Dataset Information

Identification of Unique Transcriptomic Signatures and Hub Genes Through RNA Sequencing and Integrated WGCNA and PPI Network Analysis in Nonerosive Reflux Disease.

ABSTRACT:

Purpose

Transcriptomic studies on gastroesophageal reflux disease are scarce, and gene expression signatures in nonerosive reflux disease (NERD) remain elusive. The aim of the study was to identify gene expression profiles and potential hub genes in NERD.

Patients and methods

We performed RNA sequencing on biopsy samples from nine consecutive patients with NERD and six healthy controls. Differentially expressed genes (DEGs) were analysed with the DESeq2 R package. A DEG-based protein-protein interaction (PPI) network was constructed to filter hub genes using Cytoscape. Weighted gene coexpression network analysis (WGCNA) was conducted to identify the coexpression relationships of all modules and explore the relationship between gene sets and clinical traits.

Results

In total, 1195 DEGs were identified, including 649 upregulated and 546 downregulated genes involved in regulating the inflammatory response and epithelial cell differentiation. Overlap of the PPI and WGCNA networks identified five shared genes, namely, THY1, BMP2, LOX, KDR and MMP9, as candidate hub genes in NERD. Quantitative PCR analysis of the expression of these five genes confirmed the sequencing results. Receiver operating characteristic analyses indicated that these hub genes had diagnostic potential for NERD patients. Gene set enrichment analysis confirmed that each hub gene was closely associated with the pathophysiological processes of NERD. In addition, a regulatory network comprising 42 transcription factors (TFs), 28 miRNAs and 5 hub genes was established.

Conclusion

The five core genes may be promising biomarkers of NERD. The TF/miRNA/hub gene network can improve the understanding of the molecular mechanisms underlying disease progression.

SUBMITTER: Zhao Y

PROVIDER: S-EPMC8627320 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Esophageal adenocarcinoma (EAC) is a deadly cancer with high mortality rate, especially in economically advanced countries, while Barrett's esophagus (BE) is reported to be a precursor that strongly increases the risk of EAC. Due to the complexity of these diseases, their molecular mechanisms have not been revealed clearly. This study aims to explore the gene signatures shared between BE and EAC based on integrated network analysis. We obtained EAC- and BE-associated microarray datasets GSE26886, GSE1420, GSE37200, and GSE37203 from the Gene Expression Omnibus and ArrayExpress using systematic meta-analysis. These data were accompanied by clinical data and RNAseq data from The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis were conducted to explore the relationship between gene sets and clinical traits as well as to discover the key relationships behind the co-expression modules. A differentially expressed gene-based protein-protein interaction (PPI) complex was used to extract hub genes through Cytoscape plugins. As a result, 403 DEGs were excavated, comprising 236 upregulated and 167 downregulated genes, which are involved in the cell cycle and replication pathways. Forty key genes were identified using modules of MCODE, CytoHubba, and CytoNCA with different algorithms. A dark-gray module with 207 genes was identified which having a high correlation with phenotype (gender) in the WGCNA. Furthermore, five shared hub gene signatures (SHGS), namely, pre-mRNA processing factor 4 (PRPF4), serine and arginine-rich splicing factor 1 (SRSF1), heterogeneous nuclear ribonucleoprotein M (HNRNPM), DExH-Box Helicase 9 (DHX9), and origin recognition complex subunit 2 (ORC2), were identified between BE and EAC. SHGS enrichment denotes that RNA metabolism and splicosomes play a key role in esophageal cancer development and progress. We conclude that the PPI complex and WGCNA co-expression network highlight the importance of phenotypic identifying hub gene signatures for BE and EAC.

Project description:Microglial cells become rapidly activated through interactions with pathogens, and the persistent activation of these cells is associated with various neurodegenerative diseases. Previous studies have investigated the transcriptomic signatures in microglia or macrophages using microarray technologies. However, this method has numerous restrictions, such as spatial biases, uneven probe properties, low sensitivity, and dependency on the probes spotted. To overcome this limitation and identify novel transcribed genes in response to LPS, we used RNA Sequencing (RNA-Seq) to determine the novel transcriptomic signatures in BV-2 microglial cells. Sequencing assessment and quality evaluation showed that approximately 263 and 319 genes (? 1.5 log2-fold), such as cytokines and chemokines, were strongly induced after 2 and 4 h, respectively, and the induction of several genes with unknown immunological functions was also observed. Importantly, we observed that previously unidentified transcription factors (TFs) (irf1, irf7, and irf9), histone demethylases (kdm4a) and DNA methyltransferases (dnmt3l) were significantly and selectively expressed in BV-2 microglial cells. The gene expression levels, transcription start sites (TSS), isoforms, and differential promoter usage revealed a complex pattern of transcriptional and post-transcriptional gene regulation upon infection with LPS. In addition, gene ontology, molecular networks and pathway analyses identified the top significantly regulated functional classification, canonical pathways and network functions at each activation status. Moreover, we further analyzed differentially expressed genes to identify transcription factor (TF) motifs (-950 to +50 bp of the 5' upstream promoters) and epigenetic mechanisms. Furthermore, we confirmed that the expressions of key inflammatory genes as well as pro-inflammatory mediators in the supernatants were significantly induced in LPS treated primary microglial cells. This transcriptomic analysis is the first to show a comparison of the family-wide differential expression of most known immune genes and also reveal transcription evidence of multiple gene families in BV-2 microglial cells. Collectively, these findings reveal unique transcriptomic signatures in BV-2 microglial cells required for homeostasis and effective immune responses.