Gastric cancer (GC) is a major global health concern, it caused 1,089,103 new cases and 768,793 new deaths in 2020, ranking fifth for incidence and fourth for mortality among all cancer types globally (Sung et al., 2021). However, more than 60.0% GC new cases were found in Eastern Asian countries, such as China, Japan, Korea and Mongolia (Ning et al., 2022; Sekiguchi et al., 2022). Although the GC incidence was remarkable decreasing during the past several decades with the improvement of medical treatments and public health strategies (Sekiguchi et al., 2022; Song et al., 2022), the 5-year survival rate for GC still relatively low (about 30.0%–40.0%) in most countries (Sekiguchi et al., 2022). In contrast, 2 GC high-risk countries, Japan and Korea, reported a high 5-year survival rate for GC of 60.0%–70.0%, which was due to the long-term national GC screening programs, and many of these cases were found at early stage (Hamashima, 2020; Sekiguchi et al., 2022). In Japan and Korea, the GC screening strategies included gastrointestinal X-ray and upper endoscopy detection. However, because upper endoscopy is an invasive method with several side effects and a low compliance rate, and gastrointestinal X-ray suffer the risk of radiation exposure, they were not the best choice for primary screening of GC. Other non-invasive methods, such as the CA72-4, PGI/II and the ABC method (combination assay of Helicobacter pylori and serum pepsinogen), have insufficient sensitivity and specificity (Yamaguchi et al., 2016; Xu Y. et al., 2021).

DNA methylation is the most widely studied epigenetic modification which plays a significant role in cancer progression and metastasis (Davalos and Esteller, 2022). Many of DNA methylation occur early in tumorigenesis, which allows DNA methylation-based markers to be suitable for early detection of cancer (Locke et al., 2019; Zhao et al., 2019; Zhao et al., 2020). Because DNA methylation can also be detected in body fluids such as blood, stool, urine, and cerebrospinal fluid (Liu et al., 2020; Rahat et al., 2020), it is more stable, sensitive and specific than other cell free nucleic acid markers (miRNA, lncRNA or mRNA), indicating that DNA methylation is a promising non-invasive marker for cancer early detection (Jamshidi et al., 2022). During the past decade, several commercially available DNA methylation-based assays have been developed and approved by the United States Food and Drug Administration (FDA) and Chinese National Medical Products Administration (NMPAfor clinical application. For example, the plasma SPET9 methylation test (Epi proColon) (Church et al., 2014), and the stool multiple-target assay (Cologuard), which contained two methylation markers (Imperiale et al., 2014), were successfully used for colorectal cancer (CRC) screening (Wolf et al., 2018). Moreover, many studies have proven that DNA methylation markers are more sensitive and specific than traditional blood protein markers (Young et al., 2016; Cai et al., 2021; Lin et al., 2021). Promoter CpG island hypermethylation and tumor suppressor genes inactivated in gastric carcinogenesis has also been frequently observed (Patel et al., 2017), thus DNA methylation can also be a potential marker for GC early detection. Recent advances in translational genomics and analytics, drives numerous potential DNA methylation markers have come to light in clinics.

This review provides a comprehensive overview of the DNA methylation changes in GC, and summarized the achievements, challenges and possible further direction for DNA methylation as a potential tool for GC early detection.

In this review, we performed an extensive search and analysis of previously published DNA methylation markers to evaluate their effectiveness in the detection of GC across different sample types (Table 1; Table 2; Table 3). According to our analysis, current research on DNA methylation in GC primarily focuses on tissue, gastric washes/juice, blood (plasma or serum) and stool samples. Tissue samples are primarily employed for the initial identification of methylation markers, whereas blood or stool samples are commonly used for the subsequent validation of identified markers. Regarding the sample types used in the previous studies, we found that plasma was the most commonly used sample type for DNA methylation analysis, with 26 studies utilizing this sample type. However, in 6 of these studies, plasma volumes were ≤1mL, which may have impacted the accuracy and sensitivity of the analysis. In contrast, 3.5 mL of plasma was used in 7 studies. Serum was used in 13 studies, and the volume of serum used was ≤0.5 mL in 53.8% of these studies.

TABLE 1. DNA methylation markers individually evaluated for GC detection.

TABLE 2. DNA methylation panels for GC detection.

TABLE 3. DNA Methylation-based pan-cancer test for GC detection.

The methods used for analyzing DNA methylation markers included methylation-specific PCR (MSP), nested MSP, quantitative methylation-specific PCR (qMSP), Sanger sequencing, combined restriction digital PCR (CORD), methylation-sensitive melt curve analysis (MS-MCA), pyrosequencing and bisulfite sequencing (Table 1; Table 2; Table 3). Our analysis revealed that MSP was the most commonly used technology in tissue and plasma/serum samples. However, the reported frequency of MSP has significantly decreased in the past 5 years. Similarly, most reports on pyrosequencing for GC methylation analysis were published 5 years ago and mainly focused on tissue and gastric wash samples. In contrast, qMSP has emerged as a more sensitive and convenient method, and it has been increasingly used for plasma/serum analysis in recent years. While the bisulfite sequencing, especially the target bisulfite sequencing is a commonly used method for pan-cancer early detection in recent years (Table 3).

A total of 59 DNA methylation markers were evaluated individually in 41 studies, and 13 out of the 59 markers (P16, RNF180, RUNX3, SEPT9, E-cadherin, Reprimo, ELMO1, TFPI2, RASSF1A, SFRP2, RPRM, ZIC1, and DAPK) were reported at least 2 times. Among these markers, 37 out of them were analyzed in blood samples (plasma or serum), and the repeatedly evaluated markers were also assessed in blood samples at least once. In addition to blood samples, this review also included markers evaluated in tissue and gastric washes/juice. For instance, Sox17 (Oishi et al., 2012) and BARHL2 (Yamamoto et al., 2016) were exclusively evaluated in gastric washes, and 15 markers (CDO1, DCLK1, SFRP1, ZNF331, ZSCAN18, FLT3, LINC00643, JAM2, BHLHE22, RIMS1,GUSBP5, ZNF3, CHFR, NMDAR2B, and RECK) were solely evaluated in tissue samples (fresh frozen tissue or FFPE). The remaining six markers (MINT25, RORA, PRDM5, MLF1, ADAM2, and GDNF) were evaluated simultaneously in both tissue and gastric wash samples (Table 1).

The most frequently evaluated methylated marker for GC early detection was P16, which was assessed in 7 studies across various sample types, including tissue, serum, or plasma (Lee et al., 2002; Kanyama et al., 2003; Ichikawa et al., 2004; Koike et al., 2004; Hou et al., 2005; Abbaszadegan et al., 2008; Saliminejad et al., 2020). Nonetheless, the sensitivity of P16 in blood samples was relatively low in these studies (Lee et al., 2002; Ichikawa et al., 2004; Saliminejad et al., 2020). Apart from P16, RNF180, and RUNX3 were the most frequently investigated methylation markers, each mentioned in 5 studies (Table 1). RNF180 was studied in various research conditions, including marker discovery to multiple center applications (Cheung et al., 2012; Zhang et al., 2014; Cao et al., 2020; Xu J. et al., 2021). Cheung et al. (2012) first reported the feasibility of using RNF180 methylation as an early detection marker for GC in 0.8 mL plasma, showing a sensitivity of 56.3% and specificity of 100.0%. Three studies using 3.5 mL plasma showed that RNF180 methylation had sensitivities ranging from 32.4% to 71.7%, with specificities of 59.4%–88.4% (Cao et al., 2020; Xu J. et al., 2021; Zhao et al., 2022a). RUNX3 also were explored both in tissue and blood samples, its sensitivities in blood range from 29.2% to 58.3%, with the specificities of 79.2%–100.0% (Sakakura et al., 2009; Lin et al., 2017; Hideura et al., 2020; Saliminejad et al., 2020). The three studies examining SEPT9 analyzed it under identical research conditions, yielding sensitivities of 28.4%–48.3% and specificities of 86.9%–98.2% (Cao et al., 2020; Xu J. et al., 2021; Zhao et al., 2022b). Reprimo showed 62.0%–95.3% sensitivities and 90.3%–100.0% specificities in plasma for GC detection (Bernal et al., 2008), and also exhibited 94.3% sensitivity and 92.9% specificity in 1 mL serum (Wang et al., 2016), indicated that it is a promising marker for early detection of GC. E-cadherin has exhibited 100.0% specificity in previous studies conducted on three serum cohorts, but its sensitivity remains relatively low, ranging from 22.2% to 57.4% (Lee et al., 2002; Ichikawa et al., 2004; Koike et al., 2004). Pimson et al. (2016) found that methylated RASSF1A and PCDH10 have high sensitivities (83.2% and 94.1%) and specificities (94.6% and 97.0%) in 0.2 mL plasma, while another study demonstrated that RASSF1A had only 33.3% sensitivity with a specificity of 100.0% in 2 mL plasma (Saliminejad et al., 2020). The ZIC1 was evaluated not only in GC samples but also in samples of gastric precancerous lesions, such as intraepithelial neoplasia (IN) and intestinal metaplasia (IM) (Chen et al., 2015; Lin et al., 2017). Other methylation markers such as SFRP2 (Miao et al., 2020), RPRM (Saliminejad et al., 2020), OSR2 (Li et al., 2016), PPFIA3 (Li et al., 2016), ZNF582 (Peng et al., 2022) and C9orf50 (Li et al., 2022) showed sensitivities higher than 50.0% with specificities higher than 80.0% in plasma, indicating their potential as non-invasive tools for GC early detection.

For early-stage GC detection, most methylation markers lack of the performance evaluation in stage I GC (Table 1). RNF180 and SEPT9 both showed sensitivities less than 20.0% in stage I GC (Xu J. et al., 2021). Among the markers evaluated in stage I GC, C9orf50 had sensitivities ranging from 21.4% to 44.4% (Li et al., 2022), while P16, E-cadherin and SFRP2 showed the relatively higher sensitivities ≥50.0% (Lee et al., 2002; Miao et al., 2020). Only one study containing methylated KCNQ5, C9orf50, and CLIP4 was evaluated in multiple cohorts and showed good reproducibility in three plasma cohorts (Li et al., 2022).

Single DNA methylation markers for GC detection often exhibit insufficient sensitivity, particularly for early-stage cancer, due to tumor heterogeneity and individual differences. Developing methylation panels using multiple DNA methylation markers is an effective strategy to improve sensitivity and has been successfully applied in detection of CRC (Zhao et al., 2019; Zhao et al., 2020) and lung cancer (LC) (Zhang et al., 2017; Wei et al., 2021). In the past decade, several methylation markers were evaluated in panels for GC early detection, with all studies using blood samples, including 10 plasma and three serum sample cohorts (Table 2). The SEPT9 and RNF180 combination was the first and only non-invasive panel approved by NMPA for plasma GC detection in 2020. A previous study indicated that the combination of SEPT9 and RNF180 improved sensitivities from 28.4%–32.4%–40.5% with a specificity of 85.3% (Cao et al., 2020). Another study using a combination of SEPT9, RNF180, and CA72-4, achieved 33.3% and 68.6% sensitivities for stage I and overall stage GC, with a specificity of 85.1% (Xu J. et al., 2021). Combining RNF180 with other markers, such as DAPK1 and SFRP2, showed a high sensitivity of 87.7% but a lower specificity of 38.1% (Zhang et al., 2014).

The combination of P16, E-cadherin, and RARb in a panel resulted in a significant improvement in sensitivity compared to using a single marker. However, the sensitivity of the panel remained relatively low (Koike et al., 2004). In contrast, the combination of FAM5C and MYLK demonstrated an area under the curve (AUC) of 0.84 for GC detection, indicating its potential as a non-invasive blood-based method for early detection of GC (Chen et al., 2012). One panel including Reprimo and hMLH1 identified 84.0% of GC cases with a specificity of 96.7%. This panel could also be expanded to detect gastric dysplasia and IM with sensitivities of 76.0% and 34.0%, respectively (Liu and Yang, 2015). Shin et al. (2016) reported a methylation panel involving four new markers, PYCARD, APAF1, MINT1, and BRCA1, with a sensitivity and specificity of 97.6% and 66.3%. Two other studies used low volume blood samples and included three methylation markers by using MSP, showing an increasing trend of sensitivity but a decline in specificity when compared to single markers (Li et al., 2016; Lin et al., 2017). Anderson et al. (2018) developed and validated a novel panel including methylated ELMO1, C13orf18, and ZNF569, which demonstrated a promising sensitivity of 86.0% and a specificity of 95.0%. Ren et al. (2022) used methylated CpG tandem amplification and sequencing (MCTA-Seq) to develop a panel with 153 cfDNA methylation markers, which could detect 44.0% of stage I GC in plasma. Li et al. (2022) integrated KCNQ5, C9orf50 and CLIP4 in a single tube qMSP panel, and evaluated its performance in three cohorts, finding that it had about 10.0%–30.0% in increase of sensitivity compared with single markers. Ruan et al. (2023) also developed a qMSP panel included seven methylation markers, and validated in two independent cohorts, achieving sensitivities and specificities of 80.0%–82.0% and 65.0%–69.0%, respectively.

Except for methylation panels for single cancer type early detection, detecting multiple cancer types together in one panel, called a pan-cancer test, is a new strategy for reducing cancer morbidity and mortality (Duffy et al., 2021; Jamshidi et al., 2022). Seven pan-cancer tests based on methylation markers have been summarized in this review, which have been applied to at least two cancer types, including GC (Table 3). Four of these tests were developed using bisulfite sequencing, all of which demonstrated high specificities higher than 95.0% (Chen et al., 2020; Kandimalla et al., 2021; Klein et al., 2021; Yang et al., 2023). Moreover, most of the bisulfite sequencing based pan-cancer tests can identify tissue of origin (TOO) (Table 3). As for qMSP-based method, Peng et al. (2022) developed a panel by combined using ZNF582, ELMO1, and TFPI2, which can detect GC, esophageal cancer (EC) and esophagogastric junction cancer (EJC) together, and achieved 67.9% and 71.0% sensitivities for GC and all cancer types, respectively, with a specificity of 90.0%. Ma Y. et al. (2022) reported a six methylation markers panel for detection of EC, GC and CRC in plasma, it showed 69.9% and 76.6% for GC and all cancer types with a specificity of 89.2%. In addition to blood, stool was also utilized as a sample type for detection of the pan-cancer in gastrointestinal tract. For example, methylation RASSF2 and SFRP2 were combined using for detection of CRC and GC in 0.1 g stool samples (Nagasaka et al., 2009), and Ma et al used 3 g stool as a sampling type for simultaneous detection of CRC and GC, and it could detect 67.5% GC with a specificity of 97.8% (Ma L. et al., 2022).

Indeed, DNA methylation-based cancer early detection tests face several challenges that need to be addressed before they can be widely adopted in clinical settings (Figure 1). One of the major challenges is the complexity of the DNA methylation detection process. The process often involves bisulfite treatment, PCR amplification, and sequencing or other detection methods. These steps can introduce errors or biases into the results, which can affect the accuracy and reproducibility of the test. In addition to the technical challenges, there are several other factors that can impact the performance of DNA methylation-based cancer early detection tests. For example, the quality of the sample is critical, and the performance of the test can be affected by the pre-analytical conditions, such as the time and temperature of sample storage and transportation. Moreover, the accuracy of the test can be influenced by the selection of the target CpG sites and the panel design. It is important to ensure that the selected CpG sites are informative and specific for the target cancer types, and that the panel design is optimized for sensitivity and specificity. Another critical factor is the quality of the enrolled subjects and the selection criteria. DNA methylation-based cancer early detection tests may have limited sensitivity in early-stage cancers, and false positive results may occur in some cases. Therefore, it is important to carefully select the enrolled subjects based on their clinical and pathological characteristics, such as age, gender, tumor stage, and histological type, to minimize the risk of false positives or false negatives. Lastly, the quality of the operators and the analytical methods used for the test are also important factors that can affect the performance and reproducibility of the test. Therefore, it is crucial to establish standard operating procedures and quality control measures to ensure that the test results are accurate and reliable.

FIGURE 1. The challenges and future of DNA methylation markers for early detection of GC. PPV, positive predictive value; NPV, negative predictive value. Created with MedPeer (www.medpeer.cn).

As mentioned in this review, the samples involved in GC DNA methylation tests include fresh frozen tissue, FFPE, plasma, serum, gastric washes/juice, and stool (Figure 1). However, fresh frozen tissue, FFPE, and gastric washes/juice are invasive sample types and therefore not suitable for large-scale screening or early diagnosis of GC. Tissue samples are more appropriate for discovering DNA methylation markers rather than early detection. In comparison, the methylation level of each marker is consistently higher in tissues than in plasma and serum, which due to the proportion of circulating tumor DNA (ctDNA) in the blood is much lower than that of tumor DNA in the tissues (Abbosh et al., 2018). Therefore, when we translate the markers that from discovery stage in GC tissues to blood-based assay development, a significant decrease in sensitivities might be observed in blood samples (Table 1).

Blood is a convenient, non-invasive, and high-throughput processable sample, that is, easily accessible. Plasma and serum are two main sample types for blood ctDNA analysis, as indicated by the studies summarized in this review (Table 1). Previous head-to-head studies have shown that plasma is a preferable sample type for ctDNA analysis because the ctDNA fraction in serum is lower than that in plasma, while the background and large DNA fragments are higher in serum (Lee et al., 2020; Pittella-Silva et al., 2020). Therefore, we suggest plasma as the ideal non-invasive sample type for GC early detection, and recent studies on GC early diagnosis have also confirmed that plasma is a superior sample type (Table 1; Table 2; Table 3). However, an important issue to address is the significant variation in the volume of plasma samples used in different studies (Table 1), which can have a significant impact on the performance of DNA methylation tests and affect subsequent marker selection and replication by other researchers. Furthermore, the standardized pre-analytical procedure must be considered before the clinical applications (Figure 1) (Kerachian et al., 2021). For example, blood drawn by EDTA tubes should be processed within 4–6 h after collection if storage at room temperature (Meddeb et al., 2019), and stored at 4°C in EDTA tubes for up to 24 h (Kerachian et al., 2021). During the plasma fraction separation procedure, the brake function of the centrifuge must be turned off to prevent disruption of the cell layer (Kerachian et al., 2021), and centrifuging the blood twice is recommended (Volckmar et al., 2018). The plasma should be processed for cfDNA isolation within 24 h if stored at 4°C, and for long-term storage at −20 or −80°C (Kerachian et al., 2021).

Stool is an easily accessible and non-invasive sample type that can be conveniently collected at home (Qian, 2017), making it an ideal sample for early detection of gastrointestinal cancers. However, DNA derived from the stomach has a longer residence time in the digestive tract, making it more susceptible to degradation by nucleases and gastric acid present in the gastrointestinal tract (Olson et al., 2005; Liu et al., 2015). As a result, the proportion of DNA originating from the stomach in stool samples is relatively lower compared to fresh DNA derived from the colon. Consequently, the use of stool samples for detecting GC often exhibits significantly lower sensitivity compared to the detection of CRC (Table 3).

The most commonly used analytical method for analyzing DNA methylation is the bisulfite-treat-based method, which includes MSP, qMSP, bisulfite Sanger sequencing, bisulfite next-generation sequencing (NGS), and pyrosequencing (Figure 1) (Kurdyukov and Bullock, 2016). The entire process consists of three steps: 1) DNA isolation from specimens; 2) DNA bisulfite treatment and purification; 3) converted DNA analysis. The efficiencies of the DNA isolation kit and the DNA bisulfite treatment kit are crucial factors that affect the performance of DNA methylation analysis (Cox et al., 2022). Some studies have compared the most commonly used commercial cfDNA isolation kits and bisulfite conversion kits and have observed significant differences in cfDNA recovery efficiency and bisulfite conversion efficiency (Sorber et al., 2017; Worm Ørntoft et al., 2017). Hence, carefully selecting a suitable and highly efficient kit is necessary during DNA methylation assay development and application. Among the DNA methylation analytical methods, MSP is an economical and traditional method that has been widely used in various sample types (Ramalho-Carvalho et al., 2018), but its low resolution, low-throughput, and potential for cross-contamination limit its application in liquid biopsy (Mao and Chou, 2010). qMSP, a modified method combining MSP and qPCR, can detect several DNA methylation markers simultaneously in one tube with high resolution and avoid cross-contamination from gel analysis (Sigalotti et al., 2019). Currently, several qMSP-based non-invasive cancer early detection tests have approved by FDA and NMPA due to their cost-effectiveness and convenience (Imperiale et al., 2014; Potter et al., 2014; Wu et al., 2016; Wang et al., 2020). Bisulfite NGS as a high-throughput analytical method was used for DNA methylation markers discovery and large-panel development (Luo et al., 2020), and its wider coverage of DNA markers can avoid false positives. However, the data summarized in this review suggest that the NGS-based GC early detection panels do not offer a significant advantage compared to qMSP-based panels (Table 2; Table 3), and the high-cost and complex operation process also limit their application.

In the field of GC early detection, the current methylation markers being used are predominantly pan-cancer markers rather than GC-specific markers. For example, SEPT9 is approved by the FDA and NMPA as a plasma marker for CRC detection (Potter et al., 2014; Wu et al., 2016), while SFRP2 (Li et al., 2019), KCNQ5 (Jensen et al., 2019), C9orf50 (Jensen et al., 2019), CLIP4 (Jensen et al., 2019) and TFPI2 (Hibi et al., 2011b) were found to be positive in CRC plasma. Similarly, ZNF582 (Huang et al., 2017) and ZNF569 (Salta et al., 2020) showed high methylation in esophageal cancers, and RASSF1A has been approved by NMPA as a lung cancer early detection marker (Wei et al., 2021). Meanwhile, the above data in GC samples all came from case-control study, and have not been evaluated in the real-world asymptomatic population. While the cfDNA in blood is derived from organs throughout the body, thus the blood-based GC early detection may result in numerous “false positives” in real-world application if using the pan-cancer methylation markers. To address the issue of insufficient specificity for current methods, a pan-cancer panel may be utilized, but it also raises concerns regarding the next-step examination in case of a positive result. On the bright side, many methylation markers are more likely to exhibit positivity in the digestive system while being relatively specific in other organs. Therefore, the optimal solution for GC early detection in the future may be the target digestive system pan-cancer test.

Compared to DNA methylation in CRC early detection (Nikolaou et al., 2018; Worm Ørntoft, 2018; Nassar et al., 2021), the recent milestones of methylation-based GC early detection are still in a relatively early stage, and the majority of the GC methylation studies were focused on the Asian region, especially in China and Japan (Table 1). In the future, the researchers should pay more attention to the discovery of specific markers and improve the early-stage GC sensitivity, such as through enrichment of the short-length ctDNA in plasma (Mouliere et al., 2018). Meanwhile, the cost and the payer condition (individual or medical insurance) are two important factors that must be concerned when developing and applying the GC DNA methylation test (Figure 1). In developing countries and rural regions, a qMSP-based test may be the first choice, but in developed countries and urban areas, NGS-based methods can also be an alternative solution. Furthermore, automating pre-analytical procedures will provide more consistent and reproducible detection results and reduce costs in the future (Figure 1). Finally, multiplex DNA methylation detection based on blood sample might the optimal method for early detection of GC in the future, which can avoid individual biases by combining multiple markers and improve sensitivity for early-stage cancer detection. Therefore, we provide a potential standardized flowchart for plasma DNA methylation analysis in Figure 2 based on our experience and literature.

FIGURE 2. The potential standardized flowchart for plasma DNA methylation analysis.

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