A novel dysregulated pathway-identification analysis based on global influence of within-pathway effects and crosstalk between pathways

Identifying dysregulated pathways from high-throughput experimental data in order to infer underlying biological insights is an important task. Current pathway-identification methods focus on single pathways in isolation; however, consideration of crosstalk between pathways could improve our understanding of alterations in biological states. We propose a novel method of pathway analysis based on global influence (PAGI) to identify dysregulated pathways, by considering both within-pathway effects and crosstalk between pathways. We constructed a global gene–gene network based on the relationships among genes extracted from a pathway database. We then evaluated the extent of differential expression for each gene, and mapped them to the global network. The random walk with restart algorithm was used to calculate the extent of genes affected by global influence. Finally, we used cumulative distribution functions to determine the significance values of the dysregulated pathways. We applied the PAGI method to five cancer microarray datasets, and compared our results with gene set enrichment analysis and five other methods. Based on these analyses, we demonstrated that PAGI can effectively identify dysregulated pathways associated with cancer, with strong reproducibility and robustness. We implemented PAGI using the freely available R-based and Web-based tools ( http://bioinfo.hrbmu.edu.cn/PAGI ).

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Abstract B1-35: Enrichr2: Next generation gene set enrichment analysis web-based tool

10.1158/1538-7445.compsysbio-b1-35 ◽

2015 ◽

Author(s):

Matthew R. Jones

Keyword(s):

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Next Generation ◽

Gene Set Enrichment ◽

Web Based ◽

Gene Set

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Cross-Platform Microarray Data Integration Combining Meta-Analysis and Gene Set Enrichment Analysis

Bioinformatics ◽

10.4018/978-1-4666-3604-0.ch031 ◽

2013 ◽

pp. 570-585

Author(s):

Jian Yu ◽

Jun Wu ◽

Miaoxin Li ◽

Yajun Yi ◽

Yu Shyr ◽

...

Keyword(s):

Microarray Data ◽

Molecular Mechanisms ◽

Meta Analysis ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Gene Set ◽

Gene Expression Studies ◽

Microarray Datasets ◽

Pathway Level

Integrative analysis of microarray data has been proven as a more reliable approach to deciphering molecular mechanisms underlying biological studies. Traditional integration such as meta-analysis is usually gene-centered. Recently, gene set enrichment analysis (GSEA) has been widely applied to bring gene-level interpretation to pathway-level. GSEA is an algorithm focusing on whether an a priori defined set of genes shows statistically significant differences between two biological states. However, GSEA does not support integrating multiple microarray datasets generated from different studies. To overcome this, the improved version of GSEA, ASSESS, is more applicable, after necessary modifications. By making proper combined use of meta-analysis, GSEA, and modified ASSESS, this chapter reports two workflow pipelines to extract consistent expression pattern change at pathway-level, from multiple microarray datasets generated by the same or different microarray production platforms, respectively. Such strategies amplify the advantage and overcome the disadvantage than if using each method individually, and may achieve a more comprehensive interpretation towards a biological theme based on an increased sample size. With further network analysis, it may also allow an overview of cross-talking pathways based on statistical integration of multiple gene expression studies. A web server where one of the pipelines is implemented is available at: http://lifecenter.sgst.cn/mgsea//home.htm.

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Integrated analysis of the expression, involved functions, and regulatory network of RUNX3 in melanoma

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207324666210816121833 ◽

2021 ◽

Vol 24 ◽

Author(s):

Yanxin Liu ◽

Zhang Feng ◽

Huaxia Chen

Keyword(s):

Protein Interactions ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Integrated Analysis ◽

Protein Protein Interactions ◽

Nf Kappab ◽

Microarray Datasets

Background: As a tumor suppressor or oncogenic gene, abnormal expression of RUNX family transcription factor 3 (RUNX3) has been reported in various cancers. Introduction: This study aimed to investigate the role of RUNX3 in melanoma. Methods: The expression level of RUNX3 in melanoma tissues was analyzed by immunohistochemistry and the Oncomine database. Based on microarray datasets GSE3189 and GSE7553, differentially expressed genes (DEGs) in melanoma samples were screened, followed by functional enrichment analysis. Gene Set Enrichment Analysis (GSEA) was performed for RUNX3. DEGs that co-expressed with RUNX3 were analyzed, and the transcription factors (TFs) of RUNX3 and its co-expressed genes were predicted. The protein-protein interactions (PPIs) for RUNX3 were analyzed utilizing the GeneMANIA database. MicroRNAs (miRNAs) that could target RUNX3 expression, were predicted. Results : RUNX3 expression was significantly up-regulated in melanoma tissues. GSEA showed that RUNX3 expression was positively correlated with melanogenesis and melanoma pathways. Eleven DEGs showed significant co-expression with RUNX3 in melanoma, for example, TLE4 was negatively co-expressed with RUNX3. RUNX3 was identified as a TF that regulated the expression of both itself and its co-expressed genes. PPI analysis showed that 20 protein-encoding genes interacted with RUNX3, among which 9 genes were differentially expressed in melanoma, such as CBFB and SMAD3. These genes were significantly enriched in transcriptional regulation by RUNX3, RUNX3 regulates BCL2L11 (BIM) transcription, regulation of I-kappaB kinase/NF-kappaB signaling, and signaling by NOTCH. A total of 31 miRNAs could target RUNX3, such as miR-326, miR-330-5p, and miR-373-3p. Conclusion: RUNX3 expression was up-regulated in melanoma and was implicated in the development of melanoma.

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GenomicSuperSignature: interpretation of RNA-seq experiments through robust, efficient comparison to public databases

10.1101/2021.05.26.445900 ◽

2021 ◽

Author(s):

Sehyun Oh ◽

Ludwig Geistlinger ◽

Marcel Ramos ◽

Jaclyn N. Taroni ◽

Vincent J. Carey ◽

...

Keyword(s):

High Performance ◽

Principal Component ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Training Data ◽

Rna Seq ◽

Learning Capacity ◽

New Gene ◽

Novel Method ◽

Public Archives

Millions of transcriptomic profiles have been deposited in public archives, yet remain underused for the interpretation of new experiments. We present a novel method for interpreting new transcriptomic datasets through near-instantaneous comparison to public archives without high-performance computing requirements. We performed Principal Component Analysis on 536 studies comprising 44,890 RNA sequencing profiles. Sufficiently similar loading vectors were aggregated to form Replicable Axes of Variation (RAV). RAVs were annotated with metadata of originating studies and by gene set enrichment analysis, forming a knowledge graph. Functionality to associate new datasets with RAVs, extract interpretable annotations, and provide intuitive visualization are implemented as the GenomicSuperSignature R/Bioconductor package. We demonstrated the efficient and coherent database searching, robustness to batch effects and heterogeneous training data, and transfer learning capacity of our method using TCGA and rare diseases datasets. GenomicSuperSignature will aid analyzing new gene expression data in the context of existing databases using minimal computing resources.

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Comprehensive identification of essential pathways and transcription factors related to epilepsy by gene set enrichment analysis on microarray datasets

International Journal of Molecular Medicine ◽

10.3892/ijmm.2014.1843 ◽

2014 ◽

Vol 34 (3) ◽

pp. 715-724 ◽

Cited By ~ 4

Author(s):

KAN HE ◽

WEIZHONG XIAO ◽

WENWEN LV

Keyword(s):

Transcription Factors ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Gene Set ◽

Microarray Datasets

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Clinical relevance of semaphorin-3F in patients with prostate cancer

Clinical & Investigative Medicine ◽

10.25011/cim.v42i3.33094 ◽

2019 ◽

Vol 42 (3) ◽

pp. E64-E69

Author(s):

Guanlin Wu

Keyword(s):

Prostate Cancer ◽

Specific Antigen ◽

Enrichment Analysis ◽

Tnm Staging ◽

Gene Set Enrichment Analysis ◽

Gleason Grade ◽

Grade Group ◽

Normal Prostate ◽

Semaphorin 3F ◽

Microarray Datasets

Purpose: To identify prognosis predictors for patients with prostate cancer (PCa). Methods: Four independent PCa microarray datasets (GSE32448, GSE16560, GSE79957 and GSE17951) were reanalyzed to characterize the expression of semaphorin-3F (SEMA3F) gene between PCa patients and normal prostate tissues and the correlation between SEMA3F expression and the age, tumor/nodes/metastasis (TNM) staging, Gleason Grade Group, prostate-specific antigen level and overall survival of PCa patients. Gene set enrichment analysis was applied to investigate the potential relevant mechanisms regarding the expression of SEMA3F and the proliferation of PCa cells. Results: The level of SEMA3F was significantly higher in normal prostate tissues compared with that in PCa cells (P

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SynLethDB 2.0: A web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery

10.1101/2021.12.28.474346 ◽

2021 ◽

Author(s):

Jie Wang ◽

Min Wu ◽

Xuhui Huang ◽

Li Wang ◽

Sophia Zhang ◽

...

Keyword(s):

Synthetic Lethality ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Knowledge Graph ◽

Lethal Gene ◽

Web Interface ◽

Dynamic Graph ◽

Web Based ◽

Synthetic Lethal ◽

Graph Viewer

Two genes are synthetic lethal if mutations in both genes result in impaired cell viability, while mutation of either gene does not affect the cell survival. The potential usage of synthetic lethality (SL) in anticancer therapeutics has attracted many researchers to identify synthetic lethal gene pairs. To include newly identified SLs and more related knowledge, we present a new version of the SynLethDB database to facilitate the discovery of clinically relevant SLs. We extended the first version of SynLethDB database significantly by including new SLs identified through CRISPR screening, a knowledge graph about human SLs, and new web interface, etc. Over 16,000 new SLs and 26 types of other relationships have been added, encompassing relationships among 14,100 genes, 53 cancers, and 1,898 drugs, etc. Moreover, a brand-new web interface has been developed to include modules such as SL query by disease or compound, SL partner gene set enrichment analysis and knowledge graph browsing through a dynamic graph viewer. The data can be downloaded directly from the website or through the RESTful APIs. The database is accessible online at http://synlethdb.sist.shanghaitech.edu.cn/v2.

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DrugPattern: a web-based tool for drug set enrichment analysis

10.1101/178632 ◽

2017 ◽

Author(s):

Chuanbo Huang ◽

Weili Yang ◽

Junpei Wang ◽

Yuan Zhou ◽

Bin Geng ◽

...

Keyword(s):

Low Density Lipoprotein ◽

Animal Experiments ◽

Density Lipoprotein ◽

Enrichment Analysis ◽

The Body ◽

Gene Set Enrichment Analysis ◽

Web Based ◽

Beneficial Effects ◽

Regular Patterns

ABSTRACTSet enrichment analysis based methods (e.g. gene set enrichment analysis) have provided great helps in mining patterns in biomedical datasets, however, tools for inferring regular patterns in drug-related datasets are still limited. For the above purpose, here we developed a web-based tool, DrugPattern. DrugPattern first collected and curated 7019 drug sets, including indications, adverse reaction, targets, pathways etc. For a list of interested drugs, DrugPattern then evaluates the significance of the enrichment of these drugs in each of the 7019 drug sets. To validate DrugPattern, we applied it to predict the potential protective roles of oxidized low-density lipoprotein (oxLDL), a widely accepted deleterious factor for the body. We predicted that oxLDL has beneficial effects on some diseases, most of which were supported by literature except type 2 diabetes (T2D), in which oxLDL was previously believed to be a risk factor. Animal experiments further validated that oxLDL indeed has beneficial effects on T2D. These data confirmed the prediction accuracy of our approach and revealed unexpected protective roles for oxLDL in various diseases including T2D. This study provides a tool to infer regular patterns in biomedical datasets based on drug set enrichment analysis.

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A cell atlas of the adult Drosophila midgut

10.1101/410423 ◽

2018 ◽

Cited By ~ 2

Author(s):

Ruei-Jiun Hung ◽

Yanhui Hu ◽

Rory Kirchner ◽

Fangge Li ◽

Chiwei Xu ◽

...

Keyword(s):

Cell Fate ◽

Single Cells ◽

Enrichment Analysis ◽

Cell Types ◽

Gene Set Enrichment Analysis ◽

Web Based ◽

Molecular Features ◽

Gut Hormone ◽

A Cell ◽

Fate Decision

AbstractStudies of the adult Drosophila midgut have provided a number of insights on cell type diversity, stem cell regeneration, tissue homeostasis and cell fate decision. Advances in single-cell RNA sequencing (scRNA-seq) provide opportunities to identify new cell types and molecular features. We used inDrop to characterize the transcriptome of midgut epithelial cells and identified 12 distinct clusters representing intestinal stem cells (ISCs), enteroblasts (EBs), enteroendocrine cells (EEs), enterocytes (ECs) from different regions, and cardia. This unbiased approach recovered 90% of the known ISCs/EBs markers, highlighting the high quality of the dataset. Gene set enrichment analysis in conjunction with electron micrographs revealed that ISCs are enriched in free ribosomes and possess mitochondria with fewer cristae. We demonstrate that a subset of EEs in the middle region of the midgut expresses the progenitor marker esg and that individual EEs are capable of expressing up to 4 different gut hormone peptides. We also show that the transcription factor klumpfuss (klu) is expressed in EBs and functions to suppress EE formation. Lastly, we provide a web-based resource for visualization of gene expression in single cells. Altogether, our study provides a comprehensive resource for addressing novel functions of genes in the midgut epithelium.

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