Abstract Oral squamous cell carcinoma (OSCC) represents 90% of oral malignant neoplasms. The search for specific biomarkers for OSCC is a very active field of research contributing to establishing early diagnostic methods and unraveling underlying pathogenic mechanisms. In this work we investigated the salivary metabolites and the metabolic pathways of OSCC aiming find possible biomarkers. Salivary metabolites samples from 27 OSCC patients and 41 control individuals were compared through a gas chromatography coupled to a mass spectrometer (GC-MS) technique. Our results allowed identification of pathways of the malate-aspartate shuttle, the beta-alanine metabolism, and the Warburg effect. The possible salivary biomarkers were identified using the area under receiver-operating curve (AUC) criterion. Twenty-four metabolites were identified with AUC > 0.8. Using the threshold of AUC = 0.9 we find malic acid, maltose, protocatechuic acid, lactose, 2-ketoadipic, and catechol metabolites expressed. We notice that this is the first report of salivary metabolome in South American oral cancer patients, to the best of our knowledge. Our findings regarding these metabolic changes are important in discovering salivary biomarkers of OSCC patients. However, additional work needs to be performed considering larger populations to validate our results. Keywords: metabolomics, biomarkers, metabolites, oral squamous cell carcinoma, oral cancer, saliva, mass spectrometry, GC-MS 1. Introduction Oral cancer refers to the set of malignant neoplasms that affect the lips and other intraoral regions [[46]1]. It represents the 16th most common neoplasm in the world, with 355,000 new diagnoses and 177,000 deaths in 2018 [[47]2]. It is a highly relevant problem for global public health since there is no evidence of significant improvement for fast treatment and prevention in spite of all the progress in current research and therapies [[48]3]. Among oral malignancies squamous cell carcinoma (OSCC) is the most prevalent histological type representing approximately 90% of cases. OSCC is often preceded by the presence of oral potentially malignant disorders. They are clinically identifiable as either white or red patches known as leukoplakia and erythroplakia, respectively. Non-healing ulcers may also be noticed along cancer development [[49]4]. The highest incidence of OSCC occurs in the middle-aged population although the number of young individuals diagnosed with the disease has increased [[50]5,[51]6]. The most common site for OSCC is the tongue followed by the floor of the mouth. Less common sites include the gingiva, buccal mucosa, labial mucosa, and hard palate [[52]4]. OSCC has a survival rate of approximately 80% for individuals detected with early stage disease (stage I) when compared to a rate of 20–30% in patients diagnosed at advanced stages (stages III–IV) [[53]7]. This fact emphasizes the importance of early diagnosis. Unfortunately about 50% of cases are diagnosed in advanced stages (III and IV) [[54]8,[55]9] which implies a worse prognosis, increased costs, and a high mortality rate [[56]10,[57]11]. The predominant etiological factors for oral cancer are well established in the literature and include the use of tobacco and alcohol which act as carcinogenic substances responsible for constituting the so-called “field cancer” [[58]12]. The carcinogenesis process is complex, being influenced by genetic and epigenetic alterations [[59]13,[60]14]. The fact is that the sooner these changes are detected, the earlier the disease will be discovered, contributing to a better prognosis for patients [[61]15]. Conventional biopsy is considered the gold standard for the diagnosis of OSCC. However, it is inconvenient for large population screening and monitoring of patients due to its invasiveness, high cost, and need for trained personnel and equipment [[62]16]. Thus, it is important to investigate biological molecules acting as biomarkers that may provide valuable diagnostic data on OSCC [[63]14]. The search for biomarkers for chronic diseases and malignant neoplasms is a very active field of research worldwide [[64]17]. Metabolomics employ state-of-the-art analytical techniques to recognize and study metabolic alterations in individuals who are undergoing some pathophysiological process or are undergoing pharmacological interventions and genetic modifications [[65]18,[66]19,[67]20]. Biofluids—urine, blood, and saliva—are often used as clinical specimens of patients for metabolomic analysis [[68]15]. Saliva is an oral fluid capable of reflecting the oral and systemic health conditions of individuals [[69]21]. It is a complex and valuable composition that includes proteins, peptides, nucleic acids, enzymes, hormones, antibodies, electrolytes, antimicrobial constituents, growth factors, and other molecules associated with the phenotype and even diseases of individuals [[70]22,[71]23,[72]24,[73]25]. The main functions of saliva are related to digestion, swallowing, tasting, and lubrication of the oral mucosa. However, it is known that in addition to these functions saliva acts as a protective substance against pathogens and toxins due to its specific composition [[74]26]. Previous studies have identified metabolomic biomarkers for OSCC [[75]27,[76]28,[77]29,[78]30,[79]31,[80]32,[81]33,[82]34,[83]35,[84]36, [85]37,[86]38,[87]39,[88]40,[89]41,[90]42,[91]43]. Some of these probed salivary metabolites [[92]27,[93]28,[94]29,[95]30,[96]31,[97]32,[98]33,[99]34,[100]35,[101]3 6]. The fact that the metabolites profile can be influenced by the sample collection time [[102]31,[103]44], the food intake [[104]28,[105]30,[106]31,[107]45], the general oral health status [[108]46], and even the oral microbiome [[109]38,[110]47] represents a challenge for standardization of salivary studies in order to avoid inconsistencies and reproducibility drawbacks. Ethnicity has also been shown to play an important role in the differentiation of metabolites since populations of distinct ethnicities presented distinct salivary metabolic profiles [[111]31,[112]37]. Most studies involving salivary metabolome in OSCC patients come from Asian individuals [[113]38], showing the importance of studying different ethnic groups [[114]48]. However, despite existing limitations, previous studies have shown consistent changes between OSCC and healthy patients [[115]28], mainly due to the direct contact between saliva and the oral cancer lesion [[116]30]. In the present work we investigated the salivary metabolites profile from a sample of OSCC patients from a South American population. The objectives were to identify possible salivary metabolomic biomarkers and also altered metabolic pathways. 2. Results 2.1. Demographic Data The main clinical data of patients are summarized in [117]Table 1. Data on sex and age did not show a statistically relevant difference between the groups (p < 0.05). Table 1. Demographic data of patients. Variable OSCC ^1 (n = 27) CONTROL (n = 41) p-Value * Sex ^2 Female 8 (29.6%) 20 (49%) 0.3326 Male 19 (70.4%) 21 (51%) 0.9131 Age ^3 57 ± 13.87 57.34 ± 11.66 0.9131 (28–88) (31–86) [118]Open in a new tab ^1 OSCC, oral squamous cell carcinoma group. ^2 Sex was described with their respective means and (%) percentages. ^3 Age described as mean ± standard deviation and in parentheses the minimum and maximum age of the patients. n represents the number of patients in each group. * p-values according to the Student’s t-test considering as significant p < 0.05. [119]Table 2 presents the TNM cancer staging system, smoking habits, and racial ethnicity data of the patients. Table 2. Cancer staging system, smoking habits, and racial ethnicity of patients. TNM ^1 OSCC (n = 27) Control (n = 41) T (tumor) T1 5 (19%) T2 7 (26%) Not applicable T3 6 (22%) T4 9 (33%) N (node) N0 14 (52%) N1 4 (15%) Not applicable N2 8 (30%) N3 1 (4%) M (metastasis) M0 27 (100%) Not applicable Stages I 4 (15%) II 4 (15%) Not applicable III 6 (22%) IV 13 (48%) Smokers 20 (74%) 8 (20%) Non smokers 7 (26%) 20 (49%) Ex smokers 0 (0%) 13 (32%) Racial ethnicity Leucoderma 24 (89%) 32 (78%) Melanoderm 1 (4%) 4 (10%) Pheoderm 2 (7%) 4 (10%) Xanthoderm 0 (0%) 1 (2%) [120]Open in a new tab ^1 TNM—classification of malignant tumors. The TNM system is used to describe the anatomical extension of the disease, where T—the extension of the primary tumor, N—the absence or presence and extension of metastasis in regional lymph nodes, M—the absence or presence of distant metastasis. All data are described with their respective n of each group and their respective (%) percentages. 2.2. Metabolomic Analysis A total of 108 metabolites were identified as relevant for OSCC and control discrimination. All metabolites found in both groups studied were allocated on a Venn diagram to assess their distribution between groups ([121]Figure 1). The analysis showed that the OSCC group has a higher number of specific metabolites (26 metabolites), while the control group had 5 specific metabolites. Seventy-seven metabolites were common for both groups. These metabolites are show on [122]Table 3. Figure 1. [123]Figure 1 [124]Open in a new tab Venn diagram for salivary metabolites probed on OSCC (red) and control (green) groups. Table 3. Exclusive and shared salivary metabolites for OSCC and control groups. OSCC CONTROL OSCC AND CONTROL 2-Hydroxyglutaric acid 2-Ketoadipic acid 1,6-Anhydroglucose 2-Ketoglutaric acid Catechol 1-Hexadecanol 3-Hydroxypropionic acid Lactose 2-Aminoethanol 4-Hydroxyphenyllactic acid Leucine 2-Deoxy-glucose Cystamine Urea 2-Hydroxyisovaleric acid Dihydroxyacetone phosphate 3-Aminoglutaric acid Galacturonic acid 3-Aminoisobutyric acid Gluconic acid 3-Aminopropanoic acid Hippuric acid 3-Hydroxyisovaleric acid Indol-3-acetic acid 3-Phenyllactic acid Inosine 4-Aminobutyric acid Isocitric acid 5-Aminovaleric acid Lactitol Acetoacetic acid Lyxose Adenine Malic acid Allose Maltose Arabitol Methionine Arachidonic acid O-Phospho-Serine Arginine Pantothenic acid Aspartic acid Protocatechuic acid Batyl alcohol Ribose 5-phosphate Cadaverine Sorbose Caproic acid Spermidine Citramalic acid Thymidine Citric acid Uracil Cysteine Ureidosuccinic acid Dopamine Eicosapentaenoic acid Elaidic acid Fructose Galactosamine Galactose Glucono-1,5-lactone Glucosamine Glucose Glucuronic acid Glutamic acid Glycerol Glycerol 2-phosphate [125]Open in a new tab The dispersion score plot PC2 against PC1 ([126]Figure 2) shows a clear separation among groups. Figure 2. [127]Figure 2 [128]Open in a new tab PCA score plot OSCC (green) and control (red) groups. Ellipses represent the loci of maximum variance of data. The heatmap showing the clustering of classes against metabolites is shown in [129]Figure 3. Samples in OSCC and control classes clustered in two big groups with 100% discrimination. Metabolites urea, lactose, catechol, palmitic acid, 2-ketoadipic acid and leucine appeared underexpressed in the OSCC group. On the other hand, lyxose, protocatechuic acid, uracil, 2-hydroxyglutaric, inosine, methionine, indol-3-acetic acid, 4-hydroxyphenyllac, malic acid, pantothenic acid, isocitric acid, maltose, O-phospho-serine, lactitol, dihydroxyacetone and ribose 5-phosphate were overexpressed in patients with cancer. Figure 3. [130]Figure 3 [131]Open in a new tab Heatmap using PCA data for OSCC and control classes. [132]Table 4 displays the up- and down-regulated metabolites presenting statistical relevance. Twenty metabolites were up-regulated (malic acid, methionine, maltose, protocatechuic acid, inosine, pantothenic acid, dihydroxyacetone phosphate, hydroxyphenylatic acid, galacturonic acid, indole-3-acetic acid, uracil, isocitric acid, ribose-5-phosphate, o-phospho serine, lactitol, gluconic acid, hippuric acid, 3-hydroxypropionic acid and spermidine) and 20 down-regulated (lactose, catechol, 2-ketoadipic acid, leucine, urea, maleic acid, palmitic acid, ornithine, margaric acid, sucrose, octadecanol, threitol, acetoacetic acid, methionine sulfone, phosphoric acid, elaidic acid, mannose, sorbitol, citric acid, 3-aminopropanoic acid) in OSCC samples. Table 4. Set of metabolites up- and down-regulated in OSCC samples according to PCA analyses. Metabolites OSCC Control p-Value ^1 q-Value (FDR) ^2 FC Volcano Plot ^3 Mean Standard Deviation Mean Standard Deviation Lactose * −1.090 0.492 0.718 0.673 <0.0001 3.1755 × 10^−16 0.015832 Down Malic acid ** 0.917 0.622 −0.604 0.444 <0.0001 3.7012 × 10^−16 40.712 Up Methionine ** 1.088 0.939 −0.717 0.367 <0.0001 3.0633 × 10^−15 311.66 Up Catechol * −0.952 0.521 0.627 0.734 <0.0001 7.1635 × 10^−13 0.035587 Down 2-Keto adipic acid * −0.925 0.522 0.609 0.768 <0.0001 6.363 × 10^−12 0.029706 Down Maltose ** 0.889 0.959 −0.586 0.407 <0.0001 2.0868 × 10^−11 325.18 Up Protocatechuic acid ** 0.806 0.827 −0.531 0.447 <0.0001 2.7666 × 10^−11 35.723 Up Leucine * −1.177 0.394 0.775 1.173 <0.0001 8.7168 × 10^−11 8.2595 × 10^−4 Down Inosine ** 1.070 1.317 −0.704 0.330 <0.0001 9.7882 × 10^−11 2873.0 Up Pantothenic acid ** 1.153 1.459 −0.759 0.304 <0.0001 1.4172 × 10^−10 4271.4 Up Urea * −0.861 0.530 0.567 0.810 <0.0001 1.687 × 10^−10 0.037894 Down Dihydroxyacetone phosphate ** 0.793 0.895 −0.522 0.439 <0.0001 1.687 × 10^−10 45.791 Up 4-hydroxyphenylactic acid ** 1.092 1.403 −0.719 0.318 <0.0001 2.1476 × 10^−10 2173.8 Up Galacturonic acid ** 0.725 0.831 −0.477 0.467 <0.0001 8.9307 × 10^−10 19.383 Up Indole-3-acetic acid ** 0.906 1.242 −0.597 0.365 <0.0001 3.0805 × 10^−9 341.04 Up Uracil ** 0.644 0.817 −0.424 0.491 <0.0001 3.04 × 10^−8 10.819 Up Isocitric acid ** 0.665 0.885 −0.438 0.472 <0.0001 3.6657 × 10^−8 20.802 Up Ribose-5-phosphate ** 0.647 0.969 −0.469 0.461 <0.0001 3.1666 × 10^−7 41.912 Up O-Phospho-Serina ** 0.609 0.945 −0.401 0.474 <0.0001 9.548 × 10^−7 17.64 Up Lactitol ** 0.630 1.061 −0.415 0.446 <0.0001 2.1547 × 10^−6 41.538 Up Gluconic acid ** 0.609 1.101 −0.401 0.443 <0.0001 7.7433 × 10^−6 183.99 Up 2-Ketoglutaric acid ** 0.515 0.836 −0.339 0.512 <0.0001 1.3092 × 10^−5 6.7421 Up Hipuric acid ** 0.518 0.888 −0.341 0.506 <0.0001 1.4925 × 10^−5 7.3906 Up Maleic acid −0.664 1.049 0.437 0.817 <0.0001 3.294 × 10^−5 0. 8093 Down Palmitic acid −0.430 0.657 0.283 0.551 <0.0001 3.3213 × 10^−5 0.38165 Down 3-hydroxypropionic acid ** 0.608 1.265 −0.400 0.411 0.0002 4.4319 × 10^−5 202.32 Up Spermidine ** 0.481 0.887 −0.317 0.514 0.0001 5.3374 × 10^−5 10.562 Up Ornithine −0.614 1.197 0.405 0.986 0.0003 0.0010593 0.33872 Down Margaric acid −0.453 1.055 0.298 0.648 <0.0001 0.0018846 0.28057 Down Sucrose −0.487 1.005 0.321 0.928 0.0002 0.0039383 0.25406 Down Octadecanol −0.310 0.666 0.204 0.628 0.0010 0.0064518 0.56165 Down Threitol −0.465 1.148 0.307 0.847 0.0012 0.0069549 0.37775 Down Acetoacetic acid −0.373 0.732 0.246 0.826 0.0024 0.0074047 0.25319 Down Methionine sulfone −0.306 0.767 0.202 0.582 0.0001 0.0085698 1.123 Down Phosphoric acid −0.374 0.806 0.246 0.968 0.0103 0.022159 0.12317 Down Elaidic acid −0.254 0.578 0.167 0.722 0.0134 0.038044 0.4826 Down Mannose −0.398 1.309 0.262 0.881 0.0324 0.042273 0.51969 Down Sorbitol −0.361 0.890 0.238 1.048 0.0173 0.046325 0.11612 Down Citric acid −0.416 1.200 0.274 1.111 0.0369 0.046725 0.11946 Down 3-Aminopropanoic acid −0.324 0.895 0.213 0.907 0.0004 0.048905 0.39703 Down [133]Open in a new tab ^1 p-value was calculated using the Wilcoxon-Mann-Whitney test (p-value < 0.05). ^2 All metabolites shown in the table were statistically significant with a false discovery rate (FDR) of 5%. ^3 Volcano plot shows up- and down-regulated metabolites in patients with OSCC. * Metabolites exclusively found in control patients. ** Metabolites exclusively found in OSCC patients. 2.3. Analysis of Altered Metabolic Pathways in the OSCC Group The precedent analysis enables us to investigate the altered metabolic pathways in OSCC patients and find the role of each metabolite in these pathways. We analyzed 25 metabolites which were found exclusively in the OSCC group. Cystamine was absent from the databases of the chosen metabolomic compound and was excluded from further analysis. Thus, the role of 2-ketoglutaric acid, 2-hydroxyglutaric acid, 3-hydroxypropionic acid, 4-hydroxyphenylatic acid, galacturonic acid, gluconic acid, hippuric acid, indol-3-acetic acid, isocitric acid, malic acid, pantothenic acid, protocatechuic acid, ureidosuccinic acid, spermidine, dihydroxyacetone phosphate, inosine, lactitol, lyxose, maltose, methionine, O-phospho-serine, ribose 5-phosphate, sorbose, thymidine, and uracil in metabolic pathways was investigated. The pathway enrichment analysis is shown in [134]Figure 4. Figure 4. [135]Figure 4 [136]Open in a new tab Statistically significant (p < 0.05) for OSCC. A total of 41 metabolic pathways were identified as present in OSCC salivary samples. However, only 25 presented statistical relevance. From these we can mention the malate-aspart (p = 0.0229), beta-alanine metabolism (p = 0.0467), and the Warburg effect (p = 0.048) signaling pathways. 2.4. Analysis of Possible Salivary Biomarkers for the OSCC Group A receiver operating characteristic (ROC) curve was used to establish promising biomarkers for OSCC. The area under the ROC curve value (AUC) measures the performance of the biomarkers. Thus, an excellent biomarker has an AUC value of 1.0. Good biomarkers have AUC > 0.80. Using this criterion, we list in [137]Table 5 the set of possible good salivary biomarkers for OSCC. Table 5. Area Under the Receiving—Operator Curve (AUC) for possible OSCC salivary biomarkers. Metabolite AUC Malic acid 0.98103 Lactose 0.96387 Catecol 0.94670 2-ketoadipic acid 0.94128 Maltose 0.93360 Methionine 0.92502 Urea 0.92502 Leucine 0.92322 Inosine 0.92186 Protocatechuic acid 0.91192 Dihydroxyacetone phosphate 0.89657 Galacturonic acid 0.88573 Margaric acid 0.86902 Uracil 0.86721 Isocitric acid 0.86585 Ribose 5-phosphate 0.84146 O-Phospho-Serine 0.82385 Indole-3-acetic acid 0.82204 Palmitic acid 0.82204 2-ketoglutaric acid 0.81798 Maleic acid 0.81030 Pantothenic acid 0.80307 Spermidine 0.80217 [138]Open in a new tab 3. Discussion The relevance of the investigation of the salivary metabolome of OSCC relies on the identification of predominantly altered metabolic pathways which may lead to the discovery of possible biomarkers. This could improve the capacity of early diagnosis and, consequently, the quality of life of patients. Reports of the salivary metabolome of patients with oral cancer described in the literature are presented in [139]Table 6 and compared to our findings. The present study sought the main altered salivary metabolic pathways in OSCC patients and, additionally, the main metabolites that can be used as future salivary biomarkers for early diagnosis. To the best of our knowledge, this is the first research in this area focusing on Latin American patients. Table 6. Main salivary metabolomic studies of patients with OSCC. Possible Salivary Metabolic Biomarkers Studied Population Notes References