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REVIEW 5 major objections 6 minor 55 references

In silico tool for identification of colorectal cancer from cell-free DNA biomarkers

T0 review · 5 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A 25-site blood DNA methylation panel detected colorectal cancer with AUROC 0.89.

desk verdict The reported AUROC of 0.89 is a selected maximum, not an independent estimate; the paper is a routine pipeline with a fixable but load-bearing validity problem. read the letter →

arxiv 2505.11041 v1 pith:74WIW3GX submitted 2025-05-16 q-bio.GN

Kartavya Mathur (1 , 2) , Shipra Jain (1) , Nisha Bajiya (1) , Nishant Kumar (1) , Gajendra P. S. Raghava (1) ((1) Department of Computational Biology , Indraprastha Institute of Information Technology , Delhi
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(2) School of Biotechnology Gautam Buddha University Uttar Pradesh)
This is my paper · ORCID
classification q-bio.GN
keywords colorectalcancercell-freeDNAmethylationliquidbiopsymachinelearningbiomarkerpanelCpGsitesnoninvasivescreening
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that methylation marks in cell-free DNA from a blood sample can identify colorectal cancer without a tissue biopsy. The authors start with a public dataset of 274 blood samples, find about 31,000 methylation sites that differ between cancer and normal, and reduce them to a compact 25-site signature. Their best model, a multi-layer perceptron trained on those 25 sites, reached an AUROC of 0.89 and a Matthews correlation coefficient of 0.78 on a held-out set of 55 samples, with 93% sensitivity and 85% specificity. A convolutional neural network using a much larger site set reached 0.78 AUROC. If the result holds, a simple blood test could serve as a triage step before colonoscopy, potentially widening screening coverage.

What carries the argument

The load-bearing object is a 25-CpG methylation signature selected by recursive feature elimination (RFE), an iterative procedure that trains a model, ranks features by importance, deletes the weakest, and repeats. RFE was applied to the top 100 positively correlated methylation sites chosen by univariate AUROC scoring. The resulting signature maps to genes including EVC, SLIT3, SDK1, and ADGRB1, and the paper shows that several corresponding genes are differentially expressed in colon and rectum adenocarcinomas. The multi-layer perceptron, a feed-forward neural network, carries the final classification, but the 25-site signature is the component that would be translated into a targeted clinical test.

What would settle it

Re-run the entire pipeline with all feature selection restricted to the 219 training samples, then evaluate the fixed model on the 55 held-out samples; if the AUROC falls from 0.89 toward the 0.71 achieved by the all-sites model, the claimed advantage of the compact 25-site signature would not be robust.

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Extended reading notes

Core claim

The paper's central claim is that DNA methylation profiles of circulating cell-free DNA carry enough cancer-specific information for a machine-learning classifier to separate colorectal cancer patients from healthy controls. On a discovery cohort of 142 cancer and 132 control blood samples profiled with a targeted methylated-CpG amplification sequencing assay, the authors report that a multi-layer perceptron trained on 25 CpG sites selected by recursive feature elimination achieves an AUROC of 0.89 and an MCC of 0.78 on the held-out 55-sample validation set, with 93.1% sensitivity and 84.6% specificity. A convolutional neural network trained on roughly 30,000 methylation sites reached an AUROC of 0.78 on the same validation set. The concluding summary also highlights a smaller 15-site panel run through a random forest, with 88.5% sensitivity and 86.2% specificity, which the authors describe as comparable to the approved methylated SEPT9 blood test.

Load-bearing premise

The 55 validation samples are treated as an untouched independent check, but they were used to rank and select the top CpG sites before the training/validation split was made, so the reported 0.89 AUROC is probably optimistic.

Editorial extensions

If this is right

  • A blood draw could replace or precede stool-based and invasive screening in settings where colonoscopy is not readily available, since the model needs methylation levels at only a small number of CpG sites.
  • The 25-site panel is small enough for targeted assays such as methylation-specific PCR, making clinical deployment cheaper than whole-methylome sequencing.
  • At 93.1% sensitivity and 84.6% specificity, the MLP panel sits in the same performance range as the methylated SEPT9 blood test cited in the paper, offering an alternative marker set for the same screening purpose.
  • Seven of the signature's ten annotated genes show differential expression in colon or rectum adenocarcinoma, giving the classifier a biological rationale beyond pattern matching.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural next experiment, not reported in this paper, is to freeze the 25-site panel and run it on an independent cohort collected with a different methylation assay; that would show whether the signature is portable across platforms.
  • The same pipeline could be applied to other cancer types where cell-free DNA methylation datasets exist, potentially yielding one blood-drawn panel that screens several cancers at once.
  • The 15-site random forest described in the conclusion and the 25-site MLP described in the abstract may be complementary; a consensus signature across both could be more stable than either panel alone.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. The manuscript (arXiv:2505.11041) proposes a machine-learning pipeline for colorectal cancer detection from cell-free DNA methylation profiles. Using the GSE124600 cohort (142 CRC, 132 normal), the authors retain 97,863 CpG sites, perform a t-test to select 30,791 putatively significant sites, rank the top 100 positively and negatively correlated sites by univariate AUROC, and then apply RFE, SFS, and SVC-L1 feature selection followed by 11 classifiers plus a CNN. The central reported result is an MLP trained on 25 RFE-selected positive-correlation features with AUROC 0.89 and MCC 0.78 on a randomly held-out 55-sample validation set. The paper also provides functional annotation of the selected CpG sites and GEPIA2-based gene-expression validation for seven genes.

Significance. If the reported performance were a valid out-of-sample estimate, a cfDNA methylation-based CRC classifier would have clear clinical screening value. The study uses a well-known public dataset and provides extensive supplementary tables, which is commendable. However, the central performance claim is not a valid generalization estimate because the validation set influenced both feature selection and model selection, and because the manuscript contains a major inconsistency between the abstract/Table 4 model and the conclusion's panel. A correct re-analysis with proper nested validation and an external cohort would be needed for this result to be interpretable.

major comments (5)
  1. [Section 3.4 vs 3.6] Feature selection is performed before the train/validation split: Section 3.4 ranks all 30,791 sites and selects the top 100 positively and negatively correlated sites using univariate AUROC computed on the full 274-sample dataset, and only Section 3.6 describes the 80:20 split that creates the 55-sample validation set. Because the validation samples contributed to choosing the candidate CpG sites that enter RFE/SFS/SVC-L1, the AUROC of 0.89 in Table 4 (RFE-positive, MLP-25) is not an independent out-of-sample estimate, and the abstract's claim of 'independent validation datasets' is not supported.
  2. [Section 4.2.4 and Table 4] The manuscript states 'we have reported the best-performing model over the validation dataset' after evaluating RFE, SFS, and SVC-L1 with feature counts of 25, 20, 15, 10, and 5 across 11 classifiers. Selecting the maximum AUROC on the same 55 validation samples is a form of test-set selection ('winner's curse'); therefore the reported AUROC 0.89 and MCC 0.78 are selected maxima over a large grid of correlated estimates rather than the expected performance of a pre-specified model.
  3. [Section 3.3 and Section 4.1.1] The t-test over 97,863 CpG sites with a p-value threshold of 0.05 and no multiple-testing correction is reported as yielding 30,791 significant sites. Under the null hypothesis one would expect approximately 4,893 sites to pass this threshold by chance, so the claim of 30,791 'significantly altered' sites is likely dominated by false positives. This does not by itself invalidate the classifier, but it affects the biological interpretation and the feature-ranking step that starts from these sites.
  4. [Section 3.5 (Conclusion) vs Abstract/Table 4] The conclusion describes a 'panel of 15 upregulated methylation sites on EVC, FRMD6, FRMD6-AS2, LHFPL6, LIFR, and ZFPM2' and a random forest model with specificity 86.21% and sensitivity 88.46%. Neither the panel composition nor the model matches the abstract's best-performing model (MLP with 25 RFE-selected positive-correlation sites, Table 4), and the genes FRMD6, LHFPL6, LIFR, and ZFPM2 do not appear in Table 6's list of the ten annotated genes for the best model. The manuscript therefore does not state a single, consistent best model.
  5. [Section 3.6] The text promises that 'a cross-platform validation was also conducted using data from a different colorectal cancer study, GSE149438,' but no results for GSE149438 appear anywhere in the Results or Supplementary Tables. Either the cross-platform validation results must be reported and discussed, or the sentence describing this validation should be removed.
minor comments (6)
  1. [Title and abstract] The title and abstract call the work an 'in silico tool,' but no software, web server, or code repository is described or made available; please clarify what is being delivered.
  2. [Section numbering] There are two sections numbered '3.5' (one in the methodology and one after Section 4.2.6 labeled '3.5. Conclusion'), and the conclusion is placed after the results rather than in its own numbered position; please renumber and restructure.
  3. [Table 4 and Section 4.2.4] The SVC-L1 row for negatively correlated sites in Table 4 shows 'NB 50' with 50 features, while the text states 'SVC-based ML model developed using 4 methylated sites extracted using SFS'; the row labels and the text are hard to reconcile and should be aligned.
  4. [Supplementary Table 7.1] Supplementary Table 7.1 is empty except for headers; the SFS feature lists are embedded in the caption of Table 7.2. Please move the feature lists into Table 7.1 so that each supplementary table is self-contained.
  5. [Equations (1)-(4)] The equations for sensitivity, specificity, accuracy, and MCC appear garbled in the manuscript source; please ensure they are typeset correctly with proper numerators, denominators, and parentheses.
  6. [Abstract wording] The abstract states the models were 'trained and tested using independent validation datasets,' but the validation set is a random 20% split of the same GSE124600 cohort (Section 3.6), not an independent cohort; please rephrase to avoid overclaiming.

Circularity Check

2 steps flagged · score 6.0 of 10

Validation AUROC of 0.89 is not an independent estimate: the 55-sample validation set was used in feature ranking (Section 3.4) and in choosing the best-performing model (Section 4.2.4), so the headline performance is partially fitted on the validation labels.

  1. fitted input called prediction [Section 3.4 (Feature Engineering and Feature Selection) and Section 3.6 (Performance Evaluation)]
    "Sorting was done based on maximum AUROC, and the top 100 methylation sites for both positive and negative correlation were selected. ... the dataset of a total of 274 samples was randomly split in an 80:20 ratios ... 20% data of 55 samples ... is used for validation called as validation dataset or independent dataset."

    The univariate AUROC ranking in Section 3.4 is computed on the full 274-sample dataset, before the 80:20 split described in Section 3.6. The 55 samples later designated as the 'independent' validation set therefore contribute their labels to choosing the top-100 candidate CpGs, and RFE then selects the final 25 features from this validation-informed pool. The validation AUROC of 0.89 is thus not an out-of-sample prediction: the validation labels were used to select the very features being tested. The paper's claim that the validation set was 'not involved in training or testing' is contradicted by this feature-selection-before-split pipeline.

  2. fitted input called prediction [Section 4.2.4 (Model developed on selected features by feature selection methods), Table 4]
    "In Table 4, we have reported the best-performing model over the validation dataset using RFE, SFS, and SVC-L1-based feature selection techniques. ... we observed the MLP-based model outperformed all and reported the highest AUROC as 0.89 and MCC as 0.78 with 25 methylated sites selected using the RFE feature selection technique."

    The reported model is explicitly the 'best-performing model over the validation dataset' among a large grid of configurations: three feature selectors (RFE, SFS, SVC-L1), multiple feature-set sizes, positive and negative correlation sets, and eleven classifiers. All of these candidates are evaluated on the same 55 validation samples, and the single configuration with the highest validation AUROC is then reported as the headline result. Selecting the maximum over this grid uses the validation labels as model-selection data, making the AUROC of 0.89 and MCC of 0.78 a selected maximum rather than an unbiased performance estimate for a pre-specified model.

full rationale

The paper's core claimed result is the MLP model with 25 RFE-selected features reaching AUROC 0.89 and MCC 0.78 on the validation set. Two independent procedural choices make this estimate partially circular. First, Section 3.4 ranks all CpG sites by univariate AUROC computed on the entire dataset, including the 55 samples that Section 3.6 later calls the independent validation set; the top-100 candidates from this full-data ranking are the input to RFE. Second, Section 4.2.4 reports the best-performing model over the validation dataset across a large grid of feature selectors, feature counts, and classifiers, so the reported 0.89 is the maximum validation score selected on the same 55 samples. Both practices turn the validation set into a feature-selection and model-selection set, invalidating the independence assumption that the abstract and Section 3.6 assert. There is no load-bearing self-citation chain, and the underlying data and external GEPIA2 expression check are genuine independent content; however, the central performance claim is an optimistic, partly fitted quantity. This warrants a score of 6: one or more 'predictions' reduce partially by construction, but the modeling pipeline still contains substantial independent empirical content.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim rests on a set of arbitrary analysis choices: the p-value cutoff, the top-100 site selection, the number of features retained by RFE, and the zero-imputation strategy. These are not derived from theory or external benchmarks and are fit to the same dataset used for validation. The axioms above are the key assumptions that, if false, would invalidate the reported AUROC.

free parameters (4)
  • Number of RFE-selected features = 25
    Chosen based on best validation AUROC; no independent justification or nested cross-validation.
  • p-value threshold for differential methylation = 0.05
    Standard threshold but no multiple-testing correction across 97,863 CpG sites; directly determines the 30,791-site pool.
  • Top 100 positively/negatively correlated sites = 100
    Arbitrary cut-off for the input to RFE, SFS, and SVC-L1, selected based on univariate AUROC on the full dataset.
  • Missing value imputation = 0
    All missing methylation values are replaced with zero, assuming absence of methylation rather than technical dropout.
assumptions (4)
  • domain assumption GSE124600 contains accurate MCTA-seq methylation values and correct sample labels.
    The study depends entirely on this public dataset without independent verification of sample annotations or data quality.
  • ad hoc to paper Replacing missing values with zero is a valid representation of unmethylated sites.
    In Section 3.2, missing values are set to zero; this is a strong, unvalidated assumption that affects all downstream statistics.
  • domain assumption The 80:20 random split is representative and does not introduce bias.
    A single random split is used without stratification or repeated resampling, and the feature selection is performed before the split, violating the independence of the split.
  • standard math Student's t-test assumptions (normality, equal variance) hold for each CpG site's methylation values.
    Section 3.3 applies a two-tailed t-test to 97,863 sites without checking normality or variance homogeneity, and with no multiple-testing correction.

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Pith. "Pith review of In silico tool for identification of colorectal cancer from cell-free DNA biomarkers." pith.science (2026). https://pith.science/paper/74WIW3GX

@misc{pith2026250511041,
  author       = {Pith},
  title        = {Pith review of: In silico tool for identification of colorectal cancer from cell-free DNA biomarkers},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/74WIW3GX}},
  note         = {Machine review of arXiv:2505.11041}
}
read the original abstract

Colorectal cancer remains a major global health concern, with early detection being pivotal for improving patient outcomes. In this study, we leveraged high throughput methylation profiling of cellfree DNA to identify and validate diagnostic biomarkers for CRC. The GSE124600 study data were downloaded from the Gene Expression Omnibus, as the discovery cohort, comprising 142 CRC and 132 normal cfDNA methylation profiles obtained via MCTA seq. After preprocessing and filtering, 97,863 CpG sites were retained for further analysis. Differential methylation analysis using statistical tests identified 30,791 CpG sites as significantly altered in CRC samples, where p is less than 0.05. Univariate scoring enabled the selection of top ranking features, which were further refined using multiple feature selection algorithms, including Recursive Feature Elimination, Sequential Feature Selection, and SVC L1. Various machine learning models such as Logistic Regression, Support Vector Machines, Random Forest, and Multi layer Perceptron were trained and tested using independent validation datasets. The best performance was achieved with an MLP model trained on 25 features selected by RFE, reaching an AUROC of 0.89 and MCC of 0.78 on validation data. Additionally, a deep learning based convolutional neural network achieved an AUROC of 0.78. Functional annotation of the most predictive CpG sites identified several genes involved in key cellular processes, some of which were validated for differential expression in CRC using the GEPIA2 platform. Our study highlights the potential of cfDNA methylation markers combined with ML and DL models for noninvasive and accurate CRC detection, paving the way for clinically relevant diagnostic tools.

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.