改写“We explored the biofunctions of the DEGs Gene Ontology [GO; we can get what our…
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改写“We explored the biofunctions of the DEGs Gene Ontology [GO; we can get what our target gene is mainly related to the three levels of cellular component (CC), molecular function (MF), and biological process (BP)] enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG; we can know which signal pathways of the target gene are involved) pathways using the “Bioconductor” and “Cluster Profiler” package in R 3.6.1 software”
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We investigated the biological functions of the differentially expressed genes (DEGs) through the use of Gene Ontology (GO). This allowed us to determine the primary cell components (CC), molecular functions (MF) and biological processes (BP) that were associated with our target gene. Additionally, we analyzed the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways to gain insight into the signaling pathways in which the target gene was involved. To conduct this analysis, we utilized the "Bioconductor" and "Cluster Profiler" packages within R 3.6.1 software.
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翻译“FunRich version 3 (http://www.funrich. org/) was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomics (KEGG) analysis, a common method to annotate genes and explore their biological attributes. Statistically significant biological process terms and biological pathway terms for DEGs were illustrated. Transcription factors (TFs) that might regulate DEGs were also predicted. Based on the cumulative hypergeometric distribution test, P < 0.05 was considered to indicate a statistically significant difference”
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我们使用FunRich 3.0（http://www.funrich.org/）进行Gene Ontology（GO）和Kyoto Encyclopedia of Genes and Genomics（KEGG）的分析，这是一种用于注释基因并探索其生物属性的常见方法。我们展示了DEGs的具有统计学意义的生物过程及生物通路术语。我们还预测了可能调节DEGs的转录因子（TFs）。基于累积超几何分布检验，P <0.05被认为是具有统计学意义的差异。
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翻译“KEGG annotations included 29.0% (9/31) of upregulated genes and 10.1% (9/89) of downregulated genes. Functional pathway analysis mapped genes to KEGG pathways. The Benjamini-adjusted p = 0.05 was set as the cutoff for Frontiers in Cardiovascular Medicine | www.frontiersin.org 2 July 2021 | Volume 8 | Article 656263 Xie et al. Bioinformatics Analysis for AAA screening out significant GO terms and the KEGG pathways. The protein–protein interaction (PPI) network was generated by the Cytoscape software (http://cytoscape.org/). A node in the PPI network denotes protein, and the edge denotes the interactions.”
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KEGG注释包括31个上调基因中的29.0％（9/31）和89个下调基因中的10.1％（9/89）。功能通路分析将基因映射到KEGG通路上。Benjamini调整的p = 0.05被设置为筛选出显著的GO术语和KEGG通路的阈值。蛋白质-蛋白质相互作用（PPI）网络由Cytoscape软件（http://cytoscape.org/）生成。PPI网络中的节点表示蛋白质，边表示它们之间的相互作用。
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翻译“这是一种用于注释基因并探索其生物属性的常见方法。我们展示了DEGs的具有统计学意义的生物过程及生物通路术语。基于累积超几何分布检验，P <0.05被认为是具有统计学意义的差异”。
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This is a common method used to annotate genes and explore their biological attributes. We showed statistically significant biological process and pathway terms for DEGs. Based on the cumulative hypergeometric distribution test, a P value of less than 0.05 was considered statistically significant for the observed differences.
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改写“we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.”
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In this study, we introduce an effective analytical approach known as Gene Set Enrichment Analysis (GSEA) that facilitates the interpretation of gene expression data. Rather than examining individual genes, GSEA places emphasis on gene sets, which refer to groups of genes that display common biological function, regulation, or chromosomal location, thereby leveraging the collective power of these sets.
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我们使用R (4.2.1)版本中的R包: clusterProfiler[4.4.4]进行分析， p.adj < 0.05 & FDR(qvalue) < 0.25被认为具有统计学意义
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We conducted our analysis using the clusterProfiler R package (4.4.4) in R version 4.2.1. Differences with a p.adjust value less than 0.05 and an FDR (q-value) less than 0.25 were considered statistically significant.
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翻译Functional enrichment analysis of DESRGs
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DESRGs的功能富集分析。
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DESRGs是什么
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"DESRGs"一般指"差异表达基因响应元件（Differential Expression Signature Response Genes）"，是指具有统计学上显著差异的基因组表达，可能是信号转导途径中重要的靶标或参与调控特定的生物学过程。这些基因通常是在不同条件或疾病状态下表达水平差异显著的基因。