Department of Physics and INFN, Universitá degli Studi di Torino, via P. Giuria 1, 10125 Turin, Italy.
Institut Curie, PSL Research University, 75005 Paris, France.
Int J Mol Sci. 2019 Jun 26;20(13):3114. doi: 10.3390/ijms20133114.
Matrix factorization (MF) is an established paradigm for large-scale biological data analysis with tremendous potential in computational biology. Here, we challenge MF in depicting the molecular bases of epidemiologically described disease-disease (DD) relationships. As a use case, we focus on the inverse comorbidity association between Alzheimer's disease (AD) and lung cancer (LC), described as a lower than expected probability of developing LC in AD patients. To this day, the molecular mechanisms underlying DD relationships remain poorly explained and their better characterization might offer unprecedented clinical opportunities. To this goal, we extend our previously designed MF-based framework for the molecular characterization of DD relationships. Considering AD-LC inverse comorbidity as a case study, we highlight multiple molecular mechanisms, among which we confirm the involvement of processes related to the immune system and mitochondrial metabolism. We then distinguish mechanisms specific to LC from those shared with other cancers through a pan-cancer analysis. Additionally, new candidate molecular players, such as estrogen receptor (ER), cadherin 1 (CDH1) and histone deacetylase (HDAC), are pinpointed as factors that might underlie the inverse relationship, opening the way to new investigations. Finally, some lung cancer subtype-specific factors are also detected, also suggesting the existence of heterogeneity across patients in the context of inverse comorbidity.
矩阵分解 (MF) 是一种用于大规模生物学数据分析的成熟范式，在计算生物学中有巨大的潜力。在这里，我们挑战 MF 来描述流行病学描述的疾病-疾病 (DD) 关系的分子基础。作为一个用例，我们专注于阿尔茨海默病 (AD) 和肺癌 (LC) 之间的逆共病关联，这种关联被描述为 AD 患者中 LC 的发病概率低于预期。迄今为止，DD 关系背后的分子机制仍未得到很好的解释，更好地描述这些机制可能会提供前所未有的临床机会。为此，我们扩展了我们之前设计的基于 MF 的 DD 关系分子特征描述框架。考虑到 AD-LC 逆共病作为一个案例研究，我们强调了多种分子机制，其中我们证实了与免疫系统和线粒体代谢相关的过程的参与。然后，我们通过泛癌分析将 LC 特有的机制与其他癌症共有的机制区分开来。此外，还确定了一些新的候选分子参与者，如雌激素受体 (ER)、钙黏蛋白 1 (CDH1) 和组蛋白去乙酰化酶 (HDAC)，它们可能是导致这种反向关系的因素，为新的研究开辟了道路。最后，还检测到一些肺癌亚型特异性的因素，这也表明在逆共病的情况下，患者之间存在异质性。
