Diagnostic salivary biomarkers in oral cancer and oral potentially malignant disorders and their relationships to risk factors – A systematic review

Figures & data

Figure 1. Key words used for electronic literature search

Figure 2. Study selection according to PRISMA format

Table 1. Studies reporting interleukin biomarkers in saliva

Reference	First author, year	Country	Design	Groups	Sample size	Biomarkers	Method of analysis	Sample	Statistical data/conclusions
[23]	Arellano-Garcia, 2008	USA	Case control	OSCC, periodontitis, controls	40	IL8, IL1β	ELISA, Immune bead based assay	Resting whole saliva	Both biomarkers were statistically higher in OC group than controls, AUC for cancer/controls IL1β = 0.77, IL8 = 0.8, There was no statistically significant difference between IL 8 of periodontitis and controls p value = 0.098, This study report that Luminex xMAP technology is a reliable method to validate and quantitate protein levels in saliva
[53]	Aziz, 2015	Pakistan	Case control	OSCC, controls	66	IL4, IL10, IL13, IL1 receptor antigen	Millipex human cytokine/ chemokine assay kit	Resting whole saliva	P values comparing study groups IL10=0.004, IL13=0.01, this study propose immunosuppressive cytokines as potential salivary biomarkers for OSCC
[24]	Babiuch, 2020	Poland	Case control	OSCC, OED, OLP, controls	45	IL1α, IL6, IL8	ELISA	Resting whole saliva	P values comparing OSCC to controls IL1α=0.017, IL6=0.0012, IL8=0.0001. P value comparing OED to controls IL8=0.04, P value comparing OSCC to OED IL8=0.03. This study identified IL8 in saliva as an important biomarker of malignant process in oral mucosa
[25]	Bagan, 2016	Spain	Case control	OSCC, PVL, controls	60	IL6	ELISA	Resting whole saliva	P value comparing study groups <0.01, patients with OC had the highest salivary biomarker levels followed by PVL and controls, this study suggest salivary IL6 as a potential biomarker to assess disease progression in PVL
[60]	Brailo, 2006	Croatia	Case control	Leukoplakia, controls	64	IL6	ELISA	Resting whole saliva	P value comparing study groups IL6<0.05, the results of this study demonstrate that the patients with oral leukoplakia have an increase in salivary levels of IL6 which might indicate an altered immune response
[65]	Brailo, 2012	Croatia	Case control	OSCC, Leukoplakia, controls	98	IL1β, IL6	ELISA	Resting whole saliva	P values comparing study groups IL1β <0.001, IL6 = 0.012, patients with OC have significantly higher salivary IL1β and IL6 concentrations compared to patients with leukoplakia and healthy controls
[26]	Brinkmanna, 2012	Serbia	Case control	OSCC, controls	86	IL1β, IL8,	ELISA, RT-qPCR	Resting whole saliva	P values comparing protein of IL8, IL1β and mRNA of IL8 p < 0.05, combination of biomarkers from the proteome and transcriptome yielded the highest predictive power for OC
[93]	Csosz, 2017	Hungary	Case control	OSCC, controls	107	IL1α,IL1β, IL6, IL8	Millipex magnetic bead based assay	Resting whole saliva	P values comparing study groups IL1α = 0.96, IL1β = 0.02, IL6 = 0.0002, IL8 = 0.10, OC group had higher biomarker levels, this study demonstrate the potential utility of IL6 as a biomarker in saliva for OSCC in Hungarian population
[27]	Gleber-Netto, 2016	Taiwan	Case control	OSCC, OPMD, controls	180	IL1β, IL8,	ELISA	Resting whole saliva	P values comparing study groups IL1β = 0.0061, IL8 < 0.0001, both biomarkers were higher in OSCC compared to OPMD and controls, this study propose that salivary IL8 protein levels combined with other biomarkers and risk factors have the potential for early detection of OSCC and OPMD
[28]	Hamad, 2011	Iraq	Case control	OSCC, controls	50	IL1α,IL6, IL8	ELISA	Resting whole saliva	P values comparing study groups IL1α<0.001, IL6 = 0.05, IL8 < 0.001, biomarkers were elevated in OC compared to controls, this study propose these biomarkers as diagnostic tools for OSCC
[29]	Hamzavi, 2013	Iran	Case control	Head and neck cancer/ controls	54	IL10	ELISA	Resting whole saliva	P value comparing study groups 0.5, although the salivary levels of IL10 in patients were a little higher than controls, this difference was not statistically significant, this study report that saliva levels of IL10, does not reflect tissue expression
[78]	Marton, 2019	Hungary	Case control	OSCC, controls	175	IL6	ELISA, RT-qPCR	Resting whole saliva	P values comparing study groups for the protein and mRNA <0.001, AUC for IL6 protein 0.69, AUC for IL6 mRNA 0.9, this study propose that salivary IL6 mRNA is suitable biomarkers for OC and suggest clinical validation
[66]	JuretiŰ, 2013	Croatia	Case control	OSCC, OPMD, controls	57	IL6	ELISA	Resting whole saliva	P values comparing study groups IL6 < 0.001, patients have higher salivary concentrations of biomarker compared to controls, this study suggest IL6 as a potential biomarker for OSCC
[81]	Katakura, 2007	Japan	Case control	OSCC, controls	39	IL1β,IL6,IL8	ELISA	Resting whole saliva	P values comparing study groups IL 1β< 0.05, IL 6 < 0.05, IL 8 > 0.05, IL6 was significantly elevated in patients compared to controls, this study report IL6 as a potential biomarker in saliva for OSCC
[30]	Khyani, 2017	Pakistan	Case control	OSCC, OPMD, controls	105	IL6, IL8	ELISA	Resting whole saliva	P values comparing study groups IL8 < 0.05, IL6 no significant difference, IL6 and IL8 are probable biomarker for early detection of OSCC and OPMD in Pakistan population
[31]	Korostoff, 2011	USA	Case control	Tongue cancer, controls	32	IL1α,IL6, IL8	ELISA	Resting whole saliva	P values comparing endophytic tongue cancer/controls IL1α < 0.0001, IL 6 < 0.0001,IL8 < 0.0001, all cytokines were markedly elevated in saliva of endophytic TC patients, this study propose the potential use of these biomarkers for screening, and prognosis of survival in tongue OSCC
[42]	Lee, 2018	Taiwan	Case control	OSCC, controls	65	IL1α,IL1β,IL6, IL8, IL10	Millipex magnetic bead based assay	Resting whole saliva	P values comparing study groups IL1α = 0.625,IL1β = 0.002,IL6 < 0.001, IL8 = 0.001,IL10 = 0.355.AUC for OSCC IL6 = 0.8, IL8 = 0.7, IL1β = 0.7, this study indicate that salivary biomarkers may serve useful as adjuncts for the early detection of OSCC
[32]	Lisa Cheng, 2014	USA	Case control	OSCC, OLP, Periodontitis, controls	101	IL6, IL8	ELISA	Resting whole saliva	P values comparing study groups IL6 < 0.001, IL8 = 0.001, salivary biomarkers were higher in patients with OSCC, compared to OLP, CP and controls, this study propose that IL6 as a useful biomarker in the detection of OC, not influenced by OLP or CP
[33]	Panneer, 2015	India	Case control	OSCC, Leukoplakia, controls	75	IL6	ELISA	Resting whole saliva	P values for OSCC/controls, and OSCC/Leukoplakia <0.0001, biomarker levels were highest in OC, followed by leukoplakia and controls, this biomarker is proposed for further validation to assess its clinical utility
[34]	Punyani, 2013	India	Case control	OSCC, OPMD, controls	75	IL8	ELISA	Resting whole saliva	P values comparing OC/OPMD <0.0001, OC/controls <0.0001, OPMD/ controls = 0.7, IL8 is a potential biomarker for OSCC
[35]	Rajkumar, 2014	India	Case control	OSCC, OPMD, controls	300	IL8	ELISA	Resting whole saliva	P values comparing study groups <0.05, AUC 0.9, IL8 measurement in saliva is a better medium for OC prediction than serum
[36]	Rhodus, 2005	USA	Case control	OSCC, OLP, controls	39	IL1α,IL6, IL8	ELISA	Resting whole saliva	P values comparing study groups IL1α<0.05, IL6 < 0.05, IL8 < 0.05, these biomarkers may be potential targets for disease monitoring in OLP
[37]	Sahebjamee, 2008	Iran	Case control	OSCC, controls	18	IL1α, IL6, IL8	ELISA	Resting whole saliva	P values comparing study groups IL1α>0.05, IL6 < 0.05, IL8 > 0.05, IL6 was elevated in OC compared to controls with statistical significance
[38]	Sharma, 2011	India	Case control	Leukoplakia, periodontitis, controls	60	IL6	ELISA	Resting whole saliva	P value comparing study groups <0.001, all the leukoplakia patients with coexisting periodontitis had higher IL6 levels when compared with patients with periodontitis alone, this study report an increase in IL6 levels with severity of dysplasia grading, IL6 may be a useful biomarker for monitoring of leukoplakia
[39]	Singh, 2020	India	Case control	OSCC, OPMD, controls	159	IL1β, IL8	ELISA	Resting whole saliva	P values comparing study groups IL1β<0.0001, IL8 = 0.0006. AUC for total OSCC were 0.7 for both markers, for stage three and four OSCC, IL1β = 0.9, IL8 = 0.7, these biomarker were significant in Indian population, this study propose the potential use of studied biomarkers for screening and early detection in OSCC
[40]	St. John, 2004	USA	Case control	OSCC, controls	64	IL6, IL8	ELISA RT-qPCR	Resting whole saliva	P values comparing study groups IL8 < 0.01, IL6 > 0.05. AUC for salivary IL8 = 0.9, IL8 at both the mRNA and protein levels were detected in higher concentrations in the saliva of patients with OSCC compared to controls, this study propose salivary IL8 as a potential biomarker for OSCC
[41]	Tan, 2008	USA	Case control	OSCC, controls	40	IL8	ELISA, optical protein sensor	Resting whole saliva	AUC = 0.8, optical sensor and ELISA have similar performances as diagnostic tests, this study report that accurate diagnosis is possible with proposed optical protein sensor platform compared to ELISA for the assessment of salivary IL8
[77]	Vesty, 2018	New Zealand	Case control	Head and neck cancer/ dentally compromised/ controls	30	IL8, IL1β	Cytometric bead array	Resting whole saliva	P values comparing head and neck cancer to controls IL1β = 0.03, IL8 = 0.04, significantly higher concentrations of IL1β and IL8 were detected in HNC and dentally compromised patients, IL1β and IL8 concentrations positively correlated with of C. albicans.

OSCC: oral squamous cell carcinoma, OC: oral cancer, TC: tongue cancer, OPMD: oral potentially malignant disorders, OED: oral epithelial dysplasia, HNC: head and neck cancer, OLP: oral lichen planus, PVL: proliferative verrucous Leukoplakia, CP: chronic periodontitis, IL: interleukin, ELISA: enzyme linked immune sorbent assay, RT-qPCR: reverse transcriptase quantitative polymerase chain reaction, AUC: area under the curve of receiver operating characteristics curve, p < 0.05 and AUC>0.65 was taken as significant

Table 2. Risk of bias assessment of studies reporting interleukin biomarkers

		Selection					Exposure
Reference	First author, year	Case definition	Representativeness of cases	Control selection	Definition of controls	Comparability	Ascertainment of exposure (AOE)	Same AOE for groups	Non response rate	Quality grade
[23]	Arellano-Garcia, 2008	Medical records assessed for case selection *	Appropriate sample of cases*	Hospital controls	Controls have no history of outcome*	Matched in design for age*, adjusted in analysis*	Not reported	Not reported	Not reported	Fair
[53]	Aziz, 2015	Medical records assessed for case selection *	Appropriate sample of cases*	Community controls *	Controls were selected from relatives of patients	Matched in design *	Interview not blinded to case control status	Yes *	Not reported	Fair
[24]	Babiuch, 2020	Medical records assessed for case selection *	Appropriate sample of cases*	Not reported	Controls have no history of disease*	Cases and controls not matched in design and not adjusted in analysis. But absence of significant difference of socio demographics reported	Not reported	Not reported	Not reported	Poor
[25]	Bagan, 2016	Diagnosis criteria given*	Appropriate sample of cases*	Community controls*	Controls have no history of disease*	Not matched, but the absence of age difference in groups reported	Not reported	Not reported	Not reported	Fair
[60]	Brailo, 2006	Medical records assessed for case selection *	Appropriate sample of cases*	Community controls*	Controls have no history of disease*	Absence of difference in groups reported	Not reported	Not reported	Not reported	Fair
[65]	Brailo, 2012	Medical records assessed for case selection *	Appropriate sample of cases*	Not reported	Controls have no history of disease*	Cofounders adjusted in the analysis*	Not reported	Not reported	Not reported	Fair
[26]	Brinkmanna, 2012	Medical records assessed for case selection *	Appropriate sample of cases*	Not reported	Controls have no history of disease*	Cofounders adjusted in the analysis*	Not reported	Not reported	Not reported	Fair
[93]	Csosz, 2017	Medical records assessed for case selection *	Appropriate sample of cases*	Hospital controls	Controls have no history of disease*	Matched in design* Adjusted in the analysis*	Questionnaire filled by participant	Yes*	Not reported	High
[27]	Gleber-Netto, 2016	Medical records assessed for case selection*	Appropriate sample of cases*	Hospital controls	Controls have no history of disease*	Matched in design* Adjusted in the analysis*	Questionnaire	Yes*	Not reported	High
[28]	Hamad, 2011	Medical records assessed for case selection*	Appropriate sample of cases*	Not reported	Controls have no history of disease*	Patients below the age 18 included in the control group	Not reported	Not reported	Not reported	Poor
[29]	Hamzavi, 2013	Medical records assessed for case selection*	Appropriate sample of cases*	Not reported	Controls have no history of disease*	Matched in design*	Medical records	Not reported	Not reported	Fair
[78]	Marton, 2019	Medical records assessed for case selection*	Appropriate sample of cases*	Hospital controls	Controls have no history of disease*	Matched in design* Adjusted in the analysis*	Medical records	Yes*	Not reported	High
[66]	Juretić, 2013	Medical records assessed for case selection*	Appropriate sample of cases*	Hospital controls	Controls have no history of disease*	Matched in design*	Questionnaire filled by participant	Yes*	Not reported	Fair
[81]	Katakura, 2007	No adequate details on diagnosis	Appropriate sample of cases*	Not reported	Not reported	Only the mean age and female to male ratio reported	Not reported	Not reported	Not reported	Poor
[30]	Khyani, 2017	Medical records assessed for case selection*	Appropriate sample of cases*	Hospital controls	Controls have no history of disease*	Only the age distribution of groups reported	Medical records	Yes*	Not reported	Fair
[31]	Korostoff, 2011	Medical records assessed for case selection*	Appropriate sample of cases*	Hospital controls	Controls have no history of disease*	Adjusted in the analysis*	Not reported	Not reported	Not reported	Fair
[42]	Lee, 2018	Medical records assessed for case selection*	Appropriate sample of cases*	Hospital controls	Controls have no history of disease*	Not reported	Medical records	Yes*	Not reported	Fair
[32]	Lisa Cheng, 2014	Diagnosis criteria given*	Appropriate sample of cases*	Hospital controls	Controls have no history of disease*	Matched in design*	Not reported	Not reported	Not reported	Fair
[33]	Panneer, 2015	Medical records assessed for case selection*	Appropriate sample of cases*	Hospital controls	Controls have no history of disease*	Matched in design*	Not reported	Not reported	Not reported	Fair
[34]	Punyani, 2013	Medical records assessed for case selection*	Appropriate sample of cases*	Hospital controls	Controls have no history of disease*	Matched in design*	Not reported	Not reported	Not reported	Fair
[35]	Rajkumar, 2014	Medical records assessed for case selection*	Appropriate sample of cases*	Community controls*	Controls have no history of disease*	Matched in design*	Not reported	Not reported	Not reported	Fair
[36]	Rhodus,2005	Medical records assessed for case selection*	Appropriate sample of cases*	Hospital controls	Controls have no history of disease*	Matched in design* Adjusted in the analysis*	Not reported	Not reported	Not reported	Fair
[37]	Sahebjamee, 2008	Medical records assessed for case selection*	Appropriate sample of cases*	Hospital controls	Controls have no history of disease*	Matched in design*	Medical records	Yes*	Not reported	Fair
[38]	Sharma, 2011	Medical records assessed for case selection*	Appropriate sample of cases*	Hospital controls	Controls have no history of disease*	Adjusted in the analysis*	Medical records	Yes*	Not reported	Fair
[39]	Singh, 2020	Medical records assessed for case selection*	Appropriate sample of cases*	Hospital controls	Controls have no history of disease*	Matched in design*	Medical records	Yes*	Not reported	Fair
[40]	St. John, 2004	Medical records assessed for case selection*	Appropriate sample of cases*	Hospital controls	Controls have no history of disease*	Matched in design* Adjusted in the analysis*	Interviews not blinded to case control status	Yes*	Not reported	High
[41]	Tan, 2008	No adequate data is reported	No adequate data is reported	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported	Poor
[77]	Vesty, 2018	Medical records assessed for case selection*	Appropriate sample of cases*	Community controls*	No adequate data	Age range and habits of groups reported	Medical records	Not reported	Not reported	Poor

Quality grade: High quality was given to studies with 6–9 stars. Fair quality was given to studies with 5–4 stars. Poor quality was given to studies with 3 or less stars, a star ⁽*⁾ was awarded to the feature of the study that minimized risk of bias in each category.

Table 3. Studies reporting relationships between salivary biomarkers and risk factors

Reference	Country	Design	Groups	N	Biomarker type	Salivary biomarkers	Risk factors	Relationships	Sample
[59]	Italy	Case–control	Oral cancer/ controls	104	Protein	IL6	Smoking	Smokers have higher IL6 p > 0.05	Resting whole saliva
[53]	Pakistan	Case–control	Oral cancer/ controls	63	Protein	IL 4, IL10, IL13, IL 1 receptor antigen	Smoking, smokeless tobacco	All cytokines except IL10 positively correlated with the duration of smoked tobacco in months p > 0.05	Resting whole saliva
[57]	Brazil	Case–control	Oral cancer/ controls	92	Protein	Epidermal growth factor	Smoking, alcohol	Biomarker difference in smokers p = 0.03 and alcoholics p = 0.08	Resting whole saliva
[60]	Croatia	Case–control	OPMD/ controls	64	Protein	IL 6, tumor necrosis factor-alpha (TNFα)	Smoking	No significant differences between smokers and nonsmokers. P values for OPMD IL6 = 0.243, TNF = 0.229, control IL6 = 0.051, TNF = 0.845	Resting whole saliva
[65]	Croatia	Case –control	Oral cancer/ OPMD/ controls	88	protein	IL1beta, IL6, Tumor necrosis factor-alpha (TNFα)	Smoking	No significant difference. OC p values for IL1 = 0.232, IL6 = 0.106, TNF = 0.94 Leukoplakia IL1b = 0.619 IL6 = 0.714 TNF = 0.09, controlsIL1b = 0.73,IL6 = 0.24,TNF = 0.92	Resting whole saliva
[67]	USA	Cohort	Head and neck cancer	61	DNA	Promoter hypermethylation of 7 genes	Smoking, alcohol, HPV	There were no statistically significant difference between the methylation status and smoking p = 1.0 alcohol p = 1.0 and HPV p = 0.6	Normal saline oral rinse with exfoliate brush
[47]	Brazil	Cohort	Oral cancer	50	Protein	Macrophage migration inhibitory factor (MIF)	Smoking, alcohol	Difference in smokers pre-operative p = 0.07, post-operative p = 0.04. In alcohol drinkers, pre-operative p = 0.58 and post-operative p = 0.91	Resting whole saliva
[58]	USA	Case –control	Head and neck cancer/ controls	132	DNA	Endothelin receptor type B (EDNRB)	Smoking	Current or former smokers had lower EDNRB levels than nonsmokers p = 0.026	Normal saline oral rinse with exfoliate brush
[48]	USA	Case –control	Oral cancer/controls	47	DNA	Microsatellite analysis- loss of hetero-zygocity (LOH)	Smoking	Quadruple combinations of markers identified in 15 of the 18 smokers p = 0.0001. patients who quit smoking showed an LOH frequency that was between that of smokers and nonsmokers	Resting whole saliva
[68]	USA	Case –control	Head and neck cancer/ controls	36	Protein	Soluble CD44	Smoking, alcohol	No significant difference with risk factors of patients p = 0.34	Normal saline oral rinse
[70]	USA	Case –control	Head and neck cancer/ controls	186	Protein	Soluble CD44	Smoking, tobacco, alcohol	No statistically significant relationship between smoking status (former, current, never) p = 0.46, tobacco use p = 0.68 or drinking level (light, moderate, heavy) p = 0.25	Normal saline oral rinse
[71]	India	Case–control	Oral cancer/ OPMD/ controls	50	Antioxidant	Lipid peroxidation	Smoking, betel quid chewing	No statistically significant association, smokers in the OPMD p = 0.13 oral cancer p = 0.67 and chewers in the OPMD p = 0.77 and oral cancer groups p = 0.41	Resting whole saliva
[69]	Thailand	Case –control	Oral cancer/ controls	25	Protein	Haptoglobin (HP), Ig alpha-2 chain C region (IGHA2), peroxiredoxin-2 (PRDX-2) and zinc-alpha-2-glycoprotein (ZAG)	Smoking, alcohol, betel quid chewing, HPV, EBV	No significant difference to smoking, alcohol or betel quid chewing. Up regulation of HP, PRDX-2 and ZAG and down-regulation of IGHA2 were not influenced by HPV and EBV infection	Resting whole saliva
[43]	Taiwan	Case –control	Oral cancer/ controls	18	Protein	Martrix metalloprotinase 9	Betel quid chewing	Biomarker level rises 3.1 fold after 5 min after areca quid chewing stimulation and decline up to 1.8 fold after 2–4 hours	Stimulated saliva
[54]	USA	Case –control	Head and neck cancer/ controls	747	DNA	Mitochondrial DNA for cytochrome c oxidase (Cox I and Cox II).	Smoking, alcohol	Comparing with never smoker, average Cox I (Pcurrent < 0.0001, Pformer = 0.01) and Cox II (Pcurrent < 0.0001, Pformer = 0.02) content significantly increased with smoking category in the study cohort. Increases by smoking category were limited to control group	Normal saline oral rinse with exfoliative brush
[78]	Hungary	Case–control	Oral cancer/ controls	175	Protein RNA	IL6	Smoking, alcohol, DMFT, gingival index	P values for protein smoking = 0.95, alcohol = 0.37, DMFT = 0.09, gingival index = 0.1 mRNA level smoking = 0.23, alcohol = 0.07, DMFT = 0.03, gingival index = 0.48	Resting whole saliva
[66]	Croatia	Case–control	Oral cancer/ OPMD/ controls	57	Protein	Tumor necrosis factor alpha (TNFα), IL6	Smoking	No significant differences, p values for TNFα = 0.4 and IL6 = 0.1	Normal saline oral rinse
[44]	India	Case–control	Oral cancer/ OPMD/ controls	300	Protein	Tumor necrosis factor-alpha	Smoking, alcohol, betel quid chewing	No significant difference p < 0.08	Resting whole saliva
[75]	Australia	Case–control	Head and neck cancer/ controls	255	DNA	Hyper-methylation of promoter region in a gene panel	HPV	This methylation panel could discriminate HPV-negative and HPV-positive HNC patients from normal healthy control smokers and nonsmokers	Resting whole saliva
[45]	Taiwan	Case–control	Oral cancer/ controls	18	Protein	Martrix metalloprotinase 2	Betel quid chewing	The increase after areca quid chewing stimulation was significant p < 0.05	Stimulated saliva
[73]	Sri Lanka	Case–control	Oral cancer/ oro-pharyngeal cancer/ controls	148	DNA	Hyper-methylation of promoter region in a gene panel	Smoking, alcohol, betel chewing, HPV	P vales for p16INK4a all risk factors <0.05, RASSF1A for smoking alcohol betel chewing<0.05 and HPV = 0.06, TIMP3 with smoking = 0.19 alcohol = 0.66 betel quid <0.001, HPV<0.001, PCQAP/MED15 with smoking = 0.24 alcohol = 0.06, betel quid <0.05, HPV<0.001	Resting whole saliva
[49]	USA	Case–control	Head and neck cancer/ controls	240	Protein	Secretory leukocyte protease inhibitor (SLPI)	Smoking, alcohol, education level, BMI, mouthwash	Significantly associated with smoking, current smokers having values nearly 1.5-fold higher than former smokers and six-fold higher than never smokers p = 0.035. Other risk habits were non-significant p values for education = 0.9, BMI = 0.33, mouthwash = 0.5 alcohol = 0.7	Oral rinse using mouthwash
[50]	Poland	Case–control	Oral cancer/ controls	118	Protein DNA	IL10, tumor necrosis factor (TNFα), Transforming growth factor-beta (TGF-β), vascular endothelial growth factor (VEGF)	Smoking, alcohol, HPV, EBV	Significant associations P values for smoking and IL10 = 0.0017, EBV with IL10 = 0.0002 and TNF = 0.0456, HPV with IL10 = 0.03 and TGF = 0.03. Non-significant associations p values for smoking and TNF = 0.15, VEGF = 0.46, TGF = 0.98, alcohol with IL10 = 0.13, TNF = 0.09, VEGF = 0.14, TGF = 0.32, EBV with VEGF = 0.05, TGF = 0.2, HPV with TNF = 0.73, VEGF = 0.9	Resting whole saliva
[74]	Pakistan	Case–control	OSMF/ controls	60	Protein	S100A7	Betel quid chewing, Arecanut use	P values for betel quid chewing duration = 0.03, frequency of betel quid use = 0.3, duration of Arecanut use = 0.009 frequency of Arecanut use = 0.9 in OSMF group	Resting whole saliva
[51]	Brazil	Case–control	Head and neck cancer/ controls	216	DNA	Hypermethylation of the promoter region of the genes	Smoking	No significant association CCNA1 p = 0.70, DAPK p = 0.6, DCC p = 0.83, and TIMP3 p = 1.0. Significant association MGMT p = 0.008	Normal saline oral rinse
[36]	USA	Case –control	Oral cancer/ OPMD/ controls	39	Protein	Tumor necrosis factor-alpha (TNFα), IL1a, IL6, IL8	Smoking	No significant association in the oral cancer group TNF p = 0.64, IL1a p = 0.3, IL6 p = 0.59, IL8 p = 0.5, in the OPMD group TNF p = 0.2, IL1a p = 0.8, IL6 p = 0.31, IL8 p = 0.8	Resting whole saliva
[76]	Japan	Cohort	Oral cancer/ controls	48	Protein	IL6	Smoking, periodontitis	No significant differences smokers p = 0.48, periodontal disease p = 0.37	Stimulated saliva
[56]	Syria	Case –control	Oral cancer/ OPMD/ controls	58	DNA	mitochondrial DNA	Smoking, oral health	No significant differences smoking p = 0.749, oral health p = 0.460	Resting whole saliva
[72]	India	Case –control	OSMF/ controls	60	Protein	lactate dehydrogenase	Betel quid chewing	Biomarker level in patients with habit ≤ 5 years was greater than those with habit > 5 years p = 0.539	Stimulated saliva
[77]	New Zealand	Case–control	Head and neck cancer/ dentally compromised/ controls	27	Protein	IL1β, IL8	Microbial species	Positive correlation with C. albicans IL8 p = < 0.001, IL1β p = < 0.001, Eight bacterial genera were significantly negatively correlated with both IL1β and IL8 p < 0.01	Resting whole saliva
[46]	China	Case –control	Oral cancer/ controls	60	Metabolites	choline, betaine, pipecolinic acid, L-carnitine	Smoking, alcohol	No significant differences smoking, p values for choline = 0.320, betaine = 0.928, pipecolinic acid = 0.291, L-carnitine = 0.684, alcohol choline = 0.584, betaine = 0.816, pipecolinic acid = 0.200, L-carnitine = 0.301	Resting whole saliva
[61]	Taiwan	Case –control	Oral cancer/ controls	227	Protein	Annexin II ANXA2, heat shock 70 kDa protein 5 HSPA5, kininogen KNG1, Matrix metalloproteinase MMP1	Smoking, betel nut chewing	No significant relationship smoking MMP1 p = 0.42, ANXA2 p = 0.13, KNG p = 0.49, HSPA p = 0.77 and betel nut chewing MMP p = 0.17, ANXA p = 0.55, KNG p = 0.37, HSPA p = 0.5	Resting whole saliva
[62]	Italy	Case –control	Oral cancer/ controls	100	Protein	Epidermal growth factor receptor	Smoking, alcohol	No significant relationship smoking p = 0.7 and alcohol p = 0.49	Resting whole saliva
N = total sample size included for salivary biomarker analysis, OC: oral cancer, HNC: head and neck cancer, OPMD: oral potentially malignant disorders, OSMF: oral sub mucous fibrosis, HPV: Human papillomavirus, EBV: Epstein Barr virus, BMI: body mass index, DMFT: number of decayed, missing and filled teeth due to caries, p > 0.05 was taken as significant

Table 4. Risk of bias assessment of studies reporting relationships between salivary biomarkers and risk factors using the Newcastle Ottawa scale

Reference (case control design)	Selection				Comparability	Exposure				Quality grade
	Case definition	Representativeness of cases	Control selection	Definition of controls		Ascertainment of exposure (AOE)	Same AOE for groups		Non response rate
53	Medical records assessed for case selection *	Appropriate sample of cases*	Community controls *	Controls were selected from relatives of patients	Matched in design *	Interview not blinded to case control status	Yes *		Not reported	Fair
52	Medical records assessed for case selection*	Appropriate sample of cases*	Not reported	Controls have no history of disease*	Matched in design *	Medical records	Not reported		Not reported	Fair
60	Medical records assessed for case selection *	Appropriate sample of cases*	Community controls*	Controls have no history of disease*	Absence of difference reported	Not reported	Not reported		Not reported	Fair
65	Medical records assessed for case selection *	Appropriate sample of cases*	Not reported	Controls have no history of disease*	Cofounders adjusted in the analysis*	Not reported	Not reported		Not reported	Fair
47	Medical records assessed for case selection*	Appropriate sample of cases*	No control group, comparison before and after treatment	Not applicable	Cofounders adjusted in the analysis*	Medical records	Yes*		Not reported	Fair
58	Medical records assessed for case selection*	Appropriate sample of cases*	Community controls*	Not reported	Cofounders adjusted in the analysis*	Written survey	Yes*		Not reported	Fair
48	Medical records assessed for case selection*	Appropriate sample of cases*	Hospital controls	Controls have no history of disease*	Matched in design *	From database	Not reported		Not reported	Fair
68	Medical records assessed for case selection*	Appropriate sample of cases*	Community controls*	Not reported	Cofounders adjusted in the analysis*	Not reported	Not reported		Not reported	Fair
70	Medical records assessed for case selection*	Appropriate sample of cases*	Hospital controls	Controls have no history of disease*	Matched in design* Cofounders adjusted in the analysis*	Medical records	Yes*		Not reported	High
71	Medical records assessed for case selection*	Appropriate sample of cases*	Hospital controls	Controls have no history of disease*	Cofounders adjusted in the analysis*	Not reported	Not reported		Not reported	Fair
69	Medical records assessed for case selection*	Appropriate sample of cases*	Hospital controls	Controls have no history of disease*	Matched in design* Cofounders adjusted in the analysis*	Not reported	Not reported		Not reported	Fair
43	Medical records assessed for case selection*	No adequate data	No adequate data	No adequate data	No adequate data	No adequate data	Not reported		Not reported	Poor
54	Medical records assessed for case selection*	Appropriate sample of cases*	Not reported	Controls have no history of disease*	Cofounders adjusted in the analysis*	Medical records for patients, survey for controls	No		Not reported	Fair
78	Medical records assessed for case selection*	Appropriate sample of cases*	Hospital controls	Controls have no history of disease*	Matched in design* Adjusted in the analysis*	Medical records	Yes*		Not reported	High
66	Medical records assessed for case selection*	Appropriate sample of cases*	Hospital controls	Controls have no history of disease*	Matched in design*	Questionnaire filled by participant	Yes*		Not reported	Fair
44	Medical records assessed for case selection*	Appropriate sample of cases*	Community controls*	Not reported	Cofounders adjusted in the analysis*	Not reported	Not reported		Not reported	Fair
75	Medical records assessed for case selection*	Appropriate sample of cases*	Not reported	Not reported	Cofounders adjusted in the analysis*	Not reported	Not reported		Not reported	Poor
45	Medical records assessed for case selection*	Appropriate sample of cases*	Hospital controls	Controls have no history of disease*	Absence of habits in the controls reported	Not reported	Not reported		Not reported	Poor
73	Medical records assessed for case selection*	Appropriate sample of cases*	Not reported	Controls have no history of disease*	Matched in design* Cofounders adjusted in the analysis*	Interview for patients	Not reported		Not reported	Fair
49	Medical records assessed for case selection*	Appropriate sample of cases*	Community controls*	Controls have no history of disease*	Matched in design* Cofounders adjusted in the analysis*	Questionnaire filled by participant	Yes*		Not reported	High
50	Medical records assessed for case selection*	Appropriate sample of cases*	Hospital controls	Controls have no history of disease*	Cofounders adjusted in the analysis*	Not reported	Not reported		Not reported	Fair
74	Medical records assessed for case selection*	Appropriate sample of cases*	Not reported	Controls have no history of disease*	Cofounders adjusted in the analysis*	Interviews	Yes *		Not reported	Fair
51	Medical records assessed for case selection*	Appropriate sample of cases*	Hospital controls	Not reported	Cofounders adjusted in the analysis*	Medical records	Yes*		Not reported	Fair
64	Medical records assessed for case selection*	Appropriate sample of cases*	Hospital controls	Controls have no history of disease*	Matched in design* Cofounders adjusted in the analysis*	Not reported	Not reported		Not reported	Fair
56	Medical records assessed for case selection*	Appropriate sample of cases*	Hospital controls	Controls have no history of disease*	Cofounders adjusted in the analysis*	Medical records	Yes*		Not reported	Fair
72	Medical records assessed for case selection*	Appropriate sample of cases*	Hospital controls	Not reported	Matched in design for age*	Not reported	Not reported		Not reported	Poor
77	Medical records assessed for case selection*	Appropriate sample of cases*	Community controls*	No adequate data	Age range and habits reported	Medical records	Not reported		Not reported	Poor
63	Medical records assessed for case selection*	Appropriate sample of cases*	Not reported	Controls have no history of disease*	Matched in design*	Medical records	Not reported		Not reported	Fair
61	Medical records assessed for case selection*	Appropriate sample of cases*	Hospital controls	Controls have no history of disease*	Cofounders adjusted in the analysis*	Medical records	Yes*		Not reported	Fair
62	Medical records assessed for case selection*	Appropriate sample of cases*	Not reported	Controls have no history of disease*	Matched in design*	Medical records	Not reported		Not reported	Fair
Reference (cohort design)	Selection				Comparability	Exposure				Quality grade
	Representativeness of exposed	Selection of non -exposed	Ascertainment of exposure	Outcome was absent at the start		Ascertainment of the outcome	Follow up long enough	Adequacy of follow up
59	Yes *	Yes*	Secure record*	Yes * Outcomes were recurrence and survival	Co-founders adjusted in the analysis*	Record linkage*	Mean 39.4 months, range 12 to 60 months	No loss to follow up		High
67	Yes *	No non-exposed cohort	Secure records*	Yes* outcomes were recurrence and survival	Co-founders adjusted in the analysis*	Record linkage*	Median 2.1 years, range 1 day-9.8 years	Loss to follow up not reported		Fair
76	Yes*	Yes*	Secure records*	Yes* outcome was recurrence	Non exposed were age matched*, Co-founders were reported in the analysis	Record linkage*	48 months	One patient loss to follow up, descriptionprovided*		High

Arellano-Garcia ME, Hu S, Wang J, et al. Multiplexed immunobead-based assay for detection of oral cancer protein biomarkers in saliva. Oral Dis. 2008 Nov;14(8):705–712.

 PubMed Web of Science ®Google Scholar

Aziz S, Ahmed SS, Ali A, et al. Salivary immunosuppressive cytokines IL-10 and IL-13 are significantly elevated in oral squamous cell carcinoma patients. Cancer Invest. 2015;33(7):318–328.

 PubMed Web of Science ®Google Scholar

Babiuch K, Kuśnierz-Cabala B, Kęsek B, et al. Evaluation of Proinflammatory, NF-kappaB Dependent Cytokines: IL-1α, IL-6, IL-8, and TNF-α in Tissue Specimens and Saliva of Patients with Oral Squamous Cell Carcinoma and Oral Potentially Malignant Disorders. J Clin Med. 2020;9(3):867.

 Web of Science ®Google Scholar

Bagan L, Sáez GT, Tormos MC, et al. Salivary and serum interleukin-6 levels in proliferative verrucous leukoplakia. Clin Oral Investig. 2016;20(4):737–743.

 PubMed Web of Science ®Google Scholar

Brailo V, Vučićević-Boras V, Cekić-Arambašin A, et al. The significance of salivary interleukin 6 and tumor necrosis factor alpha in patients with oral leukoplakia. Oral Oncol. 2006;42(4):370–373.

 PubMed Web of Science ®Google Scholar

Brailo V, Vucicevic-Boras V, Lukac J, et al. Salivary and serum interleukin 1 beta, interleukin 6 and tumor necrosis factor alpha in patients with leukoplakia and oral cancer. Med Oral Patol Oral Cir Bucal. 2012;17(1):6–11.

 Web of Science ®Google Scholar

Brinkmanna O, Kastratovicb DA, Milovan V, et al. Oral squamous cell carcinoma detection by salivary biomarkers in a Serbian population. Oral Oncol. 2012;47(1):51–55.

 Web of Science ®Google Scholar

Gleber-Netto FO, Yakob M, Li F, et al. Salivary biomarkers for detection of oral squamous cell carcinoma in a Taiwanese population. Clin Cancer Res. 2016;22(13):3340–3347.

 PubMed Web of Science ®Google Scholar

Hamad AWR, Gaphor SM, Shawagfeh MT, et al. Study of serum and salivary levels of proinflammatory cytokines, potential biomarkers in the diagnosis of oral squamous cell carcinoma. Acad J Cancer Res. 2011;4(2):47–55.

 Google Scholar

Hamzavi M, Tadbir AA, Rezvani G, et al. Tissue expression, serum and salivary levels of IL-10 in patients with head and necksquamous cell carcinoma. Asian Pacific J Cancer Prev. 2013;14(3):1681–1685.

 PubMed Web of Science ®Google Scholar

Márton IJ, Horváth J, Lábiscsák P, Márkus B, Dezső B, Szabó A, Tar I, Piffkó J, Jakus P, Barabás J, Barabás P, Olasz L, Kövér Z, Tőzsér J, Sándor J, Csősz É, Scholtz B, Kiss C. Salivary IL-6 mRNA is a Robust Biomarker in Oral Squamous Cell Carcinoma. J Clin Med. 2019 Nov 13;8(11):1958. doi:10.3390/jcm8111958. PMID: 31766212; PMCID: PMC6912409

 Google Scholar

Juretić M, Cerović R, Belušić-Gobić M. et al. Salivary levels of TNF-α and IL-6 in patients with oral premalignant and malignant lesions. Folia Biol. 2013;59(2):99–102.

 PubMed Web of Science ®Google Scholar

Katakura A, Kamiyama I, Takano N, et al. Comparison of salivary cytokine levels in oral cancer patients and healthy subjects. Bull Tokyo Dent Coll. 2007;48(4):199–203.

 PubMedGoogle Scholar

Khyani IAM, Qureshi MA, Mirza T, et al. Detection of interleukins-6 and 8 in saliva as potential biomarkers of oral pre-malignant lesion and oral carcinoma: a breakthrough in salivary diagnostics in Pakistan. Pak J Pharm Sci. 2017 May;30(3):817–823.

 PubMed Web of Science ®Google Scholar

Korostoff A, Reder L, Masood R, et al. The role of salivary cytokine biomarkers in tongue cancer invasion and mortality. Oral Oncol. 2011;47(4):282–287.

 PubMed Web of Science ®Google Scholar

Lee LT, Wong YK, Hsiao HY, et al. Evaluation of saliva and plasma cytokine biomarkers in patients with oral squamous cell carcinoma. Int J Oral Maxillofac Surg. 2018;47(6):699–707.

 PubMed Web of Science ®Google Scholar

Lisa Cheng Y-S, Jordan L, Gorugantula LM, et al. Salivary interleukin-6 and −8 in patients with oral cancer and patients with chronic oral inflammatory diseases. J Periodontol. 2014;85(7):956–965.

 PubMed Web of Science ®Google Scholar

Panneer Selvam N, Sadaksharam J. Salivary interleukin-6 in the detection of oral cancer and precancer. Asia Pac J Clin Oncol. 2015;11(3):236–241.

 PubMed Web of Science ®Google Scholar

Punyani SR, Sathawane RS. Salivary level of interleukin-8 in oral precancer and oral squamous cell carcinoma. Clin Oral Investig. 2013;17(2):517–524.

 PubMed Web of Science ®Google Scholar

Rajkumar K, Nandhini G, Ramya R, et al. Validation of the diagnostic utility of salivary interleukin 8 in the differentiation of potentially malignant oral lesions and oral squamous cell carcinoma in a region with high endemicity. Oral Surg Oral Med Oral Pathol Oral Radiol. 2014;118(3):309–319.

 PubMed Web of Science ®Google Scholar

Rhodus NL, Cheng B, Myers S, et al. The feasibility of monitoring NF-κB associated cytokines: TNF-α, IL-1α, IL-6, and IL-8 in whole saliva for the malignant transformation of oral lichen planus. Mol Carcinog. 2005;44(2):77–82.

 PubMed Web of Science ®Google Scholar

Sahebjamee M, Eslami M, Atarbashimoghadam F, et al. Salivary concentration of TNF? IL1? IL6, and IL8 in oral squamous cell carcinoma. Med Oral Patol Oral Cir Bucal. 2008;13(5):292–295.

 Web of Science ®Google Scholar

Sharma M, Bairy I, Pai K, et al. Salivary IL-6 levels in oral leukoplakia with dysplasia and its clinical relevance to tobacco habits and periodontitis. Clin Oral Investig. 2011;15(5):705–714.

 PubMed Web of Science ®Google Scholar

Singh P, Verma JK, Singh JK. Validation of salivary markers, IL-1β, IL-8 and Lgals3bp for detection of oral squamous cell carcinoma in an Indian population. Sci Rep. 2020 Apr;10(1):7365.

 PubMed Web of Science ®Google Scholar

St. John MAR, Li Y, Zhou X, et al. Interleukin 6 and interleukin 8 as potential biomarkers for oral cavity and oropharyngeal squamous cell carcinoma. Arch Otolaryngol - Head Neck Surg. 2004;130(8):929–935.

 PubMedGoogle Scholar

Tan W, Sabet L, Li Y, et al. Optical protein sensor for detecting cancer markers in saliva. Biosens Bioelectron. 2008;24(2):266–271.

 PubMed Web of Science ®Google Scholar

Vesty A, Gear K, Biswas K, et al. Microbial and inflammatory-based salivary biomarkers of head and neck squamous cell carcinoma. Clin Exp Dent Res. 2018 Nov 28;4(6):255–262.

 PubMed Web of Science ®Google Scholar

Arduino PG, Menegatti E, Cappello N, et al. Possible role for interleukins as biomarkers for mortality and recurrence in oral cancer. Int J Biol Markers. 2015;30(2):e262–6.

 PubMed Web of Science ®Google Scholar

Bernardes VF, Gleber-Netto FO, Sousa SF, et al. Clinical significance of EGFR, Her-2 and EGF in oral squamous cell carcinoma: a case control study. J Exp Clin Cancer Res. 2010;29(1):1–7.

 PubMedGoogle Scholar

Carvalho AL, Henrique R, Jeronimo C, et al. Detection of promoter hypermethylation in salivary rinses as a biomarker for head and neck squamous cell carcinoma surveillance. Clin Cancer Res. 2011;17(14):4782–4789.

 PubMed Web of Science ®Google Scholar

De Souza MB, De Carvalho MB, De Carvalho MB, et al. Serum and salivary macrophage migration inhibitory factor in patients with oral squamous cell carcinoma. Oncol Lett. 2014;8(5):2267–2275.

 PubMed Web of Science ®Google Scholar

Demokan S, Chang X, Chuang A, et al. KIF1A and EDNRB are differentially methylated in primary HNSCC and salivary rinses. Int J Cancer. 2010;127(10):2351–2359.

 PubMed Web of Science ®Google Scholar

El-Naggar AK, Mao L, Staerkel G, et al., Genetic heterogeneity in saliva from patients with oral squamous carcinomas. J Mol Diagnostics. 3(4): 164–170. 2001.

 PubMed Web of Science ®Google Scholar

Franzmann EJ, Reategui EP, Carraway KL, et al. Salivary soluble CD44: a potential molecular marker for head and neck cancer. Cancer Epidemiol Biomarkers Prev. 2005;14(3):735–739.

 PubMed Web of Science ®Google Scholar

Franzmann EJ, Reategui EP, Mateus Pereira LH, et al. Salivary protein and solcd44 levels as a potential screening tool for early detection of head and neck squamous cell carcinoma. Head Neck. 2012;34(5):687–695.

 PubMed Web of Science ®Google Scholar

Ganesan A, Gautham Kumar N. Assessment of lipid peroxides in multiple biofluids of leukoplakia and oral squamous cell carcinoma patients - A clinico- biochemical study. J Clin Diagn Res. 2014;8(8):10–13.

 Google Scholar

Heawchaiyaphum C, Pientong C, Phusingha P, et al. Peroxiredoxin-2 and zinc-alpha-2-glycoprotein as potentially combined novel salivary biomarkers for early detection of oral squamous cell carcinoma using proteomic approaches. J Proteomics. 2018;173:52–61.

 PubMed Web of Science ®Google Scholar

Hu SY, Lin MH, Yang SC, et al. Areca quid chewing enhances the expression of salivary matrix metalloproteinase-9. J Formos Med Assoc. 2005;2(104):113–119.

 Google Scholar

Jiang WW, Masayesva B, Zahurak M, et al. Increased mitochondrial DNA content in saliva associated with head and neck cancer. Clin Cancer Res. 2005;11(7):2486–2491.

 PubMed Web of Science ®Google Scholar

Krishnan R, Thayalan DK, Padmanaban R, et al. Association of serum and salivary tumor necrosis factor-α with histological grading in oral cancer and its role in differentiating premalignant and malignant oral disease. Asian Pacific J Cancer Prev. 2014;15(17):7141–7148.

 PubMedGoogle Scholar

Lim Y, Wan Y, Vagenas D, et al., Salivary DNA methylation panel to diagnose HPV-positive and HPV-negative head and neck cancers. BMC Cancer. 16(1): 1–12. 2016.

 Google Scholar

Liu SY, Lin MH, Yang SC, et al. Increased expression of matrix metalloproteinase-2 in oral cells after short-term stimulation and long-term usage of areca quid. J Formos Med Assoc. 2005;06(104):390–397.

 Google Scholar

Liyanage C, Wathupola A, Muraleetharan S, et al. Promoter Hypermethylation of Tumor-Suppressor Genes p16(INK4a), RASSF1A, TIMP3, and PCQAP/MED15 in Salivary DNA as a Quadruple Biomarker Panel for Early Detection of Oral and Oropharyngeal Cancers. Biomolecules. 2019;9(4):148.

 PubMed Web of Science ®Google Scholar

Pierce Campbell CM, Giuliano AR, Torres BN, et al. Salivary secretory leukocyte protease inhibitor (SLPI) and head and neck cancer: the cancer prevention study II nutrition cohort. Oral Oncol. 2016;55:1–5.

 PubMed Web of Science ®Google Scholar

Polz-Dacewicz M, Strycharz-Dudziak M, Dworzański J, et al. Salivary and serum IL-10, TNF-α, TGF-β, VEGF levels in oropharyngeal squamous cell carcinoma and correlation with HPV and EBV infections. Infect Agent Cancer. 2016;11(1):1–8.

 PubMedGoogle Scholar

Raffat MA, Hadi NI, Hosein M, et al. Differential expression of salivary S100A7 in oral submucous fibrosis. Saudi Dent J. 2019;31(1):39–44.

 PubMed Web of Science ®Google Scholar

Rettori MM, De carvalho AC, Bomfim longo AL, et al. Prognostic significance of TIMP3 hypermethylation in post-treatment salivary rinse from head and neck squamous cell carcinoma patients. Carcinogenesis. 2013;34(1):20–27.

 PubMed Web of Science ®Google Scholar

Sato J, Ohuchi M, Wada M, et al. Differences in sequential posttreatment salivary IL-6 levels between patients with and patients without locoregional recurrences of oral squamous cell carcinoma: part III of a cohort study. Oral Surg Oral Med Oral Pathol Oral Radiol. 2015;120(6):751–760.e2.

 PubMed Web of Science ®Google Scholar

Sayall L, Mashlah A, Kassem I. Evaluation of mitochondrial DNA content in saliva of oral squamous cell carcinoma and leukoplakia as non-invasive biomarker. Int J PharmTech Res. 2015;7(4):573–579.

 Google Scholar

Sivaramakrishnan M, Sivapathasundharam B, Jananni M. Evaluation of lactate dehydrogenase enzyme activity in saliva and serum of oral submucous fibrosis patients. J Oral Pathol Med. 2015;44(6):449–452.

 PubMed Web of Science ®Google Scholar

Wang Q, Gao P, Cheng F, et al. Measurement of salivary metabolite biomarkers for early monitoring of oral cancer with ultra performance liquid chromatography-mass spectrometry. Talanta. 2014;119:299–305.

 PubMed Web of Science ®Google Scholar

Yu J-S, Chen Y-T, Chiang W-F, et al. Saliva protein biomarkers To detect oral squamous cell carcinoma in a high-risk population in Taiwan. Proc Natl Acad Sci U S A. 2016;113(41):11549–11554.

 PubMed Web of Science ®Google Scholar

Zanotti L, Paderno A, Piazza C, et al. Epidermal growth factor receptor detection in serum and saliva as a diagnostic and prognostic tool in oral cancer. Laryngoscope. 2017;127(11):E408–14.

 PubMed Web of Science ®Google Scholar