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Aim To investigate the effects of daidzein(DD) on the proliferation and apoptosis of non-small cell lung cancer cells,with a focus on the possible role of the p53 signaling pathway in this regard. Methods CCK-8 method and flow cytometry were used to detect the effects of soy isoflavone crude extract and DD on the viability and apoptosis of HELF and H1299 cells. Gene microarray was used to detect the changes in gene expression after treatment of H1299 cells with DD. GSEA and differential analysis were used to screen the major pathways and key genes. RT-qPCR and Western blot were performed to verify the differences in mRNA and protein expression of key genes(p53 and CASP9) in the major pathways. After p53 inhibitor Pifithrin-α inhibited the expression of p53,the effect of DD on p53 mRNA and protein expression levels was examined,and the proliferative effect on H1299 cells was observed. Results Soy isoflavone crude extract and DD promoted proliferation and inhibited apoptosis of normal lung cells and inhibited proliferation and promoted apoptosis of lung cancer cells. p53 signaling pathway was significantly enriched in the DD-treated group(NES=1.78,P=0.000),and the expressions of p53 and CASP9 genes were found to be significantly up-regulated in the treated group. Compared with the control group,mRNA expression of CASP9 and p53 significantly increased in both HELF and H1299 cells treated with DD(P<0.05),and p53 protein expression also increased in HELF cells(P<0.05). After inhibition of p53 expression,DD significantly increased the mRNA expression of p53 in H1299 and HELF cells(P<0.05) and also markedly increased the expression of p53 protein in H1299 cells(P<0.05),and it was observed that DD inhibited the proliferation of lung cancer cells. Conclusions DD inhibits the proliferation and promotes the apoptosis of lung cancer H1299 cells,and the mechanism mainly involves the p53 signaling pathway. © 2023 Publication Centre of Anhui Medical University. All rights reserved. 相似文献
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目的基于高通量基因表达(GEO)数据库结合CT影像预测肺癌临床分期的分子标志物,并建立相应的诊断模型及预测列线图。方法从GEO数据库中获取小结节肺癌患者包括训练队列和验证队列的基因表达谱数据,按临床分期将2个队列分别分为2组:早期(Ⅰ期)组和中晚期(Ⅱ、Ⅲ期和Ⅳ期)组;分析2组基因的差异表达及功能富集;通过单因素分析筛选共差异基因后进行Logistic回归分析和ROC分析,并在训练队列中绘制诊断列线图。结果对训练队列和验证队列的差异分析分别获得161和437个差异基因,并筛选获得7个共差异表达基因(SLC16A14、LHX2、PRAME、ZNF257、SOX2、KCNJ16、GSTA1)。Logistic回归分析显示,高表达的ZNF257和低表达的SOX2、KCNJ16、GSTA1与中晚期肺癌显著相关(均P<0.05),以此四基因构建的模型灵敏度为83.3%,特异度为92.9%,建立的诊断列线图在小结节肺癌的恶性诊断预测中显示出极好的潜力。结论成功预测肺癌临床分期的4个分子标志物(ZNF257、SOX2、KCNJ16、GSTA1);以这4个差异表达基因可建立小结节肺癌诊断模型及诊断预测列线图;该诊断模型具有较好的特异度和灵敏度,列线图具有预测CT筛查小结节肺癌的潜能。 相似文献
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目的 构建肺腺癌(LUAD)预后相关坏死性凋亡基因标志物并对其进行评估。方法 KEGG数据库筛选坏死性凋亡相关基因(NRGs);TCGA和GEO数据库中分别下载LUAD样本和正常样本的RNA测序及其相应临床数据,并以TCGA数据集作为训练队列,以GEO数据集作为验证队列,对筛选的NRGs进行差异和功能富集分析;单因素Cox及Lasso Cox回归分析构建NRGs的LUAD预后标志物;采用Kaplan-Meier生存分析、timeROC分析、多因素Cox回归分析和临床列线图评估该预后标志物;以GEO数据集(GSE31210)外部验证该预后标志物。结果 筛选获得159个NRGs;在训练队列中鉴定出26个差异表达的NRGs(DENRGs);富集分析发现DENRGs主要参与坏死性凋亡和NOD样受体等信号通路;构建由6个NRGs(PLA2G4F、IL33、TLR4、RNF103.CHMP3、ALOX15、IL1A)组成的预后标志物;生存分析显示高风险组总体生存率(OS)明显低于低风险组(log-rank P<0.001);该预后标志物预测1年、3年和5年OS的AUC分别为0.672、0.631和0.624,且与LUAD患者OS显著相关(HR:2.30,95%CI:1.30~4.00,P<0.01);建立的列线图能较好地预测LUAD患者的生存;验证队列的分析结果与训练队列一致。结论 成功构建一种新的NRGs预后标志物,其可用于预测LUAD患者的预后,且该预后标志物的预测能力良好。 相似文献
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