scholarly journals Integrated Bioinformatics Analysis of Gene Expression Profiles for Potential Biomarker Identification Towards Early Therapeutic Intervention in Pancreatitis and Pancreatic Ductal Adenocarcinoma

2021 ◽  
Author(s):  
Manoj M Wagle ◽  
Ananya Rao Kedige ◽  
Shama P Kabekkodu ◽  
Sandeep Mallya
Keyword(s):  
Gene Expression ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Bioinformatics Analysis ◽  
Ductal Adenocarcinoma ◽  
Rapid Progression ◽  
Hub Genes ◽  
Altered Expression ◽  
Biochemical Pathways ◽  
Potential Biomarker ◽  
Key Genes

Abstract Pancreatic ductal adenocarcinoma (PDAC) is a malignancy associated with rapid progression and an abysmal prognosis. It has been reported that chronic pancreatitis can increase the risk of developing PDAC by 16-fold. Our study aims to identify the key genes and biochemical pathways mediating pancreatitis and PDAC. The gene expression datasets were retrieved from the EMBL-EBI ArrayExpress and NCBI GEO database. A total of 172 samples of normal pancreatic tissue, 68 samples of pancreatitis, and 306 samples of PDAC were used in this study. The differentially expressed genes (DEGs) identified were used to perform downstream analysis for ontology, interaction, and associated pathways. Furthermore, hub gene expression was validated using the GEPIA2 tool and survival analysis using the Kaplan-Meier (KM) plotter. The potential druggability of the hub genes identified was determined using the Drug-Gene Interaction Database (DGIdb). Our study identified a total of 45 genes found to have altered expression levels in both PDAC and pancreatitis. Over-representation analysis revealed that protein digestion and absorption pathway, ECM-receptor interaction pathway, PI3k-Akt signaling pathway, and proteoglycans in cancer pathways as significantly enriched. Module analysis revealed 15 hub genes with 92 edges, of which 14 were found to be in the druggable genome category. Through bioinformatics analysis, we identified key genes and biochemical pathways disrupted in pancreatitis and PDAC. The results can provide new insights into targeted therapy and intervening therapeutically at an earlier stage can be used as an effective strategy to decrease the incidence and severity of PDAC.

scholarly journals Identify key genes and pathways in pancreatic ductal adenocarcinoma via bioinformatics analysis

2020 ◽  
Author(s):  
Huatian Luo ◽  
Da-qiu Chen ◽  
Jing-jing Pan ◽  
Zhang-wei Wu ◽  
Can Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Bioinformatics Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Expression Omnibus ◽  
Ductal Adenocarcinoma ◽  
Hub Genes ◽  
Expression Levels

Abstract Background: Pancreatic cancer has many pathologic types, among which pancreatic ductal adenocarcinoma (PDAC) is the most common one. Bioinformatics has become a very common tool for the selection of potentially pathogenic genes. Methods: Three data sets containing the gene expression profiles of PDAC were downloaded from the gene expression omnibus (GEO) database. The limma package of R language was utilized to explore the differentially expressed genes (DEGs). To analyze functions and signaling pathways, the Database Visualization and Integrated Discovery (DAVID) was used. To visualize the protein-protein interaction (PPI) of the DEGs ,Cytoscape was performed under the utilization of Search Tool for the Retrieval of Interacting Genes (STRING). With the usage of the plug-in cytoHubba in cytoscape software, the hub genes were found out. To verify the expression levels of hub genes, Gene Expression Profiling Interactive Analysis (GEPIA) was performed. Last but not least, UALCAN analysis online tool was implemented to analyze the overall survival. Results: The 376 DEGs were highly enriched in biological processes including signal transduction, apoptotic process and several pathways, mainly associated with Protein digestion and absorption and Pancreatic secretion pathway. The expression levels of nucleolar and spindle associated protein 1 (NUSAP1) and SHC binding and spindle associated 1 (SHCBP1) were discovered highly expressed in pancreatic ductal adenocarcinoma tissues. NUSAP1 and SHCBP1 had a high correlation with prognosis. Conclusions: The findings of this bioinformatics analysis indicate that NUSAP1 and SHCBP1 may be key factors in the prognosis and treatment of pancreatic cancer.

scholarly journals Identification of novel genes associated with a poor prognosis in pancreatic ductal adenocarcinoma via a bioinformatics analysis

2019 ◽  
Vol 39 (8) ◽  
Author(s):  
Jun Zhou ◽  
Xiaoliang Hui ◽  
Ying Mao ◽  
Liya Fan
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Bioinformatics Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Ductal Adenocarcinoma ◽  
Peroxisome Proliferator ◽  
Integrin Subunit ◽  
Hub Genes ◽  
Potential Biomarker ◽  
Km Plotter

Abstract Pancreatic ductal adenocarcinoma (PDAC) is a class of the commonest malignant carcinomas. The present study aimed to elucidate the potential biomarker and prognostic targets in PDAC. The array data of GSE41368, GSE43795, GSE55643, and GSE41369 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) and differentially expressed microRNAs (DEmiRNAs) in PDAC were obtained by using GEO2R, and overlapped DEGs were acquired with Venn Diagrams. Functional enrichment analysis of overlapped DEGs and DEmiRNAs was conducted with Metascape and FunRich, respectively. The protein–protein interaction (PPI) network of overlapped DEGs was constructed by STRING and visualized with Cytoscape. Overall survival (OS) of DEmiRNAs and hub genes were investigated by Kaplan–Meier (KM) plotter (KM plotter). Transcriptional data and correlation analyses among hub genes were verified through GEPIA and Human Protein Atlas (HPA). Additionally, miRNA targets were searched using miRTarBase, then miRNA–DEG regulatory network was visualized with Cytoscape. A total of 32 DEmiRNAs and 150 overlapped DEGs were identified, and Metascape showed that DEGs were significantly enriched in cellular chemical homeostasis and pathways in cancer, while DEmiRNAs were mainly enriched in signal transduction and Glypican pathway. Moreover, seven hub genes with a high degree, namely, V-myc avian myelocytomatosis viral oncogene homolog (MYC), solute carrier family 2 member 1 (SLC2A1), PKM, plasminogen activator, urokinase (PLAU), peroxisome proliferator activated receptor γ (PPARG), MET proto-oncogene, receptor tyrosine kinase (MET), and integrin subunit α 3 (ITGA3), were identified and found to be up-regulated between PDAC and normal tissues. miR-135b, miR-221, miR-21, miR-27a, miR-199b-5p, miR-143, miR-196a, miR-655, miR-455-3p, miR-744 and hub genes predicted poor OS of PDAC. An integrative bioinformatics analysis identified several hub genes that may serve as potential biomarkers or targets for early diagnosis and precision target treatment of PDAC.

scholarly journals Identifying MMP14 and COL12A1 as a potential combination of prognostic biomarkers in pancreatic ductal adenocarcinoma using integrated bioinformatics analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10419
Author(s):  
Jingyi Ding ◽  
Yanxi Liu ◽  
Yu Lai
Keyword(s):  
Gene Expression ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Prognostic Biomarkers ◽  
Ductal Adenocarcinoma ◽  
Receptor Interaction ◽  
Analysis Tool ◽  
Ppi Network ◽  
Hub Genes

Background Pancreatic ductal adenocarcinoma (PDAC) is a fatal malignant neoplasm. It is necessary to improve the understanding of the underlying molecular mechanisms and identify the key genes and signaling pathways involved in PDAC. Methods The microarray datasets GSE28735, GSE62165, and GSE91035 were downloaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified by integrated bioinformatics analysis, including protein–protein interaction (PPI) network, Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The PPI network was established using the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software. GO functional annotation and KEGG pathway analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery. Hub genes were validated via the Gene Expression Profiling Interactive Analysis tool (GEPIA) and the Human Protein Atlas (HPA) website. Results A total of 263 DEGs (167 upregulated and 96 downregulated) were common to the three datasets. We used STRING and Cytoscape software to establish the PPI network and then identified key modules. From the PPI network, 225 nodes and 803 edges were selected. The most significant module, which comprised 11 DEGs, was identified using the Molecular Complex Detection plugin. The top 20 hub genes, which were filtered by the CytoHubba plugin, comprised FN1, COL1A1, COL3A1, BGN, POSTN, FBN1, COL5A2, COL12A1, THBS2, COL6A3, VCAN, CDH11, MMP14, LTBP1, IGFBP5, ALB, CXCL12, FAP, MATN3, and COL8A1. These genes were validated using The Cancer Genome Atlas (TCGA) and Genotype–Tissue Expression (GTEx) databases, and the encoded proteins were subsequently validated using the HPA website. The GO analysis results showed that the most significantly enriched biological process, cellular component, and molecular function terms among the 20 hub genes were cell adhesion, proteinaceous extracellular matrix, and calcium ion binding, respectively. The KEGG pathway analysis showed that the 20 hub genes were mainly enriched in ECM–receptor interaction, focal adhesion, PI3K-Akt signaling pathway, and protein digestion and absorption. These findings indicated that FBN1 and COL8A1 appear to be involved in the progression of PDAC. Moreover, patient survival analysis performed via the GEPIA using TCGA and GTEx databases demonstrated that the expression levels of COL12A1 and MMP14 were correlated with a poor prognosis in PDAC patients (p < 0.05). Conclusions The results demonstrated that upregulation of MMP14 and COL12A1 is associated with poor overall survival, and these might be a combination of prognostic biomarkers in PDAC.

scholarly journals ITGA2, LAMB3, and LAMC2 may be the potential therapeutic targets in pancreatic ductal adenocarcinoma: an integrated bioinformatics analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shajedul Islam ◽  
Takao Kitagawa ◽  
Byron Baron ◽  
Yoshihiro Abiko ◽  
Itsuo Chiba ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Bioinformatics Analysis ◽  
Therapeutic Targets ◽  
Ductal Adenocarcinoma ◽  
Integrin Subunit ◽  
Hub Genes ◽  
Cancerous Tissue ◽  
Tissue Samples

AbstractPancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer with an abysmal prognosis rate over the last few decades. Early diagnosis and prevention could effectively combat this malignancy. Therefore, it is crucial to discover potential biomarkers to identify asymptomatic premalignant or early malignant tumors of PDAC. Gene expression analysis is a powerful technique to identify candidate biomarkers involved in disease progression. In the present study, five independent gene expression datasets, including 321 PDAC tissues and 208 adjacent non-cancerous tissue samples, were subjected to statistical and bioinformatics analysis. A total of 20 differentially expressed genes (DEGs) were identified in PDAC tissues compared to non-cancerous tissue samples. Gene ontology and pathway enrichment analysis showed that DEGs were mainly enriched in extracellular matrix (ECM), cell adhesion, ECM–receptor interaction, and focal adhesion signaling. The protein–protein interaction network was constructed, and the hub genes were evaluated. Collagen type XII alpha 1 chain (COL12A1), fibronectin 1 (FN1), integrin subunit alpha 2 (ITGA2), laminin subunit beta 3 (LAMB3), laminin subunit gamma 2 (LAMC2), thrombospondin 2 (THBS2), and versican (VCAN) were identified as hub genes. The correlation analysis revealed that identified hub genes were significantly interconnected. Wherein COL12A1, FN1, ITGA2, LAMB3, LAMC2, and THBS2 were significantly associated with PDAC pathological stages. The Kaplan–Meier survival plots revealed that ITGA2, LAMB3, and LAMC2 expression were inversely correlated with a prolonged patient survival period. Furthermore, the Human Protein Atlas database was used to validate the expression and cellular origins of hub genes encoded proteins. The protein expression of hub genes was higher in pancreatic cancer tissue than in normal pancreatic tissue samples, wherein ITGA2, LAMB3, and LAMC2 were exclusively expressed in pancreatic cancer cells. Pancreatic cancer cell-specific expression of these three proteins may play pleiotropic roles in cancer progression. Our results collectively suggest that ITGA2, LAMB3, and LAMC2 could provide deep insights into pancreatic carcinogenesis molecular mechanisms and provide attractive therapeutic targets.

scholarly journals Identify key genes and pathways in pancreatic ductal adenocarcinoma via bioinformatics analysis

2020 ◽  
Author(s):  
Huatian Luo ◽  
Da-qiu Chen ◽  
Jing-jing Pan ◽  
Zhang-wei Wu ◽  
Can Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Bioinformatics Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Expression Omnibus ◽  
Ductal Adenocarcinoma ◽  
Hub Genes ◽  
Expression Levels

Abstract Background: Pancreatic cancer has many pathologic types, among which pancreatic ductal adenocarcinoma (PDAC) is the most common one. Bioinformatics has become a very common tool for the selection of potentially pathogenic genes.Methods: Three data sets containing the gene expression profiles of PDAC were downloaded from the gene expression omnibus (GEO) database. The limma package of R language was utilized to explore the differentially expressed genes (DEGs). To analyze functions and signaling pathways, the Database Visualization and Integrated Discovery (DAVID) was used. To visualize the protein-protein interaction (PPI) of the DEGs ,Cytoscape was performed under the utilization of Search Tool for the Retrieval of Interacting Genes (STRING). With the usage of the plug-in cytoHubba in cytoscape software, the hub genes were found out. To verify the expression levels of hub genes, Gene Expression Profiling Interactive Analysis (GEPIA) was performed. Last but not least, UALCAN analysis online tool was implemented to analyze the overall survival.Results: The 376 DEGs were highly enriched in biological processes including signal transduction, apoptotic process and several pathways, mainly associated with Protein digestion and absorption and Pancreatic secretion pathway. The expression levels of nucleolar and spindle associated protein 1 (NUSAP1) and SHC binding and spindle associated 1 (SHCBP1) were discovered highly expressed in pancreatic ductal adenocarcinoma tissues. NUSAP1 and SHCBP1 had a high correlation with prognosis.Conclusions: The findings of this bioinformatics analysis indicate that NUSAP1 and SHCBP1 may be key factors in the prognosis and treatment of pancreatic cancer.

scholarly journals ITGA2, LAMB3, and LAMC2 May Be the Potential Therapeutic Targets in Pancreatic Ductal Adenocarcinoma: an Integrated Bioinformatics Analysis

2021 ◽  
Author(s):  
Shajedul Islam ◽  
Takao Kitagawa ◽  
Byron Baron ◽  
Yoshihiro Abiko ◽  
Itsuo Chiba ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Bioinformatics Analysis ◽  
Therapeutic Targets ◽  
Ductal Adenocarcinoma ◽  
Integrin Subunit ◽  
Hub Genes ◽  
Cancerous Tissue ◽  
Tissue Samples

Abstract Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer with an abysmal prognosis rate over the last few decades. Early diagnosis and prevention could effectively combat this malignancy. Therefore, it is crucial to discover potential biomarkers to identify asymptomatic premalignant or early malignant tumors of PDAC. Gene expression analysis is a powerful technique to identify candidate biomarkers involved in disease progression. In the present study, five independent gene expression datasets, including 321 PDAC tissues and 208 adjacent non-cancerous tissue samples, were subjected to statistical and bioinformatics analysis. A total of 20 differentially expressed genes (DEGs) were identified in PDAC tissues compared to non-cancerous tissue samples. Gene ontology and pathway enrichment analysis showed that DEGs were mainly enriched in extracellular matrix (ECM), cell adhesion, ECM-receptor interaction, and focal adhesion signaling. The protein protein interaction network was constructed, and the hub genes were evaluated. Collagen type XII alpha 1 chain (COL12A1), fibronectin 1 (FN1), integrin subunit alpha 2 (ITGA2), laminin subunit beta 3 (LAMB3), laminin subunit gamma 2 (LAMC2), thrombospondin 2 (THBS2), and versican (VCAN) were identified as hub genes. The correlation analysis revealed that identified hub genes were significantly interconnected. Wherein COL12A1, FN1, ITGA2, LAMB3, LAMC2, and THBS2 were significantly associated with PDAC pathological stages. The Kaplan-Meier survival plots revealed that ITGA2, LAMB3, and LAMC2 expression were inversely correlated with a prolonged patient survival period. Furthermore, the Human Protein Atlas database was used to validate the expression and cellular origins of hub genes encoded proteins. The protein expression of hub genes was higher in pancreatic cancer tissue than in normal pancreatic tissue samples, wherein ITGA2, LAMB3, and LAMC2 were exclusively expressed in pancreatic cancer cells. Pancreatic cancer cell-specific expression of these three proteins may play pleiotropic roles in cancer progression. Our results collectively suggest that ITGA2, LAMB3, and LAMC2 could provide deep insights into pancreatic carcinogenesis molecular mechanisms and provide attractive therapeutic targets.

scholarly journals Identification of Biomarkers Related To The Diagnosis And Prognosis of Thyroid Cancer Through Bioinformatics Analysis

2021 ◽  
Author(s):  
Xiao-Li Xie ◽  
Hua-Li Yin ◽  
Yu-Lin Pan ◽  
Guo-Xia Li ◽  
Chun-Yan Yuan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Bioinformatics Analysis ◽  
Prognostic Biomarkers ◽  
Cancer Tissue ◽  
Data Sets ◽  
Hub Genes ◽  
Normal Thyroid ◽  
Key Genes ◽  
Cancer Tissues

Abstract Background: Thyroid cancer is the most common malignant tumor of the head and neck. In recent years, the incidence of thyroid cancer (THCA) worldwide has rapidly increased and shows a trend in the younger generation. This study attempted to screen key genes and potential prognostic biomarkers for thyroid cancer using bioinformatics analysis.Methods: This study attempted to screen key genes and potential prognostic biomarkers for thyroid cancer using bioinformatics analysis. 101 cases of thyroid cancer and 78 cases of normal thyroid tissue were collected from three Gene Expression Omnibus (GEO) databases, then we identified the differentially expressed genes (DEGs) and conducted downstream analyses. Moreover, we screened hub genes by constructing a protein‐protein interaction (PPI) network. Finally, we assessed the expression level of hub genes in thyroid cancer tissue and its normal tissue using GEPIA and qRT-PCR respectively. Results: 159 upregulated and 251 downregulated genes were determined after gene integration of these three GEO data sets. Through PPI analysis, we consider the top 20 DEGs with high connectivity as the hub genes of THCA. After that, this study verified 20 central genes through the GEPIA database and found that only four hub genes (TOP2A, FN1, TIMP1, and MMP9) had significantly higher expression levels in thyroid cancer tissues than in normal thyroid tissues. We further analyzed the correlation between these four hub genes and the prognosis of patients with thyroid cancer, which suggests that FN1, MMP9, TIMP1 help assess the prognosis of patients with thyroid cancer. We performed GSEA analysis on these 4 hub genes simultaneously, found that the high expression of these 4 hub genes enriched the "cell cycle." Subsequently, we collected thyroid cancer tissue specimens, verified these four hub gene expression levels by RT-PCR, and found that only FN1 and TIMP1 genes in thyroid cancer tissues had significantly higher mRNA levels than normal tissues. Conclusions: Our research has identified 20 hub genes that may be related to the occurrence and development of thyroid cancer through multiple gene expression profile data sets and a series of comprehensive bioinformatics analyses. Further database and tissue validation analysis revealed that only 2 hub genes may be considered as potential prognostic biomarkers, including FN1 and TIMP1. In addition, these two hub genes are involved in the cell cycle, suggesting that they may play a role in the occurrence and development of thyroid cancer.

scholarly journals Identification of Key Prognostic Biomarker and Its Correlation with Immune Infiltrates in Pancreatic Ductal Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Hong Luan ◽  
Chuang Zhang ◽  
Tuo Zhang ◽  
Ye He ◽  
Yanna Su ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Poor Prognosis ◽  
Immune Therapy ◽  
The Cancer Genome Atlas ◽  
Ductal Adenocarcinoma ◽  
Immune Checkpoints ◽  
Hub Genes ◽  
Immune Infiltration ◽  
The Relationship

Pancreatic ductal adenocarcinoma (PDAC) is an extremely malignant tumor. The immune profile of PDAC and the immunologic milieu of its tumor microenvironment (TME) are unique; however, the mechanism of how the TME engineers the carcinogenesis of PDAC is not fully understood. This study is aimed at better understanding the relationship between the immune infiltration of the TME and gene expression and identifying potential prognostic and immunotherapeutic biomarkers for PDAC. Analysis of data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases identified differentially expressed genes (DEGs), including 159 upregulated and 53 downregulated genes. Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes enrichment were performed and showed that the DEGs were mainly enriched for the PI3K-Akt signaling pathway and extracellular matrix organization. We used the cytoHubba plugin of Cytoscape to screen out the most significant ten hub genes by four different models (Degree, MCC, DMNC, and MNC). The expression and clinical relevance of these ten hub genes were validated using Gene Expression Profiling Interactive Analysis (GEPIA) and the Human Protein Atlas, respectively. High expression of nine of the hub genes was positively correlated with poor prognosis. Finally, the relationship between these hub genes and tumor immunity was analyzed using the Tumor Immune Estimation Resource. We found that the expression of SPARC, COL6A3, and FBN1 correlated positively with infiltration levels of six immune cells in the tumors. In addition, these three genes had a strong coexpression relationship with the immune checkpoints. In conclusion, our results suggest that nine upregulated biomarkers are related to poor prognosis in PDAC and may serve as potential prognostic biomarkers for PDAC therapy. Furthermore, SPARC, COL6A3, and FBN1 play an important role in tumor-related immune infiltration and may be ideal targets for immune therapy against PDAC.

scholarly journals Network analyses of circular RNAs expression profiles and their hub genes in pancreatic ductal adenocarcinoma

2019 ◽  
Author(s):  
Yanyan Tang ◽  
Ping Zhang
Keyword(s):  
Gene Expression ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Signaling Pathway ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Expression Profiles ◽  
Ductal Adenocarcinoma ◽  
Ppi Network ◽  
Hub Genes ◽  
Kegg Pathway Analysis

Abstract Pancreatic ductal adenocarcinoma (PDAC) is one of the most common malignant tumor in digestive system. CircRNAs involve in lots of biological processes through interacting with miRNAs and their targeted mRNA. We obtained the circRNA gene expression profiles from Gene Expression Omnibus (GEO) and identified differentially expressed genes (DEGs) between PDAC samples and paracancerous tissues. Bioinformatics analyses, including GO analysis, KEGG pathway analysis and PPI network analysis, were conducted for further investigation. We also constructed circRNA‑microRNA-mRNA co-expression network. A total 291 differentially expressed circRNAs were screened out. The GO enrichment analysis revealed that up-regulated DEGs were mainly involved metabolic process, biological regulation, and gene expression, and down-regulated DEGs were involved in cell communication, single-organism process, and signal transduction. The KEGG pathway analysis, the upregulated circRNAs were enriched cGMP-PKG signaling pathway, and HTLV-I infection, while the downregulated circRNAs were enriched in protein processing in endoplasmic reticulum, insulin signaling pathway, regulation of actin cytoskeleton, etc. Four genes were identified from PPI network as both hub genes and module genes, and their circRNA‑miRNA-mRNA regulatory network also be constructed. Our study indicated possible involvement of dysregulated circRNAs in the development of PDAC and promoted our understanding of the underlying molecular mechanisms.

scholarly journals POS0851 IDENTIFICATION OF HUB GENES AND PATHWAYS IN DERMATOMYOSITIS BY BIOINFORMATICS ANALYSIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 680.1-680
Author(s):  
C. Zheng ◽  
S. X. Zhang ◽  
R. Zhao ◽  
L. Cheng ◽  
T. Kong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signaling Pathway ◽  
Muscle Tissue ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Shanxi Province ◽  
Pathway Enrichment Analysis ◽  
Type I ◽  
Hub Genes ◽  
Key Genes

Background:Dermatomyositis (DM) is a chronic systemic autoimmune disease characterized by inflammatory infiltrates in the skin and muscle1. The genes and pathways in the inflamed myopathies in patients with DM are poorly understood2.Objectives:To identify the key genes and pathways associated with DM and further discover its pathogenesis.Methods:Muscle tissue gene expression profile (GSE143323) were acquired from the GEO database, which included 39 DM samples and 20 normal samples. The differentially expressed genes (DEGs) in DM muscle tissue were screened by adopting the R software. Gene ontology (GO) and Kyoto Encyclopedia of Genome (KEGG) pathway enrichment analysis was performed by Metascape online analysis tool. A protein-protein interaction (PPI) network was then constructed by STRING software using the genes in significantly different pathways. Network of DEGs was analyzed by Cytoscape software. And degree of nodes was used to screen key genes.Results:Totally, 126 DEGs were obtained, which contained 122 up-regulated and 4 down-regulated. GO analysis revealed that most of the DEGs were significantly enriched in type I interferon signaling pathway, response to interferon-gamma, collagen-containing extracellular matrix, response to interferon-alpha and bacterium, positive regulation of cell death, leukocyte chemotaxis. KEGG pathway analysis showed that upregulated DEGs enhanced pathways associated with the hepatitis C, complement and coagulation cascades, p53 signaling pathway, RIG-I-like receptor signaling, Osteoclast differentiation, and AGE-RAGE signaling pathway. Ten hub genes were identified in DM, they were ISG15, IRF7, STAT1, MX1, OASL, OAS2, OAS1, OAS3, GBP1, and IRF9 according to the Cytoscape software and cytoHubba plugin.Conclusion:The findings from this bioinformatics network analysis study identified the key hub genes that might provide new molecular markers for its diagnosis and treatment.References:[1]Olazagasti JM, Niewold TB, Reed AM. Immunological biomarkers in dermatomyositis. Curr Rheumatol Rep 2015;17(11):68. doi: 10.1007/s11926-015-0543-y [published Online First: 2015/09/26].[2]Chen LY, Cui ZL, Hua FC, et al. Bioinformatics analysis of gene expression profiles of dermatomyositis. Mol Med Rep 2016;14(4):3785-90. doi: 10.3892/mmr.2016.5703 [published Online First: 2016/09/08].[3]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].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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