Salivary Amino Acids as Non-Invasive Biomarkers for Oral Squamous Cell Carcinoma: A Case Control Study

Salivary Amino Acids as Non-Invasive Biomarkers for Oral Squamous Cell Carcinoma: A Case Control Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Salivary Amino Acids as Non-Invasive Biomarkers for Oral Squamous Cell Carcinoma: A Case Control Study Dipayan Mojumder, Mamudul Hasan Razu, Md. Mehedi Hasan, Minhazul Islam, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8663305/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 15 You are reading this latest preprint version Abstract Background: Oral squamous cell carcinoma (OSCC) is frequently diagnosed at advanced stages, resulting in poor survival outcomes. Salivary metabolomic profiling offers a promising, non-invasive approach for early disease detection. This study evaluated salivary free amino acid (SFAA) profiles as potential diagnostic biomarkers for OSCC. Methods: In this case–control study, six salivary free amino acids proline, valine, phenylalanine, isoleucine, leucine, and lysine were quantified from unstimulated whole-saliva samples collected from 34 histopathologically confirmed OSCC patients and 29 healthy controls. Amino acid concentrations were measured using ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC–MS/MS). Group comparisons were performed using non-parametric tests, and diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis for individual biomarkers and a composite SFAA profile index. Results: Salivary concentrations of proline, valine, phenylalanine, isoleucine, and leucine were significantly higher in OSCC patients compared with controls (all P < 0.05). Individual amino acids demonstrated moderate diagnostic accuracy, with area under the curve (AUC) values ranging from 0.54 to 0.78. A composite SFAA profile index comprising phenylalanine, leucine, isoleucine, valine, and proline achieved superior discrimination between cases and controls (AUC = 0.79; 95% CI: 0.69–0.90), with 70.6% sensitivity and 69.0% specificity. SFAA concentrations also varied significantly according to tumor stage and growth pattern. Conclusions: The salivary free amino acid profile index demonstrated improved diagnostic performance compared with individual biomarkers, supporting its potential utility as a non-invasive tool for OSCC detection. Salivary amino acid profiling may complement existing diagnostic strategies for early identification of OSCC. Further large-scale and multi-center studies are warranted to validate these findings and refine biomarker panels. Oral squamous cell carcinoma Saliva Salivary biomarkers Amino acid profiling Metabolomics Non-invasive diagnosis Diagnostic accuracy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Oral squamous cell carcinoma (OSCC) accounts for nearly 90% of malignancies arising in the oral cavity. Globally, it is the sixth most common cancer, while in South Asia including Sri Lanka, India, Bangladesh, and Pakistan it ranks among the three most prevalent cancers [ 1 – 3 ]. Established risk factors include tobacco smoking, betel-quid chewing, alcohol consumption, low socioeconomic status, and ultraviolet radiation exposure, particularly for lip cancer [ 4 ]. Other contributors such as human papillomavirus (HPV) infection, Candida colonization, nutritional deficiencies, and genetic predisposition have also been implicated in OSCC development [ 3 , 5 ]. Clinically, OSCC often presents as a painless ulcer with indurated or rolled borders, a feature that contributes to late diagnosis and the high mortality rate associated with this disease [ 6 , 7 ]. Although incisional biopsy followed by histopathological examination remains the gold standard for diagnosis [ 8 , 9 ], it is invasive, operator-dependent, and may be technically challenging in posterior oral regions or in patients with restricted mouth opening. These limitations underscore the urgent need for simple, non-invasive diagnostic strategies that may facilitate earlier detection and improve survival outcomes. Human saliva closely resembles blood in biochemical composition, containing water, electrolytes, enzymes, proteins, and a wide range of small molecules transferred from the bloodstream via ultrafiltration, passive diffusion, active transport, and both intercellular and extracellular pathways [ 10 – 12 ]. As saliva collection is non-invasive, economical, and suitable for repeated sampling, it has gained increasing attention as a promising biological matrix for large-scale screening and biomarker discovery. Salivary diagnostics have shown utility in various diseases including diabetes mellitus, HIV, dengue, breast cancer, gastric cancer, lung cancer, and oral cancer [ 13 ]. The ability to obtain saliva samples easily, even by patients themselves, further enhances its clinical applicability. Alterations in amino acid (AA) metabolism are well-recognized features of cancer biology, reflecting the heightened metabolic demands, disrupted protein turnover, and tumor-driven catabolic processes characteristic of malignancy. Early studies suggested that abnormal AA profiles in cancer patients were primarily due to malnutrition and cancer-related anorexia leading to weight loss [ 14 ]. However, subsequent research demonstrated that deviations in circulating and salivary free amino acid (SFAA) profiles also occur in weight-stable cancer patients, indicating cancer-specific metabolic reprogramming rather than simple nutritional deficiency [ 15 – 18 ]. Cachexia is common in advanced cancer which is characterized by hypermetabolism, peripheral insulin resistance, increased protein catabolism, and nitrogen imbalance, all of which further contribute to alterations in AA concentrations [ 19 ]. These metabolic changes provide a strong biological basis for investigating SFAA profiles as potential biomarkers for OSCC detection. Technological advancements have further strengthened the feasibility of salivary SFAA analysis. While high-performance liquid chromatography (HPLC) with UV or fluorescence detection has traditionally been used for AA quantification [ 20 – 22 ], these methods may be hindered by non-specific derivatization byproducts. In contrast, ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC–MS/MS) offers superior selectivity, sensitivity, and throughput for amino acid analysis [ 23 ]. Using mass-to-charge (m/z) ratios and advanced acquisition modes such as selected reaction monitoring (SRM) and multiple reaction monitoring (MRM), UPLC–MS/MS enables accurate, automated, and high-throughput identification of amino acids in complex biological samples [ 24 – 28 ]. This enhances the precision and clinical feasibility of salivary metabolite-based diagnostic approaches. To date, only a single study from the South Asian region has evaluated salivary amino acid levels in oral cancer patients [ 29 ], and no study in Bangladesh has investigated salivary amino acid profiling for OSCC diagnosis. Therefore, this study aimed to assess the association of specific salivary amino acid levels with OSCC and to explore their potential diagnostic and prognostic utility. 2. Materials and methods This The study is reported according to the STROBE statement for case–control studies & that was conducted over a 15-month period from December 2020 to February 2022 at Bangladesh Medical University (BMU) and Dhaka Dental College Hospital (DDCH), with analytical procedures performed at the Bangladesh Reference Institute for Chemical Measurements (BRiCM). A total of 110 eligible individuals were initially approached; however, due to COVID-19–related restrictions and limited laboratory capacity, 63 participants (34 histopathologically confirmed OSCC cases and 29 clinically healthy controls) were finally recruited using a convenient sampling technique. Cases were adults newly diagnosed with oral squamous cell carcinoma, while controls were healthy volunteers without any oral lesions, malignancy, or severe periodontal disease. Detailed demographic and clinical histories were obtained using a structured data sheet, followed by standardized clinical examinations to minimize measurement variability. Unstimulated whole saliva samples were collected following a 2-hour fasting period, ensuring uniform pre-analytical conditions, and immediately transported in a validated cold-chain system maintained at 2–8°C to BRiCM for biochemical analysis. Levels of six salivary free amino acids (Pro, Val, Phe, Ile, Leu, and Lys) the primary variables of interest were quantified using ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC–MS/MS) employing an Intrada Amino Acid analytical column. All laboratory procedures followed the same protocol for cases and controls to reduce differential measurement bias. The study size was determined pragmatically based on feasibility during the pandemic and the capacity of the country’s only available UPLC–MS/MS facility; nevertheless, the final sample retained adequate variability to examine associations between SFAA levels and OSCC. To minimize bias, all saliva samples were coded with anonymous ID numbers, laboratory analysts were blinded to case–control status, and uniform procedures were maintained throughout sample handling and processing. Quantitative variables were analyzed using appropriate non-parametric tests (Mann–Whitney U and Kruskal–Wallis) due to skewed distributions, while diagnostic accuracy was assessed using receiver operating characteristic (ROC) curves and area-under-the-curve (AUC) estimates with 95% confidence intervals. Ethical clearance was obtained from the Institutional Review Board of BMU (IRB Reg. No. BSMMU/MU/2021/7214 ), and the study was registered on ClinicalTrials.gov (NCT05150509). Prior to enrollment, all participants received a full explanation of study objectives, procedures, potential risks, and their rights, and written informed consent was obtained in accordance with the Declaration of Helsinki while ensuring strict confidentiality through coded documentation and restricted data access. 2.1 Reagent and materials Acetonitrile, methanol, and tetrahydrofuran (≥ 99%, LC–MS grade) were obtained from Honeywell, USA. Ultrapure water (18.2 MΩ·cm) was produced using an in-house Milli-Q purification system. Formic acid (≥ 98%) and ammonium formate (≥ 98%) were purchased from Merck, Germany. A mixed amino acid stock standard (0.2 mg/mL for each analyte, analytical grade) was prepared by dissolving the amino acids in ultrapure water. Working standards were freshly prepared before analysis by serial dilution of the stock solution using a 75:25 (v/v) acetonitrile–water mixture. All reagent solutions were stored at 4°C in amber glass vials to protect them from light and maintain stability until use 2.2 Incisional biopsy for OSCC patients Patients presenting with clinically suspicious oral lesions were evaluated for eligibility for incisional biopsy (Fig. 1 ). As part of institutional protocol during the COVID-19 pandemic, all suspected cases first underwent RT-PCR testing for SARS-CoV-2. Only individuals with a confirmed negative result within 24 hours proceeded with further clinical procedures. Following routine oral prophylaxis (scaling), patients were referred to the Department of Minor Oral Surgery for incisional biopsy under local anaesthesia and strict aseptic conditions. Tissue samples were collected from the most clinically suspicious area of each lesion and immediately preserved in 10% neutral buffered formalin. The specimens were transported to the histopathology laboratory for microscopic examination. Histopathological reports were obtained for each patient, and information for all confirmed OSCC cases was recorded in a predesigned data collection sheet. 2.3 Saliva collection and sample preparation Periodontal scaling was performed during the first visit, and participants were re-evaluated after 15 days to ensure improved periodontal status. Each participant was contacted by phone for follow-up, and an additional RT-PCR test for SARS-CoV-2 was performed. Once a negative COVID-19 result was confirmed, unstimulated whole saliva was collected the next morning between 9:00 and 11:00 a.m. Participants were instructed to refrain from eating or drinking (except water) for at least 2 hours prior to collection. They were asked to rinse their mouth with water, wait 10 minutes, sit upright with the head slightly tilted forward, and expectorate a minimum of 5 mL of unstimulated saliva into a calibrated collection tube [ 30 ]. Collected samples were immediately maintained in a 2–8°C cold-chain and transported to the Bangladesh Reference Institute for Chemical Measurements (BRiCM). Upon arrival, each sample was centrifuged at 10,000 rpm for 10 minutes at 4°C to remove insoluble debris, food particles, and cellular components. The resulting clear supernatant was aliquoted and stored at − 80°C until biochemical analysis. For UPLC–MS/MS preparation, 400 µL of thawed saliva was mixed with 800 µL of acetonitrile in a 2.0-mL Eppendorf tube, vortexed vigorously for 1 minute to precipitate proteins, and allowed to stand for 15 minutes. The mixture was then centrifuged at 10,000 rpm for 20 minutes at 4°C. The final supernatant was filtered through a 0.22 µm syringe filter to obtain a particle-free extract suitable for LC–MS/MS analysis. 2.4 SFAA Analysis by LC–MS/MS Salivary free amino acids were analyzed using a Shimadzu LCMS-8050 Ultra-Fast Liquid Chromatography system (Shimadzu Corporation, Kyoto, Japan) equipped with a triple-quadrupole mass spectrometer and an electrospray ionization (ESI) source. The system comprised binary pumps, an autosampler, an online degassing unit, and a thermostated column oven. Chromatographic separation was achieved on an Intrada Amino Acid column (100 mm × 3 mm, 3 µm), which provides high-resolution, reproducible retention of both hydrophilic and hydrophobic amino acids. The mobile phase consisted of two solutions: Solvent A: acetonitrile/tetrahydrofuran/25 mM ammonium formate/formic acid (9/75/16/0.3, v/v) and Solvent B: acetonitrile/100 mM ammonium formate (20/80, v/v). A gradient elution program was applied at a flow rate of 0.6 mL/min as follows: 0% B (0–3.0 min), 0–17% B (3.0–9.0 min), 17–100% B (9.0–16.0 min), 100% B (16.0–22.0 min), and re-equilibration at 0% B at 22.0 min. The column oven temperature was maintained at 35°C. The autosampler, operating in full-loop injection mode, was equipped with a 10 µL injection loop. The total run time was 22 minutes. Mass spectrometric detection was performed in multiple reaction monitoring (MRM) mode using electrospray ionization in the positive ion mode. The instrument was operated with the following optimized ionization parameters: capillary voltage, 4.0 kV; interface temperature, 300°C; desolvation line temperature, 300°C; block temperature, 400°C; nebulizing gas (N₂), 1.5 L/min; drying gas (N₂), 15.0 L/min; and heating gas, 10 L/min. Collision-induced dissociation (CID) was carried out using argon at 270 kPa. These settings enabled sensitive and selective detection of the target amino acids. Raw data acquired from the positive ESI mode were processed using LabSolutions (Shimadzu), with peak selection, smoothing, integration, and quantification parameters optimized for the targeted amino acids. Processed data were exported in text format for subsequent statistical and discriminant analysis. 2.5 Method Validation Validation of the LC–MS/MS method was conducted following the ICH Q2(R1) guideline to ensure suitability for quantitative determination of salivary free amino acids (SFAAs). Validation parameters included linearity, sensitivity (LOD and LOQ), accuracy, and precision (intra-day and inter-day). Linearity was evaluated by preparing six-point calibration curves using saliva matrices spiked with known concentrations of each amino acid after subtraction of endogenous levels. All six analytes demonstrated excellent linearity across the working range (2–100 µM), with correlation coefficients (R² ≥ 0.998). The calibration equations and linearity ranges are presented in Table 2 . Sensitivity was determined based on signal-to-noise ratios of approximately 3:1 for the limit of detection (LOD) and 10:1 for the limit of quantification (LOQ). LOD values ranged from 0.07 to 0.68 µM, and LOQ values ranged from 1.65 to 2.01 µM, confirming adequate analytical sensitivity for salivary biomarker detection. Accuracy was assessed using recovery tests at three concentration levels (20%, 50%, and 100% of the calibration range). Mean recoveries ranged from 90.7% to 101.9%, indicating good accuracy with minimal matrix interference. Precision was assessed through replicate analyses of spiked samples. Intra-day precision (n = 5 per level) demonstrated RSD values < 2%, while inter-day precision (over 3 consecutive days) showed RSD < 9%, supporting strong repeatability and intermediate precision. The combined validation data confirm that the LC–MS/MS method is sensitive, accurate, precise, and reproducible for quantifying salivary free amino acids. These findings support the reliability of the subsequent biomarker analysis. 2.6 Statistical Analysis Statistical analysis was performed using SPSS version 26 (IBM Corp., Armonk, NY, USA), GraphPad Prism version 8.0.2, and R version 4.3. Categorical variables were summarized as frequencies and percentages, while continuous variables were expressed as means, medians, and interquartile ranges as appropriate. Group comparisons for continuous variables were conducted using either the independent samples t-test (for normally distributed data) or the Mann–Whitney U test (for non-normally distributed data). Categorical variables were compared using the Chi-square test or two-tailed Fisher’s exact test when cell counts were small. For comparisons involving more than two groups, the Kruskal–Wallis test was applied, followed by appropriate post-hoc analysis where necessary. Mean concentration levels of the six salivary free amino acids (Pro, Leu, Val, Phe, Ile, and Lys) were calculated for both groups. Diagnostic performance of individual amino acids and the composite SFAA profile index was evaluated using receiver operating characteristic (ROC) curve analysis. Sensitivity, specificity, and area under the ROC curve (AUC) with 95% confidence intervals (CI) were estimated to assess discriminative ability for OSCC detection. Optimal cutoff values were determined using the Youden Index (maximum sum of sensitivity and specificity). A p-value < 0.05 was considered statistically significant for all analyses. 3. Results A total of 110 potential participants were initially approached for eligibility assessment. Due to COVID-19 related restrictions, difficulty in arranging hospital visits, and limited LC-MS/MS laboratory capacity at BRiCM, 47 individuals could not be enrolled. Consequently, 63 participants met the eligibility criteria and provided usable saliva samples, including 34 histopathologically confirmed OSCC cases and 29 healthy controls. No participants were lost after enrollment, and all 63 samples were successfully analyzed. A detailed participant recruitment and inclusion pathway is presented in the flow diagram below on Fig. 2 . 3.1 Demographical data analysis In this study majority of the participants in both cases 23(67.6%) and controls 17(58.6%) were aged between 51–70 years. In case group 21(61.8%) and in control group 18(62.1%) were female. Among all participants, 14(41.2%) of the cases and 13(44.8%) of the controls were from the middle class. The majority of the cases 24(70.6%) and healthy controls 17(58.6%) had no comorbidities. The majority 24(70.6%) of the cases had educational status below the secondary school certificate exam (SSC) or O-level. In contrast, only 11(38.1%) of controls were in that group. Educational status was lower among the oral squamous cell carcinoma (OSCC) cases than the controls. Betel quid chewing and smoking were the habitual factors we identified in our study. Healthy controls had a significantly lower number of oral habitual factors. Betel quid chewing was the common oral habit 22 (64.7%) among the cases (Table 1 ). By univariate logistic regression, we found that oral habitual factors, poor oral hygiene, and increased amino acid levels were significantly associated risk factors for oral squamous cell carcinoma (Table 3 ). Poor oral hygiene was determined by visible stains of teeth and periodontal pocket depth of more than 3 mm [ 31 ]. By multivariate logistic regression poor oral hygiene, increased leu, Ile, and Val level were significantly associated risk factors for oral squamous cell carcinoma (Table 4 ). Table 1 Baseline sociodemographic and habit-related characteristics of study participants (n = 63) Variable OSCC Cases (n = 34) Healthy Controls (n = 29) p-value Age (years), mean ± SD 57.8 ± 8.6 55.9 ± 9.1 0.42 Age Group 0.58 18–30 0 (0%) 1 (3.4%) 31–50 11 (32.4%) 11 (37.9%) 51–70 23 (67.6%) 17 (58.6%) Gender 0.94 Male 13 (38.2%) 11 (37.9%) Female 21 (61.8%) 18 (62.1%) Socioeconomic Status 0.21 Poor 11 (32.3%) 5 (17.2%) Middle Class 14 (41.2%) 13 (44.8%) Rich 9 (26.5%) 11 (37.9%) Educational Level 0.01* Below SSC 24 (70.6%) 11 (38.1%) SSC and above 10 (29.4%) 18 (61.9%) Comorbidities 0.26 Present 10 (29.4%) 12 (41.4%) Absent 24 (70.6%) 17 (58.6%) Oral Habits 0.003 * No Habit 2 (5.9%) 10 (34.5%) Betel Quid Chewing 22 (64.7%) 11 (37.9%) Smoking Only 2 (5.9%) 5 (17.3%) Both Chewing + Smoking 8 (23.5%) 3 (10.3%) 3.2 Distribution of OSCC Among the cases, 15 (44.1%) of the cancer was located in the vestibule, followed by the retromolar trigone and buccal mucosa, 9 (26.5%) and 7 (20.6%) respectively. Majority 27(79.4%) of the cancer growth was exophytic and clinical stage IV of cancer 17(50%) (Fig. 3 ). 3.3 Salivary Free Amino Acid Concentrations in OSCC vs Controls In comparison to healthy controls, patients with OSCC demonstrated substantially higher concentrations of all six salivary free amino acids (SFAAs). Mann–Whitney U tests revealed significant elevations of Proline, Leucine, Valine, Phenylalanine, Isoleucine, and Lysine in the case group (all p < 0.05). Table 2 summarizes the mean ± SD concentrations and associated p-values. To visualize these differences across disease severity, Fig. 4 presents boxplots comparing controls, stage 3, and stage 4 OSCC cases. As shown, amino acid concentrations rise progressively with advancing stage. Table 2 Mean salivary amino acid levels in OSCC cases and healthy controls (µmol/L) Amino Acid Cases (n = 34) Mean ± SD Controls (n = 29) Mean ± SD p-value† Proline (Pro) 47.4 ± 35.9 22.5 ± 19.1 < 0.001 Leucine (Leu) 17.2 ± 22.4 5.0 ± 4.3 0.004 Valine (Val) 22.2 ± 29.3 4.8 ± 4.1 < 0.001 Phenylalanine (Phe) 19.8 ± 19.4 8.7 ± 4.8 0.002 Isoleucine (Ile) 13.4 ± 19.0 2.2 ± 2.6 0.003 Lysine (Lys) 14.5 ± 18.7 5.4 ± 4.9 0.013 The mean normalized AA concentrations of control and clinical stage (stage 3 and stage 4 of OSCC) patients were investigated in this study showing box plots of the concentrations of the AAs, which are different. The average concentrations of SFAA from the control and clinical stage comparison are shown in Fig. 4 . SFAA concentrations were higher in case patients compared with control. Kruskal Wallis test conducted for this study shows that the concentration of Pro, Leu, Val, Phe and Ile significantly differs between the groups. Pair-wise comparison of control vs stage 3 and control vs stage 4 are significantly higher for all AAs. But for control vs stage 3, it shows significantly higher for Ile and Val. On the other hand, for control vs stage 4 Ile, Phe, and Val show significantly higher concentrations. No significant difference was found between stage 3 and stage 4. comparison (p-value was determined by Mann-Whitney and Kruskal Wallis test. P < 0.05 was considered significant) This study examined the average normalized concentrations of amino acids (AAs) in control subjects and patients with different clinical types of growth of oral squamous cell carcinoma (OSCC), including endophytic and exophytic growth. Box plots were used to display the concentrations of AAs, which were shown to be distinct. Figure 5 displays the mean concentrations of SFAA in both the control and clinical type of growth groups. The concentrations of SFAA were elevated in the case patients in comparison to the control group. The Kruskal-Wallis‟s test conducted for this study indicates that there are significant differences in the concentrations of Pro, leu, Val, Phe, and Ile among the groups. The pair- wise comparison between control and endophytic growth, as well as control and exophytic growth, shows a substantial increase in all amino acids. However, the levels of Val are notably greater in endophytic growth compared to the control group. Conversely, concentrations of Ile, Leu, Pro, and Val were significantly higher in exophytic growth compared to controls. No significant difference was seen between endophytic and exophytic growth. 3.3 Risk Factors Associated With OSCC: Univariate logistic regression analysis was performed to explore factors associated with oral squamous cell carcinoma (OSCC). Oral habitual practices and poor oral hygiene demonstrated significant unadjusted associations with OSCC. In addition, higher salivary concentrations of Proline, Leucine, Isoleucine, Valine, Phenylalanine, and Lysine were significantly associated with case status when modeled as continuous variables. These findings indicate that altered salivary amino acid profiles are associated with OSCC and warranted further evaluation in multivariate analysis (Table 4 ). Table 3 Univariate logistic regression analysis of factors associated with OSCC (n = 63) Variable Odds Ratio (OR) 95% CI p-value Oral habitual factors (any vs none) 8.42 1.67–42.59 0.010 Poor oral hygiene* 6.57 2.16–20.01 0.001 Proline (per unit increase) 0.97 0.94–0.99 0.007 Leucine (per unit increase) 0.89 0.81–0.97 0.014 Isoleucine (per unit increase) 0.81 0.69–0.95 0.008 Valine (per unit increase) 0.85 0.76–0.95 0.004 Phenylalanine (per unit increase) 0.97 0.94–0.99 0.007 Lysine (per unit increase) 0.91 0.84–0.996 0.041 *p-value was determined by Univariate logistic regression. Multivariate logistic regression analysis was performed to identify factors independently associated with OSCC after adjustment for potential confounders. Poor oral hygiene remained a significant independent predictor of OSCC. Among salivary biomarkers, Leucine, Isoleucine, and Valine retained significant associations with case status in the adjusted model. Oral habitual factors demonstrated borderline statistical significance after adjustment. Other amino acids did not show independent associations in the multivariate analysis (Table 4 ). Table 4 Risk factors analysis of oral squamous cell carcinoma by Multivariate logistic regression (n = 63) Variable Adjusted OR 95% CI p-value Oral habitual factors (any vs none) 1.84 0.99–3.42 0.052 Poor oral hygiene 23.67 1.44–388.55 0.027 Proline (per unit increase) 0.93 0.84–1.03 0.161 Leucine (per unit increase) 22.63 1.67–307.41 0.019 Isoleucine (per unit increase) 0.14 0.03–0.83 0.030 Valine (per unit increase) 0.23 0.06–0.95 0.042 Phenylalanine (per unit increase) 0.72 0.39–1.31 0.279 Lysine (per unit increase) 1.01 0.82–1.24 0.934 *p-value was determined by Multivariate logistic regression. 3.4 Diagnostic Performance of Salivary AAs and SFAA Profile Index The diagnostic values of each potential SFAA biomarker for early-stage OSCC were evaluated based on the ROC curves. The ROC curve is a graphical technique for evaluating the performance of diagnostic tests and more generally for assessing the accuracy of a statistical model (e.g., logistic regression) that classifies subjects into 1 of 2 categories, diseased or no diseased [ 21 , 22 ]. Figure 6 shows the ROC curves of all the models. The ROC curves were obtained by plotting the sensitivity for diagnosing OSCC on the y-axis against 100% -specificity (%) on the x-axis. Table 5 shows the detailed AUC (95% CI), sensitivity, and specificity and cutoff value concentration of the SFAA biomarkers for OSCC prediction. As presented in Table 5 , the AUC of a single SFAA biomarker was from 0.5365 to 0.7835 in the case vs control mode. Estimates of disease risk were calculated by fitting the case vs control mode of the validation population to the two models constructed using data from this studied population, and the accuracy of each model was compared using ROC analysis. The results showed that Val as the explanatory variable had the highest accuracy (AUC = 0.784; good) and lys as the explanatory variable had the lowest accuracy (AUC = 0.537; unsatisfactory) [ 32 ]. Other four amino acids Phe (AUC = 0.712, Leu (AUC = 0.713), Ile (AUC = 0.744) and Pro (AUC = 0.715) showed good results [ 32 ]. All of the SFAA biomarkers except lys had independent predictive potential for early-stage OSCC. As a single salivary biomarker, Val proved the highest accuracy in predicting OSCC with sensitivity and specificity of 73.5 and 65.5, respectively (AUC: 0.784 and cutoff point: 5.5 µmol/L). Further Ile show its ability to predict OSCC with sensitivity and specificity of 76.5 and 62.0 (AUC: 0.744 and cutoff point: 1.3 µmol/L). The results indicated that single salivary AA could be used as biomarkers for the early diagnosis of OSCC. To demonstrate the utility of the SFAA profile for the early diagnosis of OSCC, an SFAA profile index in statistical analysis was used with a backward selection method based on the SFAA biomarkers. The final model derived from this analysis comprised Phe, Leu, Ile, Val and Pro. Figure 6 shows the ROC curve analysis for the predictive power of SFAA profile index for distinguishing control from the case. The AUC was 0.796 (95% CI: 0.6888 to 0.9025; sensitivity: 70.6%; specificity: 69.0% and cutoff point: 52.01 µmol/L) for the SFAA profile index of control vs case, which showed a high accuracy in predicting OSCC. The AUC, sensitivity, and specificity values were all increased with the SFAA profile index compared with any single salivary AA biomarker. The results indicated that the diagnostic accuracy was improved by using the biomarker panel (SFAA profile index) compared with using any single SFAA biomarker. These results demonstrated that a panel comprising the salivary Phe, Leu, Ile, Val and Pro concentrations might have important clinical potential for the early diagnosis and identification of OSCC. Using the SFAA profile index will improve the sensitivity and specificity for early-stage OSCC detection. Table 5 ROC curve metrics for salivary free amino acids and SFAA profile index Biomarker AUC (95% CI) Sensitivity (%) Specificity (%) Optimal Cut-off (µmol/L) Valine 0.784 (0.67–0.89) 73.5 65.5 5.5 Isoleucine 0.744 (0.62–0.86) 76.5 62.0 1.3 Phenylalanine 0.712 (0.58–0.84) 67.6 62.1 10.8 Leucine 0.713 (0.59–0.84) 70.6 58.6 5.0 Proline 0.715 (0.59–0.84) 67.6 62.0 20.2 Lysine 0.537 (0.40–0.67) 55.9 55.1 6.8 SFAA Profile Index 0.796 (0.69–0.90) 70.6 69.0 52.01 3.5 Analytical Performance and Determination of Salivary Free Amino Acids by LC–MS/MS Efficient separation of salivary free amino acids (SFAAs) from endogenous metabolites is critical for reliable biomarker quantification. In the present study, the chromatographic conditions and gradient elution program were optimized to achieve satisfactory resolution of all six target amino acids within a total run time of 22 minutes. Multiple reaction monitoring (MRM) was employed for analyte detection, and the protonated molecular ions \(\:[M+H{]}^{+}\) were selected as precursor ions. The mass accuracy was within acceptable limits, with deviations between theoretical and observed m/z values remaining below 5 mDa for all analytes. The optimized MRM transitions, retention times, collision energies, and dwell times for Proline, Leucine, Isoleucine, Valine, Phenylalanine, and Lysine are summarized in Table 6 . Representative LC–MS/MS chromatograms illustrate clear peak separation and consistent signal intensity across samples. Typical MRM chromatograms obtained from a blank matrix, a 100 µM mixed amino acid standard, and an OSCC case sample are shown in Fig. 7 , while chromatograms from a healthy control saliva sample are presented in Fig. 8 . The analytical performance of the LC–MS/MS method was further evaluated to confirm its suitability for quantitative salivary amino acid analysis. As summarized in Table 7 , all six analytes demonstrated excellent linearity over the concentration range of 2–100 µM, with correlation coefficients (R²) ≥ 0.998. The method exhibited high analytical sensitivity, with limits of detection (LOD) ranging from 0.07 to 0.68 µM and limits of quantification (LOQ) between 1.65 and 2.01 µM. Accuracy, assessed by recovery experiments at multiple concentration levels, ranged from 90.7% to 101.9%, indicating minimal matrix interference. Intra-day precision remained below 2%, and inter-day precision was below 9% for all analytes, confirming strong repeatability and intermediate precision. Collectively, these findings demonstrate that the developed LC–MS/MS method is sensitive, accurate, precise, and reproducible for the quantification of salivary free amino acids, supporting the robustness of subsequent biomarker analyses in OSCC patients. Table 6 Multiple reaction monitoring (MRM) transitions and optimized LC–MS/MS parameters for the quantification of salivary free amino acids. Amino Acid Retention Time (min) Precursor (m/z) Product (m/z) CE (V) Dwell Time (ms) Phe 3.541 166.10 120.10 -15.0 50.0 Leu 4.394 132.10 86.30 -11.0 50.0 Ile 4.542 132.10 86.30 -11.0 50.0 Val 5.628 118.20 72.00 -12.0 50.0 Pro 6.944 116.10 70.10 -16.0 50.0 Lys 16.67 147.00 84.10 -17.0 50.0 Table 7 Analytical performance characteristics of the LC–MS/MS method for salivary free amino acid quantification, including linearity, sensitivity, precision, and recovery. AA S/N Ratio Calibration curve Linearity (R 2 ) Calibration Range (µM) LOD LOQ Precision (RSD %) Recovery (%) Intra-day (n = 6) Inter-day (n = 6) Mean (n = 3) Phe 27.2 y = 25989.38x − 21621.72 0.9999 2-100 0.24 1.98 0.27 3.84 101.9 Leu 8.1 y = 9497.501x − 413.9832 0.9997 2-100 0.68 1.65 0.18 0.03 90.7 Ile 10.7 y = 9279.141x + 8784.554 0.9993 2-100 0.61 1.85 0.03 2.71 100.3 Val 10.5 y = 12145.58x − 1748.107 0.9988 2-100 0.62 2.01 0.74 4.34 99.2 Pro 14.9 y = 24028.64x − 26385.82 0.9982 2-100 0.43 1.92 1.05 2.05 91.6 Lys 104.9 y = 21696.42x + 16324.84 1.0000 2-100 0.07 1.97 0.45 8.21 100.5 standard and, (C) case sample. 4. Discussion Oral cancer remains a major global health burden, with South-East Asian countries particularly India and neighboring regions contributing disproportionately to disease incidence and mortality [ 33 ]. Oral cancer accounts for approximately 30% of all malignancies diagnosed in India, with an incidence of nearly 20 per 100,000 individuals, and oral squamous cell carcinoma (OSCC) representing the predominant histological subtype [ 34 , 35 ]. Despite advances in multimodal therapy, the 5-year survival rate of OSCC remains below 50%, largely due to delayed diagnosis at advanced stages [ 36 ]. Therefore, the identification of reliable, non-invasive biomarkers for early detection is of critical clinical importance. Cancer progression is characterized by profound metabolic reprogramming, including enhanced protein catabolism and nitrogen loss. Tumor cells exhibit increased utilization of glucose and amino acids to support proliferation, energy production, and biosynthetic demands. Peripheral protein breakdown releases amino acids that are transported to tumors and visceral organs, contributing to gluconeogenesis and cellular growth [ 37 ]. Consequently, alterations in amino acid profiles have emerged as promising indicators of disrupted protein metabolism and malignant transformation [ 38 ]. Amino acids play central roles in cellular metabolism as substrates for protein synthesis and regulators of metabolic pathways. Multiple studies have demonstrated significant alterations in plasma-free amino acid profiles among cancer patients compared with healthy individuals [ 16 – 18 , 39 , 40 ]. Saliva, owing to its non-invasive accessibility and close metabolic interaction with systemic circulation, has gained increasing attention as a diagnostic biofluid. However, salivary free amino acid (SFAA) alterations in OSCC remain underexplored. In this study, a sensitive and reproducible LC–MS/MS-based method without derivatization was employed to quantify SFAAs, enabling robust metabolomic assessment. Our findings demonstrate that all six investigated amino acids Proline, Valine, Phenylalanine, Isoleucine, Leucine, and Lysine were significantly elevated in the saliva of OSCC patients compared with healthy controls. These results are consistent with previous reports. Sugimoto et al. observed significantly increased Valine, Phenylalanine, Leucine, and Isoleucine levels in OSCC patients, although Proline and Lysine did not differ significantly in their cohort [ 41 ]. Ohshima et al. similarly reported elevated salivary Valine, Isoleucine, and Leucine in Japanese OSCC patients [ 42 ], while Lohavanichbutr et al. identified Proline as a significant marker but reported inconsistent findings for other amino acids [ 43 ]. Variability across studies likely reflects differences in population characteristics, dietary patterns, tumor biology, and analytical platforms. Nevertheless, the consistent elevation of branched-chain amino acids (Valine, Leucine, Isoleucine) across multiple studies supports their role in OSCC-associated metabolic dysregulation. Increased amino acid availability may facilitate tumor cell proliferation, energy expenditure, and anabolic growth. Age-related trends observed in this study align with existing literature. The majority of OSCC patients were aged 51–70 years, consistent with findings reported by Lohavanichbutr et al. and population-based studies from India [43,44]. The increased incidence in older age groups may reflect cumulative carcinogen exposure and prolonged latency between initial exposure and clinically detectable disease. Although OSCC is traditionally more prevalent among males, this study observed a higher proportion of female patients. This divergence may be attributable to sociocultural factors, including delayed healthcare-seeking behavior among women and differential exposure to risk factors in the local population [45]. Educational status emerged as a significant determinant, with lower educational attainment associated with increased OSCC prevalence, consistent with previous observations that education influences health awareness, oral hygiene practices, and access to screening [46]. Habitual risk factors particularly betel quid chewing and tobacco use were strongly associated with OSCC in this study. These findings corroborate extensive evidence from South-East Asia identifying betel quid chewing as a dominant etiological factor [44,45,47]. Poor oral hygiene, frequently associated with these habits, further contributes to chronic inflammation and carcinogenesis. In both univariate and multivariate analyses, poor oral hygiene emerged as a significant independent risk factor for OSCC, supporting its role as a modifiable determinant. Tumor site distribution revealed the vestibule as the most commonly affected location, followed by the retromolar trigone and buccal mucosa. While buccal mucosa predominates in several Indian cohorts, site variability may reflect regional chewing practices and local exposure of carcinogens within the oral cavity [48,49]. Stage-wise analysis demonstrated increasing SFAA concentrations with advancing disease severity, although some amino acids showed differential patterns between stage III and stage IV disease. These variations may reflect metabolic heterogeneity of OSCC or limited subgroup sample sizes rather than true biological regression. Similarly, amino acid concentrations differed according to tumor growth patterns, suggesting metabolic distinctions between endophytic and exophytic lesions. Receiver operating characteristic analysis confirmed the diagnostic potential of individual SFAAs and the composite SFAA profile index. While single amino acids demonstrated moderate discriminative ability, the combined SFAA profile index yielded superior diagnostic performance (AUC = 0.796), with improved sensitivity and specificity compared with any individual biomarker. The cutoff values were derived and reported consistently in µmol/L, aligning with LC–MS/MS quantification standards. These findings indicate that metabolic disruption in OSCC manifests as a distinct salivary amino acid signature, supporting the clinical utility of multiplex biomarker approaches. This study has limitations. Only six amino acids were analyzed, and the sample size was modest due to time, budgetary constraints, and limited analytical infrastructure. Potential confounders such as stress levels and oral microbiome composition were not fully assessed. Additionally, external validation in independent cohorts is required to confirm the generalizability of these findings. Despite these limitations, the results provide compelling evidence that salivary free amino acid profiling may complement conventional diagnostic strategies for early, non-invasive OSCC detection. 5. Conclusion All the SFAAs except Lys levels showed a significant difference between OSCC patients and healthy subjects. This suggests that salivary amino acids detection might be helpful in the early and rapid diagnosis of OSCC. A larger sample size and a greater number of salivary amino acids should be included for further study and should minimize the changing of concentration of amino acids due to stressful mental conditions and diet. It is recommended that a detailed study of amino acids might help understand the mechanism of metabolic changes in cancer patients and monitor for recurrence. Abbreviations OSCC Oral Squamous Cell Carcinoma BMU Bangladesh Medical University BriCM Bangladesh Reference Institute for Chemical Measurements DDCH Dhaka Dental College Hospital IRB Institutional Review Board UPLC–MS/MS Ultra-Performance Liquid Chromatography–Tandem Mass Spectrometry MS Mass Spectrometry HPLC High-Performance Liquid Chromatography SPSS Statistical Package for the Social Sciences HIV Human Immunodeficiency Virus AA Amino Acid Pro Proline Val Valine Phe Phenylalanine Ile Isoleucine Leu Leucine Lys Lysine Declarations 6. Acknowledgement We sincerely thank Professor Dr. Md. Wares Uddin and Professor Dr. Ismat Ara Haider for granting permission and facilitating sample collection from the Department of Oral and Maxillofacial Surgery, Bangabandhu Sheikh Mujib Medical University (BSMMU), and Dhaka Dental College Hospital, respectively. We are also grateful to Dr. Pritimoy Das (PhD researcher, Federation University, Australia) for critically reviewing the manuscript prior to submission. In addition, we acknowledge BRCiM and Innoclin Research for their administrative and logistical support throughout the conduct of the study and manuscript preparation. 6. Funding Funding received from Bangladesh Medical University (BMU) (Thesis Grant no: 7214, Year- 2021). 7. Consent to Participate Written informed consent was obtained from all individual participants prior to enrollment in the study. 8. Ethics Approval The study was approved by the Institutional Review Board (IRB) Bangladesh Medical University (BMU) (IRB Reg. No. BSMMU/MU/2021/7214), 9. Human Ethics and Consent to Participate This study involved human participants and was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Institutional Review Board (IRB) of Bangladesh Medical University (BMU) (IRB Reg. No. BSMMU/MU/2021/7214), Written informed consent was obtained from all participants prior to participation. 10. Conflict of interest The authors report no conflict of interest. 11. Author’s contribution Conception and design: D.M., M.H.R., M.A., A.U.S.; Development of methodology: M.H.R., M.M.H., M.I., S.A.M., S.A., M.K. A.U.S.; Acquisition of data: D.M., S.P., M.S.I., M.A.F., K.A., A.U.S., S.A.M., S.A.; Analysis and interpretation of data: M.H.R., M.M.H., M.I., M.K., M.A.; Writing, review, and/or revision of the manuscript: D.M., M.H.R., M.M.H., M.I., S.A.M., S.A., S.P., M.S.I., M.A.F., K.A., A.U.S., M.K., M.A.; Study supervision: M.A., M.H.R. All authors contributed to the article and approved the submitted version. 12. Data availability The datasets which was analyzed during the current study are not publicly available due to confidentiality and institutional restrictions, but are available from the corresponding author on reasonable request. References Wong T, Wiesenfeld D. 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12:43:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1476637,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of participant recruitment and inclusion in the study.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8663305/v1/4604c1845a558bd4e483d698.png"},{"id":103504887,"identity":"4c44bf81-77a4-47b3-ba94-14dc2927ac5e","added_by":"auto","created_at":"2026-02-26 13:21:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":366184,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the squamous cell carcinoma among the patients (n=34)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8663305/v1/1c6d4413fce07b13c0b6b2e4.png"},{"id":103166975,"identity":"5fa0b786-c78a-4851-99b5-970a40a78e45","added_by":"auto","created_at":"2026-02-22 12:43:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":555234,"visible":true,"origin":"","legend":"\u003cp\u003eBox plots of the concentrations of the AAs of control and clinical stage comparison (p-value was determined by Mann-Whitney and Kruskal Wallis test. P\u0026lt; 0.05 was considered significant)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8663305/v1/f5e4684073acefea3cf8c5f8.png"},{"id":103505246,"identity":"d1552e49-1b92-4ce9-adba-053771b77b1f","added_by":"auto","created_at":"2026-02-26 13:28:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":438459,"visible":true,"origin":"","legend":"\u003cp\u003eBox plots of the concentrations of the AAs of control and clinical type of growth\u003c/p\u003e\n\u003cp\u003ecomparison (p-value was determined by Mann-Whitney and Kruskal Wallis test. P\u0026lt; 0.05 was considered significant)\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8663305/v1/0290bc6d2f4ef228313a489d.png"},{"id":103504964,"identity":"bcac9ff4-02ee-467f-b0fd-390242d6928e","added_by":"auto","created_at":"2026-02-26 13:22:17","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":572156,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis of AA biomarkers in the diagnosis of OSCC\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8663305/v1/24db567af0d479041760433d.png"},{"id":103166981,"identity":"60b4025e-0646-4f84-bc98-969226d27178","added_by":"auto","created_at":"2026-02-22 12:43:15","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1345633,"visible":true,"origin":"","legend":"\u003cp\u003eTypical MRM chromatograms obtained from saliva samples of an (A) blank, (B) 100 μM\u003c/p\u003e\n\u003cp\u003estandard and, (C) case sample.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8663305/v1/a9ea37be8f99db1b399c29dc.png"},{"id":103505309,"identity":"06efde38-af1b-4242-8326-b2a6dfa53050","added_by":"auto","created_at":"2026-02-26 13:29:42","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":321873,"visible":true,"origin":"","legend":"\u003cp\u003eMRM chromatograms obtained from Saliva samples of a healthy sample.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8663305/v1/9ae60d1a4a300d730875ae67.png"},{"id":103509756,"identity":"06348878-8ff3-4f3b-84d4-4a976e329a52","added_by":"auto","created_at":"2026-02-26 14:00:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6465217,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8663305/v1/f49feefe-5e45-4c88-ad2c-4071c846e419.pdf"},{"id":103166983,"identity":"24edbd16-7c1e-4b0f-8f09-c0ca6461d733","added_by":"auto","created_at":"2026-02-22 12:43:15","extension":"jpeg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":200451,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Presentation\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8663305/v1/9bcee8a010de83dfb0d4d156.jpeg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Salivary Amino Acids as Non-Invasive Biomarkers for Oral Squamous Cell Carcinoma: A Case Control Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOral squamous cell carcinoma (OSCC) accounts for nearly 90% of malignancies arising in the oral cavity. Globally, it is the sixth most common cancer, while in South Asia including Sri Lanka, India, Bangladesh, and Pakistan it ranks among the three most prevalent cancers [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Established risk factors include tobacco smoking, betel-quid chewing, alcohol consumption, low socioeconomic status, and ultraviolet radiation exposure, particularly for lip cancer [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Other contributors such as human papillomavirus (HPV) infection, Candida colonization, nutritional deficiencies, and genetic predisposition have also been implicated in OSCC development [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Clinically, OSCC often presents as a painless ulcer with indurated or rolled borders, a feature that contributes to late diagnosis and the high mortality rate associated with this disease [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Although incisional biopsy followed by histopathological examination remains the gold standard for diagnosis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], it is invasive, operator-dependent, and may be technically challenging in posterior oral regions or in patients with restricted mouth opening. These limitations underscore the urgent need for simple, non-invasive diagnostic strategies that may facilitate earlier detection and improve survival outcomes.\u003c/p\u003e \u003cp\u003eHuman saliva closely resembles blood in biochemical composition, containing water, electrolytes, enzymes, proteins, and a wide range of small molecules transferred from the bloodstream via ultrafiltration, passive diffusion, active transport, and both intercellular and extracellular pathways [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. As saliva collection is non-invasive, economical, and suitable for repeated sampling, it has gained increasing attention as a promising biological matrix for large-scale screening and biomarker discovery. Salivary diagnostics have shown utility in various diseases including diabetes mellitus, HIV, dengue, breast cancer, gastric cancer, lung cancer, and oral cancer [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The ability to obtain saliva samples easily, even by patients themselves, further enhances its clinical applicability.\u003c/p\u003e \u003cp\u003eAlterations in amino acid (AA) metabolism are well-recognized features of cancer biology, reflecting the heightened metabolic demands, disrupted protein turnover, and tumor-driven catabolic processes characteristic of malignancy. Early studies suggested that abnormal AA profiles in cancer patients were primarily due to malnutrition and cancer-related anorexia leading to weight loss [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, subsequent research demonstrated that deviations in circulating and salivary free amino acid (SFAA) profiles also occur in weight-stable cancer patients, indicating cancer-specific metabolic reprogramming rather than simple nutritional deficiency [\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Cachexia is common in advanced cancer which is characterized by hypermetabolism, peripheral insulin resistance, increased protein catabolism, and nitrogen imbalance, all of which further contribute to alterations in AA concentrations [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These metabolic changes provide a strong biological basis for investigating SFAA profiles as potential biomarkers for OSCC detection.\u003c/p\u003e \u003cp\u003eTechnological advancements have further strengthened the feasibility of salivary SFAA analysis. While high-performance liquid chromatography (HPLC) with UV or fluorescence detection has traditionally been used for AA quantification [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], these methods may be hindered by non-specific derivatization byproducts. In contrast, ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC\u0026ndash;MS/MS) offers superior selectivity, sensitivity, and throughput for amino acid analysis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Using mass-to-charge (m/z) ratios and advanced acquisition modes such as selected reaction monitoring (SRM) and multiple reaction monitoring (MRM), UPLC\u0026ndash;MS/MS enables accurate, automated, and high-throughput identification of amino acids in complex biological samples [\u003cspan additionalcitationids=\"CR25 CR26 CR27\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This enhances the precision and clinical feasibility of salivary metabolite-based diagnostic approaches.\u003c/p\u003e \u003cp\u003eTo date, only a single study from the South Asian region has evaluated salivary amino acid levels in oral cancer patients [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and no study in Bangladesh has investigated salivary amino acid profiling for OSCC diagnosis. Therefore, this study aimed to assess the association of specific salivary amino acid levels with OSCC and to explore their potential diagnostic and prognostic utility.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003eThis The study is reported according to the STROBE statement for case\u0026ndash;control studies \u0026amp; that was conducted over a 15-month period from December 2020 to February 2022 at Bangladesh Medical University (BMU) and Dhaka Dental College Hospital (DDCH), with analytical procedures performed at the Bangladesh Reference Institute for Chemical Measurements (BRiCM). A total of 110 eligible individuals were initially approached; however, due to COVID-19\u0026ndash;related restrictions and limited laboratory capacity, 63 participants (34 histopathologically confirmed OSCC cases and 29 clinically healthy controls) were finally recruited using a convenient sampling technique. Cases were adults newly diagnosed with oral squamous cell carcinoma, while controls were healthy volunteers without any oral lesions, malignancy, or severe periodontal disease. Detailed demographic and clinical histories were obtained using a structured data sheet, followed by standardized clinical examinations to minimize measurement variability. Unstimulated whole saliva samples were collected following a 2-hour fasting period, ensuring uniform pre-analytical conditions, and immediately transported in a validated cold-chain system maintained at \u003cb\u003e2\u0026ndash;8\u0026deg;C\u003c/b\u003e to BRiCM for biochemical analysis. Levels of six salivary free amino acids (Pro, Val, Phe, Ile, Leu, and Lys) the primary variables of interest were quantified using ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC\u0026ndash;MS/MS) employing an Intrada Amino Acid analytical column. All laboratory procedures followed the same protocol for cases and controls to reduce differential measurement bias. The study size was determined pragmatically based on feasibility during the pandemic and the capacity of the country\u0026rsquo;s only available UPLC\u0026ndash;MS/MS facility; nevertheless, the final sample retained adequate variability to examine associations between SFAA levels and OSCC. To minimize bias, all saliva samples were coded with anonymous ID numbers, laboratory analysts were blinded to case\u0026ndash;control status, and uniform procedures were maintained throughout sample handling and processing. Quantitative variables were analyzed using appropriate non-parametric tests (Mann\u0026ndash;Whitney U and Kruskal\u0026ndash;Wallis) due to skewed distributions, while diagnostic accuracy was assessed using receiver operating characteristic (ROC) curves and area-under-the-curve (AUC) estimates with 95% confidence intervals. Ethical clearance was obtained from the Institutional Review Board of \u003cb\u003eBMU (IRB Reg. No. BSMMU/MU/2021/7214\u003c/b\u003e), and the study was registered on ClinicalTrials.gov (NCT05150509). Prior to enrollment, all participants received a full explanation of study objectives, procedures, potential risks, and their rights, and written informed consent was obtained in accordance with the Declaration of Helsinki while ensuring strict confidentiality through coded documentation and restricted data access.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Reagent and materials\u003c/h2\u003e \u003cp\u003eAcetonitrile, methanol, and tetrahydrofuran (\u0026ge;\u0026thinsp;99%, LC\u0026ndash;MS grade) were obtained from Honeywell, USA. Ultrapure water (18.2 MΩ\u0026middot;cm) was produced using an in-house Milli-Q purification system. Formic acid (\u0026ge;\u0026thinsp;98%) and ammonium formate (\u0026ge;\u0026thinsp;98%) were purchased from Merck, Germany. A mixed amino acid stock standard (0.2 mg/mL for each analyte, analytical grade) was prepared by dissolving the amino acids in ultrapure water. Working standards were freshly prepared before analysis by serial dilution of the stock solution using a 75:25 (v/v) acetonitrile\u0026ndash;water mixture. All reagent solutions were stored at 4\u0026deg;C in amber glass vials to protect them from light and maintain stability until use\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Incisional biopsy for OSCC patients\u003c/h2\u003e \u003cp\u003ePatients presenting with clinically suspicious oral lesions were evaluated for eligibility for incisional biopsy (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As part of institutional protocol during the COVID-19 pandemic, all suspected cases first underwent RT-PCR testing for SARS-CoV-2. Only individuals with a confirmed negative result within 24 hours proceeded with further clinical procedures. Following routine oral prophylaxis (scaling), patients were referred to the Department of Minor Oral Surgery for incisional biopsy under local anaesthesia and strict aseptic conditions. Tissue samples were collected from the most clinically suspicious area of each lesion and immediately preserved in 10% neutral buffered formalin. The specimens were transported to the histopathology laboratory for microscopic examination. Histopathological reports were obtained for each patient, and information for all confirmed OSCC cases was recorded in a predesigned data collection sheet.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Saliva collection and sample preparation\u003c/h2\u003e \u003cp\u003ePeriodontal scaling was performed during the first visit, and participants were re-evaluated after 15 days to ensure improved periodontal status. Each participant was contacted by phone for follow-up, and an additional RT-PCR test for SARS-CoV-2 was performed. Once a negative COVID-19 result was confirmed, unstimulated whole saliva was collected the next morning between 9:00 and 11:00 a.m. Participants were instructed to refrain from eating or drinking (except water) for at least 2 hours prior to collection. They were asked to rinse their mouth with water, wait 10 minutes, sit upright with the head slightly tilted forward, and expectorate a minimum of 5 mL of unstimulated saliva into a calibrated collection tube [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Collected samples were immediately maintained in a 2\u0026ndash;8\u0026deg;C cold-chain and transported to the Bangladesh Reference Institute for Chemical Measurements (BRiCM). Upon arrival, each sample was centrifuged at 10,000 rpm for 10 minutes at 4\u0026deg;C to remove insoluble debris, food particles, and cellular components. The resulting clear supernatant was aliquoted and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until biochemical analysis. For UPLC\u0026ndash;MS/MS preparation, 400 \u0026micro;L of thawed saliva was mixed with 800 \u0026micro;L of acetonitrile in a 2.0-mL Eppendorf tube, vortexed vigorously for 1 minute to precipitate proteins, and allowed to stand for 15 minutes. The mixture was then centrifuged at 10,000 rpm for 20 minutes at 4\u0026deg;C. The final supernatant was filtered through a 0.22 \u0026micro;m syringe filter to obtain a particle-free extract suitable for LC\u0026ndash;MS/MS analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 SFAA Analysis by LC\u0026ndash;MS/MS\u003c/h2\u003e \u003cp\u003eSalivary free amino acids were analyzed using a Shimadzu LCMS-8050 Ultra-Fast Liquid Chromatography system (Shimadzu Corporation, Kyoto, Japan) equipped with a triple-quadrupole mass spectrometer and an electrospray ionization (ESI) source. The system comprised binary pumps, an autosampler, an online degassing unit, and a thermostated column oven. Chromatographic separation was achieved on an Intrada Amino Acid column (100 mm \u0026times; 3 mm, 3 \u0026micro;m), which provides high-resolution, reproducible retention of both hydrophilic and hydrophobic amino acids.\u003c/p\u003e \u003cp\u003eThe mobile phase consisted of two solutions: Solvent A: acetonitrile/tetrahydrofuran/25 mM ammonium formate/formic acid (9/75/16/0.3, v/v) and Solvent B: acetonitrile/100 mM ammonium formate (20/80, v/v). A gradient elution program was applied at a flow rate of 0.6 mL/min as follows: 0% B (0\u0026ndash;3.0 min), 0\u0026ndash;17% B (3.0\u0026ndash;9.0 min), 17\u0026ndash;100% B (9.0\u0026ndash;16.0 min), 100% B (16.0\u0026ndash;22.0 min), and re-equilibration at 0% B at 22.0 min. The column oven temperature was maintained at 35\u0026deg;C. The autosampler, operating in full-loop injection mode, was equipped with a 10 \u0026micro;L injection loop. The total run time was 22 minutes.\u003c/p\u003e \u003cp\u003eMass spectrometric detection was performed in multiple reaction monitoring (MRM) mode using electrospray ionization in the positive ion mode. The instrument was operated with the following optimized ionization parameters: capillary voltage, 4.0 kV; interface temperature, 300\u0026deg;C; desolvation line temperature, 300\u0026deg;C; block temperature, 400\u0026deg;C; nebulizing gas (N₂), 1.5 L/min; drying gas (N₂), 15.0 L/min; and heating gas, 10 L/min. Collision-induced dissociation (CID) was carried out using argon at 270 kPa. These settings enabled sensitive and selective detection of the target amino acids.\u003c/p\u003e \u003cp\u003eRaw data acquired from the positive ESI mode were processed using LabSolutions (Shimadzu), with peak selection, smoothing, integration, and quantification parameters optimized for the targeted amino acids. Processed data were exported in text format for subsequent statistical and discriminant analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Method Validation\u003c/h2\u003e \u003cp\u003e Validation of the LC\u0026ndash;MS/MS method was conducted following the ICH Q2(R1) guideline to ensure suitability for quantitative determination of salivary free amino acids (SFAAs). Validation parameters included linearity, sensitivity (LOD and LOQ), accuracy, and precision (intra-day and inter-day). Linearity was evaluated by preparing six-point calibration curves using saliva matrices spiked with known concentrations of each amino acid after subtraction of endogenous levels. All six analytes demonstrated excellent linearity across the working range (2\u0026ndash;100 \u0026micro;M), with correlation coefficients (R\u0026sup2; \u0026ge; 0.998). The calibration equations and linearity ranges are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Sensitivity was determined based on signal-to-noise ratios of approximately 3:1 for the limit of detection (LOD) and 10:1 for the limit of quantification (LOQ). LOD values ranged from 0.07 to 0.68 \u0026micro;M, and LOQ values ranged from 1.65 to 2.01 \u0026micro;M, confirming adequate analytical sensitivity for salivary biomarker detection. Accuracy was assessed using recovery tests at three concentration levels (20%, 50%, and 100% of the calibration range). Mean recoveries ranged from 90.7% to 101.9%, indicating good accuracy with minimal matrix interference. Precision was assessed through replicate analyses of spiked samples. Intra-day precision (n\u0026thinsp;=\u0026thinsp;5 per level) demonstrated RSD values\u0026thinsp;\u0026lt;\u0026thinsp;2%, while inter-day precision (over 3 consecutive days) showed RSD\u0026thinsp;\u0026lt;\u0026thinsp;9%, supporting strong repeatability and intermediate precision. The combined validation data confirm that the LC\u0026ndash;MS/MS method is sensitive, accurate, precise, and reproducible for quantifying salivary free amino acids. These findings support the reliability of the subsequent biomarker analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using SPSS version 26 (IBM Corp., Armonk, NY, USA), GraphPad Prism version 8.0.2, and R version 4.3. Categorical variables were summarized as frequencies and percentages, while continuous variables were expressed as means, medians, and interquartile ranges as appropriate. Group comparisons for continuous variables were conducted using either the independent samples t-test (for normally distributed data) or the Mann\u0026ndash;Whitney U test (for non-normally distributed data). Categorical variables were compared using the Chi-square test or two-tailed Fisher\u0026rsquo;s exact test when cell counts were small. For comparisons involving more than two groups, the Kruskal\u0026ndash;Wallis test was applied, followed by appropriate post-hoc analysis where necessary. Mean concentration levels of the six salivary free amino acids (Pro, Leu, Val, Phe, Ile, and Lys) were calculated for both groups.\u003c/p\u003e \u003cp\u003eDiagnostic performance of individual amino acids and the composite SFAA profile index was evaluated using receiver operating characteristic (ROC) curve analysis. Sensitivity, specificity, and area under the ROC curve (AUC) with 95% confidence intervals (CI) were estimated to assess discriminative ability for OSCC detection. Optimal cutoff values were determined using the Youden Index (maximum sum of sensitivity and specificity). A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant for all analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eA total of 110 potential participants were initially approached for eligibility assessment. Due to COVID-19 related restrictions, difficulty in arranging hospital visits, and limited LC-MS/MS laboratory capacity at BRiCM, 47 individuals could not be enrolled. Consequently, 63 participants met the eligibility criteria and provided usable saliva samples, including 34 histopathologically confirmed OSCC cases and 29 healthy controls. No participants were lost after enrollment, and all 63 samples were successfully analyzed. A detailed participant recruitment and inclusion pathway is presented in the flow diagram below on Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Demographical data analysis\u003c/h2\u003e \u003cp\u003eIn this study majority of the participants in both cases 23(67.6%) and controls 17(58.6%) were aged between 51\u0026ndash;70 years. In case group 21(61.8%) and in control group 18(62.1%) were female. Among all participants, 14(41.2%) of the cases and 13(44.8%) of the controls were from the middle class. The majority of the cases 24(70.6%) and healthy controls 17(58.6%) had no comorbidities. The majority 24(70.6%) of the cases had educational status below the secondary school certificate exam (SSC) or O-level. In contrast, only 11(38.1%) of controls were in that group. Educational status was lower among the oral squamous cell carcinoma (OSCC) cases than the controls. Betel quid chewing and smoking were the habitual factors we identified in our study. Healthy controls had a significantly lower number of oral habitual factors. Betel quid chewing was the common oral habit 22 (64.7%) among the cases (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). By univariate logistic regression, we found that oral habitual factors, poor oral hygiene, and increased amino acid levels were significantly associated risk factors for oral squamous cell carcinoma (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Poor oral hygiene was determined by visible stains of teeth and periodontal pocket depth of more than 3 mm [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. By multivariate logistic regression poor oral hygiene, increased leu, Ile, and Val level were significantly associated risk factors for oral squamous cell carcinoma (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline sociodemographic and habit-related characteristics of study participants (n\u0026thinsp;=\u0026thinsp;63)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOSCC Cases (n\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealthy Controls (n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge Group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (32.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (37.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u0026ndash;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (67.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (58.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (38.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (37.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (61.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (62.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocioeconomic Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (32.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle Class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (44.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (37.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational Level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelow SSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (70.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (38.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSC and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (61.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (41.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (70.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (58.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOral Habits\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Habit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBetel Quid Chewing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (64.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (37.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking Only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (17.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth Chewing\u0026thinsp;+\u0026thinsp;Smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (23.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (10.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Distribution of OSCC\u003c/h2\u003e \u003cp\u003eAmong the cases, 15 (44.1%) of the cancer was located in the vestibule, followed by the\u003c/p\u003e \u003cp\u003eretromolar trigone and buccal mucosa, 9 (26.5%) and 7 (20.6%) respectively. Majority\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e27(79.4%) of the cancer growth was exophytic and clinical stage IV of cancer 17(50%)\u003c/h3\u003e\n\u003cp\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Salivary Free Amino Acid Concentrations in OSCC vs Controls\u003c/h2\u003e \u003cp\u003eIn comparison to healthy controls, patients with OSCC demonstrated substantially higher concentrations of all six salivary free amino acids (SFAAs). Mann\u0026ndash;Whitney U tests revealed significant elevations of Proline, Leucine, Valine, Phenylalanine, Isoleucine, and Lysine in the case group (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD concentrations and associated p-values. To visualize these differences across disease severity, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents boxplots comparing controls, stage 3, and stage 4 OSCC cases. As shown, amino acid concentrations rise progressively with advancing stage.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean salivary amino acid levels in OSCC cases and healthy controls (\u0026micro;mol/L)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmino Acid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCases (n\u0026thinsp;=\u0026thinsp;34) Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControls (n\u0026thinsp;=\u0026thinsp;29) Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u0026dagger;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProline (Pro)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e47.4\u0026thinsp;\u0026plusmn;\u0026thinsp;35.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e22.5\u0026thinsp;\u0026plusmn;\u0026thinsp;19.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeucine (Leu)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e17.2\u0026thinsp;\u0026plusmn;\u0026thinsp;22.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValine (Val)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e22.2\u0026thinsp;\u0026plusmn;\u0026thinsp;29.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenylalanine (Phe)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e19.8\u0026thinsp;\u0026plusmn;\u0026thinsp;19.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e8.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsoleucine (Ile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e13.4\u0026thinsp;\u0026plusmn;\u0026thinsp;19.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLysine (Lys)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e14.5\u0026thinsp;\u0026plusmn;\u0026thinsp;18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe mean normalized AA concentrations of control and clinical stage (stage 3 and stage 4 of\u003c/p\u003e \u003cp\u003eOSCC) patients were investigated in this study showing box plots of the concentrations of the\u003c/p\u003e \u003cp\u003eAAs, which are different. The average concentrations of SFAA from the control and clinical\u003c/p\u003e \u003cp\u003estage comparison are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. SFAA concentrations were higher in case patients\u003c/p\u003e \u003cp\u003ecompared with control. Kruskal Wallis test conducted for this study shows that the\u003c/p\u003e \u003cp\u003econcentration of Pro, Leu, Val, Phe and Ile significantly differs between the groups. Pair-wise\u003c/p\u003e \u003cp\u003ecomparison of control vs stage 3 and control vs stage 4 are significantly higher for all AAs.\u003c/p\u003e \u003cp\u003eBut for control vs stage 3, it shows significantly higher for Ile and Val. On the other hand, for\u003c/p\u003e \u003cp\u003econtrol vs stage 4 Ile, Phe, and Val show significantly higher concentrations. No significant\u003c/p\u003e \u003cp\u003edifference was found between stage 3 and stage 4.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ecomparison (p-value was determined by Mann-Whitney and Kruskal Wallis test. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant)\u003c/p\u003e \u003cp\u003eThis study examined the average normalized concentrations of amino acids (AAs) in control\u003c/p\u003e \u003cp\u003esubjects and patients with different clinical types of growth of oral squamous cell carcinoma\u003c/p\u003e \u003cp\u003e(OSCC), including endophytic and exophytic growth. Box plots were used to display the\u003c/p\u003e \u003cp\u003econcentrations of AAs, which were shown to be distinct. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e displays the mean concentrations of SFAA in both the control and clinical type of growth groups. The\u003c/p\u003e \u003cp\u003econcentrations of SFAA were elevated in the case patients in comparison to the control\u003c/p\u003e \u003cp\u003egroup. The Kruskal-Wallis‟s test conducted for this study indicates that there are significant\u003c/p\u003e \u003cp\u003edifferences in the concentrations of Pro, leu, Val, Phe, and Ile among the groups. The pair- wise comparison between control and endophytic growth, as well as control and exophytic\u003c/p\u003e \u003cp\u003egrowth, shows a substantial increase in all amino acids. However, the levels of Val are notably greater in endophytic growth compared to the control group. Conversely, concentrations of Ile, Leu, Pro, and Val were significantly higher in exophytic growth compared to controls. No significant difference was seen between endophytic and exophytic growth.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Risk Factors Associated With OSCC:\u003c/h2\u003e \u003cp\u003eUnivariate logistic regression analysis was performed to explore factors associated with oral squamous cell carcinoma (OSCC). Oral habitual practices and poor oral hygiene demonstrated significant unadjusted associations with OSCC. In addition, higher salivary concentrations of Proline, Leucine, Isoleucine, Valine, Phenylalanine, and Lysine were significantly associated with case status when modeled as continuous variables. These findings indicate that altered salivary amino acid profiles are associated with OSCC and warranted further evaluation in multivariate analysis (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate logistic regression analysis of factors associated with OSCC (n\u0026thinsp;=\u0026thinsp;63)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOral habitual factors (any vs none)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.67\u0026ndash;42.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor oral hygiene*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.16\u0026ndash;20.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProline (per unit increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94\u0026ndash;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeucine (per unit increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81\u0026ndash;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsoleucine (per unit increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.69\u0026ndash;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValine (per unit increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76\u0026ndash;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenylalanine (per unit increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94\u0026ndash;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLysine (per unit increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84\u0026ndash;0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*p-value was determined by Univariate logistic regression.\u003c/p\u003e \u003cp\u003eMultivariate logistic regression analysis was performed to identify factors independently associated with OSCC after adjustment for potential confounders. Poor oral hygiene remained a significant independent predictor of OSCC. Among salivary biomarkers, Leucine, Isoleucine, and Valine retained significant associations with case status in the adjusted model. Oral habitual factors demonstrated borderline statistical significance after adjustment. Other amino acids did not show independent associations in the multivariate analysis (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk factors analysis of oral squamous cell carcinoma by Multivariate logistic regression (n\u0026thinsp;=\u0026thinsp;63)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOral habitual factors (any vs none)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor oral hygiene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.44\u0026ndash;388.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProline (per unit increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeucine (per unit increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.67\u0026ndash;307.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsoleucine (per unit increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u0026ndash;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValine (per unit increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u0026ndash;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenylalanine (per unit increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.39\u0026ndash;1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLysine (per unit increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82\u0026ndash;1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*p-value was determined by Multivariate logistic regression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Diagnostic Performance of Salivary AAs and SFAA Profile Index\u003c/h2\u003e \u003cp\u003eThe diagnostic values of each potential SFAA biomarker for early-stage OSCC were\u003c/p\u003e \u003cp\u003eevaluated based on the ROC curves. The ROC curve is a graphical technique for evaluating\u003c/p\u003e \u003cp\u003ethe performance of diagnostic tests and more generally for assessing the accuracy of a\u003c/p\u003e \u003cp\u003estatistical model (e.g., logistic regression) that classifies subjects into 1 of 2 categories,\u003c/p\u003e \u003cp\u003ediseased or no diseased [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the ROC curves of all the models. The ROC\u003c/p\u003e \u003cp\u003ecurves were obtained by plotting the sensitivity for diagnosing OSCC on the y-axis against\u003c/p\u003e \u003cp\u003e100% -specificity (%) on the x-axis. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the detailed AUC (95% CI), sensitivity, and specificity and cutoff value concentration of the SFAA biomarkers for OSCC prediction. As presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the AUC of a single SFAA biomarker was from 0.5365 to 0.7835 in the case vs control mode. Estimates of disease risk were calculated by fitting the case vs control mode of the validation population to the two models constructed using data from this studied population, and the accuracy of each model was compared using ROC analysis. The\u003c/p\u003e \u003cp\u003eresults showed that Val as the explanatory variable had the highest accuracy (AUC\u0026thinsp;=\u0026thinsp;0.784;\u003c/p\u003e \u003cp\u003egood) and lys as the explanatory variable had the lowest accuracy (AUC\u0026thinsp;=\u0026thinsp;0.537;\u003c/p\u003e \u003cp\u003eunsatisfactory) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Other four amino acids Phe (AUC\u0026thinsp;=\u0026thinsp;0.712, Leu (AUC\u0026thinsp;=\u0026thinsp;0.713), Ile\u003c/p\u003e \u003cp\u003e(AUC\u0026thinsp;=\u0026thinsp;0.744) and Pro (AUC\u0026thinsp;=\u0026thinsp;0.715) showed good results [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. All of the SFAA biomarkers\u003c/p\u003e \u003cp\u003eexcept lys had independent predictive potential for early-stage OSCC. As a single salivary\u003c/p\u003e \u003cp\u003ebiomarker, Val proved the highest accuracy in predicting OSCC with sensitivity and\u003c/p\u003e \u003cp\u003especificity of 73.5 and 65.5, respectively (AUC: 0.784 and cutoff point: 5.5 \u0026micro;mol/L). Further\u003c/p\u003e \u003cp\u003eIle show its ability to predict OSCC with sensitivity and specificity of 76.5 and 62.0 (AUC:\u003c/p\u003e \u003cp\u003e0.744 and cutoff point: 1.3 \u0026micro;mol/L). The results indicated that single salivary AA could be\u003c/p\u003e \u003cp\u003eused as biomarkers for the early diagnosis of OSCC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo demonstrate the utility of the SFAA profile for the early diagnosis of OSCC, an SFAA profile index in statistical analysis was used with a backward selection method based on the SFAA biomarkers. The final model derived from this analysis comprised Phe, Leu, Ile, Val and Pro. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the ROC curve analysis for the predictive power of SFAA profile index for distinguishing control from the case. The AUC was 0.796 (95% CI: 0.6888 to 0.9025; sensitivity: 70.6%; specificity: 69.0% and cutoff point: 52.01 \u0026micro;mol/L) for the SFAA profile index of control vs case, which showed a high accuracy in predicting OSCC. The AUC, sensitivity, and specificity values were all increased with the SFAA profile index compared with any single salivary AA biomarker. The results indicated that the diagnostic accuracy was improved by using the biomarker panel (SFAA profile index) compared with using any single SFAA biomarker. These results demonstrated that a panel comprising the salivary Phe, Leu, Ile, Val and Pro concentrations might have important clinical potential for the early diagnosis and identification of OSCC. Using the SFAA profile index will improve the sensitivity and specificity for early-stage OSCC detection.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eROC curve metrics for salivary free amino acids and SFAA profile index\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiomarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOptimal Cut-off (\u0026micro;mol/L)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.784\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(0.67\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsoleucine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.744\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(0.62\u0026ndash;0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenylalanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003cp\u003e(0.58\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeucine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.713 (0.59\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003cp\u003e(0.59\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLysine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003cp\u003e(0.40\u0026ndash;0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSFAA\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eProfile Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.796\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(0.69\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e70.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e69.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Analytical Performance and Determination of Salivary Free Amino Acids by LC\u0026ndash;MS/MS\u003c/h2\u003e \u003cp\u003eEfficient separation of salivary free amino acids (SFAAs) from endogenous metabolites is critical for reliable biomarker quantification. In the present study, the chromatographic conditions and gradient elution program were optimized to achieve satisfactory resolution of all six target amino acids within a total run time of 22 minutes. Multiple reaction monitoring (MRM) was employed for analyte detection, and the protonated molecular ions \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:[M+H{]}^{+}\\)\u003c/span\u003e\u003c/span\u003ewere selected as precursor ions. The mass accuracy was within acceptable limits, with deviations between theoretical and observed m/z values remaining below 5 mDa for all analytes.\u003c/p\u003e \u003cp\u003eThe optimized MRM transitions, retention times, collision energies, and dwell times for Proline, Leucine, Isoleucine, Valine, Phenylalanine, and Lysine are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Representative LC\u0026ndash;MS/MS chromatograms illustrate clear peak separation and consistent signal intensity across samples. Typical MRM chromatograms obtained from a blank matrix, a 100 \u0026micro;M mixed amino acid standard, and an OSCC case sample are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, while chromatograms from a healthy control saliva sample are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe analytical performance of the LC\u0026ndash;MS/MS method was further evaluated to confirm its suitability for quantitative salivary amino acid analysis. As summarized in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, all six analytes demonstrated excellent linearity over the concentration range of 2\u0026ndash;100 \u0026micro;M, with correlation coefficients (R\u0026sup2;)\u0026thinsp;\u0026ge;\u0026thinsp;0.998. The method exhibited high analytical sensitivity, with limits of detection (LOD) ranging from 0.07 to 0.68 \u0026micro;M and limits of quantification (LOQ) between 1.65 and 2.01 \u0026micro;M. Accuracy, assessed by recovery experiments at multiple concentration levels, ranged from 90.7% to 101.9%, indicating minimal matrix interference. Intra-day precision remained below 2%, and inter-day precision was below 9% for all analytes, confirming strong repeatability and intermediate precision. Collectively, these findings demonstrate that the developed LC\u0026ndash;MS/MS method is sensitive, accurate, precise, and reproducible for the quantification of salivary free amino acids, supporting the robustness of subsequent biomarker analyses in OSCC patients.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple reaction monitoring (MRM) transitions and optimized LC\u0026ndash;MS/MS parameters for the quantification of salivary free amino acids.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmino Acid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetention Time (min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrecursor (m/z)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProduct (m/z)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCE (V)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDwell Time (ms)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e166.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e132.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e132.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e147.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-17.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalytical performance characteristics of the LC\u0026ndash;MS/MS method for salivary free amino acid quantification, including linearity, sensitivity, precision, and recovery.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eS/N Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCalibration curve\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLinearity (R\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCalibration Range (\u0026micro;M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLOD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLOQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003ePrecision (RSD %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRecovery (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIntra-day (n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eInter-day (n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMean (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;25989.38x\u003c/p\u003e \u003cp\u003e\u0026minus;\u0026thinsp;21621.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e101.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeu\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;9497.501x\u003c/p\u003e \u003cp\u003e\u0026minus;\u0026thinsp;413.9832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e90.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIle\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;9279.141x\u003c/p\u003e \u003cp\u003e+\u0026thinsp;8784.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e100.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;12145.58x\u003c/p\u003e \u003cp\u003e\u0026minus;\u0026thinsp;1748.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e99.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePro\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;24028.64x\u003c/p\u003e \u003cp\u003e\u0026minus;\u0026thinsp;26385.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e91.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLys\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e104.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;21696.42x\u003c/p\u003e \u003cp\u003e+\u0026thinsp;16324.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e100.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003estandard and, (C) case sample.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOral cancer remains a major global health burden, with South-East Asian countries particularly India and neighboring regions contributing disproportionately to disease incidence and mortality [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Oral cancer accounts for approximately 30% of all malignancies diagnosed in India, with an incidence of nearly 20 per 100,000 individuals, and oral squamous cell carcinoma (OSCC) representing the predominant histological subtype [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Despite advances in multimodal therapy, the 5-year survival rate of OSCC remains below 50%, largely due to delayed diagnosis at advanced stages [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Therefore, the identification of reliable, non-invasive biomarkers for early detection is of critical clinical importance.\u003c/p\u003e \u003cp\u003eCancer progression is characterized by profound metabolic reprogramming, including enhanced protein catabolism and nitrogen loss. Tumor cells exhibit increased utilization of glucose and amino acids to support proliferation, energy production, and biosynthetic demands. Peripheral protein breakdown releases amino acids that are transported to tumors and visceral organs, contributing to gluconeogenesis and cellular growth [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Consequently, alterations in amino acid profiles have emerged as promising indicators of disrupted protein metabolism and malignant transformation [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmino acids play central roles in cellular metabolism as substrates for protein synthesis and regulators of metabolic pathways. Multiple studies have demonstrated significant alterations in plasma-free amino acid profiles among cancer patients compared with healthy individuals [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Saliva, owing to its non-invasive accessibility and close metabolic interaction with systemic circulation, has gained increasing attention as a diagnostic biofluid. However, salivary free amino acid (SFAA) alterations in OSCC remain underexplored. In this study, a sensitive and reproducible LC\u0026ndash;MS/MS-based method without derivatization was employed to quantify SFAAs, enabling robust metabolomic assessment.\u003c/p\u003e \u003cp\u003eOur findings demonstrate that all six investigated amino acids Proline, Valine, Phenylalanine, Isoleucine, Leucine, and Lysine were significantly elevated in the saliva of OSCC patients compared with healthy controls. These results are consistent with previous reports. Sugimoto et al. observed significantly increased Valine, Phenylalanine, Leucine, and Isoleucine levels in OSCC patients, although Proline and Lysine did not differ significantly in their cohort [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Ohshima et al. similarly reported elevated salivary Valine, Isoleucine, and Leucine in Japanese OSCC patients [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], while Lohavanichbutr et al. identified Proline as a significant marker but reported inconsistent findings for other amino acids [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVariability across studies likely reflects differences in population characteristics, dietary patterns, tumor biology, and analytical platforms. Nevertheless, the consistent elevation of branched-chain amino acids (Valine, Leucine, Isoleucine) across multiple studies supports their role in OSCC-associated metabolic dysregulation. Increased amino acid availability may facilitate tumor cell proliferation, energy expenditure, and anabolic growth.\u003c/p\u003e \u003cp\u003eAge-related trends observed in this study align with existing literature. The majority of OSCC patients were aged 51\u0026ndash;70 years, consistent with findings reported by Lohavanichbutr et al. and population-based studies from India [43,44]. The increased incidence in older age groups may reflect cumulative carcinogen exposure and prolonged latency between initial exposure and clinically detectable disease.\u003c/p\u003e \u003cp\u003eAlthough OSCC is traditionally more prevalent among males, this study observed a higher proportion of female patients. This divergence may be attributable to sociocultural factors, including delayed healthcare-seeking behavior among women and differential exposure to risk factors in the local population [45]. Educational status emerged as a significant determinant, with lower educational attainment associated with increased OSCC prevalence, consistent with previous observations that education influences health awareness, oral hygiene practices, and access to screening [46].\u003c/p\u003e \u003cp\u003eHabitual risk factors particularly betel quid chewing and tobacco use were strongly associated with OSCC in this study. These findings corroborate extensive evidence from South-East Asia identifying betel quid chewing as a dominant etiological factor [44,45,47]. Poor oral hygiene, frequently associated with these habits, further contributes to chronic inflammation and carcinogenesis. In both univariate and multivariate analyses, poor oral hygiene emerged as a significant independent risk factor for OSCC, supporting its role as a modifiable determinant.\u003c/p\u003e \u003cp\u003eTumor site distribution revealed the vestibule as the most commonly affected location, followed by the retromolar trigone and buccal mucosa. While buccal mucosa predominates in several Indian cohorts, site variability may reflect regional chewing practices and local exposure of carcinogens within the oral cavity [48,49].\u003c/p\u003e \u003cp\u003eStage-wise analysis demonstrated increasing SFAA concentrations with advancing disease severity, although some amino acids showed differential patterns between stage III and stage IV disease. These variations may reflect metabolic heterogeneity of OSCC or limited subgroup sample sizes rather than true biological regression. Similarly, amino acid concentrations differed according to tumor growth patterns, suggesting metabolic distinctions between endophytic and exophytic lesions.\u003c/p\u003e \u003cp\u003eReceiver operating characteristic analysis confirmed the diagnostic potential of individual SFAAs and the composite SFAA profile index. While single amino acids demonstrated moderate discriminative ability, the combined SFAA profile index yielded superior diagnostic performance (AUC\u0026thinsp;=\u0026thinsp;0.796), with improved sensitivity and specificity compared with any individual biomarker. The cutoff values were derived and reported consistently in \u0026micro;mol/L, aligning with LC\u0026ndash;MS/MS quantification standards. These findings indicate that metabolic disruption in OSCC manifests as a distinct salivary amino acid signature, supporting the clinical utility of multiplex biomarker approaches.\u003c/p\u003e \u003cp\u003eThis study has limitations. Only six amino acids were analyzed, and the sample size was modest due to time, budgetary constraints, and limited analytical infrastructure. Potential confounders such as stress levels and oral microbiome composition were not fully assessed. Additionally, external validation in independent cohorts is required to confirm the generalizability of these findings. Despite these limitations, the results provide compelling evidence that salivary free amino acid profiling may complement conventional diagnostic strategies for early, non-invasive OSCC detection.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eAll the SFAAs except Lys levels showed a significant difference between OSCC patients and healthy subjects. This suggests that salivary amino acids detection might be helpful in the early and rapid diagnosis of OSCC. A larger sample size and a greater number of salivary amino acids should be included for further study and should minimize the changing of concentration of amino acids due to stressful mental conditions and diet. It is recommended that a detailed study of amino acids might help understand the mechanism of metabolic changes in cancer patients and monitor for recurrence.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOSCC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOral Squamous Cell Carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBMU\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBangladesh Medical University\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBriCM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBangladesh Reference Institute for Chemical Measurements\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDDCH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDhaka Dental College Hospital\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIRB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInstitutional Review Board\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eUPLC\u0026ndash;MS/MS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUltra-Performance Liquid Chromatography\u0026ndash;Tandem Mass Spectrometry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMass Spectrometry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHPLC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-Performance Liquid Chromatography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSPSS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHIV\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHuman Immunodeficiency Virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmino Acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePro\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProline\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eVal\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eValine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePhe\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhenylalanine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIle\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIsoleucine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLeu\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeucine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLys\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLysine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e6. Acknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank Professor Dr. Md. Wares Uddin and Professor Dr. Ismat Ara Haider for granting permission and facilitating sample collection from the Department of Oral and Maxillofacial Surgery, Bangabandhu Sheikh Mujib Medical University (BSMMU), and Dhaka Dental College Hospital, respectively. We are also grateful to Dr. Pritimoy Das (PhD researcher, Federation University, Australia) for critically reviewing the manuscript prior to submission. In addition, we acknowledge BRCiM and Innoclin Research for their administrative and logistical support throughout the conduct of the study and manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6. Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding\u0026nbsp;received\u0026nbsp;from Bangladesh\u0026nbsp;Medical\u0026nbsp;University\u0026nbsp;(BMU) (Thesis Grant\u0026nbsp;no:\u0026nbsp;7214,\u0026nbsp;Year-\u0026nbsp;2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7. Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all individual participants prior to enrollment in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8. Ethics Approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Institutional Review Board (IRB) Bangladesh Medical University (BMU)\u0026nbsp;(IRB Reg. No. BSMMU/MU/2021/7214),\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e9. Human Ethics and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involved human participants and was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Institutional Review Board (IRB) of Bangladesh Medical University (BMU)\u0026nbsp;(IRB Reg. No. BSMMU/MU/2021/7214),\u0026nbsp;Written informed consent was obtained from all participants prior to participation.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e10. Conflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;authors report\u0026nbsp;no conflict\u0026nbsp;of interest.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e11. Author\u0026rsquo;s contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design: D.M., M.H.R., M.A., A.U.S.; Development of methodology: M.H.R., M.M.H., M.I., S.A.M., S.A., M.K. A.U.S.; Acquisition of data: D.M., S.P., M.S.I., M.A.F., K.A., A.U.S., S.A.M., S.A.; Analysis and interpretation of data: M.H.R., M.M.H., M.I., M.K., M.A.; Writing, review, and/or revision of the manuscript: D.M., M.H.R., M.M.H., M.I., S.A.M., S.A., S.P., M.S.I., M.A.F., K.A., A.U.S., M.K., M.A.; Study supervision: M.A., M.H.R. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e12. Data availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets which was analyzed during the current study are not publicly available due to confidentiality and institutional restrictions, but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWong T, Wiesenfeld D. Oral Cancer. Aust Dent J. 2018;63(Suppl 1):S91\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta B, Johnson NW, Kumar N. Global Epidemiology of Head and Neck Cancers: A Continuing Challenge. Oncology. 2016;91(1):13\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta S, Gupta R, Sinha DN, Mehrotra R. Relationship between type of smokeless tobacco \u0026amp; risk of cancer: A systematic review. Indian J Med Res. 2018;148(1):56\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394\u0026ndash;424.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArgiris A, Harrington KJ, Tahara M, Schulten J, Chomette P, Ferreira Castro A, et al. Evidence-Based Treatment Options in Recurrent and/or Metastatic Squamous Cell Carcinoma of the Head and Neck. Front Oncol. 2017;7:72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRivera C, Oliveira AK, Costa RAP, De Rossi T, Paes Leme AF. Prognostic biomarkers in oral squamous cell carcinoma: A systematic review. Oral Oncol. 2017;72:38\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBramati C, Abati S, Bondi S, Lissoni A, Arrigoni G, Filipello F, et al. Early diagnosis of oral squamous cell carcinoma may ensure better prognosis: A case series. Clin Case Rep. 2021;9(10):e05004.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajapakshe RMAR, Pallegama RW, Jayasooriya PR, Siriwardena BSMS, Attygalla AM, Hewapathirana S, et al. A retrospective analysis to determine factors contributing to the survival of patients with oral squamous cell carcinoma. Cancer Epidemiol. 2015;39(3):360\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubramaniam N, Balasubramanian D, Murthy S, Kumar N, Vidhyadharan S, Vijayan SN. 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Ann Oncol. 2015;26(2):259\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu Q, Chen N, Ge C, Li R, Li Z, Zeng B, et al. Prognostic value of tumor-infiltrating lymphocytes in melanoma: a systematic review and meta-analysis. OncoImmunology. 2019;8(7):1593806.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlintrup K, M\u0026auml;kinen JM, Kauppila S, V\u0026auml;re PO, Melkko J, Tuominen H, et al. Inflammation and prognosis in colorectal cancer. Eur J Cancer. 2005;41(17):2645\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Rajhi N, Soudy H, Ahmed SA, Elhassan T, Mohammed SF, Khoja HA, et al. CD3 + T-lymphocyte infiltration is an independent prognostic factor for advanced nasopharyngeal carcinoma. BMC Cancer. 2020;20(1):240.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorsetto D, Tomasoni M, Payne K, Polesel J, Deganello A, Bossi P. 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BMC Cancer. 2017;17(1):375.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Oral squamous cell carcinoma, Saliva, Salivary biomarkers, Amino acid profiling, Metabolomics, Non-invasive diagnosis, Diagnostic accuracy","lastPublishedDoi":"10.21203/rs.3.rs-8663305/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8663305/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eOral squamous cell carcinoma (OSCC) is frequently diagnosed at advanced stages, resulting in poor survival outcomes. Salivary metabolomic profiling offers a promising, non-invasive approach for early disease detection. This study evaluated salivary free amino acid (SFAA) profiles as potential diagnostic biomarkers for OSCC.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eIn this case\u0026ndash;control study, six salivary free amino acids proline, valine, phenylalanine, isoleucine, leucine, and lysine were quantified from unstimulated whole-saliva samples collected from 34 histopathologically confirmed OSCC patients and 29 healthy controls. Amino acid concentrations were measured using ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC\u0026ndash;MS/MS). Group comparisons were performed using non-parametric tests, and diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis for individual biomarkers and a composite SFAA profile index.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eSalivary concentrations of proline, valine, phenylalanine, isoleucine, and leucine were significantly higher in OSCC patients compared with controls (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Individual amino acids demonstrated moderate diagnostic accuracy, with area under the curve (AUC) values ranging from 0.54 to 0.78. A composite SFAA profile index comprising phenylalanine, leucine, isoleucine, valine, and proline achieved superior discrimination between cases and controls (AUC\u0026thinsp;=\u0026thinsp;0.79; 95% CI: 0.69\u0026ndash;0.90), with 70.6% sensitivity and 69.0% specificity. SFAA concentrations also varied significantly according to tumor stage and growth pattern.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eThe salivary free amino acid profile index demonstrated improved diagnostic performance compared with individual biomarkers, supporting its potential utility as a non-invasive tool for OSCC detection. Salivary amino acid profiling may complement existing diagnostic strategies for early identification of OSCC. Further large-scale and multi-center studies are warranted to validate these findings and refine biomarker panels.\u003c/p\u003e","manuscriptTitle":"Salivary Amino Acids as Non-Invasive Biomarkers for Oral Squamous Cell Carcinoma: A Case Control Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-22 12:43:10","doi":"10.21203/rs.3.rs-8663305/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-29T08:58:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-04T16:00:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-02T08:31:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252429782069049143471882306356292105996","date":"2026-02-27T01:22:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332798482996993820151508720299076560553","date":"2026-02-26T19:18:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-26T01:56:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149155574013247950771131353875625270165","date":"2026-02-24T13:03:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"255941655919813453446704316655114162852","date":"2026-02-20T00:43:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-17T12:44:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"220570437838962899696943825427303549160","date":"2026-02-17T11:56:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-17T08:37:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-28T06:32:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-26T09:17:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-26T09:16:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2026-01-21T20:16:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8fe1aa8f-c662-4ad8-a712-cd303df8fc07","owner":[],"postedDate":"February 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T14:23:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-22 12:43:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8663305","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8663305","identity":"rs-8663305","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}