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Min H, Zhu S, Safi L, Alkourdi M, Nguyen BH, Upadhyay A, Tran SD. Salivary Diagnostics in Pediatrics and the Status of Saliva-Based Biosensors. Biosensors (Basel) 2023; 13:206. [PMID: 36831972 PMCID: PMC9953390 DOI: 10.3390/bios13020206] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 01/16/2023] [Accepted: 01/23/2023] [Indexed: 06/18/2023]
Abstract
Salivary biomarkers are increasingly being used as an alternative to diagnose and monitor the progression of various diseases due to their ease of use, on site application, non-invasiveness, and most likely improved patient compliance. Here, we highlight the role of salivary biosensors in the general population, followed by the application of saliva as a diagnostic tool in the pediatric population. We searched the literature for pediatric applications of salivary biomarkers, more specifically, in children from 0 to 18 years old. The use of those biomarkers spans autoimmune, developmental disorders, oncology, neuropsychiatry, respiratory illnesses, gastrointestinal disorders, and oral diseases. Four major applications of salivary proteins as biomarkers are: (1) dental health (caries, stress from orthodontic appliances, and gingivitis); (2) gastrointestinal conditions (eosinophilic esophagitis, acid reflux, appendicitis); (3) metabolic conditions (obesity, diabetes); and (4) respiratory conditions (asthma, allergic rhinitis, small airway inflammation, pneumonia). Genomics, metabolomics, microbiomics, proteomics, and transcriptomics, are various other classifications for biosensing based on the type of biomarkers used and reviewed here. Lastly, we describe the recent advances in pediatric biosensing applications using saliva. This work guides scientists in fabricating saliva-based biosensors by comprehensively overviewing the potential markers and techniques that can be employed.
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Affiliation(s)
- Hayeon Min
- McGill Craniofacial Tissue Engineering and Stem Cells Laboratory, Faculty of Dental Medicine and Oral Health Science, McGill University, 3640 University Street, Montreal, QC H3A 0C7, Canada
| | - Sophie Zhu
- McGill Craniofacial Tissue Engineering and Stem Cells Laboratory, Faculty of Dental Medicine and Oral Health Science, McGill University, 3640 University Street, Montreal, QC H3A 0C7, Canada
| | - Lydia Safi
- McGill Craniofacial Tissue Engineering and Stem Cells Laboratory, Faculty of Dental Medicine and Oral Health Science, McGill University, 3640 University Street, Montreal, QC H3A 0C7, Canada
| | - Munzer Alkourdi
- McGill Craniofacial Tissue Engineering and Stem Cells Laboratory, Faculty of Dental Medicine and Oral Health Science, McGill University, 3640 University Street, Montreal, QC H3A 0C7, Canada
| | | | - Akshaya Upadhyay
- McGill Craniofacial Tissue Engineering and Stem Cells Laboratory, Faculty of Dental Medicine and Oral Health Science, McGill University, 3640 University Street, Montreal, QC H3A 0C7, Canada
| | - Simon D. Tran
- McGill Craniofacial Tissue Engineering and Stem Cells Laboratory, Faculty of Dental Medicine and Oral Health Science, McGill University, 3640 University Street, Montreal, QC H3A 0C7, Canada
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Huang Z, Yang X, Huang Y, Tang Z, Chen Y, Liu H, Huang M, Qing L, Li L, Wang Q, Jie Z, Jin X, Jia B. Saliva - a new opportunity for fluid biopsy. Clin Chem Lab Med 2023; 61:4-32. [PMID: 36285724 DOI: 10.1515/cclm-2022-0793] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 09/29/2022] [Indexed: 12/15/2022]
Abstract
Saliva is a complex biological fluid with a variety of biomolecules, such as DNA, RNA, proteins, metabolites and microbiota, which can be used for the screening and diagnosis of many diseases. In addition, saliva has the characteristics of simple collection, non-invasive and convenient storage, which gives it the potential to replace blood as a new main body of fluid biopsy, and it is an excellent biological diagnostic fluid. This review integrates recent studies and summarizes the research contents of salivaomics and the research progress of saliva in early diagnosis of oral and systemic diseases. This review aims to explore the value and prospect of saliva diagnosis in clinical application.
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Affiliation(s)
- Zhijie Huang
- Department of Oral Surgery, Stomatological Hospital, Southern Medical University, Guangzhou, P.R. China
| | - Xiaoxia Yang
- Department of Endodontics, Stomatological Hospital, Southern Medical University, Guangzhou, P.R. China
| | - Yisheng Huang
- Department of Oral Surgery, Stomatological Hospital, Southern Medical University, Guangzhou, P.R. China
| | - Zhengming Tang
- Department of Oral Surgery, Stomatological Hospital, Southern Medical University, Guangzhou, P.R. China
| | - Yuanxin Chen
- Department of Oral Surgery, Stomatological Hospital, Southern Medical University, Guangzhou, P.R. China
| | - Hongyu Liu
- Department of Oral Surgery, Stomatological Hospital, Southern Medical University, Guangzhou, P.R. China
| | - Mingshu Huang
- Department of Oral Surgery, Stomatological Hospital, Southern Medical University, Guangzhou, P.R. China
| | - Ling Qing
- Department of Oral Surgery, Stomatological Hospital, Southern Medical University, Guangzhou, P.R. China
| | - Li Li
- Department of Oral Surgery, Stomatological Hospital, Southern Medical University, Guangzhou, P.R. China
| | - Qin Wang
- Department of Oral Surgery, Stomatological Hospital, Southern Medical University, Guangzhou, P.R. China
| | - Zhuye Jie
- BGI Genomics, BGI-Shenzhen, Shenzhen, P.R. China
- Shenzhen Key Laboratory of Human Commensal Microorganisms and Health Research, BGI-Shenzhen, Shenzhen, P.R. China
- Laboratory of Genomics and Molecular Biomedicine, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Xin Jin
- BGI Genomics, BGI-Shenzhen, Shenzhen, P.R. China
- School of Medicine, South China University of Technology, Guangzhou, P.R. China
| | - Bo Jia
- Department of Oral Surgery, Stomatological Hospital, Southern Medical University, Guangzhou, P.R. China
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Feng G, He N, Xia HHX, Mi M, Wang K, Byrne CD, Targher G, Yuan HY, Zhang XL, Zheng MH, Ye F. Machine learning algorithms based on proteomic data mining accurately predicting the recurrence of hepatitis B-related hepatocellular carcinoma. J Gastroenterol Hepatol 2022; 37:2145-2153. [PMID: 35816347 DOI: 10.1111/jgh.15940] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 06/25/2022] [Accepted: 07/04/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND AND AIM Over 10% of hepatocellular carcinoma (HCC) cases recur each year, even after surgical resection. Currently, there is a lack of knowledge about the causes of recurrence and the effective prevention. Prediction of HCC recurrence requires diagnostic markers endowed with high sensitivity and specificity. This study aims to identify new key proteins for HCC recurrence and to build machine learning algorithms for predicting HCC recurrence. METHODS The proteomics data for analysis in this study were obtained from the Clinical Proteomics Tumor Analysis Consortium (CPTAC) database. We analyzed different proteins based on cases with or without recurrence of HCC. Survival analysis, Cox regression analysis, and area under the ROC curves (AUROC > 0.7) were used to screen for more significant differential proteins. Predictive models for HCC recurrence were developed using four machine learning algorithms. RESULTS A total of 690 differentially expressed proteins between 50 relapsed and 77 non-relapsed hepatitis B-related HCC patients were identified. Seven of these proteins had an AUROC > 0.7 for 5-year survival in HCC, including BAHCC1, ESF1, RAP1GAP, RUFY1, SCAMP3, STK3, and TMEM230. Among the machine learning algorithms, the random forest algorithm showed the highest AUROC values (AUROC: 0.991, 95% CI 0.962-0.999) for identifying HCC recurrence, followed by the support vector machine (AUROC: 0.893, 95% Cl 0.824-0.956), the logistic regression (AUROC: 0.774, 95% Cl 0.672-0.868), and the multi-layer perceptron algorithm (AUROC: 0.571, 95% Cl 0.459-0.682). CONCLUSIONS Our study identifies seven novel proteins for predicting HCC recurrence and the random forest algorithm as the most suitable predictive model for HCC recurrence.
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Affiliation(s)
- Gong Feng
- The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Na He
- The First Affiliated Hospital of Xi'an Medical University, Xi'an, China
| | - Harry Hua-Xiang Xia
- Department of Gastroenterology, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
| | - Man Mi
- Xi'an Medical University, Xi'an, China
| | - Ke Wang
- Xi'an Medical University, Xi'an, China
| | - Christopher D Byrne
- Southampton National Institute for Health Research Biomedical Research Centre, University Hospital Southampton, Southampton General Hospital, Southampton, UK
| | - Giovanni Targher
- Section of Endocrinology, Diabetes and Metabolism, Department of Medicine, University and Azienda Ospedaliera Universitaria Integrata of Verona, Verona, Italy
| | - Hai-Yang Yuan
- MAFLD Research Center, Department of Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xin-Lei Zhang
- MAFLD Research Center, Department of Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Ming-Hua Zheng
- MAFLD Research Center, Department of Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Institute of Hepatology, Wenzhou Medical University, Wenzhou, China
- Key Laboratory of Diagnosis and Treatment for the Development of Chronic Liver Disease in Zhejiang Province, Wenzhou, China
| | - Feng Ye
- The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
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