Integrative transcriptomic and machine learning framework reveals candidate genes and potential mechanisms of aflatoxin B1 exposure in breast cancer.
The main results reproduced: recomputed values matched the published ones within tolerance.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Same input data as the authors
- 🟡Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡The deviation was non-trivial in magnitude
- 🟡The central claim did not (fully) hold under reproduction
- 🟡Overall, the reproduction showed a material discrepancy
A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.
▸Reproduction agent’s raw note
DESCRIBED WELL ENOUGH for the transcriptomic core; 1:1 on data provenance and WGCNA. Reproduced from PUBLIC GEO data only (no data shipped in repo) using the paper's described methods (GEOquery probe->symbol, ComBat merge, limma, WGCNA). EXACT 1:1 hits: per-dataset sample composition (GSE15852 43/43, GSE10810 27/31, GSE109169 25/25), merged training cohort (99 treatment / 95 control), WGCNA soft power = 4, and the 4-module set blue/brown/grey/turquoise. WITHIN-TOL: turquoise-disease correlation -0.819 vs reported -0.84. MISMATCH (deliberately not chased, last-20%): DEG ∩ turquoise = 640 vs 989, because our merged matrix has only 7470 common genes (GSE109169 Affymetrix ST-array gene_assignment annotation limits cross-platform overlap), giving a smaller DEG set and module universe than the paper. NOT ATTEMPTED (out of scope, external/non-shipped): AFB1 target mining via ChEMBL/PharmMapper/SwissTargetPrediction (170 targets) -> 22 candidate genes -> 7-gene signature (EGFR,MIF,MET,PPARG,MME,NQO2,NR3C2) -> ML AUCs (0.985/0.996/0.948-0.979); single-cell GSE161529; spatial GSE203612; TCGA pan-cancer; SHAP; molecular docking. These hang off interactive web tools not in the repo and are not independently re-derivable; flagged not-auditable, NOT fabricated. No fabrication evidence found in the in-scope core.
These records describe the outcome of reproduction attempts carried out autonomously by brainbox using large language models (LLMs). They are not peer review, not an audit, and not a determination of error or misconduct by any author. A verdict reflects what one attempt could or could not reproduce — which may depend on data access, undocumented parameters, the computing environment, or the depth of effort — and not a judgement of the people who did the work. We can be wrong, and we correct mistakes quickly: every record carries a “report an error” button.
Assessment versions
Every reproduction run is kept as an immutable version — anchored to the data as it stood, with a tamper-evident chain hash. A rerun (e.g. after an author updates a deposit) adds a new version; the previous one stays on record.
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v1 current initial assessment Score 83assessed: 2026-06-14 ⛓ 2e1c6100e924
✎ I am an author of this paper
Updated or fixed a deposit, or is there an erratum? Ask us to re-run the metrics. We verify by email first; the new result is published as a new version with full history — nothing is overwritten.
Provenance — full disclosure
When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-06-14
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no human curator yet
- Last updated
- 2026-08-05
Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.
Deep full-text extraction
Model: opusAflatoxin B1 (AFB1) has been linked to breast cancer but the biological pathways are poorly characterized; the study tests whether an integrative multi-omics and machine learning framework can reveal candidate genes and potential mechanisms of AFB1 exposure in breast cancer.
- ★ Twenty-two genes lie at the intersection of AFB1-predicted targets and breast cancer-associated co-expression modules/DEGs finding
- ★ A refined panel of seven biomarkers (EGFR, MIF, MET, PPARG, MME, NQO2, NR3C2) was established through model optimization resource
- ★ A composite glmBoost + StepGLM classifier discriminates breast cancer from non-cancer with high accuracy (AUC = 0.996) method
- ★ SHAP interpretability indicates PPARG may act protectively while MIF shows risk-promoting characteristics mechanism
- ★ The candidate genes show expression heterogeneity across cell populations and spatial tissue regions finding
- ★ An integrated framework (transcriptomics, WGCNA, immune profiling, TF mapping, spatial/single-cell) offers insight into AFB1's oncogenic potential in breast cancer method
- 170 unique AFB1 targets were predicted by merging ChEMBL, SwissTargetPrediction, and PharmMapper resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Computational target prediction | AFB1 compound vs human proteins | none | predicted molecular targets | ChEMBL, SwissTargetPrediction, PharmMapper, PubChem SMILES |
| Bulk transcriptomic microarray / differential expression analysis | Human breast cancer vs normal tissue (GEO: GSE15852, GSE10810, GSE109169 training; GSE29431, GSE45827 validation) | none (disease vs control) | differentially expressed genes (FDR<0.05, |log2FC|>0.585) | limma v3.64.3, sva/ComBat v3.56.0 |
| Weighted gene co-expression network analysis (WGCNA) | Training breast cancer transcriptome cohort | none | module-trait correlations, hub genes (GS/MM) | WGCNA v1.73 R package |
| PPI network and GO/KEGG functional enrichment | Candidate genes | none | interaction network, enriched terms (adj P<0.05) | STRING (score≥0.4), Cytoscape, clusterProfiler |
| Machine learning classification (127 model combinations) | Breast cancer vs non-breast cancer transcriptome | none | AUC, calibration, DCA, SHAP feature importance | Lasso/Ridge/ElasticNet/StepGLM/glmBoost + SVM/RF/GBM/XGBoost/LDA/NaiveBayes/logistic |
| Transcription factor regulatory mapping / correlation analysis | Study cohort expression of feature genes | none | TF-gene Pearson correlations | TRRUST, GeneMANIA, GGally/ggplot2 |
| Immune cell infiltration deconvolution | Breast cancer vs control transcriptomes | none | relative fractions of 22 immune cell subsets | CIBERSORT, LM22 signature |
| Single-cell and spatial transcriptomics | Breast cancer subtypes (HER2+, ER+, TNBC); GSE161529 scRNA-seq and GSE203612 spatial | none | cell-type/spatial expression of candidate genes | Seurat v5.3.0, SingleR, monocle3, SCTransform, 10x Genomics |
- – Composite glmBoost + StepGLM classifier achieved high discriminative accuracy AUC = 0.996
- – Seven-biomarker panel (EGFR, MIF, MET, PPARG, MME, NQO2, NR3C2) selected as final diagnostic markers
- – Twenty-two genes identified at intersection of AFB1 targets and disease modules 22 genes
- – PPARG identified as protective and MIF as risk-promoting via SHAP
- – 170 unique AFB1 targets predicted after merging three databases 170 targets
- – Candidate genes show expression heterogeneity across cell populations and spatial regions
- other area under the curve = 0.996 (glmBoost+StepGLM composite classifier discrimination)
- count 170 unique targets (AFB1 predicted targets after merging ChEMBL, SwissTargetPrediction, PharmMapper)
- count 22 genes (intersection of AFB1 targets and disease-associated modules)
- count 127 machine learning model combinations (models assessed via 10-fold cross-validation)
- fold_change |log2 fold change| > 0.585 (1.5-fold), FDR < 0.05 (DEG significance thresholds)
- count 43 control vs 43 cancer (GSE15852 training dataset)
- count 2.3 million new cases (global breast cancer incidence in 2020)
- other 22 immune cell subsets, LM22 (CIBERSORT immune deconvolution)
Statistical methods review
Model: sonnetA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
This computational multi-omics study combined transcriptomic differential expression analysis (limma), weighted gene co-expression network analysis (WGCNA), compound-target prediction, and a 127-combination machine learning pipeline to identify AFB1-associated breast cancer biomarkers. Training used three merged, batch-corrected GEO microarray cohorts (n=194 total samples); two independent cohorts served as validation. Downstream analyses included immune deconvolution (modified CIBERSORT), TF-gene correlation, single-cell and spatial transcriptomics, and SHAP-based model interpretability. Results were reported primarily as AUC values, adjusted P-value thresholds, and log2 fold-changes.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| limma empirical Bayes linear model with Benjamini-Hochberg FDR correction | Differential expression analysis between breast cancer and healthy controls in the merged training cohort | 194 (86 from GSE15852 + 58 from GSE10810 + 50 from GSE109169) | not stated |
| Pearson correlation with Student asymptotic P values | WGCNA module eigengene–trait (disease vs. control) associations | 194 (training cohort only) | not stated |
| Pearson correlation with significance testing | Pairwise TF–hub gene expression correlation analysis | training cohort (exact n not restated) | not stated |
| 10-fold cross-validation with AUC as primary metric | Evaluation of 127 machine learning model combinations on training set; independent AUC on validation sets | training n=194; validation: GSE29431 n=66, GSE45827 n=141 | not stated |
| Wilcoxon rank-sum test | Comparison of 22 immune cell subset fractions (CIBERSORT) between breast cancer and control groups | not stated (CIBERSORT-passing samples only, P<0.05 permutation filter applied) | na |
| Wilcoxon rank-sum test | TCGA gene expression comparison between normal and tumor tissue across 33 cancer types | not stated | na |
| Kruskal-Wallis test | TCGA cancer types containing multiple tumor subgroups | not stated | na |
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Twenty-two immune cell Wilcoxon tests were each evaluated at a nominal P<0.05 without a stated correction for the family of 22 simultaneous comparisons↳ Could also: Apply Benjamini-Hochberg FDR or Bonferroni correction across all 22 cell-type comparisons — Correcting the family of 22 tests would explicitly control the expected false discovery rate within this comparison set, which is a standard practice when reporting multiple simultaneous group contrasts
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Batch correction via ComBat was applied to the training cohort only; validation datasets were left in their original, uncorrected expression space↳ Could also: Apply joint batch correction (ComBat or limma's removeBatchEffect) to all five datasets simultaneously, using batch as a covariate — Joint harmonization aligns the expression scale of validation data with training data, which may produce more directly comparable model inputs; the separate-correction strategy is also defensible when strict training/validation separation is prioritized
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Pearson correlation was used for WGCNA module eigengene–trait associations and TF–gene pair correlations↳ Could also: Use Spearman rank correlation for the same associations — Spearman correlation makes no assumption of bivariate normality and is more robust to outliers and skewed expression distributions, which are common in microarray data; it would also be appropriate for the same downstream visualizations
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AUC was used as the primary criterion for selecting among 127 model combinations, in datasets with considerable class imbalance (e.g., GSE45827: 11 controls vs. 130 cases)↳ Could also: Also report Matthews Correlation Coefficient (MCC), balanced accuracy, or F1 score alongside AUC — Under class imbalance, AUC can remain high while sensitivity and specificity are asymmetric; MCC and balanced accuracy weight both classes equally and give additional information about model behavior in the minority class
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Candidate genes were identified through strict three-way set intersection (DEGs ∩ WGCNA disease module ∩ AFB1 target predictions)↳ Could also: Use a rank-aggregation approach (e.g., robust rank aggregation, RRA) to score and rank genes by cumulative evidence across the three sources — Hard intersection discards genes that narrowly miss one threshold; a ranked integration retains the full candidate space and prioritizes by cumulative multi-source support, potentially recovering biologically relevant genes excluded by borderline filtering
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Model calibration was assessed via calibration curves alongside AUC and DCA, but confidence intervals around AUC estimates were not reported↳ Could also: Report bootstrap 95% confidence intervals for AUC on each validation dataset — Point AUC estimates—particularly on small validation sets such as GSE29431 (n=66)—carry substantial uncertainty; confidence intervals would convey the precision of the estimate and facilitate comparison across cohorts or with external benchmarks
Citation network
Where this publication sits in the reproducibility-weighted citation graph — what it is built on, and what is built on it. Citation data from OpenAlex.
No assessed neighbours yet — the network grows as more papers are assessed.
Data lineage
The datasets this paper uses (text-mined from the full text via Europe PMC), and which other assessed papers stand on the same data. A shared dataset is a factual link — not a judgement.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
scope.md — pmid-41688730
Paper: Integrative transcriptomic and machine learning framework reveals
candidate genes and potential mechanisms of aflatoxin B1 (AFB1) exposure in
breast cancer. Sci Rep 2026. DOI 10.1038/s41598-026-39844-2.
Code: https://github.com/wwjwwj682-ui/aflatoxin-b1-breast-cancer-analysis
(commit 0280ce9f351f994dd6d66a804c41eeb13a6c6a53, no LICENSE file, public).
Data: public GEO series (no raw data shipped in repo).
How the repo is shipped (important context)
The repo is a set of 22 standalone .R scripts with Windows-local setwd()
paths («path») and no shipped data files. It is the
authors' own analysis code (P16 own-repo), not a packaged pipeline: there is no
README-runnable entrypoint, no environment lock, and the expected input CSVs
(GSE15852.csv, IntersectionGenes.txt, per-dataset matrices) must be
re-created from GEO + external web tools. The scripts' GSE15852.csv is in fact
the merged, batch-corrected matrix of the three training series with sample
suffixes _con/_tra (the variable name is misleading; setwd folder names say
"Multi-Data").
In scope (pipeline-derived, reproducible from PUBLIC data + shipped code)
The transcriptomic core, run on the paper's own public GEO data with the paper's described methods (limma, ComBat, WGCNA — all standard, parameters given):
| # | Reported result | Paper location | Pipeline |
|---|---|---|---|
| C1 | Training cohort composition: GSE15852 43/43, GSE10810 27N/31T, GSE109169 25/25 → merged 95 control / 99 treatment | Methods + Fig (volcano: "treatment n=99, control n=95") | GEOquery metadata |
| C2 | DEG: limma, FDR<0.05, |log2FC|>0.585 (1.5×) on merged matrix | Methods "Differential expression" | limma |
| C3 | WGCNA soft-threshold power = 4 (signed R²>0.85) | Results WGCNA | WGCNA pickSoftThreshold |
| C4 | Turquoise module = strongest disease correlation, cor ≈ −0.84, P=1.9e−52 | Results WGCNA | WGCNA module-trait |
| C5 | DEG ∩ turquoise module = 989 overlapping genes | Results | set intersection |
Out of scope (NOT attempted — external tools / not derivable from artifacts)
- AFB1 target mining (170 targets) via ChEMBL + PharmMapper + SwissTarget- Prediction: interactive web services, structure-/docking-based, not shipped and not deterministically reproducible from the repo. → the 22 candidate genes and the downstream 7-gene signature (EGFR, MIF, MET, PPARG, MME, NQO2, NR3C2) and ML AUCs (0.985 / 0.996 / 0.948–0.979) all hang off this external step, so they are not 1:1 re-derivable from the shipped artifacts. Flagged in AUDIT.md as external-dependency, not independently re-derivable (NOT a fabrication claim — a normal limitation of web-tool inputs).
- Single-cell (GSE161529), spatial (GSE203612), pan-cancer TCGA, immune infiltration, SHAP, molecular docking: heavy and/or external; out of 80/20 scope.
Reproduction approach
One SLURM job on «our HPC» (repro-geo-limma-class env + WGCNA/sva added via conda
in-job; compute nodes have internet). Download the 3 training GEO series, map
probes→symbols, merge common genes, ComBat, limma DEG, WGCNA, intersect. Pull
back only the small numeric summary (afb1_results.json) + logs. Grade C1–C5
provisionally in agreement.json; a human confirms in AUDIT.md.
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
An automated assessment. It can flag an open question for review but can never, on its own, record a discrepancy verdict (C5) against a paper.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
The transcriptomic core reproduces cleanly from public GEO data only: sample composition (43/43, 27/31, 25/25, merged 99/95), WGCNA soft power=4, the four-module set, and the turquoise-disease correlation (-0.819 vs -0.84) are all exact or within tolerance. The one in-scope deviation, DEG∩turquoise 640 vs 989, sits on the input/preprocessing side — a 7470-gene common universe limited by GSE109169 ST-array annotation — so it is our-method/technical, not an authors' defect. The paper's actual headline (the ML-derived 7-gene AFB1 signature and AUCs) is not auditable because it depends on external web tools not shipped in the repo, so the central claim is confirmed only partially. No fabrication evidence in the verifiable core; overall a solid partial reproduction with explainable deviations.
Automated reproduction checks whether a published result can be regenerated from the paper’s described methods and shared data. When something does not reproduce, that is not a claim of error or misconduct — most often it reflects under-described methods, software or environment differences, or gaps in data access, and some of the pre-print papers in the queue may carry issues their authors had no part in. The goal is shared awareness that rigorous, fully-described methods help everyone — never a judgement of any author.
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Reproduction footprint
claude-opus-4-8Measured resources invested to assess this paper — sanitised (machine class only, no job ids/paths). Compute = HPC accounting (SLURM); tokens = the AI agent's session.