A Meta-analysis of Transcriptome Data to Investigate the Effect of Soy Isoflavones on Breast Cancer Cell.

IF 1.6 4区 生物学 Q4 BIOTECHNOLOGY & APPLIED MICROBIOLOGY Iranian Journal of Biotechnology Pub Date : 2024-04-01 DOI:10.30498/ijb.2024.407148.3762
Elham Ashrafi-Dehkordi, Ahmad Tahmasebi, Habil Zare, Seyed Mohammad Mazloomi
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Abstract

Background: Breast cancer ranks as the second highest cause of cancer-linked deaths in women, with varying rates between Western and Asian countries. The consumption of phytoestrogens can influence breast cancer occurrence.

Objective: To comprehend how soy isoflavones impact breast cancer cells, we conducted a meta-analysis, combining gene expression data from multiple studies. This approach aimed to identify crucial transcriptional characteristics driving breast cancer cell response to soy phytoestrogens.

Materials and methods: The gene expression profiles obtained from the Gene Expression Omnibus and Array Express and were grouped into control and isoflavones exposure conditions. We performed a meta-analysis based on the effect size combination method to identify the differentially expressed genes (DEGs). In addition, we performed Gene Ontology (GO) enrichment analysis, pathway analysis, weighted gene co-expression network analysis (WGCNA) and recursive support vector machine (R-SVM) algorithm.

Results: Based on this meta-analysis, we identified 3,890 DEGs, of which 2,173 were up-regulated and 1,717 were down-regulated. For example, SGCG, PLK2, and TBC1D9 were the most highly down-regulated genes and EGR3, WISP2, and FKBP4 were the most highly expressed genes in the isoflavones exposure condition. The functional enrichment and pathway analysis were revealed "cell division" and "cell cycle" among the most enriched terms. Among the identified DEGs, 269 transcription factor (TF) genes belonged to 42 TF families, where the C2H2 ZF, bZIP, and bHLH were the most prominent families. We also employed the R-SVM for detecting the most important genes to classify samples into isoflavones exposure and control conditions. It identified a subset of 100 DEGs related to regulation of cell growth, response to estradiol, and intermediate ribonucleoside monophosphate in the purine (IMP) metabolic process. Moreover, the WGCNA separated the DEGs into five discrete modules strongly enriched for genes involved in cell division, DNA replication, embryonic digit morphogenesis, and cell-cell adhesion.

Conclusion: Our analysis provides evidence suggesting that isoflavone affects various mechanisms in cells, including pathways associated with NF-κB, Akt, MAPK, Wnt, Notch, p53, and AR pathways, which can lead to the induction of apoptosis, the alteration of the cell cycle, the inhibition of angiogenesis, and interference in the redox state of cells. These findings can shed light on the molecular mechanisms that underlie the response of breast cancer cells to isoflavones.

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研究大豆异黄酮对乳腺癌细胞影响的转录组数据元分析。
背景:乳腺癌是导致女性癌症死亡的第二大原因,西方国家和亚洲国家的发病率各不相同。食用植物雌激素可影响乳腺癌的发生:为了解大豆异黄酮如何影响乳腺癌细胞,我们结合多项研究的基因表达数据进行了荟萃分析。这种方法旨在确定驱动乳腺癌细胞对大豆植物雌激素反应的关键转录特征:从基因表达总库(Gene Expression Omnibus)和 Array Express 中获得的基因表达谱被分为对照组和异黄酮暴露组。我们根据效应大小组合法进行了荟萃分析,以确定差异表达基因(DEGs)。此外,我们还进行了基因本体(GO)富集分析、通路分析、加权基因共表达网络分析(WGCNA)和递归支持向量机(R-SVM)算法:根据这项荟萃分析,我们确定了 3,890 个 DEGs,其中 2,173 个上调,1,717 个下调。例如,在异黄酮暴露条件下,SGCG、PLK2和TBC1D9是下调最多的基因,EGR3、WISP2和FKBP4是表达最多的基因。功能富集和通路分析表明,"细胞分裂 "和 "细胞周期 "是富集程度最高的术语。在鉴定出的DEGs中,269个转录因子(TF)基因隶属于42个TF家族,其中C2H2 ZF、bZIP和bHLH是最主要的家族。我们还采用了R-SVM来检测最重要的基因,以便将样本分为异黄酮暴露和对照两种情况。它识别出了与细胞生长调控、对雌二醇的反应以及嘌呤(IMP)代谢过程中的单磷酸核糖核苷中间体有关的 100 个 DEGs 子集。此外,WGCNA 将 DEGs 分成了五个离散的模块,这些模块强烈富集了参与细胞分裂、DNA 复制、胚胎指状体形态发生和细胞-细胞粘附的基因:我们的分析提供的证据表明,异黄酮会影响细胞中的各种机制,包括与 NF-κB、Akt、MAPK、Wnt、Notch、p53 和 AR 通路相关的通路,从而诱导细胞凋亡、改变细胞周期、抑制血管生成和干扰细胞的氧化还原状态。这些发现可以揭示乳腺癌细胞对异黄酮反应的分子机制。
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来源期刊
Iranian Journal of Biotechnology
Iranian Journal of Biotechnology BIOTECHNOLOGY & APPLIED MICROBIOLOGY-
CiteScore
2.60
自引率
7.70%
发文量
20
期刊介绍: Iranian Journal of Biotechnology (IJB) is published quarterly by the National Institute of Genetic Engineering and Biotechnology. IJB publishes original scientific research papers in the broad area of Biotechnology such as, Agriculture, Animal and Marine Sciences, Basic Sciences, Bioinformatics, Biosafety and Bioethics, Environment, Industry and Mining and Medical Sciences.
期刊最新文献
In-Silico Method for Predicting Pathogenic Missense Variants Using Online Tools: AURKA Gene as a Model. A Meta-analysis of Transcriptome Data to Investigate the Effect of Soy Isoflavones on Breast Cancer Cell. DNA Methyltransferase Inhibition by RG108 Improves Stemness and Multipotential Differentiation of Human Adipose Tissue-derived Stem Cells. Docking-Based Virtual Screening Method for Selecting Natural Compounds with Synergistic Inhibitory Effects Against Cancer Signalling Pathways Using a Multi-Target Approach. Increased Expression Level of Human Blood Clotting Factor VIII Using NS0 Cell Line as a Host Cells.
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