scholarly journals Identification of potential gene signatures associated with osteosarcoma by integrated bioinformatics analysis

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11496
Author(s):  
Yutao Jia ◽  
Yang Liu ◽  
Zhihua Han ◽  
Rong Tian
Keyword(s):  
Roc Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Gene Signatures ◽  
Expression Levels ◽  
Link Type ◽  
Diagnostic Values ◽  
Key Genes ◽  
Integration Analysis ◽  
Geo Database

Background Osteosarcoma (OS) is the most primary malignant bone cancer in children and adolescents with a high mortality rate. This work aims to screen novel potential gene signatures associated with OS by integrated microarray analysis of the Gene Expression Omnibus (GEO) database. Material and Methods The OS microarray datasets were searched and downloaded from GEO database to identify differentially expressed genes (DEGs) between OS and normal samples. Afterwards, the functional enrichment analysis, protein–protein interaction (PPI) network analysis and transcription factor (TF)-target gene regulatory network were applied to uncover the biological function of DEGs. Finally, two published OS datasets (GSE39262 and GSE126209) were obtained from GEO database for evaluating the expression level and diagnostic values of key genes. Results  In total 1,059 DEGs (569 up-regulated DEGs and 490 down-regulated DEGs) between OS and normal samples were screened. Functional analysis showed that these DEGs were markedly enriched in 214 GO terms and 54 KEGG pathways such as pathways in cancer. Five genes (CAMP, METTL7A, TCN1, LTF and CXCL12) acted as hub genes in PPI network. Besides, METTL7A, CYP4F3, TCN1, LTF and NETO2 were key genes in TF-gene network. Moreover, Pax-6 regulated four key genes (TCN1, CYP4F3, NETO2 and CXCL12). The expression levels of four genes (METTL7A, TCN1, CXCL12 and NETO2) in GSE39262 set were consistent with our integration analysis. The expression levels of two genes (CXCL12 and NETO2) in GSE126209 set were consistent with our integration analysis. ROC analysis of GSE39262 set revealed that CYP4F3, CXCL12, METTL7A, TCN1 and NETO2 had good diagnostic values for OS patients. ROC analysis of GSE126209 set revealed that CXCL12, METTL7A, TCN1 and NETO2 had good diagnostic values for OS patients.

scholarly journals Diagnostic model of combined ceRNA and DNA methylation related genes in esophageal carcinoma

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8831 ◽  
Author(s):  
Xiaojiao Guan ◽  
Yao Yao ◽  
Guangyao Bao ◽  
Yue Wang ◽  
Aimeng Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Esophageal Cancer ◽  
Esophageal Carcinoma ◽  
Differentially Expressed Genes ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Diagnostic Model ◽  
Link Type ◽  
Key Genes

Esophageal cancer is a common malignant tumor in the world, and the aim of this study was to screen key genes related to the development of esophageal cancer using a variety of bioinformatics analysis tools and analyze their biological functions. The data of esophageal squamous cell carcinoma from the Gene Expression Omnibus (GEO) were selected as the research object, processed and analyzed to screen differentially expressed microRNAs (miRNAs) and differential methylation genes. The competing endogenous RNAs (ceRNAs) interaction network of differentially expressed genes was constructed by bioinformatics tools DAVID, String, and Cytoscape. Biofunctional enrichment analysis was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The expression of the screened genes and the survival of the patients were verified. By analyzing GSE59973 and GSE114110, we found three down-regulated and nine up-regulated miRNAs. The gene expression matrix of GSE120356 was calculated by Pearson correlation coefficient, and the 11696 pairs of ceRNA relation were determined. In the ceRNA network, 643 lncRNAs and 147 mRNAs showed methylation difference. Functional enrichment analysis showed that these differentially expressed genes were mainly concentrated in the FoxO signaling pathway and were involved in the corresponding cascade of calcineurin. By analyzing the clinical data in The Cancer Genome Atlas (TCGA) database, it was found that four lncRNAs had an important impact on the survival and prognosis of esophageal carcinoma patients. QRT-PCR was also conducted to identify the expression of the key lncRNAs (RNF217-AS1, HCP5, ZFPM2-AS1 and HCG22) in ESCC samples. The selected key genes can provide theoretical guidance for further research on the molecular mechanism of esophageal carcinoma and the screening of molecular markers.

scholarly journals Identification of Key Genes Affecting the Tumor Microenvironment and Prognosis of Triple-Negative Breast Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Jiarong Yi ◽  
Wenjing Zhong ◽  
Haoming Wu ◽  
Jikun Feng ◽  
Xiazi Zouxu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Tumor Microenvironment ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Key Genes

Although the tumor microenvironment (TME) plays an important role in the development of many cancers, its roles in breast cancer, especially triple-negative breast cancer (TNBC), are not well studied. This study aimed to identify genes related to the TME and prognosis of TNBC. Firstly, we identified differentially expressed genes (DEG) in the TME of TNBC, using Expression data (ESTIMATE) datasets obtained from the Cancer Genome Atlas (TCGA) and Estimation of Stromal and Immune cells in Malignant Tumor tissues. Next, survival analysis was performed to analyze the relationship between TME and prognosis of TNBC, as well as determine DEGs. Genes showing significant differences were scored as alternative genes. A protein-protein interaction (PPI) network was constructed and functional enrichment analysis conducted using the DEG. Proteins with a degree greater than 5 and 10 in the PPI network correspond with hub genes and key genes, respectively. Finally, CCR2 and CCR5 were identified as key genes in TME and prognosis of TNBC. Finally, these results were verified using Gene Expression Omnibus (GEO) datasets and immunohistochemistry of TNBC patients. In conclusion, CCR2 and CCR5 are key genes in the TME and prognosis of TNBC with the potential of prognostic biomarkers in TNBC.

scholarly journals Identification of the Hub Genes in Alzheimer’s Disease

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Huiwen Gui ◽  
Qi Gong ◽  
Jun Jiang ◽  
Mei Liu ◽  
Huanyin Li
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Elderly ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Geo Database

Purpose. Alzheimer’s disease (AD) is considered to be the most common neurodegenerative disease and also one of the major fatal diseases affecting the elderly, thus bringing a huge burden to society. Therefore, identifying AD-related hub genes is extremely important for developing novel strategies against AD. Materials and Methods. Here, we extracted the gene expression profile GSE63061 from the National Center for Biotechnology Information (NCBI) GEO database. Once the unverified gene chip was removed, we standardized the microarray data after quality control. We utilized the Limma software package to screen the differentially expressed genes (DEGs). We conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of DEGs. Subsequently, we constructed a protein-protein interaction (PPI) network using the STRING database. Result. We screened 2169 DEGs, comprising 1313 DEGs with upregulation and 856 DEGs with downregulation. Functional enrichment analysis showed that the response of immune, the degranulation of neutrophils, lysosome, and the differentiation of osteoclast were greatly enriched in DEGs with upregulation; peptide biosynthetic process, translation, ribosome, and oxidative phosphorylation were dramatically enriched in DEGs with downregulation. 379 nodes and 1149 PPI edges were demonstrated in the PPI network constructed by upregulated DEGs; 202 nodes and 1963 PPI edges were shown in the PPI network constructed by downregulated DEGs. Four hub genes, including GAPDH, RHOA, RPS29, and RPS27A, were identified to be the newly produced candidates involved in AD pathology. Conclusion. GAPDH, RHOA, RPS29, and RPS27A are expected to be key candidates for AD progression. The results of this study can provide comprehensive insight into understanding AD’s pathogenesis and potential new therapeutic targets.

scholarly journals Identification of potential biomarkers and candidate small molecule drugs in glioblastoma

2020 ◽  
Author(s):  
Wei-cheng Lu ◽  
Hui Xie ◽  
Ce Yuan ◽  
Jin-jiang Li ◽  
Zhao-yang Li ◽  
...  
Keyword(s):  
Protein Expression ◽  
Small Molecule ◽  
Molecular Mechanisms ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Integrated Analysis ◽  
Hub Genes ◽  
Expression Levels ◽  
Small Molecule Drugs ◽  
Key Genes

Abstract Background and aims:Glioblastoma (GBM) is a common and aggressive primary brain tumor, and the prognosis for GBM patients remains poor. This study aimed to identify the key genes associated with the development of GBM and provide new diagnostic and therapies for GBM. Methods:Three microarray datasets (GSE111260, GSE103227, and GSE104267) were selected from Gene Expression Omnibus (GEO) database for integrated analysis. The differential expressed genes (DEGs) between GBM and normal tissues were identified. Then, prognosis-related DEGs were screened by survival analysis, followed by functional enrichment analysis. The protein-protein interaction (PPI) network was constructed to explore the hub genes associated with GBM. The mRNA and protein expression levels of hub genes were respectively validated in silico using The Cancer Genome Atlas (TCGA) and Human Protein Atlas (HPA) databases. Subsequently, the small molecule drugs of GBM were predicted by using Connectivity Map (CMAP) database. Results:A total of 78 prognosis-related DEGs were identified, of which10 hub genes with higher degree were obtained by PPI analysis. The mRNA expression and protein expression levels of CETN2, MKI67, ARL13B, and SETDB1 were overexpressed in GBM tissues, while the expression levels of CALN1, ELAVL3, ADCY3, SYN2, SLC12A5, and SOD1 were down-regulated in GBM tissues. Additionally, these genes were significantly associated with the prognosis of GBM. We eventually predicted the 10 most vital small molecule drugs, which potentially imitate or reverse GBM carcinogenic status. Cycloserine and 11-deoxy-16,16-dimethylprostaglandin E2 might be considered as potential therapeutic drugs of GBM. Conclusions:Our study provided 10 key genes for diagnosis, prognosis, and therapy for GBM. These findings might contribute to a better comprehension of molecular mechanisms of GBM development, and provide new perspective for further GBM research. However, specific regulatory mechanism of these genes needed further elaboration.

scholarly journals Bioinformatics Analysis of Gene Expression Profiles of Sex Differences in Ischemic Stroke

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Wenhao Zhu ◽  
Yinan Nan ◽  
Shaoqing Wang ◽  
Wei Liu
Keyword(s):  
Gene Expression ◽  
Immune Response ◽  
Sex Differences ◽  
Ischemic Stroke ◽  
Functional Enrichment ◽  
Biological Processes ◽  
Receptor Signaling ◽  
Ppi Network ◽  
Apoptotic Process ◽  
Key Genes

Ischemic stroke (IS) is a complex disease with sex differences in epidemiology, presentations, and outcomes. However, the sex-specific mechanism underlying IS remains unclear. The purpose of this study was to identify key genes contributing to biological differences between sexes. First, we downloaded the gene expression data of GSE22255 from Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified using R software and related packages. Second, DEGs were separately analyzed by Gene Ontology enrichment and pathways analyses. Third, protein-protein interaction (PPI) network was constructed to further investigate the interactions of DEGs. A total of 123 DEGs were identified between sexes, including 8 upregulated and 115 downregulated genes. In the PPI network, ten key genes were identified, including IL1α, IL1β, IL6, IL8, CXCL1, CXCL2, CXCL20, CCL4, ICAM1, and PTGS2. Functional enrichment analysis revealed that these genes were mainly enriched in biological processes of immune response and apoptotic process, also in pathways of TNF and NOD-like receptor signaling. In conclusion, the above ten genes may have a protective effect on IS females through their direct or indirect involvement in biological processes of immune response and apoptotic process, as well as in TNF and NOD-like receptor signaling pathways. The results of this study may help to gain new insights into the sex-specific mechanisms underlying IS females and may suggest potential therapeutic targets for disease treatment.

scholarly journals LncRNA-mRNA Expression Pattern in Invasive Pituitary Adenomas: A Microarray Analysis

2021 ◽  
Author(s):  
Chao Peng ◽  
Shuaikai Wang ◽  
Jinxiu Yu ◽  
Xiaoyi Deng ◽  
Zhishan Chen ◽  
...  
Keyword(s):  
Pituitary Adenomas ◽  
Roc Analysis ◽  
Gene Prediction ◽  
Expression Patterns ◽  
Differentially Expressed ◽  
Significant Differential Expression ◽  
Expression Levels ◽  
Qrt Pcr ◽  
Go Enrichment ◽  
Diagnostic Values

Abstract Backgrounds: Long non-coding RNAs (lncRNAs) play important roles in tumorigenesis and progression of various cancer types; however, their roles in the development of invasive pituitary adenomas (PAs) remain to be investigated.Methods: lncRNA microarray was performed in three invasive and three noninvasive PAs. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed, and coexpression networks between lncRNA and mRNA were constructed. Furthermore, three differentially expressed lncRNAs were selected for validation by real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) in PA samples. The diagnostic values of these three lncRNAs were further evaluated by receiver operating characteristic (ROC) analysis.Results: A total of 8872 lncRNAs were identified in invasive and paired noninvasive PAs using lncRNA microarray. Among these, the differentially expressed lncRNAs included 81 that were upregulated and 165 that were downregulated. GO enrichment and KEGG pathway analysis showed that these differentially expressed lncRNAs were associated with post-translational modifications of proteins. Furthermore, we performed target gene prediction and coexpression analysis. The interrelationships between the lncRNAs and mRNAs with significant differential expression were identified. Additionally, three differentially expressed lncRNAs were selected for validation in 41 PA samples by qRT-PCR. The expression levels of FAM182B, LOC105371531, and LOC105375785 in the invasive PAs were significantly (P < 0.05) lower than in the noninvasive PAs, and these results were consistent with the microarray data. ROC analysis suggested that FAM182B and LOC105375785 expression levels could be used to distinguish invasive PAs from noninvasive PAs.Conclusion: Our findings demonstrated the lncRNAs expression patterns in invasive PAs. Thus, FAM182B and LOC105375785 may be involved in the invasiveness of PAs and serve as new candidate biomarkers for the diagnosis of invasive PAs.

scholarly journals Identification of Differentially Expressed Genes in Different Glioblastoma Regions and Their Association with Cancer Stem Cell Development and Temozolomide Response

2021 ◽  
Vol 11 (11) ◽  
pp. 1047
Author(s):  
Justin Bo-Kai Hsu ◽  
Tzong-Yi Lee ◽  
Sho-Jen Cheng ◽  
Gilbert Aaron Lee ◽  
Yung-Chieh Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cell ◽  
Cancer Stem Cell ◽  
Single Cell ◽  
Differentially Expressed Genes ◽  
Tumor Recurrence ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Gene Signatures ◽  
Key Genes

The molecular heterogeneity of gene expression profiles of glioblastoma multiforme (GBM) are the most important prognostic factors for tumor recurrence and drug resistance. Thus, the aim of this study was to identify potential target genes related to temozolomide (TMZ) resistance and GBM recurrence. The genomic data of patients with GBM from The Cancer Genome Atlas (TCGA; 154 primary and 13 recurrent tumors) and a local cohort (29 primary and 4 recurrent tumors), samples from different tumor regions from a local cohort (29 tumor and 25 peritumoral regions), and Gene Expression Omnibus data (GSE84465, single-cell RNA sequencing; 3589 cells) were included in this study. Critical gene signatures were identified based an analysis of differentially expressed genes (DEGs). DEGs were further used to evaluate gene enrichment levels among primary and recurrent GBMs and different tumor regions through gene set enrichment analysis. Protein–protein interactions (PPIs) were incorporated into gene regulatory networks to identify the affected metabolic pathways. The enrichment levels of 135 genes were identified in the peritumoral regions as being risk signatures for tumor recurrence. Fourteen genes (DVL1, PRKACB, ARRB1, APC, MAPK9, CAMK2A, PRKCB, CACNA1A, ERBB4, RASGRF1, NF1, RPS6KA2, MAPK8IP2, and PPM1A) derived from the PPI network of 135 genes were upregulated and involved in the regulation of cancer stem cell (CSC) development and relevant signaling pathways (Notch, Hedgehog, Wnt, and MAPK). The single-cell data analysis results indicated that 14 key genes were mainly expressed in oligodendrocyte progenitor cells, which could produce a CSC niche in the peritumoral region. The enrichment levels of 336 genes were identified as biomarkers for evaluating TMZ resistance in the solid tumor region. Eleven genes (ARID5A, CDC42EP3, CDKN1A, FLT3, JUNB, MAP2K3, MYBPC2, RGS14, RNASEK, TBC1D30, and TXNDC11) derived from the PPI network of 336 genes were upregulated and may be associated with a high risk of TMZ resistance; these genes were identified in both the TCGA and local cohorts. Furthermore, the expression patterns of ARID5A, CDKN1A, and MAP2K3 were identical to the gene signatures of TMZ-resistant cell lines. The identified enrichment levels of the two gene sets expressed in tumor and peritumoral regions are potentially helpful for evaluating TMZ resistance in GBM. Moreover, these key genes could be used as biomarkers, potentially providing new molecular strategies for GBM treatment.

scholarly journals Bioinformatics Analysis Reveals CCR7 As A Potential Biomarker for Predicting CKD Progression

2021 ◽  
Author(s):  
Junju Lai ◽  
Huizhi Shan ◽  
Sini Cui ◽  
Lingfeng Xiao ◽  
Xiaowen Huang ◽  
...  
Keyword(s):  
Immune Response ◽  
Renal Disease ◽  
End Stage Renal Disease ◽  
Curve Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Data Set ◽  
Key Genes ◽  
Stage Renal Disease ◽  
End Stage

Abstract Background Chronic kidney disease (CKD) inevitably progresses to end-stage renal disease if intervention does not occur in time. However, there are limitations in predicting the progression of CKD by solely relying on changes in renal function. A biomarker with high sensitivity and specificity that can predict the progression of CKD early is required. Methods We used the online Gene Expression Omnibus (GEO) microarray dataset GSE45980 to identify differentially expressed genes (DEGs) in patients with progressive and stable CKD. We then performed functional enrichment and protein-protein interaction (PPI) network analysis on DEGs and identified key genes. Finally, the expression patterns of the key genes were verified using the GSE60860 data set, and the receiver operating characteristic curve analysis was performed to clarify their predictive ability of progressive CKD. Ultimately, we verified the expression profiles of these hub genes in an in vitro renal interstitial fibrosis model by RT-PCR and western blot analysis. Results Differential expression analysis identified 50 upregulated genes and 47 downregulated genes. The results of the functional enrichment analysis revealed that the upregulated DEGs were mainly enriched in immune response, inflammatory response, and NF-κB signaling pathways, whereas the downregulated DEGs were mainly related to angiogenesis and the extracellular environment. PPI network and key gene analysis identified CCR7 as the most important gene. CCR7 mainly plays a role in immune response, and its only receptors, CCL19 and CCL21, have also been identified as DEGs. The ROC curve analysis of CCR7, CCL19 and CCL21 found that CCR7 and CCL19 present good disease prediction ability. Conclusion CCR7 may be a stable biomarker for predicting the progression of CKD, and the CCR7-CCL19/CCL21 axis may be a therapeutic target for end-stage renal disease. However, further experiments are needed to explore the relationship between these genes and CKD.

scholarly journals Analysis of key genes and their functions in placental tissue of patients with gestational diabetes mellitus

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Yuxia Wang ◽  
Haifeng Yu ◽  
Fangmei Liu ◽  
Xiue Song
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Tyrosine Phosphatase ◽  
Human Leukocyte ◽  
Placental Tissue ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Tissue Samples ◽  
Key Genes

Abstract Background This study was aimed at screening out the potential key genes and pathways associated with gestational diabetes mellitus (GDM). Methods The GSE70493 dataset used for this study was obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) in the placental tissue of women with GDM in relation to the control tissue samples were identified and submitted to protein-protein interaction (PPI) network analysis and subnetwork module mining. Functional enrichment analyses of the PPI network and subnetworks were subsequently carried out. Finally, the integrated miRNA–transcription factor (TF)–DEG regulatory network was analyzed. Results In total, 238 DEGs were identified, of which 162 were upregulated and 76 were downregulated. Through PPI network construction, 108 nodes and 278 gene pairs were obtained, from which chemokine (C-X-C motif) ligand 9 (CXCL9), CXCL10, protein tyrosine phosphatase, receptor type C (PTPRC), and human leukocyte antigen (HLA) were screened out as hub genes. Moreover, genes associated with the immune-related pathway and immune responses were found to be significantly enriched in the process of GDM. Finally, miRNAs and TFs that target the DEGs were predicted. Conclusions Four candidate genes (viz., CXCL9, CXCL10, PTPRC, and HLA) are closely related to GDM. miR-223-3p, miR-520, and thioredoxin-binding protein may play important roles in the pathogenesis of this disease.

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.

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