Table 1.
Master list of CRC DNA methylation markers and their corresponding sample types based on the list of eligible research articles
| SI no. | Title, Year | Reference no. | Sample type | Population | Age range (years) | CRC stage | DNA methylation marker | ||
|---|---|---|---|---|---|---|---|---|---|
| Male | Female | 0–I | II | ||||||
| 1 | Identifying potential DNA methylation markers in early-stage CRC, 2020 | [10] | FFPE tissue; Blood plasma | 37 | 14 | 34–77 | 36 | 6 | SFMBT2, VAV3-AS1, ZNF132, KCNQ5, ITGA4, THBD, FBN1, EVC, C9orf50, TWIST1, ZNF304 |
| 2 | Blood leukocytes methylation levels analysis indicate methylated plasma test is a promising tool for CRC early detection, 2011 | [11] | Blood plasma | 55 | 36 | 22–89 | 13 | 31 | SEPT9, SDC2 |
| 3 | A novel cfDNA methylation-based model improves the early detection of CRC, 2021 | [12] | Tissue DNA samples; plasma cfDNA | 287 | 202 | 18–89 | 66 | 86 | SEPT9, BCAT1, IKZF1, cg10673833 |
| 4 | Evaluation of epigenetic methylation biomarkers for the detection of CRC using ddPCR, 2023 | [13] | FFPE tissue | 57 | 48 | - | 24 | 35 | BCAT1, GATA5, IKZF1 (V1), IKZF1 (V2), IRF4, ITGA4, HIC1, NPY, SDC2, SEPT9, WIF1 |
| 5 | Combined SEPT9 and BMP3 methylation in plasma for CRC early detection and screening in a Brazilian population, 2023 | [14] | Blood plasma | 11 | 32 | 57.1 (mean) | 9 | 9 | SEPT9, BMP3 |
| 6 | CRC detected by liquid biopsy 2 years prior to clinical diagnosis in the HUNT study, 2023 | [3] | Blood plasma | 32 | 40 | 69.8 (mean) | 0 | 11 | AGBL4, ALX4, BCAT1, BMP3, FLI1, GRIA4, IKZF1, NDRG4, NPTX2, PRIMA1, RARB, SEPT9, SDC2, SFRP1, SFRP2, SLC8A1, TWIST1, VIM, WNT5A, ZNF331 |
| 7 | Combining methylated SEPTIN9 and RNF180 plasma markers for diagnosis and early detection of gastric cancer, 2023 | [15] | Blood plasma | 384 | 176 | <65, >65 | 94 | 53 | SEPT9, RNF180 |
| 8 | cfDNA methylation profiles enable early detection of colorectal and gastric cancer, 2016 | [16] | Blood plasma | 28 | 42 | 25–89 | 10 | 25 | SEPT9, ATXN1, PCDH10, MYO1G, NGFR, IKZF1, ITGA4 |
| 9 | Circulating-tumor DNA methylation of HAND1 gene: a promising biomarker in early detection of CRC, 2017 | [17] | Blood plasma | 18 | 12 | 31–68 | - | 9 | HAND1, SEPT9 |

Figure 1.
Flow chart of study selection process. CRC, colorectal cancer; PMC, PubMed Center.

Figure 2.
Summary of study samples demographic profile based on sex and early CRC stage. CRC, colorectal cancer.

Figure 3.
Percentages of identified CRC DNA methylation markers from the list of eligible research articles. CRC, colorectal cancer.
Table 2.
Methods used for the identification of CRC DNA methylation markers
| SI No. | DOI | Sample type | Extraction & isolation method | Quantification of methylation method | Markers identified |
|---|---|---|---|---|---|
| 1 | 10.1016/j.ygeno.2020.06.007 | FFPE tissue; Blood plasma | AllPrep DNA/RNA FFPE Kit (QIAGEN) | RT-PCR | SFMBT2, VAV3-AS1, ZNF132, KCNQ5, ITGA4, THBD, FBN1, EVC, C9orf50, TWIST1, ZNF304 |
| 2 | 10.7150/jca.57114 | Blood plasma | QIAamp DNA Blood Mini Kit (QIAGEN) | Bisulfite Sequencing and PCR Assays | SEPT9, SDC2 |
| 3 | 10.1002/1878-0261.12942 | Tissue samples; plasma | MagMAX Cell-Free DNA Isolation Kit (Thermo Fisher Scientific) | Bisulfite conversion and targeted sequencing | SEPT9, BCAT1, IKZF1, cg10673833 |
| 4 | 10.1038/s41598-023-35631-5 | FFPE tissue | QIAamp DNA Kit (QIAGEN) | ddPCR using Bio-Rad QX200 system | BCAT1, GATA5, IKZF1 (V1), IKZF1 (V2), IRF4, ITGA4, HIC1, NPY, SDC2, SEPT9, WIF1 |
| 5 | 10.1002/cam4.6224 | Blood plasma | QIAmp circulating Nucleic Acid Kit (QIAGEN) | ddPCR | SEPT9, BMP3 |
| 6 | 10.1038/s41416-023-02337-4 | Blood Plasma | MagMAX Cell-Free DNA Isolation Kit (Thermo Fisher Scientific) | MSP with Zymo Lightning Conversion Reagent | AGBL4, ALX4, BCAT1, BMP3, FLI1, GRIA4, IKZF1, NDRG4, NPTX2, PRIMA1, RARB, SEPT9, SDC2, SFRP1, SFRP2, SLC8A1, TWIST1, VIM, WNT5A, ZNF331 |
| 7 | 10.1002/cac2.12478 | Blood plasma | QIAamp Circulating Nucleic Acid Kit (QIAGEN) | qPCR | SEPTIN9, RNF180 |
| 8 | 10.62347/TPTQ3682 | Blood plasma | MagMAX Cell-Free DNA Isolation Kit (Thermo Fisher Scientific) | MSP with Zymo Lightning Conversion Reagent | SEPT9, ATXN1, PCDH10, MYO1G, NGFR, IKZF1, ITGA4 |
| 9 | 10.1186/s12920-024-01893-9 | Blood plasma | AddPrep Genomic DNA Extraction Kit (Addbio) | qMS-PCR | HAND1, SEPT9 |
Table 3.
Sensitivity (%) and specificity (%) of each identified DNA methylation marker
| SI No. | DOI | Sample type | Markers identified | Sensitivity (%) | Specificity (%) |
|---|---|---|---|---|---|
| 1 | 10.1016/j.ygeno.2020.06.007 | FFPE tissue; Blood plasma | SFMBT2 | 92.20 | 94.60 |
| VAV3-AS1 | 92.20 | 89.20 | |||
| ZNF132 | 86.30 | 94.60 | |||
| KCNQ5 | 90.20 | 89.20 | |||
| ITGA4 | 90.20 | 94.60 | |||
| THBD | 86.30 | 94.60 | |||
| FBN1 | 86.30 | 91.90 | |||
| EVC | 84.30 | 86.50 | |||
| C9orf50 | 94.10 | 86.50 | |||
| TWIST1 | 72.60 | 100.0 | |||
| ZNF304 | 66.70 | 100.0 | |||
| 2 | 10.7150/jca.57114 | Blood plasma | SEPT9 | 75.80 | 94.70 |
| SDC2 | 60.40 | 86.80 | |||
| 3 | 10.1002/1878-0261.12942 | Tissue DNA samples; plasma cfDNA | SEPT9 | 48.20 | 91.50 |
| BCAT1/IKZF1 | 66.00 | 95.00 | |||
| cg10673833 | 89.70 | 86.80 | |||
| 4 | 10.1038/s41598-023-35631-5 | FFPE tissue | BCAT1 | 73.30–75.20 | 92.40–94.30 |
| GATA5 | 73.30–74.30 | 91.40–92.40 | |||
| IKZF1 (V1) | 75.20 | 93.30 | |||
| IKZF1 (V2) | 70.50 | 95.20 | |||
| IRF2 | 81.90–82.90 | 90.5–91.40 | |||
| ITGA4 | 82.90 | 88.60–89.50 | |||
| HIC1 | 43.80 | 78.10 | |||
| NPY | 80.00 | 90.50 | |||
| SDC2 | 75.20–76.20 | 94.30–95.20 | |||
| SEPT9 | 70.50–72.40 | 88.60–90.50 | |||
| WIF1 | 65.70 | 96.20 | |||
| 5 | 10.1002/cam4.6224 | Blood plasma | SEPT9 | 50.00 | 90.00 |
| BMP3 | 40.00 | 90.00 | |||
| 6 | 10.1038/s41416-023-02337-4 | Blood plasma | AGBL4 | 40.30 | 77.50 |
| ALX4 | - | - | |||
| BCAT1 | 43.10 | 64.80 | |||
| BMP3 | 41.70 | 76.10 | |||
| FLI1 | 38.90 | 76.10 | |||
| GRIA4 | - | - | |||
| IKZF1 | 43.10 | 78.90 | |||
| NDRG4 | 62.50 | 47.90 | |||
| NPTX2 | 48.60 | 74.60 | |||
| PRIMA1 | - | - | |||
| RARB | - | - | |||
| SEPT9 | 48.60 | 69.00 | |||
| SDC2 | 41.70 | 70.40 | |||
| SFRP1 | 40.30 | 77.50 | |||
| SFRP2 | 31.90 | 85.90 | |||
| SLC8A1 | 45.80 | 73.20 | |||
| TWIST1 | - | - | |||
| VIM | 45.80 | 70.40 | |||
| WNT5A | 51.40 | 60.60 | |||
| ZNF331 | 50.00 | 66.20 | |||
| 7 | 10.1002/cac2.12478 | Blood plasma | SEPT9 | 40.00 | 96.00 |
| RNF180 | 46.20 | 87.30 | |||
| 8 | 10.62347/TPTQ3682 | Blood plasma | SEPT9 | 68.00 | 80.00 |
| ATXN1 | - | - | |||
| PCDH10 | - | - | |||
| MYO1G | - | - | |||
| NGFR | - | - | |||
| IKZF1 | - | - | |||
| ITGA4 | - | - | |||
| 9 | 10.1186/s12920-024-01893-9 | Blood plasma | HAND1 | 93.33 | 80.00 |
| SEPT9 | 66.67 | 86.67 |

Figure 4.
Summary of (A) risk of bias and (B) applicability concerns of the included papers after QUADAS-2 assessment. QUADAS-2, Quality Assessment of Diagnostic Accuracy Studies-2.
Table 4.
Quality assessment ratings using the QUADAS-2 scale for reviewed studies (n = 9)
| Signaling questions | Patient selection | Index test | Reference standard | Flow and timing | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Risk of bias | Applicability concerns | Q4 | Q5 | Risk of bias | Applicability concerns | Q6 | Q7 | Risk of bias | Applicability concerns | Q8 | Q9 | Risk of bias | |
| (1) Zhang et al. [16] | Y | N | N | High | Low | N | U | Low | Unclear | U | Y | Unclear | High | Y | N | High |
| (2) Chen et al. [17] | N | N | Y | Low | High | Y | Y | Unclear | Low | Y | N | Low | Low | N | Y | High |
| (3) Wu et al. [18] | U | Y | U | Unclear | Low | N | N | High | Low | Y | N | Low | Low | Y | N | Low |
| (4) Petit et al. [19] | N | N | Y | High | Low | Y | U | Low | High | N | Y | High | Low | N | Y | High |
| (5) Lima et al. [6] | Y | Y | N | High | High | Y | Y | Low | Low | Y | Y | Low | Unclear | U | Y | Unclear |
| (6) Brenne et al. [3] | N | Y | U | Low | Unclear | N | Y | Unclear | Low | N | Y | High | Low | Y | U | Low |
| (7) Nie et al. [20] | U | N | Y | Unclear | Low | N | U | High | Unclear | Y | N | Low | High | N | Y | High |
| (8) Lei [21] | N | Y | N | Low | High | Y | N | Low | High | Y | U | Low | Low | Y | N | Low |
| (9) Shavali et al. [22] | Y | N | N | High | Low | N | U | Unclear | Low | U | N | Unclear | Low | Y | N | Low |