CDH1 gene as biomarker towards breast cancer prediction*

Breast cancer is considered to be happened due to genetic aberration. Out of several genes expressed, it is found that cadherin 1, type 1 (CDH1) is responsible in several ways to control the metabolic order in human. Deregulation of the function of protein E-cadherin, expressed from CDH1 plays an important role in lobular breast cancer. In order to understand the root cause of this recent claim, we focus on CDH1 gene: whether the genetic information translated due to any deviation/alteration/modification in its sequence is related to the occurrence of the different types breast cancer. Towards this end, quantitative analysis of different biophysical and bio-chemical properties of CDH1 gene in genomic and proteomic levels from the available genomic (cDNA) sequences of CDH1 gene (obtained from the COSMIC Database for 78 patients, suffering from various types of breast cancer) clearly emphasizes that alternation/modification in the sequence of the CDH1 gene can be detrimental. Furthermore, Random forest, K-nearest neighbour and stochastic gradient descent (SGD) algorithms are applied on the derived dataset to classify the types of breast cancer, and to validate our hypothesis regarding the acute role of CDH1 as potential bio marker for breast cancer. Analysis of the mutated CDH1 gene sequences, and their related parameters using aforesaid machine learning techniques clearly establish that CDH1 gene can take the deterministic role in predicting the chances of occurrences of different types of breast cancer with an accuracy of >90%. Such an observation opens a new paradigm in diagnostic approach of breast cancer.

乳腺癌的发生被认为源于遗传畸变。在已表达的诸多基因中，研究发现1型钙粘蛋白（cadherin 1, type 1, CDH1）可通过多种途径调控人体的代谢秩序。由CDH1编码的E-钙粘蛋白（E-cadherin）功能失调，在小叶型乳腺癌的发生发展中发挥关键作用。为厘清这一最新研究论断的根本成因，本研究聚焦CDH1基因：即其序列是否存在偏差、改变或修饰，进而引发遗传信息翻译异常，并与不同类型乳腺癌的发生相关。为此，本研究针对取自78名罹患各类乳腺癌患者的CDH1基因基因组互补脱氧核糖核酸（complementary DNA, cDNA）序列（数据来自癌症体细胞突变目录COSMIC Database），从基因组与蛋白质组层面开展CDH1基因多项生物物理与生化特性的定量分析，结果明确显示CDH1基因序列的偏差与修饰可产生有害影响。此外，本研究将随机森林（Random Forest）、K近邻（K-nearest neighbour, KNN）和随机梯度下降（stochastic gradient descent, SGD）三种机器学习算法应用于衍生数据集，以实现乳腺癌亚型分类，并验证我们的假说：即CDH1可作为乳腺癌潜在生物标志物发挥关键作用。通过上述机器学习技术对突变型CDH1基因序列及其相关参数开展分析，结果明确证实：CDH1基因可在预测不同类型乳腺癌发生风险方面发挥决定性作用，预测准确率超过90%。这一发现为乳腺癌的临床诊断开辟了全新范式。