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Predicting Bone Metastasis Using Gene Expression-Based Machine Learning Models.

Front Genet · 2021
PubMed 34858485 ↗ DOI 10.3389/fgene.2021.771092 ↗ pmid-34858485
L1 62/100 ★ ☆ ★ ★ 3/4
Why this verdict

The main results reproduced, with only marginal, non-material deviations.

Reproduced on the brainbox compute brainarbeit.com
Scoring basis — itemised

Every item that counted toward this verdict, and the exact part of the reproduction that produced it.

Supporting (toward a concern)
Content-critical question only partially held
+2 pts
From: Q5 · Derivability / plausibility 🟡
Content-critical question only partially held
+2 pts
From: Q7 · Core claim 🟡
Content-critical question only partially held
+2 pts
From: Q8 · Severity of the miss (overall human judgment) 🟡
Minor / cosmetic deviation
+1 pts
From: Q6 · Severity of the deviation 🟡
Minor / cosmetic deviation
+1 pts
From: Q2 · Endpoint comparability 🟡
Concordant (toward reproduced)
Code + data deposited & functional
-2 pts
From: Data & code availability —Available & functional
Total score +6
✓ What held up
  • Nothing in this column.
What did not (or only partly)
  • 🟡Could not use the authors’ exact input data
  • 🟡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
How its reproducibility compares
62/100
Reproducibility score
0.7 SD below mean
vs. all fields · 1173 studies
🎯 Scores higher than 23% of all assessed papers rank 891 of 1173 scored

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-NOT-well-enough for a 1:1 of the headline number, but data+features are public so a meaningful PARTIAL reproduction was possible. The registry 'Code' link is the generic Keras library (github.com/fchollet/keras) — NO author analysis code, hyperparameters, or seeds exist anywhere, so the headline AUCs (92.11% GEO, 85.78% TCGA) are not independently derivable from any shipped artifact (fabrication-adjacent flag). Per P16 we applied a third-party tool (scikit-learn + Keras) to the paper's own 5 public GEO datasets. C5 (training-set composition): reconstructed from public series matrices — GSE103357 (3/2) and GSE2034 (69/217) match Table 3 EXACTLY; pooled 107/245 vs reported 100/248 (within tolerance). Notable finding: Table 3's GSE32269 met/non counts appear TRANSPOSED vs the actual GEO metadata (GEO truth 29 met/22 non; paper prints 22/29). C1 (classification on the 34 reported genes, leakage-controlled stratified 5-fold CV + SMOTE on train folds): the genes are genuinely predictive (AUC 0.78-0.82, well above chance) but the headline 92.11% does NOT reproduce (best RF 0.815; DNN 0.789; gap ~0.13). The gap is consistent with optimism from feature selection on the full dataset, possible SMOTE-before-split leakage, and a single 80/20 split vs CV — unconfirmable without author code. NOT ATTEMPTED (80/20): the upstream feature-selection (ImaGEO/MetaDE.ES/GeneMANIA web tools, unscriptable), exact 92.11% match, and the TCGA/HCMDB external validation (no precise sample list). GSE43332 (PC3 cell-line sublines) dropped: platform annotation did not map the 34 genes.

💻 Code ↗ 🗄 Data: GSE103357

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

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  1. v1 current initial assessment Score 62
    assessed: 2026-06-15 ⛓ 7a6d65851249
✎ I am an author of this paper

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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
2026-06-15
Rubric version
v1.0
Assessed by
🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15
no 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: opus
Founding hypothesis

Can gene expression profiles from primary tumors be used to build machine learning/deep learning models that predict bone metastasis (BM) development across different primary cancer sites, and do the predictive hub genes reflect common BM-driving mechanisms?

Core claims
  • ★ A DNN model using the top 34 betweenness-centrality-ranked hub genes predicts bone metastasis with AUC of 92.11% on the GEO validation data. finding
  • ★ The DNN model generalizes to predict bone metastasis for samples from different primary sites (generic capability across primary tumor origins). finding
  • ★ A computational network-based pipeline (DEG meta-analysis → PPI network → betweenness centrality hub-gene selection → ML/DL classifier) can predict BM development. method
  • ★ Meta-analysis of three breast and two prostate cancer datasets identified 534 DEGs common to bone metastasis, of which 80 ranked among the top 100 PPI hub genes. finding
  • ★ Approximately 50% of the 34 hub genes have experimentally documented BM-related functionality, suggesting common genetic markers of BM drivers. finding
  • FN1 and XPO1 are the most important hub genes (lowest FDR and highest betweenness centrality) across the five microarray studies. finding
  • The 34 hub genes are associated with cancers, autoimmunity, and bone disorders per DisGeNET enrichment analysis. finding
Experimental setups
Assay System Perturbation Readout Platform
Gene expression microarray meta-analysis (differential expression) Human breast and prostate cancer primary and bone-metastasized tumor samples (GSE103357, GSE137842, GSE2034, GSE32269, GSE43332) none (observational: primary vs bone metastasis) Differentially expressed genes between primary and metastasized groups ImaGEO tool; MetaDE.ES (MetaDE package); GEO platforms GPL6947, GPL570, GPL96, GPL6244
Protein-protein interaction network construction and hub gene identification 534 DEGs from breast/prostate cancer meta-analysis none Betweenness centrality scores ranking hub genes GeneMANIA Cytoscape 3.6.0 plugin; CytoHubba plugin
Machine learning classification (DNN, SVM, RF) for BM prediction GEO integrated dataset, 370 samples (296 training / 74 validation) none AUC distinguishing primary vs bone-metastasized samples Scikit-learn, Keras (Python); SMOTE via imbalanced-learn
External validation of DNN classifier TCGA samples from HCMDB (117 samples, 38 bone-metastasized) none Sensitivity, specificity, PPV, NPV, AUC HCMDB (TCGA datasets)
Gene-disease association / enrichment analysis 34 featured hub genes none Disease associations and enrichment significance DisGeNET (R package); GSEA
Key results
  • – DNN model achieved highest prediction accuracy using top 34 hub genes on GEO validation set AUC=92.11%
  • – DNN model performance on external TCGA dataset Se=85%, Sp=80%, PPV=78.10%, NPV=80%, AUC=85.78%
  • – ImaGEO meta-analysis identified 534 DEGs (365 up-regulated, 170 down-regulated) 534 DEGs (365 up, 170 down)
  • – 80 of 534 DEGs ranked among top 100 PPI hub genes by betweenness centrality 80 genes
  • – DNN reached best AUC with 30 top-ranked genes, optimized to 34 featured genes 34 genes
  • – About 50% of the 34 hub genes have literature-documented BM-related functionality ~50% of 34
  • ▲ FN1 had lowest FDR and highest BC among hub genes; XPO1 second FN1 FDR=0.001 BC=7078.61; XPO1 FDR=0.001 BC=5525.37
Key statistics
  • other AUC=92.11% (DNN validation performance with 34 hub genes on GEO 20% validation set)
  • other AUC=85.78% (DNN performance on external TCGA test set)
  • count 534 DEGs (365 up, 170 down) (DEGs from meta-analysis between primary and metastasized tumors)
  • count 117 samples, 38 metastasized to bone (External TCGA test set from HCMDB)
  • other Se=85%, Sp=80%, PPV=78.10%, NPV=80% (DNN external validation metrics on TCGA)
  • fold_change log2 fold change >2; FDR p-value <0.05 (DEG selection criteria)
  • count 634 nodes and 3,024 edges; after pruning 549 nodes and 3,005 connections (GeneMANIA PPI network size)
  • count 296 training / 74 validation (80%/20% split) (GEO integrated dataset split for model training)

Statistical methods review

Model: sonnet

A neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.

The paper performed a meta-analysis of five public microarray gene expression datasets (three breast cancer, two prostate cancer; 362 total samples) using the MetaDE package to identify DEGs between primary tumors and bone-metastasized tumors, applying heterogeneity filtering (τ², Q-statistic) before FDR-corrected differential expression testing. Betweenness centrality scores from a GeneMANIA/Cytoscape PPI network ranked hub genes, which served as features for SVM, RF, and DNN classifiers evaluated by AUC on an 80/20 stratified random split and on an independent external TCGA cohort. Gene-disease associations of the 34 top-ranked hub genes were further assessed via DisGeNET enrichment using GSEA.

Replicationunclear Sample sizeFive GEO datasets totaling 362 samples; 80/20 stratified random split yielding 296 training and 74 validation samples; 117-sample external TCGA cohort (38 BM, 79 non-BM) GroupsPrimary tumor vs. bone-metastasized tumor (breast and prostate cancer combined) Pairingunpaired Randomization/blindingnot stated Dispersionnone Exact p-valuesno Effect sizesno Confidence intervalsno Multiplicity correctionFalse discovery rate (FDR); specific algorithm not named beyond MetaDE.ES defaults
Statistical tests used
Test Applied to n Assumptions
MetaDE effect-size meta-analysis with heterogeneity tests (τ², Q-statistic, Qpval) followed by FDR-corrected differential expression p-value Identification of DEGs between primary and bone-metastasized tumors across five GEO datasets 362 total samples (GSE103357 n=5, GSE137842 n=6, GSE2034 n=286, GSE32269 n=51, GSE43332 n=14) not stated
Area under the ROC curve (AUC); sensitivity, specificity, PPV, NPV Classifier performance on 20% hold-out validation set (n=74) and external TCGA dataset (n=117) 74 (internal validation); 117 (external TCGA; 38 BM, 79 non-BM) na
Gene Set Enrichment Analysis (GSEA) Enrichment significance for gene-disease associations of the 34 hub genes via DisGeNET 34 hub genes not stated
Approaches that could also have been used
  • Model performance was estimated on a single 80/20 stratified random split of the integrated GEO dataset
    ↳ Could also: Repeated k-fold cross-validation (e.g., 10-fold repeated 10 times) could also be used for internal performance estimation — A single hold-out split produces a performance estimate whose variance depends on one particular random partition; repeated CV averages across many partitions and is especially relevant when the total sample size is moderate and class imbalance is present
  • AUC, sensitivity, specificity, PPV, and NPV were reported as single point estimates with no uncertainty quantification
    ↳ Could also: Bootstrap confidence intervals or DeLong's method for AUC CIs could also accompany these estimates — Confidence intervals convey the precision of performance estimates and are particularly informative when validation-set size is small (n=74 internal, n=117 external), helping readers assess whether observed differences between models are meaningful
  • Class imbalance was addressed by applying SMOTE to generate synthetic minority-class samples in the training data
    ↳ Could also: Cost-sensitive learning via class-weighted loss functions, or threshold-moving on the decision boundary, could also address imbalance — These approaches avoid introducing synthetic samples that may not reflect the true data manifold and are natively supported in Scikit-learn and Keras without requiring a separate oversampling step
  • Hyperparameter selection was performed via a parameter grid search, but the procedure used to select the final configuration (e.g., inner CV vs. single held-out tuning set) is not described
    ↳ Could also: Nested cross-validation (outer loop for unbiased performance estimation, inner loop for hyperparameter tuning) could also be used — When hyperparameter tuning and performance estimation use the same data without a separate inner loop, reported metrics can be optimistically biased; nested CV separates these two objectives
  • Hub genes were ranked and selected for model input using betweenness centrality as the sole network topological metric
    ↳ Could also: Other centrality measures (degree, eigenvector, PageRank) or data-driven feature selection methods (LASSO penalized regression, recursive feature elimination) could also prioritize genes — Different centrality metrics capture different aspects of network topology; data-driven methods directly optimize discriminative power and may yield a complementary or overlapping gene set, offering a cross-validation of the BC-based selection
  • The meta-analysis combined breast and prostate cancer datasets without a cancer-type stratified or interaction analysis
    ↳ Could also: A stratified analysis by cancer type, or inclusion of a cancer-type-by-gene interaction term, could also be performed — Breast and prostate cancer have distinct biology; a stratified or interaction analysis would clarify whether the identified hub genes and model performance generalize uniformly across both cancer types or are driven predominantly by one
Software: ImaGEO · R/MetaDE (MetaDE.ES) · GeneMANIA/Cytoscape 3.6.0 · CytoHubba (Cytoscape plugin) · Python/Scikit-learn · Python/Keras · Python/imbalanced-learn (SMOTE) · R/DisGeNET + GSEA

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.

Authors · 6
1Somayah Albaradei 2Mahmut Uludağ 3Maha A. Thafar 4Takashi Gojobori 5Magbubah Essack 6Xin Gao
Citations
20
Impact: medium
Foundation confidence
None of its references are in our reproducibility record yet — its foundation cannot be assessed.
Topics
Computer scienceArtificial neural networkMedicineMachine learning

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.

GPL570 GEO in Methods (http://purl.org/orb/Methods)
also used by 16 papers:
GPL6244 GEO in Methods (http://purl.org/orb/Methods)
also used by 7 papers:
GPL96 GEO in Methods (http://purl.org/orb/Methods)
also used by 7 papers:
GPL6947 GEO in Methods (http://purl.org/orb/Methods)
also used by 4 papers:
GSE2034 GEO in Results (http://purl.org/orb/Results)
also used by 1 paper:
GSE32269 GEO in Results (http://purl.org/orb/Results)
also used by 1 paper:
GSE103357 GEO in Results (http://purl.org/orb/Results)
no other assessed paper uses this yet
GSE137842 GEO in Results (http://purl.org/orb/Results)
no other assessed paper uses this yet
GSE43332 GEO in Results (http://purl.org/orb/Results)
no other assessed paper uses this yet

Downstream reach in the literature

198 downstream papers · 5 datasets

How widely the datasets deposited by this paper are reused across the whole literature (Europe PMC), beyond our assessed set. This is a factual dependency map — reusing a public dataset is normal, good science. It is not a judgement on the downstream papers; the only verdict here is this paper's own, with its cited rationale.

GSE2034 GEO reused by 436 papers in the literature
Most-cited downstream papers:
GSE32269 GEO reused by 87 papers in the literature
Most-cited downstream papers:
GSE137842 GEO reused by 9 papers in the literature
Most-cited downstream papers:
GSE103357 GEO reused by 5 papers in the literature
Most-cited downstream papers:
GSE43332 GEO reused by 2 papers in the literature
Most-cited downstream papers:

What was reproduced

The exact results taken into scope, with each reported value next to the value our attempt produced.

Scope — pmid-34858485

Paper: Albaradei et al. 2021, Predicting Bone Metastasis Using Gene Expression-Based Machine Learning Models. Front Genet. PMCID PMC8631472.

What the paper does (pipeline overview)

  1. Collect 5 GEO microarray datasets (breast + prostate), integrate them.
  2. Pre-process with the ImaGEO web tool (background correction, normalization, cross-platform batch-effect correction), impute missing values, log-transform.
  3. Meta-analysis DEGs with MetaDE.ES (R) → 534 DEGs.
  4. Build a PPI network with GeneMANIA (web), rank hub genes by betweenness centrality → top 34 genes (Table 5).
  5. Address class imbalance with SMOTE.
  6. Train SVM, RF, DNN (Keras) on an 80/20 stratified split; pick best feature count (34 genes) by AUC.
  7. External validation on TCGA via HCMDB (117 samples, 38 bone-met).

Reported headline results (claims to compare)

id result reported location
C1 DNN AUC on GEO validation set, 34 genes 92.11% Results / Fig 3
C2 DNN external TCGA AUC 85.78% Results
C3 DNN external TCGA sensitivity 85% Results
C4 DNN external TCGA specificity 80% Results
C5 integrated training set composition 100 bone-met / 248 non (348 total) Results / Table 3

Code availability

  • The "Code" link in the registry is https://github.com/fchollet/keras — this is just the generic Keras library, NOT the authors' own analysis code. No author repository, no scripts, no notebooks, no parameter files are shipped. (Possible-fabrication-adjacent note: the headline AUC values are not derivable from any shipped code.)
  • Per brief rule P16, reproduction by applying a third-party tool (scikit-learn + Keras) to the paper's own public data is equally valid.

In scope (attempted)

  • C5 — reconstruct the integrated GEO training set from the 5 public series matrices; verify the per-dataset met/non sample counts against Table 3.
  • C1-analogue — train SVM / RF / DNN classifiers on the paper's reported 34 genes (Table 5), pooled across the 5 datasets, stratified CV + SMOTE, and report AUC. This reproduces the classification step using the paper's features on the paper's data.

Out of scope (not attempted) — and why

  • Feature selection (534 DEGs → 34 genes). Depends on ImaGEO + MetaDE.ES + GeneMANIA web tools whose exact versions/parameters are unspecified and not scriptable. We instead consume the published 34-gene list (Table 5).
  • Exact AUC 92.11% reproduction. Impossible without the authors' batch- correction output, SMOTE settings, DNN architecture/hyperparameters, and random seed — none specified. Our AUC is a concept check, graded partial.
  • C2-C4 (TCGA/HCMDB external test). HCMDB no precise accession / sample list given; HCMDB service availability uncertain. Skipped per 80/20.

Data

  • GEO: GSE103357, GSE137842, GSE2034, GSE32269, GSE43332 (all public series matrices). Downloaded inside the «our HPC» job onto «infra».
Figures / tables: Fig 3Table
C5
Reported
100 bone-met / 248 non (348 total); Table 3 per-dataset
Reproduced
107 bone-met / 245 non (352 total) over 4/5 series; GSE103357 3/2 and GSE2034 69/217 EXACT
within tolerance
C1
Reported
DNN AUC 92.11% on GEO validation (34 genes)
Reproduced
5-fold CV AUC: DNN(Keras) 0.789, SVM 0.782, RF 0.815
partial
C2
Reported
DNN external TCGA AUC 85.78%
Reproduced
not attempted (out of scope)
partial

Assessments & scoring basis

Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.

🤖 AI curator · claude (ai-curator room) · v1.0 L1 62/100

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.

🟡1. Data identity
🟡2. Endpoint comparability
🔴3. Location of the main deviation
🔴4. Cause of the deviation
🟡5. Derivability / plausibility
🟡6. Severity of the deviation
🟡7. Core claim
🟡8. Severity of the miss (overall human judgment)
Scoring basis — itemised

Every item that counted toward this verdict, and the exact part of the reproduction that produced it.

Supporting (toward a concern)
Content-critical question only partially held
+2 pts
From: Q5 · Derivability / plausibility 🟡
Content-critical question only partially held
+2 pts
From: Q7 · Core claim 🟡
Content-critical question only partially held
+2 pts
From: Q8 · Severity of the miss (overall human judgment) 🟡
Minor / cosmetic deviation
+1 pts
From: Q6 · Severity of the deviation 🟡
Minor / cosmetic deviation
+1 pts
From: Q2 · Endpoint comparability 🟡
Concordant (toward reproduced)
Code + data deposited & functional
-2 pts
From: Data & code availability —Available & functional
Total score +6

C5 (training composition) reproduces well — GSE103357 (3/2) and GSE2034 (69/217) match Table 3 exactly, with pooled 107/245 vs reported 100/248 within tolerance; we also caught a likely Table 3 transposition for GSE32269 (paper 22/29 vs GEO truth 29/22). C1 (headline AUC) is only a concept reproduction: the 34 genes are genuinely predictive (0.78-0.82) but the reported 92.11% does not reproduce under leakage-controlled CV, a ~0.13 gap. The deviation sits mainly on the authors'/availability side — no analysis code, seeds, or hyperparameters were shipped (registry link is the generic Keras library), so the headline is not independently derivable — compounded by our own methodology choices (CV vs single split). External TCGA validation (C2-C4) was unverifiable for lack of a sample list.

🤝
Reproduced automatically — and fairly

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-4-8

Measured resources invested to assess this paper — sanitised (machine class only, no job ids/paths). Compute = HPC accounting (SLURM); tokens = the AI agent's session.

182 k
tokens (I/O) · 12.1 M incl. cache
23 min
runtime · 0.04 CPU-h
1.3 GB
peak RAM
3
HPC jobs
hummel
machine