Background
Sepsis is one of the most lethal diseases worldwide. Pyroptosis is a unique form of cell death, and the mechanism of interaction with sepsis is not yet clear. The aim of this study was to uncover pyroptosis genes associated with sepsis and to provide early therapeutic targets for the treatment of sepsis.
Methods
Based on the GSE134347 dataset, sepsis-related genes were mined by differential expression analysis and weighted gene coexpression network analysis (WGCNA). Subsequently, the sepsis-related genes were analysed for enrichment, and a protein‒protein interaction (PPI) network was constructed. We performed unsupervised consensus clustering of sepsis patients based on 33 pyroptosis-related genes (PRGs) provided by prior reviews. We finally obtained the PRGs mostly associated with sepsis by machine learning prediction models combined with prior reviews. The GSE32707 dataset served as an external validation dataset to validate the model and PRGs via receiver operating characteristic (ROC) curves. The NetworkAnalyst online tool was utilized to create a ceRNA network of lncRNAs and miRNAs around PRGs mostly associated with sepsis.
Results
A total of 170 genes associated with sepsis and 13 hub genes were acquired by WGCNA and PPI network analysis. The results of the enrichment analysis implied that these genes were mainly involved in the regulation of the inflammatory response and the positive regulation of bacterial and fungal defence responses. The prolactin signalling pathway and IL-17 signalling pathway were the primary enrichment pathways. Thirty-three PRGs can effectively classify septic patients into two subtypes, implying that there is a reciprocal relationship between sepsis and pyroptosis. Eventually, NLRC4 was considered the PRG most strongly associated with sepsis. The validation results of the prediction model and NLRC4 based on ROC curves were 0.74 and 0.67, respectively, both of which showed better predictive values. Meanwhile, the ceRNA network consisting of 6 lncRNAs and 2 miRNAs was constructed around NLRC4.
Conclusion
NLRC4, as the PRG mostly associated with sepsis, could be considered a potential target for treatment. The 6 lncRNAs and 2 miRNAs centred on NLRC4 could serve as a further research direction to uncover the deeper pathogenesis of sepsis.
Background: Peripheral arterial disease (PAD) is a widely and complex disease which also known as arteriosclerosis obliterans(ASO). This disorder causes an increasingly prevalence all around the world. Despite its high prevalence and huge disruptive impact on the social economy globally, a large proportion of patients that suffers from PAD cannot receive early and proper therapy. Even some patients are not diagnosed so clearly that they missed the PAD optimal treatment. However, seldom research on PAD’s early diagnosis was based on bioinformatics assisted by machine learning. And the correlation between PAD, ferroptosis and cancer is still unavailable. The aim at this study is to seek potential diagnostic markers for PAD and the related genes of PAD-ferroptosis and analyze the relationship to malignant tumors.
Methods: We used PAD datasets from Gene Expression Omnibus (GEO) database. R software was used to identify differentially expressed genes (DEGs) in PAD. Then we performed functional correlation Bioinformatics analysis such as Gene Ontology(GO) analysis, Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis and Network of Cancer Gene (NCG) enrichment analysis. The protein–protein interaction analysis (PPI) network was also built and enriched for DEGs. The hub genes were acquired by Cytoscape software. Hub genes were taken to intersection with ferroptosis related genes for acquiring PAD-ferroptosis related genes. And we selected the key gene from hub genes by using support vector machine-recursive feature elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO) logistic regression methods. By the use of R software, we drew the ROC curve to evaluate the diagnostic efficiency of PAD.
Results: A total of 176 DEGs, containing 53 up-regulated and 123 down-regulated DEGs, were identified. FBXW7 and YWHAE were turned out to be the PAD-ferroptosis related genes. PAD has a potential correlation with many types of cancer. SMARCA4 and YWHAE can be treated as the diagnostic markers of PAD(AUC>0.8).
Conclusion: To summarize, SMARCA4 and YWHAE were identified as diagnostic markers of PAD. FBXW7 and YWHAE were selected to be the PAD-ferroptosis related genes. YWHAE may be the crossing gene among PAD, a part of malignant tumors and ferroptosis.
Trial registration: Our reaserch was based on bioinformatic analysis and we obtained the date from Gene Expression Omnibus database.
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