scholarly journals Compendium of skin molecular signatures identifies key pathological features associated with fibrosis in systemic sclerosis

2019 ◽  
Vol 78 (6) ◽  
pp. 817-825 ◽  
Author(s):  
Su-Jin Moon ◽  
Jung Min Bae ◽  
Kyung-Su Park ◽  
Ilias Tagkopoulos ◽  
Ki-Jo Kim
Keyword(s):  
Systemic Sclerosis ◽  
Drug Targets ◽  
Leading Edge ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Skin Fibrosis ◽  
Akt Pathway ◽  
Data Sets ◽  
Phosphoinositide 3 Kinase ◽  
Genome Scale

ObjectivesTreatment of patients with systemic sclerosis (SSc) can be challenging because of clinical heterogeneity. Integration of genome-scale transcriptomic profiling for patients with SSc can provide insights on patient categorisation and novel drug targets.MethodsA normalised compendium was created from 344 skin samples of 173 patients with SSc, covering an intersection of 17 424 genes from eight data sets. Differentially expressed genes (DEGs) identified by three independent methods were subjected to functional network analysis, where samples were grouped using non-negative matrix factorisation. Finally, we investigated the pathways and biomarkers associated with skin fibrosis using gene-set enrichment analysis.ResultsWe identified 1089 upregulated DEGs, including 14 known genetic risk factors and five potential drug targets. Pathway-based subgrouping revealed four distinct clusters of patients with SSc with distinct activity signatures for SSc-relevant pathways. The inflammatory subtype was related to significant improvement in skin fibrosis at follow-up. The phosphoinositide-3-kinase-protein kinase B (PI3K-Akt) signalling pathway showed both the closest correlation and temporal pattern to skin fibrosis score. COMP, THBS1, THBS4, FN1, and TNC were leading-edge genes of the PI3K-Akt pathway in skin fibrogenesis.ConclusionsConstruction and analysis of normalised skin transcriptomic compendia can provide useful insights on pathway involvement by SSc subsets and discovering viable biomarkers for a skin fibrosis index. Particularly, the PI3K-Akt pathway and its leading players are promising therapeutic targets.

scholarly journals Drug perturbation gene set enrichment analysis (dpGSEA): a new transcriptomic drug screening approach

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Mike Fang ◽  
Brian Richardson ◽  
Cheryl M. Cameron ◽  
Jean-Eudes Dazard ◽  
Mark J. Cameron
Keyword(s):  
Drug Targets ◽  
T Regulatory Cells ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Regulatory Cells ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Gene Sets ◽  
Gastroenteropancreatic Neuroendocrine Tumor ◽  
Public Datasets

Abstract Background In this study, we demonstrate that our modified Gene Set Enrichment Analysis (GSEA) method, drug perturbation GSEA (dpGSEA), can detect phenotypically relevant drug targets through a unique transcriptomic enrichment that emphasizes biological directionality of drug-derived gene sets. Results We detail our dpGSEA method and show its effectiveness in detecting specific perturbation of drugs in independent public datasets by confirming fluvastatin, paclitaxel, and rosiglitazone perturbation in gastroenteropancreatic neuroendocrine tumor cells. In drug discovery experiments, we found that dpGSEA was able to detect phenotypically relevant drug targets in previously published differentially expressed genes of CD4+T regulatory cells from immune responders and non-responders to antiviral therapy in HIV-infected individuals, such as those involved with virion replication, cell cycle dysfunction, and mitochondrial dysfunction. dpGSEA is publicly available at https://github.com/sxf296/drug_targeting. Conclusions dpGSEA is an approach that uniquely enriches on drug-defined gene sets while considering directionality of gene modulation. We recommend dpGSEA as an exploratory tool to screen for possible drug targeting molecules.

RNA profiling of neuroendocrine tumors (NETs) to define new targets and mechanisms of progression.

2017 ◽  
Vol 35 (4_suppl) ◽  
pp. 302-302
Author(s):  
Namrata Vijayvergia ◽  
Suraj Peri ◽  
Karthik Devarajan ◽  
Jianming Pei ◽  
Yulan Gong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Drug Targets ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Enrichment Score ◽  
Rna Profiling ◽  
Histone Lysine Methyltransferase ◽  
Canonical Pathways ◽  
Lysine Methyltransferase

302 Background: NETs lack mutations in the “classical” signaling pathways but share mutations in regulators of gene expression (Jiao; 2011). We compared gene expression in PD & WD NETs to identify novel targets and biomarkers of differentiation. Methods: High quality RNA, extracted from paraffin blocks of deidentified NETs under an IRB-approved protocol, was profiled using a 770 gene panel (nCounter PanCancer pathway, Nanostring Technologies). The resulting data was used to identify the differentially expressed genes between PD and WD NETs using limma software (Ritchie; 2015). Gene Set Enrichment Analysis (Subramanian; 2005) identified differential pathway enrichment by calculating a Normalized Enrichment Score (NES). Results: Analysis of 16 PD and 23 WD NET samples identified 154 genes as extreme outliers ( > 2 fold up/downregulation between the subtypes). Compared to WD NETS, drug targets of interest overexpressed in PD NETs were histone lysine methyltransferase EZH2, and a cell cycle regulator CHEK1 (6.5x and 8.1x, respectively, p < 0.001). In contrast, serine/threonine protein kinase PAK 3 was upregulated in WD (10.6x, p < 0.001). These and other biomarkers will be further validated by immunolabeling of tissue sections. We also found differential enrichment of canonical pathways in PD versus WD NETs (table). Conclusions: Extreme outlier transcripts identified in PD & WD NETs support investigation of inhibitors of EZH2 (e.g. EPZ6438) and CHEK1 (e.g. LY2606368) in PD and PAK3(e.g. FRAX597) in WD NETs. Genes involved in cell cycle regulation and DNA repair in PD NETs and calcium / G protein coupled receptor signaling in WD NET account for biological differences between the 2 molecular subtypes and warrant future investigation as classifiers for NETs. Our findings provide mechanistic insights into the biology of NET and targets for therapy with direct clinical implications.[Table: see text]

scholarly journals Modelling Nonalcoholic Steatohepatitis In Vivo—A Close Transcriptomic Similarity Supports the Guinea Pig Disease Model

Biomedicines ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1198
Author(s):  
Josephine Skat-Rørdam ◽  
David H. Ipsen ◽  
Stefan E. Seemann ◽  
Markus Latta ◽  
Jens Lykkesfeldt ◽  
...  
Keyword(s):  
Guinea Pig ◽  
Candidate Genes ◽  
Nonalcoholic Steatohepatitis ◽  
Guinea Pigs ◽  
High Throughput Sequencing ◽  
Disease Model ◽  
Leading Edge ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis

The successful development of effective treatments against nonalcoholic steatohepatitis (NASH) is significantly set back by the limited availability of predictive preclinical models, thereby delaying and reducing patient recovery. Uniquely, the guinea pig NASH model develops hepatic histopathology and fibrosis resembling that of human patients, supported by similarities in selected cellular pathways. The high-throughput sequencing of guinea pig livers with fibrotic NASH (n = 6) and matched controls (n = 6) showed a clear separation of the transcriptomic profile between NASH and control animals. A comparison to NASH patients with mild disease (GSE126848) revealed a 45.2% overlap in differentially expressed genes, while pathway analysis showed a 34% match between the top 50 enriched pathways in patients with advanced NASH (GSE49541) and guinea pigs. Gene set enrichment analysis highlighted the similarity to human patients (GSE49541), also when compared to three murine models (GSE52748, GSE38141, GSE67680), and leading edge genes THRSP, CCL20 and CD44 were highly expressed in both guinea pigs and NASH patients. Nine candidate genes were identified as highly correlated with hepatic fibrosis (correlation coefficient > 0.8), and showed a similar expression pattern in NASH patients. Of these, two candidate genes (VWF and SERPINB9) encode secreted factors, warranting further investigations as potential biomarkers of human NASH progression. This study demonstrates key similarities in guinea pig and human NASH, supporting increased predictability when translating research findings to human patients.

scholarly journals NPM1 Is a Prognostic Biomarker Involved in Immune Infiltration of Lung Adenocarcinoma and Associated With m6A Modification and Glycolysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Xu-Sheng Liu ◽  
Lu-Meng Zhou ◽  
Ling-Ling Yuan ◽  
Yan Gao ◽  
Xue-Yan Kui ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Prognostic Biomarker ◽  
Aerobic Glycolysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Expression Level ◽  
Data Sets ◽  
Translational Initiation ◽  
Immune Infiltration ◽  
The Relationship

BackgroundOverexpression of NPM1 can promote the growth and proliferation of various tumor cells. However, there are few studies on the comprehensive analysis of NPM1 in lung adenocarcinoma (LUAD).MethodsTCGA and GEO data sets were used to analyze the expression of NPM1 in LUAD and clinicopathological analysis. The GO/KEGG enrichment analysis of NPM1 co-expression and gene set enrichment analysis (GSEA) were performed using R software package. The relationship between NPM1 expression and LUAD immune infiltration was analyzed using TIMER, GEPIA database and TCGA data sets, and the relationship between NPM1 expression level and LUAD m6A modification and glycolysis was analyzed using TCGA and GEO data sets.ResultsNPM1 was overexpressed in a variety of tumors including LUAD, and the ROC curve showed that NPM1 had a certain accuracy in predicting the outcome of tumors and normal samples. The expression level of NPM1 in LUAD is significantly related to tumor stage and prognosis. The GO/KEGG enrichment analysis indicated that NPM1 was closely related to translational initiation, ribosome, structural constituent of ribosome, ribosome, Parkinson disease, and RNA transport. GSEA showed that the main enrichment pathway of NPM1-related differential genes was mainly related to mTORC1 mediated signaling, p53 hypoxia pathway, signaling by EGFR in cancer, antigen activates B cell receptor BCR leading to generation of second messengers, aerobic glycolysis and methylation pathways. The analysis of TIMER, GEPIA database and TCGA data sets showed that the expression level of NPM1 was negatively correlated with B cells and NK cells. The TCGA and GEO data sets analysis indicated that the NPM1 expression was significantly correlated with one m6A modifier related gene (YTHDF2) and five glycolysis related genes (ENO1, HK2, LDHA, LDHB and SLC2A1).ConclusionNPM1 is a prognostic biomarker involved in immune infiltration of LUAD and associated with m6A modification and glycolysis. NPM1 can be used as an effective target for diagnosis and treatment of LUAD.

scholarly journals A kinase-independent role for CDK8 in BCR-ABL1+ leukemia

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Ingeborg Menzl ◽  
Tinghu Zhang ◽  
Angelika Berger-Becvar ◽  
Reinhard Grausenburger ◽  
Gerwin Heller ◽  
...  
Keyword(s):  
Drug Targets ◽  
Enrichment Analysis ◽  
Mtor Inhibitors ◽  
Gene Set Enrichment Analysis ◽  
Leukemic Cells ◽  
Mtor Inhibition ◽  
Cyclin Dependent Kinases ◽  
Highly Sensitive ◽  
Transcriptional Changes ◽  
Human Leukemic Cells

Abstract Cyclin-dependent kinases (CDKs) are frequently deregulated in cancer and represent promising drug targets. We provide evidence that CDK8 has a key role in B-ALL. Loss of CDK8 in leukemia mouse models significantly enhances disease latency and prevents disease maintenance. Loss of CDK8 is associated with pronounced transcriptional changes, whereas inhibiting CDK8 kinase activity has minimal effects. Gene set enrichment analysis suggests that the mTOR signaling pathway is deregulated in CDK8-deficient cells and, accordingly, these cells are highly sensitive to mTOR inhibitors. Analysis of large cohorts of human ALL and AML patients reveals a significant correlation between the level of CDK8 and of mTOR pathway members. We have synthesized a small molecule YKL-06-101 that combines mTOR inhibition and degradation of CDK8, and induces cell death in human leukemic cells. We propose that simultaneous CDK8 degradation and mTOR inhibition might represent a potential therapeutic strategy for the treatment of ALL patients.

scholarly journals Evaluating Gene Set Enrichment Analysis via a Hybrid Data Model

2014 ◽  
Vol 13s1 ◽  
pp. CIN.S13305 ◽  
Author(s):  
Jianping Hua ◽  
Michael L. Bittner ◽  
Edward R. Dougherty
Keyword(s):  
Simulation Study ◽  
Data Model ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Data Sets ◽  
Gene Set Enrichment ◽  
Data Set ◽  
Gene Set ◽  
Ranking Performance ◽  
Hybrid Data

Gene set enrichment analysis (GSA) methods have been widely adopted by biological labs to analyze data and generate hypotheses for validation. Most of the existing comparison studies focus on whether the existing GSA methods can produce accurate P-values; however, practitioners are often more concerned with the correct gene-set ranking generated by the methods. The ranking performance is closely related to two critical goals associated with GSA methods: the ability to reveal biological themes and ensuring reproducibility, especially for small-sample studies. We have conducted a comprehensive simulation study focusing on the ranking performance of seven representative GSA methods. We overcome the limitation on the availability of real data sets by creating hybrid data models from existing large data sets. To build the data model, we pick a master gene from the data set to form the ground truth and artificially generate the phenotype labels. Multiple hybrid data models can be constructed from one data set and multiple data sets of smaller sizes can be generated by resampling the original data set. This approach enables us to generate a large batch of data sets to check the ranking performance of GSA methods. Our simulation study reveals that for the proposed data model, the Q2 type GSA methods have in general better performance than other GSA methods and the global test has the most robust results. The properties of a data set play a critical role in the performance. For the data sets with highly connected genes, all GSA methods suffer significantly in performance.

scholarly journals Towards a gold standard for benchmarking gene set enrichment analysis

2019 ◽  
Author(s):  
Ludwig Geistlinger ◽  
Gergely Csaba ◽  
Mara Santarelli ◽  
Marcel Ramos ◽  
Lucas Schiffer ◽  
...  
Keyword(s):  
Ad Hoc ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Data Sets ◽  
Expression Data ◽  
Rna Seq ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Gene Sets ◽  
Enrichment Methods

AbstractBackgroundAlthough gene set enrichment analysis has become an integral part of high-throughput gene expression data analysis, the assessment of enrichment methods remains rudimentary and ad hoc. In the absence of suitable gold standards, evaluations are commonly restricted to selected data sets and biological reasoning on the relevance of resulting enriched gene sets. However, this is typically incomplete and biased towards the goals of individual investigations.ResultsWe present a general framework for standardized and structured benchmarking of enrichment methods based on defined criteria for applicability, gene set prioritization, and detection of relevant processes. This framework incorporates a curated compendium of 75 expression data sets investigating 42 different human diseases. The compendium features microarray and RNA-seq measurements, and each dataset is associated with a precompiled GO/KEGG relevance ranking for the corresponding disease under investigation. We perform a comprehensive assessment of 10 major enrichment methods on the benchmark compendium, identifying significant differences in (i) runtime and applicability to RNA-seq data, (ii) fraction of enriched gene sets depending on the type of null hypothesis tested, and (iii) recovery of the a priori defined relevance rankings. Based on these findings, we make practical recommendations on (i) how methods originally developed for microarray data can efficiently be applied to RNA-seq data, (ii) how to interpret results depending on the type of gene set test conducted, and (iii) which methods are best suited to effectively prioritize gene sets with high relevance for the phenotype investigated.ConclusionWe carried out a systematic assessment of existing enrichment methods, and identified best performing methods, but also general shortcomings in how gene set analysis is currently conducted. We provide a directly executable benchmark system for straightforward assessment of additional enrichment methods.Availabilityhttp://bioconductor.org/packages/GSEABenchmarkeR

scholarly journals IonFlow: a galaxy tool for the analysis of ionomics data sets

Metabolomics ◽  
2021 ◽  
Vol 17 (10) ◽  
Author(s):  
J. Iacovacci ◽  
W. Lin ◽  
J. L. Griffin ◽  
R. C. Glen
Keyword(s):  
Data Analysis ◽  
Inductively Coupled Plasma ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Inductively Coupled ◽  
Icp Ms ◽  
Plasma Mass Spectrometry ◽  
Elemental Profiling

Abstract Introduction Inductively coupled plasma mass spectrometry (ICP-MS) experiments generate complex multi-dimensional data sets that require specialist data analysis tools. Objective Here we describe tools to facilitate analysis of the ionome composed of high-throughput elemental profiling data. Methods IonFlow is a Galaxy tool written in R for ionomics data analysis and is freely accessible at https://github.com/wanchanglin/ionflow. It is designed as a pipeline that can process raw data to enable exploration and interpretation using multivariate statistical techniques and network-based algorithms, including principal components analysis, hierarchical clustering, relevance network extraction and analysis, and gene set enrichment analysis. Results and Conclusion The pipeline is described and tested on two benchmark data sets of the haploid S. Cerevisiae ionome and of the human HeLa cell ionome.

scholarly journals Molecular Classification Based on Prognostic and Cell Cycle-Associated Genes in Patients With Colon Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhiyuan Zhang ◽  
Meiling Ji ◽  
Jie Li ◽  
Qi Wu ◽  
Yuanjian Huang ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Colon Cancer ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Molecular Classification ◽  
Original Data ◽  
Gene Set Enrichment Analysis ◽  
Data Sets ◽  
Data Set ◽  
Cox Regression Analysis

The molecular classification of patients with colon cancer is inconclusive. The gene set enrichment analysis (GSEA) of dysregulated genes among normal and tumor tissues indicated that the cell cycle played a crucial role in colon cancer. We performed univariate Cox regression analysis to find out the prognostic-related genes, and these genes were then intersected with cell cycle-associated genes and were further recognized as prognostic and cell cycle-associated genes. Unsupervised non-negative matrix factorization (NMF) clustering was performed based on cell cycle-associated genes. Two subgroups were identified with different overall survival, clinical features, cell cycle enrichment profile, and mutation profile. Through nearest template prediction (NTP), the molecular classification could be effectively repeated in the original data set and validated in several independent data sets indicating that the classification is highly repeatable. Furthermore, we constructed two prognostic signatures in two subgroups, respectively. Our molecular classification based on cell cycle may provide novel insight into the treatment and the prognosis of colon cancer.

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