Dataset-chemokines, cytokines, and biomarkers in the saliva of children with Sjögren's syndrome
Miyuraj Harishchandra Hikkaduwa Withanage,
M. Paula Gomez Hernandez,
Emily E. Starman,
Andrew B. Davis,
Erliang Zeng,
Scott M. Lieberman,
Kim A. Brogden,
Emily A. Lanzel
Affiliations
Miyuraj Harishchandra Hikkaduwa Withanage
Division of Biostatistics and Computational Biology, College of Dentistry, University of Iowa, Iowa City, IA United States
M. Paula Gomez Hernandez
Pediatric Dentistry, College of Dentistry, University of Iowa, Iowa City, IA, United States
Emily E. Starman
College of Dentistry, Iowa Institute for Oral Health Research, University of Iowa, Iowa City, IA, United States
Andrew B. Davis
Department of Otolaryngology, College of Medicine, University of Iowa, Iowa City, IA, United States
Erliang Zeng
Division of Biostatistics and Computational Biology, College of Dentistry, University of Iowa, Iowa City, IA United States
Scott M. Lieberman
Stead Family Department of Pediatrics-Division of Rheumatology, Allergy and Immunology, Carver College of Medicine, University of Iowa, Iowa City, IA, United States
Kim A. Brogden
Pediatric Dentistry, College of Dentistry, University of Iowa, Iowa City, IA, United States
Emily A. Lanzel
Department of Oral Pathology, Radiology and Medicine, College of Dentistry, University of Iowa, Iowa City, IA, United States; Corresponding author.
Sjögren's syndrome is an autoimmune disease that can also occur in children. The disease is not well defined and there is limited information on the presence of chemokines, cytokines, and biomarkers (CCBMs) in the saliva of children that could improve their disease diagnosis. In a recent study [1], we reported a large dataset of 105 CCBMs that were associated with both lymphocyte and mononuclear cell functions [2] in the saliva of 11 children formally diagnosed with Sjögren's syndrome and 16 normal healthy children. Here, we extend those findings and use the Mendeley dataset [2] to identify CCBMs that have predictive power for Sjögren's syndrome in female children. Datasets of CCBMs from all saliva samples and female children saliva samples were standardized. We used machine learning methods to select Sjögren's syndrome associated CCBMs and assessed the predictive power of selected CCBMs in these two datasets using receiver operating characteristic (ROC) curves and associated areas under curve (AUC) as metrics. We used eight classifiers to identify 16 datasets that contained from 2 to 34 CCBMs with AUC values ranging from 0.91 to 0.94.