Shigeo Ishikawa1, Kenichi Ishizawa2, Atsushi Tanaka3,4, Hirohito Kimura4, Kenichiro Kitabatake5, Ayako Sugano6, Kaoru Edamatsu6, Shohei Ueda6, Mitsuyoshi Iino6. 1. Department of Dentistry, Oral and Maxillofacial Plastic and Reconstructive Surgery, Faculty of Medicine, Yamagata University, Yamagata, Japan; shigeo_ishikawa2011@yahoo.co.jp. 2. Department of Neurology, Hematology, Metabolism, Endocrinology and Diabetology, Faculty of Medicine, Yamagata University, Yamagata, Japan. 3. Pharmaceutical Sciences, Graduate School of Medical Science, Yamagata University, Yamagata, Japan. 4. Institute for Promotion of Medical Science Research, Faculty of Medicine, Yamagata University, Yamagata, Japan. 5. Department Koriyama Minami Dental and Oral surgery, Fukushima, Japan. 6. Department of Dentistry, Oral and Maxillofacial Plastic and Reconstructive Surgery, Faculty of Medicine, Yamagata University, Yamagata, Japan.
Abstract
BACKGROUND/AIM: The current study aimed to identify biomarkers for differentiating between patients with oral cancer (OC) and healthy controls (HCs) on the basis of the comprehensive proteomic analyses of saliva samples by using liquid chromatography-mass spectrometry (LC-MS/MS). PATIENTS AND METHODS: Unstimulated saliva samples were collected from 39 patients with OC and from 31 HCs. Proteins in the saliva were comprehensively analyzed using LC-MS/MS. To differentiate between patients with OC and HCs, a multiple logistic regression model was developed for evaluating the discriminatory ability of a combination of multiple markers. RESULTS: A total of 23 proteins were significantly differentially expressed between the patients with OC and the HCs. Six out of the 23 proteins, namely α-2-macroglobulin-like protein 1, cornulin, hemoglobin subunit β, Ig k chain V-II region Vk167, kininogen-1 and transmembrane protease serine 11D, were selected using the forward-selection method and applied to the multiple logistic regression model. The area under the curve for discriminating between patients with OC and HCs was 0.957 when the combination of the six metabolites was used (95% confidence interval=0.915-0.998; p<0.001). Furthermore, these candidate proteins did not show a stage-specific difference. CONCLUSION: The results of the current study showed that six salivary proteins are potential non-invasive biomarkers for OC screening. Copyright
BACKGROUND/AIM: The current study aimed to identify biomarkers for differentiating between patients with oral cancer (OC) and healthy controls (HCs) on the basis of the comprehensive proteomic analyses of saliva samples by using liquid chromatography-mass spectrometry (LC-MS/MS). PATIENTS AND METHODS: Unstimulated saliva samples were collected from 39 patients with OC and from 31 HCs. Proteins in the saliva were comprehensively analyzed using LC-MS/MS. To differentiate between patients with OC and HCs, a multiple logistic regression model was developed for evaluating the discriminatory ability of a combination of multiple markers. RESULTS: A total of 23 proteins were significantly differentially expressed between the patients with OC and the HCs. Six out of the 23 proteins, namely α-2-macroglobulin-like protein 1, cornulin, hemoglobin subunit β, Ig k chain V-II region Vk167, kininogen-1 and transmembrane protease serine 11D, were selected using the forward-selection method and applied to the multiple logistic regression model. The area under the curve for discriminating between patients with OC and HCs was 0.957 when the combination of the six metabolites was used (95% confidence interval=0.915-0.998; p<0.001). Furthermore, these candidate proteins did not show a stage-specific difference. CONCLUSION: The results of the current study showed that six salivary proteins are potential non-invasive biomarkers for OC screening. Copyright
Authors: Patricia Del Vigna de Almeida; Ana Maria Trindade Grégio; Maria Angela Naval Machado; Antonio Adilson Soares de Lima; Luciana Reis Azevedo Journal: J Contemp Dent Pract Date: 2008-03-01
Authors: Franky D Shah; Rasheedunnisa Begum; Bhairavi N Vajaria; Kinjal R Patel; Jayendra B Patel; Shilin N Shukla; Prabhudas S Patel Journal: Indian J Clin Biochem Date: 2011-08-09
Authors: Hua Xiao; Alexander Langerman; Yan Zhang; Omar Khalid; Shen Hu; Cheng-Xi Cao; Mark W Lingen; David T W Wong Journal: Oral Oncol Date: 2015-08-29 Impact factor: 5.337
Authors: Xi Zhang; Daniel Broszczak; Karam Kostner; Kristyan B Guppy-Coles; John J Atherton; Chamindie Punyadeera Journal: Biomolecules Date: 2019-11-22
Authors: Camila M de Almeida; Larissa C Motta; Gabriely S Folli; Wena D Marcarini; Camila A Costa; Ana C S Vilela; Valério G Barauna; Francis L Martin; Maneesh N Singh; Luciene C G Campos; Nádia L Costa; Paula F Vassallo; Andrea R Chaves; Denise C Endringer; José G Mill; Paulo R Filgueiras; Wanderson Romão Journal: J Proteome Res Date: 2022-07-25 Impact factor: 5.370