法洛氏四联症炎症相关生物标志物的鉴定与分析

IF 1.5 4区 医学 Q2 PEDIATRICS Translational pediatrics Pub Date : 2024-07-31 Epub Date: 2024-07-29 DOI:10.21037/tp-24-8
Junzhe Du, Huaipu Liu, Pengcheng Wang, Wenzhi Wu, Fengnan Zheng, Yuanxiang Wang, Baoying Meng
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引用次数: 0

摘要

背景:研究表明,炎症反应与法洛氏四联症(TOF)有关。然而,目前还没有研究系统地探讨炎症相关基因(IRGs)在 TOF 中的作用。因此,我们以生物信息学为基础,探索了TOF中与炎症相关的生物标志物,为其深入研究奠定了理论基础:方法:从基因表达总库(GEO)数据库下载 TOF 相关数据集(GSE36761 和 GSE35776)。在 GSE36761 中确定了 TOF 组和对照组之间的差异表达基因(DEGs),在 GSE35776 中确定了 TOF 组和对照组之间的差异表达基因(DEGs)。将 TOF 组和对照组之间的 DEGs 与 IRGs 相交,得到差异表达的 IRGs(DE-IRGs)。然后,利用最小绝对收缩和选择算子(LASSO)和随机森林(RF)来识别生物标志物。接着,进行了免疫分析。建立了转录因子(TF)-mRNA、lncRNA-miRNA-mRNA 和 miRNA-单核苷酸多态性(SNP)-mRNA 网络。最后,预测了针对生物标记物的潜在药物:结果:TOF组和对照组之间有971个DEGs,通过DEGs和IRGs之间的交叉获得了29个DE-IRGs。接下来,通过两种机器学习算法共获得了 5 个生物标记物(MARCO、CXCL6、F3、SLC7A2 和 SLC7A1)。18种免疫细胞的浸润丰度在TOF组和对照组之间存在显著差异,如活化B细胞、中性粒细胞、CD56dim自然杀伤细胞等。TF-mRNA网络包含4个mRNA、31个TF和33条边,如ELF1-CXCL6、CBX8-SLC7A2、ZNF423-SLC7A1、ZNF71-F3。建立的 lncRNA-miRNA-mRNA 网络包含 4 个 mRNA、4 个 miRNA 和 228 个 lncRNA。随后,在 miRNA-SNP-mRNA 网络中确定了 9 个 SNPs 位置。共预测出21种药物,如鸟氨酸、赖氨酸、精氨酸等:我们的研究结果发现了五种与TOF相关的炎症生物标志物(MARCO、CXCL6、F3、SLC7A2和SLC7A1),为进一步研究TOF提供了科学参考。
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Identification and analysis of inflammation-related biomarkers in tetralogy of Fallot.

Background: Studies have revealed that inflammatory response is relevant to the tetralogy of Fallot (TOF). However, there are no studies to systematically explore the role of the inflammation-related genes (IRGs) in TOF. Therefore, based on bioinformatics, we explored the biomarkers related to inflammation in TOF, laying a theoretical foundation for its in-depth study.

Methods: TOF-related datasets (GSE36761 and GSE35776) were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between TOF and control groups were identified in GSE36761. And DEGs between TOF and control groups were intersected with IRGs to obtain differentially expressed IRGs (DE-IRGs). Afterwards, the least absolute shrinkage and selection operator (LASSO) and random forest (RF) were utilized to identify the biomarkers. Next, immune analysis was carried out. The transcription factor (TF)-mRNA, lncRNA-miRNA-mRNA, and miRNA-single nucleotide polymorphism (SNP)-mRNA networks were created. Finally, the potential drugs targeting the biomarkers were predicted.

Results: There were 971 DEGs between TOF and control groups, and 29 DE-IRGs were gained through the intersection between DEGs and IRGs. Next, a total of five biomarkers (MARCO, CXCL6, F3, SLC7A2, and SLC7A1) were acquired via two machine learning algorithms. Infiltrating abundance of 18 immune cells was significantly different between TOF and control groups, such as activated B cells, neutrophil, CD56dim natural killer cells, etc. The TF-mRNA network contained 4 mRNAs, 31 TFs, and 33 edges, for instance, ELF1-CXCL6, CBX8-SLC7A2, ZNF423-SLC7A1, ZNF71-F3. The lncRNA-miRNA-mRNA network was created, containing 4 mRNAs, 4 miRNAs, and 228 lncRNAs. Afterwards, nine SNPs locations were identified in the miRNA-SNP-mRNA network. A total of 21 drugs were predicted, such as ornithine, lysine, arginine, etc.

Conclusions: Our findings detected five inflammation-related biomarkers (MARCO, CXCL6, F3, SLC7A2, and SLC7A1) for TOF, providing a scientific reference for further studies of TOF.

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来源期刊
Translational pediatrics
Translational pediatrics Medicine-Pediatrics, Perinatology and Child Health
CiteScore
4.50
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5.00%
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108
期刊介绍: Information not localized
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