Department of Computer Engineering and Industrial Automation, School of Electrical and Computer Engineering, University of Campinas (UNICAMP), Campinas, SP, Brazil.
Department of Morphology, Genetics, Orthodontics and Pediatric Dentistry, School of Dentistry at Araraquara, São Paulo State University (UNESP), Araraquara, SP, Brazil.
PLoS One. 2020 Oct 2;15(10):e0240269. doi: 10.1371/journal.pone.0240269. eCollection 2020.
It is increasingly common to find patients affected by a combination of type 2 diabetes mellitus (T2DM), dyslipidemia (DLP) and periodontitis (PD), which are chronic inflammatory diseases. More studies able to capture unknown relationships among these diseases will contribute to raise biological and clinical evidence. The aim of this study was to apply association rule mining (ARM) to discover whether there are consistent patterns of clinical features (CFs) and differentially expressed genes (DEGs) relevant to these diseases. We intend to reinforce the evidence of the T2DM-DLP-PD-interplay and demonstrate the ARM ability to provide new insights into multivariate pattern discovery.
We utilized 29 clinical glycemic, lipid and periodontal parameters from 143 patients divided into five groups based upon diabetic, dyslipidemic and periodontal conditions (including a healthy-control group). At least 5 patients from each group were selected to assess the transcriptome by microarray. ARM was utilized to assess relevant association rules considering: (i) only CFs; and (ii) CFs+DEGs, such that the identified DEGs, specific to each group of patients, were submitted to gene expression validation by quantitative polymerase chain reaction (qPCR).
We obtained 78 CF-rules and 161 CF+DEG-rules. Based on their clinical significance, Periodontists and Geneticist experts selected 11 CF-rules, and 5 CF+DEG-rules. From the five DEGs prospected by the rules, four of them were validated by qPCR as significantly different from the control group; and two of them validated the previous microarray findings.
ARM was a powerful data analysis technique to identify multivariate patterns involving clinical and molecular profiles of patients affected by specific pathological panels. ARM proved to be an effective mining approach to analyze gene expression with the advantage of including patient's CFs. A combination of CFs and DEGs might be employed in modeling the patient's chance to develop complex diseases, such as those studied here.
越来越多的患者同时患有 2 型糖尿病（T2DM）、血脂异常（DLP）和牙周炎（PD），这些疾病都是慢性炎症性疾病。更多能够捕捉到这些疾病之间未知关系的研究将有助于提高生物学和临床证据。本研究旨在应用关联规则挖掘（ARM）来发现与这些疾病相关的临床特征（CFs）和差异表达基因（DEGs）是否存在一致的模式。我们旨在加强 T2DM-DLP-PD 相互作用的证据，并展示 ARM 发现多变量模式的能力。
我们利用了 143 名患者的 29 个临床血糖、血脂和牙周参数，这些患者根据糖尿病、血脂异常和牙周状况分为五组（包括一个健康对照组）。从每组中至少选择 5 名患者进行微阵列转录组学评估。ARM 用于评估相关的关联规则，考虑：（i）仅 CFs；和（ii）CFs+DEGs，即特定于每组患者的鉴定 DEGs ，通过定量聚合酶链反应（qPCR）进行基因表达验证。
我们获得了 78 个 CF 规则和 161 个 CF+DEG 规则。基于其临床意义，牙周病学家和遗传学家专家选择了 11 个 CF 规则和 5 个 CF+DEG 规则。从规则预测的五个 DEGs 中，有四个通过 qPCR 验证与对照组显著不同；其中两个验证了之前的微阵列结果。
ARM 是一种强大的数据分析技术，可用于识别涉及受特定病理组影响的患者的临床和分子特征的多变量模式。ARM 被证明是一种有效的挖掘方法，可用于分析包含患者 CFs 的基因表达。CFs 和 DEGs 的组合可用于对患者患复杂疾病的机会进行建模，例如本研究中所研究的疾病。
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