Breast-cancer (BC) is the most common invasive cancer in women, with considerable death. Given that, BC is classified as a hormone-dependent cancer, when it collides with pregnancy, different questions may arise for which there are still no convincing answers. To deal with this issue, two new frameworks are proposed within this paper: CoRaM and Dist-CoRaM . The former is the first unified framework dedicated to the extraction of a generic basis of Correlated-Rare Association rules from gene expression data. The proposed approach has been successfully applied on a breast-cancer Gene Expression Matrix (GSE1379) with very promising results. The latter, the Dist-CoRaM approach, is a big-data processing based on Apache spark framework, dealing with correlation mining from micro-array pregnancy associated breast-cancer assays (PABC) data. It is successfully applied on the (GSE31192) gene expression matrix (GEM). The correlated patterns of gene-sets shed light on the fact that PABC exhibits heightened aggressiveness compared to cancers for Non-PABC women. Our findings suggest that higher levels of estrogen and progesterone hormones, unfortunately, are very keen to the increase of the tumor aggressiveness and the proliferation of the cancer.

中文翻译：

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妊娠相关乳腺癌基因表达：基于罕见相关模式对其调控的新见解

乳腺癌 (BC) 是女性最常见的浸润性癌症，死亡率很高。鉴于此，BC 被归类为激素依赖性癌症，当它与怀孕发生冲突时，可能会出现不同的问题，但仍然没有令人信服的答案。为了解决这个问题，本文提出了两个新框架：科拉姆 和 分布式CoRaM . 前者是第一个致力于从基因表达数据中提取相关稀有关联规则的通用基础的统一框架。所提出的方法已成功应用于乳腺癌基因表达矩阵（GSE1379），并取得了非常有希望的结果。后者，分布式CoRaM方法，是一种基于 Apache spark 框架的大数据处理，处理从微阵列妊娠相关乳腺癌检测 (PABC) 数据中进行相关性挖掘。成功应用于（GSE31192）基因表达矩阵（GEM）。基因集的相关模式揭示了一个事实，即与非 PABC 女性的癌症相比，PABC 表现出更高的侵袭性。我们的研究结果表明，不幸的是，较高水平的雌激素和孕激素对肿瘤侵袭性的增加和癌症的增殖非常敏感。