Transcriptome Analysis of Gelatin Seed Treatment as a Biostimulant of Cucumber Plant Growth

The beneficial effects of gelatin capsule seed treatment on enhanced plant growth and tolerance to abiotic stress have been reported in a number of crops, but the molecular mechanisms underlying such effects are poorly understood. Using mRNA sequencing based approach, transcriptomes of one- and two-week-old cucumber plants from gelatin capsule treated and nontreated seeds were characterized. The gelatin treated plants had greater total leaf area, fresh weight, frozen weight, and nitrogen content. Pairwise comparisons of the RNA-seq data identified 620 differentially expressed genes between treated and control two-week-old plants, consistent with the timing when the growth related measurements also showed the largest differences. Using weighted gene coexpression network analysis, significant coexpression gene network module of 208 of the 620 differentially expressed genes was identified, which included 16 hub genes in the blue module, a NAC transcription factor, a MYB transcription factor, an amino acid transporter, an ammonium transporter, a xenobiotic detoxifier-glutathione S-transferase, and others. Based on the putative functions of these genes, the identification of the significant WGCNA module and the hub genes provided important insights into the molecular mechanisms of gelatin seed treatment as a biostimulant to enhance plant growth.

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Bioinformatics analysis of differentially expressed genes and identification of an miRNA–mRNA network associated with entorhinal cortex and hippocampus in Alzheimer’s disease

Hereditas ◽

10.1186/s41065-021-00190-0 ◽

2021 ◽

Vol 158 (1) ◽

Author(s):

Haoming Li ◽

Linqing Zou ◽

Jinhong Shi ◽

Xiao Han

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Differentially Expressed Genes ◽

Entorhinal Cortex ◽

Molecular Mechanisms ◽

Kegg Pathway ◽

Neurodegenerative Disorder ◽

Early Stage ◽

Differentially Expressed ◽

Hub Genes

Abstract Background Alzheimer’s disease (AD) is a fatal neurodegenerative disorder, and the lesions originate in the entorhinal cortex (EC) and hippocampus (HIP) at the early stage of AD progression. Gaining insight into the molecular mechanisms underlying AD is critical for the diagnosis and treatment of this disorder. Recent discoveries have uncovered the essential roles of microRNAs (miRNAs) in aging and have identified the potential of miRNAs serving as biomarkers in AD diagnosis. Methods We sought to apply bioinformatics tools to investigate microarray profiles and characterize differentially expressed genes (DEGs) in both EC and HIP and identify specific candidate genes and pathways that might be implicated in AD for further analysis. Furthermore, we considered that DEGs might be dysregulated by miRNAs. Therefore, we investigated patients with AD and healthy controls by studying the gene profiling of their brain and blood samples to identify AD-related DEGs, differentially expressed miRNAs (DEmiRNAs), along with gene ontology (GO) analysis, KEGG pathway analysis, and construction of an AD-specific miRNA–mRNA interaction network. Results Our analysis identified 10 key hub genes in the EC and HIP of patients with AD, and these hub genes were focused on energy metabolism, suggesting that metabolic dyshomeostasis contributed to the progression of the early AD pathology. Moreover, after the construction of an miRNA–mRNA network, we identified 9 blood-related DEmiRNAs, which regulated 10 target genes in the KEGG pathway. Conclusions Our findings indicated these DEmiRNAs having the potential to act as diagnostic biomarkers at an early stage of AD.

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A Comprehensive Data Analysis of Differentially Regulated Genes in Melanoma

10.21203/rs.3.rs-19035/v1 ◽

2020 ◽

Author(s):

Yanjie Han ◽

Xinxin Li ◽

Jiliang Yan ◽

Chunyan Ma ◽

Xin Wang ◽

...

Keyword(s):

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Growth Factor Receptor ◽

Principal Component ◽

Distant Metastases ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Hub Genes ◽

Ppi Networks

Abstract Background: Melanoma is the most deadly tumor in skin tumors and is prone to distant metastases. The incidence of melanoma has increased rapidly in the past few decades, and current trends indicate that this growth is continuing. This study was aimed to explore the molecular mechanisms of melanoma pathogenesis and discover underlying pathways and genes associated with melanoma.Methods: We used high-throughput expression data to study differential expression profiles of related genes in melanoma. The differentially expressed genes (DEGs) of melanoma in GSE15605, GSE46517, GSE7553 and the Cancer Genome Atlas (TCGA) datasets were analyzed. Differentially expressed genes (DEGs) were identified by paired t-test. Then the DEGs were performed cluster and principal component analyses and protein–protein interaction (PPI) network construction. After that, we analyzed the differential genes through bioinformatics and got hub genes. Finally, the expression of hub genes was confirmed in the TCGA databases and collected patient tissue samples.Results: Total 144 up-regulated DEGs and 16 down-regulated DEGs were identified. A total of 17 gene ontology analysis (GO) terms and 11 pathways were closely related to melanoma. Pathway of pathways in cancer was enriched in 8 DEGs, such as junction plakoglobin (JUP) and epidermal growth factor receptor (EGFR). In the PPI networks, 9 hub genes were obtained, such as loricrin (LOR), filaggrin (FLG), keratin 5 (KRT5), corneodesmosin (CDSN), desmoglein 1 (DSG1), desmoglein 3 (DSG3), keratin 1 (KRT1), involucrin (IVL) and EGFR. The pathway of pathways in cancer and its enriched DEGs may play important roles in the process of melanoma. The hub genes of DEGs may become promising melanoma candidate genes. Five key genes FLG, DSG1, DSG3, IVL and EGFR were identified in the TCGA database and melanoma tissues.Conclusions: The results suggested that FLG, DSG1, DSG3, IVL and EGFR might play important roles and potentially be valuable in the prognosis and treatment of melanoma.

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A study on regional differences in decidualization of the mouse uterus

Reproduction ◽

10.1530/rep-16-0486 ◽

2017 ◽

Vol 153 (5) ◽

pp. 645-653 ◽

Cited By ~ 3

Author(s):

Miao Zhao ◽

Wen-Qian Zhang ◽

Ji-Long Liu

Keyword(s):

Differentially Expressed Genes ◽

Regional Differences ◽

Molecular Mechanisms ◽

Cell Metabolism ◽

Gene Expression Pattern ◽

Differentially Expressed ◽

Hub Genes ◽

Promoter Regions ◽

Mouse Uterus ◽

Region Gene

Although regional differences in mouse decidualization have been recognized for decades, the molecular mechanisms remain understudied. In the present study, by using RNA-seq, we compared transcriptomic differences between the anti-mesometrial (AM) region and the mesometrial (M) region of mouse uterus on day 8 of pregnancy. A total of 1423 differentially expressed genes were identified, of which 811 genes were upregulated and 612 genes were downregulated in the AM region compared to those in the M region. Gene ontology analysis showed that upregulated genes were generally involved in cell metabolism and differentiation, whereas downregulated genes were associated with lymphocyte themes and immune response. Through network analysis, we identified a total of 6 hub genes. These hub genes are likely more important than other genes due to their key positions in the network. We also examined the promoter regions of differentially expressed genes for the enrichment of transcription factor-binding sites. In the end, we demonstrated that a similar regional gene expression pattern can be observed in the artificial decidualization model. Our study contributes to an increase in the knowledge on the molecular mechanisms underlying regional decidualization in mice.

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Identification of Hub Genes in Anaplastic Thyroid Carcinoma: Evidence From Bioinformatics Analysis

Technology in Cancer Research & Treatment ◽

10.1177/1533033820962135 ◽

2020 ◽

Vol 19 ◽

pp. 153303382096213

Author(s):

Liqi Li ◽

Mingjie Zhu ◽

Hu Huang ◽

Junqiang Wu ◽

Dong Meng

Keyword(s):

Thyroid Carcinoma ◽

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Quantitative Polymerase Chain Reaction ◽

Interaction Network ◽

Anaplastic Thyroid Carcinoma ◽

Rank Aggregation ◽

Differentially Expressed ◽

Hub Genes ◽

Rare Type

Anaplastic thyroid carcinoma (ATC) is a rare type of thyroid cancer that results in fatal clinical outcomes; the pathogenesis of this life-threatening disease has yet to be fully elucidated. This study aims to identify the hub genes of ATC that may play key roles in ATC development and could serve as prognostic biomarkers or therapeutic targets. Two microarray datasets (GSE33630 and GSE53072) were obtained from the Gene Expression Omnibus database; these sets included 16 ATC and 49 normal thyroid samples. Differential expression analyses were performed for each dataset, and 420 genes were screened as common differentially expressed genes using the robust rank aggregation method. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were conducted to explore the potential bio-functions of these differentially expressed genes (DEGs). The terms and enriched pathways were primarily associated with cell cycle, cell adhesion, and cancer-related signaling pathways. Furthermore, a protein-protein interaction network of DEG expression products was constructed using Cytoscape. Based on the whole network, we identified 7 hub genes that included CDK1, TOP2A, CDC20, KIF11, CCNA2, NUSAP1, and KIF2C. The expression levels of these hub genes were validated using quantitative polymerase chain reaction analyses of clinical specimens. In conclusion, the present study identified several key genes that are involved in ATC development and provides novel insights into the understanding of the molecular mechanisms of ATC development.

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Identification of Differentially Expressed Genes in COVID-19 and Integrated Bioinformatics Analysis of Signaling Pathways

Genetics Research ◽

10.1155/2021/2728757 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Linjie Fang ◽

Tingyu Tang ◽

Mengqi Hu

Keyword(s):

Signaling Pathway ◽

Differentially Expressed Genes ◽

Cellular Response ◽

Molecular Mechanisms ◽

Interaction Network ◽

Differentially Expressed ◽

Type I ◽

Hub Genes ◽

Gene Set ◽

Go Terms

Coronavirus disease 2019 (COVID-19) is acutely infectious pneumonia. Currently, the specific causes and treatment targets of COVID-19 are still unclear. Herein, comprehensive bioinformatics methods were employed to analyze the hub genes in COVID-19 and tried to reveal its potential mechanisms. First of all, 34 groups of COVID-19 lung tissues and 17 other diseases’ lung tissues were selected from the GSE151764 gene expression profile for research. According to the analysis of the DEGs (differentially expressed genes) in the samples using the limma software package, 84 upregulated DEGs and 46 downregulated DEGs were obtained. Later, by the Database for Annotation, Visualization, and Integrated Discovery (DAVID), they were enriched in the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. It was found that the upregulated DEGs were enriched in the type I interferon signaling pathway, AGE-RAGE signaling pathway in diabetic complications, coronavirus disease, etc. Downregulated DEGs were in cellular response to cytokine stimulus, IL-17 signaling pathway, FoxO signaling pathway, etc. Then, based on GSEA, the enrichment of the gene set in the sample was analyzed in the GO terms, and the gene set was enriched in the positive regulation of myeloid leukocyte cytokine production involved in immune response, programmed necrotic cell death, translesion synthesis, necroptotic process, and condensed nuclear chromosome. Finally, with the help of STRING tools, the PPI (protein-protein interaction) network diagrams of DEGs were constructed. With degree ≥13 as the cutoff degree, 3 upregulated hub genes (ISG15, FN1, and HLA-G) and 4 downregulated hub genes (FOXP3, CXCR4, MMP9, and CD69) were screened out for high degree. All these findings will help us to understand the potential molecular mechanisms of COVID-19, which is also of great significance for its diagnosis and prevention.

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Identification of Hub Genes related to the Progression of Bladder Cancer by an Integrated Bioinformatics Analysis

10.21203/rs.3.rs-819606/v1 ◽

2021 ◽

Author(s):

Han Wang ◽

Jieqing Chen ◽

Xinhui Liao ◽

Yang Liu ◽

Aifa Tang ◽

...

Keyword(s):

Bladder Cancer ◽

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Signalling Pathway ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

Hub Genes ◽

Immune Infiltration ◽

Ppi Networks ◽

Normal Bladder

Abstract BACKGROUND and OBJECTIVE: A better understanding of the molecular mechanisms underlying bladder cancer is necessary to identify candidate therapeutic targets. METHODS: We screened for genes associated with bladder cancer progression and prognosis. Publicly available expression data were obtained from TCGA and GEO to identify differentially expressed genes (DEGs) between bladder cancer and normal bladder tissues. Weighted co-expression networks were constructed, and Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Associations between hub genes and immune infiltration and immune therapy were evaluated. RESULTS: 3461 DEGs in TCGA-BC and 1069 DEGs in the GSE dataset were identified, with 87 overlapping differentially expressed genes between the bladder cancer and normal bladder groups. Hub genes in the tumour group were mainly enriched for cell proliferation-related GO terms and KEGG pathways, while hub genes in the normal group were related to the synthesis and secretion of neurotransmitters. PPI networks for the genes identified in the normal and tumour groups were constructed. Based on a survival analysis, CDH19, RELN, PLP1, and TRIB3 were significantly associated with prognosis (P < 0.05). Four hub genes were significantly enriched in the MAPK signalling pathway, VEGF signalling pathway, WNT signalling pathway, cell cycle, and P53 signalling pathway based on a gene set enrichment analysis; these genes were associated with immune infiltration levels in bladder cancer. CONCLUSIONS: CDH19, RELN, PLP1, and TRIB3 may play important roles in the development of bladder cancer and are potential therapeutic and prognostic targets.

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Analysis of pathways and networks influencing the differential expression of genes in coronary artery disease

Archives of Biological Sciences ◽

10.2298/abs1403983l ◽

2014 ◽

Vol 66 (3) ◽

pp. 983-988 ◽

Cited By ~ 1

Author(s):

Hui Li ◽

Xiaolan Zhong ◽

Chaomin Li ◽

Lijing Peng ◽

Wei Liu ◽

...

Keyword(s):

Gene Expression ◽

Coronary Artery Disease ◽

Coronary Artery ◽

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Hub Genes ◽

Network Analyses ◽

Artery Disease

Coronary artery disease (CAD) is the leading cause of death worldwide. Microarray analysis is a practical approach to study gene transcription changes that may reflect signatures that underlie the pathogenesis of CAD. Using gene expression profile data from the Gene Expression Omnibus database, we identified differentially expressed genes that can contribute to the pathology of CAD. Further pathway and network analyses were also implemented to identify pathways and hub genes related to the disease. We observed 466 downregulated and 560 upregulated genes. The ribosome pathway was the most significantly over-represented pathway with differentially expressed genes. Over 35% of the genes in this pathway were downregulated. Hub genes in the network, such as IL7R, FYN, CALM1 ESR1 and PLCG1, may play crucial roles in the pathogenesis of CAD. Our results facilitate the identification of molecular mechanisms that underlie CAD.

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Network Analyses of Differentially Expressed Genes in Osteoarthritis to Identify Hub Genes

BioMed Research International ◽

10.1155/2019/8340573 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Zhaoyan Li ◽

Lei Zhong ◽

Zhenwu Du ◽

Gaoyang Chen ◽

Jing Shang ◽

...

Keyword(s):

Signaling Pathway ◽

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Receptor Interaction ◽

Hub Genes ◽

Expression Levels ◽

Network Analyses ◽

Synovial Membranes

Background. Osteoarthritis (OA) is the most common degenerative disease in orthopedics. However, the cause and underlying molecular mechanisms are not clear. This study aims to identify the hub genes and pathways involved in the occurrence of osteoarthritis. Methods. The raw data of GSE89408 were downloaded from the Gene Expression Omnibus (GEO) database, and the differentially expressed genes (DEGs) were identified by R software. The DAVID database was used for pathway and gene ontology analysis, and p<0.05 and gene count >2 were set as the cut-off point. Moreover, protein-protein interaction (PPI) network construction was applied for exploring the hub genes in osteoarthritis. The expression levels of the top ten hub genes in knee osteoarthritis synovial membranes and controls were detected by quantitative real-time PCR system. Results. A total of 229 DEGs were identified in osteoarthritis synovial membranes compared with normal synovial membranes, including 145 upregulated and 84 downregulated differentially expressed genes. The KEGG pathway analysis results showed that up-DEGs were enriched in proteoglycans in cytokine-cytokine receptor interaction, chemokine signaling pathway, rheumatoid arthritis, and TNF signaling pathway, whereas down-DEGs were enriched in the PPAR signaling pathway and AMPK signaling pathway. The qRT-PCR results showed that the expression levels of ADIPOQ, IL6, and CXCR1 in the synovium of osteoarthritis were significantly increased (p <0.05).

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Identification of Aberrantly Expressed Genes during Aging in Rat Nucleus Pulposus Cells

Stem Cells International ◽

10.1155/2019/2785207 ◽

2019 ◽

Vol 2019 ◽

pp. 1-16

Author(s):

Shi Cheng ◽

Xiaochuan Li ◽

Linghan Lin ◽

Zhiwei Jia ◽

Yachao Zhao ◽

...

Keyword(s):

Nucleus Pulposus ◽

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Vital Role ◽

Differentially Expressed ◽

Ppi Network ◽

Nucleus Pulposus Cells ◽

Hub Genes ◽

Software Module ◽

A Genome

Nucleus pulposus cells (NPCs) play a vital role in maintaining the homeostasis of the intervertebral disc (IVD). Previous studies have discovered that NPCs exhibited malfunction due to cellular senescence during disc aging and degeneration; this might be one of the key factors of IVD degeneration. Thus, we conducted this study in order to investigate the altered biofunction and the underlying genes and pathways of senescent NPCs. We isolated and identified NPCs from the tail discs of young (2 months) and old (24 months) SD rats and confirmed the senescent phenotype through SA-β-gal staining. CCK-8 assay, transwell assay, and cell scratch assay were adopted to detect the proliferous and migratory ability of two groups. Then, a rat Gene Chip Clariom™ S array was used to detect differentially expressed genes (DEGs). After rigorous bioinformatics analysis of the raw data, totally, 1038 differentially expressed genes with a fold change>1.5 were identified out of 23189 probes. Among them, 617 were upregulated and 421 were downregulated. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted and revealed numerous number of enriched GO terms and signaling pathways associated with senescence of NPCs. A protein-protein interaction (PPI) network of the DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and Cytoscape software. Module analysis was conducted for the PPI network using the MCODE plugin in Cytoscape. Hub genes were identified by the CytoHubba plugin in Cytoscape. Derived 5 hub genes and most significantly up- or downregulated genes were further verified by real-time PCR. The present study investigated underlying mechanisms in the senescence of NPCs on a genome-wide scale. The illumination of molecular mechanisms of NPCs senescence may assist the development of novel biological methods to treat degenerative disc diseases.

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Identification and validation of hub genes of synovial tissue for patients with osteoarthritis and rheumatoid arthritis

10.21203/rs.3.rs-564869/v1 ◽

2021 ◽

Author(s):

Yanzhi Ge ◽

Zuxiang Chen ◽

Yanbin Fu ◽

Li Zhou ◽

Haipeng Xu ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Gene Expression ◽

Differentially Expressed Genes ◽

Large Scale ◽

Molecular Mechanisms ◽

Immune Cell ◽

Interaction Network ◽

Differentially Expressed ◽

Specific Gene ◽

Hub Genes

Abstract Osteoarthritis (OA) and rheumatoid arthritis (RA) were two major joint diseases with partially common phenotypes and genotypes. This study aimed to determine the mechanistic similarities and differences between osteoarthritis and rheumatoid arthritis by analyzing the differentially expressed genes and signaling pathways. Microarray data of osteoarthritis and rheumatoid arthritis were obtained from the Gene Expression Omnibus. By integrating multiple gene data sets, specific differentially expressed genes (DEGs) were identified in synovial membrane samples from patients and healthy donations. Then, the Gene ontology significant functions annotation, Kyoto Encyclopedia of Genes and Genomes pathways and protein-protein interaction network analysis were conducted. Moreover, CIBERSORT was used to further distinguish OA and RA in immune infiltration. Finally, animal experimentation was conducted and the establishment of model, which was verified using PCR in the mouse. As an overlapping process, we identified 1116 DEGs between OA and RA. It was indicated that specific gene signatures differed significantly between OA and RA connected with the distinct pathways. Of identified DEGs, 9 immune cell types among 22 were identified to distinguish from each other. The qRT-PCR result showed that the eight-tenths expression levels of the hub genes were significantly increased in OA samples (P < 0.05). This large-scale gene expression study provided new insights for disease-associated genes and molecular mechanisms as well as their associated function in osteoarthritis and rheumatoid arthritis, which simultaneously offer a new direction for biomarker development and the distinguishment of gene-level mechanisms between osteoarthritis and rheumatoid arthritis.

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