scholarly journals eQTLs are key players in the integration of genomic and transcriptomic data for phenotype prediction

2021 ◽  
Author(s):  
Abdou Rahmane Wade ◽  
Harold Duruflé ◽  
Leopoldo Sanchez ◽  
Vincent Segura
Keyword(s):  
Prediction Models ◽  
Enrichment Analysis ◽  
Significant Negative Correlation ◽  
Eqtl Analysis ◽  
Transcriptomic Data ◽  
Common Gardens ◽  
Omics Integration ◽  
Go Enrichment ◽  
Trait Prediction ◽  
Go Enrichment Analysis

AbstractMulti-omics represent a promising link between phenotypes and genome variation. Few studies yet address their integration to understand genetic architecture and improve predictability. Our study used 241 poplar genotypes, phenotyped in two common gardens, with their xylem and cambium RNA sequenced at one site, yielding large phenotypic, genomic and transcriptomic datasets. For each trait, prediction models were built with genotypic or transcriptomic data and compared to concatenation integrating both omics. The advantage of integration varied across traits and, to understand such differences, we made an eQTL analysis to characterize the interplay between the genome and the transcriptome and classify the predicting features into CIS or TRANS relationships. A strong and significant negative correlation was found between the change in predictability and the change in predictor importance for eQTLs (both TRANS and CIS effects) and CIS regulated transcripts, and mostly for traits showing beneficial integration and evaluated in the site of transcriptomic sampling. Consequently, beneficial integration happens when redundancy of predictors is decreased, leaving the stage to other less prominent but complementary predictors. An additional GO enrichment analysis appeared to corroborate such statistical output. To our knowledge, this is a novel finding delineating a promising way to explore data integration.One-sentence summarySuccessful multi-omics integration when predicting phenotypes makes redundant the predictors that are linked to ubiquitous connections between the omics, according to biological and statistical approaches

scholarly journals Comprehensive biological information analysis of PTEN gene in pan-cancer

2021 ◽  
Author(s):  
Hang Zhang ◽  
Wenhan Zhou ◽  
Xiaoyi Yang ◽  
Shuzhan Wen ◽  
Baicheng Zhao ◽  
...  
Keyword(s):  
3D Structure ◽  
Enrichment Analysis ◽  
Biological Information ◽  
Pten Gene ◽  
Clinical Prognosis ◽  
Regulatory Pathways ◽  
Go Enrichment ◽  
Go Enrichment Analysis ◽  
Pan Cancer ◽  
Low Expression

Abstract Background PTEN is a multifunctional tumor suppressor gene mutating at high frequency in a variety of cancers. However, its expression in pan-cancer, correlated genes, survival prognosis, and regulatory pathways are not completely described. Here, we aimed to conduct a comprehensive analysis from the above perspectives in order to provide reference for clinical application. Methods we studied the expression levels in cancers by using data from TCGA and GTEx database. Obtain expression box plot from UALCAN database. Perform mutation analysis on the cBioportal website. Obtain correlation genes on the GEPIA website. Construct protein network and perform KEGG and GO enrichment analysis on the STRING database. Perform prognostic analysis on the Kaplan-Meier Plotter website. We also performed transcription factor prediction on the PROMO database and performed RNA-RNA association and RNA-protein interaction on the RNAup Web server and RPISEq. The gene 3D structure, protein sequence and conserved domain were obtained in NCBI respectively. Results PTEN was underexpressed in all cancers we studied. It was closely related to the clinical stage of tumors, suggesting PTEN may involved in cancer development and progression. The mutations of PTEN were present in a variety of cancers, most of which were truncation mutations and missense mutations. Among cancers (KIRC, LUAD, THYM, UCEC, Gastric Cancer, Liver Cancer, Lung Cancer, Breast Cancer), patients with low expression of PTEN had a shorter OS time and poorer OS prognosis. The low expression of PTEN can cause the deterioration of RFS in certain cancers (TGCT, UCEC, LIHC, LUAD, THCA), suggesting that the expression of PTEN was related to the clinical prognosis. Our study identified genes correlated with PTEN and performed GO enrichment analysis on 100 PTEN-related genes obtained from the GEPIA website. Conclusions The understanding of PTEN gene and the in-depth exploration of its related regulatory pathways may provide insight for the discovery of tumor-specific biomarkers and clinical potential therapeutic targets.

Transcriptome Signatures Reveal Candidate Key Genes in the Peripheral Blood Mononuclear Cells of Patients With Coronary Artery Disease

2020 ◽  
Author(s):  
Vijayakrishna Kolur ◽  
Basavaraj Vastrad ◽  
Chanabasayya Vastrad ◽  
Iranna Kotturshetti ◽  
Anandkumar Tengli
Keyword(s):  
Coronary Artery Disease ◽  
Gene Ontology ◽  
Coronary Artery ◽  
Regulatory Network ◽  
Target Gene ◽  
Enrichment Analysis ◽  
Hub Genes ◽  
Go Enrichment ◽  
Artery Disease ◽  
Go Enrichment Analysis

Abstract BackgroundCoronary artery disease (CAD) is one of the most common disorders in the cardiovascular system. This study aims to explore potential signaling pathways and important biomarkers that drive CAD development. MethodsThe CAD GEO Dataset GSE113079 was featured to screen differentially expressed genes (DEGs). The pathway and Gene Ontology (GO) enrichment analysis of DEGs were analyzed using the ToppGene. We screened hub and target genes from protein-protein interaction (PPI) networks, target gene - miRNA regulatory network and target gene - TF regulatory network, and Cytoscape software. Validations of hub genes were performed to evaluate their potential prognostic and diagnostic value for CAD. Results1,036 DEGs were captured according to screening criteria (525upregulated genes and 511downregulated genes). Pathway and Gene Ontology (GO) enrichment analysis of DEGs revealed that these up and down regulated genes are mainly enriched in thyronamine and iodothyronamine metabolism, cytokine-cytokine receptor interaction, nervous system process, cell cycle and nuclear membrane. Hub genes were validated to find out potential prognostic biomarkers, diagnostic biomarkers and novel therapeutic target for CAD. ConclusionsIn summary, our findings discovered pivotal gene expression signatures and signaling pathways in the progression of CAD. CAPN13, ACTBL2, ERBB3, GATA4, GNB4, NOTCH2, EXOSC10, RNF2, PSMA1 and PRKAA1 might contribute to the progression of CAD, which could have potential as biomarkers or therapeutic targets for CAD.

scholarly journals Mining Featured Biomarkers Linked with Epithelial Ovarian Cancer Based on Bioinformatics

Diagnostics ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 39
Author(s):  
◽  
Chanabasayya Vastrad ◽  
◽  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Target Gene ◽  
Enrichment Analysis ◽  
Microrna Target ◽  
Pathway Enrichment ◽  
Protein Protein Interaction ◽  
Go Enrichment ◽  
Gene Regulatory ◽  
Go Enrichment Analysis

: Epithelial ovarian cancer (EOC) is the18th most common cancer worldwide and the 8th most common in women. The aim of this study was to diagnose the potential importance of, as well as novel genes linked with, EOC and to provide valid biological information for further research. The gene expression profiles of E-MTAB-3706 which contained four high-grade ovarian epithelial cancer samples, four normal fallopian tube samples and four normal ovarian epithelium samples were downloaded from the ArrayExpress database. Pathway enrichment and Gene Ontology (GO) enrichment analysis of differentially expressed genes (DEGs) were performed, and protein-protein interaction (PPI) network, microRNA-target gene regulatory network and TFs (transcription factors ) -target gene regulatory network for up- and down-regulated were analyzed using Cytoscape. In total, 552 DEGs were found, including 276 up-regulated and 276 down-regulated DEGs. Pathway enrichment analysis demonstrated that most DEGs were significantly enriched in chemical carcinogenesis, urea cycle, cell adhesion molecules and creatine biosynthesis. GO enrichment analysis showed that most DEGs were significantly enriched in translation, nucleosome, extracellular matrix organization and extracellular matrix. From protein-protein interaction network (PPI) analysis, modules, microRNA-target gene regulatory network and TFs-target gene regulatory network for up- and down-regulated, and the top hub genes such as E2F4, SRPK2, A2M, CDH1, MAP1LC3A, UCHL1, HLA-C (major histocompatibility complex, class I, C) , VAT1, ECM1 and SNRPN (small nuclear ribonucleoprotein polypeptide N) were associated in pathogenesis of EOC. The high expression levels of the hub genes such as CEBPD (CCAAT enhancer binding protein delta) and MID2 in stages 3 and 4 were validated in the TCGA (The Cancer Genome Atlas) database. CEBPD andMID2 were associated with the worst overall survival rates in EOC. In conclusion, the current study diagnosed DEGs between normal and EOC samples, which could improve our understanding of the molecular mechanisms in the progression of EOC. These new key biomarkers might be used as therapeutic targets for EOC.

scholarly journals A Network Pharmacology Approach to Explore the Pharmacological Mechanism of Xiaoyao Powder on Anovulatory Infertility

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Huiping Liu ◽  
Liuting Zeng ◽  
Kailin Yang ◽  
Guomin Zhang
Keyword(s):  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Signal Pathways ◽  
Active Compounds ◽  
Pharmacological Mechanism ◽  
Go Enrichment ◽  
Human Proteins ◽  
Network Pictures ◽  
Go Enrichment Analysis ◽  
Anovulatory Infertility

Aim.To explore the pharmacological mechanism of Xiaoyao powder (XYP) on anovulatory infertility by a network pharmacology approach.Method.Collect XYP’s active compounds by traditional Chinese medicine (TCM) databases, and input them into PharmMapper to get their targets. Then note these targets by Kyoto Encyclopedia of Genes and Genomes (KEGG) and filter out targets that can be noted by human signal pathway. Get the information of modern pharmacology of active compounds and recipe’s traditional effects through databases. Acquire infertility targets by Therapeutic Target Database (TTD). Collect the interactions of all the targets and other human proteins via String and INACT. Put all the targets into the Database for Annotation, Visualization, and Integrated Discovery (DAVID) to do GO enrichment analysis. Finally, draw the network by Cytoscape by the information above.Result.Six network pictures and two GO enrichment analysis pictures are visualized.Conclusion.According to this network pharmacology approach some signal pathways of XYP acting on infertility are found for the first time. Some biological processes can also be identified as XYP’s effects on anovulatory infertility. We believe that evaluating the efficacy of TCM recipes and uncovering the pharmacological mechanism on a systematic level will be a significant method for future studies.

scholarly journals The DOMINO web-server for active module identification analysis

2021 ◽  
Author(s):  
Hagai Levi ◽  
Nima Rahmanian ◽  
Ran Elkon ◽  
Ron Shamir
Keyword(s):  
Gene Network ◽  
Enrichment Analysis ◽  
Molecular Processes ◽  
Activity Profile ◽  
Module Identification ◽  
Go Enrichment ◽  
Downstream Analysis ◽  
User Friendly ◽  
Identification Analysis ◽  
Go Enrichment Analysis

Active module identification (AMI) is an essential step in many omics analyses. Such algorithms receive a gene network and a gene activity profile as input and report subnetworks that show significant over-representation of accrued activity signal ("active modules"). Such modules can point out key molecular processes in the analyzed biological conditions. We recently introduced a novel AMI algorithm called DOMINO, and demonstrated that it detects active modules that capture biological signals with markedly improved rate of empirical validation. Here, we provide an online server that executes DOMINO, making it more accessible and user-friendly. To help the interpretation of solutions, the server provides GO enrichment analysis, module visualizations, and accessible output formats for customized downstream analysis. It also enables running DOMINO with various gene identifiers of different organisms. The server is available at http://domino.cs.tau.ac.il. Its codebase is available at https://github.com/Shamir-Lab.

scholarly journals Analysis of small RNA changes in different Brassica napus synthetic allopolyploids

2019 ◽  
Author(s):  
Yunxiao Wei ◽  
Fei Li ◽  
Shujiang Zhang ◽  
Shifan Zhang ◽  
Hui Zhang ◽  
...  
Keyword(s):  
Differential Expression ◽  
Small Rna ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Small Rna Sequencing ◽  
Go Enrichment ◽  
Polyploid Formation ◽  
Differentially Expressed Mirnas ◽  
New Perspective ◽  
Go Enrichment Analysis

Allopolyploidy is an evolutionary and mechanisticaly intriguing process involving the reconciliation of two or more sets of diverged genomes and regulatory interactions, resulting in new phenotypes. In this study, we explored the small RNA changes of eight F2 synthetic B. napus using small RNA sequencing. We found that a part of miRNAs and siRNAs were non-additively expressed in the synthesized B. napus allotetraploid. Differentially expressed miRNAs and siRNAs differed among eight F2 individuals, and the differential expression of miR159 and miR172 was consistent with that of flowering time trait. The GO enrichment analysis of differential expression miRNA target genes found that most of them were concentrated in ATP-related pathways, which might be a potential regulatory process contributing to heterosis. In addition, the number of siRNAs present in the offspring was significantly higher than that of the parent, and the number of high parents was significantly higher than the number of low parents. The results have shown that the differential expression of miRNA lays the foundation for solving the trait separation phenomenon, and the significant increase of siRNA alleviates the shock of the newly synthesized allopolyploidy. It provides a new perspective of small RNA changes and trait separation in the early stages of allopolyploid polyploid formation.

scholarly journals Systematic Analyses of the Expression, Function, and Prognostic Value of CCNBs in Breast Caner

2021 ◽  
Author(s):  
Xinyu Liu ◽  
Yuqi Tang ◽  
Shuang Wang ◽  
Shutong Liu ◽  
Chenglin Li ◽  
...  
Keyword(s):  
Prognostic Value ◽  
Enrichment Analysis ◽  
Good Prognosis ◽  
Interactive Analysis ◽  
Kaplan Meier ◽  
Go Enrichment ◽  
Level Function ◽  
Go Enrichment Analysis ◽  
Kaplan Meier Plotter ◽  
Breast Caner

Abstract Background Cyclin B (CCNB) family plays key roles in the cell cycle, cell division and proliferation. Three members of CCNB family have been identified, including CCNB1, CCNB2 and CCNB3. Many studies have explored the roles of CCNBs in the tumorigenesis and pathogenesis of different types of cancer. However, the expression level, function, and prognostic value of CCNBs in breast caner (BC) are still unclear.Methods We explored the specific alterations of CCNBs in BC and predicted their prognostic value for BC patients. Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA), Kaplan-Meier plotter, cBioPortal, STRING, Database for Annotation,Visualization and Integrated Discovery (DAVID) databases were used for above analyses.Results We found that CCNB1 amd CCNB2 were significantly overexpressed in BC compared with normal samples, but not CCNB3. Survival analysis showed that upregulated CCNB1 and CCNB2 expression levels were associated with poor prognosis of BC patients, while high CCNB3 expression was related to good prognosis for BC patients. Furthermore, gene oncology (GO) enrichment analysis was performed to reveal the functions of CCNBs and the interacted genes related to CCNBs. In addition, hsa-miR-139-5p and has-miR-944 were identified to be potentially involved in the regulation of CCNB1.Conclusion Our study suggests that CCNB1, CCNB2 are potential targets of precise therapy for BC patients and that CCNB3 is a novel biomarker for the good prognosis of BC patients.

scholarly journals A Network Pharmacology Perspective Investigation of the Pharmacological Mechanisms of the Herbal Drug FDY003 in Gastric Cancer

2022 ◽  
Vol 17 (1) ◽  
pp. 1934578X2110730
Author(s):  
Ho-Sung Lee ◽  
In-Hee Lee ◽  
Kyungrae Kang ◽  
Sang-In Park ◽  
Minho Jung ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Cellular Proliferation ◽  
Malignant Tumors ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Herbal Drug ◽  
Cellular Processes ◽  
Go Enrichment ◽  
Phytochemical Compounds ◽  
Go Enrichment Analysis

Gastric cancer (GC) is one of the most common and deadly malignant tumors worldwide. While the application of herbal drugs for GC treatment is increasing, the multicompound–multitarget pharmacological mechanisms involved are yet to be elucidated. By adopting a network pharmacology strategy, we investigated the properties of the anticancer herbal drug FDY003 against GC. We found that FDY003 reduced the viability of human GC cells and enhanced their chemosensitivity. We also identified 8 active phytochemical compounds in FDY003 that target 70 GC-associated genes and proteins. Gene ontology (GO) enrichment analysis suggested that the targets of FDY003 are involved in various cellular processes, such as cellular proliferation, survival, and death. We further identified various major FDY003 target GC-associated pathways, including PIK3-Akt, MAPK, Ras, HIF-1, ErbB, and p53 pathways. Taken together, the overall analysis presents insight at the systems level into the pharmacological activity of FDY003 against GC.

scholarly journals Interpretation of biological experiments changes with evolution of Gene Ontology and its annotations

2017 ◽  
Author(s):  
Aurelie Tomczak ◽  
Jonathan M. Mortensen ◽  
Rainer Winnenburg ◽  
Charles Liu ◽  
Dominique T. Alessi ◽  
...  
Keyword(s):  
Gene Ontology ◽  
High Throughput ◽  
Enrichment Analysis ◽  
Building Blocks ◽  
Molecular Data ◽  
Biological Phenomena ◽  
Go Enrichment ◽  
Human Genes ◽  
Go Enrichment Analysis ◽  
Over Time

ABSTRACTGene Ontology (GO) enrichment analysis is ubiquitously used for interpreting high throughput molecular data and generating hypotheses about underlying biological phenomena of experiments. However, the two building blocks of this analysis — the ontology and the annotations — evolve rapidly. We used gene signatures derived from 104 disease analyses to systematically evaluate how enrichment analysis results were affected by evolution of the GO over a decade. We found low consistency between enrichment analyses results obtained with early and more recent GO versions. Furthermore, there continues to be strong annotation bias in the GO annotations where 58% of the annotations are for 16% of the human genes. Our analysis suggests that GO evolution may have affected the interpretation and possibly reproducibility of experiments over time. Hence, researchers must exercise caution when interpreting GO enrichment analyses and should reexamine previous analyses with the most recent GO version.

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