Gene set enrichment analysis of pathways and transcription factors associated with diabetic retinopathy using a microarray dataset

Purpose: To investigate the key regulators of the disease by comparing the abundance of vitreous proteins between the patients with proliferative diabetic retinopathy (PDR) and the controls with idiopathic epiretinal membrane (iERM).Methods: Vitreous humor (VH) samples were derived from patients with PDR or iERM through the pars plana vitrectomy. The VH proteins were identified by liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis. MaxQuant software and Metascape were applied to explore the enrichment of differentially expressed proteins in biological processes, cellular components, and molecular functions. Enrichr online tool and Gene Set Enrichment Analysis (GSEA) were performed to detect upstream transcriptional regulators of the highly expressed proteins.Results: The present study collected 8 vitreous humor samples from 5 PDR eyes and 3 iERM eyes and identified 88 highly expressed proteins in PDR patients. We validated our highly expressed proteome was able to distinguish the PDR patients from the non-PDR patients by using the VH proteomics data from a previous study. The majority of highly expressed proteins were involved in complement and coagulation cascades, regulating exocytosis, and hemostasis. Using the Gene Set Enrichment Analysis (GSEA), we identified that transcription factors (TFs) PPAR-α, RXR, LXR regulate these proteins.Conclusions: In this study, we identified a highly expressed proteome in VH of PDR patients. The role of the complement and coagulation system, regulating exocytosis, and hemostasis has been of great significance to PDR. Nuclear receptors PPARA, RXR, LXR were possible upstream regulators of disease progression and required further study.

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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

Journal of Clinical Medicine ◽

10.3390/jcm8101580 ◽

2019 ◽

Vol 8 (10) ◽

pp. 1580 ◽

Cited By ~ 1

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.

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

BMC Bioinformatics ◽

10.1186/s12859-020-03929-0 ◽

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.

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Key pathways involved in prostate cancer based on gene set enrichment analysis and meta analysis

Genetics and Molecular Research ◽

10.4238/2011.december.14.10 ◽

2011 ◽

Vol 10 (4) ◽

pp. 3856-3887 ◽

Cited By ~ 11

Author(s):

Q.Y. Ning ◽

J.Z. Wu ◽

N. Zang ◽

J. Liang ◽

Y.L. Hu ◽

...

Keyword(s):

Prostate Cancer ◽

Meta Analysis ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Gene Set ◽

Key Pathways

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GSEA (gene set enrichment analysis)

Encyclopedia of Genetics, Genomics, Proteomics and Informatics ◽

10.1007/978-1-4020-6754-9_7187 ◽

2008 ◽

pp. 827-827

Keyword(s):

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Gene Set

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A Novel Prognostic Model Based On Cytosolic DNA Sensing-Related Gene Signature for Pancreatic Cancer

10.21203/rs.3.rs-877931/v1 ◽

2021 ◽

Author(s):

Chuan-Qi Xu ◽

Kui-Sheng Yang ◽

Shu-Xian Zhao ◽

Jian Lv

Keyword(s):

Pancreatic Cancer ◽

Prognostic Factor ◽

Prognostic Model ◽

Risk Group ◽

Enrichment Analysis ◽

Independent Prognostic Factor ◽

Gene Set Enrichment Analysis ◽

Dna Sensing ◽

Gene Set Enrichment ◽

Gene Set

Abstract Objective: Pancreatic cancer (PC) is one of the most malignant tumors. Cytosolic DNA sensing have been found to play an essential role in tumor. In this study, a cytosolic DNA sensing-related genes (CDSRGs) signature was constructed and the potential mechanisms also been discussed.Methods: The RNA expression and clinical data of PC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Subsequently, univariate (UCR) and multivariate Cox regression (MCR) analyses were conducted to establish a prognostic model in the TCGA patients, which was verified by GEO patients. Cancer immune infiltrates were investigated via single sample gene set enrichment analysis (ssGSEA) and Tumor Immune Estimation Resource (TIMER). Finally, Gene Set Enrichment Analysis (GSEA) was used to investigate the related signaling pathways.Results: A prognostic model comprising four genes (POLR2E,IL18, MAVS, and FADD) was established. The survival rate of patients in the low-risk group was significantly higher than that of patients in the high-risk group. In addition, CDSRGs-risk score was proved as an independent prognostic factor in PC. Immune infiltrates and drug sensitivity are associated with POLR2E,IL18, MAVS, and FADD expression.Conclusions: In summary, we present and validated a CDSRGs risk model that is an independent prognostic factor and indicates the immune characteristics of PC. This prognostic model may facilitate the personalized treatment and monitoring.

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Kalirin-RAC controls nucleokinetic migration in ADRN-type neuroblastoma

Life Science Alliance ◽

10.26508/lsa.201900332 ◽

2021 ◽

Vol 4 (5) ◽

pp. e201900332

Author(s):

Elena A Afanasyeva ◽

Moritz Gartlgruber ◽

Tatsiana Ryl ◽

Bieke Decaesteker ◽

Geertrui Denecker ◽

...

Keyword(s):

Enrichment Analysis ◽

Time Lapse ◽

Dna Methyltransferases ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Gene Set ◽

Risk Characteristics ◽

Weak Points ◽

Treatment Research ◽

Time Lapse Imaging

The migrational propensity of neuroblastoma is affected by cell identity, but the mechanisms behind the divergence remain unknown. Using RNAi and time-lapse imaging, we show that ADRN-type NB cells exhibit RAC1- and kalirin-dependent nucleokinetic (NUC) migration that relies on several integral components of neuronal migration. Inhibition of NUC migration by RAC1 and kalirin-GEF1 inhibitors occurs without hampering cell proliferation and ADRN identity. Using three clinically relevant expression dichotomies, we reveal that most of up-regulated mRNAs in RAC1- and kalirin–GEF1–suppressed ADRN-type NB cells are associated with low-risk characteristics. The computational analysis shows that, in a context of overall gene set poverty, the upregulomes in RAC1- and kalirin–GEF1–suppressed ADRN-type cells are a batch of AU-rich element–containing mRNAs, which suggests a link between NUC migration and mRNA stability. Gene set enrichment analysis–based search for vulnerabilities reveals prospective weak points in RAC1- and kalirin–GEF1–suppressed ADRN-type NB cells, including activities of H3K27- and DNA methyltransferases. Altogether, these data support the introduction of NUC inhibitors into cancer treatment research.

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DPYSL2 as potential diagnostic and prognostic biomarker linked to immune infiltration in lung adenocarcinoma

World Journal of Surgical Oncology ◽

10.1186/s12957-021-02379-z ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Yang-Jie Wu ◽

Ai-Tao Nai ◽

Gui-Cheng He ◽

Fei Xiao ◽

Zhi-Min Li ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Roc Curve ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

List Type ◽

Gene Set Enrichment ◽

Gene Set ◽

Normal Tissues ◽

Kaplan Meier ◽

Kaplan Meier Plotter

Abstract Background Dihydropyrimidinase like 2 (DPYSL2) has been linked to tumor metastasis. However, the function of DPSY2L in lung adenocarcinoma (LUAD) is yet to be explored. Methods Herein, we assessed DPYSL2 expression in various tumor types via online databases such as Oncomine and Tumor Immune Estimation Resource (TIMER). Further, we verified the low protein and mRNA expressions of DPYSL2 in LUAD via the ULCAN, The TCGA and GEPIA databases. We applied the ROC curve to examine the role of DPYSL2 in diagnosis. The prognostic significance of DPYSL2 was established through the Kaplan–Meier plotter and the Cox analyses (univariate and multivariate). TIMER was used to explore DPYSL2 expression and its connection to immune infiltrated cells. Through Gene Set Enrichment Analysis, the possible mechanism of DPYSL2 in LUAD was investigated. Results In this study, database analysis revealed lower DPYSL2 expression in LUAD than in normal tissues. The ROC curve suggested that expression of DPYSL2 had high diagnostic efficiency in LUAD. The DPYSL2 expression had an association with the survival time of LUAD patients in the Kaplan–Meier plotter and the Cox analyses. The results from TIMER depicted a markedly positive correlation of DPYSL2 expression with immune cells infiltrated in LUAD, such as macrophages, dendritic cells, CD4+ T cells, and neutrophils. Additionally, many gene markers for the immune system had similar positive correlations in the TIMER analysis. In Gene Set Enrichment Analysis, six immune-related signaling pathways were associated with DPYSL2. Conclusions In summary, DPYSL2 is a novel biomarker with diagnostic and prognostic potential for LUAD as well as an immunotherapy target. Highlights Expression of DPYSL2 was considerably lower in LUAD than in normal tissues. Investigation of multiple databases showed a high diagnostic value of DPYSL2 in LUAD. DPYSL2 can independently predict the LUAD outcomes. Immune-related mechanisms may be potential ways for DPYSL2 to play a role in LUAD.

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