Identification of Critical Functional Modules and Signaling Pathways in Osteoporosis

2020 ◽  
Vol 15 ◽  
Author(s):  
Xiaowei Jiang ◽  
Pu Ying ◽  
Yingchao Shen ◽  
Yiming Miu ◽  
Wenbin Kong ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Differential Expression ◽  
Expression Analysis ◽  
Network Topology ◽  
Molecular Mechanisms ◽  
Differential Expression Analysis ◽  
Osteoclast Differentiation ◽  
Enrichment Analysis ◽  
Osteoclast Formation ◽  
Functional Modules

Background: Osteoporosis is the most common bone metabolic disease. Abnormal osteoclast formation and resorption play a fundamental role in osteoporosis pathogenesis. Recent researches have greatly broadened our understanding of molecular mechanisms of osteoporosis. However, the molecular mechanisms leading to osteoporosis are still not entirely clear. Objective: The purpose of this work is to study the critical regulatory genes, functional modules, and signaling pathways. Methods: Differential expression analysis, network topology-based analysis, and overrepresentation enrichment analysis (ORA) were used to identify differentially expressed genes (DEGs), gene subnetworks, and signaling pathways related to osteoporosis, respectively. Results: Differential expression analysis identified DEGs, such as POGLUT1, DAPK3 and NFKBIA, associated with osteoclastogenesis, which highlighted Notch, apoptosis and NF-kB signaling pathways. Network topology-based analysis identified the upregulated subnetwork characterized by EXOSC8 and DIS3L from the RNA exosome complex, and the downregulated subnetwork composed of histone deacetylases and the cofactors, MORF4L1 and JDP2. Furthermore, the overrepresentation enrichment analysis highlighted that corticotrophin-releasing hormone signaling pathway may affect osteoclastogenesis through its component NR4A1, and suppressing osteoclast differentiation and osteoclast bone resorption with urocortin (UCN). Conclusion: Our systematic analysis not only discovered novel molecular mechanisms, but also proposed potential drug targets for osteoporosis.

scholarly journals Easy and efficient ensemble gene set testing with EGSEA

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 2010 ◽  
Author(s):  
Monther Alhamdoosh ◽  
Charity W. Law ◽  
Luyi Tian ◽  
Julie M. Sheridan ◽  
Milica Ng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Gene Set ◽  
P Gene ◽  
Wide Range ◽  
Gene Set Testing

Gene set enrichment analysis is a popular approach for prioritising the biological processes perturbed in genomic datasets. The Bioconductor project hosts over 80 software packages capable of gene set analysis. Most of these packages search for enriched signatures amongst differentially regulated genes to reveal higher level biological themes that may be missed when focusing only on evidence from individual genes. With so many different methods on offer, choosing the best algorithm and visualization approach can be challenging. The EGSEA package solves this problem by combining results from up to 12 prominent gene set testing algorithms to obtain a consensus ranking of biologically relevant results.This workflow demonstrates how EGSEA can extend limma-based differential expression analyses for RNA-seq and microarray data using experiments that profile 3 distinct cell populations important for studying the origins of breast cancer. Following data normalization and set-up of an appropriate linear model for differential expression analysis, EGSEA builds gene signature specific indexes that link a wide range of mouse or human gene set collections obtained from MSigDB, GeneSetDB and KEGG to the gene expression data being investigated. EGSEA is then configured and the ensemble enrichment analysis run, returning an object that can be queried using several S4 methods for ranking gene sets and visualizing results via heatmaps, KEGG pathway views, GO graphs, scatter plots and bar plots. Finally, an HTML report that combines these displays can fast-track the sharing of results with collaborators, and thus expedite downstream biological validation. EGSEA is simple to use and can be easily integrated with existing gene expression analysis pipelines for both human and mouse data.

Circular RNA expression profiling in dorsal root ganglion of rats with peripheral nerve injury-induced neuropathic pain

2020 ◽  
Author(s):  
Wanxia Xiong ◽  
Fan Liu ◽  
jie wang ◽  
zhiyao wang
Keyword(s):  
Neuropathic Pain ◽  
Differential Expression ◽  
Expression Analysis ◽  
Functional Annotation ◽  
Molecular Mechanisms ◽  
High Throughput Sequencing ◽  
Differential Expression Analysis ◽  
Mapk Signaling ◽  
Circular Rnas ◽  
Next Generation Sequencing Technology

Abstract Background : Circular RNAs (circRNAs) comprise a class of endogenous species of RNA consisting of a covalently closed loop structure that is crucial for genetic and epigenetic regulation. The significance of circRNA in neuropathic pain remains to be investigated. Methods : The sciatic nerve chronic constriction injury (CCI) model was established to induce neuropathic pain. We performed genome-wide circRNA analysis of 4 paired DRG sample from CCI and NC rats via next generation sequencing technology. The differentially expressed circRNAs (DEcircRNAs) were identified by differential expression analysis and the expression profile of circRNAs was validated by quantitative real-time PCR (qPCR). Functional annotation analysis was performed to predict the function of DEcircRNAs. Results : A total of 374 DEcirRNAs were identified between CCI and NC rats using circRNA High-throughput sequencing (HTS). Expression levels of 9 DEcircRNAs were validated by qPCR. Functional annotation analysis showed that DEcircRNAs were mainly enriched in pathways and functions such as ‘dopaminergic synapse’, ‘renin secretion’, ‘MAPK signaling pathway’ and ‘neurogenesis’. Competing endogenous RNAs analysis showed that top 50 circRNAs exhibited interactions with four pain related miRNAs. Circ:chr2:33950934-33955969 is the largest node in the circRNA-miRNA interaction network. Conclusion : DEcircRNAs may advance our understanding of the molecular mechanisms underlying neuropathic pain. Key words : neuropathic pain, circRNA, CCI, differential expression analysis

scholarly journals Systematic Investigation of the Clinical Significance and Prognostic Value of the CBXs in Esophageal Cancer

2021 ◽  
Author(s):  
Jun Hou ◽  
Yinfeng Yang ◽  
Honglei Gao ◽  
Qiwei Liu ◽  
Ran Ding ◽  
...  
Keyword(s):  
T Cells ◽  
Esophageal Cancer ◽  
Mrna Expression ◽  
Differential Expression ◽  
Expression Analysis ◽  
Prognostic Value ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Genetic Alterations ◽  
Apoptosis Pathway

Abstract Background: Esophageal cancer (ESCA), one of the most aggressive malignant tumors, has been announced to be the ninth most common cancer and the sixth leading cause of cancer-related death in the world. Chromobox family members (CBXs) are important epigenetic regulators which are related with the transcription of target genes. The role of CBXs in carcinomas has been reported in many studies. However, the function and prognostic value of different CBXs in Esophageal cancer are still largely unknown.Methods: In this article, we first performed differential expression analysis through several methods including Oncomine and Gene Expression Profiling Interactive Analysis. The results led us to determine the differential expression of CBXs in pan-cancer, especially ESCA. Then we evaluated the prognostic value of different CBX mRNA expression in patients with ESCA through the Kaplan-Meier plotter and the Human Protein Atlas database. In addition, we used cBioPortal to explore all genetic alterations and mutations in the CBXs in ESCA. Simultaneously, the correlation between its expression and the level of immune infiltration of ESCA was visualized by TIMER. Finally, the biological function of CBXs in ESCA is obtained through Biological Enrichment Analysis including GO and KEGG.Results: The expression levels of CBX3/4/5 and CBX8 in ESCA tissues increased significantly and the expression level of CBX7 decreased through differential expression analysis. Additionally, CBX1 is significantly related to the clinical cancer stage and disease-free survival (DFS) of ESCA patients. The high mRNA expression of CBX4 is related to the short overall survival (OS) of patients with esophageal squamous cell carcinoma, and the high mRNA expression of CBX3/7/8 is related to the short OS of patients with esophageal adenocarcinoma, indicating that CBX1/3/4/7/8 may be a potential prognostic biomarker for the survival of ESCA patients. Besides, the expression of CBXs is significantly related to the infiltration of a variety of immune cells, including six types of CD4+ T cells, macrophages, neutrophils, B cells, CD8+ T cells and dendritic cells in esophageal cancer. Moreover, we found that CBXs are mainly associated with the inhibition of cell cycle and apoptosis pathway. Further, enrichment analysis indicated that CBXs and correlated genes were enriched in mismatch repair, DNA replication, cancer pathways, and spliceosomes.Conclusions: Our research may provide new insights into the choice of prognosis biomarkers of the CBXs in esophageal cancer.

scholarly journals Construction of a ceRNA network in glioma and analysis of its clinical significance

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Guangdong Liu ◽  
Haihong Li ◽  
Wenyang Ji ◽  
Haidong Gong ◽  
Yan Jiang ◽  
...  
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Gene Set Enrichment Analysis ◽  
Normal Brain ◽  
Central Nervous System Tumor ◽  
Cox Regression Analysis ◽  
Cerna Network

Abstract Background Glioma is the most common central nervous system tumor with a poor survival rate and prognosis. Previous studies have found that long non-coding RNA (lncRNA) and competitive endogenous RNA (ceRNA) play important roles in regulating various tumor mechanisms. We obtained RNA-Seq data of glioma and normal brain tissue samples from TCGA and GTEx databases and extracted the lncRNA and mRNA expression data. Further, we analyzed these data using weighted gene co-expression network analysis and differential expression analysis, respectively. Differential expression analysis was also carried out on the mRNA data from the GEO database. Further, we predicted the interactions between lncRNA, miRNA, and targeted mRNA. Using the CGGA data to perform univariate and multivariate Cox regression analysis on mRNA. Results We constructed a Cox proportional hazard regression model containing four mRNAs and performed immune infiltration analysis. Moreover, we also constructed a ceRNA network including 21 lncRNAs, two miRNAs, and four mRNAs, and identified seven lncRNAs related to survival that have not been previously studied in gliomas. Through the gene set enrichment analysis, we found four lncRNAs that may have a significant role in tumors and should be explored further in the context of gliomas. Conclusions In short, we identified four lncRNAs with research value for gliomas, constructed a ceRNA network in gliomas, and developed a prognostic prediction model. Our research enhances our understanding of the molecular mechanisms underlying gliomas, providing new insights for developing targeted therapies and efficiently evaluating the prognosis of gliomas.

scholarly journals Best practices on the differential expression analysis of multi-species RNA-seq

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Matthew Chung ◽  
Vincent M. Bruno ◽  
David A. Rasko ◽  
Christina A. Cuomo ◽  
José F. Muñoz ◽  
...  
Keyword(s):  
Best Practices ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Single Species ◽  
Rna Seq ◽  
Species Analysis ◽  
Differential Gene ◽  
Multiple Species ◽  
Downstream Analysis

AbstractAdvances in transcriptome sequencing allow for simultaneous interrogation of differentially expressed genes from multiple species originating from a single RNA sample, termed dual or multi-species transcriptomics. Compared to single-species differential expression analysis, the design of multi-species differential expression experiments must account for the relative abundances of each organism of interest within the sample, often requiring enrichment methods and yielding differences in total read counts across samples. The analysis of multi-species transcriptomics datasets requires modifications to the alignment, quantification, and downstream analysis steps compared to the single-species analysis pipelines. We describe best practices for multi-species transcriptomics and differential gene expression.

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