Torki Zahra, Ghavi Davood, Foruzandeh Zahra, Zeinali Sehrig Fatemeh, Hashemi Solmaz, Alivand Mohammad Reza, Pornour Majid
Department of Medical Genetics, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.
Department of Molecular Genetics, Ahar Branch, Islamic Azad University, Ahar, Iran.
Cell Mol Biol (Noisy-le-grand). 2024 Nov 27;70(11):16-30. doi: 10.14715/cmb/2024.70.11.3.
Breast cancer (BC) is a global health concern with a growing prevalence. Since BC is a heterogeneous cancer, transcriptome analyzes were carried out on breast tumor tissues relative to their corresponding normal tissues in order to identify gene expression signatures and perform meta-analysis. Five expression profiling by array data sets from breast tumor tissues and non-tumor neighboring tissues were retrieved following a search in the GEO database (GSE70947, GSE70905, GSE10780, GSE29044, and GSE42568). Meta-analysis of gene expression using the Network Analyst tool identified common differentially expressed genes and biological pathways in all data sets. Then, the DEGs were analyzed through PPI network construction, gene ontology, and pathway analysis. The detected hub genes underwent Kaplan-Meier (KM) plotter and UALCAN validation. Finally, Real-time PCR analysis was used on BC patients' samples to determine mRNA levels of cAMP signaling pathway members ATP1A2, FXYD1, and ADCY3. Breast tumor tissues showed 710 differentially expressed genes (DEGs), with 392 overexpressed and 318 underexpressed, compared to normal marginal tissues. On the EnrichR library, GO, and KEGG pathway analyses were performed on the DEGs list. Progesterone-mediated oocyte maturation and the NF-kappa B signaling system were upregulated DEGs' top deregulated signaling pathways. In contrast, pathways related to cancer and the cAMP signaling pathway were the most enriched terms for down-regulated genes. Next, Real-time PCR quantification of cAMP signaling cascade members ATP1A2, FXYD1, and ADCY3 was performed on 50 BC tumoral and non-tumoral tissues for validation. Results of meta-analyzed array data sets revealed DEGs representing BC gene signatures, and cAMP signaling pathway members as effective factors in BC. The results of our real-time PCR expression level determination for ATP1A2, FXYD1, and ADCY3 in breast tumor tissues relative to the normal margins contradicted our bioinformatics investigations, which found increased levels for these genes. Of these, only ATP1A2's expression levels were statistically significant. This study focused on identifying gene expression signatures that provide an invaluable source of evidence for BC-related underlying mechanisms to provide new therapeutic targets and biomarkers.
乳腺癌（BC）是一个日益受到全球关注的健康问题。由于乳腺癌是一种异质性癌症，因此对乳腺肿瘤组织及其相应的正常组织进行了转录组分析，以识别基因表达特征并进行荟萃分析。在GEO数据库（GSE70947、GSE70905、GSE10780、GSE29044和GSE42568）中搜索后，获取了来自乳腺肿瘤组织和非肿瘤邻近组织的五个基因芯片表达谱数据集。使用网络分析工具对基因表达进行荟萃分析，确定了所有数据集中常见的差异表达基因和生物学途径。然后，通过蛋白质-蛋白质相互作用（PPI）网络构建、基因本体论和途径分析对差异表达基因进行分析。对检测到的枢纽基因进行了Kaplan-Meier（KM）绘图仪和UALCAN验证。最后，对乳腺癌患者样本进行实时PCR分析，以确定环磷酸腺苷（cAMP）信号通路成员ATP1A2、FXYD1和ADCY3的mRNA水平。与正常边缘组织相比，乳腺肿瘤组织显示出710个差异表达基因（DEGs），其中392个基因过表达，318个基因低表达。在EnrichR库上，对差异表达基因列表进行了基因本体论（GO）和京都基因与基因组百科全书（KEGG）途径分析。孕酮介导的卵母细胞成熟和核因子κB信号系统是差异表达基因上调最明显的信号通路。相反，与癌症相关的途径和cAMP信号通路是下调基因最富集的术语。接下来，对50个乳腺肿瘤和非肿瘤组织进行了cAMP信号级联成员ATP1A2、FXYD1和ADCY3的实时PCR定量分析以进行验证。荟萃分析的基因芯片数据集结果揭示了代表乳腺癌基因特征的差异表达基因，以及cAMP信号通路成员是乳腺癌的有效影响因素。我们对乳腺肿瘤组织中ATP1A2、FXYD1和ADCY3相对于正常边缘的实时PCR表达水平测定结果与我们的生物信息学研究结果相矛盾，后者发现这些基因的水平升高。其中，只有ATP1A2的表达水平具有统计学意义。本研究专注于识别基因表达特征，这些特征为乳腺癌相关潜在机制提供了宝贵的证据来源，以提供新的治疗靶点和生物标志物。
