Integrated Bioinformatics Analysis of Master Regulators in Anaplastic Thyroid Carcinoma

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.

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

Frontiers in Genetics ◽

10.3389/fgene.2021.585058 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jin Ma ◽

Huan Gui ◽

Yunjia Tang ◽

Yueyue Ding ◽

Guanghui Qian ◽

...

Keyword(s):

Kawasaki Disease ◽

Signaling Pathway ◽

Regulatory Network ◽

Molecular Mechanisms ◽

Target Genes ◽

Unknown Etiology ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Ppi Networks

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

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

10.21203/rs.3.rs-506395/v1 ◽

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.

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The Identified Hub Gene GlcN in Osteoarthritis Progression and Treatment

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/5499450 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Jingsheng Liu ◽

Xiaoli Dong ◽

Yining Liu ◽

Kai Wang ◽

Shuanhu Lei ◽

...

Keyword(s):

Signaling Pathway ◽

Point Of View ◽

Receptor Interaction ◽

Ppi Network ◽

Hub Genes ◽

Data Set ◽

Treatment Groups ◽

R Language ◽

Kegg Pathways ◽

Osteoarthritis Progression

Background. As a chronic disease, osteoarthritis has caused great trouble to the health of middle-aged and elderly people. Studies have shown that glucosamine (GlcN) can be used to abate the progression and improve this disease. Based on this point of view, we try to verify the connection between GlcN and osteoarthritis and find more effective biomarkers. Methods. We downloaded the GSE72575 data set from the GEO database, and used the R language to perform DEG analysis on the gene expression profile of the samples. Next, the GO function and the KEGG signaling pathways were analyzed through the DAVID database, and then, the KEGG pathways enriched in the gene set were analyzed based on GSEA. Then, the PPI network of DEGs was constructed based on the STRING online database, and finally, the hub genes were selected by Cytoscape. Results. Three GlcN-treated MH7A cell treatment groups and 3 control groups in the GSE72575 data set were studied. Through analysis, there were 52 DEGs in these samples. Then, through GO, KEGG, and GSEA, regulation of endoplasmic reticulum stress-induced intrinsic apoptotic signaling pathway, FoxO signaling pathway, JAK-STAT signaling pathway, PI3K-Akt signaling pathway, TGF-beta signaling pathway, and ECM receptor interaction were involved in the regulatory mechanisms of the osteoarthritis pathogenesis. After that, the hub genes IL6 and DDIT3 were identified through PPI network construction and analysis. And it was found that IL6 was lowly expressed in the group with GlcN-treated MH7A cells, while DDIT3 was highly expressed. Conclusion. The above results provide a basis for GlcN to participate in the treatment of osteoarthritis and a possibility for finding effective therapeutic targets.

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Identification of hub genes and pathways in psoriasis through bioinformatics and validation by RT-qPCR

10.21203/rs.3.rs-357690/v1 ◽

2021 ◽

Author(s):

Ling Ai Zou ◽

Qichao Jian

Keyword(s):

Signaling Pathway ◽

Quantitative Polymerase Chain Reaction ◽

Cytokine Receptor ◽

Receptor Interaction ◽

Hacat Cells ◽

Receptor Signaling ◽

Ppi Network ◽

Hub Genes ◽

Network Analyses ◽

Receptor Signaling Pathway

Abstract Background Although several studies have attempted to investigate the aetiology and mechanism of psoriasis, the precise molecular mechanism remains unclear. Our study aimed to identify the hub genes and associated pathways that promote its pathogenesis in psoriasis, which would be helpful for the discovery of diagnostic and therapeutic markers. Methods GSE30999, GSE34248, GSE41662, and GSE50790 datasets were extracted from the Gene Expression Omnibus (GEO) database. The GEO profiles were integrated to obtain differentially expressed genes (DEGs) using the affy package in R software, with |logFC|> 1.5 and adjusted P < 0.05. The DEGs were utilised for Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein-protein interaction (PPI) network analyses. Hub genes were identified using Cytoscape and enriched for analysis in www.bioinformatics.com.cn. These hub genes were validated in the four aforementioned datasets and M5-induced HaCaT cells using real-time quantitative polymerase chain reaction (RT-qPCR). Results A total of 359 DEGs were identified, which were mostly associated with responses to bacterium, defence responses to other organism, and antimicrobial humoral response. These DEGs were mostly enriched in the steroid hormone biosynthesis pathway, NOD-like receptor signaling pathway, and cytokine-cytokine receptor interaction. PPI network analysis indicated seven genes (CXCL1, ISG15, CXCL10, STAT1, OASL, IFIT1, and IFIT3) as the probable hub genes of psoriasis; CXCL10 had a positive correlation with the other six hub genes. The chord plot results further supported the GO and KEGG analysis results of the 359 DEGs. Seven predicted hub genes were validated to be upregulated in four datasets and M5-induced HaCaT cells using RT-qPCR. Conclusions The pathogenesis of psoriasis may be associated with seven hub genes (CXCL1, ISG15, CXCL10, STAT1, OASL, IFIT1, and IFIT3) and pathways, such as the NOD-like receptor signaling pathway and cytokine-cytokine receptor interaction. These hub genes, especially CXCL10, can be used as potential biomarkers in psoriasis.

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Comprehensive analysis of miRNA–mRNA regulatory network and potential drugs in chronic chagasic cardiomyopathy across human and mouse

BMC Medical Genomics ◽

10.1186/s12920-021-01134-3 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Jiahe Wu ◽

Jianlei Cao ◽

Yongzhen Fan ◽

Chenze Li ◽

Xiaorong Hu

Keyword(s):

Signaling Pathway ◽

Regulatory Network ◽

Molecular Mechanisms ◽

Killer Cell ◽

Differentially Expressed ◽

Ppi Network ◽

Hub Genes ◽

Hub Gene ◽

Homologous Genes ◽

Chagasic Cardiomyopathy

Abstract Background Chronic chagasic cardiomyopathy (CCC) is the leading cause of heart failure in Latin America and often causes severe inflammation and fibrosis in the heart. Studies on myocardial function and its molecular mechanisms in patients with Chronic chagasic cardiomyopathy are very limited. In order to understand the development and progression of Chronic chagasic cardiomyopathy and find targets for its diagnosis and treatment, the field needs to better understand the exact molecular mechanisms involved in these processes. Methods The mRNA microarray datasets GSE84796 (human) and GSE24088 (mouse) were obtained from the Gene Expression Omnibus (GEO) database. Homologous genes between the two species were identified using the online database mining tool Biomart, followed by differential expression analysis, gene enrichment analysis and protein–protein interaction (PPI) network construction. Cytohubba plug-in of Cytoscape software was used to identify Hub gene, and miRNet was used to construct the corresponding miRNA–mRNA regulatory network. miRNA-related databases: miRDB, Targetscan and miRWalk were used to further evaluate miRNAs in the miRNA–mRNA network. Furthermore, Comparative Toxicogenomics Database (CTD) and L1000 Platform were used to identify hub gene-related drugs. Results A total of 86 homologous genes were significantly differentially expressed in the two datasets, including 73 genes with high expression and 13 genes with low expression. These differentially expressed genes were mainly enriched in the terms of innate immune response, signal transduction, protein binding, Natural killer cell mediated cytotoxicity, Tuberculosis, Chemokine signaling pathway, Chagas disease and PI3K−Akt signaling pathway. The top 10 hub genes LAPTM5, LCP1, HCLS1, CORO1A, CD48, TYROBP, RAC2, ARHGDIB, FERMT3 and NCF4 were identified from the PPI network. A total of 122 miRNAs were identified to target these hub genes and 30 of them regulated two or more hub genes at the same time. miRDB, Targetscan and miRWalk were further analyzed and screened out hsa-miR-34c-5p, hsa-miR-34a-5p and hsa-miR-16-5p as miRNAs regulating these hub genes. Finally, Progesterone, Flutamide, Nimesulide, Methotrexate and Temozolomide were identified to target these hub genes and might be targeted therapies for Chronic chagasic cardiomyopathy. Conclusions In this study, the potential genes associated with Chronic chagasic cardiomyopathy are identified and a miRNA–mRNA regulatory network is constructed. This study explores the molecular mechanisms of Chronic chagasic cardiomyopathy and provides important clues for finding new therapeutic targets.

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Diallyl trisulfide induces G2/M cell-cycle arrest and apoptosis in anaplastic thyroid carcinoma 8505C cells

Food & Function ◽

10.1039/c9fo00646j ◽

2019 ◽

Vol 10 (11) ◽

pp. 7253-7261

Author(s):

Jiangxia Zheng ◽

Xian Cheng ◽

Shichen Xu ◽

Li Zhang ◽

Jie Pan ◽

...

Keyword(s):

Cell Cycle ◽

Thyroid Carcinoma ◽

Cell Cycle Arrest ◽

Signaling Pathway ◽

Anaplastic Thyroid Carcinoma ◽

Diallyl Trisulfide ◽

M Cell ◽

Cycle Arrest

DATS induces G2/M cell-cycle arrest and apoptosis through ATM-Chk1-Cdc25C signaling pathway in ATC 8505C cells.

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Comprehensive Analysis of miRNA-mRNA Regulatory Network And Potential Drugs In Chronic Chagasic Cardiomyopathy Across Human And Mouse

10.21203/rs.3.rs-835491/v1 ◽

2021 ◽

Author(s):

Jiahe Wu ◽

Jianlei Cao ◽

Yongzhen Fan ◽

Chenze Li ◽

Xiaorong Hu

Keyword(s):

Signaling Pathway ◽

Regulatory Network ◽

Molecular Mechanisms ◽

Killer Cell ◽

Differentially Expressed ◽

Ppi Network ◽

Hub Genes ◽

Hub Gene ◽

Homologous Genes ◽

Chagasic Cardiomyopathy

Abstract Background: Chronic chagasic cardiomyopathy (CCC) is the leading cause of heart failure in Latin America and often causes severe inflammation and fibrosis in the heart. Studies on myocardial function and its molecular mechanisms in patients with Chronic chagasic cardiomyopathy are very limited. In order to understand the development and progression of Chronic chagasic cardiomyopathy and find targets for its diagnosis and treatment, the field needs to better understand the exact molecular mechanisms involved in these processes.Methods: The mRNA microarray datasets GSE84796 (human) and GSE24088 (mouse) were obtained from the Gene Expression Omnibus (GEO) database. Homologous genes between the two species were identified using the online database mining tool Biomart, followed by differential expression analysis, gene enrichment analysis and protein-protein interaction (PPI) network construction. Cytohubba plug-in of Cytoscape software was used to identify Hub gene, and miRNet was used to construct the corresponding miRNA-mRNA regulatory network. Furthermore, Comparative Toxicogenomics Database (CTD) was used to identify hub gene-related drugs. Results: A total of 86 homologous genes were significantly differentially expressed in the two datasets, including 73 genes with high expression and 13 genes with low expression. These differentially expressed genes were mainly enriched in the terms of innate immune response, signal transduction, protein binding, Natural killer cell mediated cytotoxicity, Tuberculosis, Chemokine signaling pathway, Chagas disease and PI3K−Akt signaling pathway. The top 10 hub genes LAPTM5, LCP1, HCLS1, CORO1A, CD48, TYROBP, RAC2, ARHGDIB, FERMT3 and NCF4 were identified from the PPI network. A total of 122 miRNAs were identified to target these hub genes and 30 of them regulated two or more hub genes at the same time. Finally, 49 drugs, such as Gentamicins, acetamide, Isotretinoin, Nimesulide, Oxyquinoline, Quercetin and Resveratrol were identified to target these hub genes.Conclusions: In this study, the potential genes associated with Chronic chagasic cardiomyopathy are identified and a miRNA-mRNA regulatory network is constructed. This study explores the molecular mechanisms of Chronic chagasic cardiomyopathy and provides important clues for finding new therapeutic targets.

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Comprehensive Analysis of a circRNA-miRNA-mRNA Network to Reveal Potential Inflammation-Related Targets for Gastric Adenocarcinoma

Mediators of Inflammation ◽

10.1155/2020/9435608 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

YunXia Liu ◽

YeFeng Xu ◽

Feng Xiao ◽

JianFeng Zhang ◽

YiQing Wang ◽

...

Keyword(s):

Regulatory Network ◽

Gene Networks ◽

Expression Profiles ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Genetic Alterations ◽

Functional Enrichment ◽

Receptor Interaction ◽

Ppi Network ◽

Hub Genes

Gastric cancer (GC) is the most common malignancy of the stomach. This study was aimed at elucidating the regulatory network of circRNA-miRNA-mRNA and identifying the precise inflammation-related targets in GC. The expression profiles of GSE83521, GSE78091, and GSE33651 were obtained from the GEO database. Interactions between miRNAs and circRNAs were investigated by the Circular RNA Interactome, and targets of miRNAs were predicted with miRTarBase. Then, a circRNA/miRNA/mRNA regulatory network was constructed. Also, functional enrichment analysis of selected differentially expressed genes (DEGs) was performed. The inflammation-/GC-related targets were collected in the GeneCards and GenLiP3 database, respectively. And a protein-protein interaction (PPI) network of DE mRNAs was constructed with STRING and Cytoscape to identify hub genes. The genetic alterations, neighboring gene networks, expression levels, and the poor prognosis of hub genes were investigated in cBioPortal, Oncomine, and Human Protein Atlas databases and Kaplan-Meier plotter, respectively. A total of 10 DE miRNAs and 33 DEGs were identified. The regulatory network contained 26 circRNAs, 10 miRNAs, and 1459 mRNAs. Functional enrichment analysis revealed that the selected 33 DEGs were involved in negative regulation of fat cell differentiation, response to wounding, extracellular matrix- (ECM-) receptor interaction, and regulation of cell growth pathways. THBS1, FN1, CALM1, COL4A1, CTGF, and IGFBP5 were selected as inflammation-related hub genes of GC in the PPI network. The genetic alterations in these hub genes were related to amplification and missense mutations. Furthermore, the genes RYR2, ERBB2, PI3KCA, and HELZ2 were connected to hub genes in this study. The hub gene levels in clinical specimens were markedly upregulated in GC tissues and correlated with poor overall survival (OS). Our results suggest that THBS1, FN1, CALM1, COL4A1, CTGF, and IGFBP5 were associated with the pathogenesis of gastric carcinogenesis and may serve as biomarkers and inflammation-related targets for GC.

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Identification of key gene biomarkers and pathways related to Dent disease in CLCN5 knockout mice by bioinformatics analysis

10.21203/rs.2.14810/v1 ◽

2019 ◽

Author(s):

Zhijian Lin ◽

Yu Wang ◽

Fanfan Guo ◽

Bing Zhang

Keyword(s):

Signaling Pathway ◽

Knockout Mice ◽

Gene Knockout ◽

Metabolic Process ◽

Dent Disease ◽

Receptor Interaction ◽

Regulation Of Transcription ◽

Ppi Network ◽

Hub Genes ◽

Positive Regulation

Abstract Background Dent disease is an X-linked inherited renal disease that occurs almost exclusively in males. Abnormal CLC-5 function might play a role in the development of Dent disease, but the genetic interaction changes and biomarkers in Dent disease are not fully understood. The aim of this study was to explore the potential key gene biomarkers and pathways related to Dent disease in CLCN5 knockout mice model.Methods The gene expression profile GSE10162 was analyzed differentially expressed genes (DEGs), between 3 samples of CLC-5 transporter gene knockout mouse model of Dent disease and 3 samples from wild type mouse. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were applied for the enriched pathway by the online tool DAVID. A protein-protein interaction (PPI) network of DEGs was constructed to find the hub genes by STRING, and visualized with Cytoscape software.Results Three samples from were incorporated into this study. A total of 500 DEGs were filtered, consisting of 231 upregulated genes and 269 downregulated genes. GO analysis indicated that the up-regulated DEGs were significantly enriched in the regulation of transcription form RNA, regulation of cell proliferation, and ion transport, whereas down-regulated genes were mainly enriched in oxidation-reduction process, and metabolic process. KEGG analysis demonstrated that the DEGs were enriched in the metabolic pathways, neuroactive ligand-receptor interaction, nicotine addiction, morphine addiction, fatty acid elongation, TNF signaling pathway, calcium signaling pathway, and cAMP signaling pathway. PPI network analysis found 17 hub genes with greater than 10 degrees of connectivity. The hub genes might participate in TNF signaling pathway, fat digestion and absorption, and enrich in lipid metabolic process, regulation of blood pressure, cellular response to hypoxia, positive regulation of angiogenesis, positive regulation of developmental growth, and positive regulation of cytosolic calcium ion concentration.Conclusions Our study suggests that Apob, Lep, C3, Cxcl1, Acly and Mmp9 may play key roles in the progression of Dent disease. Blood lipid profiles and calcium levels might be potential prognostic biomarkers for Dent disease.

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Identification of hub genes and important pathways in determination of breast cancer survival using bioinformatics approaches

10.21203/rs.2.16078/v1 ◽

2019 ◽

Author(s):

Sepideh Dashti ◽

Soudeh Ghafouri-Fard

Keyword(s):

Breast Cancer ◽

Signaling Pathway ◽

Cancer Survival ◽

Clinical Importance ◽

Gene Expression Omnibus ◽

Receptor Interaction ◽

Peroxisome Proliferator ◽

Ppi Network ◽

Hub Genes ◽

Pathologic Classification

Abstract Backgrounds Breast cancer is a highly heterogeneous disorder characterized by dysregulation of expression of numerous genes and cascades. The conventional pathologic classification of breast cancer is not sufficient for the prediction of breast cancer behavior and response to therapy.Methods We have retrieved data of two microarray datasets (GSE65194 and GSE45827) from the NCBI Gene Expression Omnibus database (GEO). R package was used for identification of differentially expressed genes (DEGs), assessment of gene ontology (GO) and pathway enrichment evaluation. The DEGs were integrated to construct a protein-protein interaction (PPI) network. Next, hub genes were recognized using the Cytoscape software and lncRNA-mRNA co-expression analysis was performed to evaluate the potential roles of lncRNAs. The interactive information among DEGs and the PPI network was obtained using the STRING online database. Finally, the clinical importance of the obtained genes was assessed using Kaplan-Meier survival analysis.Results After excluding the outliers from the GSE65194 and GSE45827 datasets and data normalization, 866 DEGs including 712 upregulated and 154 downregulated DEGs were detected between breast cancer and normal samples. Up-regulated DEGs were enriched in six pathways including ‘Cell cycle’, ‘Oocyte meiosis’ and ‘Focal adhesion’. Down-regulated DEGs were enriched in five pathways including ‘Peroxisome-proliferator-activated receptors (PPAR) signaling pathway’, ‘Metabolism of xenobiotics by cytochrome P450’, ‘Adipocytokine signaling pathway’ and ‘Cytokine-cytokine receptor interaction’ pathways. CCNA2, CDK1, MAD2L1, and CCNB2 were significantly enriched in several biological pathways. These four genes showed strong expression in breast cancer samples as compared to normal breast tissue. We also identified 12 lncRNAs with a significant correlation with MAD2L1 and CCNB2 genes. MAD2L1, CCNA2, RAD51-AS1, and LINC01089 have the most prediction potential among all candidate hub genes.Conclusion Our study offers a framework for recognition of the mRNA-lncRNA network in breast cancer and the detection of important pathways that could be used as therapeutic targets in this kind of cancer.

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