Up-regulation of GINS1 highlighted a good diagnostic and prognostic potential of survival in three different subtypes of human cancer

Abstract Cancer is a fatal malignancy and its increasing worldwide prevalence demands the discovery of more sensitive and reliable molecular biomarkers. To investigate the GINS1 expression level and its prognostic value in distinct human cancers using a series of multi-layered in silico approach may help to establish it as a potential shared diagnostic and prognostic biomarker of different cancer subtypes. The GINS1 mRNA, protein expression, and promoter methylation were analyzed using UALCAN and Human Protein Atlas (HPA), while mRNA expression was further validated via GENT2. The potential prognostic values of GINS1 were evaluated through KM plotter. Then, cBioPortal was utilized to examine the GINS1-related genetic mutations and copy number variations (CNVs), while pathway enrichment analysis was performed using DAVID. Moreover, a correlational analysis between GINS1 expression and CD8+ T immune cells and a the construction of gene-drug interaction network was performed using TIMER, CDT, and Cytoscape. The GINS1 was found down-regulated in a single subtypes of human cancer while commonly up-regulated in 23 different other subtypes. The up-regulation of GINS1 was significantly correlated with the poor overall survival (OS) of Liver Hepatocellular Carcinoma (LIHC), Lung Adenocarcinoma (LUAD), and Kidney renal clear cell carcinoma (KIRC). The GINS1 was also found up-regulated in LIHC, LUAD, and KIRC patients of different clinicopathological features. Pathways enrichment analysis revealed the involvement of GINS1 in two diverse pathways, while few interesting correlations were also documented between GINS1 expression and its promoter methylation level, CD8+ T immune cells level, and CNVs. Moreover, we also predicted few drugs that could be used in the treatment of LIHC, LUAD, and KIRC by regulating the GINS1 expression. The expression profiling of GINS1 in the current study has suggested it a novel shared diagnostic and prognostic biomarker of LIHC, LUAD, and KIRC.

Resumo O câncer é uma doença maligna fatal e sua crescente prevalência mundial exige a descoberta de biomarcadores moleculares mais sensíveis e confiáveis. Investigar o nível de expressão de GINS1 e seu valor prognóstico em cânceres humanos distintos, usando uma série de abordagens in silico em várias camadas, pode ajudar a estabelecê-lo como um potencial biomarcador de diagnóstico e prognóstico compartilhado de diferentes subtipos de câncer. O mRNA de GINS1, a expressão da proteína e a metilação do promotor foram analisados usando UALCAN e Human Protein Atlas (HPA), enquanto a expressão de mRNA foi posteriormente validada via GENT2. Os valores prognósticos potenciais de GINS1 foram avaliados por meio do plotter KM. Em seguida, o cBioPortal foi utilizado para examinar as mutações genéticas relacionadas ao GINS1 e as variações do número de cópias (CNVs), enquanto a análise de enriquecimento da via foi realizada usando DAVID. Além disso, uma análise correlacional entre a expressão de GINS1 e células imunes T CD8 + e a construção de uma rede de interação gene-droga foi realizada usando TIMER, CDT e Cytoscape. O GINS1 foi encontrado regulado negativamente em um único subtipo de câncer humano, enquanto comumente regulado positivamente em 23 outros subtipos diferentes. A regulação positiva de GINS1 foi significativamente correlacionada com a sobrevida global pobre (OS) de Carcinoma Hepatocelular de Fígado (LIHC), Adenocarcinoma de Pulmão (LUAD) e Carcinoma de Células Claras Renais de Rim (KIRC). O GINS1 também foi encontrado regulado positivamente em pacientes LIHC, LUAD e KIRC de diferentes características clínico-patológicas. A análise de enriquecimento de vias revelou o envolvimento de GINS1 em duas vias diversas, enquanto poucas correlações interessantes também foram documentadas entre a expressão de GINS1 e seu nível de metilação do promotor, nível de células imunes T CD8 + e CNVs. Além disso, também previmos poucos medicamentos que poderiam ser usados no tratamento de LIHC, LUAD e KIRC, regulando a expressão de GINS1. O perfil de expressão de GINS1 no estudo atual sugeriu que é um novo biomarcador de diagnóstico e prognóstico compartilhado de LIHC, LUAD e KIRC.

Up-regulation of GINS1 highlighted a good diagnostic and prognostic potential of survival in three different subtypes of human cancer / A regulação positiva de GINS1 destacou um bom potencial diagnóstico e prognóstico de sobrevivência em três subtipos diferentes de câncer humano

Abstract Cancer is a fatal malignancy and its increasing worldwide prevalence demands the discovery of more sensitive and reliable molecular biomarkers. To investigate the GINS1 expression level and its prognostic value in distinct human cancers using a series of multi-layered in silico approach may help to establish it as a potential shared diagnostic and prognostic biomarker of different cancer subtypes. The GINS1 mRNA, protein expression, and promoter methylation were analyzed using UALCAN and Human Protein Atlas (HPA), while mRNA expression was further validated via GENT2. The potential prognostic values of GINS1 were evaluated through KM plotter. Then, cBioPortal was utilized to examine the GINS1-related genetic mutations and copy number variations (CNVs), while pathway enrichment analysis was performed using DAVID. Moreover, a correlational analysis between GINS1 expression and CD8+ T immune cells and a the construction of gene-drug interaction network was performed using TIMER, CDT, and Cytoscape. The GINS1 was found down-regulated in a single subtypes of human cancer while commonly up-regulated in 23 different other subtypes. The up-regulation of GINS1 was significantly correlated with the poor overall survival (OS) of Liver Hepatocellular Carcinoma (LIHC), Lung Adenocarcinoma (LUAD), and Kidney renal clear cell carcinoma (KIRC). The GINS1 was also found up-regulated in LIHC, LUAD, and KIRC patients of different clinicopathological features. Pathways enrichment analysis revealed the involvement of GINS1 in two diverse pathways, while few interesting correlations were also documented between GINS1 expression and its promoter methylation level, CD8+ T immune cells level, and CNVs. Moreover, we also predicted few drugs that could be used in the treatment of LIHC, LUAD, and KIRC by regulating the GINS1 expression. The expression profiling of GINS1 in the current study has suggested it a novel shared diagnostic and prognostic biomarker of LIHC, LUAD, and KIRC.

Resumo O câncer é uma doença maligna fatal e sua crescente prevalência mundial exige a descoberta de biomarcadores moleculares mais sensíveis e confiáveis. Investigar o nível de expressão de GINS1 e seu valor prognóstico em cânceres humanos distintos, usando uma série de abordagens in silico em várias camadas, pode ajudar a estabelecê-lo como um potencial biomarcador de diagnóstico e prognóstico compartilhado de diferentes subtipos de câncer. O mRNA de GINS1, a expressão da proteína e a metilação do promotor foram analisados ​​usando UALCAN e Human Protein Atlas (HPA), enquanto a expressão de mRNA foi posteriormente validada via GENT2. Os valores prognósticos potenciais de GINS1 foram avaliados por meio do plotter KM. Em seguida, o cBioPortal foi utilizado para examinar as mutações genéticas relacionadas ao GINS1 e as variações do número de cópias (CNVs), enquanto a análise de enriquecimento da via foi realizada usando DAVID. Além disso, uma análise correlacional entre a expressão de GINS1 e células imunes T CD8 + e a construção de uma rede de interação gene-droga foi realizada usando TIMER, CDT e Cytoscape. O GINS1 foi encontrado regulado negativamente em um único subtipo de câncer humano, enquanto comumente regulado positivamente em 23 outros subtipos diferentes. A regulação positiva de GINS1 foi significativamente correlacionada com a sobrevida global pobre (OS) de Carcinoma Hepatocelular de Fígado (LIHC), Adenocarcinoma de Pulmão (LUAD) e Carcinoma de Células Claras Renais de Rim (KIRC). O GINS1 também foi encontrado regulado positivamente em pacientes LIHC, LUAD e KIRC de diferentes características clínico-patológicas. A análise de enriquecimento de vias revelou o envolvimento de GINS1 em duas vias diversas, enquanto poucas correlações interessantes também foram documentadas entre a expressão de GINS1 e seu nível de metilação do promotor, nível de células imunes T CD8 + e CNVs. Além disso, também previmos poucos medicamentos que poderiam ser usados ​​no tratamento de LIHC, LUAD e KIRC, regulando a expressão de GINS1. O perfil de expressão de GINS1 no estudo atual sugeriu que é um novo biomarcador de diagnóstico e prognóstico compartilhado de LIHC, LUAD e KIRC.

Up-regulation of GINS1 highlighted a good diagnostic and prognostic potential of survival in three different subtypes of human cancer.

Cancer is a fatal malignancy and its increasing worldwide prevalence demands the discovery of more sensitive and reliable molecular biomarkers. To investigate the GINS1 expression level and its prognostic value in distinct human cancers using a series of multi-layered in silico approach may help to establish it as a potential shared diagnostic and prognostic biomarker of different cancer subtypes. The GINS1 mRNA, protein expression, and promoter methylation were analyzed using UALCAN and Human Protein Atlas (HPA), while mRNA expression was further validated via GENT2. The potential prognostic values of GINS1 were evaluated through KM plotter. Then, cBioPortal was utilized to examine the GINS1-related genetic mutations and copy number variations (CNVs), while pathway enrichment analysis was performed using DAVID. Moreover, a correlational analysis between GINS1 expression and CD8+ T immune cells and a the construction of gene-drug interaction network was performed using TIMER, CDT, and Cytoscape. The GINS1 was found down-regulated in a single subtypes of human cancer while commonly up-regulated in 23 different other subtypes. The up-regulation of GINS1 was significantly correlated with the poor overall survival (OS) of Liver Hepatocellular Carcinoma (LIHC), Lung Adenocarcinoma (LUAD), and Kidney renal clear cell carcinoma (KIRC). The GINS1 was also found up-regulated in LIHC, LUAD, and KIRC patients of different clinicopathological features. Pathways enrichment analysis revealed the involvement of GINS1 in two diverse pathways, while few interesting correlations were also documented between GINS1 expression and its promoter methylation level, CD8+ T immune cells level, and CNVs. Moreover, we also predicted few drugs that could be used in the treatment of LIHC, LUAD, and KIRC by regulating the GINS1 expression. The expression profiling of GINS1 in the current study has suggested it a novel shared diagnostic and prognostic biomarker of LIHC, LUAD, and KIRC.

Epidemiology, etiology, diagnosis and treatment of prostate cancer.

Prostate cancer is more common in men over the age of 65 years. There are 15% cases with positive family history of prostate cancer Worldwide. Prostate cancer is the second leading cause of death among the U.S. men. Prostate cancer incidence is strongly related to age with the highest rates in older man. Globally millions of people are suffering from this disease. This study aims to provide awareness about prostate cancer as well as an updated knowledge about the epidemiology, etiology, diagnosis and treatment of prostate cancer.

Simultaneous inference and clustering of transcriptional dynamics in gene regulatory networks.

We present a novel method for simultaneous inference and nonparametric clustering of transcriptional dynamics from gene expression data. The proposed method uses gene expression data to infer time-varying TF profiles and cluster these temporal profiles according to the dynamics they exhibit. We use the latent structure of factorial hidden Markov model to model the transcription factor profiles as Markov chains and cluster these profiles using nonparametric mixture modeling. An efficient Gibbs sampling scheme is proposed for inference of latent variables and grouping of transcriptional dynamics into a priori unknown number of clusters. We test our model on simulated data and analyse its performance on two expression datasets; S. cerevisiae cell cycle data and E. coli oxygen starvation response data. Our results show the applicability of the method for genome wide analysis of expression data.

Large-scale learning of combinatorial transcriptional dynamics from gene expression.

MOTIVATION: Knowledge of the activation patterns of transcription factors (TFs) is fundamental to elucidate the dynamics of gene regulation in response to environmental conditions. Direct experimental measurement of TFs' activities is, however, challenging, resulting in a need to develop statistical tools to infer TF activities from mRNA expression levels of target genes. Current models, however, neglect important features of transcriptional regulation; in particular, the combinatorial nature of regulation, which is fundamental for signal integration, is not accounted for. RESULTS: We present a novel method to infer combinatorial regulation of gene expression by multiple transcription factors in large-scale transcriptional regulatory networks. The method implements a factorial hidden Markov model with a non-linear likelihood to represent the interactions between the hidden transcription factors. We explore our model's performance on artificial datasets and demonstrate the applicability of our method on genome-wide scale for three expression datasets. The results obtained using our model are biologically coherent and provide a tool to explore the concealed nature of combinatorial transcriptional regulation. AVAILABILITY: http://homepages.inf.ed.ac.uk/gsanguin/software.html.

Transcript profiling and inference of Escherichia coli K-12 ArcA activity across the range of physiologically relevant oxygen concentrations.

Oxygen availability is the major determinant of the metabolic modes adopted by Escherichia coli. Although much is known about E. coli gene expression and metabolism under fully aerobic and anaerobic conditions, the intermediate oxygen tensions that are encountered in natural niches are understudied. Here, for the first time, the transcript profiles of E. coli K-12 across the physiologically significant range of oxygen availabilities are described. These suggested a progressive switch to aerobic respiratory metabolism and a remodeling of the cell envelope as oxygen availability increased. The transcriptional responses were consistent with changes in the abundance of cytochrome bd and bo' and the outer membrane protein OmpW. The observed transcript and protein profiles result from changes in the activities of regulators that respond to oxygen itself or to metabolic and environmental signals that are sensitive to oxygen availability (aerobiosis). A probabilistic model (TFInfer) was used to predict the activity of the indirect oxygen-sensing two-component system ArcBA across the aerobiosis range. The model implied that the activity of the regulator ArcA correlated with aerobiosis but not with the redox state of the ubiquinone pool, challenging the idea that ArcA activity is inhibited by oxidized ubiquinone. The amount of phosphorylated ArcA correlated with the predicted ArcA activities and with aerobiosis, suggesting that fermentation product-mediated inhibition of ArcB phosphatase activity is the dominant mechanism for regulating ArcA activity under the conditions used here.

TFInfer: a tool for probabilistic inference of transcription factor activities.

SUMMARY: TFInfer is a novel open access, standalone tool for genome-wide inference of transcription factor activities from gene expression data. Based on an earlier MATLAB version, the software has now been extended in a number of ways. It has been significantly optimised in terms of performance, and it was given novel functionality, by allowing the user to model both time series and data from multiple independent conditions. With a full documentation and intuitive graphical user interface, together with an in-built data base of yeast and Escherichia coli transcription factors, the software does not require any mathematical or computational expertise to be used effectively. AVAILABILITY: http://homepages.inf.ed.ac.uk/gsanguin/TFInfer.html CONTACT: gsanguin@staffmail.ed.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.