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乳腺癌特异性诊断和预后生物标志物。

Grade-specific diagnostic and prognostic biomarkers in breast cancer.

机构信息

Department of Biotechnology, National Institute of Technology Warangal, Warangal 506004, Telangana, India.

Department of Biotechnology, National Institute of Technology Warangal, Warangal 506004, Telangana, India.

出版信息

Genomics. 2020 Jan;112(1):388-396. doi: 10.1016/j.ygeno.2019.03.001. Epub 2019 Mar 7.

Abstract

An integrative approach is presented to identify grade-specific biomarkers for breast cancer. Grade-specific molecular interaction networks were constructed with differentially expressed genes (DEGs) of cancer grade 1, 2, and 3. We observed that the molecular network of grade3 is predominantly associated with cancer-specific processes. Among the top ten connected DEGs in the grade3, the increase in the expression of UBE2C and CCNB2 genes was statistically significant across different grades. Along with UBE2C and CCNB2 genes, the CDK1, KIF2C, NDC80, and CCNB2 genes are also profoundly expressed in different grades and reduce the patient's survival. Gene set enrichment analysis of these six genes reconfirms their role in metastatic phenotype. Moreover, the coexpression network shows a strong association of these six genes promotes cancer specific biological processes and possibly drives cancer from lower to a higher grade. Collectively the identified genes can act as potential biomarkers for breast cancer diagnosis and prognosis.

摘要

本文提出了一种综合方法来鉴定乳腺癌的分级特异性生物标志物。使用癌症 1 级、2 级和 3 级的差异表达基因 (DEGs) 构建了分级特异性分子相互作用网络。我们观察到,3 级的分子网络主要与癌症特异性过程相关。在 3 级中排名前十的连接 DEG 中,UBE2C 和 CCNB2 基因的表达增加在不同分级中具有统计学意义。与 UBE2C 和 CCNB2 基因一起,CDK1、KIF2C、NDC80 和 CCNB2 基因在不同分级中也有明显表达,降低了患者的生存率。对这六个基因的基因集富集分析再次证实了它们在转移表型中的作用。此外,共表达网络显示这些六个基因的强烈关联促进了癌症特定的生物学过程,并可能促使癌症从低级别发展到高级别。综上所述,鉴定出的基因可以作为乳腺癌诊断和预后的潜在生物标志物。

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