Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, China; College of Electrical Engineering, Northwest Minzu University, Lanzhou, China.
Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, China.
Biosystems. 2021 Jan;199:104317. doi: 10.1016/j.biosystems.2020.104317. Epub 2020 Dec 3.
Breast cancer is a complex cancer which includes many different subtypes. Identifying prognostic modules, i.e., functionally related gene networks that play crucial roles in cancer development is essential in breast cancer study. Different subtypes of breast cancer correspond to different treatment methods. The purpose of this study is to use a new method to divide breast cancer into different prognostic modules, so as to provide scientific basis for improving clinical management. The method is based on comparing similarities between modules detected from different weighted gene co-expression networks. The method was applied on genomic data of breast cancer from The Cancer Genome Atlas database and was applied to select differential modules between two groups of patients with significant differences in survival times. It was compared with a previously proposed module selection method. The result shows that our method outperforms the previously proposed one. Moreover, within the identified two differential modules, the first one is highly enriched with genes involved in hormone responds, the second one is highly related with biological process engaged in M-phase. The two modules were further validated by log-rank test in the validation dataset GSE3494. Both of the two modules show significantly different with p-values less than 0.02. The identified two modules confirmed previous findings including importance of biological networks in breast cancer involved in hormone response and M-phase. Out of the top twenty hub genes in the two modules, fifteen genes were previously shown to be prognostic markers for breast cancer.
乳腺癌是一种复杂的癌症，包括许多不同的亚型。鉴定预后模块，即对癌症发展起关键作用的功能相关基因网络，对于乳腺癌研究至关重要。不同亚型的乳腺癌对应不同的治疗方法。本研究旨在使用一种新方法将乳腺癌分为不同的预后模块，为提高临床管理水平提供科学依据。该方法基于比较从不同加权基因共表达网络中检测到的模块之间的相似性。该方法应用于癌症基因组图谱数据库中的乳腺癌基因组数据，并应用于选择两组生存时间差异显著的患者之间的差异模块。并与之前提出的模块选择方法进行了比较。结果表明，我们的方法优于之前提出的方法。此外，在鉴定的两个差异模块中，第一个模块高度富集了与激素反应相关的基因，第二个模块与参与 M 期的生物学过程高度相关。在验证数据集 GSE3494 中，通过对数秩检验对这两个模块进行了进一步验证。两个模块的 p 值均小于 0.02，差异均有统计学意义。鉴定的两个模块证实了之前的研究结果，包括激素反应和 M 期涉及的乳腺癌生物网络的重要性。在两个模块的前 20 个枢纽基因中，有 15 个基因之前被证明是乳腺癌的预后标志物。
