scholarly journals Identification of the Key Genes Associated with the Yak Hair Follicle Cycle

Genes ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 32
Author(s):  
Xiaolan Zhang ◽  
Pengjia Bao ◽  
Na Ye ◽  
Xuelan Zhou ◽  
Yongfeng Zhang ◽  
...  
Keyword(s):  
Noncoding Rna ◽  
Hair Follicles ◽  
Receptor Interaction ◽  
Seasonal Cycles ◽  
Transcriptome Data ◽  
Hub Genes ◽  
Specific Expression ◽  
Kegg Analysis ◽  
Regulation Network ◽  
Hair Follicle Cycle

The development of hair follicles in yak shows significant seasonal cycles. In our previous research, transcriptome data including mRNAs and lncRNAs in five stages during the yak hair follicles (HFs) cycle were detected, but their regulation network and the hub genes in different periods are yet to be explored. This study aimed to screen and identify the hub genes during yak HFs cycle by constructing a mRNA-lncRNA co-expression network. A total of 5000 differently expressed mRNA (DEMs) and 729 differently expressed long noncoding RNA (DELs) were used to construct the co-expression network, based on weighted genes co-expression network analysis (WGCNA). Four temporally specific modules were considered to be significantly associated with the HFs cycle of yak. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that the modules are enriched into Wnt, EMC-receptor interaction, PI3K-Akt, focal adhesion pathways, and so on. The hub genes, such as FER, ELMO1, PCOLCE, and HOXC13, were screened in different modules. Five hub genes (WNT5A, HOXC13, DLX3, FOXN1, and OVOL1) and part of key lncRNAs were identified for specific expression in skin tissue. Furthermore, immunofluorescence staining and Western blotting results showed that the expression location and abundance of DLX3 and OVOL1 are changed following the process of the HFs cycle, which further demonstrated that these two hub genes may play important roles in HFs development.

scholarly journals Using RNA-Seq to Explore the Hub Genes in the Trigeminal Root Entry Zone of Rats by Compression Injury

2021 ◽  
pp. E573-E581
Keyword(s):  
Animal Model ◽  
Male Rats ◽  
Receptor Interaction ◽  
Rna Seq ◽  
Compression Injury ◽  
Hub Genes ◽  
Entry Zone ◽  
Kegg Analysis ◽  
Root Entry Zone ◽  
Trigeminal Root

BACKGROUND: Mechanical compression on the trigeminal root entry zone (TREZ) by microvascular is the main etiology of primary trigeminal neuralgia (TN). OBJECTIVES: To study the pathogenesis of TN, hub genes screening in the TREZ of TN in an animal model was performed. STUDY DESIGN: A double blind, randomized study was designed in a controlled animal trial. SETTING: The research took place in the Laboratory of Clinical Applied Anatomy at the School of Basic Medical Science of Fujian Medical University. METHODS: Twelve male rats were randomly divided into a sham operation group and a TN animal model group. TN animal model was induced by chronic compression of trigeminal nerve root (CCT) operation. Gene expression in the TREZ were analyzed by RNA sequencing (RNA-Seq) technique. KEGG analysis, GO analysis, and PPI analysis were performed in the DEGs. Key signaling pathways analyzing by GSEA and the hub genes in the DEGs were also studied. Reverse transcription real-time polymerase chain reaction (RT-qPCR) was used to verify the RNA-Seq results. RESULTS: Transcriptome data showed that 352 genes up-regulated and 59 genes down-regulated in DEGs on post-operation day 21, after CCT operation in the TN group. KEGG analysis revealed that, “neuroactive ligand receptor interaction” and “cytokine cytokine receptor interaction” may be related to the pathogenesis of TN. GO analysis showed “regulation of signing receptor activity”, “chemokine activity”, and “carbohydrate binging” may be related to TN. The RT-qPCR results were consistent with the test results, indicating that the transcriptome sequencing results were reliable. LIMITATIONS: Although the incidence of TN in female rats was higher than in male rats, we only used male SD rats to establish the TN animal model, to avoid the effect of estrogen on experimental results. This study only presents some respects of RNA-Seq technique and, therefore, did not identify the DEGs at the protein level. The relationship between the DEGs at different levels shoud be done in the future. CONCLUSIONS: Based on the results of RNA-seq, this study discovered 6 hub genes in the TREZ that are closely related to the TN animal model, which provide a potential breakthrough point to explore the pathogenesis of TN. KEY WORDS: Animal model, compression injury, hub gene, rat, RNA-seq, transcriptome, trigeminal neuralgia, trigeminal root entry zone

scholarly journals Identification of Hub Genes in Pancreatic Ductal Adenocarci-noma Using Bioinformatics Analysis

2021 ◽  
Author(s):  
Congcong Wang ◽  
Jianping Guo ◽  
Xiaoyang Zhao ◽  
Jia Jia ◽  
Wenting Xu ◽  
...  
Keyword(s):  
Bioinformatics Analysis ◽  
Expression Profiles ◽  
Gene Expression Omnibus ◽  
Receptor Interaction ◽  
Analysis Tool ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Kegg Analysis ◽  
Prognostic Assessment ◽  
Scheme Selection

Background: To address the biomarkers that correlated with the prognosis of patients with PDCA using bioinformatics analysis. Methods: The raw data of genes were obtained from the Gene Expression Omnibus. We screened differently expressed genes (DEGs) by Rstudio. Database for Annotation,Visualization and Intergrated Discovery was used to investigate their biological function by Gene Ontology(GO) and Kyoto Encyclopedia of Genes (KEGG) analysis. Protein-protein interaction of these DEGs were analyzed based on the Search Tool for the Retrieval of Interacting Genes database (STRING) and visualized by Cytoscape. Genes calculated by CytoHubba with degree >10 were identified as hub genes. Then, the identified hub genes were verified by UALCAN online analysis tool to evaluate the prognostic value in PDCA. Results: Three expression profiles (GSE15471, GSE16515 and GSE32676) were downloaded from GEO database. The three sets of DEGs exhibited an intersection consisting of 223 genes (214 upregulated DEGs and 9 downregulated DEGs). GO analysis showed that the 223 DEGs were significantly enriched in extracellular exosome, plasma membrane and extracellular space. ECM-receptor interaction, PI3K-Akt signaling pathway and Focal adhesion were the most significantly enriched pathway according to KEGG analysis. By combining the results of Cytohubba, 30 hub genes with a high degree of connectivity were picked out. Finally, we candidated 3 biomarkers by UALCAN online survival analysis, including CEP55, ANLN and PRC1. Conclusion: we identified CEP55, ANLN and PRC1 may be the potential biomarkers and therapeutic targets of PDCA, which used for prognostic assessment and scheme selection.

Construction of Long Noncoding RNA-Associated ceRNA Networks Reveals Potential Biomarkers in Alzheimer’s Disease

2021 ◽  
pp. 1-15
Author(s):  
Guan-yong Ou ◽  
Wen-wen Lin ◽  
Wei-jiang Zhao
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Noncoding Rna ◽  
Regulatory Networks ◽  
Enrichment Analysis ◽  
Brain Regions ◽  
Functional Enrichment ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Potential Biomarkers

Background: Alzheimer’s disease (AD) is a chronic neurodegenerative disease that seriously impairs both cognitive and memory functions mainly in the elderly, and its incidence increases with age. Recent studies demonstrated that long noncoding RNAs (lncRNAs) play important roles in AD by acting as competing endogenous RNAs (ceRNAs). Objective: In this study, we aimed to construct lncRNA-associated ceRNA regulatory networks composed of potential biomarkers in AD based on the ceRNA hypothesis. Methods: A total of 20 genes (10 upregulated genes and 10 downregulated genes) were identified as the hub differentially expressed genes (DEGs). The functional enrichment analysis showed that the most significant pathways of DEGs involved include retrograde endocannabinoid signaling, synaptic vesicle circle, and AD. The upregulated hub genes were mainly enriched in the cytokine-cytokine receptor interaction pathway, whereas downregulated hub genes were involved in the neuroactive ligand-receptor interaction pathway. After convergent functional genomic (CFG) ranks and expression level analysis in different brain regions of hub genes, we found that CXCR4, GFAP, and GNG3 were significantly correlated with AD. We further identified crucial miRNAs and lncRNAs of targeted genes to construct lncRNA-associated ceRNA regulatory networks. Results: The results showed that two lncRNAs (NEAT1, MIAT), three miRNAs (hsa-miR-551a, hsa-miR-133b and hsa-miR-206), and two mRNA (CXCR4 and GNG3), which are highly related to AD, were preliminarily identified as potential AD biomarkers. Conclusion: Our study provides new insights for understanding the pathogenic mechanism underlying AD, which may potentially contribute to the ceRNA mechanism in AD.

scholarly journals Human Genes Escaping X-inactivation Revealed by Single Cell Expression Data

2018 ◽  
Author(s):  
Kerem Wainer Katsir ◽  
Michal Linial
Keyword(s):  
Single Cell ◽  
X Chromosome ◽  
Noncoding Rna ◽  
Phenotypic Variability ◽  
Cell Types ◽  
X Inactivation ◽  
Specific Expression ◽  
X Chromosomes ◽  
Human Genes ◽  
Cumulative Knowledge

AbstractBackgroundIn mammals, sex chromosomes pose an inherent imbalance of gene expression between sexes. In each female somatic cell, random inactivation of one of the X-chromosomes restores this balance. While most genes from the inactivated X-chromosome are silenced, 15-25% are known to escape X-inactivation (termed escapees). The expression levels of these genes are attributed to sex-dependent phenotypic variability.ResultsWe used single-cell RNA-Seq to detect escapees in somatic cells. As only one X-chromosome is inactivated in each cell, the origin of expression from the active or inactive chromosome can be determined from the variation of sequenced RNAs. We analyzed primary, healthy fibroblasts (n=104), and clonal lymphoblasts with sequenced parental genomes (n=25) by measuring the degree of allelic-specific expression (ASE) from heterozygous sites. We identified 24 and 49 candidate escapees, at varying degree of confidence, from the fibroblast and lymphoblast transcriptomes, respectively. We critically test the validity of escapee annotations by comparing our findings with a large collection of independent studies. We find that most genes (66%) from the unified set were previously reported as escapees. Furthermore, out of the overlooked escapees, 11 are long noncoding RNA (lncRNAs).ConclusionsX-chromosome inactivation and escaping from it are robust, permanent phenomena that are best studies at a single-cell resolution. The cumulative information from individual cells increases the potential of identifying escapees. Moreover, despite the use of a limited number of cells, clonal cells (i.e., same X-chromosomes are coordinately inhibited) with genomic phasing are valuable for detecting escapees at high confidence. Generalizing the method to uncharacterized genomic loci resulted in lncRNAs escapees which account for 20% of the listed candidates. By confirming genes as escapees and propose others as candidates from two different cell types, we contribute to the cumulative knowledge and reliability of human escapees.

scholarly journals Using WGCNA (weighted gene co-expression network analysis) to identify the hub genes of skin hair follicle development in fetus stage of Inner Mongolia cashmere goat

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243507
Author(s):  
Zhihong Wu ◽  
Erhan Hai ◽  
Zhengyang Di ◽  
Rong Ma ◽  
Fangzheng Shang ◽  
...  
Keyword(s):  
Network Analysis ◽  
Hair Follicle ◽  
Quantitative Polymerase Chain Reaction ◽  
Correlation Coefficients ◽  
Hair Follicles ◽  
Functional Enrichment ◽  
Follicle Development ◽  
Hub Genes ◽  
Cashmere Goats ◽  
Hair Follicle Development

Objective Mature hair follicles represent an important stage of hair follicle development, which determines the stability of hair follicle structure and its ability to enter the hair cycle. Here, we used weighted gene co-expression network analysis (WGCNA) to identify hub genes of mature skin and hair follicles in Inner Mongolian cashmere goats. Methods We used transcriptome sequencing data for the skin of Inner Mongolian cashmere goats from fetal days 45–135 days, and divided the co expressed genes into different modules by WGCNA. Characteristic values were used to screen out modules that were highly expressed in mature skin follicles. Module hub genes were then selected based on the correlation coefficients between the gene and module eigenvalue, gene connectivity, and Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The results were confirmed by quantitative polymerase chain reaction (qPCR). Results Ten modules were successfully defined, of which one, with a total of 3166 genes, was selected as a specific module through sample and gene expression pattern analyses. A total of 584 candidate hub genes in the module were screened by the correlation coefficients between the genes and module eigenvalue and gene connectivity. Finally, GO/KEGG functional enrichment analyses detected WNT10A as a key gene in the development and maturation of skin hair follicles in fetal Inner Mongolian cashmere goats. qPCR showed that the expression trends of 13 genes from seven fetal skin samples were consistent with the sequencing results, indicating that the sequencing results were reliable.n

scholarly journals Transcriptome Dynamics Reveal Stage-Specific and Melatonin-Triggered Gene Expression Patterns During The Cashmere Growth Cycle in Capra Hircus

2021 ◽  
Author(s):  
Chun Li ◽  
Cong Feng ◽  
Guangyuan Ma ◽  
Shaoyin Fu ◽  
Ming Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signaling Pathways ◽  
Expression Patterns ◽  
Growth Cycle ◽  
Hair Follicles ◽  
Receptor Interaction ◽  
Morphological Evidence ◽  
Gene Expression Patterns ◽  
Growth Stages ◽  
Nf Kappa B

Abstract Background Cashmere goat is famous for its high-quality fibers. The growth of cashmere in secondary hair follicles exhibits a seasonal pattern arising from circannual changes in the natural photoperiod. Although several studies have compared and analyzed the differences in gene expression between different cashmere growth stages, the selection of samples in these studies relies on research experience or morphological evidence. Distinguishing cashmere growth cycle according to gene expression patterns may help to explore the regulation mechanisms related to cashmere growth and the effect of melatonin from a molecular level more accurately. Results In this study, we applied RNA-sequencing to the hair follicles of three normal and three melatonin-treated Inner Mongolian cashmere goats sampled every month during a whole cashmere growth cycle. A total of 3559 and 988 genes were subjected as seasonal changing genes (SCGs) in the control and treated groups, respectively. The SCGs in the normal group are divided into three clusters, and their specific expression patterns help to group the cashmere growth cycle into anagen, catagen and telogen stages. Some canonical pathways such as Wnt, TGF-beta and Hippo signaling pathways are detected as promoting the cashmere growth, while Cell adhesion molecules (CAMs), Cytokine-cytokine receptor interaction, Jak-STAT, Fc epsilon RI, NOD-like receptor, Rap1, PI3K-Akt, cAMP, NF-kappa B and many immune-related pathways are detected in the catagen and telogen stages. The PI3K-Akt signaling, ECM-receptor interaction and Focal adhesion are found in the transition stage between telogen to anagen, which may serve as candidate biomarkers for telogen-anagen regeneration. Pairwise comparisons between the control and melatonin-treated groups also indicate 941 monthly differentially expressed genes (monthly DEGs). These monthly DEGs are mainly distributed from April and September, which reveal a potential signal pathway map regulating the anagen stage triggered by melatonin. Enrichment analysis shows that Wnt, Hedgehog, ECM, Chemokines and NF-kappa B signaling pathways may be involved in the regulation of non-quiescence and secondary shedding under the influence of melatonin. Conclusions Our study decodes the key regulators of the whole cashmere growth cycle, laying the foundation for the control of cashmere growth and improvement of cashmere yield.

Identification of hub genes specific to pulmonary metastasis in osteosarcoma through integrated bioinformatics analysis

2021 ◽  
pp. 1-11
Author(s):  
Yinan Chai ◽  
Lihan Xu ◽  
Rui He ◽  
Liangjun Zhong ◽  
Yuying Wang
Keyword(s):  
Pulmonary Metastasis ◽  
Kegg Pathway ◽  
Expression Profiles ◽  
Experimental Studies ◽  
Genetic Alterations ◽  
Mapk Signaling ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Pathway Analyses ◽  
Geo Database

BACKGROUND: Pulmonary metastasis is the most frequent cause of death in osteosarcoma (OS) patients. Recently, several bioinformatics studies specific to pulmonary metastatic osteosarcoma (PMOS) have been applied to identify genetic alterations. However, the interpretation and reliability of the results obtained were limited for the independent database analysis. OBJECTIVE: The expression profiles and key pathways specific to PMOS remain to be comprehensively explored. Therefore, in our study, three original datasets of GEO database were selected. METHODS: Initially, three microarray datasets (GSE14359, GSE14827, and GSE85537) were downloaded from the GEO database. Differentially expressed genes (DEGs) between PMOS and nonmetastatic osteosarcoma (NMOS) were identified and mined using DAVID. Subsequently, GO and KEGG pathway analyses were carried out for DEGs. Corresponding PPI network of DEGs was constructed based on the data collected from STRING datasets. The network was visualized with Cytoscape software, and ten hub genes were selected from the network. Finally, survival analysis of these hub genes also used the TARGET database. RESULTS: In total, 569 upregulated and 1238 downregulated genes were filtered as DEGs between PMOS and NMOS. Based on the GO analysis result, these DEGs were significantly enriched in the anatomical structure development, extracellular matrix, biological adhesion, and cell adhesion terms. Based on the KEGG pathway analysis result, these DEGs were mainly enriched in the pathways in cancer, PI3K-Akt signaling, MAPK signaling, focal adhesion, cytokine-cytokine receptor interaction, and IL-17 signaling. Hub genes (ANXA1 and CXCL12) were significantly associated with overall survival time in OS patient. CONCLUSION: Our results may provide new insight into pulmonary metastasis of OS. However, experimental studies remain necessary to elucidate the biological function and mechanism underlying PMOS.

scholarly journals Anticancer Effects of Emodin on HepG2 Cell: Evidence from Bioinformatic Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Rui-sheng Zhou ◽  
Xiong-Wen Wang ◽  
Qin-feng Sun ◽  
Zeng Jie Ye ◽  
Jian-wei Liu ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Hepg2 Cells ◽  
Molecular Mechanisms ◽  
Mapk Signaling ◽  
Ion Concentration ◽  
Receptor Interaction ◽  
Pathway Enrichment Analysis ◽  
Mrna Levels ◽  
Hub Genes ◽  
Positive Regulation

Hepatocellular carcinoma (HCC) is a primary cause of cancer-related death in the world. Despite the fact that there are many methods to treat HCC, the 5-year survival rate of HCC is still at a low level. Emodin can inhibit the growth of HCC cells invitroand invivo. However, the gene regulation of emodin in HCC has not been well studied. In our research, RNA sequencing technology was used to identify the differentially expressed genes (DEGs) in HepG2 cells induced by emodin. A total of 859 DEGs were identified, including 712 downregulated genes and 147 upregulated genes in HepG2 cells treated with emodin. We used DAVID for function and pathway enrichment analysis. The protein-protein interaction (PPI) network was constructed using STRING, and Cytoscape was used for module analysis. The enriched functions and pathways of the DEGs include positive regulation of apoptotic process, structural molecule activity and lipopolysaccharide binding, protein digestion and absorption, ECM-receptor interaction, complement and coagulation cascades, and MAPK signaling pathway. 25 hub genes were identified and pathway analysis revealed that these genes were mainly enriched in neuropeptide signaling pathway, inflammatory response, and positive regulation of cytosolic calcium ion concentration. Survival analysis showed that LPAR6, C5, SSTR5, GPR68, and P2RY4 may be involved in the molecular mechanisms of emodin therapy for HCC. A quantitative real-time PCR (qRT-PCR) assay showed that the mRNA levels of LPAR6, C5, SSTR5, GPR68, and P2RY4 were significantly decreased in HepG2 cells treated with emodin. In conclusion, the identified DEGs and hub genes in the present study provide new clues for further researches on the molecular mechanisms of emodin.

scholarly journals Integrated Bioinformatics Analysis of Master Regulators in Anaplastic Thyroid Carcinoma

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Zongfu Pan ◽  
Lu Li ◽  
Qilu Fang ◽  
Yangyang Qian ◽  
Yiwen Zhang ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Thyroid Carcinoma ◽  
Signaling Pathway ◽  
Regulatory Network ◽  
Anaplastic Thyroid Carcinoma ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Hub Genes ◽  
Normal Thyroid ◽  
Master Regulators

Anaplastic thyroid carcinoma (ATC) is one of the most aggressive and rapidly lethal tumors. However, limited advances have been made to prolong the survival and to reduce the mortality over the last decades. Therefore, identifying the master regulators underlying ATC progression is desperately needed. In our present study, three datasets including GSE33630, GSE29265, and GSE65144 were retrieved from Gene Expression Omnibus with a total of 32 ATC samples and 78 normal thyroid tissues. A total of 1804 consistently changed differentially expressed genes (DEGs) were identified from three datasets. KEGG pathways enrichment suggested that upregulated DEGs were mainly enriched in ECM-receptor interaction, cell cycle, PI3K-Akt signaling pathway, focal adhesion, and p53 signaling pathway. Furthermore, key gene modules in PPI network were identified by Cytoscape plugin MCODE and they were mainly associated with DNA replication, cell cycle process, collagen fibril organization, and regulation of leukocyte migration. Additionally, TOP2A, CDK1, CCNB1, VEGFA, BIRC5, MAPK1, CCNA2, MAD2L1, CDC20, and BUB1 were identified as hub genes of the PPI network. Interestingly, module analysis showed that 8 out of 10 hub genes participated in Module 1 network and more than 70% genes of Module 2 consisted of collagen family members. Notably, transcription factors (TFs) regulatory network analysis indicated that E2F7, FOXM1, and NFYB were master regulators of Module 1, while CREB3L1 was the master regulator of Module 2. Experimental validation showed that CREB3L1, E2F7, and FOXM1 were significantly upregulated in ATC tissue and cell line when compared with normal thyroid group. In conclusion, the TFs regulatory network provided a more detail molecular mechanism underlying ATC occurrence and progression. TFs including E2F7, FOXM1, CREB3L1, and NFYB were likely to be master regulators of ATC progression, suggesting their potential role as molecular therapeutic targets in ATC treatment.

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