Quantitative proteomic analysis of gingival crevicular fluids to identify novel biomarkers of gingival recession in orthodontic patients

J Proteomics. 2022 Aug 30;266:104647. doi: 10.1016/j.jprot.2022.104647. Epub 2022 Jun 30.


Objective: To identify gingival recession-related biomarkers in orthodontic patients, we compared the proteome of gingival crevicular fluids (GCF) from healthy gingiva without orthodontic treatment (GH), healthy gingiva undergoing orthodontic treatment (OGH), and recessed gingiva undergoing orthodontic treatment (OGR).

Methods: GCF samples were obtained from the anterior teeth of 15 volunteers (n = 5/group). Quantitative proteomic analysis was performed using DIA-based liquid chromatography-tandem mass spectrometry (LC-MS/MS). Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used to annotate differentially expressed proteins (DEPs). Receiver-operating characteristic (ROC) analysis was performed to detect and filter biomarker candidates, while Protein-Protein Interaction (PPI) Networks were utilized to determine the interactions between these DEPs.

Results: A total of 253, 238, and 101 DEPs were found in OGR vs. OGH, OGR vs. GH, and OGH vs. GH groups, respectively. Based on the Venn diagram of three groups, 128 DEPs in OGR vs. OGH group were identified as specific proteins associated with progressive gingival recession (GR) during orthodontic treatment. Molecular function analysis showed that 128 DEPs were enriched in "molecular binding", including antigen binding, RNA binding, double-stranded RNA binding, cadherin binding involved in cell-cell adhesion, vinculin binding, S100 protein binding, and Ral GTPase binding. The majority of these DEPs were also involved in cytoskeletal regulation. In addition, biological process analysis showed an enrichment in translation, while cellular component analysis indicated that 128 DEPs were related to extracellular exosome. Furthermore, Ribosome and Phagosome were the top two terms in KEGG analysis. The results of ROC analysis demonstrated that 26 proteins could be potential biomarker candidates for GR. PPI networks analysis predicted that IQGAP1, ACTN1, TLN1, VASP, FN1, FERMT3, MYO1C, RALA, RPL35, SEC61G, KPNB1, and NPM1 could be involved in the development of GR via cytoskeletal regulation.

Conclusions: In summary, we identified several GCF proteins associated with GR after orthodontic treatment. These findings could contribute to the prevention of GR in susceptible patients before the initiation of orthodontic treatment.

Significance: Orthodontic patients with GR often report esthetic defects or root hypersensitivity during orthodontic treatment, especially at the anterior teeth site. GCF, rich in protein, is an easily accessible source of potential biomarkers for the diagnosis of periodontal diseases; however, little is known about the changes in GCF proteome associated with GR in orthodontic patients. In this study we firstly used DIA-based LC-MS/MS to evaluate the proteome and to identify the biomarker candidates for GR in orthodontic patients. These findings will improve our understanding of GR during orthodontic treatment, and could contribute to an earlier diagnosis, or even prevention, of GR in susceptible populations before orthodontic treatment.

Keywords: Data independent acquisition(DIA); Gingival cervical fluid(GCF); Gingival recession(GR); Orthodontic treatment; Quantitative proteomics.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biomarkers / analysis
  • Chromatography, Liquid
  • Gingival Crevicular Fluid / chemistry
  • Gingival Crevicular Fluid / metabolism
  • Gingival Recession* / metabolism
  • Humans
  • Proteome / analysis
  • Proteomics* / methods
  • SEC Translocation Channels / analysis
  • SEC Translocation Channels / metabolism
  • Tandem Mass Spectrometry


  • Biomarkers
  • Proteome
  • SEC Translocation Channels
  • SEC61G protein, human