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. 2025 Jul 21;17(1):128.
doi: 10.1186/s13148-025-01939-4.

Monitoring breast cancer progression through circulating methylated GCM2 and TMEM240 detection

Affiliations

Monitoring breast cancer progression through circulating methylated GCM2 and TMEM240 detection

Chin-Sheng Hung et al. Clin Epigenetics. .

Abstract

Background: Breast cancer is the most commonly diagnosed cancer and the second leading cause of cancer-related deaths in women worldwide. Approximately 20-30% of women diagnosed with early-stage breast cancer eventually develop metastatic disease. Current biomarkers, such as CA15-3 and CEA, detect metastasis in only 60-80% of cases, underscoring the need for improved diagnostic tools. This study investigates the potential of circulating methylated GCM2 and TMEM240 as biomarkers for noninvasive monitoring of breast cancer progression.

Methods: In a prospective study conducted in Taiwan, 396 patients were enrolled, alongside a retrospective study of 134 plasma samples from Western populations. cfDNA was extracted, subjected to sodium bisulfite conversion, and the methylation levels of GCM2 and TMEM240 were measured using QMSP. Monte Carlo analysis assigned 70% of the dataset to a training set and 30% to a validation set, repeated 1000 times. Performance metrics such as sensitivity, specificity, and accuracy were averaged to ensure robustness, supporting the use of combined GCM2 and TMEM240 for monitoring treatment response and tumor burden.

Results: The training set, consisting of 166 breast cancer patients (13.3% with recurrence or metastasis), was utilized to establish the biomarker detection cutoff. Validation in a separate cohort of 325 patients (20% with recurrence or metastasis) demonstrated superior performance compared to CA15-3 and CEA, achieving 95.1% accuracy, 89.4% sensitivity, 96.5% specificity, 86.8% positive predictive value (PPV), and 97.3% negative predictive value (NPV). Monte Carlo analysis of the training data revealed an average sensitivity of 95.7%, specificity of 90.3%, and accuracy of 91.5%, while validation data achieved 92.8% sensitivity, 89.5% specificity, and 90.3% accuracy across 1000 replicates. Positive cases were significantly associated with late-stage disease (P < 0.001), larger tumors (P = 0.002), distant metastasis (P < 0.001), and disease progression (P < 0.001). For monitoring treatment response and tumor burden, decreased methylation levels were observed in patients responding well to treatment, whereas increased levels were noted in cases of cancer progression or prior to metastasis.

Conclusions: Overall, detecting methylated GCM2 and TMEM240 in plasma offers a novel, accurate, and noninvasive method for monitoring breast cancer progression.

Keywords: GCM2; TMEM240; Breast cancer; Circulating cell-free DNA; Methylation biomarker.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: The study has been approved by the Taipei Medical University—Joint Institutional Review Board and the Institutional Review Board. Written informed consent was obtained from all patients. Consent for publication: Not applicable. Competing interests: Part authors are employees of EG BioMed Co. Ltd. and EG BioMed US Inc., whose company funded this study. HTS, RKL are employees of EG BioMed Co. Ltd. and EG BioMed US Inc., HTS, CMS, CSH and RKL hold stock in EG BioMed Co. Ltd. and EG BioMed US Inc., and report other support from EG BioMed Co. Ltd. and EG BioMed US Inc., during the conduct of the study. In addition, RKL has multiple patents in the field of cancer detection pending to EG BioMed Co. Ltd. The remaining authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Study design and patient grouping in the establishment of the monitoring model
Fig. 2
Fig. 2
Distribution and performance of TMEM240 and GCM2 methylation levels in breast cancer progression. In the training group, box plots compare the average Cp values of TMEM240 (A) and GCM2 (B) between nonprogression and progression groups (166 subjects). Scatter plots display cfDNA methylation levels for TMEM240 (C) and GCM2 (D), showing progression patients in the orange area (left) and nonprogression patients in the green area (right). The Y-axis represents Cp values from qPCR, and the X-axis corresponds to individual patient numbers, with data based on three technical replicates per patient. In the validation group, bar charts show the average Cp values of TMEM240 (E) and GCM2 (F) for nonprogressive (left) and progressive (right) patients (325 subjects), with differences evaluated using the Mann–Whitney U test (***, p < 0.001). (G) ROC curves are based on classifications using the defined cutoff value for combined circulating GCM2 and TMEM240 methylation levels and are compared with CA15-3 and CEA levels for monitoring breast cancer progression. (H) Bar chart displaying combined GCM2 and TMEM240 methylation levels, calculated via the second Monte Carlo replicates module using Su and Liu’s method. (I) Methylated cfDNA of GCM2 and TMEM240 in plasma across different stages at diagnosis. (J) ROC curves further compare the predictive performance of combined GCM2 and TMEM240 methylation levels against CA15-3 and CEA levels in monitoring breast cancer progression
Fig. 3
Fig. 3
Real-time monitoring of circulating methylated GCM2 and TMEM240 in patients with breast cancer following treatment. Breast cancer patients with a good prognosis (A), with distant metastasis (B), and with distant metastasis who subsequently received adequate treatment (C). The Y-axis represents the relative methylation level, which was calculated with the Monte Carlo replicates module and analyzed with Su and Liu’s method. The X-axis represents months after treatment

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References

    1. Taiwan Health Promotion Administration: Cancer Registry Annual Report, 2017. Taiwan: Ministry of Health and Welfare; 2019.
    1. Statistics on causes of death in China in 2017 [https://www.mohw.gov.tw/cp-16-48057-1.html]
    1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394–424. - PubMed
    1. Arnold M, Morgan E, Rumgay H, Mafra A, Singh D, Laversanne M, et al. Current and future burden of breast cancer: global statistics for 2020 and 2040. Breast (Edinburgh, Scotland). 2022;66:15–23. - PMC - PubMed
    1. Chaffer CL, Weinberg RA. A perspective on cancer cell metastasis. Science. 2011;331:1559–64. - PubMed

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