Comprehensive Analysis of Potential ceRNA Network and Different Degrees of Immune Cell Infiltration in Acute Respiratory Distress Syndrome

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Acute respiratory distress syndrome (ARDS) is a leading cause of death in critically ill patients due to hypoxemic respiratory failure. The specific pathogenesis underlying ARDS has not been fully elucidated. In this study, we constructed a triple regulatory network involving competing endogenous RNA (ceRNA) to investigate the potential mechanism of ARDS and evaluated the immune cell infiltration patterns in ARDS patients. Overall, we downloaded three microarray datasets that included 60 patients with sepsis-induced ARDS and 79 patients with sepsis alone from the public Gene Expression Omnibus (GEO) database and identified differentially expressed genes (DEGs, including 9 DElncRNAs, 9 DEmiRNAs, and 269 DEmRNAs) by R software. The DEGs were subjected to the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for functional enrichment analysis, and a protein–protein interaction (PPI) network was generated for uncovering interactive relationships among DEmRNAs. Then, a ceRNA network that contained 5 DElncRNAs, 7 DEmiRNAs, and 71 DEmRNAs was established according to the overlapping genes in both DEGs and predicted genes by public databases. Finally, we identified the TUG1/miR-140-5p/NFE2L2 pathway as the hub pathway in the whole network through Cytoscape. In addition, we evaluated the distribution of 22 subtypes of immune cells and recognized three differentially expressed immune cells in patients with sepsis-induced ARDS by “Cell Type Identification by Estimating Relative Subsets of Known RNA Transcripts (CIBERSORT)” algorithm, namely, naive B cells, regulatory T cells, and eosinophils. Correlations between differentially expressed immune cells and hub genes in the ceRNA network were also performed. In conclusion, we demonstrated a new potential regulatory mechanism underlying ARDS (the TUG1/miR-140-5p/NFE2L2 ceRNA regulatory pathway), which may help in further exploring the pathogenesis of ARDS.

Related collections

Most cited references 69

Record: found
Abstract: found
Article: found

Is Open Access

limma powers differential expression analyses for RNA-sequencing and microarray studies

Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

0 comments Cited 11214 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

Damian Szklarczyk, Annika L Gable, David Lyon … (2018)

Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.

0 comments Cited 6225 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Robust enumeration of cell subsets from tissue expression profiles

Aaron Newman, Chih Long Liu, Michael Green … (2015)

We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu).

0 comments Cited 4918 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Guangfa Zhu: URI : https://loop.frontiersin.org/people/1709801/overview

Journal

Journal ID (nlm-ta): Front Genet

Journal ID (iso-abbrev): Front Genet

Journal ID (publisher-id): Front. Genet.

Title: Frontiers in Genetics

Publisher: Frontiers Media S.A.

ISSN (Electronic): 1664-8021

Publication date (Electronic): 01 June 2022

Publication date Collection: 2022

Volume: 13

Electronic Location Identifier: 895629

Affiliations

Department of Respiratory and Critical Care Medicine , Beijing Anzhen Hospital , Capital Medical University , Beijing, China

Author notes

Edited by: Pu-Feng Du, Tianjin University, China

Reviewed by: Cheng Liang, Shandong Normal University, China

Qi Zhao, University of Science and Technology Liaoning, China

*Correspondence: Guangfa Zhu, guangfa_zhu@ 123456ccmu.edu.cn

This article was submitted to Computational Genomics, a section of the journal Frontiers in Genetics

Article

Publisher ID: 895629

DOI: 10.3389/fgene.2022.895629

PMC ID: 9198558

SO-VID: 497935bf-5c3c-4c02-9a2b-5798c1a99c5b

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

History

Date received : 14 March 2022

Date accepted : 04 April 2022

Funding

Funded by: National Natural Science Foundation of China , doi 10.13039/501100001809;

Comprehensive Analysis of Potential ceRNA Network and Different Degrees of Immune Cell Infiltration in Acute Respiratory Distress Syndrome

Read this article at

Abstract

Related collections

G3: Genes|Genomes|Genetics

Most cited references 69

limma powers differential expression analyses for RNA-sequencing and microarray studies

STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

Robust enumeration of cell subsets from tissue expression profiles

Author and article information

Contributors

Journal

Affiliations

Author notes

Article

History

Funding

Categories

Comments

Comment on this article

Similar content 320

Most referenced authors 1,445