Identification of potential key genes in anaplastic thyroid cancer using bioinformatics analysis

Anaplastic thyroid cancer (ATC) has a high degree of malignancy and poor prognosis. The purpose of this study was to determine differentially expressed genes (DEGs) in ATC through biometric analysis technology, clarify potential interactions between them, and screen genes related to the prognosis of ATC. Using obtained DEGs, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Protein-protein interaction (PPI), and survival analysis were performed. After R integration analysis of the four datasets, 764 DEGs were obtained, i.e., 314 upregulated genes and 450 downregulated genes. Among the hub DEGs obtained from the PPI network, the expression levels of TYMS, FN1, CHRDL1, SDC2, ITGA2, COL1A1, COL9A3, and COL23A1 were associated with ATC prognosis. These results showed that the recurrence-free survival (RFS) of ATC was associated with TYMS, FN1, ITGA2, COL23A1, SDC2, and CHRDL1 statistically significantly in the KM plotter (P<0.05). Thus, the study suggests that TYMS, FN1, ITGA2, COL23A1, SDC2, and CHRDL1 may be used as potential biomarkers of ATC. These findings provide new insights for the detection of novel diagnostic and therapeutic biomarkers for ATC.     

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.     

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.     

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...     

Bioinformatics analysis reveals disturbance mechanism of MAPK signaling pathway and cell cycle in Glioblastoma multiforme

Background & objectives

To analyze the reversal gene pairs and identify featured reversal genes related to mitogen-activated protein kinases (MAPK) signaling pathway and cell cycle in Glioblastoma multiforme (GBM) to reveal its pathogenetic mechanism.

Methods

We downloaded the gene expression profile GSE4290 from the Gene Expression Omnibus database, including 81 gene chips of GBM and 23 gene chips of controls. The t test was used to analyze the DEGs (differentially expressed genes) between 23 normal and 81 GBM samples. Then some perturbing metabolic pathways, including MAPK (mitogen-activated protein kinases) and cell cycle signaling pathway, were extracted from KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway database. Cancer genes were obtained from the database of Cancer Gene Census. The reversal gene pairs between DEGs and cancer genes were further analyzed in MAPK and cell cycle signaling pathway.

Results

A total 8523 DEGs were obtained including 4090 up-regulated and 4433 down-regulated genes. Among them, ras-related protein rab-13(RAB13), neuroblastoma breakpoint family member 10 (NBPF10) and disks large homologue 4 (DLG4) were found to be involved in GBM for the first time. We obtained MAPK and cell cycle signaling pathways from KEGG database. By analyzing perturbing mechanism in these two pathways, we identified several reversal gene pairs, including NRAS (neuroblastoma RAS) and CDK2 (cyclin-dependent kinase 2), CCND1 (cyclin D1) and FGFR (fibroblast growth factor receptor). Further analysis showed that NRAS and CDK2 were positively related with GBM. However, FGFR2 and CCND1 were negatively related with GBM.

Interpretation & conclusions

These findings suggest that newly identified DEGs and featured reversal gene pairs participated in MAPK and cell cycle signaling pathway may provide a new therapeutic line of approach to GBM.     

金针菇菌柄发育的转录组与蛋白组分析

菌柄是金针菇等食用菌的主要商品部位,但其生长机制仍不明确。本研究对金针菇伸长期和成熟期菌柄进行了转录组联合蛋白组分析,结果显示,两样本显著性差异表达基因和蛋白分别为721个和61个,均以上调表达为主。GO（gene ontology）功能聚类分析表明：有72.41%的差异表达基因富集在催化活性（catalytic activity）条目下。细胞组分（cell part）和绑定结合（binding）条目同时富集了较多的差异表达基因和蛋白。KEGG通路富集分析显示：碳水化合物代谢通路（carbohydrate metabolism）和氨基酸代谢通路（amino acid metabolism）富集的差异表达基因较多。差异表达蛋白富集较多的通路是单环菌素生物合成（monobactam biosynthesis,ko00261）、链霉素生物合成（streptomycin biosynthesis,ko00521）和有机含硒化合物代谢（selenocompound metabolism,ko00450）等。内质网蛋白质加工（protein processing in endoplasmic reticulum,ko04141）和MAPK信号通路（MAPK signaling pathway-yeast,ko04011）在转录组和蛋白组的KEGG富集分析中均为差异通路。本研究联合转录组和蛋白组数据筛选了40个金针菇菌柄发育中差异表达基因,为深入研究揭示食用菌菌柄发育过程提供候选基因。     

Identification of potential key genes associated with osteosarcoma based on integrated bioinformatics analyses

Due to high rates of metastasis and poor clinical outcomes for patients, it is important to study the pathomechanisms of osteosarcoma. However, due to the fact that osteosarcoma shows significant interindividual variation and high heterogeneity, the identification of differentially expressed genes (DEGs) at the population level cannot answer many important questions related to osteosarcoma tumorigenesis. Therefore, a new strategy to identify dysregulated genes in osteosarcoma samples is required. The aim of this study was to improve our understanding of osteosarcoma pathogenesis by identifying genes with universal aberrant expression in osteosarcoma samples. Because the relative expression ordering of genes is stable in normal bone tissues but is disrupted in osteosarcoma tissues, we used the RankComp algorithm to identify DEGs in normal and osteosarcoma tissue samples. We then calculated the dysregulation frequency for each gene. Genes with deregulation frequencies above 80% were deemed to be universal DEGs. Next, coexpression, pathway enrichment, and protein-protein interaction network analyses were performed to characterize the functions of these genes. From 188 samples of osteosarcoma obtained from four datasets measured on different platforms, 51 universal DEGs were identified, including 4 universally upregulated genes and 47 universally downregulated genes. Genes that were differentially coexpressed with these universal DEGs were found to be enriched in 46 cancer-related pathways. In addition, functional and network analyses showed that genes with high dysregulation frequencies were involved in cancer-related functions. Thus, the commonly aberrant genes identified in osteosarcoma tissues may be important targets for osteosarcoma diagnosis and therapy.     

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).

Five candidate biomarkers associated with the diagnosis and prognosis of cervical cancer

Purpose: Cervical cancer (CC) is one of the most general gynecological malignancies and is associated with high morbidity and mortality. We aimed to select candidate genes related to the diagnosis and prognosis of CC.Methods: The mRNA expression profile datasets were downloaded. We also downloaded RNA-sequencing gene expression data and related clinical materials from TCGA, which included 307 CC samples and 3 normal samples. Differentially expressed genes (DEGs) were obtained by R software. GO function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed in the DAVID dataset. Using machine learning, the optimal diagnostic mRNA biomarkers for CC were identified. We used qRT-PCR and Human Protein Atlas (HPA) database to exhibit the differences in gene and protein levels of candidate genes.Results: A total of 313 DEGs were screened from the microarray expression profile datasets. DNA methyltransferase 1 (DNMT1), Chromatin Assembly Factor 1, subunit B (CHAF1B), Chromatin Assembly Factor 1, subunit A (CHAF1A), MCM2, CDKN2A were identified as optimal diagnostic mRNA biomarkers for CC. Additionally, the GEPIA database showed that the DNMT1, CHAF1B, CHAF1A, MCM2 and CDKN2A were associated with the poor survival of CC patients. HPA database and qRT-PCR confirmed that these genes were highly expressed in CC tissues.Conclusion: The present study identified five DEmRNAs, including DNMT1, CHAF1B, CHAF1A, MCM2 and Kinetochore-related protein 1 (KNTC1), as potential diagnostic and prognostic biomarkers of CC.     

RNA-seq analysis provide new insights into mapk signaling of apolipoproteinciii-induced inflammation in porcine vascular endothelial cells

Apolipoprotein CIII (ApoCIII) has been shown to be associated with the inflammatory response, but the mechanism of its inflammatory effects remains unclear. Because vascular endothelial cells (VECs) play a key role in the development of inflammation, the present study was performed to investigate inflammatory mechanisms induced by ApoCIII in VECs. In this study, we screened differentially expressed genes (DEGs) using RNA-sequencing. The results identified 390 up-regulated genes and 257 down-regulated genes. We performed GO functional classification and KEGG pathway analysis for DEGs. Analysis of sequencing data showed that 21 genes were related to the MAPK pathway. Finally, we investigated whether ApoCIII regulates the expression of pro-inflammatory cytokines via MAPK signaling pathway. The results showed that ApoCIII increased the expression levels of IL-6, TNF-α, VCAM-1 and ICAM-1 in VECs. ApoCIII activated the phosphorylation of ERK1/2 and p38 MAPK. An inhibitor of ERK1/2 and p38 MAPK decreased the protein levels of IL-6 and TNF-α. Our findings demonstrate that ApoCIII induces pro-inflammatory cytokine production in VECs via activation of ERK1/2 and p38 MAPK phosphorylation.     

基质金属蛋白酶及其抑制剂在肝纤维化中的表达变化

目的 观察肝纤维化形成过程中基质金属蛋白酶MMP-1及其抑制剂TIMP-1的表达变化,从细胞外基质降解代谢的角度研究四氯化碳(CCl4)中毒性肝纤维化发生的机制.方法 雄性Wistar大鼠20只,分为正常组和肝纤维化模型组.肝纤维化组采用CCl4、饮酒、高脂低蛋白饮食等复合病因刺激制备肝纤维化动物模型,造模时间为8周.实验结束后测定肝脏指数、血清透明质酸(HA)、谷丙转氨酶(ALT)及尿羟脯氨酸(HYP)排出量,光镜下观察肝组织纤维化程度,并用免疫组化SABC法检测肝组织中Ⅰ、Ⅲ型胶原蛋白及MMP-1、TIMP-1的表达,同时用荧光实时定量PCR(RT-PCR)的方法检测肝组织中MMP-1、TIMP-1 mRNA的表达.结果 与正常对照组比较,肝纤维化模型组大鼠肝脏指数、血清HA及ALT显著增高,尿羟脯氨酸的排出量明显增加,病理组织学检查发现肝组织内纤维结缔组织增生明显,有假小叶形成;免疫组化的结果显示肝组织内Ⅰ、Ⅲ型胶原蛋白、MMP-1及TIMP-1的表达较正常组显著增加.结论 肝组织中MMP-1及TIMP-1的表达变化可能是导致肝纤维化的重要机制之一.     

Identification of Gene Expression Changes from Colitis to CRC in the Mouse CAC Model

A connection between colorectal carcinogenesis and inflammation is well known, but the underlying molecular mechanisms have not been elucidated. Chemically induced colitis-associated cancer (CAC) is an outstanding mouse model for studying the link between inflammation and cancer. Additionally, the CAC model is used for examining novel diagnostic, prognostic, and predictive markers for use in clinical practice. Here, a CAC model was established in less than 100 days using azoxymethane (AOM) with dextran sulfate sodium salt (DSS) in BALB/c mice. We examined the mRNA expression profiles of three groups: control untreated mice (K), DSS-induced chronic colitis mice (D), and AOM/DSS-induced CAC (AD) mice. We identified 6301 differentially expressed genes (DEGs) among the three groups, including 93 persistently upregulated genes and 139 persistently downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed that the most persistent DEGs were significantly enriched in metabolic or inflammatory components in the tumor microenvironment. Furthermore, several associated DEGs were identified as potential DEGs by protein-protein interaction (PPI) network analysis. We selected 14 key genes from the DEGs and potential DEGs for further quantitative real-time PCR (qPCR) verification. Six persistently upregulated, 3 persistently downregulated DEGs, and the other 3 genes showed results consistent with the microarray data. We demonstrated the regulation of 12 key genes specifically involved in Wnt signaling, cytokine and cytokine receptor interactions, homeostasis, and tumor-associated metabolism during colitis-associated CRC. Our results suggest that a close relationship between metabolic and inflammatory mediators of the tumor microenvironment is present in CAC.     

High-dose dexamethasone injection disordered metabolism and multiple protein kinases expression in the mouse kidney

Glucocorticoids (GCs) have been widely used in clinical treatment as anti-inflammatory, anti-shock and immunosuppressive medicines. However, the effect of excessive GCs on immune response and metabolism of kidney remains unclear. Here, we profiled the gene expression of kidney from mice with high-dose dexamethasone (DEX) treatment. A total of 1193 differentially expressed genes (DEGs) were screened in DEX treatment group compared with the saline group, including 715 down- regulated and 478 up-regulated. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of these DEGs showed extracellular matrix (ECM)–receptor interaction, cell adhesion molecules signaling pathway were significantly enriched, and that the vast majority of DEGs were involved in monocarboxylic acid metabolism, leukocyte cell–cell adhesion and fatty acid metabolism. Gene set enrichment analysis (GSEA) revealed that DEGs were strongly associated with immune-response and cell adhesion gene sets, such as Fc γ R-mediated phagocytosis, leukocyte transendothelial migration, T-cell receptor signaling pathway, cell adhesion, ECM–receptor interaction and focal adhesion-associated pathways. KEGG pathway analysis of differentially expressed kinases (DEKs) showed T-cell receptor and forkhead box class O signaling pathway were enriched. Furthermore, we found multiple protein kinases expression were dysregulated greatly after dexamethasone treatment, including classical effector of GCs stimulation-serum and GC-regulated kinase. These protein kinases are involved in multiple signaling pathways in mice kidney, such as mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase (PI3K)/Akt signaling pathway. We profiled the gene expression of the kidney from high-dose dexamethasone-treated mice and provided important information for further study the mechanism of side effects of GCs in clinical therapy.