Long-term dynamic compression enhancement TGF-β3-induced chondrogenesis in bovine stem cells: a gene expression analysis

Abstract Background Bioengineering has demonstrated the potential of utilising mesenchymal stem cells (MSCs), growth factors, and mechanical stimuli to treat cartilage defects. However, the underlying genes and pathways are largely unclear. This is the first study on screening and identifying the hub genes involved in mechanically enhanced chondrogenesis and their potential molecular mechanisms. Methods The datasets were downloaded from the Gene Expression Omnibus (GEO) database and contain six transforming growth factor-beta-3 (TGF-β3) induced bovine bone marrow-derived MSCs specimens and six TGF-β3/dynamic-compression-induced specimens at day 42. Screening differentially expressed genes (DEGs) was performed and then analysed via bioinformatics methods. The Database for Annotation, Visualisation, and Integrated Discovery (DAVID) online analysis was utilised to obtain the Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment. The protein-protein interaction (PPI) network of the DEGs was constructed based on data from the STRING database and visualised through the Cytoscape software. The functional modules were extracted from the PPI network for further analysis. Results The top 10 hub genes ranked by their connection degrees were IL6, UBE2C, TOP2A, MCM4, PLK2, SMC2, BMP2, LMO7, TRIM36, and MAPK8. Multiple signalling pathways (including the PI3K-Akt signalling pathway, the toll-like receptor signalling pathway, the TNF signalling pathway, and the MAPK pathway) may impact the sensation, transduction, and reaction of external mechanical stimuli. Conclusions This study provides a theoretical finding showing that gene UBE2C, IL6, and MAPK8, and multiple signalling pathways may play pivotal roles in dynamic compression-enhanced chondrogenesis.

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110 Temporal Changes in Endometrial Gene Expression Between Ipsi- and Contralateral Uterine Horns in Cattle

Reproduction Fertility and Development ◽

10.1071/rdv30n1ab110 ◽

2018 ◽

Vol 30 (1) ◽

pp. 194

Author(s):

J. M. Sánchez ◽

C. Passaro ◽

N. Forde ◽

S. Behura ◽

J. A. Browne ◽

...

Keyword(s):

Gene Expression ◽

Stem Cells ◽

Luteal Phase ◽

Expression Patterns ◽

Uterine Horn ◽

Oestrous Cycle ◽

Signalling Pathway ◽

Signalling Pathways ◽

Robust Analysis ◽

The Impact

The transfer of an embryo into the uterine horn contralateral to the ovary bearing the corpus luteum has been associated with a decreased pregnancy rate in cattle compared with transfer into the ipsilateral horn. These findings suggest that the environment in the contralateral horn is less conducive to supporting conceptus development than that of the ipsilateral horn. Therefore, this study compared the endometrial transcriptome of the ipsi- and contralateral uterine horns during the luteal phase. Endometrial samples from the ipsi- (IPSI) and contralateral (CONTRA) horns were collected from synchronized nonpregnant beef heifers on Days 5, 7, 13 or 16 post-oestrus (n = 5 heifers per time point). Total RNA was isolated and sequenced. Differences in the transcriptome were determined by edgeR-robust analysis. Principal component analysis found that IPSI and CONTRA have distinct patterns of gene expression on each day, with Day 5 exhibiting the most variation and Day 16 being least variable. Further, the 2 uterine horns had distinct expression patterns on Day 5, with IPSI exhibiting significantly higher variation in gene expression compared twitho CONTRA. EdgeR-robust analysis found 217 (201 up- and 16 down-regulated), 54 (44 up- and 10 down-regulated), 14 (13 up- and 1 down-regulated), and 18 (14 up- and 4 down-regulated) differentially expressed genes (DEG; >2-fold change, false discovery rate P < 0.05) between IPSI and CONTRA endometria on Days 5, 7, 13, and 16 of the oestrous cycle, respectively. The top 5 canonical pathways associated with DEG between IPSI and CONTRA during the luteal phase of the oestrous cycle were involved in signalling pathways regulating pluripotency of stem cells (73/138), progesterone-mediated oocyte maturation (55/89), endometrial cancer (31/51), ErbB signalling pathway (50/87), and mTOR signalling pathway (36/61). The impact of DEG on signalling pathways was assessed using a pathway perturbation algorithm called Signalling Pathway Impact Analysis (SPIA). This topology-based pathway analysis was conducted using the Bioconductor ToPAseq package (https://bioconductor.org/packages/release/bioc/html/ToPASeq.html) and revealed that signalling pathways regulating pluripotency of stem cells showed the highest perturbation score when IPSI was compared with CONTRA irrespective of day. Discovering and cataloguing which pathways are perturbed in each uterine horn throughout the oestrous cycle may contribute to our understanding of the mechanisms underlying early embryonic loss. Ths study was supported by Science Foundation Ireland (13/IA/1983) and the Irish Department of Agriculture, Food and The Marine (13S528).

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Meta-Analyses of Multiple Gene Expression Profiles to Screen Hub Genes Related to Osteoarthritis

Public Health Genomics ◽

10.1159/000517308 ◽

2021 ◽

Vol 24 (5-6) ◽

pp. 267-279

Author(s):

Xianyang Zhu ◽

Wen Guo

Keyword(s):

Gene Expression ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Meta Analysis ◽

Wnt Signaling Pathway ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Receptor Interaction ◽

Ppi Network ◽

Hub Genes

Background: This study aimed to screen and validate the crucial genes involved in osteoarthritis (OA) and explore its potential molecular mechanisms. Methods: Four expression profile datasets related to OA were downloaded from the Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) from 4 microarray patterns were identified by the meta-analysis method. The weighted gene co-expression network analysis (WGCNA) method was used to investigate stable modules most related to OA. In addition, a protein-protein interaction (PPI) network was built to explore hub genes in OA. Moreover, OA-related genes and pathways were retrieved from Comparative Toxicogenomics Database (CTD). Results: A total of 1,136 DEGs were identified from 4 datasets. Based on these DEGs, WGCNA further explored 370 genes included in the 3 OA-related stable modules. A total of 10 hub genes were identified in the PPI network, including AKT1, CDC42, HLA-DQA2, TUBB, TWISTNB, GSK3B, FZD2, KLC1, GUSB, and RHOG. Besides, 5 pathways including “Lysosome,” “Pathways in cancer,” “Wnt signaling pathway,” “ECM-receptor interaction” and “Focal adhesion” in CTD and enrichment analysis and 5 OA-related hub genes (including GSK3B, CDC42, AKT1, FZD2, and GUSB) were identified. Conclusion: In this study, the meta-analysis was used to screen the central genes associated with OA in a variety of gene expression profiles. Three OA-related modules (green, turquoise, and yellow) containing 370 genes were identified through WGCNA. It was discovered through the gene-pathway network that GSK3B, CDC42, AKT1, FZD2, and GUSB may be key genes related to the progress of OA and may become promising therapeutic targets.

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Identification of potential crucial genes and pathways associated with vein graft restenosis based on gene expression analysis in experimental rabbits

PeerJ ◽

10.7717/peerj.4704 ◽

2018 ◽

Vol 6 ◽

pp. e4704 ◽

Cited By ~ 1

Author(s):

Qiang Liu ◽

Xiujie Yin ◽

Mingzhu Li ◽

Li Wan ◽

Liqiao Liu ◽

...

Keyword(s):

Gene Expression ◽

High Throughput ◽

Vein Graft ◽

Molecular Mechanisms ◽

Rapid Development ◽

Bypass Graft ◽

Functional Enrichment ◽

Ppi Network ◽

Hub Genes ◽

Network Analyses

Occlusive artery disease (CAD) is the leading cause of death worldwide. Bypass graft surgery remains the most prevalently performed treatment for occlusive arterial disease, and veins are the most frequently used conduits for surgical revascularization. However, the clinical efficacy of bypass graft surgery is highly affected by the long-term potency rates of vein grafts, and no optimal treatments are available for the prevention of vein graft restenosis (VGR) at present. Hence, there is an urgent need to improve our understanding of the molecular mechanisms involved in mediating VGR. The past decade has seen the rapid development of genomic technologies, such as genome sequencing and microarray technologies, which will provide novel insights into potential molecular mechanisms involved in the VGR program. Ironically, high throughput data associated with VGR are extremely scarce. The main goal of the current study was to explore potential crucial genes and pathways associated with VGR and to provide valid biological information for further investigation of VGR. A comprehensive bioinformatics analysis was performed using high throughput gene expression data. Differentially expressed genes (DEGs) were identified using the R and Bioconductor packages. After functional enrichment analysis of the DEGs, protein–protein interaction (PPI) network and sub-PPI network analyses were performed. Finally, nine potential hub genes and fourteen pathways were identified. These hub genes may interact with each other and regulate the VGR program by modulating the cell cycle pathway. Future studies focusing on revealing the specific cellular and molecular mechanisms of these key genes and pathways involved in regulating the VGR program may provide novel therapeutic targets for VGR inhibition.

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Identification of prognostic risk factors for pancreatic cancer using bioinformatics analysis

PeerJ ◽

10.7717/peerj.9301 ◽

2020 ◽

Vol 8 ◽

pp. e9301

Author(s):

Dandan Jin ◽

Yujie Jiao ◽

Jie Ji ◽

Wei Jiang ◽

Wenkai Ni ◽

...

Keyword(s):

Gene Expression ◽

Pancreatic Cancer ◽

Gene Networks ◽

Molecular Mechanisms ◽

Functional Module ◽

Disease Free Survival ◽

Ductal Adenocarcinoma ◽

Literature Mining ◽

Ppi Network ◽

Hub Genes

Background Pancreatic cancer is one of the most common malignant cancers worldwide. Currently, the pathogenesis of pancreatic cancer remains unclear; thus, it is necessary to explore its precise molecular mechanisms. Methods To identify candidate genes involved in the tumorigenesis and proliferation of pancreatic cancer, the microarray datasets GSE32676, GSE15471 and GSE71989 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between Pancreatic ductal adenocarcinoma (PDAC) and nonmalignant samples were screened by GEO2R. The Database for Annotation Visualization and Integrated Discovery (DAVID) online tool was used to obtain a synthetic set of functional annotation information for the DEGs. A PPI network of the DEGs was established using the Search Tool for the Retrieval of Interacting Genes (STRING) database, and a combination of more than 0.4 was considered statistically significant for the PPI. Subsequently, we visualized the PPI network using Cytoscape. Functional module analysis was then performed using Molecular Complex Detection (MCODE). Genes with a degree ≥10 were chosen as hub genes, and pathways of the hub genes were visualized using ClueGO and CluePedia. Additionally, GenCLiP 2.0 was used to explore interactions of hub genes. The Literature Mining Gene Networks module was applied to explore the cocitation of hub genes. The Cytoscape plugin iRegulon was employed to analyze transcription factors regulating the hub genes. Furthermore, the expression levels of the 13 hub genes in pancreatic cancer tissues and normal samples were validated using the Gene Expression Profiling Interactive Analysis (GEPIA) platform. Moreover, overall survival and disease-free survival analyses according to the expression of hub genes were performed using Kaplan-Meier curve analysis in the cBioPortal online platform. The relationship between expression level and tumor grade was analyzed using the online database Oncomine. Lastly, the eight snap-frozen tumorous and adjacent noncancerous adjacent tissues of pancreatic cancer patients used to detect the CDK1 and CEP55 protein levels by western blot. Conclusions Altogether, the DEGs and hub genes identified in this work can help uncover the molecular mechanisms underlying the tumorigenesis of pancreatic cancer and provide potential targets for the diagnosis and treatment of this disease.

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JAK2 Inhibition by Fedratinib Reduces Osteoblast Differentiation and Mineralisation of Human Mesenchymal Stem Cells

Molecules ◽

10.3390/molecules26030606 ◽

2021 ◽

Vol 26 (3) ◽

pp. 606

Author(s):

Nihal AlMuraikhi ◽

Hanouf Alaskar ◽

Sarah Binhamdan ◽

Amal Alotaibi ◽

Moustapha Kassem ◽

...

Keyword(s):

Gene Expression ◽

Stem Cells ◽

Mesenchymal Stem Cells ◽

Molecular Mechanisms ◽

Osteoblastic Differentiation ◽

Molecular Signature ◽

Signalling Pathways ◽

Alizarin Red ◽

Jak2 Inhibitor

Several signalling pathways, including the JAK/STAT signalling pathway, have been identified to regulate the differentiation of human bone marrow skeletal (mesenchymal) stem cells (hBMSCs) into bone-forming osteoblasts. Members of the JAK family mediate the intracellular signalling of various of cytokines and growth factors, leading to the regulation of cell proliferation and differentiation into bone-forming osteoblastic cells. Inhibition of JAK2 leads to decoupling of its downstream mediator, STAT3, and the subsequent inhibition of JAK/STAT signalling. However, the crucial role of JAK2 in hBMSCs biology has not been studied in detail. A JAK2 inhibitor, Fedratinib, was identified during a chemical biology screen of a small molecule library for effects on the osteoblastic differentiation of hMSC-TERT cells. Alkaline phosphatase activity and staining assays were conducted as indicators of osteoblastic differentiation, while Alizarin red staining was used as an indicator of in vitro mineralised matrix formation. Changes in gene expression were assessed using quantitative real-time polymerase chain reaction. Fedratinib exerted significant inhibitory effects on the osteoblastic differentiation of hMSC-TERT cells, as demonstrated by reduced ALP activity, in vitro mineralised matrix formation and downregulation of osteoblast-related gene expression, including ALP, ON, OC, RUNX2, OPN, and COL1A1. To identify the underlying molecular mechanisms, we examined the effects of Fedratinib on a molecular signature of several target genes known to affect hMSC-TERT differentiation into osteoblasts. Fedratinib inhibited the expression of LIF, SOCS3, RRAD, NOTCH3, TNF, COMP, THBS2, and IL6, which are associated with various signalling pathways, including TGFβ signalling, insulin signalling, focal adhesion, Notch Signalling, IL-6 signalling, endochondral ossification, TNF-α, and cytokines and inflammatory response. We identified a JAK2 inhibitor (Fedratinib) as a powerful inhibitor of the osteoblastic differentiation of hMSC-TERT cells, which may be useful as a therapeutic option for treating conditions associated with ectopic bone formation or osteosclerotic metastases.

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Identification of Core Genes and Pathways in Melanoma Metastasis via Bioinformatics Analysis

International Journal of Molecular Sciences ◽

10.3390/ijms23020794 ◽

2022 ◽

Vol 23 (2) ◽

pp. 794

Author(s):

Renjian Xie ◽

Bifei Li ◽

Lee Jia ◽

Yumei Li

Keyword(s):

Gene Expression ◽

Molecular Mechanisms ◽

Primary Melanoma ◽

Melanoma Metastasis ◽

Ppi Network ◽

Hub Genes ◽

Receptor Interactions ◽

Upregulated Genes ◽

Core Genes ◽

A375 Cells

Metastasis is the leading cause of melanoma-related mortality. Current therapies are rarely curative for metastatic melanoma, revealing the urgent need to identify more effective preventive and therapeutic targets. This study aimed to screen the core genes and molecular mechanisms related to melanoma metastasis. A gene expression profile, GSE8401, including 31 primary melanoma and 52 metastatic melanoma clinical samples, was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between melanoma metastases and primary melanoma were screened using GEO2R tool. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analyses of DEGs were performed using the Database for Annotation Visualization and Integrated Discovery (DAVID). The Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape with Molecular Complex Detection (MCODE) plug-in tools were utilized to detect the protein–protein interaction (PPI) network among DEGs. The top 10 genes with the highest degrees of the PPI network were defined as hub genes. In the results, 425 DEGs, including 60 upregulated genes and 365 downregulated genes, were identified. The upregulated genes were enriched in ECM–receptor interactions and the regulation of actin cytoskeleton, while 365 downregulated genes were enriched in amoebiasis, melanogenesis, and ECM–receptor interactions. The defined hub genes included CDK1, COL17A1, EGFR, DSG1, KRT14, FLG, CDH1, DSP, IVL, and KRT5. In addition, the mRNA and protein levels of the hub genes during melanoma metastasis were verified in the TCGA database and paired post- and premetastatic melanoma cells, respectively. Finally, KRT5-specific siRNAs were utilized to reduce the KRT5 expression in melanoma A375 cells. An MTT assay and a colony formation assay showed that KRT5 knockdown significantly promoted the proliferation of A375 cells. A Transwell assay further suggested that KRT5 knockdown significantly increased the cell migration and cell invasion of A375 cells. This bioinformatics study provided a deeper understanding of the molecular mechanisms of melanoma metastasis. The in vitro experiments showed that KRT5 played the inhibitory effects on melanoma metastasis. Therefore, KRT5 may serve important roles in melanoma metastasis.

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MCM2 and NUSAP1 Are Potential Biomarkers for the Diagnosis and Prognosis of Pancreatic Cancer

BioMed Research International ◽

10.1155/2020/8604340 ◽

2020 ◽

Vol 2020 ◽

pp. 1-20

Author(s):

Yajun Deng ◽

Hanyun Ma ◽

Jinyong Hao ◽

Qiqi Xie ◽

Ruochen Zhao

Keyword(s):

Gene Expression ◽

Pancreatic Cancer ◽

Molecular Mechanisms ◽

Malignant Tumors ◽

The Cancer Genome Atlas ◽

Receptor Interaction ◽

Ppi Network ◽

Hub Genes ◽

Diagnosis And Prognosis ◽

Potential Biomarkers

Pancreatic cancer (PC) is one of the most malignant tumors. Despite considerable progress in the treatment of PC, the prognosis of patients with PC is poor. The aim of this study was to identify potential biomarkers for the diagnosis and prognosis of PC. First, the original data of three independent mRNA expression datasets were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas databases and screened for differentially expressed genes (DEGs) using the R software. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the DEGs were performed, and a protein-protein interaction (PPI) network was constructed to screen for hub genes. The hub genes were analyzed for genetic variations, as well as for survival, prognostic, and diagnostic value, using the cBioPortal and Gene Expression Profiling Interactive Analysis (GEPIA) databases and the pROC package. After screening for potential biomarkers, the mRNA and protein levels of the biomarkers were verified at the tissue and cellular levels using the Cancer Cell Line Encyclopedia, GEPIA, and the Human Protein Atlas. As a result, a total of 248 DEGs were identified. The GO terms enriched in DEGs were related to the separation of mitotic sister chromatids and the binding of the spindle to the extracellular matrix. The enriched pathways were associated with focal adhesion, ECM-receptor interaction, and phosphatidylinositol 3-kinase (PI3K)/AKT signaling. The top 20 genes were selected from the PPI network as hub genes, and based on the analysis of multiple databases, MCM2 and NUSAP1 were identified as potential biomarkers for the diagnosis and prognosis of PC. In conclusion, our results show that MCM2 and NUSAP1 can be used as potential biomarkers for the diagnosis and prognosis of PC. The study also provides new insights into the underlying molecular mechanisms of PC.

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Bioinformatics Analysis of Candidate Genes and Pathways Related to Hepatocellular Carcinoma in China: A Study Based on Public Databases

Pathology & Oncology Research ◽

10.3389/pore.2021.588532 ◽

2021 ◽

Vol 27 ◽

Author(s):

Peng Zhang ◽

Jing Feng ◽

Xue Wu ◽

Weike Chu ◽

Yilian Zhang ◽

...

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Candidate Genes ◽

Molecular Mechanisms ◽

Kegg Pathway ◽

Reduction Process ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Chronic Hepatitis B Virus

Background and Objective: Hepatocellular carcinoma (HCC) is a highly aggressive malignant tumor of the digestive system worldwide. Chronic hepatitis B virus (HBV) infection and aflatoxin exposure are predominant causes of HCC in China, whereas hepatitis C virus (HCV) infection and alcohol intake are likely the main risk factors in other countries. It is an unmet need to recognize the underlying molecular mechanisms of HCC in China.Methods: In this study, microarray datasets (GSE84005, GSE84402, GSE101685, and GSE115018) derived from Gene Expression Omnibus (GEO) database were analyzed to obtain the common differentially expressed genes (DEGs) by R software. Moreover, the gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by using Database for Annotation, Visualization and Integrated Discovery (DAVID). Furthermore, the protein-protein interaction (PPI) network was constructed, and hub genes were identified by the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape, respectively. The hub genes were verified using Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, and Kaplan-Meier Plotter online databases were performed on the TCGA HCC dataset. Moreover, the Human Protein Atlas (HPA) database was used to verify candidate genes’ protein expression levels.Results: A total of 293 common DEGs were screened, including 103 up-regulated genes and 190 down-regulated genes. Moreover, GO analysis implied that common DEGs were mainly involved in the oxidation-reduction process, cytosol, and protein binding. KEGG pathway enrichment analysis presented that common DEGs were mainly enriched in metabolic pathways, complement and coagulation cascades, cell cycle, p53 signaling pathway, and tryptophan metabolism. In the PPI network, three subnetworks with high scores were detected using the Molecular Complex Detection (MCODE) plugin. The top 10 hub genes identified were CDK1, CCNB1, AURKA, CCNA2, KIF11, BUB1B, TOP2A, TPX2, HMMR and CDC45. The other public databases confirmed that high expression of the aforementioned genes related to poor overall survival among patients with HCC.Conclusion: This study primarily identified candidate genes and pathways involved in the underlying mechanisms of Chinese HCC, which is supposed to provide new targets for the diagnosis and treatment of HCC in China.

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Screening and identification of key biomarkers and related transcription factors in ovarian cancer by integrated bioinformatics analysis

10.21203/rs.3.rs-20327/v1 ◽

2020 ◽

Author(s):

Wenqiong Qin ◽

Qiang Yuan ◽

Yi Liu ◽

Ying Zeng ◽

Dandan Ke ◽

...

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Molecular Mechanisms ◽

Malignant Tumors ◽

Expression Profiles ◽

Expression Patterns ◽

Gene Expression Profiles ◽

Transcriptional Factor ◽

Ppi Network ◽

Hub Genes

Abstract Background Ovarian tumors are the most malignant tumors of all gynecological tumors, and although multiple efforts have been made to elucidate the pathogenesis, the molecular mechanisms of ovarian cancer remain unclear. Methods In this study, we used bioinformatics to identify genes involved in the carcinogenesis and progression of ovarian cancer. Three microarray datasets (GSE14407, GSE29450, and GSE54388) were downloaded from Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified. For a more in-depth understanding of the DEGs, functional and pathway enrichment analyses were performed and a protein-protein interaction (PPI) network was constructed. The associated transcriptional factor (TFs) regulation network of the DEGs was also constructed. Kaplan Meier-plotter, Gene Expression Profiling Interactive Analysis (GEPIA), the Human Protein Atlas (HPA) database and the Oncomine database were implemented to validated hub genes. Results A total of 514 DEGs were detected after the analysis of the three gene expression profiles, including 171 upregulated and 343 downregulated genes. Nine hub genes ( CCNB1, CDK1, BUB1, CDC20, CCNA2, BUB1B, AURKA, RRM2, TTK) were obtained from the PPI network. Survival analysis showed that high expression levels of seven hub genes ( CCNB1, BUB1, BUB1B, CCNA2, AURKA, CDK1, and RRM2) were associated with worse overall survival (OS). All of seven hub genes were discovered highly expressed in ovarian cancer samples compared to normal ovary samples in GEPIA. Immunostaining results from the HPA database suggested that the expressions of CCNB1, CCNA2, AURKA, and CDK1 proteins were increased in ovarian cancer tissues, and Oncomine analysis indicated that the expression patterns of BUB1B, CCNA2, AURKA, CCNB1, CDK1, and BUB1 have associated with patient clinicopathological information. From the gene-transcriptional factor network, key transcriptional factors, such as POLR2A, ZBTB11, KLF9, and ELF1, were identified with close interactions with these hub genes. Conclusion We identified six significant DEGs with poor prognosis in ovarian cancer, which could be of potential biomarkers for ovarian cancer patients.

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Identification of key pathways and hub genes in the myogenic process of pluripotent stem cell: A bioinformatics study

10.21203/rs.3.rs-41291/v1 ◽

2020 ◽

Author(s):

Wenyong Fei ◽

Mingsheng Liu ◽

Yao Zhang ◽

Shichao Cao ◽

Xuanqi Wang ◽

...

Keyword(s):

Gene Expression ◽

Stem Cells ◽

Pluripotent Stem Cells ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Ppi Network ◽

Hub Genes ◽

Myogenic Stem Cells ◽

Key Pathways

Abstract Purpose: The study aims to determine the process of myoginc differentiation in human pluripotent stem cells and to figure out that the key pathways and hub genes in the process, which do helpful for the further research of muscular regeneration.Method: Three gene expression profiles, GSE131125, GSE148994, GSE149055, about the comparisons of pluripotent stem cells and myogenic stem cells from the Gene Expression Omnibus (GEO) data base. Common differentially expressed genes (DEGs) were obtained and for the further analysis as Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and GSEA analysis and protein‑protein interaction (PPI) network. In vitro cell research to verify the hub genes and key pathways.Result:824 DEGs were co-expressed in the three GSEs. 19 hub genes were identified from the PPI network. The GO and KEGG pathway analysis were performed to determine the functions of DEGs. GSEA analysis indicated the differentiated cells were enriched in muscle cell development and myogenesis.Conclusion: Our research revealed the main hub genes and modules in the myogeinc process of stem cells which contribute to further study about the molecular mechanism of myogenesis regeneration. Paving a way for more accurate treatment for muscle dysfunction.

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