scholarly journals PROGgene: gene expression based survival analysis web application for multiple cancers

2013 ◽  
Vol 3 (1) ◽  
pp. 22 ◽  
Author(s):  
Chirayu Goswami ◽  
Harikrishna Nakshatri
Keyword(s):  
Gene Expression ◽  
Survival Analysis ◽  
Web Application ◽  
Multiple Cancers

scholarly journals Comprehensive Pan-Cancer Analysis and the Regulatory Mechanism of ASF1B, a Gene Associated With Thyroid Cancer Prognosis in the Tumor Micro-Environment

2021 ◽  
Vol 11 ◽  
Author(s):  
Jing Ma ◽  
Wei Han ◽  
Kai Lu
Keyword(s):  
Gene Expression ◽  
Survival Analysis ◽  
Thyroid Cancer ◽  
Prognostic Marker ◽  
Enrichment Analysis ◽  
Differential Analysis ◽  
Expression Levels ◽  
Multiple Cancers ◽  
Mtorc1 Signaling ◽  
Pan Cancer

BackgroundThe incidence of thyroid cancer, whose local recurrence and metastasis lead to death, has always been high and the pathogenesis of papillary thyroid carcinoma (PTC) has not been clearly elucidated. Therefore, the research for more accurate prognosis-related predictive biomarkers is imminent, and a key gene can often be a prognostic marker for multiple tumors.MethodsGene expression profiles of various cancers in the TCGA and GTEx databases were downloaded, and genes significantly associated with the prognosis of THCA were identified by combining differential analysis with survival analysis. Then, a series of bioinformatics tools and methods were used to analyze the expression of the gene in each cancer and the correlation of each expression with prognosis, tumor immune microenvironment, immune neoantigens, immune checkpoints, DNA repair genes, and methyltransferases respectively. The possible biological mechanisms were also investigated by GSEA enrichment analysis.Results656 differentially expressed genes were identified from two datasets and 960 DEGs that were associated with disease-free survival in THCA patients were screened via survival analysis. The former and the latter were crossed to obtain 7 key genes, and the gene with the highest risk factor, ASF1B, was selected for this study. Differential analysis of multiple databases showed that ASF1B was commonly and highly expressed in pan-cancer. Survival analysis showed that high ASF1B expression was significantly associated with poor patient prognosis in multiple cancers. In addition, ASF1B expression levels were found to be associated with tumor immune infiltration in THCA, KIRC, LGG, and LIHC, and with tumor microenvironment in BRCA, LUSC, STAD, UCEC, and KIRC. Further analysis of the relationship between ASF1B expression and immune checker gene expression suggested that ASF1B may regulate tumor immune patterns in most tumors by regulating the expression levels of specific immune checker genes. Finally, GSEA enrichment analysis showed that ASF1B high expression was mainly enriched in cell cycle, MTORC1 signaling system, E2F targets, and G2M checkpoints pathways.ConclusionsASF1B may be an independent prognostic marker for predicting the prognosis of THCA patients. The pan-cancer analysis suggested that ASF1B may play an important role in the tumor micro-environment and tumor immunity and it has the potential of serving as a predictive biomarker for multiple cancers.

Identification of Hub Genes Associated with Development of Lung Adenocarcinoma by Integrated Bioinformatics Analysis

2021 ◽  
Author(s):  
Jinglei Li ◽  
Wei Hou
Keyword(s):  
Gene Expression ◽  
Survival Analysis ◽  
Network Analysis ◽  
Lung Adenocarcinoma ◽  
Normal Control ◽  
Gene Expression Analysis ◽  
Normal Control Group ◽  
Control Group ◽  
Differential Gene Expression Analysis ◽  
Differential Gene

Abstract Purpose: Lung adenocarcinoma (LUAD) has high heterogeneity and poor prognosis, posing a major challenge to human health worldwide. Therefore, it is necessary to improve our understanding of the molecular mechanism of LUAD in order to be able to better predict its prognosis and develop new therapeutic strategies for target genes.Methods: The Cancer Genome Atlas and Gene Expression Omnibus, were selected to comprehensively analyze and explore the differences between LUAD tumors and adjacent normal tissues. Critical gene information was obtained through weighted gene co-expression network analysis (WGCNA), differential gene expression analysis, and survival analysis.Results: Using WGCNA and differential gene expression analysis, 29 differentially expressed genes were screened. The functional annotation analysis showed these genes to be mainly concentrated in heart trabecula formation, regulation of inflammatory response, collagen-containing extracellular matrix, and metalloendopeptidase inhibitor activity. Also, in the protein–protein interaction network analysis, 10 central genes were identified using Cytoscape's CytoHubba plug-in. The expression of CDH5, TEK, TIMP3, EDNRB, EPAS1, MYL9, SPARCL1, KLF4, and TGFBR3 in LUAD tissue was found to be lower than that in the normal control group, while the expression of MMP1 in LUAD tissue was higher than that in the normal control group. According to survival analysis, the low expression of MYL9 and SPARCL1 was correlated with poor overall survival in patients with LUAD. Finally, through the verification of the Oncomine database, it was found that the expression levels of MYL9 and SPARCL1 were consistent with the mRNA levels in LUAD samples, and both were downregulated.Conclusion: Two survival-related genes, MYL9 and SPARCL1, were determined to be highly correlated with the development of LUAD. Both may play an essential role in the development LUAD and may be potential biomarkers for its diagnosis and treatment in the future.

scholarly journals NCR-PCOPGene: An Exploratory Tool for Analysis of Sample-Classes Effect on Gene-Expression Relationships

2008 ◽  
Vol 2008 ◽  
pp. 1-7
Author(s):  
Juan Cedano ◽  
Mario Huerta ◽  
Enrique Querol
Keyword(s):  
Gene Expression ◽  
Web Application ◽  
Biological Information ◽  
Microarray Technology ◽  
Previous Knowledge ◽  
Type B ◽  
Gene Expressions ◽  
Microarray Experiments ◽  
Maximum Value ◽  
Class Definition

Background. Microarray technology is so expensive and powerful that it is essential to extract maximum value from microarray data. Our tools allow researchers to test and formulate from a hypothesis to entire models. Results. The objective of the NCRPCOPGene is to study the relationships among gene expressions under different conditions, to classify these conditions, and to study their effect on the different relationships. The web application makes it easier to define the sample classes, grouping the microarray experiments either by using (a) biological, statistical, or any other previous knowledge or (b) their effect on the expression relationship maintained among specific genes of interest. By means of the type (a) class definition, the researcher can add biological information to the gene-expression relationships. The type (b) class definition allows for linking genes correlated neither linearly nor nonlinearly. Conclusions. The PCOPGene tools are especially suitable for microarrays with large sample series. This application helps to identify cellular states and the genes involved in it in a flexible way. The application takes advantage of the ability of our system to relate gene expressions; even when these relationships are noncontinuous and cannot be found using linear or nonlinear analytical methods.

scholarly journals Potential Common Key Genes Associated with Chronic Periodontitis and Low Birth Weight: A Case Control Study Using Bioinformatics Analysis of Pooled mRNA Expression Datasets

2020 ◽  
Author(s):  
Asami Suzuki ◽  
Tetsuro Horie ◽  
Akihito Nakai ◽  
Eriko Kikuchi ◽  
Yukihiro Numabe
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Birth Weight ◽  
Low Birth Weight ◽  
Chronic Periodontitis ◽  
Web Application ◽  
Coiled Coil ◽  
Growth Development ◽  
Key Genes ◽  
Upstream Regulators

Abstract Background: Chronic periodontitis (CP) is a multifactorial disease associated with many systemic diseases. However, the precise association between CP and low birth weight (LBW) remains unclear. Therefore, this study aimed to elucidate common differentially expressed genes (DEGs), biomarker candidates, and upstream regulators related to key genes between CP and LBW.Methods: We investigated molecular relations and biomarker candidates using pooled microarray datasets of CP (GSE12484) and LBW (GSE29807) in the Gene Expression Omnibus (GEO). Datasets were analyzed for common DEGs using GEO2R, an R-based web application for GEO data analysis. Common DEGs, biomarker candidates, and upstream regulators in DEGs between CP and LBW were analyzed using the Database for Annotation Visualization and Integrated Discovery (DAVID), Search Tool for the Retrieval of Interacting Genes (STRING), and QIAGEN’s Ingenuity Pathway Analysis (IPA).Results: Three significantly upregulated and 20 significantly downregulated common DEGs between CP and LBW were identified. Some biological processes and pathways of these downregulated genes were associated with the cell cycle. Biomarker candidates among common DEGs were proline-rich coiled-coil 2A (PPRC2A), topoisomerase (DNA) II alpha (TOP2A), neural cell adhesion molecule 1 (NCAM1), and calcium channel, voltage-dependent, alpha 2/delta subunit 3 (CACNA2D3). Many upstream regulators of these biomarker candidates were factors associated with inflammation, immunity, the cell cycle, and growth development, and were hormones related to pregnancy.Conclusions: The results of this study suggest that PPRC2A, TOP2A, NCAM1, and CACNA2D3 are common biomedical key genes between CP and LBW. The expression states of these genes, which are related to inflammation, hormones, the cell cycle, and growth development, were common in both CP and LBW in blood. To the best of our knowledge, the relations of PPRC2A, TOP2A, and CACNA2D3 to CP and LBW are reported for the first time. Thus, in the bloodstream, inflammatory-related upstream regulators of these key genes may control gene expression associated with fetal growth, and conversely, changes in female hormones due to pregnancy may affect the progress of CP.

Exploring Prognostic Signatures of Hepatocellular Carcinoma and the potential implications in Tumor Immune Microenvironment

2021 ◽  
Vol 24 ◽  
Author(s):  
Hongxu Chen ◽  
Zhijing Jiang ◽  
Bingshi Yang ◽  
Guiling Yan ◽  
Xiaochen Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Survival Analysis ◽  
Liver Cancer ◽  
Gene Expression Data ◽  
Immune Cells ◽  
Roc Curve ◽  
Prognostic Model ◽  
Risk Groups ◽  
Low Risk ◽  
Expression Data

Objective: The objective of this study is to construct a prognostic model using genetic markers of liver cancer and explore the signature genes associated with the tumor immune microenviroment. Methods: Cox proportional hazards regression analysis was carried out to screen the significant HR using dataset of TCGA Liver Cancer (LIHC) gene expression data, then LASSO (Least absolute shrinkage and selection operator) was performed to select the minimal variables with significant HR of genes. Thus, the prognostic model was constructed by the minimal variables with their HR and time-dependent receiver-operating characteristic (ROC) curve and area under the ROC curve (AUC) value used to assess the prognostic performance. Then dividing the patients into high and low risk groups by the median of the model, survival analysis was performed by two groups with testing and independent dataset. Furthermore, enrichment analysis of signature mRNAs and lncRNAs and their co-expression genes were performed, then, spearman rank correlation used to calculate the correlation between immune cells and genes in the prognostic model, and testing abundance difference of the immune cells in high and low risks groups. Results: A total of 5989 genes with significant HR were identified, then 6 key genes (three mRNAs: DHX37, SMIM7 and MFSD1, three lncRNAs: PIWIL4, KCNE5 and LOC100128398) screened by LASSO were used to construct the model with their HR value respectively. The AUC values of 1 and 5 year overall survival were 0.78 and 0.76 in discovery data and 0.67 and 0.68 in testing data. Survival analysis performed significantly in discriminating high and low groups with testing and independent data. Furthermore, many immune cells such as nTreg found a significant correlation with the genes in the prognostic model, and many immune cells show significantly different abundance in high and low risk groups. Conclusion: In the study, we used Univariate Cox analyses and LASSO algorithm with TCGA gene expression data to construct the prognostic model in liver cancer patients. And the prognostic model comprising three mRNAs including DHX37, SMIM7, MFSD1, and three lncRNAs including PIWIL4, KCNE5 and LOC100128398. Furthermore, these genes expression levels were associated with the abundance of some immune cells, such as nTreg. Also, many immune cells have significantly different abundance in high and low risk groups. All these results indicated combination with all these six genes could be the potential biomarker for the prognosis of liver cancer.

scholarly journals Identification of Differentially Expressed Genes and miRNAs Associated with Esophageal Squamous Cell Carcinoma by Integrated Analysis of Microarray Data

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Lemeng Zhang ◽  
Jianhua Chen ◽  
Tianli Cheng ◽  
Hua Yang ◽  
Changqie Pan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Squamous Cell Carcinoma ◽  
Survival Analysis ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Differentially Expressed Genes ◽  
Microarray Data ◽  
Regulatory Network ◽  
Differentially Expressed ◽  
Pathway Enrichment

To identify candidate key genes and miRNAs associated with esophageal squamous cell carcinoma (ESCC) development and prognosis, the gene expression profiles and miRNA microarray data including GSE20347, GSE38129, GSE23400, and GSE55856 were downloaded from the Gene Expression Omnibus (GEO) database. Clinical and survival data were retrieved from The Cancer Genome Atlas (TCGA). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of differentially expressed genes (DEGs) was analyzed via DAVID, while the DEG-associated protein-protein interaction network (PPI) was constructed using the STRING database. Additionally, the miRNA target gene regulatory network and miRNA coregulatory network were constructed, using the Cytoscape software. Survival analysis and prognostic model construction were performed via the survival (version 2.42-6) and rbsurv R packages, respectively. The results showed a total of 2575, 2111, and 1205 DEGs, and 226 differentially expressed miRNAs (DEMs) were identified. Pathway enrichment analyses revealed that DEGs were mainly enriched in 36 pathways, such as the proteasome, p53, and beta-alanine metabolism pathways. Furthermore, 448 nodes and 1144 interactions were identified in the PPI network, with MYC having the highest random walk score. In addition, 7 DEMs in the microarray data, including miR-196a, miR-21, miR-205, miR-194, miR-103, miR-223, and miR-375, were found in the regulatory network. Moreover, several reported disease-related miRNAs, including miR-198a, miR-103, miR-223, miR-21, miR-194, and miR-375, were found to have common target genes with other DEMs. Survival analysis revealed that 85 DEMs were related to prognosis, among which hsa-miR-1248, hsa-miR-1291, hsa-miR-421, and hsa-miR-7-5p were used for a prognostic survival model. Taken together, this study revealed the important roles of DEGs and DEMs in ESCC development, as well as DEMs in the prognosis of ESCC. This will provide potential therapeutic targets and prognostic predictors for ESCC.

scholarly journals RankerGUI: A Computational Framework to Compare Differential Gene Expression Profiles Using Rank Based Statistics

2019 ◽  
Vol 20 (23) ◽  
pp. 6098 ◽  
Author(s):  
Amarinder Singh Thind ◽  
Kumar Parijat Tripathi ◽  
Mario Rosario Guarracino
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Differential Gene Expression ◽  
Web Application ◽  
Cellular Response ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Experimental Conditions ◽  
Differential Gene

The comparison of high throughput gene expression datasets obtained from different experimental conditions is a challenging task. It provides an opportunity to explore the cellular response to various biological events such as disease, environmental conditions, and drugs. There is a need for tools that allow the integration and analysis of such data. We developed the “RankerGUI pipeline”, a user-friendly web application for the biological community. It allows users to use various rank based statistical approaches for the comparison of full differential gene expression profiles between the same or different biological states obtained from different sources. The pipeline modules are an integration of various open-source packages, a few of which are modified for extended functionality. The main modules include rank rank hypergeometric overlap, enriched rank rank hypergeometric overlap and distance calculations. Additionally, preprocessing steps such as merging differential expression profiles of multiple independent studies can be added before running the main modules. Output plots show the strength, pattern, and trends among complete differential expression profiles. In this paper, we describe the various modules and functionalities of the developed pipeline. We also present a case study that demonstrates how the pipeline can be used for the comparison of differential expression profiles obtained from multiple platforms’ data of the Gene Expression Omnibus. Using these comparisons, we investigate gene expression patterns in kidney and lung cancers.

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