Unraveling the molecular mechanisms of hyperlipidemia using integrated lncRNA and mRNA microarray data

Abstract Background. Exploring alterations in the host transcriptome following SARS-CoV-2 infection is not only highly warranted to help us understand molecular mechanisms of the disease, but also provide new prospective for screening effective antiviral drugs, finding new therapeutic targets, and evaluating the risk of systemic inflammatory response syndrome (SIRS) early.Methods. We downloaded three gene expression matrix files from the Gene Expression Omnibus (GEO) database, and extracted the gene expression data of the SARS-CoV-2 infection and non-infection in human samples and different cell line samples, and then performed gene set enrichment analysis (GSEA), respectively. Thereafter, we integrated the results of GSEA and obtained co-enriched gene sets and co-core genes in three various microarray data. Finally, we also constructed a protein-protein interaction (PPI) network and molecular modules for co-core genes and performed Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis for the genes from modules to clarify their possible biological processes and underlying signaling pathway. Results. A total of 11 co-enriched gene sets were identiﬁed from the three various microarray data. Among them, 10 gene sets were activated, and involved in immune response and inflammatory reaction. 1 gene set was suppressed, and participated in cell cycle. The analysis of molecular modules showed that 2 modules might play a vital role in the pathogenic process of SARS-CoV-2 infection. The KEGG enrichment analysis showed that genes from module one enriched in signaling pathways related to inflammation, but genes from module two enriched in signaling of cell cycle and DNA replication. Particularly, necroptosis signaling, a newly identified type of programmed cell death that differed from apoptosis, was also determined in our findings. Additionally, for patients with SARS-CoV-2 infection, genes from module one showed a relatively high-level expression while genes from module two showed low-level. Conclusions. We identified two molecular modules were used to assess severity and predict the prognosis of the patients with SARS-CoV-2 infection. In addition, these results provide a unique opportunity to explore more molecular pathways as new potential targets on therapy in COVID 19.

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Identification of Egr1 Direct Target Genes in the Uterus by In Silico Analyses with Expression Profiles from mRNA Microarray Data

Development & Reproduction ◽

10.12717/dr.2014.18.1.001 ◽

2014 ◽

Vol 18 (1) ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Bong-jong Seo ◽

Ji Won Son ◽

Hye-Ryun Kim ◽

Seok-Ho Hong ◽

Haengseok Song

Keyword(s):

Microarray Data ◽

In Silico ◽

Target Genes ◽

Expression Profiles ◽

Direct Target ◽

Mrna Microarray ◽

In Silico Analyses

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Association of microRNA-21 with efficacy of cetuximab in RAS wild-type patients in the FIRE-3 study (AIO KRK-0306) and microRNA-21’s influence on gene expression in the EGFR signaling pathway.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.3593 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. 3593-3593

Author(s):

Lisa Miller-Phillips ◽

Volker Heinemann ◽

Arndt Stahler ◽

Ludwig Fischer von Weikersthal ◽

Florian Kaiser ◽

...

Keyword(s):

Gene Expression ◽

Microarray Data ◽

Multiple Testing ◽

Predictive Biomarker ◽

Cancer Gene ◽

Egfr Signaling ◽

Wild Type ◽

Egfr Signaling Pathway ◽

Gene Sets ◽

Mrna Microarray

3593 Background: FIRE-3 compared first-line therapy with FOLFIRI plus cetuximab (cet) or bevacizumab (bev) in KRAS exon 2 wild-type (wt) patients with metastatic colorectal cancer. Recent analyses showed mircoRNA-21 (miR-21) expression level may be a predictive biomarker for anti-EGFR-therapy raising the question whether miR-21 influences gene expression in the EGFR signaling pathway. Methods: Reverse-transcription quantitative polymerase chain reaction assay identified quantitative miR-21 expression. Median expression was defined as a threshold value to discriminate FIRE-3 population into miR-21 low and high groups. Differential gene expression based on additional mRNA microarray data (Almac Inc, Xcel Array) was calculated by linear models adjusted for multiple testing followed by single sample gene set enrichment analysis (ssGSEA) to compare differentially enriched hallmarks of cancer gene sets. Overall response rate (ORR) was compared using Fisher´s exact test. Median progression-free (PFS) and overall survival (OS) were analyzed using Kaplan-Meier estimation and log-rank test. Results: 333 RAS wt patients provided material for miR-21 expression analysis. In these patients, low miR-21 expression was associated with higher ORR (80.0% vs. 57.9%; p = 0.005) and longer OS (35.8 months (mo) vs. 25.9 mo; p = 0.005) when cet vs bev was added to FOLFIRI. High miR-21 expression was associated with comparable ORR (74.6% vs. 64.0%; p = 0.21) and OS (24.5 mo vs. 23.8 mo; p = 0.4). There was no significant difference in PFS in either group. By comparing miR-21 low and high groups using normalized mRNA microarray data, 538 genes were found to be significantly differentially expressed in RAS wt patients after adjustment for multiple testing. Including data from the two groups into ssGSEA yielded 23 hallmark of cancer gene sets that were significantly differentially enriched; among them, KRAS-signaling showed higher enrichment in the miR-21 high group (adjusted p = 2.09 E-13). Conclusions: MiR-21 expression level might be a predictive biomarker for anti-EGFR-therapy by modulating KRAS signaling in FIRE-3 patients.

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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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An Intelligent Search Algorithm for Feature Gene Selection of Chinese Gastric Cancer with mRNA Microarray Data

2007 1st International Conference on Bioinformatics and Biomedical Engineering ◽

10.1109/icbbe.2007.63 ◽

2007 ◽

Author(s):

Xiaogang Ruan ◽

Jinlian Wang ◽

Hui Li

Keyword(s):

Gastric Cancer ◽

Microarray Data ◽

Gene Selection ◽

Search Algorithm ◽

Intelligent Search ◽

Mrna Microarray ◽

Selection Of

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Host transcriptome alterations in vitro and in vivo following severe acute respiratory syndrome coronavirus 2 infection

10.21203/rs.3.rs-37567/v2 ◽

2021 ◽

Author(s):

Yannian Luo ◽

Juan Xu ◽

Mingzhen Zhou ◽

Xiaomei Lei ◽

Wen Cao ◽

...

Keyword(s):

Gene Expression ◽

Cell Cycle ◽

Microarray Data ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Gene Set ◽

Gene Sets ◽

Core Genes

Abstract Background. Exploring alterations in the host transcriptome following SARS-CoV-2 infection is not only highly warranted to help us understand molecular mechanisms of the disease, but also provide new prospective for screening effective antiviral drugs, finding new therapeutic targets, and evaluating the risk of systemic inflammatory response syndrome (SIRS) early.Methods. We downloaded three gene expression matrix files from the Gene Expression Omnibus (GEO) database, and extracted the gene expression data of the SARS-CoV-2 infection and non-infection in human samples and different cell line samples, and then performed gene set enrichment analysis (GSEA), respectively. Thereafter, we integrated the results of GSEA and obtained co-enriched gene sets and co-core genes in three various microarray data. Finally, we also constructed a protein-protein interaction (PPI) network and molecular modules for co-core genes and performed Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis for the genes from modules to clarify their possible biological processes and underlying signaling pathway. Results. A total of 11 co-enriched gene sets were identiﬁed from the three various microarray data. Among them, 10 gene sets were activated, and involved in immune response and inflammatory reaction. 1 gene set was suppressed, and participated in cell cycle. The analysis of molecular modules showed that 2 modules might play a vital role in the pathogenic process of SARS-CoV-2 infection. The KEGG enrichment analysis showed that genes from module one enriched in signaling pathways related to inflammation, but genes from module two enriched in signaling of cell cycle and DNA replication. Particularly, necroptosis signaling, a newly identified type of programmed cell death that differed from apoptosis, was also determined in our findings. Additionally, for patients with SARS-CoV-2 infection, genes from module one showed a relatively high-level expression while genes from module two showed low-level. Conclusions. We identified two molecular modules were used to assess severity and predict the prognosis of the patients with SARS-CoV-2 infection. In addition, these results provide a unique opportunity to explore more molecular pathways as new potential targets on therapy in COVID 19.

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Pathway Analysis Based on Attractor and Cross Talk in Colon Cancer

Disease Markers ◽

10.1155/2016/2619828 ◽

2016 ◽

Vol 2016 ◽

pp. 1-7 ◽

Cited By ~ 4

Author(s):

Yanxia Liu ◽

Lin Wang ◽

Bingping Wang ◽

Meng Yue ◽

Yufeng Cheng

Keyword(s):

Colon Cancer ◽

Cross Talk ◽

Protein Interactions ◽

Microarray Data ◽

Molecular Mechanisms ◽

Linear Models ◽

Impact Factors ◽

Protein Protein Interactions ◽

Common Cancer ◽

Differential Expressed Genes

Colon cancer is the third and second most common cancer form in men and women worldwide. It is generally accepted that colon cancer mainly results from diet. The aim of this study was to identify core pathways which elucidated the molecular mechanisms in colon cancer. The microarray data of E-GEOD-44861 was downloaded from ArrayExpress database. All human pathways were obtained from Kyoto Encyclopedia of Genes and Genomes database. In total, 135 differential expressed genes (DEG) were identified using Linear Models for Microarray Data package. Differential pathways were identified with the method of attractor after overlapping with DEG. Pathway cross talk network (PCN) was constructed by combining protein-protein interactions and differential pathways. Cross talks of all pathways were obtained in PCN. There were 65 pathways with RankProd (RP) values < 0.05 and 16 pathways with Impact Factors (IF) values > 100. Five pathways were satisfied withPvalue < 0.05, RP values < 0.05, and IF values > 100, which were considered to be the most important pathways in colon cancer. In conclusion, the five pathways were identified in the center status of colon cancer, which may contribute to understanding the mechanism and development of colon cancer.

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