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.     

Identifying breast cancer subtypes associated modules and biomarkers by integrated bioinformatics analysis

Breast cancer is the most common form of cancer afflicting women worldwide. Patients with breast cancer of different molecular classifications need varied treatments. Since it is known that the development of breast cancer involves multiple genes and functions, identification of functional gene modules (clusters of the functionally related genes) is indispensable as opposed to isolated genes, in order to investigate their relationship derived from the gene co-expression analysis. In total, 6315 differentially expressed genes (DEGs) were recognized and subjected to the co-expression analysis. Seven modules were screened out. The blue and turquoise modules have been selected from the module trait association analysis since the genes in these two modules are significantly correlated with the breast cancer subtypes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment show that the blue module genes engaged in cell cycle, DNA replication, p53 signaling pathway, and pathway in cancer. According to the connectivity analysis and survival analysis, 8 out of 96 hub genes were filtered and have shown the highest expression in basal-like breast cancer. Furthermore, the hub genes were validated by the external datasets and quantitative real-time PCR (qRT-PCR). In summary, hub genes of Cyclin E1 (CCNE1), Centromere Protein N (CENPN), Checkpoint kinase 1 (CHEK1), Polo-like kinase 1 (PLK1), DNA replication and sister chromatid cohesion 1 (DSCC1), Family with sequence similarity 64, member A (FAM64A), Ubiquitin Conjugating Enzyme E2 C (UBE2C) and Ubiquitin Conjugating Enzyme E2 T (UBE2T) may serve as the prognostic markers for different subtypes of breast cancer.     

COPD 差异表达基因的生物信息学分析及在LUSC样本中的表达

为确定慢性阻塞性肺病(COPD)的分子标记物及COPD与肺鳞状细胞癌(LUSC)共存的差异表达基因,探寻COPD合并肺癌的预测因子,发现新的治疗靶点。本研究采用生物信息学方法,从GEO数据库中筛选3套基因芯片数据集,挖掘COPD患者小气道上皮细胞(SAEC)的差异表达基因(DEG)以及潜在的生物标记物,并通过基因本体(GO)、京都基因与基因组百科全书(KEGG)富集分析预测DEGs的功能及参与的代谢途径。继而对DEGs构建PPI网络,使用Cytoscape软件筛选子模块和Hub基因,并将Hub基因通过TCGA数据库分析其在LUSC中的差异表达情况及差异基因间的相关性。结果共获得52个上调基因和24个下调基因,代谢通路主要集中在细胞色素P450对外源物质的代谢、化学致癌、花生四烯酸代谢及甲状腺激素合成四条途径上,通过Cytoscape软件从PPI网络中筛选得到2个功能模块和10个Hub基因,进一步验证发现其中5个基因在TCGA数据库中的LUSC样本中同样差异表达。由此推测SPP1、ALDH3A1、SPRR3、KRT6A和SPRR1B 可能为COPD 分子标记物及COPD与LUSC共存的DEGs,从而为研究COPD和LUSC的发病机制及二者潜在关系奠定良好的基础。     

Gene regulation network analysis reveals core genes associated with survival in glioblastoma multiforme

Glioblastoma multiforme (GBM) is a very serious mortality of central nervous system cancer. The microarray data from GSE2223 , GSE4058 , GSE4290 , GSE13276 , GSE68848 and GSE70231 (389 GBM tumour and 67 normal tissues) and the RNA‐seq data from TCGA‐GBM dataset (169 GBM and five normal samples) were chosen to find differentially expressed genes (DEGs). RRA (Robust rank aggregation) method was used to integrate seven datasets and calculate 133 DEGs (82 up‐regulated and 51 down‐regulated genes). Subsequently, through the PPI (protein‐protein interaction) network and MCODE/ cytoHubba methods, we finally filtered out ten hub genes, including FOXM1, CDK4, TOP2A, RRM2, MYBL2, MCM2, CDC20, CCNB2, MYC and EZH2, from the whole network. Functional enrichment analyses of DEGs were conducted to show that these hub genes were enriched in various cancer‐related functions and pathways significantly. We also selected CCNB2, CDC20 and MYBL2 as core biomarkers, and further validated them in CGGA, HPA and CCLE database, suggesting that these three core hub genes may be involved in the origin of GBM. All these potential biomarkers for GBM might be helpful for illustrating the important role of molecular mechanisms of tumorigenesis in the diagnosis, prognosis and targeted therapy of GBM cancer.     

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.     

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 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.     

Bioinformatics identification of the methylerythritol phosphate pathway associated genes in <Emphasis Type="Italic">Arabidopsis thaliana</Emphasis> with <Emphasis Type="Italic">ceh1</Emphasis> mutant

The methylerythritol phosphate (MEP) pathway for the production of isoprenoids is recently discovered. The current study aimed to identify MEP pathway disorder-related molecular mechanisms and potential genes in Arabidopsis thaliana. Microarray data (GSE61675) obtained from ceh1 mutant plants and corresponding parental lines were retrieved from Gene Expression Omnibus (GEO) database and were applied for differentially expressed genes (DEGs) screening. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed. Protein-protein interaction (PPI) network was then constructed and displayed by Cytoscape software. Total 762 DEGs including 620 up-regulated and 142 down-regulated genes were screened. In addition, a great many of DEGs were mainly involved in biosynthesis and metabolism-related pathways, such as stilbenoid, diarylheptanoid, and gingerol biosynthesis, and biosynthesis of terpenoids and steroids. Moreover, a PPI network contained 90 down-regulated genes and 497 up-regulated genes were obtained. Up-regulated DEGs including glutaredoxin (GRX480, cytochrome BC1 synthase (BCS1, syntaxin of plants 121 (SYP121) and A. thaliana MAP kinase 11 (ATMPK11) with higher degree in this network were hub nodes. Pathways including stilbenoid, diarylheptanoid, and gingerol biosynthesis obtained in our study were consistent with previous studies. Importantly, GRX480, BCS1 and ATMPK11 could have close interactions with the MEP pathway and may play important roles in the biosynthesis of isoprenoids.     

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。     

Network Pharmacology and Molecular Docking Reveal the Mechanism of Corydalis saxicola Bunting Total Alkaloids in Treating Radiation-Induced Oral Mucositis

The study aims to explore the effect and mechanism of total alkaloids of Corydalis saxicola Bunting (CSBTA) in the treatment of radiation induced oral mucositis (RIOM) through network pharmacology and molecular docking. The components and corresponding targets of Corydalis saxicola Bunting were screened by literature review. RIOM related targets were obtained in GeneCards. Cytoscape software was used to construct the component-target-pathway network. Protein-Protein Interaction (PPI) networks was constructed by String database. GO and KEGG enrichment analyses were performed by Metascape. AutoDock Vina 4.2 software was used for molecular docking. There were 26 components of CSBTA targeting 61 genes related to RIOM. Through Cytoscape and PPI analysis, 15 core target genes of CSBTA for treating RIOM were identified. GO functional analysis indicated that CSBTA might play a role through kinase binding and protein kinase activation. KEGG pathway analysis showed that the core targets of CSBTA were mainly focused on cancer and reactive oxygen species (ROS) pathway. The results of molecular docking showed that CSBTA had strong binding energy with target protein including SRC, AKT and EGFR. The study demonstrates that CSBTA may treat RIOM by affecting SRC, AKT and EGFR through ROS pathway.     

Identification of novel biomarkers and key pathways of condyloma acuminata

Condyloma acuminata (CA) is a prevalent sexually transmitted disease, associated with human papilloma viruses (HPV) infections and host immune status. In this present study, we aimed to explore immune landscape and biomarkers for CA prevention and treatment. We obtained differentially expressed genes (DEGs) of CA vs normal tissues in GSE140662 and screened out hub genes from the protein–protein interaction (PPI) network. Hub genes were then subjected to microRNA (miRNA) analysis. Besides, CCK-8, transwell, flow cytometry assays were employed to assess the cell proliferation, migration and apoptosis in Hela cells. ImmuCellAI was firstly applied to identify immune cell infiltration levels of CA. We obtained 275 DEGs, 23 hub genes and key miRNAs. Subsequently, we verified four up-regulated hub genes IFIT1, IFI27, OASL, SAMD9L and down-regulated mir-146a-5p in CA tissues by RT-qPCR. Moreover, over-expression of miR-146a-5p reduced Hela cells proliferation, migration, blocked cell cycle and induced apoptosis. Up-regulated miR-146a-5p attenuated PI3K/AKT and activated p38/ MAPK signaling pathway. Proportions of Monocyte, NK cells, Gamma delta cells, Th17 cells were relatively low, while Th1 and CD8+ T cells were relatively high in CA skin. Our study revealed that mir-146a-5p contribute to CA progression through PI3K/AKT and p38/MAPK signaling pathway.     

Screening and identification of hub genes in pancreatic cancer by integrated bioinformatics analysis

Pancreatic cancer (Pa) is a malignant tumor of the digestive tract with high degree of malignancy, this study aimed to obtain the hub genes in the tumorigenesis of Pa. Microarray datasets GSE15471, GSE16515, and GSE62452 were downloaded from Gene Expression Omnibus (GEO) database, GEO2R was conducted to screen the differentially expressed genes (DEGs), and functional enrichment analyses were carried out by Database for Annotation, Visualization and Integrated Discovery (DAVID). The protein-protein interaction (PPI) network was constructed with the Search Tool for the Retrieval of Interacting Genes (STRING), and the hub genes were identified by Cytoscape. Totally 205 DEGs were identified, consisting of 51 downregulated genes and 154 upregulated genes enriched in Gene Ontology terms including extracellular matrix (ECM) organization, collagen binding, cell adhesion, and pathways associated with ECM-receptor interaction, focal adhesion, and protein digestion. Two modules in the PPI were chosen and biological process analyses showed that the module genes were mainly enriched in ECM and cell adhesion. Twenty-four hub genes were confirmed, the survival analyses from the cBioPortal online platform revealed that topoisomerase (DNA) II α (TOP2A), periostin (POSTN), plasminogen activator, urokinase (PLAU), and versican (VCAN) may be involved in the carcinogenesis and progression of Pa, and the receiver-operating characteristic curves indicated their diagnostic value for Pa. Among them, TOP2A, POSTN, and PLAU have been previously reported as biomarkers for Pa, and far too little attention has been paid to VCAN. Analysis from R2 online platform showed that Pa patients with high VCAN expression were more sensitive to gemcitabine than those with low level, suggesting that VCAN may be an indicator to guide the use of the chemotherapeutic drug. In vitro experiments also showed that the sensitivity of the VCAN siRNA group to gemcitabine was lower than that of the control group. In conclusion, this study discerned hub genes and pathways related to the development of Pa, and VCAN was identified as a novel biomarker for the diagnose and therapy of Pa.