PMID- 33007040
OWN - NLM
STAT- MEDLINE
DCOM- 20201207
LR  - 20231112
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 15
IP  - 10
DP  - 2020
TI  - Using association rule mining to jointly detect clinical features and 
      differentially expressed genes related to chronic inflammatory diseases.
PG  - e0240269
LID - 10.1371/journal.pone.0240269 [doi]
LID - e0240269
AB  - OBJECTIVE: 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. METHODS: 
      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). RESULTS: 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. CONCLUSIONS: 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.
FAU - Veroneze, Rosana
AU  - Veroneze R
AUID- ORCID: 0000-0003-4007-9350
AD  - Department of Computer Engineering and Industrial Automation, School of 
      Electrical and Computer Engineering, University of Campinas (UNICAMP), Campinas, 
      SP, Brazil.
FAU - Cruz Tfaile Corbi, Samia
AU  - Cruz Tfaile Corbi S
AD  - Department of Morphology, Genetics, Orthodontics and Pediatric Dentistry, School 
      of Dentistry at Araraquara, Sao Paulo State University (UNESP), Araraquara, SP, 
      Brazil.
FAU - Roque da Silva, Barbara
AU  - Roque da Silva B
AD  - Department of Morphology, Genetics, Orthodontics and Pediatric Dentistry, School 
      of Dentistry at Araraquara, Sao Paulo State University (UNESP), Araraquara, SP, 
      Brazil.
FAU - de S Rocha, Cristiane
AU  - de S Rocha C
AUID- ORCID: 0000-0001-5678-2070
AD  - Department of Medical Genetics and Genomic Medicine, University of Campinas 
      (UNICAMP), Campinas, SP, Brazil.
FAU - V Maurer-Morelli, Claudia
AU  - V Maurer-Morelli C
AD  - Department of Medical Genetics and Genomic Medicine, University of Campinas 
      (UNICAMP), Campinas, SP, Brazil.
FAU - Perez Orrico, Silvana Regina
AU  - Perez Orrico SR
AD  - Department of Diagnosis and Surgery, School of Dentistry at Araraquara, Sao Paulo 
      State University (UNESP), Araraquara, SP, Brazil.
AD  - Advanced Research Center in Medicine, Union of the Colleges of the Great Lakes 
      (UNILAGO), Sao Jose do Rio Preto, SP, Brazil.
FAU - Cirelli, Joni A
AU  - Cirelli JA
AUID- ORCID: 0000-0002-7082-9290
AD  - Department of Diagnosis and Surgery, School of Dentistry at Araraquara, Sao Paulo 
      State University (UNESP), Araraquara, SP, Brazil.
FAU - Von Zuben, Fernando J
AU  - Von Zuben FJ
AD  - Department of Computer Engineering and Industrial Automation, School of 
      Electrical and Computer Engineering, University of Campinas (UNICAMP), Campinas, 
      SP, Brazil.
FAU - Mantuaneli Scarel-Caminaga, Raquel
AU  - Mantuaneli Scarel-Caminaga R
AD  - Department of Morphology, Genetics, Orthodontics and Pediatric Dentistry, School 
      of Dentistry at Araraquara, Sao Paulo State University (UNESP), Araraquara, SP, 
      Brazil.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20201002
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
SB  - IM
MH  - Adult
MH  - Computational Biology/*methods
MH  - Data Mining
MH  - Diabetes Mellitus, Type 2/*genetics/*pathology
MH  - Female
MH  - Gene Expression Profiling/methods
MH  - Humans
MH  - Inflammation/genetics/pathology
MH  - Leukocytes, Mononuclear/metabolism/pathology
MH  - Male
MH  - Middle Aged
MH  - Multivariate Analysis
MH  - Real-Time Polymerase Chain Reaction
PMC - PMC7531780
COIS- The authors have declared that no competing interests exist.
EDAT- 2020/10/03 06:00
MHDA- 2020/12/15 06:00
PMCR- 2020/10/02
CRDT- 2020/10/02 17:08
PHST- 2020/06/25 00:00 [received]
PHST- 2020/09/23 00:00 [accepted]
PHST- 2020/10/02 17:08 [entrez]
PHST- 2020/10/03 06:00 [pubmed]
PHST- 2020/12/15 06:00 [medline]
PHST- 2020/10/02 00:00 [pmc-release]
AID - PONE-D-20-19597 [pii]
AID - 10.1371/journal.pone.0240269 [doi]
PST - epublish
SO  - PLoS One. 2020 Oct 2;15(10):e0240269. doi: 10.1371/journal.pone.0240269. 
      eCollection 2020.