Bioinformatics Analysis Identifies Hub Genes and Molecular Pathways Involved in Sepsis-Induced Myopathy

Ning, Yile; Yang, Zhong-Qi; Xian, Shaoxiang; Lin, Jian-Zhong; Lin, Xinfeng; Chen, Weitao

doi:10.12659/msm.919665

Cited by 8 publications

(5 citation statements)

References 36 publications

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“…For example, Ning et al screened and identified key biomarkers in patients with SIM. [ 15 ] However, bioinformatic analysis of OS-genes in SIM has not yet been performed. In the present study, 1089 DEGs and 453 OS-genes were used to identify 25 OS-DEGs, including 15 upregulated genes and 10 downregulated genes (Table 1 ).…”

Section: Discussionmentioning

confidence: 99%

Identification and validation of oxidative stress-related genes in sepsis-induced myopathy

Zhang,

Huang,

et al. 2024

Medicine

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Background: Sepsis-induced myopathy (SIM) a complication of sepsis that results in prolonged mechanical ventilation, long-term functional disability, and increased patient mortality. This study was performed to identify potential key oxidative stress-related genes (OS-genes) as biomarkers for the diagnosis of SIM using bioinformatics. Methods: The GSE13205 was obtained from the Gene Expression Omnibus (GEO) database, including 13 SIM samples and 8 healthy samples, and the differentially expressed genes (DEGs) were identified by limma package in R language. Simultaneously, we searched for the genes related to oxidative stress in the Gene Ontology (GO) database. The intersection of the genes selected from the GO database and the genes from the GSE13205 was considered as OS-genes of SIM, where the differential genes were regarded as OS-DEGs. OS-DEGs were analyzed using GO enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein-protein interaction (PPI) networks. Hub genes in OS-DEGs were selected based on degree, and diagnostic genes were further screened by gene expression and receiver operating characteristic (ROC) curve. Finally, a miRNA-gene network of diagnostic genes was constructed. Results: A total of 1089 DEGs were screened from the GSE13205, and 453 OS-genes were identified from the GO database. The overlapping DEGs and OS-genes constituted 25 OS-DEGs, including 15 significantly upregulated and 10 significantly downregulated genes. The top 10 hub genes, including CD36, GPX3, NQO1, GSR, TP53, IDH1, BCL2, HMOX1, JAK2, and FOXO1, were screened. Furthermore, 5 diagnostic genes were identified: CD36, GPX3, NQO1, GSR, and TP53. The ROC analysis showed that the respective area under the curves (AUCs) of CD36, GPX3, NQO1, GSR, and TP53 were 0.990, 0.981, 0.971, 0.971, and 0.971, which meant these genes had very high diagnostic values of SIM. Finally, based on these 5 diagnostic genes, we found that miR-124-3p and miR-16-5p may be potential targets for the treatment of SIM. Conclusions: The results of this study suggest that OS-genes might play an important role in SIM. CD36, GPX3, NQO1, GSR, and TP53 have potential as specific biomarkers for the diagnosis of SIM.

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Section: Discussionmentioning

confidence: 99%

Identification and validation of oxidative stress-related genes in sepsis-induced myopathy

Zhang,

Huang,

et al. 2024

Medicine

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“…Hmox1 is a kind of stress protein-encoded gene. In addition, some researches show that Hmox1 protects against heme injury ( Ning et al, 2020 ), and can regulate Hmox1 protein expression in response to oxidative damage, including the oxidative damage in sepsis ( Piantadosi et al, 2011 ; Vazquez-Armenta et al, 2013 ). Hmox1 was confirmed in this study playing an important role in sepsis.…”

Section: Discussionmentioning

confidence: 99%

Screening of ferroptosis-related genes in sepsis-induced liver failure and analysis of immune correlation

Chen

Liu

2022

PeerJ

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Purpose Sepsis-induced liver failure is a kind of liver injury with a high mortality, and ferroptosis plays a key role in this disease. Our research aims to screen ferroptosis-related genes in sepsis-induced liver failure as targeted therapy for patients with liver failure. Methods Using the limma software, we analyzed the differentially expressed genes (DEGs) in the GSE60088 dataset downloaded from the Gene Expression Omnibus (GEO) database. Clusterprofiler was applied for enrichment analysis of DEGs enrichment function. Then, the ferroptosis-related genes of the mice in the FerrDb database were crossed with DEGs. Sepsis mice model were prepared by cecal ligation and perforation (CLP). ALT and AST in the serum of mice were measured using detection kit. The pathological changes of the liver tissues in mice were observed by hematoxylin-eosin (H & E) staining. We detected the apoptosis of mice liver tissues using TUNEL. The expression of Hmox1, Epas1, Sirt1, Slc3a2, Jun, Plin2 and Zfp36 were detected by qRT-PCR. Results DEGs analysis showed 136 up-regulated and 45 down-regulated DEGs. Meanwhile, we found that the up-regulated DEGs were enriched in pathways including the cytokine biosynthesis process while the down-regulated DEGs were enriched in pathways such as organic hydroxy compound metabolic process. In this study, seven genes (Hmox1, Epas1, Sirt1, Slc3a2, Jun, Plin2 and Zfp36) were obtained through the intersection of FerrDb database and DEGs. However, immune infiltration analysis revealed that ferroptosis-related genes may promote the development of liver failure through B cells and natural killer (NK) cells. Finally, it was confirmed by the construction of septic liver failure mice model that ferroptosis-related genes of Hmox1, Slc3a2, Jun and Zfp36 were significantly correlated with liver failure and were highly expressed. Conclusion The identification of ferroptosis-related genes Hmox1, Slc3a2, Jun and Zfp36 in the present study contribute to our understanding of the molecular mechanism of sepsis-induced liver failure, and provide candidate targets for the diagnosis and treatment of the disease.

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“…Then, we generate network and node file and analyzed using Cytoscape software (version 3.5.1; www.cytoscape.org ). P < 0.05 was considered to be a statistically significant difference [ 15 ].…”

Section: Methodsmentioning

confidence: 99%

Identification of key genes and pathways of BMP-9-induced osteogenic differentiation of mesenchymal stem cells by integrated bioinformatics analysis

Mao

Liu

et al. 2021

J Orthop Surg Res

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Background The purpose of present study was to identify the differentially expressed genes (DEGs) associated with BMP-9-induced osteogenic differentiation of mesenchymal stem cells (MSCs) by using bioinformatics methods. Methods Gene expression profiles of BMP-9-induced MSCs were compared between with GFP-induced MSCs and BMP-9-induced MSCs. GSE48882 containing two groups of gene expression profiles, 3 GFP-induced MSC samples and 3 from BMP-9-induced MSCs, was downloaded from the Gene Expression Omnibus (GEO) database. Then, DEGs were clustered based on functions and signaling pathways with significant enrichment analysis. Pathway enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) demonstrated that the identified DEGs were potentially involved in cytoplasm, nucleus, and extracellular exosome signaling pathway. Results A total of 1967 DEGs (1029 upregulated and 938 downregulated) were identified from GSE48882 datasets. R/Bioconductor package limma was used to identify the DEGs. Further analysis revealed that there were 35 common DEGs observed between the samples. GO function and KEGG pathway enrichment analysis, among which endoplasmic reticulum, protein export, RNA transport, and apoptosis was the most significant dysregulated pathway. The result of protein-protein interaction (PPI) network modules demonstrated that the Hspa5, P4hb, Sec61a1, Smarca2, Pdia3, Dnajc3, Hyou1, Smad7, Derl1, and Surf4 were the high-degree hub nodes. Conclusion Taken above, using integrated bioinformatical analysis, we have identified DEGs candidate genes and pathways in BMP-9 induced MSCs, which could improve our understanding of the key genes and pathways for BMP-9-induced osteogenic of MSCs.

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Bioinformatics Analysis Identifies Hub Genes and Molecular Pathways Involved in Sepsis-Induced Myopathy

Cited by 8 publications

References 36 publications

Identification and validation of oxidative stress-related genes in sepsis-induced myopathy

Identification and validation of oxidative stress-related genes in sepsis-induced myopathy

Screening of ferroptosis-related genes in sepsis-induced liver failure and analysis of immune correlation

Identification of key genes and pathways of BMP-9-induced osteogenic differentiation of mesenchymal stem cells by integrated bioinformatics analysis

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