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2.
Gene ; 720: 144088, 2019 Dec 15.
Artigo em Inglês | MEDLINE | ID: mdl-31476404

RESUMO

BACKGROUND: Secretory leukocyte protease inhibitor (SPLI) was a secreted protein which belongs to a member of whey acidic protein four-disulfide core family. In breast cancer (BC) it may inhibit cell proliferation and promote cancer metastasis. In this study, a comprehensive bioinformatics analysis was performed to identify the expression and prognostic value of SLPI in breast cancer. METHODS: SLPI expression in breast cancer was analyzed in Oncomine online database, which was subsequently confirmed by quantitative PCR (qPCR) in 18 BC samples and western blotting in 26 BC samples. Breast cancer gene-expression miner v4.1 was used to access the expression level with clinicopathological parameters in breast cancer patients. The prognostic values of SLPI in breast cancer were evaluated using the PrognoScan database. RESULTS: Our results indicated that SLPI was downregulated in breast cancer than in normal tissues. SLPI expression was found to be negatively correlated with estrogen receptor (ER) and progesterone receptor (PR) status. SLPI expression level was decreased in negative basal-like status patients compared with positive basal-like status. Meanwhile, triple-negative breast cancer status positive correlated with SLPI. We confirmed a positive correlation between SLPI and interleukin 17 receptor B (IL17RB) express in breast cancer tissues via oncomine co-expression analysis. Ten proteins: Elastase, Granulin, Lipocalin, Defensin beta 103B, Defensin beta 103A, Tubulin, Heparin-binding EGF-like growth factor, Interleukin 6, Epidermal growth factor, Phospholipid scramblase 1 were determinate interactions with SLPI by STRING. CONCLUSION: SLPI could as a biomarker to predict the prognosis values of breast cancer. However, further comprehensive study and mining more evidence are needed to clarify our results.


Assuntos
Biologia Computacional/métodos , Regulação Neoplásica da Expressão Gênica , Inibidor Secretado de Peptidases Leucocitárias/genética , Neoplasias de Mama Triplo Negativas/genética , Adulto , Idoso , Feminino , Humanos , Pessoa de Meia-Idade , Prognóstico , Mapas de Interação de Proteínas , Inibidor Secretado de Peptidases Leucocitárias/metabolismo , Neoplasias de Mama Triplo Negativas/metabolismo , Neoplasias de Mama Triplo Negativas/patologia
3.
Gene ; 720: 144103, 2019 Dec 15.
Artigo em Inglês | MEDLINE | ID: mdl-31491435

RESUMO

Clear cell renal cell carcinoma (ccRCC) is a highly invasive urological malignant tumor that results in shorter patient survival. At present, the mechanism of ccRCC metastasis is not clear. We explored the possible mechanisms of ccRCC metastasis by analyzing the transcriptome of ccRCC patients from the Cancer Genome Atlas (TCGA) database. Comparing the differences in transcriptome in patients with and without metastasis, we found 323 differential genes (|log2FoldChange| > 1 and P < 0.001). KEGG and GO enrichment analyses of differentially expressed genes (DEGs) suggest that the transfer mechanism of ccRCC may be related to complement and coagulation cascades and cholesterol metabolism. To explore the key genes affecting tumor metastasis, we analyzed the association of these genes with patient survival time and found that 16 genes were significantly associated (P < 0.05). We compared the differences in expression of these 16 genes between ccRCC patients and the normal population, and the results showed that TF and B4GALNT1 were overexpressed in patients. Co-expression gene analysis indicated that TF may participate in the metastasis of cancer through the complement system and mucopolysaccharide biosynthesis. B4GALNT1 may affect metastasis through focal adhesion, calcium signaling pathways, and Hippo signaling pathways. Our studies suggest that the complement system and the coagulation cascade, cholesterol metabolism, calcium pathway and iron transport may be associated in the mechanism of metastasis. TF and B4GALNT1 may be the key genes for metastasis, and they may be potential diagnostic markers and therapeutic targets for ccRCC.


Assuntos
Biomarcadores Tumorais/genética , Carcinoma de Células Renais/genética , Biologia Computacional/métodos , Regulação Neoplásica da Expressão Gênica , Neoplasias Renais/genética , Transcriptoma , Biomarcadores Tumorais/metabolismo , Carcinoma de Células Renais/metabolismo , Carcinoma de Células Renais/secundário , Estudos de Casos e Controles , Feminino , Perfilação da Expressão Gênica , Redes Reguladoras de Genes , Humanos , Neoplasias Renais/metabolismo , Neoplasias Renais/patologia , Masculino , Prognóstico , Mapas de Interação de Proteínas , Transdução de Sinais , Taxa de Sobrevida
4.
5.
BMC Bioinformatics ; 20(1): 423, 2019 Aug 14.
Artigo em Inglês | MEDLINE | ID: mdl-31412762

RESUMO

BACKGROUND: Computational drug repositioning, which aims to find new applications for existing drugs, is gaining more attention from the pharmaceutical companies due to its low attrition rate, reduced cost, and shorter timelines for novel drug discovery. Nowadays, a growing number of researchers are utilizing the concept of recommendation systems to answer the question of drug repositioning. Nevertheless, there still lie some challenges to be addressed: 1) Learning ability deficiencies; the adopted model cannot learn a higher level of drug-disease associations from the data. 2) Data sparseness limits the generalization ability of the model. 3)Model is easy to overfit if the effect of negative samples is not taken into consideration. RESULTS: In this study, we propose a novel method for computational drug repositioning, Additional Neural Matrix Factorization (ANMF). The ANMF model makes use of drug-drug similarities and disease-disease similarities to enhance the representation information of drugs and diseases in order to overcome the matter of data sparsity. By means of a variant version of the autoencoder, we were able to uncover the hidden features of both drugs and diseases. The extracted hidden features will then participate in a collaborative filtering process by incorporating the Generalized Matrix Factorization (GMF) method, which will ultimately give birth to a model with a stronger learning ability. Finally, negative sampling techniques are employed to strengthen the training set in order to minimize the likelihood of model overfitting. The experimental results on the Gottlieb and Cdataset datasets show that the performance of the ANMF model outperforms state-of-the-art methods. CONCLUSIONS: Through performance on two real-world datasets, we believe that the proposed model will certainly play a role in answering to the major challenge in drug repositioning, which lies in predicting and choosing new therapeutic indications to prospectively test for a drug of interest.


Assuntos
Algoritmos , Biologia Computacional/métodos , Reposicionamento de Medicamentos , Bases de Dados como Assunto , Descoberta de Drogas , Humanos , Modelos Teóricos , Reprodutibilidade dos Testes
6.
BMC Bioinformatics ; 20(1): 422, 2019 Aug 14.
Artigo em Inglês | MEDLINE | ID: mdl-31412768

RESUMO

BACKGROUND: One of the main issues in the automated protein function prediction (AFP) problem is the integration of multiple networked data sources. The UNIPred algorithm was thereby proposed to efficiently integrate -in a function-specific fashion- the protein networks by taking into account the imbalance that characterizes protein annotations, and to subsequently predict novel hypotheses about unannotated proteins. UNIPred is publicly available as R code, which might result of limited usage for non-expert users. Moreover, its application requires efforts in the acquisition and preparation of the networks to be integrated. Finally, the UNIPred source code does not handle the visualization of the resulting consensus network, whereas suitable views of the network topology are necessary to explore and interpret existing protein relationships. RESULTS: We address the aforementioned issues by proposing UNIPred-Web, a user-friendly Web tool for the application of the UNIPred algorithm to a variety of biomolecular networks, already supplied by the system, and for the visualization and exploration of protein networks. We support different organisms and different types of networks -e.g., co-expression, shared domains and physical interaction networks. Users are supported in the different phases of the process, ranging from the selection of the networks and the protein function to be predicted, to the navigation of the integrated network. The system also supports the upload of user-defined protein networks. The vertex-centric and the highly interactive approach of UNIPred-Web allow a narrow exploration of specific proteins, and an interactive analysis of large sub-networks with only a few mouse clicks. CONCLUSIONS: UNIPred-Web offers a practical and intuitive (visual) guidance to biologists interested in gaining insights into protein biomolecular functions. UNIPred-Web provides facilities for the integration of networks, and supplies a framework for the imbalance-aware protein network integration of nine organisms, the prediction of thousands of GO protein functions, and a easy-to-use graphical interface for the visual analysis, navigation and interpretation of the integrated networks and of the functional predictions.


Assuntos
Biologia Computacional/métodos , Internet , Mapas de Interação de Proteínas , Proteínas/metabolismo , Software , Algoritmos , Interface Usuário-Computador
8.
Bioengineered ; 10(1): 345-352, 2019 12.
Artigo em Inglês | MEDLINE | ID: mdl-31411110

RESUMO

This study aimed to detect serum miR-203 expression levels in AML and explore its potential clinical significance. Quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) was performed to measure the serum miR-203 levels in 134 patients with AML and 70 healthy controls. The results demonstrated that serum miR-203 expression was significantly reduced in AML patients compared with healthy controls. Receiver operating characteristic curve (ROC) analysis revealed miR-203 could distinguish AML cases from normal controls. Low serum miR-203 levels were associated with worse clinical features, as well as poorer overall survival and relapse free survival of AML patients. Moreover, multivariate analysis confirmed low serum miR-203 expression to be an independent unfavorable prognostic predictor for AML. The bioinformatics analysis showed that the downstream genes and pathways of miR-203 was closely associated with tumorigenesis. Downregulation of miR-203 in AML cell lines upregulated the expression levels of oncogenic promoters such as CREB1, SRC and HDAC1. Thus, these findings demonstrated that serum miR-203 might be a promising biomarker for the diagnosis and prognosis of AML.


Assuntos
Biomarcadores Tumorais/genética , Carcinogênese/genética , Regulação Leucêmica da Expressão Gênica , Leucemia Mieloide Aguda/genética , MicroRNAs/genética , Proteínas de Neoplasias/genética , Antagomirs/genética , Antagomirs/metabolismo , Biomarcadores Tumorais/sangue , Carcinogênese/metabolismo , Carcinogênese/patologia , Estudos de Casos e Controles , Linhagem Celular Tumoral , Biologia Computacional/métodos , Proteína de Ligação ao Elemento de Resposta ao AMP Cíclico/sangue , Proteína de Ligação ao Elemento de Resposta ao AMP Cíclico/genética , Perfilação da Expressão Gênica , Ontologia Genética , Histona Desacetilase 1/sangue , Histona Desacetilase 1/genética , Humanos , Leucemia Mieloide Aguda/sangue , Leucemia Mieloide Aguda/mortalidade , Leucemia Mieloide Aguda/patologia , MicroRNAs/antagonistas & inibidores , MicroRNAs/sangue , Anotação de Sequência Molecular , Análise Multivariada , Proteínas de Neoplasias/sangue , Prognóstico , Curva ROC , Recidiva , Transdução de Sinais , Análise de Sobrevida , Quinases da Família src/sangue , Quinases da Família src/genética
9.
BMC Bioinformatics ; 20(1): 409, 2019 Jul 30.
Artigo em Inglês | MEDLINE | ID: mdl-31362694

RESUMO

BACKGROUND: Internal ribosome entry sites (IRES) are segments of mRNA found in untranslated regions that can recruit the ribosome and initiate translation independently of the 5' cap-dependent translation initiation mechanism. IRES usually function when 5' cap-dependent translation initiation has been blocked or repressed. They have been widely found to play important roles in viral infections and cellular processes. However, a limited number of confirmed IRES have been reported due to the requirement for highly labor intensive, slow, and low efficiency laboratory experiments. Bioinformatics tools have been developed, but there is no reliable online tool. RESULTS: This paper systematically examines the features that can distinguish IRES from non-IRES sequences. Sequence features such as kmer words, structural features such as QMFE, and sequence/structure hybrid features are evaluated as possible discriminators. They are incorporated into an IRES classifier based on XGBoost. The XGBoost model performs better than previous classifiers, with higher accuracy and much shorter computational time. The number of features in the model has been greatly reduced, compared to previous predictors, by including global kmer and structural features. The contributions of model features are well explained by LIME and SHapley Additive exPlanations. The trained XGBoost model has been implemented as a bioinformatics tool for IRES prediction, IRESpy (https://irespy.shinyapps.io/IRESpy/), which has been applied to scan the human 5' UTR and find novel IRES segments. CONCLUSIONS: IRESpy is a fast, reliable, high-throughput IRES online prediction tool. It provides a publicly available tool for all IRES researchers, and can be used in other genomics applications such as gene annotation and analysis of differential gene expression.


Assuntos
Biologia Computacional/métodos , Sítios Internos de Entrada Ribossomal/genética , Software , Regiões 5' não Traduzidas/genética , Algoritmos , Sequência de Bases , Humanos , Modelos Teóricos , Probabilidade , RNA Viral/genética
10.
BMC Bioinformatics ; 20(1): 410, 2019 Jul 30.
Artigo em Inglês | MEDLINE | ID: mdl-31362714

RESUMO

BACKGROUND: Antiretroviral drugs are a very effective therapy against HIV infection. However, the high mutation rate of HIV permits the emergence of variants that can be resistant to the drug treatment. Predicting drug resistance to previously unobserved variants is therefore very important for an optimum medical treatment. In this paper, we propose the use of weighted categorical kernel functions to predict drug resistance from virus sequence data. These kernel functions are very simple to implement and are able to take into account HIV data particularities, such as allele mixtures, and to weigh the different importance of each protein residue, as it is known that not all positions contribute equally to the resistance. RESULTS: We analyzed 21 drugs of four classes: protease inhibitors (PI), integrase inhibitors (INI), nucleoside reverse transcriptase inhibitors (NRTI) and non-nucleoside reverse transcriptase inhibitors (NNRTI). We compared two categorical kernel functions, Overlap and Jaccard, against two well-known noncategorical kernel functions (Linear and RBF) and Random Forest (RF). Weighted versions of these kernels were also considered, where the weights were obtained from the RF decrease in node impurity. The Jaccard kernel was the best method, either in its weighted or unweighted form, for 20 out of the 21 drugs. CONCLUSIONS: Results show that kernels that take into account both the categorical nature of the data and the presence of mixtures consistently result in the best prediction model. The advantage of including weights depended on the protein targeted by the drug. In the case of reverse transcriptase, weights based in the relative importance of each position clearly increased the prediction performance, while the improvement in the protease was much smaller. This seems to be related to the distribution of weights, as measured by the Gini index. All methods described, together with documentation and examples, are freely available at https://bitbucket.org/elies_ramon/catkern.


Assuntos
Algoritmos , Biologia Computacional/métodos , Farmacorresistência Viral/genética , HIV-1/genética , Fármacos Anti-HIV/farmacologia , Farmacorresistência Viral/efeitos dos fármacos , Infecções por HIV/virologia , HIV-1/efeitos dos fármacos , HIV-1/isolamento & purificação , Humanos , Modelos Lineares , Análise de Componente Principal
11.
Gene ; 720: 144035, 2019 Dec 15.
Artigo em Inglês | MEDLINE | ID: mdl-31404595

RESUMO

Alcoholic hepatitis (AH) is a severe form of alcoholic liver disease associated with high mortality. Current pharmacological treatment options are not fully effective, and novel target therapies are urgently needed. Until now, key genes, miRNAs and potential signaling pathways in AH remain unclear. Here, we integrated mRNA and miRNA expression profiles to reveal 1411 differentially expressed genes (DEG) and 69 differentially expressed miRNAs (DEM) in AH. And then 51 overlapping genes were identified by compared with miRNA target genes and DEGs, which named as consistent expression genes (CEGs). Pathway analysis showed that CEGs were mainly enriched in PI3K-Akt signaling pathway, MicroRNAs in cancer, FoxO signaling pathway, TNF signaling pathway and P53 signaling pathway. A total of 8 hub genes,FOS, FOXO1, SIRT1, ESR1, BCL2L11, CDK1, CCNB1 and CDKN1A, were screened using protein-protein interaction network analysis. In the regulatory network of miRNA and hub genes, a total of five miRNAs, miR-29c, miR-92b, miR-132, miR-221, miR-222, were identified as key miRNAs. Among them, miR-132 has been shown to target SIRT1, FOXO1, CDKN1A and BCL2L11, and miR-92b targets SIRT1 and BCL2L11. miR-221 and miR-222 both target FOS, ESR1, and BCL2L11. In addition, miR-29c is one of the major down-regulated miRNAs in AH, targeting FOS. Western blot analysis showed that SIRT1 and FoxO1 were expressed at low levels (P < 0.05) and CDK1 was highly expressed in the AH group (P < 0.05). The other five proteins were not significantly different between the two groups (P > 0.05). RT-PCR results showed that miR-132 was significantly higher in the AH group than in the normal group (P < 0.05), while miR-29c was lower than the normal group (P < 0.05), and the other three miRNAs were not significantly different between the two groups (P > 0.05). Therefore, SIRT1, FOXO1, CDK1, miR-132 and miR-29c are involved in the regulation of FoxO and P53 signaling pathways, cell cycle and other biological processes, which may play a key role in the pathogenesis of AH.


Assuntos
Biomarcadores/análise , Biologia Computacional/métodos , Regulação da Expressão Gênica , Redes Reguladoras de Genes , Hepatite Alcoólica/genética , MicroRNAs/genética , Transdução de Sinais , Estudos de Casos e Controles , Feminino , Perfilação da Expressão Gênica , Humanos , Masculino , Pessoa de Meia-Idade
12.
BMC Bioinformatics ; 20(1): 417, 2019 Aug 13.
Artigo em Inglês | MEDLINE | ID: mdl-31409281

RESUMO

BACKGROUND: The development of high throughput sequencing techniques provides us with the possibilities to obtain large data sets, which capture the effect of dynamic perturbations on cellular processes. However, because of the dynamic nature of these processes, the analysis of the results is challenging. Therefore, there is a great need for bioinformatics tools that address this problem. RESULTS: Here we present DynOVis, a network visualization tool that can capture dynamic dose-over-time effects in biological networks. DynOVis is an integrated work frame of R packages and JavaScript libraries and offers a force-directed graph network style, involving multiple network analysis methods such as degree threshold, but more importantly, it allows for node expression animations as well as a frame-by-frame view of the dynamic exposure. Valuable biological information can be highlighted on the nodes in the network, by the integration of various databases within DynOVis. This information includes pathway-to-gene associations from ConsensusPathDB, disease-to-gene associations from the Comparative Toxicogenomics databases, as well as Entrez gene ID, gene symbol, gene synonyms and gene type from the NCBI database. CONCLUSIONS: DynOVis could be a useful tool to analyse biological networks which have a dynamic nature. It can visualize the dynamic perturbations in biological networks and allows the user to investigate the changes over time. The integrated data from various online databases makes it easy to identify the biological relevance of nodes in the network. With DynOVis we offer a service that is easy to use and does not require any bioinformatics skills to visualize a network.


Assuntos
Redes Reguladoras de Genes , Interface Usuário-Computador , Acetaminofen/farmacologia , Biologia Computacional/métodos , Bases de Dados Factuais , Humanos , NF-kappa B/metabolismo , Transdução de Sinais/efeitos dos fármacos , Transdução de Sinais/genética
13.
BMC Bioinformatics ; 20(1): 420, 2019 Aug 13.
Artigo em Inglês | MEDLINE | ID: mdl-31409290

RESUMO

BACKGROUND: Lineage rate heterogeneity can be a major source of bias, especially in multi-gene phylogeny inference. We had previously tackled this issue by developing LS3, a data subselection algorithm that, by removing fast-evolving sequences in a gene-specific manner, identifies subsets of sequences that evolve at a relatively homogeneous rate. However, this algorithm had two major shortcomings: (i) it was automated and published as a set of bash scripts, and hence was Linux-specific, and not user friendly, and (ii) it could result in very stringent sequence subselection when extremely slow-evolving sequences were present. RESULTS: We address these challenges and produce a new, platform-independent program, LSX, written in R, which includes a reprogrammed version of the original LS3 algorithm and has added features to make better lineage rate calculations. In addition, we developed and included an alternative version of the algorithm, LS4, which reduces lineage rate heterogeneity by detecting sequences that evolve too fast and sequences that evolve too slow, resulting in less stringent data subselection when extremely slow-evolving sequences are present. The efficiency of LSX and of LS4 with datasets with extremely slow-evolving sequences is demonstrated with simulated data, and by the resolution of a contentious node in the catfish phylogeny that was affected by an unusually high lineage rate heterogeneity in the dataset. CONCLUSIONS: LSX is a new bioinformatic tool, with an accessible code, and with which the effect of lineage rate heterogeneity can be explored in gene sequence datasets of virtually any size. In addition, the two modalities of the sequence subsampling algorithm included, LS3 and LS4, allow the user to optimize the amount of non-phylogenetic signal removed while keeping a maximum of phylogenetic signal.


Assuntos
Evolução Biológica , Evolução Molecular , Software , Algoritmos , Biologia Computacional/métodos , Filogenia
14.
Medicine (Baltimore) ; 98(27): e16225, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-31277135

RESUMO

MicroRNAs (miRNAs) play a great contribution to the development of diabetic nephropathy (DN). The aim of this study was to explore potential miRNAs-genes regulatory network and biomarkers for the pathogenesis of DN using bioinformatics methods.Gene expression profiling data related to DN (GSE1009) was obtained from the Gene Expression Omnibus (GEO) database, and then differentially expressed genes (DEGs) between DN patients and normal individuals were screened using GEO2R, followed by a series of bioinformatics analyses, including identifying key genes, conducting pathway enrichment analysis, predicting and identifying key miRNAs, and establishing regulatory relationships between key miRNAs and their target genes.A total of 600 DEGs associated with DN were identified. An additional 7 key DEGs, including 6 downregulated genes, such as vascular endothelial growth factor α (VEGFA) and COL4A5, and 1 upregulated gene (CCL19), were identified in another dataset (GSE30528) from glomeruli samples. Pathway analysis showed that the down- and upregulated DEGs were enriched in 14 and 6 pathways, respectively, with 7 key genes mainly involved in extracellular matrix-receptor interaction, PI3K/Akt signaling, focal adhesion, and Rap1 signaling. The relationships between miRNAs and target genes were constructed, showing that miR-29 targeted COL4A and VEGFA, miR-200 targeted VEGFA, miR-25 targeted ITGAV, and miR-27 targeted EGFR.MiR-29 and miR-200 may play important roles in DN. VEGFA and COL4A5 were targeted by miR-29 and VEGFA by miR-200, which may mediate multiple signaling pathways leading to the pathogenesis and development of DN.


Assuntos
Biologia Computacional/métodos , Nefropatias Diabéticas/genética , Perfilação da Expressão Gênica/métodos , MicroRNAs/genética , Regulação para Cima , Redes Reguladoras de Genes , Humanos , Análise em Microsséries
15.
Medicine (Baltimore) ; 98(27): e16240, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-31277141

RESUMO

Osteoarthritis (OA), also known as degenerative arthritis, affects millions of people all over the world. OA occurs when the cartilage wears down over time, which is a worldwide complaint. The aim of this study was to screen and verify hub genes involved in developmental chondrogenesis as well as to explore potential molecular mechanisms.The expression profiles of GSE51812 were downloaded from the Gene Expression Omnibus (GEO) database, which contained 9 samples, including 6-week pre-chondrocytes (PC, 6 independent specimens) and 17-week fetal periarticular resting chondrocytes (RC, 3 independent specimens). The raw data were integrated to obtain differentially expressed genes (DEGs) and were further analyzed with bioinformatics analysis. The Gene Ontology (GO) and pathway enrichment of DEGs were conducted via Database for Annotation, Visualization, and Integrated Discovery (DAVID). The protein-protein interaction (PPI) networks of the DEGs were constructed based on data from the search tool for the retrieval of interacting genes (STRING) database. An intersection figure was provided to show the relationship between the DEGs identified in this study and genes from any existed related studies.A total of 9486 DEGs, including 4821 upregulated genes and 4665 downregulated genes were observed. The top 30 developmental chondrogenesis associated genes were identified, including matrix metalloproteinase (MMP)1, MMP3, MMP13, prostaglandin-endoperoxide synthase 2 (PTGS2), and so on. The majority of DEGs, including PTGS2, CCL20, CHI3L1, LIF, CXCL8, and CXCL12 were intensively enriched in immune-associated biological process terms, including inflammatory, and immune responses. Additionally, the majority of DEGs were mainly enriched in NF-kappa ß (NF-kß) signaling pathway and tumor necrosis factor (TNF) signaling pathway. The hub genes identified in STRING and Cytoscape databases included MMP1, MMP3, MMP13, PTGS2 and so on. Among the top 30 upregulated and downregulated DEGs, there were 15 genes have been reported to be associated with OA or developmental chondrogenesis.This large scale gene expression study observed genes associated with human developmental chondrogenesis and their relative GO function, which may offer opportunities for the research for cartilage tissue engineering and novel insights into the prevention of OA in the near future.


Assuntos
Condrogênese/genética , Biologia Computacional/métodos , Perfilação da Expressão Gênica/métodos , Regulação da Expressão Gênica , Osteoartrite/genética , Biomarcadores/metabolismo , Bases de Dados Genéticas , Ontologia Genética , Redes Reguladoras de Genes , Humanos , Osteoartrite/patologia , Transdução de Sinais
16.
Medicine (Baltimore) ; 98(27): e16269, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-31277149

RESUMO

Esophageal squamous cell carcinoma (ESCC) is a malignancy that severely threatens human health and carries a high incidence rate and a low 5-year survival rate. MicroRNAs (miRNAs) are commonly accepted as a key regulatory function in human cancer, but the potential regulatory mechanisms of miRNA-mRNA related to ESCC remain poorly understood.The GSE55857, GSE43732, and GSE6188 miRNA microarray datasets and the gene expression microarray datasets GSE70409, GSE29001, and GSE20347 were downloaded from Gene Expression Omnibus databases. The differentially expressed miRNAs (DEMs) and differentially expressed genes (DEGs) were obtained using GEO2R. Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for DEGs were performed by Database for Annotation, Visualization and Integrated Discovery (DAVID). A protein-protein interaction (PPI) network and functional modules were established using the STRING database and were visualized by Cytoscape. Kaplan-Meier analysis was constructed based on The Cancer Genome Atlas (TCGA) database.In total, 26 DEMs and 280 DEGs that consisted of 96 upregulated and 184 downregulated genes were screened out. A functional enrichment analysis showed that the DEGs were mainly enriched in the ECM-receptor interaction and cytochrome P450 metabolic pathways. In addition, MMP9, PCNA, TOP2A, MMP1, AURKA, MCM2, IVL, CYP2E1, SPRR3, FOS, FLG, TGM1, and CYP2C9 were considered to be hub genes owing to high degrees in the PPI network. MiR-183-5p was with the highest connectivity target genes in hub genes. FOS was predicted to be a common target gene of the significant DEMs. Hsa-miR-9-3p, hsa-miR-34c-3p and FOS were related to patient prognosis and higher expression of the transcripts were associated with a poor OS in patients with ESCC.Our study revealed the miRNA-mediated hub genes regulatory network as a model for predicting the molecular mechanism of ESCC. This may provide novel insights for unraveling the pathogenesis of ESCC.


Assuntos
Biologia Computacional/métodos , Neoplasias Esofágicas/genética , Carcinoma de Células Escamosas do Esôfago/genética , Perfilação da Expressão Gênica/métodos , Regulação Neoplásica da Expressão Gênica , MicroRNAs/genética , RNA Neoplásico/genética , Bases de Dados Genéticas , Neoplasias Esofágicas/metabolismo , Carcinoma de Células Escamosas do Esôfago/metabolismo , Ontologia Genética , Redes Reguladoras de Genes , Humanos , Análise em Microsséries
17.
Medicine (Baltimore) ; 98(27): e16277, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-31277155

RESUMO

Kaposi sarcoma (KS) is an endothelial tumor etiologically related to Kaposi sarcoma herpesvirus (KSHV) infection. The aim of our study was to screen out candidate genes of KSHV infected endothelial cells and to elucidate the underlying molecular mechanisms by bioinformatics methods. Microarray datasets GSE16354 and GSE22522 were downloaded from Gene Expression Omnibus (GEO) database. the differentially expressed genes (DEGs) between endothelial cells and KSHV infected endothelial cells were identified. And then, functional enrichment analyses of gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were performed. After that, Search Tool for the Retrieval of Interacting Genes (STRING) was used to investigate the potential protein-protein interaction (PPI) network between DEGs, Cytoscape software was used to visualize the interaction network of DEGs and to screen out the hub genes. A total of 113 DEGs and 11 hub genes were identified from the 2 datasets. GO enrichment analysis revealed that most of the DEGs were enrichen in regulation of cell proliferation, extracellular region part and sequence-specific DNA binding; KEGG pathway enrichments analysis displayed that DEGs were mostly enrichen in cell cycle, Jak-STAT signaling pathway, pathways in cancer, and Insulin signaling pathway. In conclusion, the present study identified a host of DEGs and hub genes in KSHV infected endothelial cells which may serve as potential key biomarkers and therapeutic targets, helping us to have a better understanding of the molecular mechanism of KS.


Assuntos
Biomarcadores Tumorais/genética , Biologia Computacional/métodos , Células Endoteliais/metabolismo , Regulação Neoplásica da Expressão Gênica , Herpesvirus Humano 8 , Mapas de Interação de Proteínas/genética , Sarcoma de Kaposi/genética , Biomarcadores Tumorais/biossíntese , DNA de Neoplasias/genética , Células Endoteliais/patologia , Células Endoteliais/virologia , Perfilação da Expressão Gênica/métodos , Ontologia Genética , Humanos , Mapeamento de Interação de Proteínas/métodos , Sarcoma de Kaposi/metabolismo , Sarcoma de Kaposi/virologia
18.
Gene ; 712: 143961, 2019 Sep 05.
Artigo em Inglês | MEDLINE | ID: mdl-31279709

RESUMO

Since international federation of gynecology and obstetrics (FIGO) staging is mainly based on clinical assessment, an integrated approach for mining RNA based biomarkers for understanding the molecular deregulation of signaling pathways and RNAs in cervical cancer was proposed in this study. Publicly available data were mined for identifying significant RNAs after patient staging. Significant miRNA families were identified from mRNA-miRNA and lncRNA-miRNA interaction network analyses followed by stage specific mRNA-miRNA-lncRNA association network generation. Integrated bioinformatic analyses of selected mRNAs and lncRNAs were performed. Results suggest that HBA1, HBA2, HBB, SLC2A1, CXCL10 (stage I), PKIA (stage III) and S100A7 (stage IV) were important. miRNA family enrichment of interacting miRNA partners of selected RNAs indicated the enrichment of let-7 family. Assembly of collagen fibrils and other multimeric structures_Homosapiens_R-HSA-2022090 in pathway analysis and progesterone_CTD_00006624 in DSigDB analysis were the most significant and SLC2A1, hsa-miR-188-3p, hsa-miR-378a-3p and hsa-miR-150-5p were selected as survival markers.


Assuntos
Biomarcadores Tumorais/metabolismo , Biologia Computacional/métodos , Mineração de Dados/métodos , RNA Neoplásico/metabolismo , Neoplasias do Colo do Útero/genética , Neoplasias do Colo do Útero/metabolismo , Colágeno/química , Metilação de DNA , Progressão da Doença , Feminino , Perfilação da Expressão Gênica , Regulação Neoplásica da Expressão Gênica , Redes Reguladoras de Genes , Humanos , MicroRNAs/metabolismo , Análise de Sequência com Séries de Oligonucleotídeos , Papillomaviridae/metabolismo , Infecções por Papillomavirus/complicações , Neoplasias do Colo do Útero/virologia
19.
BMC Bioinformatics ; 20(1): 374, 2019 Jul 03.
Artigo em Inglês | MEDLINE | ID: mdl-31269897

RESUMO

BACKGROUND: One of the major challenges facing investigators in the microbiome field is turning large numbers of reads generated by next-generation sequencing (NGS) platforms into biological knowledge. Effective analytical workflows that guarantee reproducibility, repeatability, and result provenance are essential requirements of modern microbiome research. For nearly a decade, several state-of-the-art bioinformatics tools have been developed for understanding microbial communities living in a given sample. However, most of these tools are built with many functions that require an in-depth understanding of their implementation and the choice of additional tools for visualizing the final output. Furthermore, microbiome analysis can be time-consuming and may even require more advanced programming skills which some investigators may be lacking. RESULTS: We have developed a wrapper named iMAP (Integrated Microbiome Analysis Pipeline) to provide the microbiome research community with a user-friendly and portable tool that integrates bioinformatics analysis and data visualization. The iMAP tool wraps functionalities for metadata profiling, quality control of reads, sequence processing and classification, and diversity analysis of operational taxonomic units. This pipeline is also capable of generating web-based progress reports for enhancing an approach referred to as review-as-you-go (RAYG). For the most part, the profiling of microbial community is done using functionalities implemented in Mothur or QIIME2 platform. Also, it uses different R packages for graphics and R-markdown for generating progress reports. We have used a case study to demonstrate the application of the iMAP pipeline. CONCLUSIONS: The iMAP pipeline integrates several functionalities for better identification of microbial communities present in a given sample. The pipeline performs in-depth quality control that guarantees high-quality results and accurate conclusions. The vibrant visuals produced by the pipeline facilitate a better understanding of the complex and multidimensional microbiome data. The integrated RAYG approach enables the generation of web-based reports, which provides the investigators with the intermediate output that can be reviewed progressively. The intensively analyzed case study set a model for microbiome data analysis.


Assuntos
Microbiota , Software , Bactérias/classificação , Bactérias/genética , Sequência de Bases , Biologia Computacional/métodos , Filogenia , RNA Ribossômico 16S/química , RNA Ribossômico 16S/classificação , RNA Ribossômico 16S/genética
20.
Nat Commun ; 10(1): 3100, 2019 07 15.
Artigo em Inglês | MEDLINE | ID: mdl-31308405

RESUMO

Of the 473 genes in the genome of the bacterium with the smallest genome generated to date, 149 genes have unknown function, emphasising a universal problem; less than 1% of proteins have experimentally determined annotations. Here, we combine the results from state-of-the-art in silico methods for functional annotation and assign functions to 66 of the 149 proteins. Proteins that are still not annotated lack orthologues, lack protein domains, and/ or are membrane proteins. Twenty-four likely transporter proteins are identified indicating the importance of nutrient uptake into and waste disposal out of the minimal bacterial cell in a nutrient-rich environment after removal of metabolic enzymes. Hence, the environment shapes the nature of a minimal genome. Our findings also show that the combination of multiple different state-of-the-art in silico methods for annotating proteins is able to predict functions, even for difficult to characterise proteins and identify crucial gaps for further development.


Assuntos
Adaptação Biológica/genética , Bactérias/genética , Genoma Bacteriano/genética , Biologia Computacional/métodos , Genes Essenciais/genética , Anotação de Sequência Molecular/métodos , Software
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