Integrating Salivary Biomarkers CST4 and miR-223 With Health-Related Factors for Gastric Cancer Detection and Risk Assessment

Abstract

Background:

Gastric cancer remains a leading cause of cancer-related death. Early detection is crucial, but effective non-invasive screening methods are lacking. This study investigates the diagnostic potential of salivary Cystatin S (CST4) and MicroRNA-223 (miR-223) biomarkers, integrated with health-related factors for early gastric cancer detection.

Methods:

Forty-five patients with gastric cancer and 45 healthy controls participated in this case-control study. Saliva samples were collected and analyzed for CST4 and miR-223 levels. Demographic data and health-related factors, including hot drink consumption, gastric ulcer history, Helicobacter pylori infection, body mass index (BMI), DMFT (Dental Decay, Missing, and Filled Teeth), and salty food intake, were also collected through a standardized questionnaire. Salivary CST4 levels were determined using enzyme-linked immunosorbent assay (ELISA), and miR-223 levels were quantified using real-time polymerase chain reaction (PCR). Statistical analyses, encompassing multiple logistic regression, were conducted to evaluate the diagnostic efficacy of the biomarkers alongside health-related parameters.

Results:

Significant differences in salivary CST4 and miR-223 levels were observed between gastric cancer patients and healthy controls (P < 0.001). Combining salivary biomarkers and health-related factors yielded high accuracy, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.89 for the model using CST4 and miR-223. Multiple logistic regression analysis identified several health-related factors, including gastric ulcer history and Helicobacter pylori infection, as significant predictors of gastric cancer risk. The inclusion of health factors, along with biomarkers, enhanced early detection’s sensitivity and specificity.

Conclusions:

The study highlights the potential of salivary biomarkers CST4 and miR-223 as non-invasive tools for early gastric cancer detection. Integrating these biomarkers with health-related factors, such as gastric ulcer history and Helicobacter pylori infection, enhances the risk assessment and diagnostic accuracy for gastric cancer.

Keywords

Saliva gastric cancer biomarkers cystatin S (CST4)health-related factors

Introduction

Stomach cancer is the fifth most common malignancy worldwide and represents a major health challenge due to its high mortality rate.¹ The prevalence of gastric cancer (GC) varies significantly across different countries and cultural contexts, with developing countries accounting for half of all newly diagnosed cases. In certain regions of the world, particularly in Central Asian countries and Iran, as well as parts of Latin America, GC is the predominant cause of cancer-related death.^2,3 Despite substantial progress in medicine, the early detection of GC continues to pose a significant challenge, frequently leading to poor prognoses and low survival rates.^4,5 In contrast, countries like Japan that implement systematic screening and early detection programs exhibit improved survival rates.^6-9

The etiology of GC is multifactorial, involving a complex interplay between environmental and genetic influences.¹⁰ Crucial variables linked to GC encompass infection with Helicobacter pylori,^11,12 tobacco¹³ and alcohol consumption, high salt intake,¹⁴ obesity,¹⁵ physical inactivity,¹⁶ and consumption of coffee.^17,18 To tackle the burden of this malignancy, researchers have been actively exploring both primary and secondary prevention strategies. Primary prevention strategies emphasize lifestyle modifications,¹⁹ and secondary prevention focuses on early diagnosis and timely treatment, particularly for individuals with a familial history of GC who are at elevated risk.²⁰

Endoscopic screening is recognized as a cost-effective strategy in regions with a high prevalence of GC.²¹ It is important to note that endoscopy is an invasive procedure,²² associated with potential risks such as bleeding,^23,24 and mucosal perforation.²⁵ However, these imaging modalities face limitations, including restricted accessibility, challenges training healthcare professionals to interpret complex images, variability in imaging acquisition parameters, and concerns regarding their diagnostic accuracy.²⁶ Primary prevention strategies emphasize lifestyle modifications,¹⁹ and secondary prevention focuses on early diagnosis and timely treatment, particularly for individuals with a familial history of GC who are at elevated risk.²⁰ Recent advances in systemic therapy, particularly immunotherapy,²⁷ nivolumab/chemotherapy in unresectable GC,^28,29 and targeted agents,³⁰ have transformed survival outcomes in advanced disease. However, their efficacy remains limited to patients diagnosed at non-metastatic stages. Thus, reliable early detection tools are fundamental to enabling curative-intent treatment and maximizing therapeutic benefit from these novel regimens.

Given that stomach cancer frequently presents with severe symptoms, effective non-invasive screening for early detection is crucial in reducing mortality associated with this malignancy. Among the promising avenues of cancer research, the role of novel biomarkers has emerged as a focal point. Saliva, a non-invasive and readily accessible biofluid, has attracted considerable interest as a potential diagnostic tool for various malignancies.³¹ Recent technological advancements have effectively overcome earlier limitations, positioning salivary biomarkers as promising options for early diagnosis.^32,33 Cystatin S (CST4), also known as Cystatin SA-III, a non-glycosylated protein found in saliva and tears, exhibits characteristics similar to blood biomarkers, with increased expression levels noted in GC cells.^34,35 CST4 has been shown to enhance the invasiveness of GC and promote its progression.³⁶ Notably, serum CST4 has been well-defined as a marker for detecting certain malignancies, particularly gastrointestinal tumors, where it is significantly elevated.^34,37 The differential gene expression analysis tool showed that CST4 had the highest expression among the cystatin protein group in saliva.^38,39 Salivary CST4 is a promising biomarker in various oral health conditions, particularly in disease such as primary Sjögren’s syndrome.^40,41

CST4, found in the cytoplasm, has potential as a biomarker due to its low molecular weight and its secretion in saliva.^42,43 Moreover, studies have confirmed that CST4 overexpression in tissues demonstrates high specificity and sensitivity, suggesting it could become a novel blood biomarker for GC.^37,44,45 However, the clinical value of CST4, in combination with miRNA tumor markers, in diagnosing gastrointestinal malignancies has not been well-established in the literature. In conclusion, while CST4 remains a promising tumor marker, further clinical data are needed. The diagnostic potential of MicroRNA-223 (miR-223) in GC has gained considerable attention due to its strong association with tumor progression.^46-48 Since miR-223 can be detected in both plasma and tissue samples, it emerges as a promising non-invasive biomarker for diagnosis GC.⁴⁸ Higher miR-223 levels are linked to worse outcomes in GC, showing a positive correlation with advanced TNM stages and lymph node metastasis. This suggests that miR-223 could be a valuable biomarker for risk-based patient stratification.^49,50 Helicobacter pylori infection, a major risk factor for GC, can increase miR-223 expression through its CagA protein, connecting chronic inflammation to cancer progression.⁵¹ When analyzed alongside demographic and clinical data, salivary CST4 and miR-223 levels’ potential diagnostic significance offers a promising opportunity to transform early detection strategies in GC. The main goal of this article is to explore the diagnostic accuracy of salivary CST4 and miR-223 levels, combined with demographic and clinical factors, for GC detection.

Methods

Ethical statement

The study was approved by the Ethical Committee of the Tehran University of Medical Sciences (Ethical Code: IR.TUMS.DENTISTRY.REC.1399.229). Following the elucidation of the study aims, all participants voluntarily furnished signed informed consent before their involvement. Individuals were made aware that they could opt out of the study without any obligation. All methods were performed in accordance with the Helsinki Declaration of 1975, as revised in 2024.⁵²

Samples

This retrospective case-control research involved 45 healthy persons, and 45 patients with early-stage gastric adenocarcinoma were selected as a convenience series from Imam Khomeini Hospital in Tehran. Gastroenterologists have diagnosed GC based on histological and endoscopic evaluations. This study was conducted over a period of six months, from January 2023 to June 2023. The time interval between the histopathological diagnosis and saliva sampling was within four weeks for all patients, and no clinical interventions such as surgery, chemotherapy, or radiotherapy were administered before saliva collection. Patients with a history of such treatments before saliva sampling were excluded from the study to prevent confounding effects on biomarker levels. To ensure the integrity of the study, specific exclusion criteria were established for participants. These criteria included:

Individuals exhibiting active oral or periodontal diseases.

Patients with additional malignancies or a documented history of any tumors and systemic diseases.

Patients who have undergone surgery, chemotherapy, or radiotherapy prior to saliva collection.

Patients possess a record of blood transfusions during the prior 3 years.

Women who were pregnant.

The control consisted of healthy individuals referred to Imam Khomeini Hospital (Tehran, Iran) for medical checkups, confirming the absence of current oral infections, inflammatory conditions, cancers, or systemic diseases. In addition, in the control group, individuals with blood in their stool, unexplained weight loss, hyperactive intestinal movements, or long-term fatigue were also excluded. Efforts were made to match the patients with GC and the control group for sex and age to minimize potential confounding variables (Figure 1). The reporting of this study adheres to STARD guidelines (Standard Protocol for Reporting Diagnostic Accuracy Studies) (Supplemental Table 1-STARD-2015).⁵³

Figure 1.

A STARD flow diagram illustrating the selection process for GC cases and healthy controls.

All participants underwent a comprehensive clinical evaluation, including a review of medical history, which led to the exclusion of patients with a documented history of cancer (either self-reported or clinically diagnosed). Screening tests, including complete blood count (CBC) and tumor marker assessments (carcinoembryonic antigen (CEA),⁵⁴ alpha-fetoprotein (AFP),⁵⁵ cancer antigen 15-3 (CA15-3),⁵⁶ cancer antigen 125 (CA125),^57,58 prostate-specific antigen (PSA),⁵⁹ carbohydrate antigen 19-9 (CA19-9)),^60,61 were conducted to identify occult malignancies for case and control groups. Patients with a history of chemotherapy, radiotherapy, or blood transfusions within the past 3 years were excluded to reduce the risk of undiagnosed hematologic malignancies. The healthy control group reporting alarm symptoms, such as unexplained weight loss, hematemesis, melena, dysphagia, lymphadenopathy, abdominal masses, or persistent vomiting, were excluded.

During the study, participants were requested to complete a standardized and comprehensive checklist to provide their information and habits potentially related to GC progression. The data included demographic data, hereditary cancer predisposition, smoking habits, consumption of salty and fast foods, hot drinks, Helicobacter pylori infection, and the DMFT (Decayed, Missing, and Filled Permanent Teeth) index.^62,63 The form-filling process was supervised by a trained author who remained neutral throughout. Clarifications were provided when necessary, ensuring that no guidance or bias influenced the participants’ responses. The information from the questionnaires was categorized for analysis, ensuring both the validity and completeness of the data.

Demographic characteristics (sex, age, BMI, occupational status, ethnicity) and clinical history (gastric ulcers) were assessed using the World Health Organization (WHO) STEPwise Approach to Surveillance (STEPS) Questionnaire,⁶⁴ with ethnicity categories standardized to Iran’s national census. Family history of malignancy was evaluated via an adapted Cancer Family History Questionnaire.⁶⁵ Smoking status (current/past) was determined using the WHO Global Adult Tobacco Survey (GATS), Section B.⁶⁶ At the same time, dietary factors (salty food, fast food, and hot drink) were quantified using the food frequency questionnaire (FFQ) of PERSIAN Cohort.^67,68 The frequency score was defined as 0 for those who did not consume these items weekly and 1 for those who consumed them at least once per week.⁶⁹ The DMFT index was determined using the participants’ panoramic X-rays⁶² and oral examinations by three oral medicine specialists based on the WHO criteria for DMFT.⁷⁰ Participants’ occupations were categorized into three groups: employed, homemaker, and retired.^71,72 The participants were categorized into two groups according to their smoking status: tobacco users and those who had never used tobacco.^73,74 All participants were encouraged to report any gastroesophageal reflux disease (GERD) history.

Saliva sample collection

To mitigate circadian rhythm influences on salivary secretions, saliva was obtained at 9:00 and 11:00 a.m. A 90-minute abstention from drinking, smoking, eating, and mouth hygiene was mandated prior to the sample to remove gustatory or mechanical stimulation. Following a comprehensive dental and periodontal examination, unstimulated whole saliva was obtained the spitting. Participants were instructed to accumulate saliva in their oral cavity and spit into a sterilized container every minute up to 15 minutes. Centrifuge the saliva samples, designated with coded labels for confidentiality, to isolate the liquid contaminants. Further examination was conducted utilizing the supernatant. The index tests were performed blinded to the reference standard results and clinical information. Samples were processed using coded identifiers, and laboratory personnel had no access to participants’ diagnostic status during the analysis. The reference standard results were only unblinded after completion of all laboratory measurements for final comparative analysis. Following centrifugation, samples were stored at −80°C to preserve their integrity.

Measurement of salivary CST4

Samples were preserved at −80°C until subjected to enzyme-linked immunosorbent assay (ELISA) analysis. Salivary CST4 levels were quantified utilizing a 96-well human CST4 ELISA kit (Cat. No. ZB-12150 C-H9648 (PRO-1097), ZellBio GmbH, Germany) in accordance with the manufacturer’s instructions. In this process, the CST4 protein was introduced into wells pre-coated with anti-human CST4 monoclonal antibodies. Subsequently, biotin-labeled anti-CST4 antibodies were added, which then formed an immune complex by binding with streptavidin-HRP. With a sensitivity of 2.5 ng/ml, the CST4 ELISA kit measured 50–1600 ng/ml. A Hyperion ELISA microplate reader measured sample absorbance. The spectrometer software computed CST4 concentrations using standard curves. The average of triplicate sample measurements was given.

Extraction and Purification of RNA

To achieve sample homogenization, 500 μL was transferred to sterile microtubes, followed by the addition of 800 μL of TRIzol reagent (Selecta, Spain). The mixture was thoroughly vortexed to achieve homogeneity and allowed to stand for 5 minutes. The solution was subjected to 200 μL of chloroform (EMSURE) to initiate phase separation. Following a 5-minute incubation, the tubes underwent centrifugation at 12,000 revolutions per minute for a duration of 10 minutes in a refrigerated centrifuge. This resulted in three distinct layers: the upper aqueous layer containing RNA, the middle layer including DNA, and the lower organic layer with proteins and other organic compounds.

To precipitate and wash RNA, the top liquid layer was moved to a fresh sterile microtube, and 800 μL of pre-cooled −20°C isopropanol was added. To ensure thorough mixing, the tubes were gently shaken and centrifuged at 12,000 rpm for 15 minutes. After a white RNA pellet had developed at the tube bottom, the supernatant was eliminated. A centrifuge was run for 3 minutes at 14,000 rpm after 500 μL of 70% ethanol was added to remove any leftover contaminants. This ethanol washing step was repeated once more. After removing ethanol, the tubes were air-dried for 2–3 minutes. The RNA pellet was then left to dry at room temperature on a dry block. The RNA was dissolved in 20 μL of RNase-free water (GENEX), protecting it from enzymatic degradation (DEPC-treated), and the solution was stored in the freezer for long-term preservation.

Real-Time Quantitative Polymerase Chain Reaction (qPCR) Assessment

The samples were extracted using a nanodrop technique, and RNA concentration was measured. For a blank measurement, 2 μL of DEPC-treated water and samples were added to the BioTek Epoch (BioTek, Gen5) microplate. The Gen program was set to measure nucleic acids using the quantification function. Absorbance was measured at 260 nm for nucleic acids and 280 nm for proteins. A reference range of 1.8–2 was used to compute the nucleic acid to protein absorption ratio, and RNU6 (U6) was used as the internal control. Stem-loop primers were used for the target to appropriately adjust the data (Supplemental File). Polymerase chain reaction (PCR) was conducted on two saliva samples, and each primer was validated against them. To confirm primer specificity, the PCR results of these two samples were subjected to electrophoresis on an agarose gel.

Synthesis of Complementary DNA (cDNA)

The production of cDNA is essential for amplifying genes through PCR, as cDNA serves as the substrate for DNA polymerase. For the synthesis of cDNA from RNA templates, we employed the FIREScript RT cDNA Synthesis Kit provided by Solis BioDyne. This kit contains reverse transcriptase, an enzyme that reads RNA sequences and synthesizes cDNA strands. Consequently, the amount of synthesized cDNA is directly related to the expression levels of the target RNA.

Initially, mix RNA with primer, combine, and dNTPs for each gene. The samples were subsequently placed in a thermal cycler and modified according to the kit’s specifications, with program instructions adhered to. Following the reaction, the samples were frozen.

Real-Time PCR

Real-time PCR was conducted by the Ampliqon SYBR assay technique, which enables the monitoring of reaction progress in real time through the fluorescent dye SYBR Green. Prepare total reaction by mixing water, GENEX Master Mix, primers, and synthesized cDNA. This solution was then placed in instrument-compatible microtubes. Capped microtubes were put in the Step One Plus device (Applied Biosystems).

Samples were heated to 95°C for 10 minutes to ensure the complete inactivation of any remaining reverse transcriptase enzyme. The amplification process consisted of 45 cycles, each including a 15-second denaturation step at 95°C and a 30-second annealing phase at 60°C to support primer binding and enzyme function. Data, including amplification curves, were generated by the instrument software as outlined in the protocol. Each sample was analyzed in duplicate, and consistent Ct values were obtained for analysis.

Helicobacter pylori Infection

Stool samples (fresh without macroscopic evidence of blood) were collected in sterile containers. Stool samples were preserved at −20°C until subjected to ELISA analysis. Helicobacter pylori antigen levels were quantified using a commercial ELISA kit (PISHTAZ TEB, Tehran, Iran) according to the manufacturer’s protocol.⁷⁵

Statistical analysis

The statistical analysis used SPSS 18.0.0 (SPSS Inc., USA) and GraphPad Prism 9.0 (GraphPad Software, USA). A p-value (p) below 0.05 was deemed statistically significant. For comparing quantitative factors between the two groups, a Student’s t-test was used. The relationship between the presence of GC and explanatory variables was assessed using multiple logistic regression (MLR) analyses. To determine the diagnostic value of salivary CST4 and miR-223 in distinguishing patients with GC, a receiver operating characteristic (ROC) curve was generated. The optimal cutoff points for the ROC curves were identified using Youden’s index. Furthermore, principal component analysis (PCA) and MLR were implemented to investigate the cumulative impacts of salivary microRNAs (miRNAs), clinical characteristics, and demographic data. The sample size of 45 participants per group was determined using PASS software v15.0 (NCSS, LLC, USA) for ROC curve analysis. Assuming an AUC of 0.85, a significance level of 0.05, and a 95% confidence interval (CI) width of 0.161, this sample size provided adequate power to detect statistically significant differences between groups.

Results

Patient demographics

Table 1 provides a summary of the demographics, clinical features, and laboratory results for cases and controls. The two groups exhibited significant differences in relation to a positive history of gastric ulcers (P < 0.001), hot drinks (P < 0.001), salivary CST4 level, ng/mL (P < 0.001), salivary miR-223 level (2^−ΔΔCt) (P < 0.001), DMFT (P = 0.003), salty food consumption (P = 0.005), body mass index (BMI) (P = 0.005), and Helicobacter pylori infection (P = 0.025) (Table 1).

Table 1.

Demographic, clinical, nutritional, and laboratory comparisons between patients with GC and controls.

Characteristics	patients with GC(n = 45)	Healthy controls(n = 45)	P ^a
Sex			1.000
female	11 (24.44%)	11 (24.44%)
male	34 (75.56%)	34 (75.56%)
Age (years)	58.91 ± 10.90	58.64 ± 9.54	0.902
BMI	23.96 ± 3.83	26.04 ± 4.41	0.019
Occupational status			0.496
employed	30 (66.67%)	28 (62.22%)
homemaker	9 (20.00%)	8 (17.78%)
retired	6 (13.33%)	9 (20.00%)
Family history of malignancy			0.671
history of malignancy in first-degree blood relatives	11 (24.44%)	11 (24.44%)
history of malignancy in other blood relatives	8 (17.78%)	3 (13.33%)
Ethnicity			0.501
Azerbaijanis	21 (46.67%)	14 (31.11%)
Lurs and Persians	13 (28.89%)	20 (44.44%)
Gilaks, Mazanderanis, and Turkmens	7 (15.55%)	8 (17.78%)
Kurds	4 (8.89%)	3 (6.67%)
Current or past smoking status (at least 1 per day)	20 (44.44%)	12 (26.67%)	0.080
Salty food consumption	24 (53.33%)	11 (24.44%)	0.005
Hot drinks	34 (75.55%)	16 (35.55%)	<0.001
Fast food consumption	6 (13.33%)	9 (20.00%)	0.402
Helicobacter pylori infection	4 (8.89%)	2 (4.44%)	0.025
Positive history of gastric ulcers	11 (24.44%)	0 (0.00%)	<0.001
DMFT	20.69 ± 8.89	15.20 ± 8.31	0.003
Salivary CST4 level, ng/mL	0.070 ± 0.047	0.032 ± 0.032	<0.001
Salivary miR-223 level (2^−ΔΔCt)	28.46 ± 34.59	2.34 ± 2.49	<0.001

Values are expressed as mean ± SD or No. (%). Abbreviations: BMI, body mass index; DMFT, decayed, missing, and filled permanent teeth.

P-value for the comparison between patients with GC and healthy control groups.

The analysis revealed no statistically significant distinctions between the case and control groups regarding sex, age, smoking status (current or past), fast food consumption, or family history of malignancy.

Salivary CST4 and miR-223-3p

The levels of salivary miR-223-3p (Figure 2A) and CST4 (Figure 2B) were analyzed in GC and healthy individuals. ROC curve analysis (Figure 2C) was employed to evaluate the salivary miR-223-3p and CST4, as well as their combined MLR model for differentiating cases from controls. The area under the ROC curve (AUC) values were determined as 0.84 (95% CI: 0.76–0.93) for miR-223-3p, 0.78 (95% CI: 0.69–0.88) for CST4, and 0.89 (95% CI: 0.83–0.96) for their MLR model. CST4 and miR-223-3p cutoffs were set using Youden’s index at 4.77 (2^-∆∆Ct) and 0.035 (ng/mL), respectively.

Figure 2.

Salivary levels of (A) miR-223-3p, (B) CST4 of GC and healthy individuals, (C) ROC curve of salivary miR-223-3p, CST4 levels, and MLP of them.

PCA was conducted on salivary CST4 and miR-223 levels, alongside demographic data, to define the important factors for differentiation of GC cases from controls. The analysis combined salivary CST4 and miR-223 levels, gastric ulcer experience, DMFT status, and Helicobacter pylori infection (Figure 3).

Figure 3.

PCA score plots using salivary CST4 and miR-223 levels, history of gastric (stomach) ulcers, DMFT status, and Helicobacter pylori infection.

The ROC curve was constructed based on PCA model to assess the ability to differentiate between GC cases and controls. The PCA score distinguishes GC cases with a high sensitivity of 0.80, specificity of 0.71, and an AUC of 0.80 (95% CI = 0.71–0.89). The results of distinguishing between GC cases and controls using salivary miR-223-3p, CST4, and clinodemographic data through MLR are shown in Table 2.

Table 2.

Summary of the diagnostic model outcomes, comparing GC cases to controls.

Biomarker and model	Sensitivity	Specificity	Accuracy	DOR	AUC
miRNA
miR-223-3p	0.73	0.91	0.82	27.34	0.84
CST4	0.82	0.64	0.73	8.90	0.78
MLR
MLR of miR-223 and CST4	0.75	0.91	0.83	30.33	0.89
MLR of miR-223, CST4, hot drinks, gastric ulcer, Helicobacter pylori, DMFT	0.82	0.82	0.82	20.75	0.93
MLR of miR-223, CST4, hot drinks, gastric ulcer, Helicobacter pylori infection, DMFT, BMI, salty food consumption	0.87	0.84	0.86	35.13	0.94
PCA
PCA of miR-223, CST4, gastric ulcer, Helicobacter pylori infection, DMFT	0.80	0.71	0.76	9.79	0.80

Abbreviations: AUC, area under the ROC curve; DOR, diagnostic odds ratio; MLR, multiple logistic regression; PCA, principal component analysis.

Discussion

GC ranks among the most fatal malignancies worldwide, frequently being diagnosed at advanced stages. Consequently, studies highlight the need for implementing early detection and control programs for GC in regions with high prevalence and mortality rates associated with the disease.⁷⁶ Development of efficient early detection and diagnostic methods is consistent with this overall objective. In this context, salivary biomarkers, which include proteins, genes, and metabolic components, are crucial.⁷⁷ The discovery of miRNAs has spurred research aimed at identifying biomarkers in oral biofluids that can be utilized for diagnosing various cancers, including miRNAs in salivary exosomes. The identification of miRNA salivary biomarkers presents significant potential for the early diagnosis of malignancies of the gastrointestinal tract, including pancreatic cancer and GC.^78,79 Moreover, studies have confirmed that CST4 overexpression in tissues demonstrates high specificity and sensitivity, suggesting it could become a novel blood biomarker for GC.^37,44,45 Compared to the numerous studies that have focused on blood and tissue biomarkers for diagnosing GC, limited studies have examined the role of salivary biomarkers in GC detection.^78,80,81 However, when integrated with demographic and clinical data, salivary CST4 and miR-223 levels show significant promise in enhancing early detection strategies for GC.

Li et al⁴⁸ conducted a study on miR-223 as a potential biomarker for early GC detection, revealing significantly increased miR-223 in the blood of patients with GC relative to controls. Increased levels of serum miR-223 have been reported in patients diagnosed with GC, establishing a significant correlation between miR-223 expression and TNM stage.⁸² Aalami et al⁸³ further associated serum miR-223 expression with cancer grade. Our analysis revealed an elevation of salivary miR-223, aligning with previous observations of increased expression of miR-223 in blood or tissues.⁴⁸,^82-84

Li and colleagues demonstrated a significant association between hot tea consumption and an increased risk of GC.⁸⁵ In ours, the habit of daily hot tea consumption was more than twice as common in the patients with GC compared with the control group, with 34 individuals in the patients with GC versus 16 in the control group. Although the underlying mechanism of this association is unclear, hot tea consumption may potentially damage the gastric mucosa, increasing its vulnerability to the development of cancer.^86,87 It is also possible that hot tea affects the equilibrium of the gastric microbiota, resulting in inflammatory responses and oncogenesis.^85,88,89

A recent meta-analysis found a strong link between high salt intake and increased GC risk.⁹⁰ Our study’s findings of higher salted food consumption in patients with GC support the need for dietary interventions to lower this risk. In our study, we found that 11 out of 45 patients with GC had a history of gastric ulcer, whereas none of the 45 individuals in the control group had gastric ulcers. This finding aligns with the observed association between gastric ulcer and increased GC risk,⁹¹ further supporting the notion that gastric ulcer may play a significant role in GC incidence.

According to our findings, there was a statistically significant difference in the DMFT score index between the control group and patients with GC. As a measure of dental health, the DMFT score was significantly higher in patients with GC that aligns with findings from other studies.^92-94 This study revealed that persons who cleaned their teeth less than once daily had a markedly increased chance of acquiring stomach cardia adenocarcinoma.⁹² GC and tooth loss may be associated with microbiota that facilitates the production of carcinogenic compounds. Specifically, absent teeth may harbor an oral microbiome that converts nitrate to nitrite. Nitrite may interact with bodily amines to produce carcinogenic nitrosamines.⁹⁵ Among the models examined, the MLP model, including miR-223 levels, CST4, clinical features, and risk variables, produced the greatest diagnostic odds ratio (DOR). Conversely, concentrating just on salivary CST4 resulted in the lowest diagnostic DOR, underscoring the significance of integrating many prognostic parameters.

The integration of machine learning models that incorporate salivary biomarkers, imaging data, and electronic health records will enable dynamic and personalized GC risk prediction, moving beyond traditional static diagnostics. In addition, rapid salivary tests, such as CRISPR-based miRNA detection, will transition from laboratories to point-of-care settings, facilitating large-scale community-level screening, especially in resource-limited environments. Moreover, microbiome-targeted prevention strategies, including probiotic or antibiotic interventions to modulate oral-gastric microbial flux, are expected to emerge as important adjunct therapies for high-risk individuals, potentially reducing disease progression and improving outcomes.

Salivary biomarkers like CST4 and miR-223 show promise in diagnosing GC, but challenges and limitations remain. First, the absence of endoscopic screening in our rigorously matched healthy controls precludes definitive exclusion of asymptomatic pre-malignant lesions (e.g., atrophic gastritis, intestinal metaplasia) or early-stage GC, potentially leading to underestimation of biomarker performance. Second, despite adjustment for known risk factors, residual confounding from unmeasured or imprecisely quantified variables, including exact dietary salt intake, socioeconomic status, specific occupational exposures, and medication use, may influence observed associations. Third, salivary biomarker levels are inherently susceptible to pre-analytical variability. However, stringent standardization was applied, and external validation using harmonized methodologies across independent laboratories remains essential. Our exclusion of oral diseases enhances internal validity but may reduce Generalizability to populations with a high oral inflammation burden. Further studies in dentally diverse cohorts are needed to verify clinical utility. Fourth, the significant association between higher DMFT index and GC, while mechanistically plausible via oral microbiome dysbiosis and nitrosamine production, remains fundamentally observational; this study cannot establish causality or define specific microbial pathways. Fifth, prospective validation in larger, multicentre cohorts is critical to confirm diagnostic performance in real-world screening contexts. Finally, the role of oral microbiota in GC is being explored through metagenomic sequencing of paired saliva and gastric samples to identify pro-carcinogenic pathways. To evaluate real-world utility, prospective multicenter trials are investigating the performance of these biomarkers in diverse populations, including those with dental compromise. In addition, standardization of saliva collection and storage protocols is critical for reproducibility, with international efforts like SALIVARES developing evidence-based guidelines.

Conclusion

This study highlights the potential of salivary biomarkers, particularly miR-223 and CST4, as promising tools for GC early diagnosis. Significant differences in the salivary miR-223 and CST4 were detected between GC cases and controls, with biomarkers showing strong diagnostic performance in differentiating the two groups. The MLR model, incorporating these biomarkers along with clinical and demographic factors, demonstrated high sensitivity, specificity, and accuracy, making it a promising tool for GC detection. In addition, the study identified several lifestyle and clinical factors, such as hot drink consumption, salty food intake, and a history of gastric ulcers, that were significantly associated with GC risk, reinforcing the importance of these factors in GC pathogenesis. The combination of salivary biomarkers and clinodemographic data offers a comprehensive approach for early detection, which is critical in improving patient outcomes given the typically late-stage diagnosis of GC. Our findings support the further exploration of salivary biomarkers, particularly in combination with clinical risk factors, as a non-invasive strategy for GC diagnosis, potentially enhancing early detection programs in high-risk groups.

Supplemental Material

sj-docx-1-onc-10.1177_11795549251375898 – Supplemental material for Integrating Salivary Biomarkers CST4 and miR-223 With Health-Related Factors for Gastric Cancer Detection and Risk Assessment

Supplemental material, sj-docx-1-onc-10.1177_11795549251375898 for Integrating Salivary Biomarkers CST4 and miR-223 With Health-Related Factors for Gastric Cancer Detection and Risk Assessment by Maryam Koopaie, Saba Manafi, Soheila Manifar, Shima Younespour, Sajad Kolahdooz and Mahdi Karimipour Pareshkooh in Clinical Medicine Insights: Oncology

Supplemental Material

sj-docx-2-onc-10.1177_11795549251375898 – Supplemental material for Integrating Salivary Biomarkers CST4 and miR-223 With Health-Related Factors for Gastric Cancer Detection and Risk Assessment

Supplemental material, sj-docx-2-onc-10.1177_11795549251375898 for Integrating Salivary Biomarkers CST4 and miR-223 With Health-Related Factors for Gastric Cancer Detection and Risk Assessment by Maryam Koopaie, Saba Manafi, Soheila Manifar, Shima Younespour, Sajad Kolahdooz and Mahdi Karimipour Pareshkooh in Clinical Medicine Insights: Oncology

Supplemental Material

sj-jpg-3-onc-10.1177_11795549251375898 – Supplemental material for Integrating Salivary Biomarkers CST4 and miR-223 With Health-Related Factors for Gastric Cancer Detection and Risk Assessment

Supplemental material, sj-jpg-3-onc-10.1177_11795549251375898 for Integrating Salivary Biomarkers CST4 and miR-223 With Health-Related Factors for Gastric Cancer Detection and Risk Assessment by Maryam Koopaie, Saba Manafi, Soheila Manifar, Shima Younespour, Sajad Kolahdooz and Mahdi Karimipour Pareshkooh in Clinical Medicine Insights: Oncology

Supplemental Material

sj-jpg-4-onc-10.1177_11795549251375898 – Supplemental material for Integrating Salivary Biomarkers CST4 and miR-223 With Health-Related Factors for Gastric Cancer Detection and Risk Assessment

Supplemental material, sj-jpg-4-onc-10.1177_11795549251375898 for Integrating Salivary Biomarkers CST4 and miR-223 With Health-Related Factors for Gastric Cancer Detection and Risk Assessment by Maryam Koopaie, Saba Manafi, Soheila Manifar, Shima Younespour, Sajad Kolahdooz and Mahdi Karimipour Pareshkooh in Clinical Medicine Insights: Oncology

Supplemental Material

sj-jpg-5-onc-10.1177_11795549251375898 – Supplemental material for Integrating Salivary Biomarkers CST4 and miR-223 With Health-Related Factors for Gastric Cancer Detection and Risk Assessment

Supplemental material, sj-jpg-5-onc-10.1177_11795549251375898 for Integrating Salivary Biomarkers CST4 and miR-223 With Health-Related Factors for Gastric Cancer Detection and Risk Assessment by Maryam Koopaie, Saba Manafi, Soheila Manifar, Shima Younespour, Sajad Kolahdooz and Mahdi Karimipour Pareshkooh in Clinical Medicine Insights: Oncology

Supplemental Material

sj-jpg-6-onc-10.1177_11795549251375898 – Supplemental material for Integrating Salivary Biomarkers CST4 and miR-223 With Health-Related Factors for Gastric Cancer Detection and Risk Assessment

Supplemental material, sj-jpg-6-onc-10.1177_11795549251375898 for Integrating Salivary Biomarkers CST4 and miR-223 With Health-Related Factors for Gastric Cancer Detection and Risk Assessment by Maryam Koopaie, Saba Manafi, Soheila Manifar, Shima Younespour, Sajad Kolahdooz and Mahdi Karimipour Pareshkooh in Clinical Medicine Insights: Oncology

Footnotes

ORCID iD

Maryam Koopaie

Ethical Considerations

This study was approved by the Tehran University of Medical Sciences Ethical Committee (ethical code: IR.TUMS.DENTISTRY.REC.1399.229). After describing the study objectives, all participants signed the informed consent before participating in this study. All methods were performed in accordance with the Helsinki Declaration of 1975, as revised in 2024.⁵³ (Online version: https://ethics.research.ac.ir/ProposalCertificateEn.php?id=180288&Print=true&NoPrintHeader=true&NoPrintFooter=true&NoPrintPageBorder=true&LetterPrint=true)

Consent for Publication

Not applicable.

Authors’ Contributions

MK, SM^b, and SM^c conceived the study idea and led data collection, created the study protocol, and wrote the original draft. MK and SY contributed to data analysis/interpretation and preparation of the manuscript. MK, SM^b, SY, and SM^c led the writing, review & editing. MK, SY, SK, and SM^b interpreted the results. All authors read and approved the final manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data used and/or analyzed during the current study are available upon request from the corresponding author.

Supplemental Material

Supplemental material for this article is available online.

References

Smyth

Nilsson

Grabsch

van Grieken

Lordick

Gastric cancer. Lancet. 2020;396:635-648.

Bray

Ferlay

Soerjomataram

Siegel

Torre

Jemal

Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394-424.

Sung

Ferlay

Siegel

, et al Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209-249.

Yamamoto

Watanabe

Sato

Maehata

Itoh

Non-invasive early molecular detection of gastric cancers. Cancers. 2020;12:2880.

Roy

Kanda

Nomura

, et al Diagnostic efficacy of circular RNAs as noninvasive, liquid biopsy biomarkers for early detection of gastric cancer. Mol Cancer. 2022;21:42.

Hamashima

Group

, Guidelines GDGfGCS. Update version of the Japanese guidelines for gastric cancer screening. Jpn J Clin Oncol. 2018;48:673-683.

Katagiri

Goto

Shimazu

, et al Dietary fiber intake and risk of gastric cancer: the Japan public health center-based prospective study. Int J Cancer. 2021;148:2664-2673.

Sumiyama

Past and current trends in endoscopic diagnosis for early stage gastric cancer in Japan. Gastric Cancer. 2017;20:20-27.

Yashima

Shabana

Kurumi

Kawaguchi

Isomoto

Gastric cancer screening in Japan: a narrative review. J Clin Med. 2022;11:4337.

10.

Yang

Liu

, et al A comprehensive update on early gastric cancer: defining terms, etiology, and alarming risk factors. Expert Rev Gastroenterol Hepatol. 2021;15:255-273.

11.

Reyes

VE.

Helicobacter pylori and its role in gastric cancer. Microorganisms. 2023;11:1312.

12.

Lee

Chiang

Chou

, et al Association between Helicobacter pylori eradication and gastric cancer incidence: a systematic review and meta-analysis. Gastroenterology. 2016;150:1113-1124.

13.

Poorolajal

Moradi

Mohammadi

Cheraghi

Gohari-Ensaf

Risk factors for stomach cancer: a systematic review and meta-analysis. Epidemiol Health. 2020;42:e2020004.

14.

Richa, Sharma

Sageena

Dietary factors associated with gastric cancer-a review. Transl Med. 2022;7:7.

15.

Cani

Jordan

BF.

Gut microbiota-mediated inflammation in obesity: a link with gastrointestinal cancer. Nat Rev Gastroenterol Hepatol. 2018;15:671-682.

16.

Fang

Wei

, et al Landscape of dietary factors associated with risk of gastric cancer: a systematic review and dose-response meta-analysis of prospective cohort studies. Eur J Cancer. 2015;51:2820-2832.

17.

Liu

Hua

Zheng

, et al Effect of coffee consumption on the risk of gastric cancer: a systematic review and meta-analysis of prospective cohort studies. PLoS ONE. 2015;10:e0128501.

18.

Fakhri

Fakheri

Moosazadeh

Ramezanpour

Yousofpour

The relationship between coffee consumption and the risk of gastric cancer; a systematic review and meta-analysis. Immunopathologia Persa. 2022;9:e33406.

19.

Sitarz

Skierucha

Mielko

Offerhaus

GJA

Maciejewski

Polkowski

WP.

Gastric cancer: epidemiology, prevention, classification, and treatment. Cancer Manag Res. 2018;10:239-248.

20.

Huang

Epplein

Hamashima

, et al An approach to the primary and secondary prevention of gastric cancer in the United States. Clin Gastroenterol Hepatol. 2022;20:2218-2228.

21.

Ascherman

Hur

International cost-effectiveness analysis evaluating endoscopic screening for gastric cancer for populations with low and high risk. Gastric Cancer. 2021;24:878-887.

22.

Conti

Agnesi

Scaravaglio

, et al Early gastric cancer: update on prevention, diagnosis and treatment. Int J Environ Res Public Health. 2023;20:2149.

23.

Sugimoto

Hatta

Tsuji

, et al Risk factors for bleeding after endoscopic submucosal dissection for gastric cancer in elderly patients older than 80 years in Japan. Clin Transl Gastroenterol. 2021;12:e00404.

24.

Yao

Uedo

Kamada

, et al Guidelines for endoscopic diagnosis of early gastric cancer. Dig Endosc. 2020;32:663-698.

25.

Akashi

Yamaguchi

Shiota

, et al Characteristics and risk factors of delayed perforation in endoscopic submucosal dissection for early gastric cancer. J Clin Med. 2024;13:1317.

26.

Giandola

Maino

Marrapodi

, et al Imaging in gastric cancer: current practice and future perspectives. Diagnostics. 2023;13:1276.

27.

Jia

Chen

Xie

, et al Helicobacter pylori and immunotherapy for gastrointestinal cancer. Innovation. 2024;5:100561.

28.

Nakazawa

Sohda

Hosoi

, et al Identification of biomarkers for assessing treatment efficacy of chemotherapy plus nivolumab as the first line in patients with unresectable advanced or recurrent gastric cancer: a multicenter study. Oncology. 2025;103:201-208.

29.

Park

Kim

Hyung

Park

Ryu

MH.

Factors associated with the efficacy of first-line nivolumab plus chemotherapy in advanced gastric cancer patients with deficient mismatch repair. Gastric Cancer. 2024;27:840-849.

30.

Alsina

Arrazubi

Diez

Tabernero

Current developments in gastric cancer: from molecular profiling to treatment strategy. Nat Rev Gastroenterol Hepatol. 2023;20:155-170.

31.

Liao

Chen

Salivary analysis: an emerging paradigm for non-invasive healthcare diagnosis and monitoring. Interdiscip Med. 2023;1:e20230009.

32.

Goldoni

Scolaro

Boccalari

, et al Malignancies and biosensors: a focus on oral cancer detection through salivary biomarkers. Biosensors. 2021;11:396.

33.

Afsaneh Abadi

Koopaie

Montazeri

. Comparison of salivary nitric oxide and oral health in diabetic patients with and without xerostomia. Diabetes Metab Syndr. 2020;14:11-15.

34.

Zhou

Fan

Xie

, et al Clinical application of serum CST4 combined with tumor markers in the diagnosis of digestive system malignant tumors. Oncol Lett. 2024;28:384.

35.

Di Giorgi

Cecchettini

Michelucci

, et al Salivary proteomics markers for preclinical Sjögren’s syndrome: a pilot study. Biomolecules. 2022;12:738.

36.

Zhang

Song

DW.

Overexpression of CST4 promotes gastric cancer aggressiveness by activating the ELFN2 signaling pathway. Am J Cancer Res. 2017;7:2290-2304.

37.

Dou

Zhou

, et al Antibody-sandwich ELISA analysis of a novel blood biomarker of CST4 in gastrointestinal cancers. Onco Targets Ther. 2018;11:1743-1756.

38.

Martini

Gallo

Vella

, et al Cystatin S—a candidate biomarker for severity of submandibular gland involvement in Sjögren’s syndrome. Rheumatology. 2017;56:1031-1038.

39.

Lau

Hardt

Zhang

Freire

Ruhl

The human salivary proteome wiki: a community-driven research platform. J Dent Res. 2021;100:1510-1519.

40.

Kamounah

Sembler-Møller

Nielsen

Pedersen

AML

. Sjögren’s syndrome: novel insights from proteomics and miRNA expression analysis. Front Immunol. 2023;14:1183195.

41.

Boto de los Bueis

de la Fuente

Montejano-Milner

del Hierro Zarzuelo

Vecino

Acera

A pilot study of a panel of ocular inflammation biomarkers in patients with primary Sjögren’s syndrome. Curr Issues Mol Biol. 2023;45:2881-2894.

42.

Wang

Liu

Lin

Prognostic value of immune-related cells and genes in the tumor microenvironment of ovarian cancer, especially CST4. Life Sci. 2021;277:119461.

43.

Huang

Yang

Ren

, et al The diagnosis significance of serum cysteine protease inhibitors (CST4) in colorectal cancer. Technol Cancer Res Treat. 2023;22:15330338231164232.

44.

Khalili-Tanha

Rouzbahani

, et al Diagnostic, prognostic, and predictive biomarkers in gastric cancer: from conventional to novel biomarkers. Transl Res. 2024;274:35-48.

45.

Chen

Y-Y

B-P

Wang

J-F

, et al Investigating the prognostic and predictive value of the type II cystatin genes in gastric cancer. BMC Cancer. 2023;23:1122.

46.

Tan

SW.

May circulating microRNAs be gastric cancer diagnostic biomarkers?

J Cancer. 2015;6:1206-1213.

47.

Aalami

Abdeahad

Mesgari

Sahebkar

MicroRNA-223 in gastrointestinal cancers: a systematic review and diagnostic meta-analysis. Eur J Clin Invest. 2021;51:e13448.

48.

Zhao

Guo

, et al Plasma microRNAs, miR-223, miR-21 and miR-218, as novel potential biomarkers for gastric cancer detection. PLoS ONE. 2012;7:e41629.

49.

Aziz

Chakraborty

Khan

Monts

Relevance of miR-223 as potential diagnostic and prognostic markers in cancer. Biology. 2022;11:249.

50.

Yin

, et al Low serum miR-223 expression predicts poor outcome in patients with acute myeloid leukemia. J Clin Lab Anal. 2020;34:e23096.

51.

Yang

Liu

, et al NF-κB/miR-223-3p/ARID1A axis is involved in Helicobacter pylori CagA-induced gastric carcinogenesis and progression. Cell Death Dis. 2018;9:12.

52.

Bibbins-Domingo

Brubaker

Curfman

The 2024 revision to the declaration of Helsinki: modern ethics for medical research. JAMA. 2025;333:30-31.

53.

Cohen

Korevaar

Altman

, et al STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration. BMJ Open. 2016;6:e012799.

54.

Zhang

Chen

Zhang

, et al The threshold of alpha-fetoprotein (AFP) for the diagnosis of hepatocellular carcinoma: a systematic review and meta-analysis. PLoS ONE. 2020;15:e0228857.

55.

Sørensen

Karlsson

Pommergaard

Burcharth

Rosenberg

The diagnostic accuracy of carcinoembryonic antigen to detect colorectal cancer recurrence–A systematic review. Int J Surg. 2016;25:134-144.

56.

Duffy

Evoy

McDermott

EW.

CA 15-3: uses and limitation as a biomarker for breast cancer. Clinica Chimica Acta. 2010;411:1869-1874.

57.

Charkhchi

Cybulski

Gronwald

Wong

Narod

Akbari

MR.

CA125 and ovarian cancer: a comprehensive review. Cancers. 2020;12:3730.

58.

Medeiros

Rosa

da Rosa

Bozzetti

MC.

Accuracy of CA 125 in the diagnosis of ovarian tumors: a quantitative systematic review. Eur J Obstet Gynecol Reprod Biol. 2009;142:99-105.

59.

Ilic

Djulbegovic

Jung

, et al Prostate cancer screening with prostate-specific antigen (PSA) test: a systematic review and meta-analysis. BMJ. 2018;362:k3519.

60.

Liang

Zhong

, et al Diagnostic accuracy of serum CA19-9 in patients with cholangiocarcinoma: a systematic review and meta-analysis. Med Sci Monit. 2015;21:3555.

61.

Pan

Song

, et al Comparing the diagnostic accuracy of five common tumour biomarkers and CA19-9 for pancreatic cancer: a protocol for a network meta-analysis of diagnostic test accuracy. BMJ Open. 2017;7:e018175.

62.

Marin

Morales

Jiménez

, et al Characterization of oral health status in Chilean patients with X-linked hypophosphatemia. Calcif Tissue Int. 2021;109:132-138.

63.

Moradi

Mohamadi Bolbanabad

Moinafshar

Adabi

Sharafi

Zareie

Evaluation of oral health status based on the decayed, missing and filled teeth (DMFT) index. Iran J Public Health. 2019;48:2050-2057.

64.

Organization

WH.

Noncommunicable Disease Surveillance, Monitoring and Reporting. WHO; 2023.

65.

Genetics ACoM. Genetic Susceptibility to Breast and Ovarian Cancer: Assessment, Counseling, and Testing Guidelines. American College of Medical Genetics; 1999.

66.

Group GATSC. Global Adult Tobacco Survey (GATS): Core Questionnaire With Optional Questions. Centers for Disease Control and Prevention; 2020.

67.

Eghtesad

Hekmatdoost

Faramarzi

, et al Validity and reproducibility of a food frequency questionnaire assessing food group intake in the PERSIAN Cohort Study. Front Nutr. 2023;10:1059870.

68.

Islami

Pourshams

Nasrollahzadeh

, et al Tea drinking habits and oesophageal cancer in a high risk area in northern Iran: population based case-control study. BMJ. 2009;338:b929.

69.

Vahid

Davoodi

SH.

Nutritional factors involved in the etiology of gastric cancer: a systematic review. Nutr Cancer. 2021;73:376-390.

70.

Organization

WH.

Oral Health Surveys: Basic Methods. World Health Organization; 2013.

71.

de Boer

Bruinvels

Tytgat

Schoorlemmer

Klinkenbijl

Frings-Dresen

MH.

Employment status and work-related problems of gastrointestinal cancer patients at diagnosis: a cross-sectional study. BMJ Open. 2011;1:e000190.

72.

Kuwahara

Takachi

Tsubono

, et al Socioeconomic status and gastric cancer survival in Japan. Gastric Cancer. 2010;13:222-230.

73.

Truong

DTT

Tran

, et al A prospective cohort study on the association between waterpipe tobacco smoking and gastric cancer mortality in Northern Vietnam. BMC Cancer. 2022;22:803.

74.

Scherübl

Tobacco smoking and gastrointestinal cancer risk. Visc Med. 2022;38:217-222.

75.

Teimoorian

Ranaei

Hajian Tilaki

Shokri Shirvani

Vosough

Association of Helicobacter pylori infection with colon cancer and adenomatous polyps. Iran J Pathol. 2018;13:325-332.

76.

Morgan

Arnold

Camargo

, et al The current and future incidence and mortality of gastric cancer in 185 countries, 2020–40: a population-based modelling study. EClinicalMedicine. 2022;47:101404.

77.

Koopaie

Ghafourian

Manifar

, et al Evaluation of CSTB and DMBT1 expression in saliva of gastric cancer patients and controls. BMC Cancer. 2022;22:1-16.

78.

Yoshizawa

Kim

, et al Discovery and validation of salivary extracellular RNA biomarkers for noninvasive detection of gastric cancer. Clin Chem. 2018;64:1513-1521.

79.

Radhika

Gopalakrishnan

Muthukumar

, et al Role of serum and salivary microRNAs as diagnostic biomarkers in gastric cancer. J Orofac Sci. 2023;15:182-188.

80.

Kaczor-Urbanowicz

Saad

Grogan

, et al Performance of salivary extracellular RNA biomarker panels for gastric cancer differs between distinct populations. Cancers. 2022;14:3632.

81.

Koopaie

Arian-Kia

Manifar

Fatahzadeh

Kolahdooz

Davoudi

Expression of salivary miRNAs, clinical, and demographic features in the early detection of gastric cancer: a statistical and machine learning analysis. J Gastrointest Cancer. 2025;56:15.

82.

Wang

, et al Three dysregulated microRNAs in serum as novel biomarkers for gastric cancer screening. Med Oncol. 2014;31:298-297.

83.

Aalami

Pouresmaeil

Amirabadi

Mojahed

Rad

Sahebkar

Evaluation of the diagnostic properties of serum hsa-miR-223-5p in the detection of gastric cancer: a case-control study. Anticancer Agents Med Chem. 2020;20:800-808.

84.

Juzėnas

Saltenienė

Kupcinskas

, et al Analysis of deregulated microRNAs and their target genes in gastric cancer. PLoS ONE. 2015;10:e0132327.

85.

Mao

Jia

Zhou

, et al Green tea drinking habits and gastric cancer in southwest China. Asian Pac J Cancer Prev. 2011;12:2179-2182.

86.

Luo

Hot tea consumption and esophageal cancer risk: a meta-analysis of observational studies. Front Nutr. 2022;9:831567.

87.

Guo

, et al Association between tea consumption and risk of cancer: a prospective cohort study of 0.5 million Chinese adults. Eur J Epidemiol. 2019;34:753-763.

88.

Islami

Boffetta

Ren

Pedoeim

Khatib

Kamangar

High-temperature beverages and foods and esophageal cancer risk–a systematic review. Int J Cancer. 2009;125:491-524.

89.

Islami

Poustchi

Pourshams

, et al A prospective study of tea drinking temperature and risk of esophageal squamous cell carcinoma. Int J Cancer. 2020;146:18-25.

90.

Chen

Cheng

Qian

Fang

Effect of dietary salt intake on risk of gastric cancer: a systematic review and meta-analysis of case-control studies. Nutrients. 2022;14:4260.

91.

Paragomi

Dabo

Pelucchi

, et al The association between peptic ulcer disease and gastric cancer: results from the stomach cancer pooling (StoP) project consortium. Cancers (Basel). 2022;14:4905.

92.

Shakeri

Malekzadeh

Etemadi

, et al Association of tooth loss and oral hygiene with risk of gastric adenocarcinoma. Cancer Prev Res (Phila). 2013;6:477-482.

93.

Yano

Abnet

Poustchi

, et al Oral health and risk of upper gastrointestinal cancers in a large prospective study from a high-risk region: Golestan cohort study. Cancer Prev Res (Phila). 2021;14:709-718.

94.

Nasrollahzadeh

Malekzadeh

Aghcheli

, et al Gastric atrophy and oesophageal squamous cell carcinoma: possible interaction with dental health and oral hygiene habit. Br J Cancer. 2012;107:888-894.

95.

Abnet

Qiao

Dawsey

Dong

Taylor

Mark

SD.

Tooth loss is associated with increased risk of total death and death from upper gastrointestinal cancer, heart disease, and stroke in a Chinese population-based cohort. Int J Epidemiol. 2005;34:467-474.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.03 MB

0.01 MB

1.89 MB

2.03 MB

1.59 MB

1.93 MB