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基于系统辨识和致癌机制的三阴性乳腺癌与非三阴性乳腺癌的系统医学设计。

Systems Medicine Design for Triple-Negative Breast Cancer and Non-Triple-Negative Breast Cancer Based on Systems Identification and Carcinogenic Mechanisms.

机构信息

Laboratory of Automatic Control, Signaling Processing and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan.

出版信息

Int J Mol Sci. 2021 Mar 17;22(6):3083. doi: 10.3390/ijms22063083.

Abstract

Triple-negative breast cancer (TNBC) is a heterogeneous subtype of breast cancers with poor prognosis. The etiology of triple-negative breast cancer (TNBC) is involved in various biological signal cascades and multifactorial aberrations of genetic, epigenetic and microenvironment. New therapeutic for TNBC is urgently needed because surgery and chemotherapy are the only available modalities nowadays. A better understanding of the molecular mechanisms would be a great challenge because they are triggered by cascade signaling pathways, genetic and epigenetic regulations, and drug-target interactions. This would allow the design of multi-molecule drugs for the TNBC and non-TNBC. In this study, in terms of systems biology approaches, we proposed a systematic procedure for systems medicine design toward TNBC and non-TNBC. For systems biology approaches, we constructed a candidate genome-wide genetic and epigenetic network (GWGEN) by big databases mining and identified real GWGENs of TNBC and non-TNBC assisting with corresponding microarray data by system identification and model order selection methods. After that, we applied the principal network projection (PNP) approach to obtain the core signaling pathways denoted by KEGG pathway of TNBC and non-TNBC. Comparing core signaling pathways of TNBC and non-TNBC, essential carcinogenic biomarkers resulting in multiple cellular dysfunctions including cell proliferation, autophagy, immune response, apoptosis, metastasis, angiogenesis, epithelial-mesenchymal transition (EMT), and cell differentiation could be found. In order to propose potential candidate drugs for the selected biomarkers, we designed filters considering toxicity and regulation ability. With the proposed systematic procedure, we not only shed a light on the differences between carcinogenetic molecular mechanisms of TNBC and non-TNBC but also efficiently proposed candidate multi-molecule drugs including resveratrol, sirolimus, and prednisolone for TNBC and resveratrol, sirolimus, carbamazepine, and verapamil for non-TNBC.

摘要

三阴性乳腺癌(TNBC)是一种预后较差的乳腺癌异质性亚型。三阴性乳腺癌(TNBC)的病因涉及多种生物信号级联和遗传、表观遗传和微环境的多因素异常。由于目前仅可采用手术和化疗,因此急需为 TNBC 开发新的治疗方法。由于它们是由级联信号通路、遗传和表观遗传调控以及药物靶点相互作用触发的,因此更好地理解分子机制将是一项巨大的挑战。这将允许为 TNBC 和非 TNBC 设计多分子药物。在这项研究中,根据系统生物学方法,我们提出了一种针对 TNBC 和非 TNBC 的系统医学设计的系统程序。对于系统生物学方法,我们通过大数据挖掘构建了候选全基因组遗传和表观遗传网络(GWGEN),并通过系统识别和模型阶次选择方法,利用相应的微阵列数据确定了 TNBC 和非 TNBC 的真实 GWGEN。之后,我们应用主网络投影(PNP)方法获得 TNBC 和非 TNBC 的KEGG 途径表示的核心信号通路。比较 TNBC 和非 TNBC 的核心信号通路,可以找到导致包括细胞增殖、自噬、免疫反应、细胞凋亡、转移、血管生成、上皮-间充质转化(EMT)和细胞分化在内的多种细胞功能障碍的关键致癌生物标志物。为了针对选定的生物标志物提出潜在的候选药物,我们设计了考虑毒性和调节能力的滤波器。通过提出的系统程序,我们不仅阐明了 TNBC 和非 TNBC 的致癌分子机制之间的差异,还为 TNBC 提出了候选多分子药物,包括白藜芦醇、西罗莫司和泼尼松龙,以及非 TNBC 的候选多分子药物,包括白藜芦醇、西罗莫司、卡马西平和维拉帕米。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/25f9/8002730/26343f48a5c7/ijms-22-03083-g001.jpg

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