Weighted Gene Co-Expression Network Analysis Reveals Six Hub Genes Involved in and Tight Junction Function in Pancreatic Adenocarcinoma and their Potential Use in Prognosis

Investigating Key Targets of Dajianzhong Decoction for Treating Crohn’s Disease Using Weighted Gene Co-Expression Network

Background: Crohn’s disease (CD) is an inflammatory bowel disease, cases of which have substantially increased in recent years. The classical formula Dajianzhong decoction (DD, Japanese: Daikenchuto) is often used to treat CD, but few studies have evaluated related therapeutic mechanisms. In this study, we investigated the potential targets and mechanisms of DD used for treating CD at the molecular level through the weighted gene co-expression network. Methods: The main chemical components of the three DD herbs (Zanthoxylum bungeanum Maxim., Zingiber officinale (Willd.) Rosc., and Ginseng Radix et Rhizoma) were searched for using the HERB database. The targets for each component were identified using the SwissTargetPrediction and HERB databases, whereas the disease targets for CD were retrieved from the GeneCards and DisGeNET databases. The functional enrichment analysis was performed on the common targets of DD and CD. High-throughput sequencing data for CD patients were retrieved from the Gene Expression Omnibus database, and WGCNA was performed to identify the key targets. The association between the key targets and DD ingredients was verified using molecular docking. Results: By analyzing the interaction targets between DD and CD, 196 overlapping genes were identified. The enrichment results indicated that the PI3K-AKT, TNF, MAPK, and IL-17 signaling pathways influenced the mechanism of action of DD in counteracting CD. Combined with WGCNA, four differentially expressed genes (SLC6A4, NOS2, SHBG, and ABCB1) and their corresponding 24 compounds were closely related to the occurrence of CD. Conclusions: By integrating gene co-expression network analysis, this study preliminarily reveals the internal molecular mechanism of DD in treating CD from a systematic perspective, validated by molecular docking. However, these findings require further validation.

Integrated gene co-expression network analysis reveals unique developmental processes of Aurelia aurita

The typical life cycle of the moon jellyfish (Aurelia aurita) includes the planula, polyp, strobila, ephyra, and medusa developmental stages. These stages exhibit huge differences in both external morphology and internal physiological functions. However, the gene co-expression network involved in these post-embryonic developmental processes has not been studied yet. Here, based on 15 RNA sequencing samples covering all five stages of the A. aurita life cycle, we systematically analyzed the gene co-expression network and obtained 35 relevant modules. Furthermore, we identified the highly correlated modules and hub genes for each stage. These hub genes are implicated to play important roles in the developmental processes of A. aurita, which should help improve our understanding of the jellyfish life cycle.

Identification and validation of key non-coding RNAs and mRNAs using co-expression network analysis in pre-eclampsia

Pre-eclampsia (PE) is a common complication of pregnancy, associated with maternal and fetal morbidity and mortality. In this study, we aimed to explore important long non-coding RNAs (lncRNAs) and their possible mechanisms in PE.GSE60438 expression profile including 25 PE samples and 23 normal samples were obtained from gene expression omnibus (GEO) database. After normalization with betaqn package in R, differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) were identified using the limma package. Gene Ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway were analyzed using DAVID 6.7 and GSEA 3.0. LncRNAs-mRNAs coexpression was implemented using weighted gene co-expression network analysis (WGCNA). MicroRNAs linked with these DElncRNAs and DEmRNAs were predicted and a competitive endogenous RNA (ceRNA) network was built.A total of 53 DElncRNAs and 301 DEmRNAs were identified between control and PE samples. These DEmRNAs were enriched into pathways such as protein digestion and absorption, osteoclast differentiation. WGCNA constructed a lncRNA-mRNA coexpression network, among which SUMO1P3, NACAP1, NCF1C, ANXA2P1, GTF2IP1, NAPSB, OR7E37P were hub genes. ceRNA network was constructed together with microRNAs (miRNAs), and functional analysis indicated cellular membrane and sugar binding were involved in PE progression. Five lncRNAsANXA2P1, GTF2IP1, NACAP1, NCF1C and OR7E37P were successfully validated in our clinical specimens.The DElncRNAs, including ANXA2P1, GTF2IP1, NACAP1, NCF1C and OR7E37P might play important roles in PE. However, the exact mechanism of these lncRNAs in prediction and diagnosis of PE should be further explored.

Prognostic prediction of a 12-methylation gene-based risk score system on pancreatic adenocarcinoma

Pancreatic adenocarcinoma (PAAD) accounts for ~85% of all pancreatic cancer cases and is associated with a less favorable prognosis. Aberrant DNA methylation may influence the progression of PAAD by inducing abnormal gene expression. Methylation data of PAAD samples with prognosis information were obtained from The Cancer Genome Atlas (training set) and European Bioinformatics Institute Array Express databases (validation sets). Using the limma package, the differentially methylated genes in the training dataset were screened. Combined with the Weighted Gene Co-expression Network Analysis package, the co-methylated genes in key modules were identified. Then, a cor.test function in R software was applied to explore the functions of key the methylated genes. Correlation analyses of the expression levels and methylation levels of key methylated genes were performed, followed by identification of methylated genes associated with prognosis using Univariate Cox regression analysis. The optimal combination of prognosis related methylated genes was determined using a Cox-Proportional Hazards (Cox-PH) model. Subsequently, the risk score prognostic prediction system was constructed by combining the Cox-PH prognosis coefficients of the selected optimized genes. Based on the constructed risk score system, samples in all datasets were divided into high and low risk samples and the survival status was compared using survival curves. Furthermore, the correlation between independent prognostic factors and the risk score system was determined using the survival package. A total of 50 genes associated with prognosis of PAAD and a 12-gene optimal combination were obtained, including: CCAAT/enhancer binding protein α, histone cluster 1 H4E, STAM binding protein-like 1, phospholipase D3, centrosomal protein 55, ssDNA binding protein 4, glutamate AMPA receptor subunit 1, switch-associated protein 70, adenylate-cyclase activating polypeptide 1 receptor 1, yippee-like 3, homeobox C4 and insulin-like growth factor binding protein 1. Subsequently, a risk score prognostic prediction system of these 12 genes was constructed and validated. In addition, pathological N category, radiotherapy and risk status were identified as independent prognostic factors. Overall, the risk score prognostic prediction system constructed in the present study may be effective for predicting the prognosis of patients with PAAD.