scholarly journals TMA Navigator: network inference, patient stratification and survival analysis with tissue microarray data

2013 ◽  
Vol 41 (W1) ◽  
pp. W562-W568 ◽  
Author(s):  
Alexander L. R. Lubbock ◽  
Elad Katz ◽  
David J. Harrison ◽  
Ian M. Overton
Keyword(s):  
Survival Analysis ◽  
Tissue Microarray ◽  
Microarray Data ◽  
Network Inference ◽  
Patient Stratification

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.

MiRNA-4665-3p Regulates Expression of PLD5 in Thyroid Cancer Patients and Predicts Death

2019 ◽  
Vol 9 (7) ◽  
pp. 871-880
Author(s):  
Yifan Han ◽  
Lei Zhou
Keyword(s):  
Survival Analysis ◽  
Thyroid Cancer ◽  
Cancer Patients ◽  
Microarray Data ◽  
Enrichment Analysis ◽  
Mapk Signaling ◽  
Core Gene ◽  
Differential Analysis ◽  
The Core ◽  
Differential Genes

Thyroid cancer has become an increasingly common malignant tumor around the world, and its incidence is increasing year by year. In this study, mRNA microarray data of thyroid cancer patients from four periods were collected from the TCGA database. We performed a series of bioinformatics analyses on these mRNA expression profiles, including differential analysis, co-expression analysis, enrichment analysis, regulator prediction, and survival analysis. There were 13126, 10914, 13585, and 13241 differential genes in the four periods; 4822 differential genes were obtained by union and deduplication (p < 0.01). Weighted gene co-expression network analysis indicated a total of 21 functional disorder modules. In each module, PLD5, CHD4, ADGRA3, ITGA3, etc. were the key genes. Enrichment analysis showed that the dysfunctional module genes were mainly related to pre-replicative complex assembly, Cytokine–cytokine receptor interaction, and MAPK signaling pathway. We downloaded thyroid cancer-associated miRNA microarray data from the GEO database for differential analysis. Then, we crossed the predicted ncRNA with the differential miRNA to obtain thyroid cancer-associated regulatory factors. Finally, we found that miRNA-4665-3p regulates the core gene PLD5, and six regulators such as miRNA-3140-3p and miRNA-324-3p regulate the core gene CHD4. Survival analysis showed that both up-regulation of PLD5 expression and down-regulation of CHD4 expression accelerated patient death. According to the above analysis, we believe miRNA-4665-3p regulates the expression of PLD5 and affects the development of thyroid cancer. Its up-regulation promotes the death of patients.

Hierarchical clustering analysis of tissue microarray immunostaining data on renal cell carcinomas.

2011 ◽  
Vol 29 (7_suppl) ◽  
pp. 393-393
Author(s):  
L. M. Antón Aparicio ◽  
V. Medina Villaamil ◽  
R. García Campelo ◽  
G. Aparicio Gallego ◽  
I. Santamarina Caínzos ◽  
...  
Keyword(s):  
Tissue Microarray ◽  
Hierarchical Clustering ◽  
Microarray Data ◽  
Renal Cell ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Rank Test ◽  
Renal Cell Carcinomas ◽  
Fuhrman Grade ◽  
Molecular Expression

393 Background: The clinical use of molecular expression profiles could result in more accurate and objective diagnoses of cancers as well as prognoses of disease or response to the treatment. Unsupervised hierarchical clustering (UHC) analysis is a common method to profile the molecular expression of tissue microarray data. Methods: TM4: a free, open-source system for microarray data management and analysis was used in order to identify expression patterns of interest in our cohort of renal cell carcinomas (RCC) (n=80). We investigated 5 pathological predictors: (a) the histological type, (b) Fuhrman grade, (c) depth of infiltration, (d) metastasis in lymph node, (e) TNM stages, and 27 immunohistochemical molecular predictors involved in different pathways of tumor development and progression including: p53 and VHL tumor suppressor proteins; Bcl-2 and Survivin antiapoptotic proteins; Hif1-alpha and Notch proteins as a transcription factors; EGFR, PDGFR-α and VEGF proteins involved in tumor growth and proliferation; Glut proteins involved in tumor cell metabolism. Results: Unfavorable prognosis was significantly correlated with pathological predictors. Clear RCC histological subtype had a worse prognosis than the other ones studied to be accompanied by increased expression of Hif1-alpha and CAIX (p<0.001 in both cases) in patients with a number of metastatic nodes weighed (p<0.001). We found in Fuhrman Grade III samples a higher expression of VEGF (p=0.029). UHC analysis was done using average method with Spearman rank test to group the data with different predictor profiles. A preliminary analysis by hierarchical clustering of RCC produced three separate clusters groups. Conclusions: UHC based on a extended immunoprofile might be a useful, promising and powerful tool for further translational studies and should lead us to define a diagnostic and prognostic signature for RCC. Our laboratory is currently involved in this issue. No significant financial relationships to disclose.

scholarly journals An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients

2009 ◽  
Vol 123 (3) ◽  
pp. 725-731 ◽  
Author(s):  
Balazs Györffy ◽  
Andras Lanczky ◽  
Aron C. Eklund ◽  
Carsten Denkert ◽  
Jan Budczies ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Survival Analysis ◽  
Microarray Data ◽  
Breast Cancer Prognosis ◽  
Cancer Prognosis ◽  
Analysis Tool

scholarly journals GPseudoRank: a permutation sampler for single cell orderings

2017 ◽  
Author(s):  
Magdalena E Strauß ◽  
John E Reid ◽  
Lorenz Wernisch
Keyword(s):  
Single Cell ◽  
Microarray Data ◽  
Network Inference ◽  
Biological Process ◽  
Supplementary Information ◽  
Established Method ◽  
Data Set ◽  
Point Estimates ◽  
Biological Development ◽  
Selection Of

AbstractMotivationA number of pseudotime methods have provided point estimates of the ordering of cells for scRNA-seq data. A still limited number of methods also model the uncertainty of the pseudotime estimate. However, there is still a need for a method to sample from complicated and multi-modal distributions of orders, and to estimate changes in the amount of the uncertainty of the order during the course of a biological development, as this can support the selection of suitable cells for the clustering of genes or for network inference.ResultsIn an application to a microarray data set our proposed method, GPseudoRank, identifies two modes of the distribution, each of them corresponding to point estimates of orders obtained by a different established method. In an application to scRNA-seq data we demonstrate the potential of GPseudoRank to identify phases of lower and higher pseudotime uncertainty during a biological process. GPseudoRank also correctly identifies cells precocious in their antiviral response.Availability and implementationOur method is available on github: https://github.com/magStra/GPseudoRank.Contactmagdalena.strauss@mrc-bsu.cam.ac.ukSupplementary informationSupplementary materials are available.

CaPSSA: visual evaluation of cancer biomarker genes for patient stratification and survival analysis using mutation and expression data

2019 ◽  
Vol 35 (24) ◽  
pp. 5341-5343 ◽  
Author(s):  
Yeongjun Jang ◽  
Jihae Seo ◽  
Insu Jang ◽  
Byungwook Lee ◽  
Sun Kim ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Web Application ◽  
Predictive Biomarkers ◽  
Primary Source ◽  
The Cancer Genome Atlas ◽  
Supplementary Information ◽  
Molecular Characteristics ◽  
Omics Data ◽  
Patient Stratification ◽  
Visual Evaluation

AbstractSummaryPredictive biomarkers for patient stratification play critical roles in realizing the paradigm of precision medicine. Molecular characteristics such as somatic mutations and expression signatures represent the primary source of putative biomarker genes for patient stratification. However, evaluation of such candidate biomarkers is still cumbersome and requires multistep procedures especially when using massive public omics data. Here, we present an interactive web application that divides patients from large cohorts (e.g. The Cancer Genome Atlas, TCGA) dynamically into two groups according to the mutation, copy number variation or gene expression of query genes. It further supports users to examine the prognostic value of resulting patient groups based on survival analysis and their association with the clinical features as well as the previously annotated molecular subtypes, facilitated with a rich and interactive visualization. Importantly, we also support custom omics data with clinical information.Availability and implementationCaPSSA (Cancer Patient Stratification and Survival Analysis) runs on a web-browser and is freely available without restrictions at http://www.kobic.re.kr/capssa/. The source code is available on https://github.com/yjjang/capssa.Supplementary informationSupplementary data are available at Bioinformatics online.

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