Early Diagnosis and Prognostic Prediction of Colorectal Cancer through Plasma Methylation Regions

  1. Lingqin Zhu
  2. Lang Yang
  3. Fangli Men
  4. Jianwei Yu
  5. Shuyang Sun
  6. Chenguang Li
  7. Xianzong Ma
  8. Junfeng Xu
  9. Yangjie Li
  10. Ju Tian
  11. Xin Wang
  12. Hui Xie
  13. Qian Kang
  14. Linghui Duan
  15. Xiang Yi
  16. Wei Guo
  17. Xueqing Gong
  18. Ni Guo
  19. Youyong Lu
  20. Joseph Leung
  21. Yuqi He
  22. Jianqiu Sheng

  • Curated by eLife

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    eLife Assessment

    This study presents an important finding that has identified 27 differentially methylated regions as a signature for non-invasive early cancer detection and predicting prognosis for colorectal cancer. The findings demonstrate promising clinical potential, particularly for improving cancer screening and patient monitoring. However, the evidence supporting the claims of the authors is incomplete due to a small sample size and some methodological concerns. The work will be of interest to researchers interested in cancer diagnosis or colorectal cancer monitoring.

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Abstract

The methylation of plasma cell-free DNA (cfDNA) has emerged as a valuable diagnostic and prognostic biomarker in various cancers including colorectal cancer (CRC). Currently, there are no biomarkers that serve simultaneously for early diagnosis and prognostic prediction in CRC patients. Herein, we developed a plasma panel (27 DMRs, differential methylated regions) and validated its superior performance across CRC diagnosis and prognosis prediction in an independent cohort. We first conducted a preliminary screening of 119 CRC tissue samples to identify CRC-specific methylation features. Subsequently, a CRC-specific methylation panel was developed by further filtering 161 plasma samples. Then machine learning algorithms were applied to develop diagnosis and prognosis models using cfDNA samples from 51 CRC patients and 33 normal controls. The diagnosis model was tested in a cohort consisting of 30 CRC, 37 advanced adenoma (AA), and 14 healthy plasma samples, independently validated in a cohort consisting of 18 CRC, 91 NAA, 23 AA and 34 healthy plasma samples. In the tissue external validation cohort (GSE48684), the cfDNA methylation diagnosis model conducted with the panel, have the area under the curve (AUC) reached 0.983, and for the plasma cfDNA model in the external validation cohort, the sensitivities for NAA, AA and CRC 0 -Ⅱ are 48.4%. 52.2% and 66.7% respectively, with a specificity of 88%. Additionally, the panel was applied to patient staging and metastasis, performing well in predicting CRC distant metastasis (AUC = 0.955) and prognosis (AUC = 0.867). Using normal samples as control, the changes in methylation score in both tissue and plasma were consistent across different lesions, although the degree of alterations varied with severity. The methylation scores vary between paired tissue and blood samples, suggesting distinct mechanisms of migration from tumor tissue to blood for the 27 DMRs. Together, Our cfDNA methylation models based on 27 DMRs can identify different stages of CRC and predict metastasis and prognosis, ultimately enabling early intervention and risk stratification for CRC patients.

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  1. eLife

    eLife Assessment

    This study presents an important finding that has identified 27 differentially methylated regions as a signature for non-invasive early cancer detection and predicting prognosis for colorectal cancer. The findings demonstrate promising clinical potential, particularly for improving cancer screening and patient monitoring. However, the evidence supporting the claims of the authors is incomplete due to a small sample size and some methodological concerns. The work will be of interest to researchers interested in cancer diagnosis or colorectal cancer monitoring.

  2. eLife

    Reviewer #1 (Public review):

    Summary:

    Colorectal cancer (CRC) is the third most common cancer globally and the second leading cause of cancer-related deaths. Colonoscopy and fecal immunohistochemical testing are among the early diagnostic tools that have significantly enhanced patient survival rates in CRC. Methylation dysregulation has been identified in the earliest stages of CRC, offering a promising avenue for screening, prediction, and diagnosis. The manuscript entitled "Early Diagnosis and Prognostic Prediction of Colorectal Cancer through Plasma Methylation Regions" by Zhu et al. presents that a panel of genes with methylation pattern derived from cfDNA (27 DMRs), serving as a noninvasive detection method for CRC early diagnosis and prognosis.

    Strengths:

    The authors provided evidence that the 27 DMRs pattern worked well in predicting CRC distant metastasis, and the methylation score remarkably increased in stage III-IV.

    Weaknesses:

    The major concerns are the design of DMR screening, the relatively low sensitivity of this DMR pattern in detecting early-stage CRC, the limited size of the cohorts, and the lack of comparison with the traditional diagnosis test.

  3. eLife

    Reviewer #2 (Public review):

    This work presents a 27-region DMR model for early diagnosis and prognostic prediction of colorectal cancer using plasma methylation markers. While this non-invasive diagnostic and prognostic tool could interest a broad readership, several critical issues require attention.

    Major Concerns:

    (1) Inconsistencies and clarity issues in data presentation

    a) Sample size discrepancies
    - The abstract mentions screening 119 CRC tissue samples, while Figure 1 shows 136 tissues. Please clarify if this represents 119 CRC and 17 normal samples.
    - The plasma sample numbers vary across sections: the abstract cites 161 samples, Figure 1 shows 116 samples, and the Supplementary Methods mentions 77 samples (13 Normal, 15 NAA, 12 AA, 37 CRC).

    b) Methodological inconsistencies
    - The Supplementary Material reports 477 hypermethylated sites from TCGA data analysis (Δβ>0.20, FDR<0.05), but Figure 1 indicates 499 sites.
    - The manuscript states that analyzing TCGA data across six cancer types identified 499 CRC-specific methylation sites, yet Figure 1 shows 477. Please also explain the rationale for selecting these specific cancer types from TCGA.
    - "404 CRC-specific DMRs" mentioned in the main text while "404 MCBs" in Figure 1, the authors need to clarify if these terms are interchangeable or how MCBs are defined.

    (2) Methodological documentation

    - The Results section requires a more detailed description of marker identification procedures and justification of methodological choices.
    - Figure 3 panels need reordering for sequential citation.

    (3) Quality control and data transparency

    - No quality control metrics are presented for the in-house sequencing data (e.g., sequencing quality, alignment rate, BS conversion rate, coverage, PCA plots for each cohort).
    - The analysis code should be publicly available through GitHub or Zenodo.
    - At a minimum, processed data should be made publicly accessible to ensure reproducibility.

  4. eLife

    Reviewer #3 (Public review):

    Summary:

    This article provides a model for early diagnosis and prognostic prediction of Colorectal Cancer and demonstrates its accuracy and usability. However, there are still some minor issues that need to be revised and paid attention to.

    Strengths:

    A large amount of external datasets were used for verification, thus demonstrating robustness and accuracy. Meanwhile, various influencing factors of multiple samples were taken into account, providing usability.

    Weaknesses:

    There are notable language issues that hinder readability, as well as a lack of some key conclusions provided.

  5. Version published to 10.7554/elife.104093.1 on eLife
  6. Version published to 10.7554/elife.104093 on eLife
  7. Version published to 10.1101/2024.11.28.24317652v1 on medRxiv