The Identified Hub Gene GlcN in Osteoarthritis Progression and Treatment

Background. As a chronic disease, osteoarthritis has caused great trouble to the health of middle-aged and elderly people. Studies have shown that glucosamine (GlcN) can be used to abate the progression and improve this disease. Based on this point of view, we try to verify the connection between GlcN and osteoarthritis and find more effective biomarkers. Methods. We downloaded the GSE72575 data set from the GEO database, and used the R language to perform DEG analysis on the gene expression profile of the samples. Next, the GO function and the KEGG signaling pathways were analyzed through the DAVID database, and then, the KEGG pathways enriched in the gene set were analyzed based on GSEA. Then, the PPI network of DEGs was constructed based on the STRING online database, and finally, the hub genes were selected by Cytoscape. Results. Three GlcN-treated MH7A cell treatment groups and 3 control groups in the GSE72575 data set were studied. Through analysis, there were 52 DEGs in these samples. Then, through GO, KEGG, and GSEA, regulation of endoplasmic reticulum stress-induced intrinsic apoptotic signaling pathway, FoxO signaling pathway, JAK-STAT signaling pathway, PI3K-Akt signaling pathway, TGF-beta signaling pathway, and ECM receptor interaction were involved in the regulatory mechanisms of the osteoarthritis pathogenesis. After that, the hub genes IL6 and DDIT3 were identified through PPI network construction and analysis. And it was found that IL6 was lowly expressed in the group with GlcN-treated MH7A cells, while DDIT3 was highly expressed. Conclusion. The above results provide a basis for GlcN to participate in the treatment of osteoarthritis and a possibility for finding effective therapeutic targets.

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Integrated Bioinformatics Analysis of Master Regulators in Anaplastic Thyroid Carcinoma

BioMed Research International ◽

10.1155/2019/9734576 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13 ◽

Cited By ~ 3

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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The Important Role of Perituberal Tissue in Epileptic Patients with Tuberous Sclerosis Complex by the Transcriptome Analysis

BioMed Research International ◽

10.1155/2020/4980609 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Shuqiang Li ◽

Huijie Shao ◽

Liansheng Chang

Keyword(s):

Tuberous Sclerosis ◽

Tuberous Sclerosis Complex ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Receptor Interaction ◽

Ppi Network ◽

Hub Genes ◽

R Language ◽

Protein Protein Interaction

Epilepsy is most common in patients with tuberous sclerosis complex (TSC). However, in addition to the challenging treatment, the pathogenesis of epilepsy is still controversial. To determine the transcriptome characteristics of perituberal tissue (PT) and clarify its role in the pathogenesis of epilepsy, GSE16969 was downloaded from the GEO database for further study by comprehensive bioinformatics analysis. Identification of differentially expressed genes (DEGs), functional enrichment analysis, construction of protein-protein interaction (PPI) network, and selection of Hub genes were performed using R language, Metascape, STRING, and Cytoscape, respectively. Comparing with cortical tuber (CT), 220 DEGs, including 95 upregulated and 125 downregulated genes, were identified in PT and mainly enriched in collagen-containing extracellular matrix and positive regulation of receptor-mediated endocytosis, as well as the pathways of ECM-receptor interaction and neuroactive ligand-receptor interaction. As for normal cortex (NC), 1549 DEGs, including 30 upregulated and 1519 downregulated genes, were identified and mainly enriched in presynapse, dendrite and axon, and also the pathways of dopaminergic synapse and oxytocin signaling pathway. In the PPI network, 4 hub modules were found between PT and CT, and top 5 hub modules were selected between PT and NC. C3, APLNR, ANXA2, CD44, CLU, CP, MCHR2, HTR1E, CTSG, APP, and GNG2 were identified as Hub genes, of which, C3, CD44, ANXA2, HTR1E, and APP were identified as Hub-BottleNeck genes. In conclusion, PT has the unique characteristics different from CT and NC in transcriptome and makes us further understand its importance in the TSC-associated epilepsy.

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Identification of hub genes and pathways in psoriasis through bioinformatics and validation by RT-qPCR

10.21203/rs.3.rs-357690/v1 ◽

2021 ◽

Author(s):

Ling Ai Zou ◽

Qichao Jian

Keyword(s):

Signaling Pathway ◽

Quantitative Polymerase Chain Reaction ◽

Cytokine Receptor ◽

Receptor Interaction ◽

Hacat Cells ◽

Receptor Signaling ◽

Ppi Network ◽

Hub Genes ◽

Network Analyses ◽

Receptor Signaling Pathway

Abstract Background Although several studies have attempted to investigate the aetiology and mechanism of psoriasis, the precise molecular mechanism remains unclear. Our study aimed to identify the hub genes and associated pathways that promote its pathogenesis in psoriasis, which would be helpful for the discovery of diagnostic and therapeutic markers. Methods GSE30999, GSE34248, GSE41662, and GSE50790 datasets were extracted from the Gene Expression Omnibus (GEO) database. The GEO profiles were integrated to obtain differentially expressed genes (DEGs) using the affy package in R software, with |logFC|> 1.5 and adjusted P < 0.05. The DEGs were utilised for Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein-protein interaction (PPI) network analyses. Hub genes were identified using Cytoscape and enriched for analysis in www.bioinformatics.com.cn. These hub genes were validated in the four aforementioned datasets and M5-induced HaCaT cells using real-time quantitative polymerase chain reaction (RT-qPCR). Results A total of 359 DEGs were identified, which were mostly associated with responses to bacterium, defence responses to other organism, and antimicrobial humoral response. These DEGs were mostly enriched in the steroid hormone biosynthesis pathway, NOD-like receptor signaling pathway, and cytokine-cytokine receptor interaction. PPI network analysis indicated seven genes (CXCL1, ISG15, CXCL10, STAT1, OASL, IFIT1, and IFIT3) as the probable hub genes of psoriasis; CXCL10 had a positive correlation with the other six hub genes. The chord plot results further supported the GO and KEGG analysis results of the 359 DEGs. Seven predicted hub genes were validated to be upregulated in four datasets and M5-induced HaCaT cells using RT-qPCR. Conclusions The pathogenesis of psoriasis may be associated with seven hub genes (CXCL1, ISG15, CXCL10, STAT1, OASL, IFIT1, and IFIT3) and pathways, such as the NOD-like receptor signaling pathway and cytokine-cytokine receptor interaction. These hub genes, especially CXCL10, can be used as potential biomarkers in psoriasis.

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Identification of Key Genes and Pathways Associated With Irradiation in Breast Cancer Tissue and Breast Cancer Cell Lines

Dose-Response ◽

10.1177/1559325820931252 ◽

2020 ◽

Vol 18 (2) ◽

pp. 155932582093125

Author(s):

Changchun Zhu ◽

Chang Ge ◽

Junbo He ◽

Xueying Zhang ◽

Guoxing Feng ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Signaling Pathway ◽

Breast Cancer Cell Lines ◽

Cancer Tissue ◽

Interleukin 17 ◽

Ppi Network ◽

Hub Genes ◽

Data Set ◽

Key Genes

Radiotherapy is mainly a traditional treatment for breast cancer; however, the key genes and pathways in breast cancer associated with irradiation are not clear. In this study, we aimed to explore the messenger RNA expression changes between preradiation and postradiation breast cancer. The gene expression data set (GSE59733) was downloaded from Gene Expression Omnibus database. According to |log2FC (fold change) | ≥ 1 and with false discovery rate adjusted P value <.05, differentially expressed genes (DEGs) were screened and annotated by R programming software. The protein–protein interaction (PPI) network was conducted through STRING database, and subnetworks and hub genes were extracted by plug-in in Cytoscape. A total of 82 DEGs (74 upregulated and 8 downregulated genes) were identified. These DEGs mainly enriched in an intrinsic apoptotic signaling pathway and G-protein-coupled receptor binding. What’s more, tumor necrosis factor signaling pathway and interleukin 17 signaling pathway abnormally activated in postradiation tumor samples. Two characteristic subnetworks and 3 hub genes ( FOS, CCL2, and CXCL12) were strongly distinguished in PPI network. Moreover, the expression level of the hub genes was confirmed in irradiated MCF-7 cell and SUM-159 cell using quantitative real-time polymerase chain reaction assay. These findings imply that these hub genes may play momentous function in breast cancer to irradiation.

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Identification of key gene biomarkers and pathways related to Dent disease in CLCN5 knockout mice by bioinformatics analysis

10.21203/rs.2.14810/v1 ◽

2019 ◽

Author(s):

Zhijian Lin ◽

Yu Wang ◽

Fanfan Guo ◽

Bing Zhang

Keyword(s):

Signaling Pathway ◽

Knockout Mice ◽

Gene Knockout ◽

Metabolic Process ◽

Dent Disease ◽

Receptor Interaction ◽

Regulation Of Transcription ◽

Ppi Network ◽

Hub Genes ◽

Positive Regulation

Abstract Background Dent disease is an X-linked inherited renal disease that occurs almost exclusively in males. Abnormal CLC-5 function might play a role in the development of Dent disease, but the genetic interaction changes and biomarkers in Dent disease are not fully understood. The aim of this study was to explore the potential key gene biomarkers and pathways related to Dent disease in CLCN5 knockout mice model.Methods The gene expression profile GSE10162 was analyzed differentially expressed genes (DEGs), between 3 samples of CLC-5 transporter gene knockout mouse model of Dent disease and 3 samples from wild type mouse. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were applied for the enriched pathway by the online tool DAVID. A protein-protein interaction (PPI) network of DEGs was constructed to find the hub genes by STRING, and visualized with Cytoscape software.Results Three samples from were incorporated into this study. A total of 500 DEGs were filtered, consisting of 231 upregulated genes and 269 downregulated genes. GO analysis indicated that the up-regulated DEGs were significantly enriched in the regulation of transcription form RNA, regulation of cell proliferation, and ion transport, whereas down-regulated genes were mainly enriched in oxidation-reduction process, and metabolic process. KEGG analysis demonstrated that the DEGs were enriched in the metabolic pathways, neuroactive ligand-receptor interaction, nicotine addiction, morphine addiction, fatty acid elongation, TNF signaling pathway, calcium signaling pathway, and cAMP signaling pathway. PPI network analysis found 17 hub genes with greater than 10 degrees of connectivity. The hub genes might participate in TNF signaling pathway, fat digestion and absorption, and enrich in lipid metabolic process, regulation of blood pressure, cellular response to hypoxia, positive regulation of angiogenesis, positive regulation of developmental growth, and positive regulation of cytosolic calcium ion concentration.Conclusions Our study suggests that Apob, Lep, C3, Cxcl1, Acly and Mmp9 may play key roles in the progression of Dent disease. Blood lipid profiles and calcium levels might be potential prognostic biomarkers for Dent disease.

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Identification of hub genes and important pathways in determination of breast cancer survival using bioinformatics approaches

10.21203/rs.2.16078/v1 ◽

2019 ◽

Author(s):

Sepideh Dashti ◽

Soudeh Ghafouri-Fard

Keyword(s):

Breast Cancer ◽

Signaling Pathway ◽

Cancer Survival ◽

Clinical Importance ◽

Gene Expression Omnibus ◽

Receptor Interaction ◽

Peroxisome Proliferator ◽

Ppi Network ◽

Hub Genes ◽

Pathologic Classification

Abstract Backgrounds Breast cancer is a highly heterogeneous disorder characterized by dysregulation of expression of numerous genes and cascades. The conventional pathologic classification of breast cancer is not sufficient for the prediction of breast cancer behavior and response to therapy.Methods We have retrieved data of two microarray datasets (GSE65194 and GSE45827) from the NCBI Gene Expression Omnibus database (GEO). R package was used for identification of differentially expressed genes (DEGs), assessment of gene ontology (GO) and pathway enrichment evaluation. The DEGs were integrated to construct a protein-protein interaction (PPI) network. Next, hub genes were recognized using the Cytoscape software and lncRNA-mRNA co-expression analysis was performed to evaluate the potential roles of lncRNAs. The interactive information among DEGs and the PPI network was obtained using the STRING online database. Finally, the clinical importance of the obtained genes was assessed using Kaplan-Meier survival analysis.Results After excluding the outliers from the GSE65194 and GSE45827 datasets and data normalization, 866 DEGs including 712 upregulated and 154 downregulated DEGs were detected between breast cancer and normal samples. Up-regulated DEGs were enriched in six pathways including ‘Cell cycle’, ‘Oocyte meiosis’ and ‘Focal adhesion’. Down-regulated DEGs were enriched in five pathways including ‘Peroxisome-proliferator-activated receptors (PPAR) signaling pathway’, ‘Metabolism of xenobiotics by cytochrome P450’, ‘Adipocytokine signaling pathway’ and ‘Cytokine-cytokine receptor interaction’ pathways. CCNA2, CDK1, MAD2L1, and CCNB2 were significantly enriched in several biological pathways. These four genes showed strong expression in breast cancer samples as compared to normal breast tissue. We also identified 12 lncRNAs with a significant correlation with MAD2L1 and CCNB2 genes. MAD2L1, CCNA2, RAD51-AS1, and LINC01089 have the most prediction potential among all candidate hub genes.Conclusion Our study offers a framework for recognition of the mRNA-lncRNA network in breast cancer and the detection of important pathways that could be used as therapeutic targets in this kind of cancer.

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Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

BMC Cancer ◽

10.1186/s12885-021-08412-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Baojie Wu ◽

Shuyi Xi

Keyword(s):

Gene Expression ◽

Cervical Cancer ◽

Differentially Expressed Genes ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Molecular Targets ◽

Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Kegg Pathways

Abstract Background This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis. Methods Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein–protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets. Results A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. Moreover, a PPI network of 476 DEGs was constructed, hub genes from 12 critical subnetworks were explored, and a total of 14 potential molecular targets were obtained. Conclusions These findings promote the understanding of the molecular mechanism of and clinically related molecular targets for cervical cancer.

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Identification of Potential Genes Associated with Vemurafenib Efficacy and Melanoma Prognosis

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207324666210603152044 ◽

2021 ◽

Vol 24 ◽

Author(s):

Yue Qi ◽

GuiE Ma

Keyword(s):

Signaling Pathway ◽

Estrogen Signaling ◽

Akt Signaling Pathway ◽

Ppi Network ◽

Hub Genes ◽

Ampk Signaling ◽

Melanoma Patients ◽

Key Genes ◽

Neurotrophin Signaling ◽

Upregulated Genes

Objective: This work aimed to investigate the molecular mechanisms underlying the efficacy of vemurafenib as a treatment for melanoma. Methods: The GSE52882 dataset, which includes A375 and A2058 melanoma cell lines treated with vemurafenib and dimethyl sulfoxide (DMSO), and clinical information associated with melanoma patients, were acquired from the Gene Expression Omnibus (GEO) database and University of California Santa Cruz (UCSC), respectively. Functional enrichment analysis, protein-protein interaction (PPI) network construction, sub-module analysis, and transcriptional regulation analysis were performed on overlapping differentially expressed genes (DEGs) identified in both cell lines. Finally, we performed a survival analysis based on the genes identified. Results: A total of 447 consistently overlapping DEGs (176 up- and 271 down-regulated DEGs) were screened. Upregulated genes were enriched in pathways of neurotrophin signaling, estrogen signaling, and transcriptional misregulation in cancer. Downregulated DEGs played essential roles in melanogenesis, pathways of cancer, PI3K-Akt signaling pathway, and AMPK signaling pathway. Upregulated (MMP2, JUN, KAT28, and PIK3R3) and downregulated genes (CXCL8, CCND1, IGF1R, and ITGB3) were considered as hub genes in the PPI network. Additionally, PIK3R3 and LEF1 served as key genes in the regulatory network. The overexpression of MMP2 and CXCL8 was associated with a poor prognosis in melanoma patients. Results: A total of 447 consistently overlapping DEGs (176 up- and 271 down-regulated DEGs) were screened. Upregulated genes were enriched in pathways of neurotrophin signaling, estrogen signaling, and transcriptional misregulation in cancer. Downregulated DEGs played essential roles in melanogenesis, pathways of cancer, PI3K-Akt signaling pathway, and AMPK signaling pathway. Upregulated (MMP2, JUN, KAT28, and PIK3R3) and downregulated genes (CXCL8, CCND1, IGF1R, and ITGB3) were considered as hub genes in the PPI network. Additionally, PIK3R3 and LEF1 served as key genes in the regulatory network. The overexpression of MMP2 and CXCL8 was associated with a poor prognosis in melanoma patients. Conclusion: MMP2, CXCL8, PIK3R3, ITGB3, and LEF1 may play roles in the efficacy of vemurafenib treatment in melanoma; for example, MMP2 and PIK3R3 are likely associated with vemurafenib resistance. These findings will contribute to the development of novel therapies for melanoma.

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Molecular profile reveals immune-associated markers of medulloblastoma for different subtypes

10.21203/rs.3.rs-816357/v2 ◽

2021 ◽

Author(s):

shenglan li ◽

Zhuang Kang ◽

jinyi Chen ◽

Can Wang ◽

Zehao Cai ◽

...

Keyword(s):

Immune Cell ◽

Intracranial Tumor ◽

Cell Infiltration ◽

Molecular Profile ◽

Immune Cell Infiltration ◽

Ppi Network ◽

Hub Genes ◽

Data Set ◽

Hub Gene ◽

External Data

Abstract Background Medulloblastoma is a common intracranial tumor among children. In recent years, research on cancer genome has established four distinct subtypes of medulloblastoma: WNT, SHH, Group3, and Group4. Each subtype has its own transcriptional profile, methylation changes, and different clinical outcomes. Treatment and prognosis also vary depending on the subtype. Methods Based on the methylation data of medulloblastoma samples, methylCIBERSORT was used to evaluate the level of immune cell infiltration in medulloblastoma samples and identified 10 kinds of immune cells with different subtypes. Combined with the immune database, 293 Imm-DEGs were screened. Imm-DEGs were used to construct the co-expression network, and the key modules related to the level of differential immune cell infiltration were identified. Three immune hub genes (GAB1, ABL1, CXCR4) were identified according to the gene connectivity and the correlation with phenotype in the key modules, as well as the PPI network involved in the genes in the modules. Results The subtype marker was recognized according to the immune hub, and the subtype marker was verified in the external data set, the methylation level of immune hub gene among different subtypes was compared and analyzed, at the same time, tissue microarray was used for immunohistochemical verification, and a multi-factor regulatory network of hub gene was constructed. Conclusions Identifying subtype marker is helpful to accurately identify the subtypes of medulloblastoma patients, and can accurately evaluate the treatment and prognosis, so as to improve the overall survival of patients.

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High-dose pyruvate treatment alters skeletal muscle differentiation and expression of inflammation-related genes

10.1101/2020.09.21.305847 ◽

2020 ◽

Author(s):

Kazuya Hasegawa ◽

Yuya Yamaguchi ◽

Yutthana Pengjam

Keyword(s):

Gene Expression ◽

Skeletal Muscle ◽

Inflammatory Response ◽

Muscle Contraction ◽

Signaling Pathway ◽

Receptor Interaction ◽

High Dose ◽

Ppi Network ◽

Acid Therapy ◽

High Doses

ABSTRACTPyruvic acid therapy is used for various diseases, but the therapeutic effect decreases at high doses. The molecular mechanism of high-dose pyruvate is not well understood. The purpose of this study was to identify the effects of high dose pyruvate addition on skeletal muscle using C2C12. The gene expression profile for the GSE5497 dataset was taken from the Gene Expression Omnibus database. GEO2R was used to identify specifically expressed genes (DEGs). Functional analysis and pathway enrichment analysis of DEG were performed using the DAVID database. The protein-protein interaction (PPI) network was built in the STRING database and visualized using Cytoscape. GO analysis showed that up-regulated DEG was primarily involved in angiogenesis, cell adhesion, and inflammatory response. We also showed that down-regulated DEG is involved in the regulation of muscle contraction, skeletal muscle fiber development. In addition, the upregulated KEGG pathway of DEG included Rheumatoid arthritis, Chemokine signaling pathway, and Cytokine-cytokine receptor interaction. Downregulated DEG included Calcium signaling pathway, hypertrophic cardiomyopathy (HCM), Dilated cardiomyopathy, Neuroactive ligand-receptor interaction, and Cardiac muscle contraction. Further, analysis of two modules selected from the PPI network showed that high-dose pyruvate exposure to C2C12 was primarily associated with muscle contraction, muscle organ morphogenesis, leukocyte chemotaxis, and chemokine activity. In conclusion, High-dose pyruvate treatment of C2C12 was found to be associated with an increased inflammatory response and decreased skeletal muscle formation. However, further studies are still needed to verify the function of these molecules at high doses of pyruvate.

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