
Abstract
The 2 major forms of periodontitis, chronic (CP) and aggressive (AgP), do not display sufficiently distinct histopathological characteristics or microbiological/immunological features. We used molecular profiling to explore biological differences between CP and AgP and subsequently carried out supervised classification using machine-learning algorithms including an internal validation. We used whole-genome gene expression profiles from 310 healthy' or diseased' gingival tissue biopsies from 120 systemically healthy non-smokers, 65 with CP and 55 with AgP, each contributing with 2 diseased' gingival papillae (n = 241; with bleeding-on-probing, probing depth 4 mm, and clinical attachment loss 3 mm), and, when available, a healthy' papilla (n = 69; no bleeding-on-probing, probing depth 4 mm, and clinical attachment loss 4 mm). Our analyses revealed limited differences between the gingival tissue transcriptional profiles of AgP and CP, with genes related to immune responses, apoptosis, and signal transduction overexpressed in AgP, and genes related to epithelial integrity and metabolism overexpressed in CP. Different classifying algorithms discriminated CP from AgP with an area under the curve ranging from 0.63 to 0.99. The small differences in gene expression and the highly variable classifier performance suggest limited dissimilarities between established AgP and CP lesions. Future analyses may facilitate the development of a novel, intrinsic' classification of periodontitis based on molecular profiling.
Item Type: | Journal article |
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Form of publication: | Publisher's Version |
Keywords: | pathogenesis; gene expression; transcriptome; microarray analysis; classification; machine learning |
Faculties: | Medicine > Institute for Medical Information Processing, Biometry and Epidemiology |
Subjects: | 600 Technology > 610 Medicine and health |
URN: | urn:nbn:de:bvb:19-epub-23194-6 |
ISSN: | 0022-0345 |
Alliance/National Licence: | This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively. |
Language: | English |
Item ID: | 23194 |
Date Deposited: | 04. Mar 2015, 10:36 |
Last Modified: | 04. Nov 2020, 13:04 |