GC is one of the major health problems in the world. Most cases of GCs are diagnosed in the later stages of the disease and become symptomatic in an advanced stage, while there is no formal screening program for the diseases. Although several screening approaches have been proposed, such as detecting gastric mucosal atrophy by measuring pepsinogens in the bloodstream, none of these methods are usually applied due to the nature of the disease and the deterioration of patients [59, 60]. Despite potential preventive measures and screening methods such as PET-CT and endoscopy, no effective method has been proposed for future clinical trials to reduce GC [61, 62]. Endoscopy and biopsy of the gastric remain the standard diagnostic criteria for GC [61, 63]. Due to the invasiveness of this method and the high cost and difficult access to this diagnostic method, endoscopy cannot be considered a suitable method for screening GC .
Biomarkers may serve as a non-invasive diagnosis in the early detection of GC, but due to the nature of GC, no specific and sensitive biomarkers are yet available . It is possible to determine these biomarkers in blood, urine, and saliva that saliva can provide the appropriate way to detect patients, better prognosis, prevent recurrence, and control patient mortality . Several studies have used salivary proteins as potential diagnostic markers to monitor disease, prognosis, patient survival, and treatment [67-69]. It has also been shown that there are blood transfusions in saliva; therefore, it is almost equal to serum [70, 71].
CSTB is a protease inhibitor of cathepsin that is increased in cancer and acts as an intracellular thiol protease inhibitor [72, 73]. Evidence suggests the role of CSTB in various diseases [45, 74]. Animal models have been shown to increase the expression of CSTB inhibiting GC metastasis by involving biological processes involved in proliferation, apoptosis, and migration . Overexpression of CSTB suppresses activation of the PI3K/Akt/mTOR pathway. PI3K/Akt/mTOR pathway is widely involved in regulating cell processes, including angiogenesis, cell proliferation and metabolism [45, 76]. CSTB Downregulation promotes the development and progression of GC by affecting cell proliferation and migration. Previous studies have shown that CSTB plays different roles in ovarian cancer [77, 78], colon cancer , and myoclonic epilepsy .
This study indicated that salivary CSTB in GC patients was significantly lower than in the control group. Furthermore, this biomarker had an acceptable sensitivity (83.87%) and specificity (70.97%) in GC differentiation from the healthy control. Previous studies have shown that CSTB downregulates both protein and mRNA levels in GC and can be used as a marker in GC diagnosis . Xiao et al. examined the salivary proteome of patients with GC. Five proteins were selected for further study, including interleukin-1 receptor antagonist (ILIRA), CSTB, isomerase triphosphate (TPI1) and DMBT1. ELISA examination of these proteins showed that their expression varied significantly in GC patients and healthy individuals with 85% sensitivity and 80% specificity in diagnosing GC .
The DMBT1 gene encodes a protein involved in cell proliferation and is considered a tumor suppressor for the brain and epithelial cancer [81-84]. Some studies have shown conflicting results in reducing or increasing the expression of DMBT1 in various cancers [85, 86]. Preliminary studies have shown that DMBT1 is eliminated or reduced in a variety of tumors . DMBT1 mucosal levels increase significantly (2.5-fold) in patients with gastric mucosal dysplasia and atrophic gastric mucosa . An increase was seen in advanced gastritis associated with Helicobacter pylori infection. In addition, the increased expression of DMBT1 wa observed in precancerous lesions of the gastric mucosa and the role of DMBT1 in gastric carcinogenesis is complex [47, 88]. Conde et al. showed that DMBT1 downregulates mRNA levels in 38% of GC patients and upregulates in 62% of GC patients. Loss of DMBT1 is likely to occur in differentiated GCs, while DMBT1 upregulation occurs in all types of GC . Increased expression of DMBT1 in GC was shown in several studies, which confirms our results. Considering the acceptable sensitivity and specificity of salivary DMBT1 in GC detection, DMBT1 may be suggested as a noninvasive marker in GC detection.
Our results showed a significant relationship between consumption of a diet containing fruits and vegetables with GC. Thus, low consumption of vegetables and fruits is associated with an increased risk of GC. These results are in line with Wang et al., who stated that high fruit intake might decrease the risk of non-cardia GC . According to ours, there is a relationship between salty taste preference and GC. Lin et al., in their study, stated that salt taste preference in the diet showed a dose-response relationship with GC. Reducing salt and salt processed food in diets might be one practical measure to preventing GC . Yang et al. stated a significant relationship between salt taste sensitivity threshold and GC . Excessive consumption might act as a gastric mucosa stimulant, leading to atrophic gastritis, increased DNA synthesis, and cell proliferation, thereby providing the basis for GC incidence . Our study indicated that higher consumption of vegetables was less likely to develop GC; this result is confirmed in several studies [92-95].
According to our results, a higher educational level is associated with a lower incidence of GC. Lower educational level is accompanied by risk factors such as Helicobacter pylori infection and lifestyle factors such as dietary habits, obesity, and cigarette smoking, which may increase the risk of GC [96-98]. These results are in line with Rota et al. and Lagergren et al. showed that the high level of education was associated with a modest decrease in the GC rate [99, 100].
Individuals with a positive history of GERD were less likely to develop GC than those without a positive history of GERD. These results are in contrast to other studies. They stated that a history of GERD is a risk factor for cardiac GC, which arises from dysplastic intestinal metaplasia, and one potentially involving dysplasia of the cardiac-type mucosa [21, 101-103]. One reason for the difference is the type of cancer examined in the present study and the low sample size compared with other studies.
Participants were classified regarding occupation in three groups; low-stress level, moderate-stress level, high-stress level. Participants with lower stress jobs were less likely to develop GC. These results were in line by Kuwahara et al. results . Also, Eguchi et al. stated that individuals working in coal and tin mining, metal processing (particularly steel and iron), and rubber manufacturing industries had increased risks of GC . Yoshinaga revealed that occupations and industries still impact men's and women's health in terms of mortality due to GC in Japan .
The sensitivity of CSTB in GC diagnosis is 83.87%, and its specificity is 70.97%. AUC is close to one, and it can be concluded that this protein has an acceptable function in diagnosing GC. Yang et al. examined serum markers for the diagnosis of GC. They showed COPS2, CTSF, NT5E, and TERF1 biomarkers with 95% diagnostic sensitivity and 92% specificity for differentiating GC patients from healthy individuals. They concluded that these four serum biomarkers could be used as a non-invasive diagnostic indicator for GC, and a combination of them could potentially be used as a predictor of overall GC survival .
In this study, in addition to studying demographic information and salivary level of CSTB and DMBT1, the relationship between demographic data by taking the Salivary CSTB and DMBT1 into account was investigated to diagnose GC. Applying the information mentioned above to a set of machine learning methods confirmed our achieved findings. Utilizing machine learning methods in cancer diagnosis improves diagnostic accuracy and introduces novel and complex cause-and-effect relationships, which is not easily possible by examining and receiving a patient’s history [107-109]. Hirasawa et al. used a neural network for detecting GC in endoscopic images. They correctly diagnosed GC lesions with a sensitivity of 92.2% and a positive predictive value of 30.6% . Although several studies have used machine learning and artificial intelligence to interpret patients’ images to diagnose GC, the use of machine learning to analyze biomarkers as well as patient demographics has been limited.
Machine learning methods do not cause crucial factors to diagnose GC but help us develop computer algorithms that can consider a set of variables and their complicated relationship. Machine learning is known as the most common engine of artificial intelligence. By taking advantage of machine learning in clinical issues, many useful facilities in public health are provided. The best model of Liu et al. exactly predicted the risk of early GC with the accuracy of 77.84% and the AUC of 0.66 by data mining method of patients’ demographic data using C5.0 decision tree algorithm . Zhu et al. used machine learning analysis of demographic data in the diagnosis of GC. They stated that machine learning is a non-invasive method with a sensitivity of 87.0, specificity of 84.1, and AUC equal to 0.91 for GC diagnosis, reducing medical costs . These results are in accord with ours, indicating the ability of machine learning to analyze demographic data.
Aslam et al. showed that using machine learning and support vector machine (SVM) for analysis the results of high-performance liquid chromatography-mass spectrometry (HPLC-MS) of saliva lead to overall acuracy of 97.18%, specificity of 97.44%, and sensitivity of 96.88% for diagnosis of GC . In this study, in addition to statistical analysis of the salivary CSTB and DMBT1, using various machine learning methods, we simultaneously analyzed the CSTB and DMBT1 salivary levels as a non-invasive method as well as demographic data, clinical characteristics and nutrition habits of patients and control group.