Skip to main content
NCBI home page
Frontiers in Genetics logoLink to Frontiers in Genetics
. 2023 Jul 25;14:1234645. doi: 10.3389/fgene.2023.1234645

An overview of DNA methylation markers for early detection of gastric cancer: current status, challenges, and prospects

Ying Xue 1,*,†, Chao Huang 1,†, Bing Pei 2, ZhenZhen Wang 3, Yanmiao Dai 4,*
PMCID: PMC10407555  PMID: 37560387

Abstract

Background: Gastric cancer (GC) is one of the most common malignancies, with a low 5-year survival rate. However, if diagnosed at an early stage, it can be cured by endoscopic treatment and has a good prognosis. While gastrointestinal X-ray and upper endoscopy are used as national GC screening methods in some GC high-risk countries, such as Japan and Korea, their radiation exposure, invasiveness, and high cost suggest that they are not the optimal tools for early detection of GC in many countries. Therefore, a cost-effective, and highly accurate method for GC early detection is urgently needed in clinical settings. DNA methylation plays a key role in cancer progression and metastasis and has been demonstrated as a promising marker for cancer early detection.

Aims and methods: This review provides a comprehensive overview of the current status of DNA methylation markers associated with GC, the assays developed for GC early detection, challenges in methylation marker discovery and application, and the future prospects of utilizing methylation markers for early detection of GC. Through our analysis, we found that the currently reported DNA methylation markers related to GC are mainly in the early discovery stage. Most of them have only been evaluated in tissue samples. The majority of non-invasive assays developed based on blood lack standardized sampling protocols, pre-analytical procedures, and multicenter validation, and they exhibit insufficient sensitivity for early-stage GC detection. Meanwhile, the reported GC DNA methylation markers are generally considered pan-cancer markers.

Conclusion: Therefore, future endeavors should focus on identifying additional methylation markers specific to GC and establishing non-invasive diagnostic assays that rely on these markers. These assays should undergo multicenter, large-scale prospective validation in diverse populations.

Keywords: sample types, analytical methods, non-invasive, gastric cancer, DNA methylation

1 Introduction

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.

2 Sample types and analytical methods for DNA methylation analysis

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.

Markers Authors Year Country Sample type Number of subjects Method SN (%) Stage I SN (%) SP (%) AUC
P16 Lee et al. (2002) 2002 China (Hong Kong) Tissue 44 GC MSP 66.7 71.4 — —
Serum 44 GC, 30 Control MSP 51.9 57.1 100.0 —
P16 Kanyama et al. (2003) 2003 Japan Tissue 60 GC, 60 Control MSP 38.3 — 100.0 —
Serum 60 GC, 16 Control MSP 10.0 — 100.0 —
P16 Koike et al. (2004) 2004 Japan Serum 41 GC, 10 Control MSP 22.0 — 100.0 —
P16 Ichikawa et al. (2004) 2004 Japan 0.4 mL serum 109 GC, 10 Control MSP 18.3 — 100.0 —
P16 Hou et al. (2005) 2005 China Tissue 60 GC, 60 Control Nested-MSP 86.7 — 100.0 —
P16 Abbaszadegan et al. (2008) 2008 Iran Tissue 52 GC, 50 Control MSP 44.2 — 100.0 —
Serum 52 GC, 50 Control MSP 26.9 — 100.0 —
P16 Hu et al. (2010) 2010 China Tissue 70 GC, 30 Control MSP 68.6 — 100.0 —
P16 Saliminejad et al. (2020) 2020 Iran 2 mL plasma 96 GC, 88 Control MSP 41.7 — 84.1 0.63
RNF180 Cheung et al. (2012) 2012 China (Hong Kong) Tissue 198 GC, 20 IM, 23 Control Sanger Sequencing GC: 75.8, IM: 55.0 — 100.0 —
0.8 mL plasma 109 GC, 190 Control qMSP 56.3 — 100.0 —
RNF180 Zhang et al. (2014) 2014 China 0.4 mL plasma 57 GC, 42 Control MSP 57.9 — 76.2 —
RNF180 Cao et al. (2020) 2020 China 3.5 mL plasma 74 GC, 99 BGD, 57 NED qMSP GC: 32.4, GD: 13.1 — 86.9 0.64
RNF180 Xu et al. (2021b) 2021 China 3.5 mL plasma 151 GC, 56 AG, 87 Other GIDs, 224 NED qMSP GC: 37.1, AG: 19.8, Other GIDs: 14.3 <20 88.4 0.72
RNF180 Zhao et al. (2022a) 2022 China 3.5 mL plasma 60 GC, 96 Control qMSP 71.7 — 59.4 0.73
RUNX3 Sakakura et al. (2009) 2009 Japan 0.4 mL serum 65 GC, 50 Control qMSP 29.2 — 100.0 —
RUNX3 Hu et al. (2010) 2010 China Tissue 70 GC, 30 Control MSP 60.0 — 100.0 —
RUNX3 Lin et al. (2017) 2017 China 0.4 mL plasma 131 GC, 56 IN, 30 IM, 34 Control MSP GC: 42.7 — 79.2 —
RUNX3 Saliminejad et al. (2020) 2020 Iran 2 mL plasma 96 GC, 88 Control MSP 58.3 — 95.5 0.77
RUNX3 Hideura et al. (2020) 2020 Japan 0.4 mL serum 50 GC, 61 Control CORD Assay 50.0 — 80.3 0.70
SEPT9 Cao et al. (2020) 2020 China 3.5 mL plasma 74 GC, 99 BGD, 57 NED qMSP GC: 28.4, BGD: 6.1 — 94.7 0.62
SEPT9 Xu et al. (2021b) 2021 China 3.5 mL plasma 151 GC, 56 AG, 87 Other GIDs, 224 NED qMSP GC: 48.3, AG: 9.3, Other GIDs: 6.7 <15 98.2 0.77
SEPT9 Zhao et al. (2022b) 2022 China 3.5 mL plasma 60 GC, 96 Control qMSP 48.3 — 86.9 0.70
Reprimo Bernal et al. (2008) 2008 Chile Plasma 43 GC, 31 Control MSP 95.3 — 90.3 —
Reprimo Liu and Yang (2015) 2015 China Plasma 50 IM, 50 Dysplasia, 50 GC, 30 Control MSP IM: 28.0, Dysplasia: 56.0, GC: 62.0 — 100.0 —
Reprimo Wang et al. (2016) 2016 China Tissue 42 GC, 28 Control MS-MCA 70.0 — 46.4 —
1 mL Serum 35 GC, 28 Control MS-MCA 94.3 — 92.9 —
E-cadherin Lee et al. (2002) 2002 China (Hong Kong) Tissue 44 GC MSP 75.9 71.4 — —
Serum 44 GC, 30 Control MSP 57.4 57.1 100.0 —
E-cadherin Ichikawa et al. (2004) 2004 Japan 0.4 mL serum 109 GC, 10 Control MSP 23.8 — 100.0 —
E-cadherin Koike et al. (2004) 2004 Japan Serum 41 GC, 10 Control MSP 22.0 — 100.0 —
ZIC1 Chen et al. (2015) 2015 China Plasma 104GC, 50 IN, 20 Control MSP GC: 60.6, IN: 54.0 — 100.0 —
ZIC1 Lin et al. (2017) 2017 China 0.4 mL plasma 131 GC, 56 IN, 30 IM, 34 Control MSP GC: 69.5 — 69.2 —
ELMO1 Maeda et al. (2018) 2018 Japan Tissue 52 GC, 50 Control Pyrosequencing — — — 0.75
ELMO1 Peng et al. (2022) 2022 China 3.5 mL plasma 32 GC, 64 Control qMSP 33.9 — 100.0 0.64
TFPI2 Hibi et al. (2011a) 2011 Japan 0.2 mL serum 73 GC, 20 Control qMSP 9.6 — 100.0 —
TFPI2 Peng et al. (2022) 2022 China 3.5 mL plasma 109 GC, 190 Control qMSP 27.5 — 97.9 0.63
RASSF1A Pimson et al. (2016) 2016 Thailand 0.2 mL plasma 101 GC, 202 Control MSP 83.2 — 94.6 —
RASSF1A Saliminejad et al. (2020) 2020 Iran 2 mL plasma 96 GC, 88 Control MSP 33.3 — 100.0 0.67
SFRP2 Zhang et al. (2014) 2014 China 0.4 mL plasma 57 GC, 42 Control MSP 71.9 — 57.1 —
SFRP2 Miao et al. (2020) 2020 China 3.5 mL plasma 92 GC, 16 IM, 26 GFGP, 13 AP, 39 HP, 50 Control qMSP GC: 60.9, IM: 56.3, GFGP: 34.6, AP: 23.1, HP: 30.8 50.0 86.0 0.78
RPRM Maeda et al. (2018) 2018 Japan Tissue 52 GC, 50 Control Pyrosequencing — — — 0.75
RPRM Saliminejad et al. (2020) 2020 Iran 2 mL plasma 96 GC, 88 Control MSP 66.7 — 93.2 0.80
DAPK Lee et al., 2002) 2002 China (Hong Kong) Tissue 44 GC MSP 70.3 57.1 — —
Serum 44 GC, 30 Control MSP 48.1 28.6 100.0 —
DAPK Hu et al. (2010) 2010 China Tissue 70 GC, 30 Control MSP 60.0 — 100.0 —
GSTP1 Lee et al. (2002) 2002 China (Hong Kong) Tissue 44 GC MSP 18.5 14.3 — —
Serum 44 GC, 30 Control MSP 14.8 14.3 100.0 —
p15 Lee et al. (2002) 2002 China (Hong Kong) Tissue 44 GC MSP 68.5 71.4 — —
Serum 44 GC, 30 Control MSP 55.6 42.9 100.0 —
RARb Koike et al. (2004) 2004 Japan Serum 41 GC, 10 Control MSP 14.6 — 100.0 —
NMDAR2B Liu et al. (2007) 2007 China Tissue 28 GC, 20 Control qMSP 60.7 — 95.0 —
CDH1 Muretto et al. (2008) 2008 Italy Gastric juice 20 GC, 14 Control MSP 65.0 — 100.0 —
MINT25 Watanabe et al. (2009) 2009 United States, Japan and Korea Tissue 22 Control, 40 Dysplasias, 91 GC Pyrosequencing 84.1 — 90.9 0.94
Gastric washes 20 GC, 48 Control Pyrosequencing 90.0 — 95.8 0.96
RORA Watanabe et al. (2009) 2009 United States, Japan and Korea Tissue 22 Control, 40 Dysplasias, 91 GC Pyrosequencing 83.2 — 86.4 0.89
Gastric washes 20 GC, 48 Control Pyrosequencing 60.0 — 85.4 0.71
PRDM5 Watanabe et al. (2009) 2009 United States, Japan and Korea Tissue 22 Control, 40 Dysplasias, 91 GC Pyrosequencing 64.2 — 94.7 0.75
Gastric washes 20 GC, 48 Control Pyrosequencing 65.0 — 93.7 0.83
MLF1 Watanabe et al. (2009) 2009 United States, Japan and Korea Tissue 22 Control, 40 Dysplasias, 91 GC Pyrosequencing 61.8 — 81.8 0.73
Gastric washes 20 GC, 48 Control Pyrosequencing 60.0 — 85.4 0.68
ADAM2 Watanabe et al. (2009) 2009 United States, Japan and Korea Tissue 22 Control, 40 Dysplasias, 91 GC Pyrosequencing 65.9 — 90.0 0.81
Gastric washes 20 GC, 48 Control Pyrosequencing 70.0 — 83.3 0.86
GDNF Watanabe et al. (2009) 2009 United States, Japan and Korea Tissue 22 Control, 40 Dysplasias, 91 GC Pyrosequencing 81.9 — 90.9 0.88
Gastric washes 20 GC, 48 Control Pyrosequencing 65.0 — 89.6 0.74
CHFR Hu et al. (2010) 2010 China Tissue 70 GC, 30 Control MSP 48.6 — 100.0 —
RECK Du et al. (2010) 2010 China Tissue 40 GC, 40 Control MSP 47.5 — 76.5 —
CHRM2 Chen et al. (2012) 2012 China Serum 58 GC, 46 GPL, 30 Control MSP GC: 31.1, GPL: 15.2 — 93.3 —
FAM5C Chen et al. (2012) 2012 China Serum 58 GC, 46 GPL, 30 Control MSP GC: 31.0, GPL: 6.5 — 96.7 —
MYLK Chen et al. (2012) 2012 China Serum 58 GC, 46 GPL, 30 Control MSP GC: 70.7, GPL: 28.3 — 93.3 —
Sox17 Oishi et al. (2012) 2012 Japan Gastric washes 64 GC, 64 Control Pyrosequencing — 20.6 — —
VIM Shirahata et al. (2012) 2012 Japan 0.2 mL serum 74 GC qMSP 33.8 41.2 — —
21 Control
BCL6B Yang et al. (2013) 2013 China (Hong Kong) 1 mL plasma 40 GC, 22 Control Sanger sequencing 42.5 — 100.0 —
DAPK1 Zhang et al. (2014) 2014 China 0.4 mL plasma 57 GC, 42 Control MSP 49.1 — 71.4 —
CDO1 Vedeld et al. (2015) 2015 Norway FFPE 25 GC qMSP 88.0 — — —
DCLK1 Vedeld et al. (2015) 2015 Norway FFPE 25 GC qMSP 96.0 — — —
SFRP1 Vedeld et al. (2015) 2015 Norway FFPE 25 GC qMSP 92.0 — — —
ZNF331 Vedeld et al. (2015) 2015 Norway FFPE 25 GC qMSP 80.0 — — —
ZSCAN18 Vedeld et al. (2015) 2015 Norway FFPE 25 GC qMSP 76.0 — — —
hMLH1 Liu and Yang (2015) 2015 China Plasma 50 IM, 50 Dysplasia, 50 GC, 30 Control MSP IM: 20.0, Dysplasia: 44.0, GC: 48.0 — 96.7 —
PCDH10 Pimson et al. (2016) 2016 Thailand 0.2 mL plasma 101 GC, 202 Control MSP 94.1 — 97.0 —
BARHL2 Yamamoto et al. (2016) 2016 Japan Gastric washes 70 GC, 70 Control Pyrosequencing — — — —
OSR2 Li et al. (2016) 2016 China Tissue 48 GC, 25 Control MSP 70.8 — 96.0 —
0.4 mL serum 48 GC, 25 Control MSP 62.5 — 92.0 —
VAV3 Li et al. (2016) 2016 China Tissue 48 GC, 25 Control MSP 54.2 — 100.0 —
0.4 mL serum 48 GC, 25 Control MSP 45.8 — 100.0 —
PPFIA3 Li et al. (2016) 2016 China Tissue 48 GC, 25 Control MSP 60.4 — 96.0 —
0.4 mL serum 48 GC, 25 Control MSP 56.3 — 96.0 —
HOXD10 Lin et al. (2017) 2017 China 0.4 mL plasma 131 GC, 56 IN, 30 IM, 34 Control MSP GC: 48.1 — 80.0 —
FLT3 Maeda et al. (2018) 2018 Japan Tissue 52 GC, 50 Control Pyrosequencing — — — 0.77
LINC00643 Maeda et al. (2018) 2018 Japan Tissue 52 GC, 50 Control Pyrosequencing — — — 0.76
JAM2 Maeda et al. (2018) 2018 Japan Tissue 52 GC, 50 Control Pyrosequencing — — — 0.73
BHLHE22 Maeda et al. (2018) 2018 Japan Tissue 52 GC, 50 Control Pyrosequencing — — — 0.72
RIMS1 Maeda et al. (2018) 2018 Japan Tissue 52 GC, 50 Control Pyrosequencing — — — 0.76
GUSBP5 Maeda et al. (2018) 2018 Japan Tissue 52 GC, 50 Control Pyrosequencing — — — 0.70
ZNF3 Maeda et al. (2018) 2018 Japan Tissue 52 GC, 50 Control Pyrosequencing — — — 0.80
PAX5 Haghverdi and Moslemi (2018) 2018 Iran Blood 35 GC, 35 Control MSP 28.6 — 100.0 —
SPG20 Wei et al. (2019) 2019 China (Taiwan) 0.5 mL serum 53 GC, 20 control MSP 88.6 — 75.0 —
RPRML Alarcón et al. (2020) 2020 Chile 0.5 mL plasma 25 GC, 25 Control qMSP 56.0 — 88.0 0.73
ZNF582 Peng et al. (2022) 2022 China 3.5 mL plasma 109 GC, 190 Control qMSP 56.0 — 91.1 0.84
KCNQ5 Li et al. (2022) 2022 China 3.5 mL plasma Train cohort qMSP Train cohort:22.6 Train cohort:12.5 Train cohort: 98.5 Train cohort:0.64
53 GC, 67 Control
Validation cohort1 Validation cohort1: 34.6 Validation cohort1:21.4 Validation cohort1:100.0 Validation cohort1: 0.69
55 GC, 50 Control
Validation cohort2 Validation cohort2:22.8 Validation cohort2: 22.2 Validation cohort2: 100.0 Validation cohort2:0.63
57 GC, 82 Control
C9orf50 Li et al. (2022) 2022 China 3.5 mL plasma Train cohort qMSP Train cohort:50.9 Train cohort:37.5 Train cohort: 95.5 Train cohort:0.73
53 GC, 67 Control
Validation cohort1 Validation cohort1:50.9 Validation cohort1:21.4 Validation cohort1:98.0 Validation cohort1:0.74
55 GC, 50 Control
Validation cohort2 Validation cohort2:64.9 Validation cohort2: 44.4 Validation cohort2: 93.9 Validation cohort2:0.82
57 GC, 82 Control
CLIP4 Li et al. (2022) 2022 China 3.5 mL plasma Train cohort qMSP Train cohort:37.7 Train cohort:25.0 Train cohort: 92.5 Train cohort: 0.65
53 GC, 67 Control
Validation cohort1 Validation cohort1:25.5 Validation cohort1:21.4 Validation cohort1:92.0 Validation cohort1: 0.60
55 GC, 50 Control
Validation cohort2 Validation cohort2:43.9 Validation cohort2: 33.3 Validation cohort2: 90.2 Validation cohort2: 0.68
57 GC, 82 Control
Open in a new tab

SN, sensitivity; SP, specificity; AUC, area under the curve; AG, atrophic gastritis; GC, gastric cancer; GID, gastrointestinal disease; NED, no evidence of disease; BGD, benign gastric diseases; IN, intraepithelial neoplasia; IM, intestinal metaplasia; GFGP, gastric fundic gland polyp; AP, small adenoma; HP, hyperplastic polyp; GPL, gastric precancerous lesions; MSP, methylation specific PCR; qMSP, quantitative methylation specific PCR; CORD, combined restriction digital PCR; FFPE, Formalin-Fixed and Parrffin-Embedded; MS-MCA, Methylation-sensitive melt curve analysis.

TABLE 2.

DNA methylation panels for GC detection.

Markers Authors Year Country Sample types Number of subjects Methods SN (%) Stage I SN (%) SP (%) AUC
P16, E-cadherin and RARb Koike et al. (2004) 2004 Japan Serum 41 GC, 10 Control MSP 44.0 36.8 100.0 —
FAM5C and MYLK Chen et al. (2012) 2012 China Serum 58 GC, 46 GPL, 30 Control MSP GC: 77.6, GPL: 30.4 — 90.0 0.84
RNF180, DAPK1 and SFRP2 Zhang et al. (2014) 2014 China 0.4 mL plasma 57 GC, 42 Control MSP 87.7 — 38.1 —
SEPT9 and RNF180 Cao et al. (2020) 2020 China 3.5 mL plasma 74 GC, 57 NED qMSP 40.5 85.3 0.65
SEPT9, RNF180 and CA7-24 Xu et al. (2021b) 2021 China 3.5 mL plasma 151 GC, 56 AG, 87 Other GIDs, 224 NED qMSP 68.6 33.3 85.1 —
Reprimo and hMLH1 Liu and Yang (2015) 2015 China Plasma 50 IM, 50 Dysplasia MSP IM: 34.0, Dysplasia: 76.0, GC: 84.0 — 96.7 —
50 GC, 30 Control
PYCARD, APAF1, MINT1, and BRCA1 Shin et al. (2016) 2016 Korea 2 mL plasma 41 GC, 104 Control MSP 97.6 — 66.3 —
OSR2, VAV3, and PPFIA3 Li et al. (2016) 2016 China 0.4 mL serum 48 GC, 25 Control MSP 83.3 — 88.0 —
ZIC1, HOXD10 and RUNX3 Lin et al. (2017) 2017 China 0.4 mL plasma 131 GC, 56 IN, 30 IM, 34 Control MSP 91.6 — 50.0 —
ELMO1, ZNF569 and C13orf18 Anderson et al. (2018) 2018 United States 2 mL plasma 36 GC, 38 Control qMSP 86.0 — 95.0 —
153 cfDNA methylation biomarkers Ren et al. (2022) 2021 China Plasma 89 GC, 82 Control MCTA-Seq 67.0 44.0 92.0 0.87
KCNQ5, C9orf50 and CLIP4 Li et al. (2022) 2022 China 3.5 mL plasma Train cohort: 53 GC, 67 Control qMSP Train cohort:67.9 Train cohort:62.5 Train cohort: 86.6 Train cohort:0.79
Validation cohort1: 55 GC, 50 Control Validation cohort1: 65.5 Validation cohort1:42.9 Validation cohort1:90.0 Validation cohort1: 0.81
Validation cohort2: 57 GC, 82 Control Validation cohort2:73.7 Validation cohort2: 55.6 Validation cohort2: 84.1 Validation cohort2:0.85
Seven methylation marker panel Ruan et al. (2023) 2023 China Plasma Traing cohort: 54 GC, 79 Control qMSP Traing cohort: 80.0 Traing cohort: 80.0 Traing cohort: 65.0 —
Validation cohort: 117 GC, 309 Control Validation cohort: 82.0 Validation cohort: 78.0 Validation cohort: 69.0
Open in a new tab

SN, sensitivity; SP, specificity; AUC, area under the curve; AG, atrophic gastritis; GC, gastric cancer; GID, gastrointestinal disease; NED, no evidence of disease; BGD, benign gastric diseases; IN, intraepithelial neoplasia; IM, intestinal metaplasia; MSP, methylation specific PCR; qMSP, quantitative methylation specific PCR; MCTA-Seq, methylated CpG tandem amplification and sequencing; cfDNA, cell-free DNA.

TABLE 3.

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

Cancer types Markers Authors Year Country Sample types Number of subjects Methods SN (%) GC SN (%) SP (%) AUC TOO (%)
GC, CRC RASSF2 and SFRP2 Nagasaka et al. (2009) 2009 Japan 0.1 g stool 21 GC, 84 CRC, 27 AA, 113 control Hi-SA 67.6 44.4 89.4 0.78 —
GC, CRC, EC, HCC and LC cfDNA methylation Chen et al. (2020) 2020 China 1 mL plasma Training cohort: 207 healthy, 203 cancers Targeted bisulfite sequencing Training cohort: 88.2–91.4 — Training cohort: 94.7 — —
Validation cohort: 207 healthy, 211 cancers Validation cohort:87.6–94.9 Validation cohort: 96.1
12 cancer classes (anus, bladder, colon/rectum, esophagus, head and neck, liver/bile duct, lung, lymphoma, ovary, pancreas, plasma cell neoplasm, and stomach) Targeted methylation Klein et al. (2021) 2021 United States 10 mL plasma 15,254 participants (56% with cancer, and 44% without cancer), included 30 GC Bisulfite sequencing 51.5 66.7 99.5 — 88.7
CRC, HCC, ESCC, GC, EAC, PC cfDNA methylation Kandimalla et al. (2021) 2021 United States 1–2 mL plasma 46 healthy, 40 CRC, 74 PC, 43 HCC, 12 EAC, 48 ESCC, 37 GC Targeted bisulfite sequencing — — 96.0 0.88 0.53–0.94
GC, EJC and EC ELMO1, ZNF582 and TFPI2 Peng et al. (2022) 2022 China 3.5 mL plasma 109 GC, 29 EJC, 48 EC, 190 Control qMSP 71.0 67.9 90.0 0.87 —
GC, CRC and EC Six methylation biomarkers Ma et al. (2022a) 2022 China 3.5 mL plasma 136 GC, 98 CRC, 48 EC, 195 Control qMSP 76.6 69.9 89.2 0.90 —
GC and CRC SDC2, TFPI2, WIF1 and NDRG4 Ma et al. (2022b) 2022 China 3 g stool 35 GC, 39 CRC, 6 AA, 107 healthy, 30 other cancers qMSP 68.8 67.5 97.8 — —
CRC, GC, HCC, EC, and PC cfDNA methylation and fragmentation signatures Yang et al. (2023) 2023 China Plasma 787 healthy, 342 HCC, 239 GC, 209 EC, 180 CRC, and 87 PC Targeted bisulfite sequencing 86.2 70.3 96.7 0.96 82.0
Open in a new tab

SN, sensitivity; SP, specificity; AUC, area under the curve; GC, gastric cancer; CRC, colorectal cancer; EC, esophageal cancer; HCC, hepatocellular carcinoma; LC, lung cancer; PC, pancreatic adenocarcinoma; EJC, esophagogastric junction cancer; ESCC, esophageal squamous cell carcinoma; EAC, esophageal adenocarcinoma; AA, advanced adenomas; TOO, tissue of origin; MSP, methylation specific PCR; qMSP, quantitative methylation specific PCR; cfDNA, cell-free DNA; Hi-SA, Fluoroscence High-sensitivity assay for bisulfite DNA.

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).

3 DNA methylation markers individually evaluated for GC detection

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).

4 DNA methylation panels for GC detection

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.

5 DNA methylation-based pan-cancer test for GC detection

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).

6 Challenges of GC early detection using DNA methylation

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.

FIGURE 1

Open in a new tab

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.

7 Future of GC early detection using DNA methylation

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.

FIGURE 2

Open in a new tab

The potential standardized flowchart for plasma DNA methylation analysis.

8 Conclusion

In conclusion, DNA methylation as a robust and sensitive marker has also been widely studied in GC samples, numerous potential DNA methylation markers have already been identified in GC, and some of them have been developed as commercial kits. But the lack of early-stage GC sensitivity and specificity should be improved in the future, and the standardized sampling, pre-analytical, cfDNA isolation and conversion procedures must be considered in development of the assay. With the rapid development of new technology and the discovery of more methylation markers, it is expected that DNA methylation will become a cost-effective and non-invasive tool for GC early detection in the near future.

Funding Statement

This work was supported by Suzhou Science and Technology Development Plan Project (Grant No. SKJY2021011) and Key Technologies R & D Program for Social Development of Kunshan (Grant No. KS2228).

Author contributions

Writing—original draft, YX and YD; writing—review and editing, CH; supervision, BP and ZW; approval of final manuscript, YX, CH, and YD. All authors contributed to the article and approved the submitted version.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Glossary

SN Sensitivity
SP Specificity
AUC Area Under the Curve
AG Atrophic Gastritis
GC Gastric Cancer
CRC Colorectal Cancer
EC Esophageal Cancer
HCC Hepatocellular Carcinoma
LC Lung Cancer
PC Pancreatic Adenocarcinoma
EJC Esophagogastric Junction Cancer
ESCC Esophageal Squamous Cell Carcinoma
EAC Esophageal Adenocarcinoma
AA Advanced Adenomas
GID gastrointestinal Disease
NED No Evidence of Disease
BGD Bbenign Gastric Diseases
IN Intraepithelial Neoplasia
IM Intestinal Metaplasia
GFGP Gastric Fundic Gland Polyp
AP Small Adenoma
HP Hyperplastic Polyp
MSP Methylation Specific PCR
qMSP quantitative Methylation Specific PCR
CORD Combined Restriction Digital PCR
FFPE Formalin-Fixed and Parrffin-Embedded
ctDNA circulating tumor DNA
cfDNA cell-free DNA
MCTA-Seq Methylated CpG Tandem Amplification and Sequencing
TOO Tissue of Origin
PPV Positive Predictive Value
NPV Negative Predictive Value
FDA Food and Drug Administration
NMPA Chinese National Medical Products Administration
Open in a new tab

References

  1. Abbaszadegan M. R., Moaven O., Sima H. R., Ghafarzadegan K., A'rabi A., Forghani M. N., et al. (2008). p16 promoter hypermethylation: a useful serum marker for early detection of gastric cancer. World J. Gastroenterology 14, 2055–2060. 10.3748/wjg.14.2055 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Abbosh C., Birkbak N. J., Swanton C. (2018). Early stage NSCLC - challenges to implementing ctDNA-based screening and MRD detection. Nat. Rev. Clin. Oncol. 15, 577–586. 10.1038/s41571-018-0058-3 [DOI] [PubMed] [Google Scholar]
  3. Alarcón M. A., Olivares W., Córdova-Delgado M., Muñoz-Medel M., de Mayo T., Carrasco-Aviño G., et al. (2020). The reprimo-like gene is an epigenetic-mediated tumor suppressor and a candidate biomarker for the non-invasive detection of gastric cancer. Int. J. Mol. Sci. 21, 9472. 10.3390/ijms21249472 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Anderson B. W., Suh Y. S., Choi B., Lee H. J., Yab T. C., Taylor W. R., et al. (2018). Detection of gastric cancer with novel methylated DNA markers: Discovery, tissue validation, and pilot testing in plasma. Clin. cancer Res. official J. Am. Assoc. Cancer Res. 24, 5724–5734. 10.1158/1078-0432.Ccr-17-3364 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bernal C., Aguayo F., Villarroel C., Vargas M., Díaz I., Ossandon F. J., et al. (2008). Reprimo as a potential biomarker for early detection in gastric cancer. Clin. cancer Res. official J. Am. Assoc. Cancer Res. 14, 6264–6269. 10.1158/1078-0432.Ccr-07-4522 [DOI] [PubMed] [Google Scholar]
  6. Cai G., Cai M., Feng Z., Liu R., Liang L., Zhou P., et al. (2021). A multilocus blood-based assay targeting circulating tumor DNA methylation enables early detection and early relapse prediction of colorectal cancer. Gastroenterology 161, 2053–2056.e2. e2052. 10.1053/j.gastro.2021.08.054 [DOI] [PubMed] [Google Scholar]
  7. Cao C. Q., Chang L., Wu Q. (2020). Circulating methylated Septin 9 and ring finger protein 180 for noninvasive diagnosis of early gastric cancer. Transl. Cancer Res. 9, 7012–7021. 10.21037/tcr-20-1330 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Chen L., Su L., Li J., Zheng Y., Yu B., Yu Y., et al. (2012). Hypermethylated FAM5C and MYLK in serum as diagnosis and pre-warning markers for gastric cancer. Dis. markers 32, 195–202. 10.3233/dma-2011-0877 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Chen X., Lin Z., Xue M., Si J., Chen S. (2015). Zic1 promoter hypermethylation in plasma DNA is a potential biomarker for gastric cancer and intraepithelial neoplasia. PloS one 10, e0133906. 10.1371/journal.pone.0133906 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Chen X., Gole J., Gore A., He Q., Lu M., Min J., et al. (2020). Non-invasive early detection of cancer four years before conventional diagnosis using a blood test. Nat. Commun. 11, 3475. 10.1038/s41467-020-17316-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Cheung K. F., Lam C. N., Wu K., Ng E., Chong W. W. S., Cheng A. S. L., et al. (2012). Characterization of the gene structure, functional significance, and clinical application of RNF180, a novel gene in gastric cancer. Cancer 118, 947–959. 10.1002/cncr.26189 [DOI] [PubMed] [Google Scholar]
  12. Church T. R., Wandell M., Lofton-Day C., Mongin S. J., Burger M., Payne S. R., et al. (2014). Prospective evaluation of methylated SEPT9 in plasma for detection of asymptomatic colorectal cancer. Gut 63, 317–325. 10.1136/gutjnl-2012-304149 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Cox D. R., Wong B. K. L., Lee E., Testro A., Muralidharan V., Dobrovic A., et al. (2022). Evaluating DNA recovery efficiency following bisulphite modification from plasma samples submitted for cell-free DNA methylation analysis. Epigenetics 17, 1956–1960. 10.1080/15592294.2022.2091821 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Davalos V., Esteller M. (2022). Cancer epigenetics in clinical practice. CA a cancer J. Clin. 73, 376–424. 10.3322/caac.21765 [DOI] [PubMed] [Google Scholar]
  15. Du Y. Y., Dai D. Q., Yang Z. (2010). Role of RECK methylation in gastric cancer and its clinical significance. World J. gastroenterology 16, 904–908. 10.3748/wjg.v16.i7.904 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Duffy M. J., Diamandis E. P., Crown J. (2021). Circulating tumor DNA (ctDNA) as a pan-cancer screening test: Is it finally on the horizon? Clin. Chem. Laboratory Med. 59, 1353–1361. 10.1515/cclm-2021-0171 [DOI] [PubMed] [Google Scholar]
  17. Haghverdi M. K., Moslemi E. (2018). Expression rate and PAX5 gene methylation in the blood of people suffering from gastric cancer. Open access Macedonian J. Med. Sci. 6, 1571–1576. 10.3889/oamjms.2018.269 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Hamashima C. (2020). The burden of gastric cancer. Ann. Transl. Med. 8, 734. 10.21037/atm.2020.03.166 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Hibi K., Goto T., Shirahata A., Saito M., Kigawa G., Nemoto H., et al. (2011a). Detection of TFPI2 methylation in the serum of gastric cancer patients. Anticancer Res. 31, 3835–3838. [PubMed] [Google Scholar]
  20. Hibi K., Goto T., Shirahata A., Saito M., Kigawa G., Nemoto H., et al. (2011b). Detection of TFPI2 methylation in the serum of colorectal cancer patients. Cancer Lett. 311, 96–100. 10.1016/j.canlet.2011.07.006 [DOI] [PubMed] [Google Scholar]
  21. Hideura E., Suehiro Y., Nishikawa J., Shuto T., Fujimura H., Ito S., et al. (2020). Blood free-circulating DNA testing of methylated RUNX3 is useful for diagnosing early gastric cancer. Cancers 12, 789. 10.3390/cancers12040789 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Hou P., Ji M. J., Shen J. Y., Lu Z. H. (2005). Detection of p16 hypermethylation in gastric carcinomas using a seminested methylation-specific PCR. Biochem. Genet. 43, 1–9. 10.1007/s10528-005-1062-8 [DOI] [PubMed] [Google Scholar]
  23. Hu S. L., Kong X. Y., Cheng Z. D., Sun Y. B., Shen G., Xu W. P., et al. (2010). Promoter methylation of p16, Runx3, DAPK and CHFR genes is frequent in gastric carcinoma. Tumori 96, 726–733. 10.1177/030089161009600515 [DOI] [PubMed] [Google Scholar]
  24. Huang J., Wang G., Tang J., Zhuang W., Wang L. P., Liou Y. L., et al. (2017). DNA methylation status of PAX1 and ZNF582 in esophageal squamous cell carcinoma. Int. J. Environ. Res. public health 14, 216. 10.3390/ijerph14020216 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Ichikawa D., Koike H., Ikoma H., Ikoma D., Tani N., Otsuji E., et al. (2004). Detection of aberrant methylation as a tumor marker in serum of patients with gastric cancer. Anticancer Res. 24, 2477–2481. [PubMed] [Google Scholar]
  26. Imperiale T. F., Ransohoff D. F., Itzkowitz S. H., Levin T. R., Lavin P., Lidgard G. P., et al. (2014). Multitarget stool DNA testing for colorectal-cancer screening. N. Engl. J. Med. 370, 1287–1297. 10.1056/NEJMoa1311194 [DOI] [PubMed] [Google Scholar]
  27. Jamshidi A., Liu M. C., Klein E. A., Venn O., Hubbell E., Beausang J. F., et al. (2022). Evaluation of cell-free DNA approaches for multi-cancer early detection. Cancer Cell. 40, 1537–1549.e12. e1512. 10.1016/j.ccell.2022.10.022 [DOI] [PubMed] [Google Scholar]
  28. Jensen S., Øgaard N., Ørntoft M. W., Rasmussen M. H., Bramsen J. B., Kristensen H., et al. (2019). Novel DNA methylation biomarkers show high sensitivity and specificity for blood-based detection of colorectal cancer-a clinical biomarker discovery and validation study. Clin. epigenetics 11, 158. 10.1186/s13148-019-0757-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Kandimalla R., Xu J., Link A., Matsuyama T., Yamamura K., Parker M. I., et al. (2021). EpiPanGI dx: A cell-free DNA methylation fingerprint for the early detection of gastrointestinal cancers. Clin. cancer Res. official J. Am. Assoc. Cancer Res. 27, 6135–6144. 10.1158/1078-0432.Ccr-21-1982 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Kanyama Y., Hibi K., Nakayama H., Kodera Y., Ito K., Akiyama S., et al. (2003). Detection of p16 promoter hypermethylation in serum of gastric cancer patients. Cancer Sci. 94, 418–420. 10.1111/j.1349-7006.2003.tb01457.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Kerachian M. A., Azghandi M., Mozaffari-Jovin S., Thierry A. R. (2021). Guidelines for pre-analytical conditions for assessing the methylation of circulating cell-free DNA. Clin. epigenetics 13, 193. 10.1186/s13148-021-01182-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Klein E. A., Richards D., Cohn A., Tummala M., Lapham R., Cosgrove D., et al. (2021). Clinical validation of a targeted methylation-based multi-cancer early detection test using an independent validation set. Ann. Oncol. official J. Eur. Soc. Med. Oncol. 32, 1167–1177. 10.1016/j.annonc.2021.05.806 [DOI] [PubMed] [Google Scholar]
  33. Koike H., Ichikawa D., Ikoma H., Otsuji E., Kitamura K., Yamagishi H. (2004). Comparison of methylation-specific polymerase chain reaction (MSP) with reverse transcriptase-polymerase chain reaction (RT-PCR) in peripheral blood of gastric cancer patients. J. Surg. Oncol. 87, 182–186. 10.1002/jso.20106 [DOI] [PubMed] [Google Scholar]
  34. Kurdyukov S., Bullock M. (2016). DNA methylation analysis: Choosing the right method. Biology 5, 3. 10.3390/biology5010003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Lee T. L., Leung W. K., Chan M. W., Ng E., Tong J. H. M., Lo K. W., et al. (2002). Detection of gene promoter hypermethylation in the tumor and serum of patients with gastric carcinoma. Clin. cancer Res. official J. Am. Assoc. Cancer Res. 8, 1761–1766. [PubMed] [Google Scholar]
  36. Lee J. S., Kim M., Seong M. W., Kim H. S., Lee Y. K., Kang H. J. (2020). Plasma vs. serum in circulating tumor DNA measurement: Characterization by DNA fragment sizing and digital droplet polymerase chain reaction. Clin. Chem. laboratory Med. 58, 527–532. 10.1515/cclm-2019-0896 [DOI] [PubMed] [Google Scholar]
  37. Li W. H., Zhou Z. J., Huang T. H., Guo K., Chen W., Wang Y., et al. (2016). Detection of OSR2, VAV3, and PPFIA3 methylation in the serum of patients with gastric cancer. Dis. markers 2016, 5780538. 10.1155/2016/5780538 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Li H., Wang Z., Zhao G., Ma Y., Chen Y., Xue Q., et al. (2019). Performance of a MethyLight assay for methylated SFRP2 DNA detection in colorectal cancer tissue and serum. Int. J. Biol. markers 34, 54–59. 10.1177/1724600818820536 [DOI] [PubMed] [Google Scholar]
  39. Li H., Zhao G., Guo Y., Fang Y., Wang K., Ma Y., et al. (2022). Feasibility and reproducibility of a plasma-based multiplex DNA methylation assay for early detection of gastric cancer. Pathology, Res. Pract. 238, 154086. 10.1016/j.prp.2022.154086 [DOI] [PubMed] [Google Scholar]
  40. Lin Z., Luo M., Chen X., He X., Qian Y., Lai S., et al. (2017). Combined detection of plasma ZIC1, HOXD10 and RUNX3 methylation is a promising strategy for early detection of gastric cancer and precancerous lesions. J. Cancer 8, 1038–1044. 10.7150/jca.18169 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Lin W. H., Xiao J., Ye Z. Y., Wei D. L., Zhai X. H., Xu R. H., et al. (2021). Circulating tumor DNA methylation marker MYO1-G for diagnosis and monitoring of colorectal cancer. Clin. epigenetics 13, 232. 10.1186/s13148-021-01216-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Liu L., Yang X. (2015). Implication of Reprimo and hMLH1 gene methylation in early diagnosis of gastric carcinoma. Int. J. Clin. Exp. pathology 8, 14977–14982. [PMC free article] [PubMed] [Google Scholar]
  43. Liu J. W., Kim M. S., Nagpal J., Yamashita K., Poeta L., Chang X., et al. (2007). Quantitative hypermethylation of NMDAR2B in human gastric cancer. Int. J. cancer 121, 1994–2000. 10.1002/ijc.22934 [DOI] [PubMed] [Google Scholar]
  44. Liu Y., Zhang Y., Dong P., An R., Xue C., Ge Y., et al. (2015). Digestion of nucleic acids starts in the stomach. Sci. Rep. 5, 11936. 10.1038/srep11936 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Liu Y., Zhao G., Miao J., Li H., Ma Y., Liu X., et al. (2020). Performance comparison between plasma and stool methylated SEPT9 tests for detecting colorectal cancer. Front. Genet. 11, 324. 10.3389/fgene.2020.00324 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Locke W. J., Guanzon D., Ma C., Liew Y. J., Duesing K. R., Fung K. Y. C., et al. (2019). DNA methylation cancer biomarkers: Translation to the clinic. Front. Genet. 10, 1150. 10.3389/fgene.2019.01150 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Luo H., Zhao Q., Wei W., Zheng L., Yi S., Li G., et al. (2020). Circulating tumor DNA methylation profiles enable early diagnosis, prognosis prediction, and screening for colorectal cancer. Sci. Transl. Med. 12, eaax7533. 10.1126/scitranslmed.aax7533 [DOI] [PubMed] [Google Scholar]
  48. Ma Y., Zhao G., Wang K., Song L., Xiong S., Li H., et al. (2022a). Clinical performance of a liquid biopsy test based on the detection of multiple DNA methylation biomarkers for early detection of gastrointestinal cancers. J. Clin. Oncol. 40, e16096. 10.1200/JCO.2022.40.16_suppl.e16096 [DOI] [Google Scholar]
  49. Ma L., Gong J., Zhao M., Kong X., Gao P., Jiang Y., et al. (2022b). A novel stool methylation test for the non-invasive screening of gastric and colorectal cancer. Front. Oncol. 12, 860701. 10.3389/fonc.2022.860701 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Maeda M., Yamashita S., Shimazu T., Iida N., Takeshima H., Nakajima T., et al. (2018). Novel epigenetic markers for gastric cancer risk stratification in individuals after Helicobacter pylori eradication. Gastric cancer official J. Int. Gastric Cancer Assoc. Jpn. Gastric Cancer Assoc. 21, 745–755. 10.1007/s10120-018-0803-4 [DOI] [PubMed] [Google Scholar]
  51. Mao R., Chou L. S. (2010). Methylation analysis by restriction endonuclease digestion and real-time PCR. Clin. Chem. 56, 1050–1052. 10.1373/clinchem.2010.146654 [DOI] [PubMed] [Google Scholar]
  52. Meddeb R., Pisareva E., Thierry A. R. (2019). Guidelines for the preanalytical conditions for analyzing circulating cell-free DNA. Clin. Chem. 65, 623–633. 10.1373/clinchem.2018.298323 [DOI] [PubMed] [Google Scholar]
  53. Miao J., Liu Y., Zhao G., Liu X., Ma Y., Li H., et al. (2020). Feasibility of plasma-methylated SFRP2 for early detection of gastric cancer. Cancer control J. Moffitt Cancer Cent. 27, 559. 10.1177/1073274820922559 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Mouliere F., Chandrananda D., Piskorz A. M., Moore E. K., Morris J., Ahlborn L. B., et al. (2018). Enhanced detection of circulating tumor DNA by fragment size analysis. Sci. Transl. Med. 10, eaat4921. 10.1126/scitranslmed.aat4921 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Muretto P., Ruzzo A., Pizzagalli F., Graziano F., Maltese P., Zingaretti C., et al. (2008). Endogastric capsule for E-cadherin gene (CDH1) promoter hypermethylation assessment in DNA from gastric juice of diffuse gastric cancer patients. Ann. Oncol. official J. Eur. Soc. Med. Oncol. 19, 516–519. 10.1093/annonc/mdm493 [DOI] [PubMed] [Google Scholar]
  56. Nagasaka T., Tanaka N., Cullings H. M., Sun D. S., Sasamoto H., Uchida T., et al. (2009). Analysis of fecal DNA methylation to detect gastrointestinal neoplasia. J. Natl. Cancer Inst. 101, 1244–1258. 10.1093/jnci/djp265 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Nassar F. J., Msheik Z. S., Nasr R. R., Temraz S. N. (2021). Methylated circulating tumor DNA as a biomarker for colorectal cancer diagnosis, prognosis, and prediction. Clin. epigenetics 13, 111. 10.1186/s13148-021-01095-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Nikolaou S., Qiu S., Fiorentino F., Rasheed S., Tekkis P., Kontovounisios C. (2018). Systematic review of blood diagnostic markers in colorectal cancer. Tech. coloproctology 22, 481–498. 10.1007/s10151-018-1820-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Ning F. L., Lyu J., Pei J. P., Gu W. J., Zhang N. N., Cao S. Y., et al. (2022). The burden and trend of gastric cancer and possible risk factors in five Asian countries from 1990 to 2019. Sci. Rep. 12, 5980. 10.1038/s41598-022-10014-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Oishi Y., Watanabe Y., Yoshida Y., Sato Y., Hiraishi T., Oikawa R., et al. (2012). Hypermethylation of Sox17 gene is useful as a molecular diagnostic application in early gastric cancer. Tumour Biol. J. Int. Soc. Oncodevelopmental Biol. Med. 33, 383–393. 10.1007/s13277-011-0278-y [DOI] [PubMed] [Google Scholar]
  61. Olson J., Whitney D. H., Durkee K., Shuber A. P. (2005). DNA stabilization is critical for maximizing performance of fecal DNA-based colorectal cancer tests. Diagnostic Mol. pathology Am. J. Surg. pathology, part B 14, 183–191. 10.1097/01.pas.0000176768.18423.7e [DOI] [PubMed] [Google Scholar]
  62. Patel T. N., Roy S., Ravi R. (2017). Gastric cancer and related epigenetic alterations. Ecancermedicalscience 11, 714. 10.3332/ecancer.2017.714 [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Peng C., Zhao G., Pei B., Wang K., Li H., Fei S., et al. (2022). A novel plasma-based methylation panel for upper gastrointestinal cancer early detection. Cancers 14, 5282. 10.3390/cancers14215282 [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Pimson C., Ekalaksananan T., Pientong C., Promthet S., Putthanachote N., Suwanrungruang K., et al. (2016). Aberrant methylation of PCDH10 and RASSF1A genes in blood samples for non-invasive diagnosis and prognostic assessment of gastric cancer. PeerJ 4, e2112. 10.7717/peerj.2112 [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Pittella-Silva F., Chin Y. M., Chan H. T., Nagayama S., Miyauchi E., Low S. K., et al. (2020). Plasma or serum: Which is preferable for mutation detection in liquid biopsy? Clin. Chem. 66, 946–957. 10.1093/clinchem/hvaa103 [DOI] [PubMed] [Google Scholar]
  66. Potter N. T., Hurban P., White M. N., Whitlock K. D., Lofton-Day C. E., Tetzner R., et al. (2014). Validation of a real-time PCR-based qualitative assay for the detection of methylated SEPT9 DNA in human plasma. Clin. Chem. 60, 1183–1191. 10.1373/clinchem.2013.221044 [DOI] [PubMed] [Google Scholar]
  67. Qian C. N. (2017). At-home cancer screening: A solution for China and other developing countries with a large population and limited number of healthcare practitioners. Chin. J. cancer 36, 68. 10.1186/s40880-017-0235-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Rahat B., Ali T., Sapehia D., Mahajan A., Kaur J. (2020). Circulating cell-free nucleic acids as epigenetic biomarkers in precision medicine. Front. Genet. 11, 844. 10.3389/fgene.2020.00844 [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Ramalho-Carvalho J., Henrique R., Jerónimo C. (2018). “Methylation-specific PCR,” in DNA methylation protocols. Editor Tost J. (New York, NY: Springer New York; ), 447–472. [DOI] [PubMed] [Google Scholar]
  70. Ren J., Lu P., Zhou X., Liao Y., Liu X., Li J., et al. (2022). Genome-scale methylation analysis of circulating cell-free DNA in gastric cancer patients. Clin. Chem. 68, 354–364. 10.1093/clinchem/hvab204 [DOI] [PubMed] [Google Scholar]
  71. Ruan W., Lei X., Sha W., Li B., Wang H., Xia Y., et al. (2023). A multiplex DNA methylation assay for noninvasive early detection of gastric cancer. J. Clin. Oncol. 41, 300. 10.1200/JCO.2023.41.4_suppl.300 [DOI] [Google Scholar]
  72. Sakakura C., Hamada T., Miyagawa K., Nishio M., Miyashita A., Nagata H., et al. (2009). Quantitative analysis of tumor-derived methylated RUNX3 sequences in the serum of gastric cancer patients. Anticancer Res. 29, 2619–2625. [PubMed] [Google Scholar]
  73. Saliminejad K., Soleymani Fard S., Khorram Khorshid H. R., Yaghmaie M., Mahmoodzadeh H., Mousavi S. A., et al. (2020). Methylation analysis of P16, RASSF1A, RPRM, and RUNX3 in circulating cell-free DNA for detection of gastric cancer: A validation study. Avicenna J. Med. Biotechnol. 12, 99–106. [PMC free article] [PubMed] [Google Scholar]
  74. Salta S., Macedo-Silva C., Miranda-Gonçalves V., Lopes N., Gigliano D., Guimarães R., et al. (2020). A DNA methylation-based test for esophageal cancer detection. Biomark. Res. 8, 68. 10.1186/s40364-020-00248-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Sekiguchi M., Oda I., Matsuda T., Saito Y. (2022). Epidemiological trends and future perspectives of gastric cancer in eastern asia. Digestion 103, 22–28. 10.1159/000518483 [DOI] [PubMed] [Google Scholar]
  76. Shin D. G., Kim K., Choi S., Joo H. J., Baik S. H., Park M. (2016). A methylation profile of circulating cell free DNA for the early detection of gastric cancer and the effects after surgical resection. J. Clin. Exp. Oncol. 5. 10.4172/2324-9110.1000151 [DOI] [Google Scholar]
  77. Shirahata A., Sakuraba K., Kitamura Y., Yokomizo K., Gotou T., Saitou M., et al. (2012). Detection of vimentin methylation in the serum of patients with gastric cancer. Anticancer Res. 32, 791–794. [PubMed] [Google Scholar]
  78. Sigalotti L., Covre A., Colizzi F., Fratta E. (2019). Quantitative methylation-specific PCR: A simple method for studying epigenetic modifications of cell-free DNA. Methods Mol. Biol. Clift. NJ) 1909, 137–162. 10.1007/978-1-4939-8973-7_11 [DOI] [PubMed] [Google Scholar]
  79. Song Y., Liu X., Cheng W., Li H., Zhang D. (2022). The global, regional and national burden of stomach cancer and its attributable risk factors from 1990 to 2019. Sci. Rep. 12, 11542. 10.1038/s41598-022-15839-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Sorber L., Zwaenepoel K., Deschoolmeester V., Roeyen G., Lardon F., Rolfo C., et al. (2017). A comparison of cell-free DNA isolation kits: Isolation and quantification of cell-free DNA in plasma. J. Mol. diagnostics JMD 19, 162–168. 10.1016/j.jmoldx.2016.09.009 [DOI] [PubMed] [Google Scholar]
  81. Sung H., Ferlay J., Siegel R. L., Laversanne M., Soerjomataram I., Jemal A., et al. (2021). Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA a cancer J. Clin. 71, 209–249. 10.3322/caac.21660 [DOI] [PubMed] [Google Scholar]
  82. Vedeld H. M., Andresen K., Eilertsen I. A., Nesbakken A., Seruca R., Gladhaug I. P., et al. (2015). The novel colorectal cancer biomarkers CDO1, ZSCAN18 and ZNF331 are frequently methylated across gastrointestinal cancers. Int. J. cancer 136, 844–853. 10.1002/ijc.29039 [DOI] [PMC free article] [PubMed] [Google Scholar]
  83. Volckmar A. L., Sültmann H., Riediger A., Fioretos T., Schirmacher P., Endris V., et al. (2018). A field guide for cancer diagnostics using cell-free DNA: From principles to practice and clinical applications. Genes., chromosomes cancer 57, 123–139. 10.1002/gcc.22517 [DOI] [PubMed] [Google Scholar]
  84. Wang H., Zheng Y., Lai J., Luo Q., Ke H., Chen Q. (2016). Methylation-sensitive melt curve analysis of the Reprimo gene methylation in gastric cancer. PloS one 11, e0168635. 10.1371/journal.pone.0168635 [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Wang J., Liu S., Wang H., Zheng L., Zhou C., Li G., et al. (2020). Robust performance of a novel stool DNA test of methylated SDC2 for colorectal cancer detection: A multicenter clinical study. Clin. epigenetics 12, 162. 10.1186/s13148-020-00954-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. Watanabe Y., Kim H. S., Castoro R. J., Chung W., Estecio M. R. H., Kondo K., et al. (2009). Sensitive and specific detection of early gastric cancer with DNA methylation analysis of gastric washes. Gastroenterology 136, 2149–2158. 10.1053/j.gastro.2009.02.085 [DOI] [PMC free article] [PubMed] [Google Scholar]
  87. Wei K. L., Chou J. L., Chen Y. C., Jin H., Chuang Y. M., Wu C. S., et al. (2019). Methylomics analysis identifies a putative STAT3 target, SPG20, as a noninvasive epigenetic biomarker for early detection of gastric cancer. PloS one 14, e0218338. 10.1371/journal.pone.0218338 [DOI] [PMC free article] [PubMed] [Google Scholar]
  88. Wei B., Wu F., Xing W., Sun H., Yan C., Zhao C., et al. (2021). A panel of DNA methylation biomarkers for detection and improving diagnostic efficiency of lung cancer. Sci. Rep. 11, 16782. 10.1038/s41598-021-96242-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  89. Wolf A. M. D., Fontham E. T. H., Church T. R., Flowers C. R., Guerra C. E., LaMonte S. J., et al. (2018). Colorectal cancer screening for average-risk adults: 2018 guideline update from the American cancer society. CA a cancer J. Clin. 68, 250–281. 10.3322/caac.21457 [DOI] [PubMed] [Google Scholar]
  90. Worm Ørntoft M. B., Jensen S., Hansen T. B., Bramsen J. B., Andersen C. L. (2017). Comparative analysis of 12 different kits for bisulfite conversion of circulating cell-free DNA. Epigenetics 12, 626–636. 10.1080/15592294.2017.1334024 [DOI] [PMC free article] [PubMed] [Google Scholar]
  91. Worm Ørntoft M. B. (2018). Review of blood-based colorectal cancer screening: How far are circulating cell-free DNA methylation markers from clinical implementation? Clin. colorectal cancer 17, e415–e433. 10.1016/j.clcc.2018.02.012 [DOI] [PubMed] [Google Scholar]
  92. Wu D., Zhou G., Jin P., Zhu J., Li S., Wu Q., et al. (2016). Detection of colorectal cancer using a simplified SEPT9 gene methylation assay is a reliable method for opportunistic screening. J. Mol. diagnostics JMD 18, 535–545. 10.1016/j.jmoldx.2016.02.005 [DOI] [PubMed] [Google Scholar]
  93. Xu Y., Zhang P., Zhang K., Huang C. (2021a). The application of CA72-4 in the diagnosis, prognosis, and treatment of gastric cancer. Biochimica biophysica acta Rev. cancer 1876, 188634. 10.1016/j.bbcan.2021.188634 [DOI] [PubMed] [Google Scholar]
  94. Xu J., Song J., Wang T., Zhu W., Zuo L., Wu J., et al. (2021b). A combination of methylation and protein markers is capable of detecting gastric cancer detection by combined markers. Epigenomics 13, 1557–1570. 10.2217/epi-2021-0080 [DOI] [PubMed] [Google Scholar]
  95. Yamaguchi Y., Nagata Y., Hiratsuka R., Kawase Y., Tominaga T., Takeuchi S., et al. (2016). Gastric cancer screening by combined assay for serum anti-Helicobacter pylori IgG antibody and serum pepsinogen levels-the ABC method. Digestion 93, 13–18. 10.1159/000441742 [DOI] [PubMed] [Google Scholar]
  96. Yamamoto H., Watanabe Y., Oikawa R., Morita R., Yoshida Y., Maehata T., et al. (2016). BARHL2 methylation using gastric wash DNA or gastric juice exosomal DNA is a useful marker for early detection of gastric cancer in an H. Pylori-independent manner. Clin. Transl. gastroenterology 7, e184. 10.1038/ctg.2016.40 [DOI] [PMC free article] [PubMed] [Google Scholar]
  97. Yang Q., Gao J., Xu L., Zeng Z., Sung J. J. Y., Yu J. (2013). Promoter hypermethylation of BCL6B gene is a potential plasma DNA biomarker for gastric cancer. Biomarkers Biochem. Indic. Expo. response susceptibility Chem. 18, 721–725. 10.3109/1354750x.2013.853839 [DOI] [PubMed] [Google Scholar]
  98. Yang X., He D., Xiong Z., Yan B., Li Q., Guo D., et al. (2023). Early detection and localization of major gastrointestinal cancers by combining DNA methylation and fragmentomic signatures generated from a single cfDNA targeted sequencing assay. J. Clin. Oncol. 41, 330. 10.1200/JCO.2023.41.4_suppl.330 [DOI] [Google Scholar]
  99. Young G. P., Pedersen S. K., Mansfield S., Murray D. H., Baker R. T., Rabbitt P., et al. (2016). A cross-sectional study comparing a blood test for methylated BCAT1 and IKZF1 tumor-derived DNA with CEA for detection of recurrent colorectal cancer. Cancer Med. 5, 2763–2772. 10.1002/cam4.868 [DOI] [PMC free article] [PubMed] [Google Scholar]
  100. Zhang X., Zhang X., Sun B., Lu H., Wang D., Yuan X., et al. (2014). Detection of aberrant promoter methylation of RNF180, DAPK1 and SFRP2 in plasma DNA of patients with gastric cancer. Oncol. Lett. 8, 1745–1750. 10.3892/ol.2014.2410 [DOI] [PMC free article] [PubMed] [Google Scholar]
  101. Zhang C., Yu W., Wang L., Zhao M., Guo Q., Lv S., et al. (2017). DNA methylation analysis of the SHOX2 and RASSF1A panel in bronchoalveolar lavage fluid for lung cancer diagnosis. J. Cancer 8, 3585–3591. 10.7150/jca.21368 [DOI] [PMC free article] [PubMed] [Google Scholar]
  102. Zhao G., Li H., Yang Z., Wang Z., Xu M., Xiong S., et al. (2019). Multiplex methylated DNA testing in plasma with high sensitivity and specificity for colorectal cancer screening. Cancer Med. 8, 5619–5628. 10.1002/cam4.2475 [DOI] [PMC free article] [PubMed] [Google Scholar]
  103. Zhao G., Ma Y., Li H., Li S., Zhu Y., Liu X., et al. (2020). A novel plasma based early colorectal cancer screening assay base on methylated SDC2 and SFRP2. Clin. chimica acta; Int. J. Clin. Chem. 503, 84–89. 10.1016/j.cca.2020.01.010 [DOI] [PubMed] [Google Scholar]
  104. Zhao L., Liu Y., Zhang S., Li M. (2022a). Plasma methylated RNF180 for noninvasive diagnosis of gastric cancer. BioMed Res. Int. 2022, 6548945. 10.1155/2022/6548945 [DOI] [PMC free article] [PubMed] [Google Scholar]
  105. Zhao L., Li M., Zhang S., Liu Y. (2022b). Plasma-methylated SEPT9 for the noninvasive diagnosis of gastric cancer. J. Clin. Med. 11, 6399. 10.3390/jcm11216399 [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Frontiers in Genetics are provided here courtesy of Frontiers Media SA

RESOURCES