基于转录组的肝豆状核变性调控网络的构建和分析

本研究旨在利用生物信息学方法构建经铜诱导的ATP7B基因敲除HepG2细胞系的转录调控网络。探讨关键转录因子在肝豆状核变性发生、发展中的潜在作用机制。收集公共基因表达数据库(gene expression omnibus, GEO)中包含野生型、ATP7B基因敲除型、铜诱导的野生型和铜诱导的ATP7B基因敲除型HepG2细胞系数据。筛选由铜诱导产生的差异表达基因(differentially expressed genes,DEGs)后进行基因本体论(gene ontology,GO)、京都基因和基因组百科全书(Kyoto encyclopedia of genes and genomes, KEGG)富集分析。基于蛋白相互作用网络,识别疾病关键基因和功能模块,并对关键功能模块中的基因进行富集分析。最后,构建转录调控网络,筛选核心转录因子。共筛选出1 034个差异表达基因,其中上调525个,下调509个。上、下调关键功能模块分别包括了3785个和3931个基因。关键功能模块中的基因主要定位于细胞-基质连接、染色体、剪接复合体、核糖体等区域,共同参与了mRNA加工、组蛋白修饰、RNA剪切...     

Identification of hub genes and pathways in adrenocortical carcinoma by integrated bioinformatic analysis

Adrenocortical carcinoma (ACC), a rare malignant neoplasm originating from adrenal cortical cells, has high malignancy and few treatments. Therefore, it is necessary to explore the molecular mechanism of tumorigenesis, screen and verify potential biomarkers, which will provide new clues for the treatment and diagnosis of ACC. In this paper, three gene expression profiles (GSE10927, GSE12368 and GSE90713) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained using the Limma package. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched by DAVID. Protein‐protein interaction (PPI) network was evaluated by STRING database, and PPI network was constructed by Cytoscape. Finally, GEPIA was used to validate hub genes’ expression. Compared with normal adrenal tissues, 74 up‐regulated DEGs and 126 down‐regulated DEGs were found in ACC samples; GO analysis showed that up‐regulated DEGs were enriched in organelle fission, nuclear division, spindle, et al, while down‐regulated DEGs were enriched in angiogenesis, proteinaceous extracellular matrix and growth factor activity; KEGG pathway analysis showed that up‐regulated DEGs were significantly enriched in cell cycle, cellular senescence and progesterone‐mediated oocyte maturation; Nine hub genes (CCNB1, CDK1, TOP2A, CCNA2, CDKN3, MAD2L1, RACGAP1, BUB1 and CCNB2) were identified by PPI network; ACC patients with high expression of 9 hub genes were all associated with worse overall survival (OS). These hub genes and pathways might be involved in the tumorigenesis, which will offer the opportunities to develop the new therapeutic targets of ACC.     

Identification of core genes and outcomes in hepatocellular carcinoma by bioinformatics analysis

Hepatocellular carcinoma (HCC) is the most common malignant liver disease in the world. However, the mechanistic relationships among various genes and signaling pathways are still largely unclear. In this study, we aimed to elucidate potential core candidate genes and pathways in HCC. The expression profiles GSE14520, GSE25097, GSE29721, and GSE62232, which cover 606 tumor and 550 nontumour samples, were downloaded from the Gene Expression Omnibus (GEO) database. Furthermore, HCC RNA-seq datasets were also downloaded from the Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were filtered using R software, and we performed gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using the online databases DAVID 6.8 and KOBAS 3.0. Furthermore, the protein-protein interaction (PPI) network complex of these DEGs was constructed by Cytoscape software, the molecular complex detection (MCODE) plug-in and the online database STRING. First, a total of 173 DEGs (41 upregulated and 132 downregulated) were identified that were aberrantly expressed in both the GEO and TCGA datasets. Second, GO analysis revealed that most of the DEGs were significantly enriched in extracellular exosomes, cytosol, extracellular region, and extracellular space. Signaling pathway analysis indicated that the DEGs had common pathways in metabolism-related pathways, cell cycle, and biological oxidations. Third, 146 nodes were identified from the DEG PPI network complex, and two important modules with a high degree were detected using the MCODE plug-in. In addition, 10 core genes were identified, TOP2A, NDC80, FOXM1, HMMR, KNTC1, PTTG1, FEN1, RFC4, SMC4, and PRC1. Finally, Kaplan-Meier analysis of overall survival and correlation analysis were applied to these genes. The abovementioned findings indicate that the identified core genes and pathways in this bioinformatics analysis could significantly enrich our understanding of the development and recurrence of HCC; furthermore, these candidate genes and pathways could be therapeutic targets for HCC treatment.     

Gene microarray analysis of expression profiles in Suberoyllanilide hyroxamic acid-treated Dendritic cells

Objective

The purpose of this study is to provide a further theoretical basis for the role of Suberoyllanilide hyroxamic acid (SAHA) affect on Dendritic cells (DCs).

Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks

BackgroundGlioma is the most lethal nervous system cancer. Recent studies have made great efforts to study the occurrence and development of glioma, but the molecular mechanisms are still unclear. This study was designed to reveal the molecular mechanisms of glioma based on protein-protein interaction network combined with machine learning methods. Key differentially expressed genes (DEGs) were screened and selected by using the protein-protein interaction (PPI) networks.ResultsAs a result, 19 genes between grade I and grade II, 21 genes between grade II and grade III, and 20 genes between grade III and grade IV. Then, five machine learning methods were employed to predict the gliomas stages based on the selected key genes. After comparison, Complement Naive Bayes classifier was employed to build the prediction model for grade II-III with accuracy 72.8%. And Random forest was employed to build the prediction model for grade I-II and grade III-VI with accuracy 97.1% and 83.2%, respectively. Finally, the selected genes were analyzed by PPI networks, Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and the results improve our understanding of the biological functions of select DEGs involved in glioma growth. We expect that the key genes expressed have a guiding significance for the occurrence of gliomas or, at the very least, that they are useful for tumor researchers.ConclusionMachine learning combined with PPI networks, GO and KEGG analyses of selected DEGs improve our understanding of the biological functions involved in glioma growth.     

Identification of key genes and pathways associated with sex difference in osteoarthritis based on bioinformatics analysis

Objectives:The present study aimed to identify different key genes and pathways between postmenopausal females and males by studying differentially expressed genes (DEGs).Methods:{"type":"entrez-geo","attrs":{"text":"GSE32317","term_id":"32317"}}GSE32317 and {"type":"entrez-geo","attrs":{"text":"GSE55457","term_id":"55457"}}GSE55457 gene expression data were downloaded from the GEO database, and DEGs were discovered using R software to obtain overlapping DEGs. The interaction between overlapping DEGs was further analyzed by establishing the protein-protein interaction (PPI) network. Finally, GO and KEGG were used for enrichment analysis.Results:924 overlapping DEGs between postmenopausal women and men with osteoarthritis (OA) were identified, including 674 up-regulated genes and 249 down-regulated ones. And 10 hub genes were identified in the PPI network, including BMP4, KDM6A, JMJD1C, NFATC1, PRKX, SRF, ZFX, LAMTOR5, UFD1L and AMBN. The findings of the functional enrichment analysis suggested that these genes were predominantly expressed in MAPK signaling pathway as well as the Thyroid hormone signaling pathway, indicating that those two pathways may be involved in onset and disease progression of OA in postmenopausal patients.Conclusion:BMP4, KDM6A, JMJD1C, PRKX, ZFX and LAMTOR5 are expected to play crucial roles in disease development in postmenopausal patients and may be ideal targets or prognostic markers for the treatment of OA.     

Differences in potential key genes and pathways between primary and radiation-associated angiosarcoma of the breast

BackgroundAngiosarcoma of the breast is a high-grade malignant soft tissue tumor, it can be divided into primary and radiation-associated angiosarcoma(secondary). However, the differences between primary and secondary angiosarcomas in terms of pathogenesis, clinical behavior, early diagnosis biomarkers, genetic abnormalities, and therapeutic targets remain to be fully elucidated. At the same time, due to its rarity, most of current information relating to angiosarcoma is provided by case reports. Therefore, exploring the mechanisms of primary and secondary breast angiosarcoma have important value for the discovery of new biomarkers and research into potential therapeutic targets.MethodsThe differentially expressed genes (DEGs) between 36 cases of primary angiosarcoma and 54 cases of secondary angiosarcoma were screened. Then, the DEGs were used to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Then, a protein-protein interaction (PPI) network was constructed using the STRING database.ResultsA total of 18 DEGs were identified, of which 13 were upregulated and 5 were downregulated in secondary breast angiosarcoma. The GO enrichment analysis showed that the DEGs were most enriched in metabolism, energy pathways, and protein metabolism in biological processes. The enriched signaling pathways of DEGs were the transforming growth factor-β (TGF-β), Wnt, Hippo and PI3K-Akt signaling pathways. Then, the PPI network was conducted and hub genes were identified and they were involved in thyroid hormone, Hippo and other signaling pathways.ConclusionThis study lay the foundation for the discovery of effective and reliable molecular biomarkers and essential therapeutic targets for these malignancies.     

Identification of key genes and pathways in benign prostatic hyperplasia

Benign prostatic hyperplasia (BPH) is one of the most common causes of lower urinary tract symptoms (LUTS) in elderly man. However, the underlying molecular mechanisms of BPH have not been completely elucidated. We identified the key genes and pathways by using analysis of Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using edgeR. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed for the DEGs by Database for Annotation, Visualization and Integrated Discovery (DAVID) database and ConsensusPathDB, respectively. Then, protein–protein interaction (PPI) networks were established by the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized by Cytoscape software. Finally, we identified 660 DEGs ultimately including 268 upregulated genes and 392 downregulated genes. GO analysis revealed that DEGs were mainly enriched in extracellular exosome, identical protein binding, mitochondrial adenosine triphosphate (ATP) synthesis coupled proton transport, extracelluar matrix, focal adhesion, cytosol, Golgi apparatus, cytoplasm, protein binding, and Golgi membrane. Focal adhesion pathway, FoxO signaling pathway, and autophagy pathway were selected. Ubiquitin-conjugating enzyme E2 C (UBE2C), serine/threonine kinase (AKT1), mitogen-activated protein kinase 1 (MAPK1), cyclin B1 (CCNB1), polo-like kinase 1 (PLK1) were filtrated as the hub genes according to the degree of connectivity from the PPI network. The five hub genes including UBE2C, AKT1, MAPK1, CCNB1, PLK1 may play key roles in the pathogenesis of benign prostatic hyperplasia (BPH). Focal adhesion pathway, FoxO signaling pathway, and autophagy pathway may be crucial for the progression of BPH.     

RNA sequence analysis identified bone morphogenetic protein-2 (BMP2) as a biomarker underlying form deprivation myopia

PurposeForm deprivation myopia (FDM) is an urgent public issue characterized by pathological changes, but the underlying mechanism remained unclear. The aim was to investigate bone morphogenetic proteins (BMPs) utilizing the pathogenesis of FDM.Material and methodsGene expression omnibus (GEO) database was used to analyze one mRNA profile (GSE89325) of FDM. Sixteen retina samples (8 FDM and 8 controls) were randomly divided into seven groups for differential gene expression analysis in R. software. The gene pathway and protein-protein interaction (PPI) analysis were performed by the DAVID and STRING databases. Cytoscape was used to draw the PPI network. The gene ontology (GO) enrichment and Kyoto encyclopedia of genes and Genomes (KEGG) analysis were determined to achieve gene annotation and visualization.ResultsA total of 18420 differentially expressed genes (DEGs) were identified associated with FDM. The only non-significant gene (BEND6) was separately analyzed between two groups. Thirteen hub genes were discovered, ACVR1, ACVR2A, ACVR2B, RGMB, BMPR2, BMPR1A, BMP2, BMPR1B, CHRD, PTH, PTH1R, PTHLH, and WNT9A. The expression alteration in FDM were mainly enriched in cytokine-cytokine, and neuroactive ligand receptor interaction pathways. BMP2 was the key gene in myopia progression.ConclusionsOf clinical perspective, our findings reveal that expression of BMP2 as an underlying mechanism of FDM, providing an insight for therapeutic interventions.     

Identification of lncRNA–miRNA–mRNA regulatory network associated with epithelial ovarian cancer cisplatin-resistant

To construct a long noncoding RNA (lncRNA)–microRNA (miRNA)–messenger RNA (mRNA) regulatory network related to epithelial ovarian cancer (EOC) cisplatin-resistant, differentially expressed genes (DEGs), differentially expressed lncRNAs (DELs), and differentially expressed miRNAs (DEMs) between MDAH and TOV-112D cells lines were identified. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to analyze the biological functions of DEGs. Downstream mRNAs or upstream lncRNAs for miRNAs were analyzed at miRTarBase 7.0 or DIANA-LncBase V2, respectively. A total of 485 significant DEGs, 85 DELs, and 5 DEMs were identified. Protein–protein interaction (PPI) network of DEGs contrains 81 nodes and 141 edges was constructed, and 25 hub genes related to EOC cisplatin-resistant were identified. Subsequently, a lncRNA–miRNA–mRNA regulatory network contains 4 lncRNAs, 4 miRNAs, and 35 mRNAs was established. Taken together, our study provided evidence concerning the alteration genes involved in EOC cisplatin-resistant, which will help to unravel the mechanisms underlying drug resistant.     

家兔前体脂肪细胞分化不同时期基因表达谱分析

脂肪的过度积累严重危害人类健康。前体脂肪细胞分化是脂肪发育的关键过程,研究前体脂肪细胞分化相关基因的表达有助于认识脂肪沉积的机理。尽管家兔是一种理想的研究脂肪发育的动物模型,但是针对其前体脂肪细胞分化不同时期基因表达谱的研究鲜见报道。本研究通过诱导家兔前体脂肪细胞分化,在分化第0 d、3 d和9 d收集脂肪细胞,利用转录组测序(RNA-seq),在分化第3 d样本与第0 d样本的比较中筛选出1352个差异表达基因(differentially expressed genes, DEGs),在分化第9 d样本与第3 d样本的比较中筛选出888个DEGs。GO (gene ontology)功能富集和KEGG (kyoto encyclopedia of genes and genomes)通路分析发现,0~3 d分化期上调的DEGs显著富集在PPAR信号通路和PI3K-Akt信号通路上,3~9d分化期上调的DEGs显著富集到与细胞周期调控有关的GO条目和KEGG信号通路,0~3d和3~9d阶段特异上调的DEGs可能分别作用于细胞质和细胞核。通过DEGs的蛋白-蛋白互作(protein-protein interaction, PPI)网络分析发现,筛选出的核心节点(hub node)基因可能通过调控细胞周期而影响家兔前体脂肪细胞分化。     

Analysis of the potential pathways and target genes in spinal cord injury using bioinformatics methods

Spinal cord injury (SCI) remains to be the most devastating type of trauma for patients because of long lasting disability and limited response to the acute drug administration and efforts at rehabilitation. With the purpose to identify potential targets for SCI treatment and to gain more insights into the mechanisms of SCI, the microarray data of GSE2270, including 119 raphe magnus (RM) samples and 125 sensorimotor cortex (SMTC) samples, was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened in RM group and SMTC group compared with their corresponding controls, respectively. A protein–protein interaction (PPI) network was constructed based on the common DEGs identified in both RM group and SMTC group. Gene ontology (GO) and pathway enrichment analyses of the overlapping DEGs were performed. Furthermore, the common DEGs enriched in each pathway were analyzed to identify significant regulatory elements. Totally, 173 overlapping DEGs (130 up-regulated and 43 down-regulated) were identified in both RM and SMTC samples. These overlapping DEGs were enriched in different GO terms. Pathway enrichment analysis revealed that DEGs were mainly related to inflammation and immunity. CD68 molecule (CD68) was a hub protein in the PPI network. Moreover, the regulatory network showed that ras-related C3 botulinum toxin substrate 2 (RAC2), CD44 molecule (CD44), and actin related protein 2/3 complex (ARPC1B) were hub genes. RAC2, CD44, and ARPC1B may be significantly involved in the pathogenesis of SCI by participating significant pathways such as extracellular matrix-receptor signaling pathway and Toll-like receptor signaling pathway.     

Prognostic value of metabolic genes in lung adenocarcinoma via integrative analyses

BackgroundLung adenocarcinoma (LUAD) is the most common malignant lung tumor. Metabolic pathway reprogramming is an important hallmark of physiologic changes in cancers. However, the mechanisms through which these metabolic genes and pathways function in LUAD as well as their prognostic values have not been fully established.MethodsFour publicly available datasets from GEO and TCGA were used to identify differentially expressed genes (DEGs) in LUAD, which were then subjected to GO and KEGG pathway enrichment analysis. Associations between metabolic gene expressions with overall survival, tumor stage, TP53 mutation status, and infiltrated immune cells were investigated. Protein-protein interactions were evaluated using GeneMANIA and Metascape.ResultsBy integrating four public datasets, 247 DEGs were identified in LUAD. These DEGs were significantly enriched in regulation of chromosome segregation, centromeric region, and histone kinase activity GO terms, as well as in cell cycle, p53 signaling pathway, metabolic pathways, and other KEGG pathways. Elevated expressions of ten metabolic genes in LUAD were significantly associated with poor survival outcomes. These metabolic genes were highly expressed in more advanced tumor stage and TP53 mutated patients. Moreover, expression levels were significantly correlated with tumor-infiltrating immune cells. PPI interaction analysis revealed that the top 20 genes interacting with each metabolic gene were significantly enriched in DNA replication, response to radiation, and central carbon metabolism in cancer.ConclusionThis study elucidates on molecular changes in metabolic genes in LUAD, which may inform the development of genetically oriented diagnostic approaches and effective treatment options.     

IgA 肾病患者与膜性肾病患者外周血单核细胞mRNA分析

为了寻找诊断、鉴别IgA肾病(IgAN)和膜性肾病(MN)的血液特异性标记物,利用公共数据库中的IgAN和MN患者的外周血单核细胞(PBMCs)的转录组表达谱数据集识别特异性生物标记物,为诊断和鉴别提供简便、可靠的依据补充。从公共基因表达数据库(GEO)下载IgAN患者组(n=15)和MN患者组(n=8)芯片数据集,筛选前250个差异表达基因(DEGs)。通过分析筛选关键基因和途径,进行基因本体(GO)富集分析、京都基因与基因组百科全书(KEGG)通路分析和蛋白质与蛋白质相互作用关系(PPI)分析等进一步了解DEGs。通过分析共发现75个显著DEGs,其中73个上调基因,2个下调基因。GO富集分析的生物学过程(BP)主要包括蛋白质转运、内溶酶体到溶酶体转运、趋化因子介导的信号通路作用等。显著富集差异表达基因KEGG通路分析包括Endocytosis和Hepatitis B的相关信号通路。PPI筛选出EPS15、STAT4、CCL2、SUN2、SEC24C、SEC31A、GOLGB1、F2R,RAB12和PTK2B等关键基因。成功筛选出核心差异表达基因,为IgAN和MN的诊断和鉴别提供简便、可靠的依据补充,甚至提供治疗的新靶点。     

Uncovering the pathogenesis and identifying novel targets of pancreatic cancer using bioinformatics approach

Pancreatic cancer is a uniformly lethal disease that can be difficult to diagnose at its early stage. Thus, our present study aimed to explore the underlying mechanism and identify new targets for this disease. The data GSE16515, including 36 tumor and 16 normal samples were available from Gene Expression Omnibus. Differentially expressed genes (DEGs) were screened out using Robust Multichip Averaging and LIMMA package. Moreover, gene ontology and pathway enrichment analyses were performed to DEGs. Followed with protein–protein interaction (PPI) network construction by STRING and Cytoscape, module analysis was conducted using ClusterONE. Finally, based on PubMed, text mining about these DEGs was carried out. Total 274 up-regulated and 93 down-regulated genes were identified as the common DEGs and these genes were discovered significantly enriched in cell adhesion and extracellular region terms, as well as ECM-receptor interaction pathway. In addition, five modules were screened out from the up-regulated PPI network with none in down-regulated network. Finally, the up-regulated genes, including MIA, MET and CEACAMS, and down-regulated genes, such as FGF, INS and LAPP, had the most references in text mining analysis. Our findings demonstrate that the up- and down-regulated genes play important roles in pancreatic cancer development and might be new targets for the therapy.