Identification of a DNA Methylation-Driven Genes-Based Prognostic Model and Drug Targets in Breast Cancer: In silico Screening of Therapeutic Compounds and in vitro Characterization.
Identification of a DNA Methylation-Driven Genes-Based Prognostic Model and Drug Targets in Breast Cancer: In silico Screening of Therapeutic Compounds and in vitro Characterization.
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DOI：
10.3389/fimmu.2021.761326
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发表时间：
2021
影响因子：
7.3
通讯作者：
Zhang W
中科院分区：
文献类型：
--
作者：
Tian S;Fu L;Zhang J;Xu J;Yuan L;Qin J;Zhang W
DNA甲基化是一种重要的表观遗传学变化，它调节基因转录并有助于保持基因组的稳定。人类癌症的去调控特征通常由异常的DNA甲基化来定义，DNA甲基化对肿瘤的形成至关重要，并控制着几个肿瘤相关基因的表达。在多种癌症中，抑癌基因高甲基化、癌基因低甲基化等甲基化改变在肿瘤发生中起重要作用，尤其是在乳腺癌中。因此，检测DNA甲基化驱动的基因，了解这些基因的分子特征，有助于加深我们对乳腺癌发病机制和分子机制的理解，促进精确医学和药物发现的发展。在目前的研究中，我们回顾分析了1000多名乳腺癌患者，并基于DNA甲基化驱动的基因建立了一个可靠的预后标志。然后，我们计算了每个患者的免疫细胞丰度，高危患者存在免疫活性低下。白细胞抗原家族基因和免疫检查点基因的表达与上述结果一致。此外，在高危人群中观察到更多的突变基因。此外，对CTRP和PRISM数据库中的可用药靶点和化合物进行了电子筛选，结果鉴定了5个靶基因(HMMR、CCNB1、CDC25C、AURKA和CENPE)和5个药物(寡霉素A、泛诺比妥、(+)-JQ1、伏沙利西和阿昔洛黄素A)，它们可能在治疗高危乳腺癌患者中具有治疗潜力。进一步的体外评价证实，(+)-JQ1具有最好的癌细胞选择性，并通过CENPE发挥其抗乳腺癌活性。综上所述，我们的研究为个性化预测提供了新的见解，并可能启发风险分层和精确治疗的整合。
DNA methylation is a vital epigenetic change that regulates gene transcription and helps to keep the genome stable. The deregulation hallmark of human cancer is often defined by aberrant DNA methylation which is critical for tumor formation and controls the expression of several tumor-associated genes. In various cancers, methylation changes such as tumor suppressor gene hypermethylation and oncogene hypomethylation are critical in tumor occurrences, especially in breast cancer. Detecting DNA methylation-driven genes and understanding the molecular features of such genes could thus help to enhance our understanding of pathogenesis and molecular mechanisms of breast cancer, facilitating the development of precision medicine and drug discovery. In the present study, we retrospectively analyzed over one thousand breast cancer patients and established a robust prognostic signature based on DNA methylation-driven genes. Then, we calculated immune cells abundance in each patient and lower immune activity existed in high-risk patients. The expression of leukocyte antigen (HLA) family genes and immune checkpoints genes were consistent with the above results. In addition, more mutated genes were observed in the high-risk group. Furthermore, a in silico screening of druggable targets and compounds from CTRP and PRISM databases was performed, resulting in the identification of five target genes (HMMR, CCNB1, CDC25C, AURKA, and CENPE) and five agents (oligomycin A, panobinostat, (+)-JQ1, voxtalisib, and arcyriaflavin A), which might have therapeutic potential in treating high-risk breast cancer patients. Further in vitro evaluation confirmed that (+)-JQ1 had the best cancer cell selectivity and exerted its anti-breast cancer activity through CENPE. In conclusion, our study provided new insights into personalized prognostication and may inspire the integration of risk stratification and precision therapy.