrays, significantly correlated with M1 and M2 macrophages. The intersection of newly screened 17 genes based on 163 key genes through Cytoscape and 2682 DEGs were defined as hub genes including CCT2, CCL2, and TXN. The results were finally verified via GSE116250 datasets.Conclusions: The three hub genes associated with two immune cells identified by comprehensive bioinformatics analysis may play crucial roles in the pathophysiological mechanism of DCM, which provided potential immunological therapeutic targets and new insights into the treatment of DCM.

scholarly journals Bioinformatics analysis of potential key genes and mechanisms in type 2 diabetes mellitus

2021 ◽  
Author(s):  
Basavaraj Vastrad ◽  
Chanabasayya Vastrad
Keyword(s):  
Regulatory Network ◽  
Roc Analysis ◽  
Bioinformatics Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Rt Pcr ◽  
Hub Genes ◽  
Hub Gene ◽  
Gene Regulatory

AbstractType 2 diabetes mellitus (T2DM) is etiologically related to metabolic disorder. The aim of our study was to screen out candidate genes of T2DM and to elucidate the underlying molecular mechanisms by bioinformatics methods. Expression profiling by high throughput sequencing data of GSE154126 was downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between T2DM and normal control were identified. And then, functional enrichment analyses of gene ontology (GO) and REACTOME pathway analysis was performed. Protein–protein interaction (PPI) network and module analyses were performed based on the DEGs. Additionally, potential miRNAs of hub genes were predicted by miRNet database . Transcription factors (TFs) of hub genes were detected by NetworkAnalyst database. Further, validations were performed by receiver operating characteristic curve (ROC) analysis and real-time polymerase chain reaction (RT-PCR). In total, 925 DEGs were identified in T2DM, including 447 up regulated genes and 478 down-regulated genes. Functional enrichment analysis results showed that up regulated DEGs were significantly enriched in defense response, neutrophil degranulation, cell adhesion and extracellular matrix organization. The top 10 hub genes, JUN, VCAM1, RELA, U2AF2, ADRB2, FN1, CDK1, TK1, A2M and ACTA2 were identified from the PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network. Furthermore, ROC analysis and RT-PCR revealed that JUN, VCAM1, RELA, U2AF2, ADRB2, FN1, CDK1, TK1, A2M and ACTA2 might serve as biomarkers in T2DM. Bioinformatics analysis is a useful tool to explore the molecular mechanism and pathogenesis of T2DM. The identified hub genes may participate in the onset and advancement of T2DM and serve as therapeutic targets.

Identification of Key Genes and Signaling Pathways Involved in Acute Myocardial Infarction and Potential Biomarkers of Its Consequent Heart Failure Based On Bioinformatics Analysis

2021 ◽  
Author(s):  
Yuyan Xiong ◽  
Yuejin Yang
Keyword(s):  
Myocardial Infarction ◽  
Heart Failure ◽  
Acute Myocardial Infarction ◽  
Ventricular Remodeling ◽  
Bioinformatics Analysis ◽  
Roc Curves ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Key Genes ◽  
Potential Biomarkers

Abstract Backgrounds : Acute myocardial infarction (AMI) is the predominant cause of cardiac death and ischemic heart failure (IHF) worldwide in coronary artery disease (CAD). Although it results from coronary acute occlusion, we in the study explored some key genes involved in the development of AMI and consequent IHF using bioinformatics analysis. Methods Utilizing expression data of 52 patients with AMI and 53 controls from GSE66360 and GSE97320 datasets, we screened shared differentially expressed genes (DEGs) in the independent datasets. Functional enrichment analysis and protein-protein interaction (PPI) network were employed. GSE58967 of 111 AMI patients and 46 controls was used to validate the shared DEGs and further analyzed to identify the DEGs in AMI patients with and without heart failure (HF) with the dynamic changes also being evaluated. The receiver operating characteristic (ROC) curves and area under the curve (AUC) were used to validate the diagnostic efficiency. Results In the comparison of AMI patients with controls, we identified 105 shared DEGs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed the shared DEGs mainly enriched in immune-related inflammation process and pathways. Filtered with PPI network, 5 genes of CXCL8, CXCL1, MMP9, FPR1 and TLR2 were considered as hub genes, which were further validated in GSE59867. Compared with the genes in AMI patients without HF, those of TNFAIP6, ADM, TRIB1, AQP9 and IL1R2 associated with ventricular remodeling were found to be significantly high expressed in patients with HF on admission with the AUC of ROC curves was 0.792–0.847 (all p < 0.05), which can be used as the potential biomarkers for early prediction of HF after AMI. Conclusions These findings based on integrated bioinformatic analysis provide new insights into the important roles of genes to play in the patients with AMI and consequent HF.

scholarly journals Bioinformatic Analysis Reveals Novel Hub Genes pathways associated with IgA nephropathy

2020 ◽  
Author(s):  
xue Jiang ◽  
Zhijie Xu ◽  
Yuanyuan Du ◽  
Hongyu Chen
Keyword(s):  
Inflammatory Response ◽  
Molecular Mechanism ◽  
Biological Networks ◽  
Tissue Sample ◽  
Bioinformatic Analysis ◽  
Functional Enrichment ◽  
Control Group ◽  
Ppi Network ◽  
Hub Genes ◽  
Ppi Networks

Abstract Background Immunoglobulin A nephropathy (IgAN) is the most common primary glomerulopathy worldwide. However, the molecular events underlying IgAN remain to be fully elucidated. The aim of the study is to identify novel biomarkers of IgAN through bioinformatics analysis and elucidate the possible molecular mechanism. Methods Based on the microarray data GSE93798 and GSE37460 were downloaded from the Gene Expression Omnibus database, the differentially expressed genes (DEGs) between IgAN samples and normal controls were identified. With DEGs, we further performed a series of functional enrichment analyses. Protein-protein interaction (PPI) networks of the DEGs were built with the STRING online search tool and visualized by using Cytoscape, then further identified the hub gene and most important module in DEGs, Biological Networks Gene Oncology tool (BiNGO) were then performed to elucidate the molecular mechanism of IgAN. Results A total of 148 DEGs were recognized, consisting of 53 upregulated genes and 95 downregulated genes. GO analysis indicates that DEGs for IgAN were mainly enriched in extracellular exosome, region and space, fibroblast growth factor stimulus, inflammatory response, and innate immunity. The modules analysis showed that genes in the top 1 significant modules of PPI network were mainly associated with innate immune response, integrin-mediated signaling pathway and inflammatory response respectively. The top 10 hub genes were constructed in PPI network, which could well distinguish the IgAN and control group in monocytes sample and tissue sample. We finally identified ITGB2 and FCER1G gene may have important roles in the development of IgAN. Conclusions We identified a series of key genes along with the pathways that were most closely related with IgAN initiation and progression. Our results provide a more detailed molecular mechanism for the development of IgAN and novel candidate genes targets of IgAN.

scholarly journals Identification of miRNA-target gene regulatory networks in liver fibrosis based on bioinformatics analysis

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11910
Author(s):  
Yang Tai ◽  
Chong Zhao ◽  
Jinhang Gao ◽  
Tian Lan ◽  
Huan Tong
Keyword(s):  
Immune Response ◽  
Liver Cirrhosis ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Differentially Expressed Mirnas ◽  
And Control

Background Liver cirrhosis is one of the leading causes of death worldwide. MicroRNAs (miRNAs) can regulate liver fibrosis, but the underlying mechanisms are not fully understood, and the interactions between miRNAs and mRNAs are not clearly elucidated. Methods miRNA and mRNA expression arrays of cirrhotic samples and control samples were acquired from the Gene Expression Omnibus database. miRNA-mRNA integrated analysis, functional enrichment analysis and protein-protein interaction (PPI) network construction were performed to identify differentially expressed miRNAs (DEMs) and mRNAs (DEGs), miRNA-mRNA interaction networks, enriched pathways and hub genes. Finally, the results were validated with in vitro cell models. Results By bioinformatics analysis, we identified 13 DEMs between cirrhotic samples and control samples. Among these DEMs, six upregulated (hsa-miR-146b-5p, hsa-miR-150-5p, hsa-miR-224-3p, hsa-miR-3135b, hsa-miR-3195, and hsa-miR-4725-3p) and seven downregulated (hsa-miR-1234-3p, hsa-miR-30b-3p, hsa-miR-3162-3p, hsa-miR-548aj-3p, hsa-miR-548x-3p, hsa-miR-548z, and hsa-miR-890) miRNAs were further validated in activated LX2 cells. miRNA-mRNA interaction networks revealed a total of 361 miRNA-mRNA pairs between 13 miRNAs and 245 corresponding target genes. Moreover, PPI network analysis revealed the top 20 hub genes, including COL1A1, FBN1 and TIMP3, which were involved in extracellular matrix (ECM) organization; CCL5, CXCL9, CXCL12, LCK and CD24, which participated in the immune response; and CDH1, PECAM1, SELL and CAV1, which regulated cell adhesion. Functional enrichment analysis of all DEGs as well as hub genes showed similar results, as ECM-associated pathways, cell surface interaction and adhesion, and immune response were significantly enriched in both analyses. Conclusions We identified 13 differentially expressed miRNAs as potential biomarkers of liver cirrhosis. Moreover, we identified 361 regulatory pairs of miRNA-mRNA and 20 hub genes in liver cirrhosis, most of which were involved in collagen and ECM components, immune response, and cell adhesion. These results would provide novel mechanistic insights into the pathogenesis of liver cirrhosis and identify candidate targets for its treatment.

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