Department of Radiology, Mayo Clinic, Rochester, MN, USA.
School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India.
J Integr Bioinform. 2022 Apr 8;19(3). doi: 10.1515/jib-2021-0041. eCollection 2022 Sep 1.
Breast cancer metastases are most commonly found in bone, an indication of poor prognosis. Pathway-based biomarkers identification may help elucidate the cellular signature of breast cancer metastasis in bone, further characterizing the etiology and promoting new therapeutic approaches. We extracted gene expression profiles from mouse macrophages from the GEO dataset, GSE152795 using the GEO2R webtool. The differentially expressed genes (DEGs) were filtered by log2 fold-change with threshold 1.5 (FDR < 0.05). STRING database and Enrichr were used for GO-term analysis, miRNA and TF analysis associated with DEGs. Autodock Vienna was exploited to investigate interaction of anti-cancer drugs, Actinomycin-D and Adriamycin. Sensitivity and specificity of DEGs was assessed using receiver operating characteristic (ROC) analyses. A total of 61 DEGs, included 27 down-regulated and 34 up-regulated, were found to be significant in breast cancer bone metastasis. Major DEGs were associated with lipid metabolism and immunological response of tumor tissue. Crucial DEGs, Bcl3, ADGRG7, FABP4, VCAN, and IRF4 were regulated by miRNAs, miR-497, miR-574, miR-138 and TFs, CCDN1, STAT6, IRF8. Docking analysis showed that these genes possessed strong binding with the drugs. ROC analysis demonstrated Bcl3 is specific to metastasis. DEGs Bcl3, ADGRG7, FABP4, IRF4, their regulating miRNAs and TFs have strong impact on proliferation and metastasis of breast cancer in bone tissues. In conclusion, present study revealed that DEGs are directly involved in of breast tumor metastasis in bone tissues. Identified genes, miRNAs, and TFs can be possible drug targets that may be used for the therapeutics. However, further experimental validation is necessary.
乳腺癌转移最常发生在骨骼中，这是预后不良的指征。基于途径的生物标志物识别可能有助于阐明乳腺癌骨转移的细胞特征，进一步阐明其病因，并促进新的治疗方法。我们从 GEO 数据集 GSE152795 中使用 GEO2R 网络工具提取了来自小鼠巨噬细胞的基因表达谱。通过 log2 倍数变化阈值为 1.5（FDR < 0.05）过滤差异表达基因 (DEGs)。使用 STRING 数据库和 Enrichr 进行 GO-term 分析、与 DEGs 相关的 miRNA 和 TF 分析。利用 Autodock Vienna 研究抗癌药物放线菌素 D 和阿霉素的相互作用。使用接收者操作特征 (ROC) 分析评估 DEGs 的敏感性和特异性。在乳腺癌骨转移中发现 61 个 DEGs，包括 27 个下调和 34 个上调，具有显著意义。主要的 DEGs 与肿瘤组织的脂质代谢和免疫反应有关。关键的 DEGs，Bcl3、ADGRG7、FABP4、VCAN 和 IRF4，受 miRNA（miR-497、miR-574、miR-138）和 TF（CCDN1、STAT6、IRF8）的调控。对接分析表明这些基因与药物具有很强的结合能力。ROC 分析表明 Bcl3 是转移的特异性标志物。DEGs Bcl3、ADGRG7、FABP4、IRF4、它们的调节 miRNA 和 TF 对乳腺癌在骨骼中的增殖和转移有很强的影响。总之，本研究表明 DEGs 直接参与了乳腺癌在骨骼中的转移。鉴定出的基因、miRNA 和 TF 可能成为药物靶点，可用于治疗。然而，还需要进一步的实验验证。
