scholarly journals Transcriptome Co-expression Network Analysis Identifies Key Genes Regulating Conchosporangia Maturation of Pyropia haitanensis

2021 ◽  
Vol 12 ◽  
Author(s):  
Yinghui Lin ◽  
Kai Xu ◽  
Yan Xu ◽  
Dehua Ji ◽  
Changsheng Chen ◽  
...  
Keyword(s):  
Lipid Metabolism ◽  
Network Analysis ◽  
Molecular Mechanisms ◽  
Transcript Level ◽  
Pyropia Haitanensis ◽  
Rna Seq ◽  
Hub Genes ◽  
Early Maturing ◽  
Pigment Contents ◽  
Rapid Amplification

Conchosporangia maturation is crucial for the yield of Pyropia/Porphyra. However, the molecular mechanisms underlying this process are poorly understood. In this study, we selected two strains of Pyropia haitanensis that show significant differences in conchosporangia maturation as materials to produce RNA-Seq libraries. Then, we identified key molecular pathways and genes involved in conchosporangia maturation by conducting a weighted gene co-expression network analysis. Two specific modules were identified, and included functions such as phosphorus metabolism, lipid metabolism, and the phosphatidylinositol signaling system. The hub genes that responded positively during conchosporangia maturation encoded diacylglycerol kinase (DGK) and phosphatidylinositol-3-phosphate-5-kinase, which are involved in the synthesis of phosphatidic acid, a key component of lipid metabolism. A full-length DGK sequence of P. haitanensis, designated as PhDGK1, was obtained by rapid-amplification of cDNA ends. Conserved motif and phylogenetic tree analyses showed that PhDGK1 belongs to DGK Cluster II. The transcript level of PhDGK1 increased during conchosporangia maturation in both strains, but increased earlier, and to higher levels, in the early-maturing strain than in the late-maturing strain. This pattern of gene expression was consistent with the patterns of maturity and changes in pigment contents. These results indicate that lipid metabolism plays a key role in regulating conchosporangia maturation in Pyropia spp., and that PhDGK1 might be a useful molecular marker for breeding new early-maturing strains.

Identification of Four Key Biomarkers and Small Molecule Drugs Innasopharyngeal Carcinoma by Weighted Gene Co-expression Network Analysis

2020 ◽  
Author(s):  
Xi Pan ◽  
Jian-Hao Liu
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Small Molecule ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Tumor Stage ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Therapeutic Goals ◽  
Small Molecule Drugs

Abstract Background Nasopharyngeal carcinoma (NPC) is a heterogeneous carcinoma that the underlying molecular mechanisms involved in the tumor initiation, progression, and migration are largely unclear. The purpose of the present study was to identify key biomarkers and small-molecule drugs for NPC screening, diagnosis, and therapy via gene expression profile analysis. Methods Raw microarray data of NPC were retrieved from the Gene Expression Omnibus (GEO) database and analyzed to screen out the potential differentially expressed genes (DEGs). The key modules associated with histology grade and tumor stage was identified by using weighted correlation network analysis (WGCNA). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of genes in the key module were performed to identify potential mechanisms. Candidate hub genes were obtained, which based on the criteria of module membership (MM) and high connectivity. Then we used receiver operating characteristic (ROC) curve to evaluate the diagnostic value of hub genes. The Connectivity map database was further used to screen out small-molecule drugs of hub genes. Results A total of 430 DEGs were identified based on two GEO datasets. The green gene module was considered as key module for the tumor stage of NPC via WGCNA analysis. The results of functional enrichment analysis revealed that genes in the green module were enriched in regulation of cell cycle, p53 signaling pathway, cell part morphogenesis. Furthermore, four DEGs-related hub genes in the green module were considered as the final hub genes. Then ROC revealed that the final four hub genes presented with high areas under the curve, suggesting these hub genes may be diagnostic biomarkers for NPC. Meanwhile, we screened out several small-molecule drugs that have provided potentially therapeutic goals for NPC. Conclusions Our research identified four potential prognostic biomarkers and several candidate small-molecule drugs for NPC, which may contribute to the new insights for NPC therapy.

scholarly journals Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257343
Author(s):  
Shaoshuo Li ◽  
Baixing Chen ◽  
Hao Chen ◽  
Zhen Hua ◽  
Yang Shao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Network Analysis ◽  
Postmenopausal Osteoporosis ◽  
Molecular Mechanism ◽  
Molecular Mechanisms ◽  
Functional Enrichment ◽  
Support Vector ◽  
Hub Genes ◽  
Genetic Biomarkers

Objectives Smoking is a significant independent risk factor for postmenopausal osteoporosis, leading to genome variations in postmenopausal smokers. This study investigates potential biomarkers and molecular mechanisms of smoking-related postmenopausal osteoporosis (SRPO). Materials and methods The GSE13850 microarray dataset was downloaded from Gene Expression Omnibus (GEO). Gene modules associated with SRPO were identified using weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, and pathway and functional enrichment analyses. Feature genes were selected using two machine learning methods: support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF). The diagnostic efficiency of the selected genes was assessed by gene expression analysis and receiver operating characteristic curve. Results Eight highly conserved modules were detected in the WGCNA network, and the genes in the module that was strongly correlated with SRPO were used for constructing the PPI network. A total of 113 hub genes were identified in the core network using topological network analysis. Enrichment analysis results showed that hub genes were closely associated with the regulation of RNA transcription and translation, ATPase activity, and immune-related signaling. Six genes (HNRNPC, PFDN2, PSMC5, RPS16, TCEB2, and UBE2V2) were selected as genetic biomarkers for SRPO by integrating the feature selection of SVM-RFE and RF. Conclusion The present study identified potential genetic biomarkers and provided a novel insight into the underlying molecular mechanism of SRPO.

scholarly journals Gene Co-expression Network Analysis for Identifying Modules and Functionally Enriched Pathways in Vitiligo Disease: A Systems Biology Study

2020 ◽  
Author(s):  
Afshin Derakhshani ◽  
Homa Mollaei ◽  
Negin Parsamanesh ◽  
Mohammad Fereidouni ◽  
Ebrahim Miri-Moghaddam ◽  
...  
Keyword(s):  
Systems Biology ◽  
Network Analysis ◽  
Messenger Rna ◽  
Molecular Mechanisms ◽  
Biological Activities ◽  
Neutrophil Migration ◽  
Enrichment Analysis ◽  
Autoimmune Disorder ◽  
Hub Genes ◽  
Novel Biomarkers

Vitiligo is the most common cause of skin, hair, and oral depigmentation which is known as an autoimmune disorder. Genetic and environmental factors have important roles in the progression of the disease. Dysregulation of gene expression, like microRNAs (miRNA), may serve as major relevant factors. Several biological processes are involved in vitiligo disease and developing a comprehensive approach helps us to better understand the molecular mechanisms of disease. In this research, we describe how a weighted gene co-expression network analysis as a systems biology approach assists to define the primary gene modules, hub genes, and messenger RNA (mRNA)-miRNA regulatory network in vitiligo disease as the novel biomarkers. The results demonstrated a module with a high correlation with vitiligo state. Moreover, gene enrichment analysis showed that this module's genes were mostly involved in some biological activities including G protein-coupled receptors signaling pathway, lymphocyte chemotaxis, chemokine activity, neutrophil migration, granulocyte chemotaxis, etc. The co-expression network was constructed using top hub genes of the correlated module which are named as CXCL10, ARL9, AKR1B10, COX7B, RPL26, SPA17, NDUFAF2, RPF2, DAPL1, RPL34, CWC15, NDUFB3, RPL26L1, ACOT13, HSPB11, and NSA2. MicroRNAs prediction tool (miRWalk) revealed top miRNAs correlated with the interested module. Finally, a drug-target network was constructed which indicated interactions of some food and drug administration (FDA) approved drugs with hub genes. Our findings specified one important module and main hub genes which can be considered as novel biomarkers for vitiligo therapeutic purposes.   

scholarly journals Comparative Transcriptomic Analyses Provide Insights into the Enzymatic Browning Mechanism of Fresh-Cut Sand Pear Fruit

Horticulturae ◽  
2021 ◽  
Vol 7 (11) ◽  
pp. 502
Author(s):  
Jing Fan ◽  
Wei Du ◽  
Qi-Liang Chen ◽  
Jing-Guo Zhang ◽  
Xiao-Ping Yang ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Transcript Level ◽  
Enzymatic Browning ◽  
Rna Seq ◽  
Pear Fruit ◽  
Total Polyphenol ◽  
Phenylpropanoid Biosynthesis ◽  
Key Enzyme ◽  
Fresh Cut ◽  
High Level

Pear (Pyrus spp.) is one of the most commonly consumed temperate fruits, having considerable economic and health importance. Fresh-cut or processed pear fruits are prone to browning because of the abundant phenolic compounds; however, little is known about the molecular mechanisms underlying enzymatic browning of fresh-cut sand pear fruit. In this study, fruits of two sand pear genotypes (low browning cultivar ‘Eli No.2′ and high browning cultivar ‘Weiningdahuangli’) were used to analyze the molecular mechanism of enzymatic browning by SMRT-seq and RNA-seq. The results generated 69,122 consensus isoforms, 21,336 new transcripts, 7105 alternative splicing events, and 254 long non-coding RNAs (lncRNAs). Furthermore, five genes related to enzymatic browning were predicted to be targets of six lncRNAs, and 9930 differentially expressed genes (DEGs) were identified between two different flesh browning cultivars. Meanwhile, most DEGs (e.g., PAL, 4CL, CAD, CCR, CHS, and LAR) involved in the phenylpropanoid biosynthesis pathway were up-regulated, and the expression of PPO and POD were highly expressed in the high-browning cultivar. Interestingly, the transcript level of PbrPPO4 (Pbr000321.4) was significantly higher than other PPO and POD genes, and a high level of total polyphenol and PPO activity were observed in the high browning cultivar. We found that the expression of lncRNA PB.156.1 was significantly positively correlated with the target gene PbrPPO4 (Pbr000321.4). The results suggest that PbrPPO4 might act as a major contributor and a key enzyme encoding gene in regulating fresh-cut sand pear fruit enzymatic browning; the expression of PbrPPO4 was probably regulated by lncRNA PB.156.1. Altogether, the transcriptomic and physiological analyses expand the knowledge of sand pear flesh enzymatic browning at the molecular level and provide a foundation for germplasm resources for molecular breeding of high polyphenol and low browning cultivars in sand pears.

scholarly journals Transmembrane signaling molecules play a key role in the pathogenesis of IgA nephropathy: a weighted gene co-expression network analysis study

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Alieh Gholaminejad ◽  
Amir Roointan ◽  
Yousof Gheisari
Keyword(s):  
Network Analysis ◽  
Molecular Mechanisms ◽  
Immunoglobulin A ◽  
Cell Communication ◽  
Signaling Molecules ◽  
Health Concern ◽  
Transmembrane Signaling ◽  
Ppi Network ◽  
Hub Genes ◽  
Efficient Treatment

Abstract Background Immunoglobulin A nephropathy (IgAN) is one of the most common primary glomerulonephritis and a serious health concern worldwide; though still the underlying molecular mechanisms of IgAN are yet to be known and there is no efficient treatment for this disease. The main goal of this study was to explore the IgAN underlying pathogenic pathways, plus identifying the disease correlated modules and genes using the weighted gene co-expression network analysis (WGCNA) algorithm. Results GSE104948 dataset (the expression data from glomerular tissue of IgAN patients) was analyzed and the identified differentially expressed genes (DEGs) were introduced to the WGCNA algorithm for building co-expression modules. Genes were classified into six co-expression modules. Genes of the disease’s most correlated module were mainly enriched in the immune system, cell–cell communication and transmembrane cell signaling pathways. The PPI network was constructed by genes in all the modules and after hub-gene identification and validation steps, 11 genes, mostly transmembrane proteins (CD44, TLR1, TLR2, GNG11, CSF1R, TYROBP, ITGB2, PECAM1), as well as DNMT1, CYBB and PSMB9 were identified as potentially key players in the pathogenesis of IgAN. In the constructed regulatory network, hsa-miR-129-2-3p, hsa-miR-34a-5p and hsa-miR-27a-3p, as well as STAT3 were spotted as top molecules orchestrating the regulation of the hub genes. Conclusions The excavated hub genes from the hearts of co-expressed modules and the PPI network were mostly transmembrane signaling molecules. These genes and their upstream regulators could deepen our understanding of IgAN and be considered as potential targets for hindering its progression.

scholarly journals Development and Validation of Novel Biomarkers Related to M2 Macrophages Infiltration by Weighted Gene Co-Expression Network Analysis in Prostate Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Ning Xu ◽  
Ru-Nan Dong ◽  
Ting-Ting Lin ◽  
Tian Lin ◽  
Yun-Zhi Lin ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Network Analysis ◽  
Functional Annotation ◽  
Molecular Mechanisms ◽  
Cancer Genomics ◽  
Interaction Network ◽  
The Cancer Genome Atlas ◽  
Pathway Enrichment Analysis ◽  
Castration Resistance ◽  
Hub Genes

M2-tumor-associated macrophages (TAMs) work as a promoter in the processes of bone metastases, chemotherapy resistance, and castration resistance in prostate cancer (PCa), but how M2-TAMs affect PCa has not been fully understood. In this study, we analyzed the proportion of tumor-infiltrating immune cells using the CIBERSORT algorithm, based on samples from the Cancer Genome Atlas database. Then we performed weighted gene co-expression network analysis to examine the modules concerning infiltrated M2-TAMs. Gene Ontology analysis and pathway enrichment analysis were performed for functional annotation and a protein–protein interaction network was constructed. The International Cancer Genomics Consortium cohort was used as a validation cohort. The red module showed the most correlation with M2-TAMs in PCa. Biological processes and pathways were mainly associated with the immune-related processes, as revealed by functional annotation. Four hub genes were screened: ACSL1, DLGAP5, KIF23 and NCAPG. Further validation showed that the four hub genes had a higher expression level in tumor tissues than that in normal tissues, and they were good prognosis biomarkers for PCa. In conclusion, these findings contribute to understanding the underlying molecular mechanisms of how M2-TAMs affect PCa, and looking for the potential biomarkers and therapeutic targets for PCa patients.

scholarly journals Hub Genes of Stroke Identified by Weighted Gene Co-expression Network Analysis

2020 ◽  
Author(s):  
Junhong Li ◽  
Yang Zhai ◽  
Peng Wu ◽  
Yueqiang Hu ◽  
Wei Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Common Disease ◽  
Hub Genes ◽  
Neuron Death ◽  
Roc Curve Analysis

Abstract BACKGROUD: Microarray-based gene expression profiling is widely used in biomedical research. Weighted gene co-expression network analysis (WGCNA) links microarray data directly to clinical traits and identifies rules for predicting pathological stage and prognosis of disease.WGCNA is useful in understandingmany biological processes. Stroke is a common disease worldwide, however, molecular mechanisms of its pathogenesis are largely unknown. The aim of this study was to construct gene co-expression networks for identification of key modules and hub genes associated with stroke pathogenesis.METHODS: Gene microarray expression profiles of stroke samples were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened by the limma package in R software. WGCNA was used to construct free-scale gene co-expression networks to explore the associations between gene sets and clinical features, and to identify key modules and hub genes. Subsequently, functional enrichment analyses were performed. Further, receiver operating characteristic (ROC) curve analysis was carried out to validate expression of hub genes and literature validation was performed as well.RESULTS: A total of 11,747 most variant genes were used for co-expression network construction. Pink and yellow modules were significantly correlated to stroke pathogenesis. Functional enrichment analysis showed that the pink module was mainly involved in regulation of neuron regeneration, and repair of DNA damage.On the other hand, yellow module was mainly enriched in ion transport system dysfunction which was correlated with neuron death. A total of eight hub genes (PRR11, NEDD9, Notch2, RUNX1-IT1, ANP32A-IT1, ASTN2, SAMHD1 and STIM1) were identified and validated at transcriptional levels and through existing literature.CONCLUSION: The eight hub genes (PRR11, NEDD9, Notch2, RUNX1-IT1, ANP32A-IT1, ASTN2, SAMHD1 and STIM1) identified in the study are potentialbiomarkers and therapeutic targets for effective diagnosis and treatment of stroke.

scholarly journals RPL19 Is a Prognostic Biomarker and Promotes Tumor Progression in Hepatocellular Carcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Benchen Rao ◽  
Jianhao Li ◽  
Tong Ren ◽  
Jing Yang ◽  
Guizhen Zhang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Network Analysis ◽  
Tumor Progression ◽  
Poor Prognosis ◽  
Molecular Mechanisms ◽  
The Cancer Genome Atlas ◽  
Bile Acid Metabolism ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Tumor Tissues

BackgroundHepatocellular carcinoma (HCC) is one of the most common malignancies, and the therapeutic outcome remains undesirable due to its recurrence and metastasis. Gene dysregulation plays a pivotal role in the occurrence and progression of cancer, and the molecular mechanisms are largely unknown.MethodsThe differentially expressed genes of HCC screened from the GSE39791 dataset were used to conduct weighted gene co-expression network analysis. The selected hub genes were validated in The Cancer Genome Atlas (TCGA) database and 11 HCC datasets from the Gene Expression Omnibus (GEO) database. Then, a tissue microarray comprising 90 HCC specimens and 90 adjacent normal specimens was used to validate the hub genes. Moreover, the Hallmark, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to identify enriched pathways. Then, we conducted the immune infiltration analysis.ResultsA total of 17 co-expression modules were obtained by weighted gene co-expression network analysis. The green, blue, and purple modules were the most relevant to HCC samples. Four hub genes, RPL19, RPL35A, RPL27A, and RPS12, were identified. Interestingly, we found that all four genes were highly expressed in HCC and that their high expression was related to a poor prognosis by analyzing the TCGA and GEO databases. Furthermore, we investigated RPL19 in HCC tissue microarrays and demonstrated that RPL19 was overexpressed in tumor tissues compared with non-tumor tissues (p = 0.016). Moreover, overexpression of RPL19 predicted a poor prognosis in hepatocellular carcinoma (p < 0.0007). Then, enrichment analysis revealed that cell cycle pathways were significantly enriched, and bile acid metabolism-related pathways were significantly down-regulated when RPL19 was highly expressed. Furthermore, immune infiltration analysis showed that immune response was suppressed.ConclusionOur study demonstrates that RPL19 may play an important role in promoting tumor progression and is correlated with a poor prognosis in HCC. RPL19 may serve as a promising biomarker and therapeutic target for the precise diagnosis and treatment of HCC in the future.

scholarly journals Hub genes of stroke identified by weighted gene co-expression network analysis

2019 ◽  
Author(s):  
Junhong Li ◽  
Yang Zhai ◽  
Peng Wu ◽  
Yueqiang Hu ◽  
Wei Chen ◽  
...  
Keyword(s):  
Network Analysis ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Neuron Death ◽  
Roc Curve Analysis ◽  
Precise Diagnosis

Abstract Background Microarray-based gene expression profiling has been widely used in biomedical research. Weighted gene co-expression network analysis (WGCNA) can link microarray data directly to clinical traits and to identify rules for predicting pathological stage and prognosis of disease, it has been found useful in many biological processes. Stroke is one of the most common diseases worldwide, yet molecular mechanisms of its pathogenesis are largely unknown. We aimed to construct gene co-expression networks to identify key modules and hub genes associated with the pathogenesis of stroke.Results In this study, we screened out the differentially expressed genes from gene microarray expression profiles, then constructed the free-scale gene co-expression networks to explore the associations between gene sets and clinical features, and to identify key modules and hub genes. Subsequently, functional enrichment and the receiver operating characteristic (ROC) curve analysis were performed. And the results show that a total of 11,747 most variant genes were used for co-expression network construction. Pink and yellow modules were found to be the most significantly related to stroke. Functional enrichment analysis showed that the pink module was mainly involved in regulation of neuron regeneration, and the repair of DNA damage, while the yellow module was mainly enriched in ion transport system dysfunction which were correlated with neuron death. A total of 8 hub genes (PRR11, NEDD9, Notch2, RUNX1-IT1, ANP32A-IT1, ASTN2, SAMHD1 and STIM1) were identified and validated at transcriptional levels (other datasets) and by existing literatures.Conclusions Eight hub genes (PRR11, NEDD9, Notch2, RUNX1-IT1, ANP32A-IT1, ASTN2, SAMHD1 and STIM1) may serve as biomarkers and therapeutic targets for precise diagnosis and treatment of stroke in the future.

scholarly journals Network-based analysis reveals the potential involvement of proteasome subunit alpha-2 in tetralogy of Fallot

2020 ◽  
Vol 24 (4) ◽  
pp. 298-313
Author(s):  
Hassan Karami ◽  
◽  
Maryam Moosavi ◽  
Afshin Derakhshani ◽  
Ebrahim Miri-Moghaddam ◽  
...  
Keyword(s):  
Network Analysis ◽  
Neural Crest ◽  
Tetralogy Of Fallot ◽  
Molecular Mechanisms ◽  
Heart Defects ◽  
Neural Crest Cell ◽  
Ventricular Myocardium ◽  
P Value ◽  
Hub Genes ◽  
Positive Regulation

Introduction: Tetralogy of Fallot (TOF) is the most common cyanotic form of congenital heart defects. However, there is no effective therapeutic approach and current therapies have limited curative efficacy. Moreover, the exact etiology of TOF has remained largely unknown. Improved understanding of molecular mechanisms can give an insight into TOF pathogenesis and development of therapeutic approaches. Methods: Here, we conducted a systematic study on the right ventricular myocardium of 24 infants (16 ToF/8 control) using weighted gene co-expression network analysis (WGCNA) to identify meaningful modules or candidate biomarkers. Results: Co-expression network analysis by WGCNA suggested that a highly preserved turquoise module with 2,493 genes and a P-value of 3×10-11 was significantly correlated to TOF. The top 5 hub genes of this module were PSMA2, MYL12A, C11ORF71, COMMD6, and CREG1. The result of turquoise module enrichment showed that the most correlation topic in biological processes and KEGG pathways were positive regulation of cardiac neural crest migration involved in outflow tract morphogenesis and positive regulation of neural crest cell differentiation. Also, we recognized 4 FDA-approved drug candidates for other indications could potentially use for the treatment of TOF patients through regulation of two hub genes of the co-expression network (PSMA2 and NDUFA4). Our findings also showed that the 13 experimentally validated microRNAs regulated the co-expression network through 5 hub genes. Conclusion: We systematically recognized co-expressed gene modules and hub genes associated with TOF progression, which offered insights into the mechanisms underlying TOF progression and some potential drugs for the treatment of TOF.

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