H3K36me3和H3K79me2调控基因在乳腺癌中的风险模型构建。

Ling-Yu Wang, Lu-Qiang Zhang, Qian-Zhong Li, Hui Bai
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引用次数: 0

摘要

异常组蛋白修饰(HMs)可促进乳腺癌的发生。为了阐明HM与基因表达的关系,我们分析了HM的结合模式,并计算了其在乳腺肿瘤细胞和正常细胞之间的信号变化。在此基础上,通过三种不同的方法估计HM信号变化对乳腺癌相关基因表达变化的影响。结果表明,H3K79me2和H3K36me3可能对基因表达变化的贡献更大。随后,通过Shannon熵鉴定出2109个在癌变过程中具有H3K79me2或H3K36me3水平差异的基因,并提交进行功能富集分析。富集分析表明,这些基因参与了癌症、人乳头瘤病毒感染和病毒癌变的途径。采用单因素Cox、LASSO和多因素Cox回归分析,从TCGA队列中H3K79me2/H3K36me3水平差异的基因中提取9个潜在的乳腺癌相关驱动基因。为了便于应用,将9个驱动基因的表达水平转化为风险评分模型,并在TCGA数据集和独立GEO数据集中通过随时间变化的受试者工作特征曲线检验其稳健性。最后,重新分析9个驱动基因中H3K79me2和H3K36me3在两株细胞系中的分布水平,定位信号变化显著的区域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

The risk model construction of the genes regulated by H3K36me3 and H3K79me2 in breast cancer.

The risk model construction of the genes regulated by H3K36me3 and H3K79me2 in breast cancer.

The risk model construction of the genes regulated by H3K36me3 and H3K79me2 in breast cancer.

The risk model construction of the genes regulated by H3K36me3 and H3K79me2 in breast cancer.

Abnormal histone modifications (HMs) can promote the occurrence of breast cancer. To elucidate the relationship between HMs and gene expression, we analyzed HM binding patterns and calculated their signal changes between breast tumor cells and normal cells. On this basis, the influences of HM signal changes on the expression changes of breast cancer-related genes were estimated by three different methods. The results showed that H3K79me2 and H3K36me3 may contribute more to gene expression changes. Subsequently, 2109 genes with differential H3K79me2 or H3K36me3 levels during cancerogenesis were identified by the Shannon entropy and submitted to perform functional enrichment analyses. Enrichment analyses displayed that these genes were involved in pathways in cancer, human papillomavirus infection, and viral carcinogenesis. Univariate Cox, LASSO, and multivariate Cox regression analyses were then adopted, and nine potential breast cancer-related driver genes were extracted from the genes with differential H3K79me2/H3K36me3 levels in the TCGA cohort. To facilitate the application, the expression levels of nine driver genes were transformed into a risk score model, and its robustness was tested via time-dependent receiver operating characteristic curves in the TCGA dataset and an independent GEO dataset. At last, the distribution levels of H3K79me2 and H3K36me3 in the nine driver genes were reanalyzed in the two cell lines and the regions with significant signal changes were located.

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