scholarly journals Transcriptomic Prediction of Pig Liver-Enriched Gene 1 Functions in a Liver Cell Line

Genes ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 412
Author(s):  
Zhe Zhang ◽  
Zizengchen Wang ◽  
Yanna Dang ◽  
Jinyang Wang ◽  
Sakthidasan Jayaprakash ◽  
...  
Keyword(s):  
Enrichment Analysis ◽  
Liver Cells ◽  
Cellular Level ◽  
Gene Set Enrichment Analysis ◽  
Functional Study ◽  
Rna Seq ◽  
Gene Set Enrichment ◽  
Functional Studies ◽  
Er Stress Response ◽  
Domain Of Unknown Function

The newly identified liver-enriched gene 1 (LEG1) encodes a protein with a characteristic domain of unknown function 781 (DUF781/LEG1), constituting a protein family with only one member in mammals. A functional study in zebrafish suggested that LEG1 genes are involved in liver development, while the platypus LEG1 homolog, Monotreme Lactation Protein (MLP), which is enriched in the mammary gland and milk, acts as an antibacterial substance. However, no functional studies on eutherian LEG1s have been published to date. Thus, we here report the first functional prediction study at the cellular level. As previously reported, eutherian LEG1s can be classified into three paralogous groups. Pigs have all three LEG1 genes (pLEG1s), while humans and mice have retained only LEG1a. Hence, pLEG1s might represent an ideal model for studying LEG1 gene functions. RNA-seq was performed by the overexpression of pLEG1s and platypus MLP in HepG2 cells. Enrichment analysis showed that pLEG1a and pLEG1b might exhibit little function in liver cells; however, pLEG1c is probably involved in the endoplasmic reticulum (ER) stress response and protein folding. Additionally, gene set enrichment analysis revealed that platypus MLP shows antibacterial activity, confirming the functional study in platypus. Therefore, our study showed from the transcriptomic perspective that mammalian LEG1s have different functions in liver cells due to the subfunctionalization of paralogous genes.

Genomic drivers of poor prognosis and enzalutamide resistance in metastatic castration-resistant prostate cancer.

2019 ◽  
Vol 37 (7_suppl) ◽  
pp. 146-146
Author(s):  
William S. Chen ◽  
Rahul Raj Aggarwal ◽  
Li Zhang ◽  
Shuang Zhao ◽  
Tomasz M. Beer ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Overall Survival ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Beta Catenin ◽  
Rna Seq ◽  
Castration Resistant Prostate Cancer ◽  
Gene Set Enrichment ◽  
Poor Overall Survival ◽  
Castration Resistant

146 Background: Metastatic castration-resistant prostate cancer (mCRPC) is the lethal form of the disease. Several recent efforts have identified genomic alterations in mCRPC, but the clinical implications of these alterations have not been fully elucidated. We conducted a prospective cohort study (n = 101) using whole genome sequencing (WGS) to analyze the association between key driver gene alterations and overall survival. We also performed whole-transcriptome RNA sequencing (RNA-seq) analyses to identify potential mechanisms of enzalutamide resistance in mCRPC. Methods: Metastasis biopsies were obtained in 101 mCRPC patients as part of the multi-institutional West Coast Prostate Cancer Dream Team project. Samples underwent WGS and RNA-seq. The resulting mutation, copy number, and structural variant calls were integrated to determine functional copy number status of candidate genes for downstream clinical analyses. We performed univariate and multivariable analyses to assess the prognostic significance of candidate genomic events with respect to overall survival. To nominate and investigate genomic pathways associated with enzalutamide resistance, we performed expression-based gene set enrichment analysis followed by cross-sectional enrichment and survival analyses related to the top nominated pathway. Results: RB1 loss was associated with poor overall survival (median 14.1 vs. 42.0 months, p < 0.001). When we compared enzalutamide resistant versus naïve samples using gene set enrichment analysis, we identified the Wnt/beta-catenin pathway as the top differentially expressed pathway in enzalutamide-resistant patients. Furthermore, CTNNB1 (beta-catenin) activating mutations were exclusive to enzalutamide-resistant patients (p = 0.013) and predictive of poor overall survival (median 13.6 vs. 41.7 months, p < 0.001). Conclusions: Impaired survival in mCRPC patients is associated with RB1 loss, identified by integrated genomic analysis of CRPC metastasis biopsies. Among men with mCRPC that was enzalutamide-resistant, the Wnt/beta-catenin pathway is nominated as an important predictive (and potentially therapeutic) pathway.

scholarly journals Comprehensive Analysis of Immune Correlation of KIF20A in Pan-cancer

2020 ◽  
Author(s):  
Xiao-Han Cui ◽  
Qiu-Ju Peng ◽  
Peng Gao ◽  
Xu-Dong Zhang ◽  
Ren-Zhi Li ◽  
...  
Keyword(s):  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Rna Seq ◽  
Gene Set Enrichment ◽  
Related Information ◽  
Common Causes ◽  
Cancer Genome Atlas ◽  
Pan Cancer

Abstract Background: Cancer is one of the most common causes of death, and the morbidity and mortality are gradually increasing in the world. KIF20A plays an important role in tumors, but its immune relevance in pan-cancer needs to be further studied.Methods: KIF20A-related information was download from The Cancer Genome Atlas (TCGA). Collecting RNA-seq data is fragments per kilobase million (FPKM) style data. The ESTIMATE algorithm was used for estimating the stromal and immune scores for 33 tumors. Then, we analyzed the correlation between KIF20A in pan-cancer and immune checkpoints and performed gene set enrichment analysis (GSEA) analysis on the co-expressed genes of KIF20A in pan-cancer.Results: We have confirmed that the expression of KIF20A has a intensive correlation with prognosis in 33 kinds of tumors. Its expression of KIF20A was related to a variety of immune cells and immune checkpoints. Based on the results of GSEA for further analysis, in multiple tumors, KIF20A is related to immune-related pathways.Conclusion: We have demonstrated that KIF20A played an important role in pan-cancer and could affect the occurrence or development of a variety of tumors. Moreover, KIF20A was related to immunity, and KIF20A- related immune research in pan-cancer also needs to be further demonstrate.

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 Improving Gene-Set Enrichment Analysis of RNA-Seq Data with Small Replicates

PLoS ONE ◽  
2016 ◽  
Vol 11 (11) ◽  
pp. e0165919 ◽  
Author(s):  
Sora Yoon ◽  
Seon-Young Kim ◽  
Dougu Nam
Keyword(s):  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Rna Seq ◽  
Gene Set Enrichment ◽  
Gene Set

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

2020 ◽  
Author(s):  
Ludwig Geistlinger ◽  
Gergely Csaba ◽  
Mara Santarelli ◽  
Marcel Ramos ◽  
Lucas Schiffer ◽  
...  
Keyword(s):  
Ad Hoc ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Rna Seq ◽  
Gene Expression Data Analysis ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Gene Sets ◽  
High Throughput Gene Expression ◽  
Enrichment Methods

Abstract Motivation Although 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 datasets and biological reasoning on the relevance of resulting enriched gene sets. Results We develop an extensible framework for reproducible 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 datasets investigating 42 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, identifying significant differences in runtime and applicability to RNA-seq data, fraction of enriched gene sets depending on the null hypothesis tested and recovery of the predefined relevance rankings. We make practical recommendations on how methods originally developed for microarray data can efficiently be applied to RNA-seq data, how to interpret results depending on the type of gene set test conducted and which methods are best suited to effectively prioritize gene sets with high phenotype relevance. Availability http://bioconductor.org/packages/GSEABenchmarkeR Contact [email protected]

scholarly journals iRAP - an integrated RNA-seq Analysis Pipeline

2014 ◽  
Author(s):  
Nuno A. Fonseca ◽  
Robert Petryszak ◽  
John Marioni ◽  
Alvis Brazma
Keyword(s):  
Enrichment Analysis ◽  
Transcriptome Profiling ◽  
Transcript Level ◽  
Gene Set Enrichment Analysis ◽  
Rna Seq ◽  
Gene Set Enrichment ◽  
Analysis Pipeline ◽  
Advantages And Disadvantages ◽  
Computational Resources ◽  
Whole Transcriptome

RNA-sequencing (RNA-Seq) has become the technology of choice for whole-transcriptome profiling. However, processing the millions of sequence reads generated requires considerable bioinformatics skills and computational resources. At each step of the processing pipeline many tools are available, each with specific advantages and disadvantages. While using a specific combination of tools might be desirable, integrating the different tools can be time consuming, often due to specificities in the formats of input/output files required by the different programs. Here we present iRAP, an integrated RNA-seq analysis pipeline that allows the user to select and apply their preferred combination of existing tools for mapping reads, quantifying expression, testing for differential expression. iRAP also includes multiple tools for gene set enrichment analysis and generates web browsable reports of the results obtained in the different stages of the pipeline. Depending upon the application, iRAP can be used to quantify expression at the gene, exon or transcript level. iRAP is aimed at a broad group of users with basic bioinformatics training and requires little experience with the command line. Despite this, it also provides more advanced users with the ability to customise the options used by their chosen tools.

scholarly journals Dysregulation of ribosome-related genes in ankylosing spondylitis: a systems biology approach and experimental method

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Arezou Lari ◽  
Hamid Gholami Pourbadie ◽  
Ali Sharifi-Zarchi ◽  
Maryam Akhtari ◽  
Leila Nejatbakhsh Samimi ◽  
...  
Keyword(s):  
Ankylosing Spondylitis ◽  
Ribosome Biogenesis ◽  
Enrichment Analysis ◽  
Microarray Dataset ◽  
Gene Set Enrichment Analysis ◽  
Rna Seq ◽  
Gene Set Enrichment ◽  
Bioinformatics Analyses ◽  
Autoimmune Rheumatic Disease ◽  
Gene Associations

Abstract Background Ankylosing spondylitis (AS) is an autoimmune rheumatic disease. Few candidate gene associations have been reported for AS and the current understanding of its pathogenesis remains still poor. Thus, the exact mechanism of AS is needed to urgently be disclosed. The purpose of this study was to identify candidate genes involving in AS disease. Methods and results GSE25101 publicly available microarray and GSE117769 RNA-seq datasets of AS patients were obtained for bioinformatics analyses. Gene set enrichment analysis showed that in the microarray dataset, the ribosome pathway was significantly up-regulated in AS compared with controls. Furthermore, some ribosomal components demonstrated overexpression in patients in the RNA-seq dataset. To confirm the findings, 20 AS patients and 20 matching controls were selected from the Rheumatology Research Center clinic, Shariati Hospital. PBMCs were separated from whole blood and RNA contents were extracted. Following the results of datasets analysis, the expression level of rRNA5.8S pseudogene, rRNA18S pseudogene, RPL23, RPL7, and RPL17 genes were measured through real-time PCR. Our findings showed dysregulation of rRNA5.8S and rRNA18S pseudogenes, and also the RPL17 gene in patients. Conclusion Considering that genes involved in ribosome biogenesis contributed to some AS-associated biological processes as well as diseases that have comorbidities with AS, our results might advance our understanding of the pathological mechanisms of ankylosing spondylitis.

Sign in / Sign up

Export Citation Format

Share Document