■研究表明,炎症反应与法洛四联症(TOF)有关。然而,目前尚无系统探讨炎症相关基因(IRGs)在TOF中的作用的研究。因此,基于生物信息学,我们探索了与TOF炎症相关的生物标志物,为其深入研究奠定理论基础。
■从基因表达综合(GEO)数据库下载TOF相关数据集(GSE36761和GSE35776)。在GSE36761中鉴定了TOF和对照组之间的差异表达基因(DEGs)。将TOF组和对照组之间的DEG与IRG相交,以获得差异表达的IRG(DE-IRG)。之后,利用最小绝对收缩和选择算子(LASSO)和随机森林(RF)来鉴定生物标志物.接下来,进行免疫分析。转录因子(TF)-mRNA,lncRNA-miRNA-mRNA,并建立了miRNA-单核苷酸多态性(SNP)-mRNA网络。最后,预测了靶向生物标志物的潜在药物.
■TOF组和对照组之间有971个DEG,通过DEG和IRG之间的交点获得了29个DE-IRG。接下来,总共有五种生物标志物(MARCO,CXCL6、F3、SLC7A2和SLC7A1)通过两种机器学习算法获得。18种免疫细胞的浸润丰度在TOF组和对照组之间有显著差异。如激活的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,31TFs,和33个边缘,例如,ELF1-CXCL6、CBX8-SLC7A2、ZNF423-SLC7A1、ZNF71-F3。创建了lncRNA-miRNA-mRNA网络,含有4个mRNA,4个miRNA,和228个lncRNAs。之后,在miRNA-SNP-mRNA网络中鉴定了9个SNP位置。总共预测了21种药物,如鸟氨酸,赖氨酸,精氨酸等。
■我们的发现检测到了五种与炎症相关的生物标志物(MARCO,CXCL6、F3、SLC7A2和SLC7A1)用于TOF,为TOF的进一步研究提供科学参考。
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: Our findings detected five inflammation-related biomarkers (MARCO, CXCL6, F3, SLC7A2, and SLC7A1) for TOF, providing a scientific reference for further studies of TOF.