scholarly journals Organoids Are Limited in Modeling the Colon Adenoma–Carcinoma Sequence

Cells ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 488
Author(s):  
Yoshihisa Tokumaru ◽  
Masanori Oshi ◽  
Ankit Patel ◽  
Wanqing Tian ◽  
Li Yan ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Immune Response ◽  
Immune Cells ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Related Gene ◽  
Gene Set Enrichment ◽  
Colon Adenoma ◽  
Tissue Samples ◽  
Gene Sets

The colon adenoma–carcinoma sequence is a multistep genomic-altering process that occurs during colorectal cancer (CRC) carcinogenesis. Organoids are now commonly used to model both non-cancerous and cancerous tissue. This study aims to investigate how well organoids mimic tissues in the adenoma–carcinoma sequence by comparing their transcriptomes. A total of 234 tissue samples (48 adenomas and 186 CRC) and 60 organoid samples (15 adenomas and 45 CRC) were analyzed. We found that cell-proliferation-related gene sets were consistently enriched in both CRC tissues and organoids compared to adenoma tissues and organoids by gene set enrichment analysis (GSEA). None of the known pathways in the colon adenoma–carcinoma sequence were consistently enriched in CRC organoids. There was no enrichment of the tumor microenvironment-related gene sets in CRC organoids. CRC tissues enriched immune-response-related gene sets, whereas CRC organoids did not. The proportions of infiltrating immune cells were different between tissues and organoids, whereas there was no difference between cancer and adenoma organoids. The amounts of cancer stem cells and progenitor cells were not different between CRC and adenoma organoids, whereas a difference was noted between CRC and adenoma tissues. In conclusion, we demonstrated that organoids model only part of the adenoma–carcinoma sequence and should be used with caution after considering their limitations.

scholarly journals Higher Acid-Base Imbalance Associated with Respiratory Failure Could Decrease the Survival of Patients with Scrub Typhus during Intensive Care Unit Stay: A Gene Set Enrichment Analysis

2019 ◽  
Vol 8 (10) ◽  
pp. 1580 ◽  
Author(s):  
Kyoung Min Moon ◽  
Kyueng-Whan Min ◽  
Mi-Hye Kim ◽  
Dong-Hoon Kim ◽  
Byoung Kwan Son ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Respiratory Failure ◽  
Scrub Typhus ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Acid Base ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Gene Sets

Ninety percent of patients with scrub typhus (SC) with vasculitis-like syndrome recover after mild symptoms; however, 10% can suffer serious complications, such as acute respiratory failure (ARF) and admission to the intensive care unit (ICU). Predictors for the progression of SC have not yet been established, and conventional scoring systems for ICU patients are insufficient to predict severity. We aimed to identify simple and robust indicators to predict aggressive behaviors of SC. We evaluated 91 patients with SC and 81 non-SC patients who were admitted to the ICU, and 32 cases from the public functional genomics data repository for gene expression analysis. We analyzed the relationships between several predictors and clinicopathological characteristics in patients with SC. We performed gene set enrichment analysis (GSEA) to identify SC-specific gene sets. The acid-base imbalance (ABI), measured 24 h before serious complications, was higher in patients with SC than in non-SC patients. A high ABI was associated with an increased incidence of ARF, leading to mechanical ventilation and worse survival. GSEA revealed that SC correlated to gene sets reflecting inflammation/apoptotic response and airway inflammation. ABI can be used to indicate ARF in patients with SC and assist with early detection.

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.

scholarly journals Identification of potential therapeutic targets for atherosclerosis by analysing the gene signature related to different immune cells and immune regulators in atheromatous plaques

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Yang Shen ◽  
Li-rong Xu ◽  
Xiao Tang ◽  
Chang-po Lin ◽  
Dong Yan ◽  
...  
Keyword(s):  
Immune Cells ◽  
Plasma Cells ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Gamma Delta ◽  
Hub Genes ◽  
Gene Set Enrichment ◽  
Bioinformatics Analyses ◽  
Immune Infiltration ◽  
Atheromatous Plaques

Abstract Background Atherosclerosis is a chronic inflammatory disease that affects multiple arteries. Numerous studies have shown the inherent immune diversity in atheromatous plaques and suggest that the dysfunction of different immune cells plays an important role in atherosclerosis. However, few comprehensive bioinformatics analyses have investigated the potential coordinators that might orchestrate different immune cells to exacerbate atherosclerosis. Methods Immune infiltration of 69 atheromatous plaques from different arterial beds in GSE100927 were explored by single-sample-gene-set enrichment analysis (presented as ssGSEA scores), ESTIMATE algorithm (presented as immune scores) and CIBERSORT algorithm (presented as relative fractions of 22 types of immune cells) to divide these plaques into ImmuneScoreL cluster (of low immune infiltration) and ImmuneScoreH cluster (of high immune infiltration). Subsequently, comprehensive bioinformatics analyses including differentially-expressed-genes (DEGs) analysis, protein–protein interaction networks analysis, hub genes analysis, Gene-Ontology-terms and KEGG pathway enrichment analysis, gene set enrichment analysis, analysis of expression profiles of immune-related genes, correlation analysis between DEGs and hub genes and immune cells were conducted. GSE28829 was analysed to cross-validate the results in GSE100927. Results Immune-related pathways, including interferon-related pathways and PD-1 signalling, were highly enriched in the ImmuneScoreH cluster. HLA-related (except for HLA-DRB6) and immune checkpoint genes (IDO1, PDCD-1, CD274(PD-L1), CD47), RORC, IFNGR1, STAT1 and JAK2 were upregulated in the ImmuneScoreH cluster, whereas FTO, CRY1, RORB, and PER1 were downregulated. Atheromatous plaques in the ImmuneScoreH cluster had higher proportions of M0 macrophages and gamma delta T cells but lower proportions of plasma cells and monocytes (p < 0.05). CAPG, CECR1, IL18, IGSF6, FBP1, HLA-DPA1 and MMP7 were commonly related to these immune cells. In addition, the advanced-stage carotid plaques in GSE28829 exhibited higher immune infiltration than early-stage carotid plaques. Conclusions Atheromatous plaques with higher immune scores were likely at a more clinically advanced stage. The progression of atherosclerosis might be related to CAPG, IGSF6, IL18, CECR1, FBP1, MMP7, FTO, CRY1, RORB, RORC, PER1, HLA-DPA1 and immune-related pathways (IFN-γ pathway and PD-1 signalling pathway). These genes and pathways might play important roles in regulating immune cells such as M0 macrophages, gamma delta T cells, plasma cells and monocytes and might serve as potential therapeutic targets for atherosclerosis.

scholarly journals Deficiency of 15-LOX-1 Induces Radioresistance through Downregulation of MacroH2A2 in Colorectal Cancer

Cancers ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1776 ◽  
Author(s):  
Yoo Jin Na ◽  
Bo Ram Kim ◽  
Jung Lim Kim ◽  
Sanghee Kang ◽  
Yoon A. Jeong ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Dna Damage ◽  
Specific Treatment ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Patient Specific ◽  
Histone H2a ◽  
Gene Set Enrichment ◽  
Damage Response ◽  
Radiation Induced

Despite the importance of radiation therapy, there are few radiation-related markers available for use in clinical practice. A larger catalog of such biomarkers is required to help clinicians decide when radiotherapy should be replaced with a patient-specific treatment. Arachidonate 15-lipoxygenase (15-LOX-1) enzyme is involved in polyunsaturated fatty acid metabolism. When colorectal cancer (CRC) cells were exposed to radiation, 15-LOX-1 was upregulated. To verify whether 15-LOX-1 protects against or induces DNA damage, we irradiated sh15-LOX-1 stable cells. We found that low 15-LOX-1 is correlated with radioresistance in CRC cells. These data suggest that the presence of 15-LOX-1 can be used as a marker for radiation-induced DNA damage. Consistent with this observation, gene-set-enrichment analysis based on microarray experiments showed that UV_RESPONSE was decreased in sh15-LOX-1 cells compared to shCon cells. Moreover, we discovered that the expression of the histone H2A variant macroH2A2 was sevenfold lower in sh15-LOX-1 cells. Overall, our findings present mechanistic evidence that macroH2A2 is transcriptionally regulated by 15-LOX-1 and suppresses the DNA damage response in irradiated cells by delaying H2AX activation.

scholarly journals Weighted Kolmogorov Smirnov testing: an alternative for Gene Set Enrichment Analysis

2015 ◽  
Vol 14 (3) ◽  
Author(s):  
Konstantina Charmpi ◽  
Bernard Ycart
Keyword(s):  
Weight Function ◽  
Null Hypothesis ◽  
Computing Time ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Test Statistic ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Gene Sets ◽  
Kolmogorov Smirnov

AbstractGene Set Enrichment Analysis (GSEA) is a basic tool for genomic data treatment. Its test statistic is based on a cumulated weight function, and its distribution under the null hypothesis is evaluated by Monte-Carlo simulation. Here, it is proposed to subtract to the cumulated weight function its asymptotic expectation, then scale it. Under the null hypothesis, the convergence in distribution of the new test statistic is proved, using the theory of empirical processes. The limiting distribution needs to be computed only once, and can then be used for many different gene sets. This results in large savings in computing time. The test defined in this way has been called Weighted Kolmogorov Smirnov (WKS) test. Using expression data from the GEO repository, tested against the MSig Database C2, a comparison between the classical GSEA test and the new procedure has been conducted. Our conclusion is that, beyond its mathematical and algorithmic advantages, the WKS test could be more informative in many cases, than the classical GSEA test.

scholarly journals Gastroesophageal reflux activates the NF-κB pathway and impairs esophageal barrier function in mice

2013 ◽  
Vol 305 (1) ◽  
pp. G58-G65 ◽  
Author(s):  
Yu Fang ◽  
Hao Chen ◽  
Yuhui Hu ◽  
Zorka Djukic ◽  
Whitney Tevebaugh ◽  
...  
Keyword(s):  
Gastroesophageal Reflux ◽  
Barrier Function ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Esophageal Epithelium ◽  
Gene Set Enrichment ◽  
Gene Sets ◽  
Epithelial Resistance

The barrier function of the esophageal epithelium is a major defense against gastroesophageal reflux disease. Previous studies have shown that reflux damage is reflected in a decrease in transepithelial electrical resistance associated with tight junction alterations in the esophageal epithelium. To develop novel therapies, it is critical to understand the molecular mechanisms whereby contact with a refluxate impairs esophageal barrier function. In this study, surgical models of duodenal and mixed reflux were developed in mice. Mouse esophageal epithelium was analyzed by gene microarray. Gene set enrichment analysis showed upregulation of inflammation-related gene sets and the NF-κB pathway due to reflux. Significance analysis of microarrays revealed upregulation of NF-κB target genes. Overexpression of NF-κB subunits (p50 and p65) and NF-κB target genes (matrix metalloproteinases-3 and -9, IL-1β, IL-6, and IL-8) confirmed activation of the NF-κB pathway in the esophageal epithelium. In addition, real-time PCR, Western blotting, and immunohistochemical staining also showed downregulation and mislocalization of claudins-1 and -4. In a second animal experiment, treatment with an NF-κB inhibitor, BAY 11-7085 (20 mg·kg−1·day−1 ip for 10 days), counteracted the effects of duodenal and mixed reflux on epithelial resistance and NF-κB-regulated cytokines. We conclude that gastroesophageal reflux activates the NF-κB pathway and impairs esophageal barrier function in mice and that targeting the NF-κB pathway may strengthen esophageal barrier function against reflux.

Differential gene expression in prostate tissue according to vasectomy.

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 298-298
Author(s):  
Kathryn M Wilson ◽  
Travis Gerke ◽  
Ericka Ebot ◽  
Jennifer A Sinnott ◽  
Jennifer R. Rider ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Cancer Diagnosis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Prostate Tissue ◽  
Gene Set Enrichment ◽  
Normal Prostate ◽  
Gene Sets ◽  
Normal Prostate Tissue

298 Background: We previously found that vasectomy was associated with an increased risk of prostate cancer, and particularly, risk of lethal prostate cancer in the Health Professionals Follow-up Study (HPFS). However, the possible biological basis for this finding is unclear. In this study, we explored possible biological mechanisms by assessing differences in gene expression in the prostate tissue of men with and without a history of vasectomy prostate cancer diagnosis. Methods: Within the HPFS, vasectomy data and gene expression data (20,254 genes) was available from archival tumor tissue from 263 cases, 124 of whom also had data for adjacent normal tissue. To relate expression of individual genes to vasectomy we used linear regression adjusting for age and year at diagnosis. We ran gene set enrichment analysis to identify pathways of genes associated with vasectomy. Results: Among 263 cases, 67 (25%) reported a vasectomy prior to cancer diagnosis. Mean age at diagnosis was 66 years among men without and 65 years among men with vasectomy. Median time between vasectomy and prostate cancer diagnosis was 25 years. Gene expression in tumor tissue was not associated with vasectomy status. In adjacent normal tissue, three individual genes were associated with vasectomy with Bonferroni-corrected p-values of < 0.10: RAPGEF6, OR4C3, and SLC35F4. Gene set enrichment analysis found five pathways upregulated and seven pathways downregulated in men with vasectomy compared to those without in normal prostate tissue with a FDR < 0.05. Upregulated pathways included several immune-related gene sets and G-protein-coupled receptor gene sets. Conclusions: We identified significant differences in gene expression profiles in normal prostate tissue according to vasectomy status among men treated for prostate cancer. The fact that such differences existed several decades after vasectomy provides support for the idea that vasectomy may play a role in the etiology of prostate cancer.

scholarly journals GSEA-InContext Explorer: An interactive visualization tool for putting gene set enrichment analysis results into biological context

2019 ◽  
Author(s):  
Rani K. Powers ◽  
Anthony Sun ◽  
James C. Costello
Keyword(s):  
Statistical Significance ◽  
Null Distribution ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Link Type ◽  
Interactive Interface ◽  
Gene Sets ◽  
Shiny App

AbstractSummaryGSEA-InContext Explorer is a Shiny app that allows users to perform two methods of gene set enrichment analysis (GSEA). The first, GSEAPreranked, applies the GSEA algorithm in which statistical significance is estimated from a null distribution of enrichment scores generated for randomly permuted gene sets. The second, GSEA-InContext, incorporates a user-defined set of background experiments to define the null distribution and calculate statistical significance. GSEA-InContext Explorer allows the user to build custom background sets from a compendium of over 5,700 curated experiments, run both GSEAPreranked and GSEA-InContext on their own uploaded experiment, and explore the results using an interactive interface. This tool will allow researchers to visualize gene sets that are commonly enriched across experiments and identify gene sets that are uniquely significant in their experiment, thus complementing current methods for interpreting gene set enrichment results.Availability and implementationThe code for GSEA-InContext Explorer is available at: https://github.com/CostelloLab/GSEA-InContext_Explorer and the interactive tool is at: http://gsea-incontext_explorer.ngrok.io

scholarly journals Identification of a Hypoxia - Related lncRNA Signature For The Prognosis of Colorectal Cancer

2021 ◽  
Author(s):  
HUA HUANG ◽  
Shanshan Xu ◽  
Youran Li ◽  
Yunfei Gu ◽  
Lijiang Ji
Keyword(s):  
Colorectal Cancer ◽  
Roc Analysis ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Receptor Interaction ◽  
Prognostic Signature ◽  
Gene Set Enrichment ◽  
Gene Set

Abstract Background: Colorectal cancer (CRC), the commonly seen malignancy, ranks the 3rd place among the causes of cancer-associated mortality. As suggested by more and more studies, long coding RNAs (lncRNAs) have been considered as prognostic biomarkers for CRC. But the significance of hypoxic lncRNAs in predicting CRC prognosis remains unclear.Methods: The gene expressed profiles for CRC cases were obtained based on the Cancer Genome Atlas (TCGA) and applied to estimate the hypoxia score using a single-sample gene set enrichment analysis (ssGSEA) algorithm. Overall survival (OS) of high- and low-hypoxia score group was analyzed by the Kaplan–Meier (KM) plot. To identify differentially expressed lncRNAs (DELs) between two hypoxia score groups, this study carried out differential expression analysis, and then further integrated with the DELs between controls and CRC patients to generate the hypoxia-related lncRNAs for CRC. Besides, prognostic lncRNAs were screened by the univariate Cox regression, which were later utilized for constructing the prognosis nomogram for CRC by adopting the least absolute shrinkage and selection operator (LASSO) algorithm. In addition, both accuracy and specificity of the constructed prognostic signature were detected through the receiver operating characteristic (ROC) analysis. Moreover, our constructed prognosis signature also was validated in the internal testing test. This study operated gene set enrichment analysis (GSEA) for exploring potential biological functions associated with the prognostic signature. Finally, the ceRNA network of the prognostic lncRNAs was constructed.Results: Among 2299 hypoxia-related lncRNAs of CRC in total, LINC00327, LINC00163, LINC00174, SYNPR-AS1, and MIR31HG were identified as prognostic lncRNAs by the univariate Cox regression, and adopted for constructing the prognosis signature for CRC. ROC analysis showed the predictive power and accuracy of the prognostic signature. Additionally, the GSEA revealed that ECM-receptor interaction, PI3K-Akt pathway, phagosome, and Hippo pathway were mostly associated with the high-risk group. 352 miRNAs-mRNAs pairs and 177 lncRNAs-miRNAs were predicted.Conclusion: To conclude , we identified 5 hypoxia-related lncRNAs to establish an accurate prognostic signature for CRC, providing important prognostic markers and therapeutic target.

Next Generation Sequencing reveals profound transcriptomic differences between reticulated and non-reticulated platelets from healthy donors, CCS- and STEMI patients

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
L Hille ◽  
T.G Nuehrenberg ◽  
L Hein ◽  
F.J Neumann ◽  
D Trenk
Keyword(s):  
Healthy Subjects ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Gene Set Enrichment ◽  
Reticulated Platelets ◽  
Gene Sets ◽  
Healthy Donors ◽  
Go Terms ◽  
Generation Sequencing

Abstract Background The youngest circulating platelets – so called reticulated platelets (RP) – represent a highly prothrombotic platelet subpopulation. Previous studies showed that patients with chronic coronary syndrome (CCS) as well as patients with ST-elevation myocardial infarction (STEMI) have higher amounts of RP compared to healthy subjects. It has been suggested that intrinsic properties of RP impact on cardiovascular risk. However, it is unknown if transcriptomic alterations contribute to the prothrombotic properties of RP. Purpose This study sought to investigate differences in the transcriptomic landscape of sorted RP versus non-RP, i.e. young and old platelets, in healthy subjects, CCS- and STEMI-patients. Methods Blood samples were obtained from healthy subjects as well as from patients with CCS/STEMI (n=8 each) the day after PCI. After staining with SYTO 13, platelets from each donor were sorted into a RP and a non-RP fraction based on their RNA-content. Next Generation Sequencing (NGS) was applied to generate sequencing reads for sorted RP and non-RP from the 3 cohorts. Data was analyzed by use of the Freiburg bioinformatics platform “Galaxy”. Results Investigation of transcriptomic alterations in non-RP versus RP by differential gene expression analysis revealed a total number of 2,476 transcripts that were differentially expressed in platelets from healthy donors, 2,075 in CCS-patients and 1,852 in STEMI patients, respectively (adj. p&lt;0.05 in all analyses). Comparison of these transcripts revealed a large overlap of 500 mRNAs which were downregulated and 660 mRNAs which were upregulated in RP in all 3 cohorts. However, there are also distinct groups of transcripts that are differentially expressed in only one of the 3 cohorts. Gene ontology (GO)-analysis of the 500 uniformly enriched transcripts in RP yielded 38 overrepresented GO-terms. A large group was related to cytoskeleton and shape change. Furthermore, GO-terms associated to the platelet activation cascade were overrepresented. Upregulated transcripts included well-known examples like GP6 and GP9, P-selectin, integrin β3, integrin a-IIb, and tubulin α4a. GO-analysis of enriched transcripts in non-RP showed a large group associated to mitosis and cell nucleus/DNA which is surprising since platelets neither contain DNA nor a nucleus. Gene set enrichment analysis (GSEA) determined higher normalized enrichment scores for several gene sets associated to platelet degranulation, aggregation and activation in the STEMI-cohort. Gene sets affecting cell adhesion and platelet calcium homeostasis were overexpressed in particular in CCS-patients. Conclusion NGS-results indicate a highly prothrombotic transcriptome of RP from each cohort with high amounts of differentially expressed transcripts overlapping. However, GSEA identified gene sets that are particularly overexpressed in CCS- or STEMI-patients which might contribute to platelet hyperreactivity in these cohorts. Gene set enrichment analysis Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): PharmCompNet Baden-Wuerttemberg: Kompetenznetzwerk Pharmakologie Baden-Wuerttemberg - Wirkstoffnetzwerke als Grundlagen der individualisierten Arzneistofftherapie

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