Genome-wide expression reveals potential biomarkers in breast cancer bone metastasis

IF 1.5 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Yashbir Singh, N. Subbarao, Abhinav Jaimini, Quincy A. Hathaway, Amina Kunovac, Bradley J. Erickson, V. Swarup, H. Singh
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Abstract

Abstract 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.
全基因组表达揭示了乳腺癌症骨转移的潜在生物标志物
摘要癌症转移最常见于骨,预示预后不良。基于途径的生物标志物鉴定可能有助于阐明癌症骨转移的细胞特征,进一步表征病因并促进新的治疗方法。我们使用GEO2R网络工具从GEO数据集GSE152795中提取了小鼠巨噬细胞的基因表达谱。通过阈值1.5的log2倍变化过滤差异表达基因(DEGs)(FDR<0.05)。STRING数据库和Enrichr用于GO术语分析、miRNA和与DEGs相关的TF分析。维也纳自动仓库被用来研究抗癌药物放线菌素-D和阿霉素的相互作用。DEG的敏感性和特异性使用受试者操作特征(ROC)分析进行评估。共发现61个DEG,包括27个下调和34个上调,在乳腺癌症骨转移中具有显著意义。主要DEG与肿瘤组织的脂质代谢和免疫反应有关。关键DEG、Bcl3、ADGRG7、FABP4、VCAN和IRF4受miRNA、miR-497、miR-574、miR-138和TF、CCDN1、STAT6、IRF8的调节。对接分析表明,这些基因与药物具有较强的结合力。ROC分析显示Bcl3对转移具有特异性。DEGs-Bcl3、ADGRG7、FABP4、IRF4及其调控的miRNA和TF对乳腺癌症骨组织的增殖和转移具有强烈的影响。总之,本研究表明DEGs直接参与了乳腺肿瘤在骨组织中的转移。已鉴定的基因、miRNA和TF可能是可用于治疗的药物靶点。然而,进一步的实验验证是必要的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Integrative Bioinformatics
Journal of Integrative Bioinformatics Medicine-Medicine (all)
CiteScore
3.10
自引率
5.30%
发文量
27
审稿时长
12 weeks
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