胃癌：
    粪便中SDC2、SFRP2、TERT、RASSF2基因甲基化标志物在胃癌筛查中具有一定的灵敏度和较高的特异度，是胃癌早期筛查中的潜在粪便生物标志物。
    这些基因甲基化标志物可以很容易地从患者的体液如血液、胃液及粪便中检测出来。
    RASSF2、SFRP2、TERT基因甲基化检测胃癌的灵敏度分别为31.8%、22.7%、36.4%，特异度均达到了90%左右。
    reference：
    http://rs.yiigle.com/CN112137202111/1313455.htm

    胃癌中基本不存在WTX基因启动子区域高甲基化状态。
    http://www.cqvip.com/qk/91170a/201303/45117810.html

    CDH1基因启动子区甲基化可能是导致散发性胃癌上皮型钙粘附素表达下调的重要原因。
    http://www.cqvip.com/qk/91170a/201301/44478711.html

    ELMO1基因启动子区甲基化异化具有特异性，在GC组织及胃液中检测其甲基化异化具有敏感性，并且在早期GC中也具有较高的敏感性，ELMO1基因甲基化可作为GC早期诊断的分子靶标。
    https://www.wjgnet.com/1009-3079/articlehighlights/v27/i17/1055.htm
    高灵敏和特异性检测NDRG4基因启动子区的甲基化能够辅助诊断早期胃癌。
    https://patents.google.com/patent/CN106119361A/zh
    6 genes (MINT25, RORA, GDNF, ADAM23, PRDM5, MLF1) showed frequent differential methylation between gastric cancer and normal mucosa in the training, test and validation sets.
    These findings suggest MINT25 is a sensitive and specific marker for screening in gastric cancer.
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2722957/

    a large number of genes with different biological functions have been found to be methylated in gastric cancer (Table 1). Promoter methylation is an important hallmark of cancer cells, which plays a key role in the initiation and progression of tumor, including gastric cancer. Additionally, aberrant methylation of a number of genes is significantly associated with clinicopathological characteristics and clinical outcomes in gastric cancer (Table 2).
    Table 1. Genes commonly methylated in gastric cancer.

    Functions Gene Assay Methylation prevalence (%) References
    Normal Para-cancer Cancer
    DNA repair hMLH1 MSP 20.0 N/A 8.8–72.9 [61]
    , [66]
    , [67]
    , [229]
    , [230]
    MGMT MSP 5.7–8.0 N/A 26.7–36.8 [67]
    , [68]
    , [69]
    , [229]
    , [230]
    Cell cycle CDKN1C Q-MSP 66.7 96.0 36.0 [182]
    IGFBP3 Q-MSP N/A N/A 58.3 [231]
    P16 MSP 3.8–35.0 N/A 21.3–45.0 [31]
    , [67]
    , [75]
    , [79]
    , [80]
    , [81]
    , [82]
    , [229]
    , [230]
    TCF4 Pyrosequencing 40.0 N/A 67.0 [232]
    PRDM5 MSP, BS 0.0 N/A 50.0–88.0 [233]
    Cell adherent/invasion/migration CDH1 MSP 16.0–36.1 N/A 50.6–84.0 [31]
    , [75]
    , [90]
    , [182]
    , [229]
    FLNc MSP 8.0 N/A 37.0–41.3 [67]
    , [229]
    GRIK2 Q-MSP 7.41–30.0 N/A 50.0–66.6 [97]
    , [98]
    HOXA10 Q-MSP 7.4 0.0 24.0 [182]
    LOX MSP 12.0 N/A 27.0–41.3 [229]
    , [234]
    TIMP3 MSP 3.8 N/A 13.2 [230]
    TSP1 MSP 3.1 N/A 35.4 [235]
    Cell growth/differentiation HAI-2/SPINT2 MSP 0.0 N/A 75.0 [100]
    HOXA1 Q-MSP 18.5 72.0 48.0 [182]
    HoxD10 MSP 0.0 N/A 85.7 [104]
    NDRG2 MSP 20.0 N/A 54.0 [236]
    RARRES1 Q-MSP 3.0–51.9 84.0 10.0–36.0 [171]
    , [182]
    SHP1 MSP 20.8 25.0 [230]
    Apoptosis BNIP3 Q-MSP 15.0 N/A 39.0–65.0 [97]
    , [112]
    CACNA1G Q-MSP 11.1 4.0 48.0 [182]
    CMTM3 MSP 14.0 N/A 44.0 [237]
    DAPK MSP 24.5–42.2 N/A 30.9–83.2 [75]
    , [112]
    , [116]
    , [230]
    GPX3 Pyrosequencing 39.0 N/A 30.1–60.0 [127]
    , [128]
    GSTP1 MSP 1.9 N/A 20.6 [230]
    PCDH10 MSP, BS 37.0 N/A 82.0 [238]
    PCDH17 MSP N/A N/A 95.0 [239]
    RBP1 Q-MSP 44.4 80.0 64.0 [182]
    SFRP2 Q-MSP 10.0–20.0 N/A 55.0–73.3 [97]
    , [240]
    Transcriptional regulation ZNF545 MSP 0.0 27.0 51.9 [241]
    CHD5 Q-MSP 20.0 N/A 40.0 [97]
    , [134]
    HLTF MSP 8.3–12.0 N/A 45.8–53.3 [229]
    , [242]
    ZIC1 MSP N/A N/A 94.6 [144]
    RUNX3 Q-MSP 7.4 8.0 56.0–75. 2 [141]
    , [182]
    , [243]
    Ras pathway hDAB2IP MSP 6.0 N/A 46.0 [244]
    HRASLS MSP N/A N/A 40.0–46.0 [67]
    , [229]
    , [234]
    RASSF1A MSP 5.7 N/A 45.6–61.8 [116]
    , [149]
    , [230]
    RASSF2 Q-MSP 35.0 N/A 14.0–70.0 [67]
    , [75]
    , [97]
    , [245]
    RKIP MSP 4.1 N/A 62.1 [246]
    STAT pathway SOCS-1 MSP 12.0 N/A 44.0 [156]
    , [157]
    , [158]
    Wnt pathway APC MSP 37.7 N/A 52.9 [230]
    , [247]
    Dkk-3 MSP 34.6 N/A 67.6 [163]
    , [166]
    SFRP5 Q-MSP 66.7 76.0 56.0 [182]
    Retinoic acid pathway RARß MSP 16.0–20.0 N/A 36.0–50.7 [75]
    , [116]
    , [229]
    CRBP1 MSP 0.0 N/A 33.0 [171]
    Others KL MSP 0.0 47.5 [181]
    , [182]
    ITGA4 Q-MSP 29.6 24.0 96.0 [182]
    CDKN2A MSP, Q-MSP 29.6 20.0 30.4–36 [182]
    TP73 Q-MSP 3.7 0.0 24.0 [182]
    BTG4 MSP 0.0 N/A 73.7 [248]
    DACT1 MSP, BS 0.0 N/A 29.3 [249]
    NPR1 MSP N/A N/A 42.5 [231]
    ECRG4 MSP 6.7 53.3 69.4 [250]
    EDNRB Pyrosequencing 6.5 N/A 50.4 [250]
    CHFR Q-MSP 5.0 N/A 48–65 [97]
    , [182]
    HACE1 Q-MSP N/A N/A 26.0 [31]
    LRP1B Q-MSP 23.0 N/A 61.0 [251]
    NR3C1 Q-MSP 15.0 N/A 24.0–30.0 [97]
    , [182]
    TFPI2 Q-MSP 0.0 N/A 18.0–80.9.0 [127]
    , [175]
    , [176]

    Table 2. Correlation of gene methylation with clinical outcomes in gastric cancer.

    Functions Gene Correlation with clinical outcomes References
    DNA repair hMLH1 Association with poor prognosis [67]
    MGMT Association with lymph node metastasis, TNM stage and poor survival [67]
    , [68]
    , [73]
    , [75]
    Cell cycle p16 Correlation with poor tumor differentiation, lymph node metastasis, and poor survival [38]
    , [67]
    , [91]
    , [92]
    , [93]
    TCF4 Correlation with tumor size, Lauren classification, depth of invasion, and lymph node metastasis [232]
    Cell adherent/invasion/migration CDH1 Association with worse prognosis, tumor size, lymph vascular invasion, infiltration depth, lymph node and distant metastasis [93]
    , [182]
    FLNc Association with a poor prognosis [67]
    LOX Association with depth of tumor invasion, lymph node metastasis, TNM stage and poor survival [234]
    TIMP3 Associated with tumor localization [116]
    TSP1 Correlation with TNM stage [235]
    Cell growth/differentiation HoxD10 Association with poor prognosis [104]
    HAI-2/SPINT2 Association with poor differentiation and lymph node metastasis [107]
    NDRG2 Association with lymph node metastasis, tumor invasion, Borrmann classification and TNM stage [236]
    Apoptosis BNIP3 Association with poor survival [112]
    , [122]
    CACNA2D3 Correlation with lymph node metastasis [38]
    DAPK Correlation with poorly differentiated tumors and lymph node metastasis [75]
    , [112]
    , [114]
    , [116]
    GPX3 Correlation with lymph node metastasis [127]
    , [128]
    PCDH10 Association with poor survival [238]
    PCDH17 Correlation with low tumor stage and lymph node metastasis [239]
    Transcriptional regulation HLTF Association with TNM stage [242]
    PAX6 Association with tumor stage, lymph node metastasis and poor prognosis [116]
    ZNF545 Association with poor prognosis [241]
    RUNX3 Correlation with depth of tumor invasion, lymph node and distant metastasis [141]
    Ras pathway RASSF1A Association with TNM stage and poor prognosis [75]
    , [116]
    , [252]
    RASSF2 Association with poor prognosis, histological differentiation, depth of tumor invasion, regional lymph node and distant metastasis, and TNM stage [67]
    , [245]
    RKIP Association with TNM stage, histological differentiation, depth of invasion, lymph node and distant metastasis. [246]
    STAT pathway SOCS-1 Association with poor prognosis and metastasis [157]
    Wnt pathway Dkk-3 Association with cancer-related death [163]
    Retinoic acid pathway RAR-ß Correlation with lymph node metastasis [116]
    Others KL Association with the poor prognosis [181]
    DACT1 Association with tumor size, lymph node and distant metastasis [249]
    BTG4 Correlation with cell differentiation, lymph node metastasis [248]
    ECRG4 Correlation with tumor stage [250]
    EDNRB Correlation with lymph node and distant metastasis [250]
    LRP1B Correlation with tumorigenicity in nude mice [251]
    TFPI2 Correlation with poor prognosis [127]
    CALCA Correlation with lymph node metastasis [116]
    QKI Correlation with poor differentiation status, depth of invasion, lymph node and distant metastasis, advanced TNM stage, and poor survival [253]

    reference
    https://www.sciencedirect.com/science/article/pii/S0009898113001964?via%3Dihub

    大肠癌
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    SEPT9、NDRG4和SDC2的甲基化可能是大肠癌早期筛查的生物标志物。SEPT9、NDRG4和SDC2甲基化的联合检测结直肠癌和腺瘤具有高度可行性。
    reference:
    https://www.x-mol.com/paper/1213056304859516961/t?recommendPaper=1213063552906235929
    http://blog.sciencenet.cn/blog-3419762-1256870.html
    CRC中SDC2和TFPI2的启动子和大多数CpG位点甲基化水平显著高于正常组织。
    https://www.x-mol.com/paper/1388225434525655040/t?recommendPaper=1213056304859516961