Plasma-based digital PCR assay for early detection of gastric cancer using multiple methylation biomarkers

Plasma-based digital PCR assay for early detection of gastric cancer using multiple methylation biomarkers | 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 Article Plasma-based digital PCR assay for early detection of gastric cancer using multiple methylation biomarkers Yun Young Lee, Joon An, Jinil Han, Youngho Moon, Sang-Il Lee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7579093/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Endoscopy is the gold standard for diagnosing gastric cancer (GC), but its invasiveness limits widespread participation and has not substantially reduced GC-related mortality. This study developed and validated a blood-based digital PCR assay for early GC detection using DNA methylation biomarkers. Genome-wide methylation profiles from over 10,000 samples were screened, and two candidates were validated in GC cell lines, tumors, matched non-cancerous tissues, and plasma. Plasma from 60 GC patients, including 38 with stage I disease, and 40 healthy controls was analyzed with a digital PCR assay targeting the selected biomarkers, using ACTB as a reference. GHR and GLRB methylation were identified as novel GC biomarkers, showing consistent hypermethylation in GC cell lines and tumor tissues. In plasma, the two-marker assay achieved 83.3% (95% CI, 71.5%-91.7%) sensitivity and 90% (95% CI, 76.3%-97.2%) specificity, clearly outperforming carcinoembryonic antigen (CEA) testing (10.0%; 95% CI, 3.8%-20.5%). Incorporation of GATM methylation as a third marker increased sensitivity to 86.7% (95% CI, 75.4%-94.1%) overall and 81.6% (95% CI, 65.7%-92.3%) for stage I disease, while maintaining 90.0% specificity. This methylation-based digital PCR assay enabled accurate, non-invasive detection of GC, particularly at early stages, and may facilitate timely diagnosis and curative treatment. Health sciences/Biomarkers Biological sciences/Cancer Health sciences/Oncology Gastric cancer Early diagnosis DNA methylation Digital PCR Circulating tumor DNA Biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Gastric cancer (GC) is a major global health concern. It ranks fifth in global incidence and cancer-related mortality according to the GLOBOCAN 2022 1 . The burden is particularly high in East Asia where rates exceed those in Western countries 1 , 2 . Despite reduced GC incidence following widespread Helicobacter pylori eradication and improvements in early diagnosis through endoscopic screening in high-incidence countries, GC-specific mortality is substantial due to diagnosis at advanced stages 3 , 4 . Early-stage GC is often asymptomatic or presents with nonspecific symptoms, leading to delayed detection and poor prognosis 5 , 6 . Upper gastrointestinal endoscopy is the gold standard for diagnosing and screening gastric neoplasms due to its high accuracy in detecting early lesions. However, it is invasive, expensive, and uncomfortable for patients, limiting patient participation especially those who are asymptomatic 7 , 8 . Other diagnostic options, including serum tumor markers such as carcinoembryonic antigen (CEA) and imaging modalities such as abdominal CT (computed tomography) or positron emission tomography (PET), have limited sensitivity for early-stage disease 9 , 10 . In countries that have implemented national screening programs, such as Korea and Japan, adherence is still suboptimal and many cases continue to be diagnosed at advanced stages 11 , 12 . These limitations underscore the urgent need for noninvasive molecular diagnostic approaches to improve screening participation and facilitate earlier detection of GC. To address the limitations of conventional diagnostic tools, liquid biopsy was developed and is a promising noninvasive strategy for cancer detection and disease monitoring 13 . Circulating tumor DNA (ctDNA) allows detection of tumor-derived genetic and epigenetic alterations in blood to enable earlier and less invasive diagnosis 13 , 14 . Particularly, DNA methylation profiling of ctDNA in early-stage cancers is a promising technique because of its tumor specificity and higher abundance relative to somatic mutations 15 , 16 . Digital PCR (dPCR) further offers a highly sensitive and quantitative detection method for low-abundance methylated DNA, making it well suited for ctDNA analysis 17 – 19 . Several recent studies evaluated ctDNA methylation biomarkers in GC and demonstrated their diagnostic potential in plasma specimens 20 – 22 . Nevertheless, no ctDNA methylation assay has been clinically implemented for routine GC diagnostics. This study aimed to develop a plasma-based dPCR assay using novel methylation biomarkers for early detection of GC. Candidate markers were identified through large-scale methylome analysis, validated in GC cell lines and tissues, and clinically evaluated in a well-characterized plasma cohort. This approach may improve screening accessibility and address the current gap in clinically available ctDNA methylation diagnostics for GC. RESULTS This study proceeded through sequential phases: Genome-wide biomarker discovery; assay development using dPCR; preclinical verification in cell lines and tissue specimens; and clinical evaluation in plasma samples (Fig. 1 ). Genome-wide identification of methylation biomarkers for GC Genome-wide DNA methylation profiles from The Cancer Genome Atlas (TCGA) datasets generated with the Infinium HumanMethylation450K BeadChip array were analyzed to identify GC-specific candidate biomarkers. After filtering the CpG sites based on the criteria stated above, GHR and GLRB exhibited markedly elevated methylation in gastric tissues with GC samples showing further elevation (Fig. 2 ). Low methylation was consistently demonstrated in GHR across diverse non-GC cancers and normal tissues, indicating a high specificity for GC. GLRB displayed high methylation in both GC and colorectal cancer (CRC), warranting a further investigation. Preclinical validation using cell lines and tumor tissues To validate the selected biomarkers, methylation levels of GHR and GLRB were assessed in genomic DNA from GC cell lines and primary tumor tissues. dPCR analysis revealed high methylation at both loci in the three GC cell lines (AGS, KATO-III, and MKN-1), consistent with the in silico predictions (Fig. 3 a). In frozen tissue samples from patients with GC and CRC, both markers showed significantly higher methylation in GC tumor tissues compared with adjacent non-cancerous gastric mucosa (Fig. 3 b; p < 0.0001). GHR methylation remained low in CRC tissues, supporting its specificity to GC, whereas GLRB methylation was markedly elevated in both GC and CRC tissues ( Supplementary Fig. S1 ). Evaluation of GHR and GLRB methylation biomarkers in plasma samples To assess the clinical utility of the selected methylation biomarkers, plasma cell-free DNA (cfDNA) from a validation cohort of 100 individuals including 60 patients with GC and 40 healthy controls was analyzed using the dPCR assay (Table 1 ). Among the patients with GC, 73% ( N = 44) had early-stage disease (stage I or II), enabling evaluation of the diagnostic performance for early GC detection. Both GHR and GLRB showed significantly higher methylation levels in the patients with GC than in the healthy controls (Fig. 4 a). Methylation levels increased progressively with advancing stage. Each stage differed significantly from the control group, suggesting a correlation with tumor burden and progression (Fig. 4 b). The receiver operating characteristic (ROC) curve analysis demonstrated high diagnostic performance with AUC (area under the curve) values of 0.87 (95% CI, 0.80–0.94) for GHR and 0.91 (95% CI, 0.85–0.98) for GLRB (Fig. 4 c). These results supported the utility of GHR and GLRB methylation as noninvasive biomarkers for early GC detection. Table 1 Characteristics and demographics of the validation cohort Characteristics Gastric cancer Controls Total, N 60 40 Sex, N (%) Male 49 (81.7) 12 (30.0) Female 11 (18.3) 28 (70.0) Age in years, median (range) 65.5 (39.0–85.0) 32.0 (24.0–61.0) TNM stage, N (%) Stage I 38 (63.3) - Stage II 6 (10.0) - Stage III 10 (16.7) - Stage IV 6 (10.0) - pT stage, N (%) T1-T2 39 (65.0) - T3-T4 21 (35.0) - pN stage, N (%) N0 39 (65.0) - N1-3 21 (35.0) - pM stage, N (%) M0 54 (90.0) - M1 6 (10.0) - Helicobacter pylori status, N (%) Positive 28 (46.7) - Negative 32 (53.3) - Tumor location, N (%) Upper (from cardia to upper body) 10 (16.7) - Middle (middle body) 6 (10.0) - Lower (from lower body to pylorus) 40 (66.6) - Mixed 4 (6.7) - Lauren’s classification, N (%) Intestinal type 39 (65.0) - Diffuse type 7 (11.7) - Mixed type 14 (23.3) - EGC type, N (%) Elevated (IIa) 4 (6.7) - Flat (IIb) 7 (11.7) - Depressed (IIc) 20 (33.3) - Mixed 3 (5.0) - Not applicable 26 (43.3) - Abbreviation: EGC, Early gastric cancer (by endoscopic classification) Mitigation of GLRB specificity using a colorectal-specific biomarker Although GLRB exhibited strong diagnostic accuracy for GC, its elevated methylation in CRC tissues raised concerns regarding potential cross-reactivity ( Supplementary Fig. S1 ). Therefore, GATM was investigated as a potential negative selection marker based on its methylation profile. Bioinformatic analysis of pan-cancer datasets revealed that GATM was highly methylated in colorectal tumors and adjacent normal tissues but remained minimally methylated in other cancer types and normal tissues, including GC and normal gastric tissue ( Supplementary Fig. S2a ). This tissue specificity was further confirmed in CRC tissue specimens in which GATM methylation was significantly higher in tumors than in matched non-cancerous tissues ( Supplementary Fig. S2b ). However, analysis in the GC plasma cohort yielded a modest AUC of 0.66 (95% CI, 0.55–0.77), indicating limited diagnostic relevance for GC ( Supplementary Fig. S2c ). While GATM alone lacked diagnostic utility for GC, the absence of its methylation in GC supports its role as a reference marker to improve assay specificity when combined with GLRB . Diagnostic performance of the assay in plasma In the case-control plasma cohort, the single-marker assays demonstrated high diagnostic accuracy. GHR achieved a sensitivity of 76.7% (46/60; 95% CI, 64.0%-86.6%) and specificity of 92.5% (37/40; 95% CI, 79.6%-98.4%) while GLRB showed a sensitivity of 73.3% (44/60; 95% CI, 60.3%-83.9%) and specificity of 90.0% (36/40; 95% CI, 76.3%-97.2%) (Table 2 ). The dual-marker combination of GHR and GLRB further improved performance, achieving a sensitivity of 83.3% (50/60; 95% CI, 71.5%-91.7%) and specificity of 90.0% (36/40; 95% CI, 76.3%-97.2%). Serum CEA testing in the same cohort yielded a sensitivity of only 10.0% (6/60; 95% CI, 3.8%-20.5%), underscoring the superior performance of the ctDNA methylation assay. Table 2 Clinical performance of three methylation biomarkers in diagnosing GC in the validation cohort Indicators (95%CI) GHR a GLRB b GHR/GLRB c GLRB/GATM d STOM eDX e Specificity 92.5% (79.6%-98.4%) 90.0% (76.3%-97.2%) 90.0% (76.3%-97.2%) 90.0% (76.3%-97.2%) 90.0% (76.3%-97.2%) Overall sensitivity 76.7% (64.0%-86.6%) 73.3% (60.3%-83.9%) 83.3% (71.5%-91.7%) 71.7% (58.6%-82.6%) 86.7% (75.4%-94.1%) Stage I sensitivity 68.4% (51.4%-82.5%) 68.4% (51.4%-82.5%) 76.3% (59.8%-88.6%) 73.7% (56.9%-86.6%) 81.6% (65.7%-92.3%) Stage II sensitivity 83.3% (35.9%-99.6%) 50.0% (11.8%-88.2%) 83.3% (35.9%-99.6%) 33.3% (4.3%-77.7%) 83.3% (35.9%-99.6%) Stage III sensitivity 90.0% (55.5%-99.8%) 90.0% (55.5%-99.8%) 100.0% (69.2%-100.0%) 90.0% (55.5%-99.8%) 100.0% (69.2%-100.0%) Stage IV sensitivity 100.0% (54.1%-100.0%) 100.0% (54.1%-100.0%) 100.0% (54.1%-100.0%) 66.7% (22.3%-95.7%) 100.0% (54.1%-100.0%) PPV 93.9% (83.1%-98.7%) 91.7% (80.0%-97.7%) 92.6% (82.1%-97.9%) 91.5% (79.6%-97.6%) 92.9% (82.7%-98.0%) NPV 72.6% (58.3%-84.1%) 69.2% (54.9%-81.3%) 78.3% (63.6%-89.1%) 67.9% (53.7%-80.1%) 81.8% (67.3%-91.8%) Accuracy 83.0% (74.2%-89.8%) 80.0% (70.8%-87.3%) 86.0% (77.6%-92.1%) 79.0% (69.7%-86.5%) 88.0% (80.0%-93.6%) a Samples were positively called when GHR > 3.5; b Samples were positively called when GLRB > 8.0; c Samples were positively called when GHR > 3.5 or GLRB > 8.0; d Samples were positively called when GLRB > 7.0 and GLRB / GATM > 4.0; e Combination of three methylation biomarkers for detecting gastric cancer. Abbreviation: GC, Gastric cancer; CI, Confidence interval; NPV, Negative predictive value; PPV, Positive predictive value; STOM eDX, Stomach cancer early diagnosis using three methylation biomarkers. To enhance specificity and minimize the effect of detecting GLRB from CRC, GATM was incorporated as a reference control. This three-marker panel, termed STOM eDX, achieved the highest diagnostic metrics with a sensitivity of 86.7% (52/60; 95% CI, 75.4%-94.1%) and specificity of 90.0% (36/40; 95% CI, 76.3%-97.2%). Notably, the assay maintained a strong performance for detecting early-stage GC with a stage I sensitivity of 81.6% (31/38; 95% CI, 65.7%-92.3%), reinforcing its value in early detection. These findings suggested that integrating a tissue-specific control marker enhanced robustness and precision of the assay in GC screening. Correlation of methylation biomarkers with clinicopathologic parameters The association between GHR or GLRB methylation levels and clinicopathological features was evaluated in the plasma validation cohort (Table 3 ). Neither marker showed significant correlations with sex, age, tumor location, nor histological classification. Although Helicobacter pylori infection is a major risk factor for GC, there was no significant association between infection status and methylation levels of either marker. Both GHR and GLRB methylation levels were significantly correlated with clinical stage, with higher methylation observed in more advanced disease (Fig. 4 ). These findings suggested that these biomarkers may serve as indicators of tumor burden in addition to early detection and could aid in risk stratification and longitudinal monitoring. Table 3 Association between clinicopathological factors and two methylation biomarkers in plasma samples Parameters GHR a p -value b GLRB a p -value b Sex Male ( N = 49) 3.2 ± 1.7 0.879 4.0 ± 1.5 0.554 Female ( N = 11) 3.2 ± 2.2 1.4 ± 1.5 Age ≤ 60 ( N = 16) 3.0 ± 3.3 0.349 4.0 ± 4.1 0.682 > 60 ( N = 44) 1.8 ± 1.7 1.3 ± 1.4 Stage I ( N = 38) 2.6 ± 1.4 0.001 ** 3.8 ± 1.2 0.011 * II ( N = 6) 3.2 ± 1.2 3.4 ± 1.2 III ( N = 10) 3.7 ± 1.2 4.6 ± 0.9 IV ( N = 6) 6.0 ± 1.9 5.9 ± 1.9 Tumor location Upper ( N = 10) 3.4 ± 1.4 0.474 4.3 ± 1.2 0.650 Middle ( N = 6) 3.4 ± 2.4 3.9 ± 2.4 Lower ( N = 40) 3.1 ± 1.8 4.1 ± 1.3 Mixed ( N = 4) 3.6 ± 1.9 4.2 ± 1.3 Helicobacter pylori Negative ( N = 32) 3.2 ± 1.8 0.906 4.1 ± 1.3 0.192 Positive ( N = 28) 3.2 ± 1.8 4.1 ± 1.5 Lauren’s classification Intestinal ( N = 39) 3.2 ± 1.8 0.756 4.0 ± 1.5 0.172 Diffuse ( N = 7) 3.2 ± 1.2 4.6 ± 1.6 Mixed ( N = 14) 3.2 ± 1.9 4.2 ± 1.1 EGC type IIa ( N = 4) 2.7 ± 1.2 0.619 3.5 ± 0.7 0.673 IIb ( N = 7) 2.3 ± 0.6 3.5 ± 1.5 IIc ( N = 20) 2.4 ± 1.5 3.7 ± 1.0 Mixed ( N = 3) 3.3 ± 0.7 4.2 ± 0.7 CEA ≤ 5 ng/mL ( N = 54) 3.9 ± 2.0 0.312 4.2 ± 1.6 0.805 > 5 ng/mL ( N = 6) 3.1 ± 1.7 4.1 ± 1.4 a Log 2 (Methylated copies + 1). Data were expressed as mean ± standard deviation; b Statistics were analyzed using the Mann-Whitney test for two-group comparisons and the Kruskal-Wallis test for comparisons among three or more groups ( * p < 0.05, ** p < 0.01). Abbreviation: CEA, Carcinoembryonic antigen; EGC, Early gastric cancer (by endoscopic classification). DISCUSSION A novel plasma ctDNA assay was developed for the noninvasive detection of GC using a methylation-specific dPCR technique targeting three epigenetic markers: GHR , GLRB , and GATM . The assay was established through genome-wide bioinformatic screening and preclinical and clinical validation. In a case-control cohort in which early-stage disease (stage I-II) accounted for 73% of cases, the triple-marker assay demonstrated a high diagnostic performance with an overall sensitivity of 87% and a sensitivity of 82% in patients with early-stage GC along with a specificity of 90% (Table 2 ). The assay yielded a positive predictive value (PPV) of 93% and a negative predictive value (NPV) of 82%, showing substantial concordance with clinical diagnosis (Cohen’s κ = 0.75). Given its strong performance in a predominantly early-stage cohort, the assay may achieve an even greater diagnostic sensitivity in broader clinical populations. Endoscopic examination remains the gold standard for GC detection. However, it is an invasive procedure, which limits its suitability for large-scale population screening. Although several non-invasive approaches, such as CEA, CA72-4, and the ABC method (a combination of anti- Helicobacter pylori antibody and serum pepsinogen), have been evaluated, their clinical utility is limited because of unacceptable diagnostic performance 9 , 23 , 24 . CEA demonstrated a 10% sensitivity in the current study, supporting the sensitivity of CEA in other studies 9 . The need for better diagnostic techniques led to the development of liquid biopsy-based methods including methylation biomarker research in GC. Multiple studies have reported encouraging diagnostic performance. For example, a plasma-based panel targeting methylated ELMO1 , ZNF569 , and C13orf18 achieved a sensitivity of 86% and specificity of 95% 20 , while a serum-based assay targeting OSR2 , VAV3 , and PPFIA3 methylation showed a sensitivity of 83% and specificity of 88% 20,25 . An MCTA-seq assay analyzing 153 cfDNA methylation biomarkers reported an AUC of 0.87 with a sensitivity of 67% and specificity of 92% 26,27 . However, most of these assays demonstrated limited sensitivity for early-stage GC. In the present study, the assay maintained a high sensitivity even for patients with stage I disease, underscoring its potential clinical utility as a noninvasive diagnostic tool. Beyond diagnostic accuracy the biological functions of the three target genes further support their relevance as cancer biomarkers ( Supplementary Table S2 ). GHR, a key regulator of the GH/IGF-1 signaling axis, promotes cell growth, metabolism, and survival through JAK2-STAT activation. Its dysregulation is implicated in cancer development 28 , 29 . In GC, aberrant GHR methylation correlates with altered gene expression and is associated with tumor growth and poor prognosis 30 , 31 . GLRB, a neuron-specific subunit of the glycine receptor involved in inhibitory neurotransmission, is frequently hypermethylated in gastrointestinal cancers including GC and CRC based on MBD-seq, RRBS (Reduced Representation Bisulfite Sequencing), and genome-wide methylation profiling studies 32 – 34 . GATM encodes a mitochondrial enzyme essential for de novo creatine biosynthesis. It is upregulated in colorectal liver metastases, suggesting a potential role in metastatic progression 35 . Collectively, these genes exhibit distinct epigenetic alterations and tissue-specific methylation patterns, making them informative and complementary targets for ctDNA methylation assays. Notably, DNA methylation patterns often exhibit substantial overlap across gastrointestinal malignancies. TriMeth is a dPCR assay that was originally developed for CRC detection using three methylated markers ( C9orf50 , KCNQ5 , and CLIP4 ). It has demonstrated predictive value for gastroesophageal cancer recurrence, supporting cross-applicability of certain methylation biomarkers 36 – 39 . In the present study, GLRB was confirmed by in silico analysis and tissue validation to have high methylation signals in both GC and CRC (Fig. 2 and Supplementary Fig. S1 ). To improve cancer specificity and reduce false positives from CRC-derived signals, the colorectal tissue-specific biomarker GATM was incorporated as a negative selection marker. While this strategy was primarily intended to enhance specificity, normalization to GATM also slightly improved the sensitivity compared with the dual-marker panel ( GHR and GLRB ; Table 2 ). These findings highlight the utility of integrating tissue-specific markers to improve the precision of methylation panels and expand their applicability across cancer types in liquid biopsy platforms. This study has certain limitations. First, it was conducted in a single-center retrospective cohort, which may limit generalizability. However, the high proportion of early-stage cases provided a valuable opportunity to evaluate assay performance in the most clinically relevant setting. Second, although Helicobacter pylori infection is a major risk factor for GC, the assay was not tested in infected individuals without cancer. Within the GC cohort, however, methylation levels did not differ between infected and uninfected patients, suggesting robustness irrespective of infection status and supporting future studies in high-risk Helicobacter pylori -positive populations. Third, while GATM was not validated in an independent CRC plasma cohort, tissue analyses in both gastric and colorectal cancers confirmed its colorectal specificity, providing indirect evidence of its utility as a negative selection control. Despite these considerations, the study demonstrates the feasibility and strong diagnostic potential of a methylation-based dPCR assay targeting GHR , GLRB , and GATM as a noninvasive early GC detection. The integration of tissue-specific methylation markers further highlights a methodological framework with potential applicability across liquid biopsy platforms and cancer types. With prospective validation, this approach holds promise for clinical translation as a scalable, accessible, and accurate diagnostic tool to improve early detection and reduce GC-related mortality. METHODS Study design and specimens This study consisted of two phases: Biomarker discovery and clinical validation of the plasma-based dPCR assay (Fig. 1 ). For biomarker discovery, primary tumor and matched normal tissues from 40 patients with GC were obtained from the Biobank of Chonnam National University Hwasun Hospital (P01-202204-02-001). Paired tissues from 37 patients with CRC were acquired additionally from the Biobank of Ajou University Hospital (P01-202003-31-001). The use of these specimens was exempted from review by the Public Institutional Review Board (IRB) designated by the Ministry of Health and Welfare, Republic of Korea. The requirement for written informed consent was waived because the study posed minimal risk to participants and all data were anonymized. Clinical characteristics are provided in Supplementary Table S1 . For clinical validation, a retrospective case-control design was used to evaluate the diagnostic performance in plasma samples from 60 patients with histologically confirmed GC and 40 healthy controls. Blood samples were collected between May 2021 and December 2023 at Chungnam National University Hospital. This study was exempted from review by the IRB of Chungnam National University Hospital (IRB No. 2023-06-096) and conducted in accordance with the Declaration of Helsinki. Written informed consent was waived as the study posed minimal risk to participants and all clinical information were anonymized. Most of the patients with GC had early-stage disease (73.3%) according to TNM staging (stage I, N = 38; stage II, N = 6; stage III, N = 14; stage IV, N = 2) as summarized in Table 1 . All biospecimens met the quality control standards of the respective institutions. Biomarker discovery Genome-wide DNA methylation profiles from over 10,000 publicly available tissue samples, including GC, adjacent normal gastric mucosa, other tumor types, and hematologic cells, were analyzed. Raw data were processed and normalized using the Chip analysis methylation pipeline (also known as ChAMP) package to obtain β-values, defined as the ratio of methylated signal intensity 36 , 40 . The analysis covered approximately 450,000 CpG sites from The Cancer Genome Atlas, generated using the Infinium HumanMethylation450K BeadChip array (Illumina, San Diego, CA, USA). Candidate biomarkers were selected based on the following criteria: (1) Low methylation in peripheral blood leukocytes; (2) High methylation in GC tissues (including adjacent non-cancerous gastric mucosa); and (3) Low methylation in other tissues and cancer types. Preclinical verification Oligonucleotide sets (IDT, Coralville, IA, USA) targeting the selected CpG sites were designed and optimized using Human HCT116 DKO Methylated and Non-methylated DNA standards (Zymo Research, Irvine, CA, USA) according to established protocols ( Supplementary Table S2 ) 41 , 42 . Candidate markers were verified using genomic DNA from GC cell lines (AGS, KATO-III, and MKN-1; Korean Cell Line Bank, Seoul, Republic of Korea) and from tissue specimens obtained from the Biobank of Chonnam National University Hwasun Hospital (Hwasun, Republic of Korea) and Ajou University Hospital (Suwon, Republic of Korea). Genomic DNA was extracted using the Wizard Genomic DNA Purification Kit (Promega, Madison, WI, USA) and quantified with a NanoDrop 2000 spectrophotometer (Thermo Scientific, Waltham, MA, USA). Bisulfite conversion was performed with the EZ DNA Methylation-Lightning Kit (Zymo Research) following the manufacturer’s instructions. Bisulfite-converted DNA was analyzed by dPCR with both positive and negative controls included to ensure assay validity. Plasma ctDNA methylation assay Prior to any therapeutic intervention, peripheral blood was collected in K2EDTA tubes (Becton Dickinson, Franklin Lakes, NJ, USA). Plasma was separated within 4 h by two-step centrifugation at 3000 ×g for 10 min and stored at -70󠄎 °C until further analysis. Cell-free DNA was extracted from 1.5-2.0 mL of plasma using the QIAamp Circulating Nucleic Acid Kit (Qiagen, Hilden, Germany) following the manufacturer’s instructions. The entire volume of purified cfDNA was subjected to bisulfite conversion using the EZ DNA Methylation-Lightning Kit (Zymo Research) immediately after extraction. Bisulfite-converted DNA was analyzed using the QX600 Droplet Digital PCR System (Bio-Rad Laboratories, Hercules, CA, USA). Each 22 µL reaction mixture contained 10 µL of bisulfite-treated DNA, 3.3 µL of 10 µM oligonucleotide mix (Gencurix, Seoul, Republic of Korea), 5.5 µL 4X Droplet Digital PCR Multiplex Supermix, 0.33 µL of 0.3 M dithiothreitol, and nuclease-free water. Droplets were generated using a QXDx Droplet Generator and amplified on a Veriti 96-Well Thermal Cycler (Applied Biosystems Inc., Foster City, CA, USA) using the following protocol: 95℃ for 10 min; 45 cycles of 94℃ for 30 s and 59℃ for 1 min; and 98℃ for 10 min. PCR products were read on the QX600 Droplet Digital PCR Reader, and fluorescence signals were analyzed with the QX Manager version 2.1 software. Results were expressed as copies/mL with ACTB serving as the internal control and cfDNA reference. Positive and negative controls were included in all runs. Positive thresholds were determined automatically, and all procedures followed the Minimum Information for Publication of Quantitative Digital PCR Experiments (known as dMIQE) guidelines 4 2 . Statistical analysis All statistical analyses were performed using GraphPad Prism version 7 (GraphPad Software, La Jolla, CA, USA) and MedCalc version 15.8 (MedCalc, Ostend, Belgium). ROC curves were generated to determine assay sensitivity, specificity, and AUC with 95% CIs. Cutoff values were defined using the Youden index. Group differences were assessed by one-way ANOVA followed by Tukey’s post hoc test. Associations between methylation levels and clinicopathological variables were examined using the Mann-Whitney U test or Kruskal-Wallis test. A p < 0.05 was considered statistically significant. Declarations Competing Interests Y.Y.L., J.A., J.H., and Y.M. are employees of Gencurix, Inc. and hold stock or stock options in the company. The remaining authors have no conflicts to report. FUNDING The authors received no funding for this work. Author Contribution J.H., Y.M., and S.-I.L. conceptualized and designed this study. Y.Y.L., J.A., J.H., and S.-I.L. acquired and analyzed data. Y.Y.L., J.A., J.H., Y.M., and S.-I.L. conducted interpretation of data. Y.Y.L. and S.-I.L. wrote the original draft of the manuscript. All authors reviewed and edited the final draft of the manuscript, had final responsibility for the decision to submit for publication. Acknowledgement The biospecimens and clinical data used for tissue verification were provided by the Biobank of Chonnam National University Hwasun Hospital (P01-202204-02-001) and Ajou University Hospital (P01-202003-31-001), both members of the Korea Biobank Network. The plasma samples and data were provided by the Biobank of Chungnam National University Hospital (2023-06-096). Data Availability The raw data and analytical methods used in the study are available from the corresponding author upon reasonable request. 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19:41:44","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":147844,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7579093/v1/05966703c5bb4529902c3bb1.html"},{"id":94140504,"identity":"4b2bcfb1-28e9-490a-b28e-f21c9119cfef","added_by":"auto","created_at":"2025-10-22 19:41:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":211417,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic overview of the study. Abbreviation: eGC, Early gastric cancer (stage I or II); GC, Gastric cancer.\u003c/p\u003e","description":"","filename":"SciRepFig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-7579093/v1/da97b69ff3f75fbcbe26bde4.png"},{"id":94139458,"identity":"86e5d231-fccd-4882-9862-63f449869bed","added_by":"auto","created_at":"2025-10-22 19:33:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":659925,"visible":true,"origin":"","legend":"\u003cp\u003eDNA methylation levels of two candidate markers across 33 cancer types and corresponding normal tissues. The β-value represents the methylation level at specific genomic regions, ranging from 0 (unmethylated) to 1 (fully methylated). Abbreviation: COAD, Colon adenocarcinoma; READ, Rectum adenocarcinoma; STAD, Stomach adenocarcinoma.\u003c/p\u003e","description":"","filename":"SciRepFig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-7579093/v1/b83c97a97ae3ae18aab48a7f.png"},{"id":94139482,"identity":"b7e27d0b-12c7-44d5-9c2d-cf1b819db935","added_by":"auto","created_at":"2025-10-22 19:33:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":277599,"visible":true,"origin":"","legend":"\u003cp\u003eDNA methylation status of two biomarkers in gastric cancer cell lines and primary tissues. (\u003cstrong\u003ea\u003c/strong\u003e).\u003cstrong\u003e \u003c/strong\u003eDNA methylation levels of \u003cem\u003eGHR\u003c/em\u003eand\u003cem\u003e GLRB\u003c/em\u003e in three gastric cancer cell lines (AGS, KATO-III, and MKN-1). Digital PCR results are presented on a logarithmic scale and normalized to \u003cem\u003eACTB\u003c/em\u003ecopy numbers. They are expressed as mean ± standard deviation from three independent experiments. For assay validation, genomic DNA from HCT116 DKO cells with 0% and 100% methylation was used as negative and positive controls, respectively. (\u003cstrong\u003eb\u003c/strong\u003e) DNA methylation levels of \u003cem\u003eGHR\u003c/em\u003e and \u003cem\u003eGLRB\u003c/em\u003e in primary tumor tissues and paired adjacent non-tumor tissues from 40 patients with gastric cancer. Matched samples from the same patient are connected by lines. Statistical differences were evaluated using a paired \u003cem\u003et\u003c/em\u003e-test. Abbreviation: NC, Negative control; NT, Non-tumor tissues; PC, Positive control; T, Tumor tissues.\u003c/p\u003e","description":"","filename":"SciRepFig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-7579093/v1/1a7eb4d9dffef9c96fed1f79.png"},{"id":94139472,"identity":"1003f21e-e7ca-4d76-9fda-dc18491928dd","added_by":"auto","created_at":"2025-10-22 19:33:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":548802,"visible":true,"origin":"","legend":"\u003cp\u003eDetection of\u003cem\u003e GHR\u003c/em\u003e and \u003cem\u003eGLRB\u003c/em\u003e methylation in plasma specimens from the validation cohort. (\u003cstrong\u003ea-b\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eMethylation levels of \u003cem\u003eGHR \u003c/em\u003eand \u003cem\u003eGLRB\u003c/em\u003e were assessed by digital PCR in plasma samples from patients with gastric cancer and healthy controls. Results are presented as medians with interquartile ranges. Statistical differences were evaluated using one-way ANOVA. (\u003cstrong\u003ec\u003c/strong\u003e) Receiver operating characteristic curves for each biomarker in distinguishing patients with gastric cancer from patients without cancer. The area under the curve (AUC) and 95% confidence intervals (CIs) are shown.\u003c/p\u003e","description":"","filename":"SciRepFig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-7579093/v1/853226b5d7c3955318400ef9.png"},{"id":98243869,"identity":"ae261dc1-4243-411c-9178-efe2c22aac08","added_by":"auto","created_at":"2025-12-15 16:10:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3357311,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7579093/v1/ccbd837f-65f6-49c0-9ab5-30b8d055722b.pdf"},{"id":94140503,"identity":"7d58919a-a7b0-4db3-b8e5-4544a1eb248a","added_by":"auto","created_at":"2025-10-22 19:41:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":533724,"visible":true,"origin":"","legend":"","description":"","filename":"SciRepSupplementaryMaterialv1.0.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7579093/v1/790408b78b27aa15e7a17ef6.pdf"}],"financialInterests":"Competing interest reported. Y.Y.L., J.A., J.H., and Y.M. are employees of Gencurix, Inc. and hold stock or stock options in the company. The remaining authors have no conflicts to report.","formattedTitle":"Plasma-based digital PCR assay for early detection of gastric cancer using multiple methylation biomarkers","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eGastric cancer (GC) is a major global health concern. It ranks fifth in global incidence and cancer-related mortality according to the GLOBOCAN 2022\u003csup\u003e1\u003c/sup\u003e. The burden is particularly high in East Asia where rates exceed those in Western countries\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Despite reduced GC incidence following widespread \u003cem\u003eHelicobacter pylori\u003c/em\u003e eradication and improvements in early diagnosis through endoscopic screening in high-incidence countries, GC-specific mortality is substantial due to diagnosis at advanced stages\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Early-stage GC is often asymptomatic or presents with nonspecific symptoms, leading to delayed detection and poor prognosis\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eUpper gastrointestinal endoscopy is the gold standard for diagnosing and screening gastric neoplasms due to its high accuracy in detecting early lesions. However, it is invasive, expensive, and uncomfortable for patients, limiting patient participation especially those who are asymptomatic\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Other diagnostic options, including serum tumor markers such as carcinoembryonic antigen (CEA) and imaging modalities such as abdominal CT (computed tomography) or positron emission tomography (PET), have limited sensitivity for early-stage disease\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In countries that have implemented national screening programs, such as Korea and Japan, adherence is still suboptimal and many cases continue to be diagnosed at advanced stages\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. These limitations underscore the urgent need for noninvasive molecular diagnostic approaches to improve screening participation and facilitate earlier detection of GC.\u003c/p\u003e\u003cp\u003eTo address the limitations of conventional diagnostic tools, liquid biopsy was developed and is a promising noninvasive strategy for cancer detection and disease monitoring\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Circulating tumor DNA (ctDNA) allows detection of tumor-derived genetic and epigenetic alterations in blood to enable earlier and less invasive diagnosis\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Particularly, DNA methylation profiling of ctDNA in early-stage cancers is a promising technique because of its tumor specificity and higher abundance relative to somatic mutations\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Digital PCR (dPCR) further offers a highly sensitive and quantitative detection method for low-abundance methylated DNA, making it well suited for ctDNA analysis\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Several recent studies evaluated ctDNA methylation biomarkers in GC and demonstrated their diagnostic potential in plasma specimens\u003csup\u003e\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Nevertheless, no ctDNA methylation assay has been clinically implemented for routine GC diagnostics.\u003c/p\u003e\u003cp\u003eThis study aimed to develop a plasma-based dPCR assay using novel methylation biomarkers for early detection of GC. Candidate markers were identified through large-scale methylome analysis, validated in GC cell lines and tissues, and clinically evaluated in a well-characterized plasma cohort. This approach may improve screening accessibility and address the current gap in clinically available ctDNA methylation diagnostics for GC.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThis study proceeded through sequential phases: Genome-wide biomarker discovery; assay development using dPCR; preclinical verification in cell lines and tissue specimens; and clinical evaluation in plasma samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eGenome-wide identification of methylation biomarkers for GC\u003c/h2\u003e\u003cp\u003eGenome-wide DNA methylation profiles from The Cancer Genome Atlas (TCGA) datasets generated with the Infinium HumanMethylation450K BeadChip array were analyzed to identify GC-specific candidate biomarkers. After filtering the CpG sites based on the criteria stated above, \u003cem\u003eGHR\u003c/em\u003e and \u003cem\u003eGLRB\u003c/em\u003e exhibited markedly elevated methylation in gastric tissues with GC samples showing further elevation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Low methylation was consistently demonstrated in \u003cem\u003eGHR\u003c/em\u003e across diverse non-GC cancers and normal tissues, indicating a high specificity for GC. \u003cem\u003eGLRB\u003c/em\u003e displayed high methylation in both GC and colorectal cancer (CRC), warranting a further investigation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePreclinical validation using cell lines and tumor tissues\u003c/h3\u003e\n\u003cp\u003eTo validate the selected biomarkers, methylation levels of \u003cem\u003eGHR\u003c/em\u003e and \u003cem\u003eGLRB\u003c/em\u003e were assessed in genomic DNA from GC cell lines and primary tumor tissues. dPCR analysis revealed high methylation at both loci in the three GC cell lines (AGS, KATO-III, and MKN-1), consistent with the \u003cem\u003ein silico\u003c/em\u003e predictions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). In frozen tissue samples from patients with GC and CRC, both markers showed significantly higher methylation in GC tumor tissues compared with adjacent non-cancerous gastric mucosa (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). \u003cem\u003eGHR\u003c/em\u003e methylation remained low in CRC tissues, supporting its specificity to GC, whereas \u003cem\u003eGLRB\u003c/em\u003e methylation was markedly elevated in both GC and CRC tissues (\u003cb\u003eSupplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eEvaluation of\u003c/b\u003e \u003cb\u003eGHR\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eGLRB\u003c/b\u003e \u003cb\u003emethylation biomarkers in plasma samples\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo assess the clinical utility of the selected methylation biomarkers, plasma cell-free DNA (cfDNA) from a validation cohort of 100 individuals including 60 patients with GC and 40 healthy controls was analyzed using the dPCR assay (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among the patients with GC, 73% (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;44) had early-stage disease (stage I or II), enabling evaluation of the diagnostic performance for early GC detection. Both \u003cem\u003eGHR\u003c/em\u003e and \u003cem\u003eGLRB\u003c/em\u003e showed significantly higher methylation levels in the patients with GC than in the healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Methylation levels increased progressively with advancing stage. Each stage differed significantly from the control group, suggesting a correlation with tumor burden and progression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). The receiver operating characteristic (ROC) curve analysis demonstrated high diagnostic performance with AUC (area under the curve) values of 0.87 (95% CI, 0.80\u0026ndash;0.94) for \u003cem\u003eGHR\u003c/em\u003e and 0.91 (95% CI, 0.85\u0026ndash;0.98) for \u003cem\u003eGLRB\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). These results supported the utility of \u003cem\u003eGHR\u003c/em\u003e and \u003cem\u003eGLRB\u003c/em\u003e methylation as noninvasive biomarkers for early GC detection.\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\u003eCharacteristics and demographics of the validation cohort\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGastric cancer\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControls\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal, \u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eSex, \u003cem\u003eN\u003c/em\u003e (%)\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\u003e49 (81.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (30.0)\u003c/p\u003e\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\u003e11 (18.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 (70.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge in years, median (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.5 (39.0\u0026ndash;85.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.0 (24.0\u0026ndash;61.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eTNM stage, \u003cem\u003eN\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38 (63.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003epT stage, \u003cem\u003eN\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1-T2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39 (65.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3-T4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (35.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003epN stage, \u003cem\u003eN\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39 (65.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN1-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (35.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003epM stage, \u003cem\u003eN\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54 (90.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHelicobacter pylori\u003c/em\u003e status, \u003cem\u003eN\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28 (46.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (53.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eTumor location, \u003cem\u003eN\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper (from cardia to upper body)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle (middle body)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLower (from lower body to pylorus)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40 (66.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMixed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eLauren\u0026rsquo;s classification, \u003cem\u003eN\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntestinal type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39 (65.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiffuse type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (11.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMixed type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (23.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eEGC type, \u003cem\u003eN\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElevated (IIa)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFlat (IIb)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (11.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepressed (IIc)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMixed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (43.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eAbbreviation: EGC, Early gastric cancer (by endoscopic classification)\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\u003e\u003cb\u003eMitigation of\u003c/b\u003e \u003cb\u003eGLRB\u003c/b\u003e \u003cb\u003especificity using a colorectal-specific biomarker\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAlthough \u003cem\u003eGLRB\u003c/em\u003e exhibited strong diagnostic accuracy for GC, its elevated methylation in CRC tissues raised concerns regarding potential cross-reactivity (\u003cb\u003eSupplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Therefore, \u003cem\u003eGATM\u003c/em\u003e was investigated as a potential negative selection marker based on its methylation profile. Bioinformatic analysis of pan-cancer datasets revealed that \u003cem\u003eGATM\u003c/em\u003e was highly methylated in colorectal tumors and adjacent normal tissues but remained minimally methylated in other cancer types and normal tissues, including GC and normal gastric tissue (\u003cb\u003eSupplementary Fig. S2a\u003c/b\u003e). This tissue specificity was further confirmed in CRC tissue specimens in which \u003cem\u003eGATM\u003c/em\u003e methylation was significantly higher in tumors than in matched non-cancerous tissues (\u003cb\u003eSupplementary Fig. S2b\u003c/b\u003e). However, analysis in the GC plasma cohort yielded a modest AUC of 0.66 (95% CI, 0.55\u0026ndash;0.77), indicating limited diagnostic relevance for GC (\u003cb\u003eSupplementary Fig. S2c\u003c/b\u003e). While \u003cem\u003eGATM\u003c/em\u003e alone lacked diagnostic utility for GC, the absence of its methylation in GC supports its role as a reference marker to improve assay specificity when combined with \u003cem\u003eGLRB\u003c/em\u003e.\u003c/p\u003e\n\u003ch3\u003eDiagnostic performance of the assay in plasma\u003c/h3\u003e\n\u003cp\u003eIn the case-control plasma cohort, the single-marker assays demonstrated high diagnostic accuracy. \u003cem\u003eGHR\u003c/em\u003e achieved a sensitivity of 76.7% (46/60; 95% CI, 64.0%-86.6%) and specificity of 92.5% (37/40; 95% CI, 79.6%-98.4%) while \u003cem\u003eGLRB\u003c/em\u003e showed a sensitivity of 73.3% (44/60; 95% CI, 60.3%-83.9%) and specificity of 90.0% (36/40; 95% CI, 76.3%-97.2%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The dual-marker combination of \u003cem\u003eGHR\u003c/em\u003e and \u003cem\u003eGLRB\u003c/em\u003e further improved performance, achieving a sensitivity of 83.3% (50/60; 95% CI, 71.5%-91.7%) and specificity of 90.0% (36/40; 95% CI, 76.3%-97.2%). Serum CEA testing in the same cohort yielded a sensitivity of only 10.0% (6/60; 95% CI, 3.8%-20.5%), underscoring the superior performance of the ctDNA methylation assay.\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\u003eClinical performance of three methylation biomarkers in diagnosing GC in the validation cohort\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=\"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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndicators \u003c/p\u003e\u003cp\u003e(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eGHR\u003c/em\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eGLRB\u003c/em\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eGHR/GLRB\u003c/em\u003e\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eGLRB/GATM\u003c/em\u003e\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSTOM eDX\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e92.5%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(79.6%-98.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e90.0%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(76.3%-97.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e90.0%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(76.3%-97.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e90.0%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(76.3%-97.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e90.0%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(76.3%-97.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall sensitivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e76.7%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(64.0%-86.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e73.3%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(60.3%-83.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e83.3%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(71.5%-91.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e71.7%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(58.6%-82.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e86.7%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(75.4%-94.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage I sensitivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e68.4%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(51.4%-82.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e68.4%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(51.4%-82.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e76.3%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(59.8%-88.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e73.7%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(56.9%-86.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e81.6%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(65.7%-92.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage II sensitivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e83.3%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(35.9%-99.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e50.0%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(11.8%-88.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e83.3%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(35.9%-99.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e33.3%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(4.3%-77.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e83.3%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(35.9%-99.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage III sensitivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e90.0%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(55.5%-99.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e90.0%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(55.5%-99.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e100.0%\u003c/b\u003e (69.2%-100.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e90.0%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(55.5%-99.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e100.0%\u003c/b\u003e (69.2%-100.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage IV sensitivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e100.0%\u003c/b\u003e (54.1%-100.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e100.0%\u003c/b\u003e (54.1%-100.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e100.0%\u003c/b\u003e (54.1%-100.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e66.7%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(22.3%-95.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e100.0%\u003c/b\u003e (54.1%-100.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePPV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e93.9%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(83.1%-98.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e91.7%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(80.0%-97.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e92.6%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(82.1%-97.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e91.5%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(79.6%-97.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e92.9%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(82.7%-98.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e72.6%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(58.3%-84.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e69.2%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(54.9%-81.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e78.3%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(63.6%-89.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e67.9%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(53.7%-80.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e81.8%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(67.3%-91.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e83.0%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(74.2%-89.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e80.0%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(70.8%-87.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e86.0%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(77.6%-92.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e79.0%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(69.7%-86.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e88.0%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(80.0%-93.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eSamples were positively called when \u003cem\u003eGHR\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;3.5;\u003c/p\u003e\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eSamples were positively called when \u003cem\u003eGLRB\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;8.0;\u003c/p\u003e\u003cp\u003e\u003csup\u003ec\u003c/sup\u003eSamples were positively called when \u003cem\u003eGHR\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;3.5 or \u003cem\u003eGLRB\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;8.0;\u003c/p\u003e\u003cp\u003e\u003csup\u003ed\u003c/sup\u003eSamples were positively called when \u003cem\u003eGLRB\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;7.0 and \u003cem\u003eGLRB\u003c/em\u003e/\u003cem\u003eGATM\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;4.0;\u003c/p\u003e\u003cp\u003e\u003csup\u003ee\u003c/sup\u003eCombination of three methylation biomarkers for detecting gastric cancer.\u003c/p\u003e\u003cp\u003eAbbreviation: GC, Gastric cancer; CI, Confidence interval; NPV, Negative predictive value; PPV, Positive predictive value; STOM eDX, Stomach cancer early diagnosis using three methylation biomarkers.\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\u003eTo enhance specificity and minimize the effect of detecting \u003cem\u003eGLRB\u003c/em\u003e from CRC, \u003cem\u003eGATM\u003c/em\u003e was incorporated as a reference control. This three-marker panel, termed STOM eDX, achieved the highest diagnostic metrics with a sensitivity of 86.7% (52/60; 95% CI, 75.4%-94.1%) and specificity of 90.0% (36/40; 95% CI, 76.3%-97.2%). Notably, the assay maintained a strong performance for detecting early-stage GC with a stage I sensitivity of 81.6% (31/38; 95% CI, 65.7%-92.3%), reinforcing its value in early detection. These findings suggested that integrating a tissue-specific control marker enhanced robustness and precision of the assay in GC screening.\u003c/p\u003e\n\u003ch3\u003eCorrelation of methylation biomarkers with clinicopathologic parameters\u003c/h3\u003e\n\u003cp\u003eThe association between \u003cem\u003eGHR\u003c/em\u003e or \u003cem\u003eGLRB\u003c/em\u003e methylation levels and clinicopathological features was evaluated in the plasma validation cohort (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Neither marker showed significant correlations with sex, age, tumor location, nor histological classification. Although \u003cem\u003eHelicobacter pylori\u003c/em\u003e infection is a major risk factor for GC, there was no significant association between infection status and methylation levels of either marker. Both \u003cem\u003eGHR\u003c/em\u003e and \u003cem\u003eGLRB\u003c/em\u003e methylation levels were significantly correlated with clinical stage, with higher methylation observed in more advanced disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These findings suggested that these biomarkers may serve as indicators of tumor burden in addition to early detection and could aid in risk stratification and longitudinal monitoring.\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\u003eAssociation between clinicopathological factors and two methylation biomarkers in plasma samples\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=\"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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eGHR\u003c/em\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eGLRB\u003c/em\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.554\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;60 (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.682\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;60 (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eStage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.011\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eII (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIII (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIV (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eTumor location\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUpper (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.650\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLower (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMixed (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eHelicobacter pylori\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNegative (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.192\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePositive (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eLauren\u0026rsquo;s classification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntestinal (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.172\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiffuse (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMixed (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eEGC type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIIa (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.673\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIIb (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIIc (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMixed (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;5 ng/mL (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.805\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;5 ng/mL (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eLog\u003csub\u003e2\u003c/sub\u003e (Methylated copies\u0026thinsp;+\u0026thinsp;1). Data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation;\u003c/p\u003e\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eStatistics were analyzed using the Mann-Whitney test for two-group comparisons and the Kruskal-Wallis test for comparisons among three or more groups (\u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003eAbbreviation: CEA, Carcinoembryonic antigen; EGC, Early gastric cancer (by endoscopic classification).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eA novel plasma ctDNA assay was developed for the noninvasive detection of GC using a methylation-specific dPCR technique targeting three epigenetic markers: \u003cem\u003eGHR\u003c/em\u003e, \u003cem\u003eGLRB\u003c/em\u003e, and \u003cem\u003eGATM\u003c/em\u003e. The assay was established through genome-wide bioinformatic screening and preclinical and clinical validation. In a case-control cohort in which early-stage disease (stage I-II) accounted for 73% of cases, the triple-marker assay demonstrated a high diagnostic performance with an overall sensitivity of 87% and a sensitivity of 82% in patients with early-stage GC along with a specificity of 90% (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The assay yielded a positive predictive value (PPV) of 93% and a negative predictive value (NPV) of 82%, showing substantial concordance with clinical diagnosis (Cohen’s κ = 0.75). Given its strong performance in a predominantly early-stage cohort, the assay may achieve an even greater diagnostic sensitivity in broader clinical populations.\u003c/p\u003e\u003cp\u003eEndoscopic examination remains the gold standard for GC detection. However, it is an invasive procedure, which limits its suitability for large-scale population screening. Although several non-invasive approaches, such as CEA, CA72-4, and the ABC method (a combination of anti-\u003cem\u003eHelicobacter pylori\u003c/em\u003e antibody and serum pepsinogen), have been evaluated, their clinical utility is limited because of unacceptable diagnostic performance\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. CEA demonstrated a 10% sensitivity in the current study, supporting the sensitivity of CEA in other studies\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The need for better diagnostic techniques led to the development of liquid biopsy-based methods including methylation biomarker research in GC.\u003c/p\u003e\u003cp\u003eMultiple studies have reported encouraging diagnostic performance. For example, a plasma-based panel targeting methylated \u003cem\u003eELMO1\u003c/em\u003e, \u003cem\u003eZNF569\u003c/em\u003e, and \u003cem\u003eC13orf18\u003c/em\u003e achieved a sensitivity of 86% and specificity of 95%\u003csup\u003e20\u003c/sup\u003e, while a serum-based assay targeting \u003cem\u003eOSR2\u003c/em\u003e, \u003cem\u003eVAV3\u003c/em\u003e, and \u003cem\u003ePPFIA3\u003c/em\u003e methylation showed a sensitivity of 83% and specificity of 88%\u003csup\u003e20,25\u003c/sup\u003e. An MCTA-seq assay analyzing 153 cfDNA methylation biomarkers reported an AUC of 0.87 with a sensitivity of 67% and specificity of 92%\u003csup\u003e26,27\u003c/sup\u003e. However, most of these assays demonstrated limited sensitivity for early-stage GC. In the present study, the assay maintained a high sensitivity even for patients with stage I disease, underscoring its potential clinical utility as a noninvasive diagnostic tool.\u003c/p\u003e\u003cp\u003eBeyond diagnostic accuracy the biological functions of the three target genes further support their relevance as cancer biomarkers (\u003cb\u003eSupplementary Table S2\u003c/b\u003e). GHR, a key regulator of the GH/IGF-1 signaling axis, promotes cell growth, metabolism, and survival through JAK2-STAT activation. Its dysregulation is implicated in cancer development\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. In GC, aberrant \u003cem\u003eGHR\u003c/em\u003e methylation correlates with altered gene expression and is associated with tumor growth and poor prognosis\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. GLRB, a neuron-specific subunit of the glycine receptor involved in inhibitory neurotransmission, is frequently hypermethylated in gastrointestinal cancers including GC and CRC based on MBD-seq, RRBS (Reduced Representation Bisulfite Sequencing), and genome-wide methylation profiling studies\u003csup\u003e\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e–\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. GATM encodes a mitochondrial enzyme essential for \u003cem\u003ede novo\u003c/em\u003e creatine biosynthesis. It is upregulated in colorectal liver metastases, suggesting a potential role in metastatic progression\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Collectively, these genes exhibit distinct epigenetic alterations and tissue-specific methylation patterns, making them informative and complementary targets for ctDNA methylation assays.\u003c/p\u003e\u003cp\u003eNotably, DNA methylation patterns often exhibit substantial overlap across gastrointestinal malignancies. TriMeth is a dPCR assay that was originally developed for CRC detection using three methylated markers (\u003cem\u003eC9orf50\u003c/em\u003e, \u003cem\u003eKCNQ5\u003c/em\u003e, and \u003cem\u003eCLIP4\u003c/em\u003e). It has demonstrated predictive value for gastroesophageal cancer recurrence, supporting cross-applicability of certain methylation biomarkers\u003csup\u003e\u003cspan additionalcitationids=\"CR37 CR38\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e–\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. In the present study, \u003cem\u003eGLRB\u003c/em\u003e was confirmed by \u003cem\u003ein silico\u003c/em\u003e analysis and tissue validation to have high methylation signals in both GC and CRC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cb\u003eSupplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). To improve cancer specificity and reduce false positives from CRC-derived signals, the colorectal tissue-specific biomarker \u003cem\u003eGATM\u003c/em\u003e was incorporated as a negative selection marker. While this strategy was primarily intended to enhance specificity, normalization to \u003cem\u003eGATM\u003c/em\u003e also slightly improved the sensitivity compared with the dual-marker panel (\u003cem\u003eGHR\u003c/em\u003e and \u003cem\u003eGLRB\u003c/em\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These findings highlight the utility of integrating tissue-specific markers to improve the precision of methylation panels and expand their applicability across cancer types in liquid biopsy platforms.\u003c/p\u003e\u003cp\u003eThis study has certain limitations. First, it was conducted in a single-center retrospective cohort, which may limit generalizability. However, the high proportion of early-stage cases provided a valuable opportunity to evaluate assay performance in the most clinically relevant setting. Second, although \u003cem\u003eHelicobacter pylori\u003c/em\u003e infection is a major risk factor for GC, the assay was not tested in infected individuals without cancer. Within the GC cohort, however, methylation levels did not differ between infected and uninfected patients, suggesting robustness irrespective of infection status and supporting future studies in high-risk \u003cem\u003eHelicobacter pylori\u003c/em\u003e-positive populations. Third, while \u003cem\u003eGATM\u003c/em\u003e was not validated in an independent CRC plasma cohort, tissue analyses in both gastric and colorectal cancers confirmed its colorectal specificity, providing indirect evidence of its utility as a negative selection control. Despite these considerations, the study demonstrates the feasibility and strong diagnostic potential of a methylation-based dPCR assay targeting \u003cem\u003eGHR\u003c/em\u003e, \u003cem\u003eGLRB\u003c/em\u003e, and \u003cem\u003eGATM\u003c/em\u003e as a noninvasive early GC detection. The integration of tissue-specific methylation markers further highlights a methodological framework with potential applicability across liquid biopsy platforms and cancer types. With prospective validation, this approach holds promise for clinical translation as a scalable, accessible, and accurate diagnostic tool to improve early detection and reduce GC-related mortality.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"METHODS","content":"\u003ch2\u003eStudy design and specimens\u003c/h2\u003e\u003cp\u003eThis study consisted of two phases: Biomarker discovery and clinical validation of the plasma-based dPCR assay (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For biomarker discovery, primary tumor and matched normal tissues from 40 patients with GC were obtained from the Biobank of Chonnam National University Hwasun Hospital (P01-202204-02-001). Paired tissues from 37 patients with CRC were acquired additionally from the Biobank of Ajou University Hospital (P01-202003-31-001). The use of these specimens was exempted from review by the Public Institutional Review Board (IRB) designated by the Ministry of Health and Welfare, Republic of Korea. The requirement for written informed consent was waived because the study posed minimal risk to participants and all data were anonymized. Clinical characteristics are provided in \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eFor clinical validation, a retrospective case-control design was used to evaluate the diagnostic performance in plasma samples from 60 patients with histologically confirmed GC and 40 healthy controls. Blood samples were collected between May 2021 and December 2023 at Chungnam National University Hospital. This study was exempted from review by the IRB of Chungnam National University Hospital (IRB No. 2023-06-096) and conducted in accordance with the Declaration of Helsinki. Written informed consent was waived as the study posed minimal risk to participants and all clinical information were anonymized. Most of the patients with GC had early-stage disease (73.3%) according to TNM staging (stage I, \u003cem\u003eN\u003c/em\u003e = 38; stage II, \u003cem\u003eN\u003c/em\u003e = 6; stage III, \u003cem\u003eN\u003c/em\u003e = 14; stage IV, \u003cem\u003eN\u003c/em\u003e = 2) as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. All biospecimens met the quality control standards of the respective institutions.\u003c/p\u003e\n\u003ch3\u003eBiomarker discovery\u003c/h3\u003e\n\u003cp\u003eGenome-wide DNA methylation profiles from over 10,000 publicly available tissue samples, including GC, adjacent normal gastric mucosa, other tumor types, and hematologic cells, were analyzed. Raw data were processed and normalized using the Chip analysis methylation pipeline (also known as ChAMP) package to obtain β-values, defined as the ratio of methylated signal intensity\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The analysis covered approximately 450,000 CpG sites from The Cancer Genome Atlas, generated using the Infinium HumanMethylation450K BeadChip array (Illumina, San Diego, CA, USA). Candidate biomarkers were selected based on the following criteria: (1) Low methylation in peripheral blood leukocytes; (2) High methylation in GC tissues (including adjacent non-cancerous gastric mucosa); and (3) Low methylation in other tissues and cancer types.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003ePreclinical verification\u003c/h2\u003e\u003cp\u003eOligonucleotide sets (IDT, Coralville, IA, USA) targeting the selected CpG sites were designed and optimized using Human HCT116 DKO Methylated and Non-methylated DNA standards (Zymo Research, Irvine, CA, USA) according to established protocols (\u003cb\u003eSupplementary Table S2\u003c/b\u003e)\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Candidate markers were verified using genomic DNA from GC cell lines (AGS, KATO-III, and MKN-1; Korean Cell Line Bank, Seoul, Republic of Korea) and from tissue specimens obtained from the Biobank of Chonnam National University Hwasun Hospital (Hwasun, Republic of Korea) and Ajou University Hospital (Suwon, Republic of Korea). Genomic DNA was extracted using the Wizard Genomic DNA Purification Kit (Promega, Madison, WI, USA) and quantified with a NanoDrop 2000 spectrophotometer (Thermo Scientific, Waltham, MA, USA). Bisulfite conversion was performed with the EZ DNA Methylation-Lightning Kit (Zymo Research) following the manufacturer\u0026rsquo;s instructions. Bisulfite-converted DNA was analyzed by dPCR with both positive and negative controls included to ensure assay validity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003ePlasma ctDNA methylation assay\u003c/h2\u003e\u003cp\u003ePrior to any therapeutic intervention, peripheral blood was collected in K2EDTA tubes (Becton Dickinson, Franklin Lakes, NJ, USA). Plasma was separated within 4 h by two-step centrifugation at 3000 \u0026times;g for 10 min and stored at -70\u0026#917774; \u0026deg;C until further analysis. Cell-free DNA was extracted from 1.5-2.0 mL of plasma using the QIAamp Circulating Nucleic Acid Kit (Qiagen, Hilden, Germany) following the manufacturer\u0026rsquo;s instructions. The entire volume of purified cfDNA was subjected to bisulfite conversion using the EZ DNA Methylation-Lightning Kit (Zymo Research) immediately after extraction. Bisulfite-converted DNA was analyzed using the QX600 Droplet Digital PCR System (Bio-Rad Laboratories, Hercules, CA, USA). Each 22 \u0026micro;L reaction mixture contained 10 \u0026micro;L of bisulfite-treated DNA, 3.3 \u0026micro;L of 10 \u0026micro;M oligonucleotide mix (Gencurix, Seoul, Republic of Korea), 5.5 \u0026micro;L 4X Droplet Digital PCR Multiplex Supermix, 0.33 \u0026micro;L of 0.3 M dithiothreitol, and nuclease-free water. Droplets were generated using a QXDx Droplet Generator and amplified on a Veriti 96-Well Thermal Cycler (Applied Biosystems Inc., Foster City, CA, USA) using the following protocol: 95℃ for 10 min; 45 cycles of 94℃ for 30 s and 59℃ for 1 min; and 98℃ for 10 min. PCR products were read on the QX600 Droplet Digital PCR Reader, and fluorescence signals were analyzed with the QX Manager version 2.1 software. Results were expressed as copies/mL with \u003cem\u003eACTB\u003c/em\u003e serving as the internal control and cfDNA reference. Positive and negative controls were included in all runs. Positive thresholds were determined automatically, and all procedures followed the Minimum Information for Publication of Quantitative Digital PCR Experiments (known as dMIQE) guidelines\u003csup\u003e4\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using GraphPad Prism version 7 (GraphPad Software, La Jolla, CA, USA) and MedCalc version 15.8 (MedCalc, Ostend, Belgium). ROC curves were generated to determine assay sensitivity, specificity, and AUC with 95% CIs. Cutoff values were defined using the Youden index. Group differences were assessed by one-way ANOVA followed by Tukey\u0026rsquo;s post hoc test. Associations between methylation levels and clinicopathological variables were examined using the Mann-Whitney U test or Kruskal-Wallis test. A \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cp\u003eY.Y.L., J.A., J.H., and Y.M. are employees of Gencurix, Inc. and hold stock or stock options in the company. The remaining authors have no conflicts to report.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFUNDING\u003c/h2\u003e\u003cp\u003eThe authors received no funding for this work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.H., Y.M., and S.-I.L. conceptualized and designed this study. Y.Y.L., J.A., J.H., and S.-I.L. acquired and analyzed data. Y.Y.L., J.A., J.H., Y.M., and S.-I.L. conducted interpretation of data. Y.Y.L. and S.-I.L. wrote the original draft of the manuscript. All authors reviewed and edited the final draft of the manuscript, had final responsibility for the decision to submit for publication.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003e The biospecimens and clinical data used for tissue verification were provided by the Biobank of Chonnam National University Hwasun Hospital (P01-202204-02-001) and Ajou University Hospital (P01-202003-31-001), both members of the Korea Biobank Network. The plasma samples and data were provided by the Biobank of Chungnam National University Hospital (2023-06-096).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe raw data and analytical methods used in the study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray, F. et al. 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Chem.\u003c/em\u003e \u003cb\u003e66\u003c/b\u003e, 1012\u0026ndash;1029. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/clinchem/hvaa125\u003c/span\u003e\u003cspan address=\"10.1093/clinchem/hvaa125\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gastric cancer, Early diagnosis, DNA methylation, Digital PCR, Circulating tumor DNA, Biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-7579093/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7579093/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEndoscopy is the gold standard for diagnosing gastric cancer (GC), but its invasiveness limits widespread participation and has not substantially reduced GC-related mortality. This study developed and validated a blood-based digital PCR assay for early GC detection using DNA methylation biomarkers. Genome-wide methylation profiles from over 10,000 samples were screened, and two candidates were validated in GC cell lines, tumors, matched non-cancerous tissues, and plasma. Plasma from 60 GC patients, including 38 with stage I disease, and 40 healthy controls was analyzed with a digital PCR assay targeting the selected biomarkers, using \u003cem\u003eACTB\u003c/em\u003e as a reference. \u003cem\u003eGHR\u003c/em\u003e and \u003cem\u003eGLRB\u003c/em\u003e methylation were identified as novel GC biomarkers, showing consistent hypermethylation in GC cell lines and tumor tissues. In plasma, the two-marker assay achieved 83.3% (95% CI, 71.5%-91.7%) sensitivity and 90% (95% CI, 76.3%-97.2%) specificity, clearly outperforming carcinoembryonic antigen (CEA) testing (10.0%; 95% CI, 3.8%-20.5%). Incorporation of \u003cem\u003eGATM\u003c/em\u003e methylation as a third marker increased sensitivity to 86.7% (95% CI, 75.4%-94.1%) overall and 81.6% (95% CI, 65.7%-92.3%) for stage I disease, while maintaining 90.0% specificity. This methylation-based digital PCR assay enabled accurate, non-invasive detection of GC, particularly at early stages, and may facilitate timely diagnosis and curative treatment.\u003c/p\u003e","manuscriptTitle":"Plasma-based digital PCR assay for early detection of gastric cancer using multiple methylation biomarkers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-22 19:33:38","doi":"10.21203/rs.3.rs-7579093/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-05T13:37:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-04T00:30:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269676560922983975505148950342593708461","date":"2025-10-26T19:23:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-26T14:44:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"85021004752926487712872745582437638368","date":"2025-10-09T15:16:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-09T09:09:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-07T05:10:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-13T05:32:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-12T08:33:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-10T05:39:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"97e38744-e57c-4f63-a481-f9325f724e9e","owner":[],"postedDate":"October 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":56537716,"name":"Health sciences/Biomarkers"},{"id":56537717,"name":"Biological sciences/Cancer"},{"id":56537718,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-12-15T16:03:01+00:00","versionOfRecord":{"articleIdentity":"rs-7579093","link":"https://doi.org/10.1038/s41598-025-31314-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-11 15:58:09","publishedOnDateReadable":"December 11th, 2025"},"versionCreatedAt":"2025-10-22 19:33:38","video":"","vorDoi":"10.1038/s41598-025-31314-5","vorDoiUrl":"https://doi.org/10.1038/s41598-025-31314-5","workflowStages":[]},"version":"v1","identity":"rs-7579093","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7579093","identity":"rs-7579093","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}