Ashrafi-Dehkordi Elham, Tahmasebi Ahmad, Zare Habil, Mazloomi Seyed Mohammad
Nutrition Research Center, Department of Food Hygiene and Quality Control, School of Nutrition and Food Sciences, Shiraz University of Medical Sciences, Shiraz, Iran.
Biotechnology Institute, College of Agriculture, Shiraz University, Shiraz, Iran.
Iran J Biotechnol. 2024 Apr 1;22(2):e3762. doi: 10.30498/ijb.2024.407148.3762. eCollection 2024 Apr.
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.
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.
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.
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, , , and were the most highly down-regulated genes and , , and 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 CH 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.
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.
乳腺癌是女性癌症相关死亡的第二大原因，在西方国家和亚洲国家的发病率有所不同。植物雌激素的摄入会影响乳腺癌的发生。
为了解大豆异黄酮如何影响乳腺癌细胞，我们进行了一项荟萃分析，整合了多项研究的基因表达数据。该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。
从基因表达综合数据库（Gene Expression Omnibus）和Array Express获取基因表达谱，并分为对照和异黄酮暴露条件。我们基于效应大小组合方法进行荟萃分析，以鉴定差异表达基因（DEG）。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。
基于此荟萃分析，我们鉴定出3890个差异表达基因，其中2173个上调，1717个下调。例如，[此处原文可能缺失具体基因名称]是异黄酮暴露条件下下调程度最高的基因，而[此处原文可能缺失具体基因名称]是表达程度最高的基因。功能富集和通路分析显示，“细胞分裂”和“细胞周期”是最富集的术语。在鉴定出的差异表达基因中，269个转录因子（TF）基因属于42个TF家族，其中CH ZF、bZIP和bHLH是最突出的家族。我们还使用R-SVM检测将样本分类为异黄酮暴露和对照条件的最重要基因。它鉴定出了100个与细胞生长调节、对雌二醇的反应以及嘌呤（IMP）代谢过程中的中间核糖核苷单磷酸相关的差异表达基因子集。此外，WGCNA将差异表达基因分为五个离散模块，这些模块强烈富集了参与细胞分裂、DNA复制、胚胎指形态发生和细胞间粘附的基因。
我们的分析提供了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通路相关的途径，这可能导致细胞凋亡的诱导、细胞周期的改变、血管生成的抑制以及对细胞氧化还原状态的干扰。这些发现可以揭示乳腺癌细胞对异黄酮反应的分子机制。
