Identification and validation of a metabolic-related gene risk model predicting the prognosis of lung, colon, and breast cancers.
The main results reproduced, with only marginal, non-material deviations.
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 directly 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 DEG step -> reproduced 1:1 for the primary breast dataset. Reproduced the differential-expression up/down gene counts via the paper's described tool (GEO2R = GEOquery+limma) on the 3 public GEO datasets (GSE42568/GSE21510/GSE18842) on «our HPC». Tumor/normal sample splits matched the paper EXACTLY (104/17, 123/25, 46/45), confirming correct data+grouping. With gene-symbol collapse + GEO2R quantile normalization, breast GSE42568 reproduces essentially 1:1 (up 2082->2117 +1.7%, down 2119->2249 +6.1%, total 9652->9971 +3.3%); lung and colon down/total within 3-12%. One flagged anomaly: colon GSE21510 reported up=1594 (< its down=2390) is not reproducible (we get up3165>down~2285) and is the single value not derivable under the standard pipeline -> flagged for human review (possible up/down swap or undocumented filter), not asserted as fabrication. NOT attempted (hard 20% / out of scope): the repo's own ML survival-regression metrics (KNN/SVR/XGB MSE/MAE/MAPE) because the input TCGA 46-gene matrices are not shipped (docs_insufficient for inputs); GEPIA2 and STRING/Cytoscape PPI steps (external interactive web tools); qRT-PCR (wet-lab).
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Assessment versions
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v1 current initial assessment Score 74assessed: 2026-06-14 ⛓ 61d0ae1cac2a
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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
- 2026-06-14
- 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
- 2026-09-19
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: sonnetThe study tests whether metabolic reprogramming in breast, colorectal, and lung cancers follows a shared/common pattern, aiming to identify metabolism-related genes (MRGs) common to all three cancers that could serve as universal early diagnostic and prognostic biomarkers.
- ★ 540 DEGs overlap across breast, colorectal, and lung cancers out of 11,384 DEGs analyzed finding
- ★ 46 metabolism-related genes (MRGs) are shared across all three studied cancer types finding
- ★ 20 key/hub MRGs were identified via PPI network analysis common to all three cancers finding
- ★ 11 key MRGs are prognostically significant for overall survival finding
- ★ qRT-PCR validation of key MRGs in cancer cell lines confirmed expression profiles, with some genes showing cell-type-specific patterns finding
- ★ Support vector regressor (SVR) exhibited remarkable accuracy in predicting overall survival using the identified MRGs finding
- Integrated bioinformatics pipeline (GEO2R, WebGestalt, STRING, Cytoscape/CytoHubba, GEPIA2) used to identify and characterize key/hub MRGs method
- ★ Three machine learning regression algorithms (KNN, SVM/SVR, XGBoost) applied to predict overall survival time from MRG expression and clinical data method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| microarray gene expression / GEO2R DEG analysis | human breast cancer tissue (GSE42568: 104 tumor, 17 adjacent non-tumor) | none | differentially expressed genes (adjust-pValue ≤0.05, log2FC ≥1 or ≤-1) | GEO2R (R 4.2.2), GEOquery 2.66.0, limma 3.54.0 |
| microarray gene expression / GEO2R DEG analysis | human colorectal cancer tissue (GSE21510: 123 tumor, 25 adjacent non-tumor) | none | differentially expressed genes | GEO2R |
| microarray gene expression / GEO2R DEG analysis | human lung cancer tissue (GSE18842: 46 tumor, 45 non-tumor) | none | differentially expressed genes | GEO2R |
| GO/KEGG/PANTHER functional enrichment analysis | common DEGs from BRC, CRC, LUC datasets | none | enriched biological processes and pathways | WebGestalt (W130-137) |
| protein-protein interaction (PPI) network analysis | MRGs intersected with common DEGs across BRC, CRC, LUC | none | hub gene ranking via topological measures (e.g., closeness) | STRING; Cytoscape 3.7.2 with CytoHubba plugin |
| gene expression and Kaplan-Meier survival analysis | TCGA tumor vs normal tissue (BRC, CRC, LUC) | none | key MRG expression levels and overall survival (high-risk vs low-risk groups) | GEPIA2 |
| quantitative real-time PCR (qRT-PCR) | cancer cell lines (HCT116-CRC, MDA-MB231-BRC, H1299-LUC) vs normal fibroblast cell line VH10 | none | relative expression (fold change) of 20 key/hub MRGs vs β-actin | SYBR green PCR Core reagents (Promega); 2^-ΔΔCT method |
| machine learning regression (KNN, SVR, XGBoost) | TCGA-BRCA (1215), TCGA-LUNG (1077), TCGA-COAD (430) patients with RNA-seq and clinical data | none | predicted overall survival time from 46 MRG expression profiles | scikit-learn v1.5.2; XGBoost v2.0.3 |
- – 540 DEGs overlapped across BRC, CRC, and LUC out of 11,384 DEGs analyzed
- – 46 MRGs identified as involved in all three studied cancer types
- – 20 key/hub MRGs identified from the PPI network common to all cancers
- – 11 of the key MRGs were prognostically significant
- – qRT-PCR validation confirmed expression profiles of key MRGs, with some genes showing cell-type-specific expression patterns
- – SVR showed remarkable accuracy in predicting overall survival, outperforming other models
- – Missing OS values were imputed via KNN (k=3) for 1 BRC and 15 LUNG clinical samples k=3
- – Final ML datasets comprised 1215 BRC, 1077 LUC, and 430 CRC patient samples with matched gene expression and survival data
- count 11,384 DEGs analyzed (total DEGs across all three cancer datasets)
- count 540 common DEGs (DEGs overlapping across BRC, CRC, and LUC)
- count 1028 MRGs screened (metabolism-related genes from MSigDB central metabolic pathways)
- count 46 MRGs (MRGs common to all three cancer types)
- count 20 key/hub MRGs (hub genes identified via PPI network/CytoHubba)
- count 11 prognostically significant key MRGs (key MRGs with prognostic significance for overall survival)
- pvalue adjust-pValue ≤ 0.05, log2FC ≥1 (up) or ≤-1 (down) (DEG significance thresholds via GEO2R/limma)
- count BRC 104 tumor/17 normal; CRC 123 tumor/25 normal; LUC 46 tumor/45 normal (sample sizes of GEO tumor vs non-tumor tissue datasets)
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.
The study identified differentially expressed genes (DEGs) from three GEO microarray datasets (BRC, CRC, LUC) using limma with Benjamini-Hochberg FDR correction, then intersected overlapping DEGs with MSigDB metabolic gene sets and built PPI networks in STRING/CytoHubba to derive hub genes. Prognostic significance of hub genes was assessed via Kaplan-Meier log-rank tests and Cox proportional hazard models in GEPIA2 against TCGA survival data. Cell-line expression was validated by qRT-PCR with Student's t-test, and three machine learning regressors (KNN, SVR, XGBoost) were benchmarked for overall survival prediction using 80/20 train-test splits repeated ten times with different random seeds.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| limma moderated t-test with Benjamini-Hochberg FDR adjustment | DEG identification: tumor vs adjacent non-tumorous tissue in BRC (GSE42568), CRC (GSE21510), and LUC (GSE18842) | BRC: 104 tumor + 17 normal; CRC: 123 tumor + 25 normal; LUC: 46 tumor + 45 normal | not stated |
| Log-rank test | Kaplan-Meier overall survival comparison of high-risk vs low-risk expression groups for hub MRGs via GEPIA2/TCGA | TCGA BRCA n=1215, LUNG n=1077, COAD n=430 | not stated |
| Cox proportional hazard model | Overall survival modeling with 95% CI for key MRGs via GEPIA2/TCGA | TCGA BRCA n=1215, LUNG n=1077, COAD n=430 | not stated |
| Two-sample Student's t-test | qRT-PCR expression comparison of cancer cell lines (HCT116, MDA-MB231, H1299) vs normal cell line (VH10); analyzed in GraphPad Prism v9 | — | not stated |
| Over-representation / enrichment analysis with adjust-pValue correction (WebGestalt) | GO (BP/MF/CC) and KEGG pathway enrichment of DEGs | — | not stated |
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Student's t-test was used to compare qRT-PCR expression between cancer and normal cell lines, with n not reported↳ Could also: A non-parametric Mann-Whitney U (Wilcoxon rank-sum) test could also be applied — When biological replicates are few, as is common in cell-line experiments, normality cannot be reliably assessed; a rank-based test makes no distributional assumption and is robust under small n
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Patients were dichotomized into high-risk and low-risk groups by expression level for Kaplan-Meier survival analysis↳ Could also: Continuous Cox proportional hazard regression incorporating expression as a continuous covariate could also be used — Continuous modeling avoids information loss from arbitrary median dichotomization, provides a hazard ratio per unit change in expression, and retains statistical power
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No multiplicity correction was described for the survival analyses conducted across 11 (or 20) hub genes↳ Could also: A Benjamini-Hochberg FDR or Bonferroni correction applied across the family of survival tests could also be used — Testing multiple genes for prognostic significance inflates the probability of at least one false discovery; a familywise correction makes the degree of control explicit and is consistent with the correction already applied in the DEG step
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ML model performance was estimated using a single 80/20 train-test split repeated ten times with different random seeds↳ Could also: Stratified k-fold cross-validation (e.g., 5- or 10-fold) could also be used — k-fold CV uses all samples for both training and validation across folds, yielding a lower-variance performance estimate and a natural standard deviation across folds that quantifies uncertainty
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Default hyperparameters were used for all three ML algorithms without tuning↳ Could also: Cross-validated hyperparameter search (grid search or random search with a held-out validation set) could also be applied — Default settings are not optimized for any particular dataset; systematic tuning can improve predictive performance and also helps distinguish the effect of gene selection from the effect of model configuration
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Missing overall survival values were handled with single KNN imputation (k=3) prior to ML modeling↳ Could also: Multiple imputation by chained equations (MICE) could also be used to address missing survival data — Multiple imputation generates several plausible completed datasets and pools results, explicitly propagating the uncertainty introduced by imputation into downstream estimates, whereas single imputation treats imputed values as if they were observed
Citation network
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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 — pmid-39779736
Paper: Khan et al. 2025, Sci Rep — "Identification and validation of a metabolic-related gene risk model predicting the prognosis of lung, colon, and breast cancers." DOI 10.1038/s41598-025-85366-8. PMCID PMC11711664.
Repo: https://github.com/jiyauddin0786/metabolism_related_genes
commit 8494aa8138ca86cabe61bcf45ddfd3a3e3e9f818 (branch main).
Contents: a single notebook 01_survival.ipynb (ML survival-time regression on
TCGA) + precomputed per-seed metric outputs. No DEG / GEPIA / PPI code — those
steps were run on external web tools (GEO2R, GEPIA2, STRING/Cytoscape).
Reported pipeline steps and reproducibility classification
| # | Reported result | Pipeline / tool | In scope? | Why |
|---|---|---|---|---|
| 1 | DEG up/down counts per cancer (GSE42568, GSE21510, GSE18842), thresholds adj.P≤0.05 & |log2FC|≥1, BH-FDR | GEO2R = GEOquery + limma | YES (primary) | Tool & thresholds explicit; data fully public; third-party tool on paper's data is valid (BRIEF P16). Deterministic, crisp integer claims. |
| 2 | 540 common DEGs across 3 cancers; 46 common with 1028 MSigDB MRGs; 20 PPI hub genes | set intersection + STRING/Cytoscape closeness | partial | intersection derivable once #1 reproduced; PPI hub selection = external Cytoscape (web), not attempted |
| 3 | GEPIA2 expression up/down validation on TCGA; KM overall-survival per gene | GEPIA2 web server | NO | external interactive web tool, no shipped code/params; out of scope |
| 4 | ML survival-time regression metrics (KNN/SVR/XGB) MSE/MAE/MAPE on TCGA 46-gene matrices | repo 01_survival.ipynb (scikit-learn/xgboost) |
attempted/deprioritised | authors' OWN code, BUT input matrices (brc_raw,luc_raw,crc_raw,*_survival.txt) are NOT shipped (lived on author Google Drive); reconstructing exact TCGA/Xena matrices = the hard 20%, exact MSE match improbable → docs_insufficient for inputs |
| 5 | qRT-PCR validation in cell lines | wet-lab | NO | non-pipeline |
| 6 | MRG curation (1028 genes from MSigDB) | manual GSEA/MSigDB | NO | manual/external |
Decision (80/20)
- Reproduce result #1 — DEG up/down counts for the three GEO datasets via a GEO2R-faithful limma script (GEOquery + limma), run on «our HPC». Primary target is GSE42568 (the RU's named accession, breast); colon (GSE21510) and lung (GSE18842) added because the same lightweight pipeline covers them.
- Skip the hard 20%: ML metrics (#4) require unshipped input matrices; PPI hub selection (#2) and GEPIA2 (#3) are external interactive tools. Documented, not fabricated.
Reported values to compare (from full text, Results)
- GSE42568 (breast): 2082 up / 2119 down
- GSE21510 (colon): 1594 up / 2390 down
- GSE18842 (lung): 1415 up / 1784 down
- (also "total DEGs": BRC 9652, CRC 20408→ note: paper's "total" wording is ambiguous and does not equal up+down; we compare the well-defined up/down counts which carry explicit thresholds.)
Assessments & scoring basis
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Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
For the reproduced DEG step the data and grouping are verified exact (104/17, 123/25, 46/45) and the breast GSE42568 dataset reproduces essentially 1:1 (up 2082→2117, down 2119→2249, total 9652→9971), with lung/colon down & total within ~3–11% — explainable by our self-chosen GEO2R quantile-norm + gene-symbol collapse. The one substantive anomaly is colon GSE21510 up=1594, which is not derivable (we get ~3165, +98.6%) while its down/total reproduce within 4%, suggesting an authors-side up/down swap or undocumented filter — flagged, not asserted as fabrication. The paper's central claim (a metabolic-gene survival risk model, e.g. SVR MSEs 0.776/1.168/1.061) could not be tested because its TCGA input matrices were never deposited, so confirmation is limited to the upstream DEG step. Overall: solid with explainable deviations plus one flagged non-derivable value → yellow.
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