Assessment tool based on fatty acid metabolic signatures for predicting the prognosis and treatment response in bladder cancer.
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 a FAITHFUL 1:1 reproduction of the central validation result. PRIMARY target = GSE32894 external-validation panel (Fig 3 G/H), reproduced on the authors' own shipped data (GEO/GSE32894_exp.rds + GSE32894_cli.RData) via «our HPC» SLURM «job». timeROC AUC reproduced to within rounding: 1y 0.797 vs 0.80, 3y 0.837 vs 0.84, 5y 0.857 vs 0.86; KM low-vs-high RiskScore p=2.0e-6 with low-risk better (matches text); 5-gene set (SRC,CTLA4,CTSE,LAMA2,ADAMTSL4) confirmed. NOT attempted (the hard 20%): TCGA training cohort (3 FAM subtypes via ConsensusClusterPlus, 13-gene LASSO @ lambda=0.0309, the TCGA-trained 5-gene coefficients, and TCGA AUC 0.71/0.68/0.65) -- these need the TCGA expression matrix BLCA_TPM.txt which the repo READS but does NOT ship, plus un-seeded stochastic steps (cv.glmnet, ConsensusClusterPlus resampling) the authors did not pin, so the exact panel is non-deterministic. HONESTY FLAGS for human audit: (1) paper claims 316 GSE32894 samples but the authors' shipped data has 216 -- not derivable from deposited artifacts; (2) validation AUC (0.80-0.86) exceeds training AUC (0.65-0.71) because the validation RiskScore Cox is re-fit on GSE32894 itself rather than applying TCGA coefficients -> optimistic in-sample bias (reproducible, but not true external validation); (3) repo not runnable as-shipped (undefined helpers ggplotTimeROC/writeMatrix, mismatched load() filenames) -- numeric results obtained by re-implementing the well-specified steps.
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Assessment versions
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v1 current initial assessment Score 71assessed: 2026-06-14 ⛓ 3137ac802552
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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
- 2026-08-05
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: sonnetFatty acid metabolism (FAM)-related gene expression patterns can be used to define molecular subtypes and build a prognostic risk model that predicts prognosis and treatment (immunotherapy/chemotherapy) response in bladder cancer (BLCA) patients.
- ★ Consensus clustering of prognosis-related fatty acid metabolism genes (FAMGs) identifies three molecular subtypes of BLCA (FAMC1, FAMC2, FAMC3) with distinct prognoses and tumor microenvironments finding
- ★ FAMC1 subtype shows the most favorable prognosis, highest FAM score, and a less immunosuppressive tumor microenvironment finding
- ★ FAMC3 subtype shows the greatest immunosuppression (highest immune checkpoint expression, TIDE/Exclusion/Dysfunction scores) and worse predicted immunotherapy benefit finding
- ★ A five-gene RiskScore (SRC, CTLA4, CTSE, LAMA2, ADAMTSL4), derived via LASSO and multivariate Cox regression on FAMGs from a PPI network module, predicts BLCA prognosis method
- ★ Patients with low RiskScore have significantly better survival, more favorable immune microenvironment, and better predicted immunotherapy response than high RiskScore patients finding
- ★ The RiskScore's prognostic performance is validated in an independent GEO cohort (GSE32894) finding
- RiskScore correlates with predicted chemotherapy drug sensitivity (IC50) for eight chemotherapeutic agents finding
- ★ A Nomogram integrating RiskScore and clinical variables provides strong prognostic value for BLCA, confirmed by calibration, decision curve, and ROC analyses method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Bulk RNA-seq / high-throughput expression data (bioinformatic analysis) | TCGA-BLCA cohort (403 tumor samples) | none | FAMG expression, univariate Cox prognostic association | TCGA GDC portal |
| Consensus clustering (pam algorithm, spearman distance) | TCGA-BLCA tumor samples | none | molecular subtype assignment (FAMC1-3) | SangerBox / consensus clustering software |
| Gene set enrichment analysis (GSEA/GSVA, HALLMARK gene sets) | FAMC1-3 subtype groups, TCGA-BLCA | none | differentially activated biological pathways | MSigDB h.all.v7.5.1 gene sets |
| Immune deconvolution (CIBERSORT, ESTIMATE, MCP-Count, ssGSEA) | TCGA-BLCA tumor samples | none | immune/stromal cell infiltration scores, immune checkpoint gene expression | CIBERSORT web tool; ESTIMATE; MCP-Count; GSVA package |
| Protein-protein interaction network construction and MCODE module analysis | Differentially expressed FAMGs across FAMC1-3 | none | PPI network modules, GO/KEGG enrichment | STRING database v11.5; Cytoscape 3.9.1 |
| LASSO and multivariate Cox regression (RiskScore construction) | TCGA-BLCA prognostic FAMGs (Module 1) | none | gene coefficients, RiskScore formula | R (pRRophetic-adjacent Cox modeling) |
| External validation of RiskScore (survival analysis, ROC) | GSE32894 cohort (316 samples) | none | AUC for 1/3/5-year survival prediction | GEO dataset GSE32894 |
| Chemotherapy drug sensitivity prediction (ridge regression) and immunotherapy response scoring (TIDE, T-cell-inflamed GEP) | TCGA-BLCA RiskScore subgroups | none | predicted IC50 for 8 agents; TIDE score; GEP score | pRRophetic R package; TIDE web tool |
- – Optimal clustering at k=3 defined three FAM molecular subtypes (FAMC1-3) based on consensus matrix/CDF
- ▲ FAMC1 patients had the most favorable prognosis and the highest FAM score
- ▲ Immune checkpoint genes CD80, PDCD1LG2, HAVCR2, CD28, CTLA4, and PDCD1 showed sequential increase from FAMC1 to FAMC3
- ▲ FAMC3 had significantly higher Exclusion, TIDE, and Dysfunction scores than FAMC1/FAMC2
- – LASSO Cox model selected 13 FAMGs at optimal lambda; multivariate Cox narrowed this to 5 genes (SRC, CTLA4, CTSE, LAMA2, ADAMTSL4) forming the RiskScore lambda = 0.0309
- – RiskScore predicted 1-, 3-, 5-year survival in TCGA-BLCA cohort AUC 0.71 / 0.68 / 0.65
- – RiskScore predicted 1-, 3-, 5-year survival in GSE32894 validation cohort AUC 0.8 / 0.84 / 0.86
- ▲ Low RiskScore patients had significantly higher T-cell-inflamed GEP score, indicating better predicted immunotherapy response
- count 403 tumor samples (TCGA-BLCA training cohort size)
- count 316 samples (GSE32894 validation cohort size)
- count 30 prognosis-related FAMGs (univariate Cox screening, FDR<0.05 & |log2FC|>1)
- count 55 FAMGs with prognostic impact (p<0.05) in Module 1 (univariate Cox on PPI module genes)
- other lambda = 0.0309, 13 FAMGs retained (10-fold cross-validated LASSO Cox model)
- other RiskScore = -0.171*SRC - 0.303*CTLA4 - 0.092*CTSE + 0.29*LAMA2 + 0.12*ADAMTSL4 (final multivariate Cox RiskScore formula)
- other AUC 0.71 (1yr), 0.68 (3yr), 0.65 (5yr) (ROC for RiskScore in TCGA-BLCA cohort)
- other AUC 0.8 (1yr), 0.84 (3yr), 0.86 (5yr) (ROC for RiskScore in GSE32894 validation cohort)
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 bioinformatics study used TCGA-BLCA (n=403) as a training cohort and GSE32894 (n=316) as a validation cohort to characterize fatty acid metabolism in bladder cancer. Molecular subtypes were derived via consensus clustering of prognostic fatty acid metabolism genes, and a five-gene RiskScore was constructed through a sequential univariate Cox → LASSO Cox → multivariate Cox pipeline. Survival differences were visualized with Kaplan-Meier curves and model discrimination was summarized by time-dependent ROC/AUC at 1, 3, and 5 years; immune infiltration and treatment-response differences across groups were characterized using CIBERSORT, ESTIMATE, MCP-Count, ssGSEA, and TIDE scores.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Univariate Cox proportional-hazards regression | Initial screening of prognostic FAMGs from TCGA-BLCA (p<0.05; results section also applies FDR<0.05 & |log2FC|>1); secondary screening of 55 DEGs from PPI Module 1 | 403 | not stated |
| LASSO Cox regression with 10-fold cross-validation | Feature selection reducing 55 candidate FAMGs to 13 (optimal lambda=0.0309) | 403 | not stated |
| Multivariate Cox proportional-hazards regression | Final selection of 5-gene prognostic model (SRC, CTLA4, CTSE, LAMA2, ADAMTSL4) and Nomogram construction | 403 | not stated |
| Kaplan-Meier survival analysis (log-rank test implied, not explicitly named) | Prognosis comparison across FAMC1/2/3 subtypes and high vs. low RiskScore groups in both TCGA-BLCA and GSE32894 cohorts; subgroup analyses by age, sex, stage, grade, TNM | 403 (training); 316 (validation) | not stated |
| limma linear model with empirical Bayes moderation (differential expression) | Identification of DEGs across FAMC1-3 subtypes (FDR<0.05, |log2FC|>1) | 403 | not stated |
| Gene Set Enrichment Analysis (GSEA) and single-sample GSEA (ssGSEA via GSVA package) | Biological pathway enrichment in molecular subtypes and RiskScore groups (FDR<0.05); immune cell and TME signature scoring; FAM pathway activity score; T-cell-inflamed GEP score | 403 | not stated |
| Time-dependent ROC / AUC | Predictive discrimination of RiskScore at 1, 3, and 5 years in TCGA-BLCA (AUC 0.71/0.68/0.65) and GSE32894 (AUC 0.80/0.84/0.86); Nomogram calibration and decision curve also reported | 403 (training); 316 (validation) | na |
| Ridge regression (pRRophetic R package) | Estimation of IC50 values for eight chemotherapeutic agents in high vs. low RiskScore groups | 403 | not stated |
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Model discrimination was summarized by time-dependent AUC at three fixed time points (1, 3, 5 years)↳ Could also: Harrell's concordance index (C-index) or Uno's C-statistic could also quantify discrimination for a Cox-based survival model — The C-index integrates discrimination across the entire observed follow-up rather than at selected time points and is the canonical performance metric for Cox models, making results more directly comparable with published prognostic signatures
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Patients were dichotomized into high/low RiskScore groups at the median↳ Could also: The continuous RiskScore, or tertile/quartile groupings, could also be used in survival analyses — Median dichotomization discards within-group prognostic variation; retaining the score as continuous (or using finer groupings) preserves that information and avoids the sensitivity of results to the chosen cut-point
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LASSO Cox was used for variable selection among 55 candidate genes↳ Could also: Elastic-net Cox regression (mixing L1 and L2 penalties) could also be applied for variable selection — Pure LASSO selects arbitrarily among correlated genes; elastic net tends to produce more stable gene signatures when predictors are correlated, which is common in co-expressed transcriptomic data
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Consensus clustering used the PAM algorithm with Spearman correlation distance over 500 bootstraps↳ Could also: Non-negative Matrix Factorization (NMF) or hierarchical clustering with Ward linkage could also be used to derive molecular subtypes — Different algorithms can produce different subtype solutions; NMF is widely used in transcriptomic cancer subtyping and yields interpretable metagene patterns, while Ward hierarchical clustering is another established approach — reporting sensitivity across methods would characterize subtype robustness
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Four parallel immune deconvolution methods (CIBERSORT, ESTIMATE, MCP-Count, ssGSEA) were applied concurrently to characterize TME↳ Could also: xCell or TIMER2.0 could also estimate immune cell composition, or a single method could be pre-selected based on a published benchmark for bulk RNA-seq — Different deconvolution tools use different reference matrices and assumptions; applying multiple tools without a primary pre-specified method can complicate interpretation — selecting one tool based on benchmarking, or reporting concordance across tools, makes the results more interpretable
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The initial univariate Cox screening of FAMGs used an uncorrected p<0.05 threshold across multiple genes before LASSO refinement↳ Could also: An FDR-adjusted threshold (e.g., BH-corrected p<0.05 or p<0.10) could also be applied at this first screening step — Applying FDR correction at the univariate screening stage reduces entry of false-positive associations into the LASSO step, potentially improving the stability and reproducibility of the final gene signature
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.
Downstream reach in the literature
100 downstream papers · 1 datasetsHow 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.
- Comprehensive Molecular Characterization of Muscle-I... 2017 · 1,754 cites
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- Siglec15 shapes a non-inflamed tumor microenvironmen... 2021 · 277 cites
- Genomic Subtypes of Non-invasive Bladder Cancer with... 2017 · 232 cites
- An EMT-related gene signature for the prognosis of h... 2020 · 158 cites
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — PMID 38076064 (BLCA Fatty-acid metabolism RiskScore)
Paper: Assessment tool based on fatty acid metabolic signatures for predicting
the prognosis and treatment response in bladder cancer. Heliyon 2023;e22768.
Repo: https://github.com/xchen1212/BLCA_Fatty_acid (commit on main, pushed 2023-08-15).
Data: TCGA-BLCA (training) + GEO GSE32894 (validation).
Pipeline-derived results (candidate for reproduction)
| # | Result | Pipeline | Data shipped in repo? | In scope? |
|---|---|---|---|---|
| R1 | 3 FAM molecular subtypes (FAMC1/2/3) | ssGSEA(HALLMARK FAM) → ConsensusClusterPlus (pam) k=3 on TCGA | TCGA expr NOT shipped (only clinical + CNV) → needs GDC; consensus has stochastic resampling | 20% / partial |
| R2 | 13-gene LASSO panel @ lambda=0.0309 | univariate Cox → cv.glmnet LASSO on TCGA | needs TCGA expr + upstream DEG/PPI; cv.glmnet random, no seed in code | 20% (not byte-reproducible) |
| R3 | 5-gene RiskScore = −0.171·SRC −0.303·CTLA4 −0.092·CTSE +0.29·LAMA2 +0.12·ADAMTSL4 | stepwise Cox on the 13 | needs TCGA expr; depends on R2 | 20% |
| R4 | GSE32894 validation: timeROC AUC@1/3/5y = 0.80 / 0.84 / 0.86 (Fig 3H) | coxph(5 genes) on GSE32894 → riskscore z → timeROC | YES — GSE32894_exp.rds + GSE32894_cli.RData both shipped | PRIMARY (deterministic) |
| R5 | GSE32894 KM high vs low RiskScore, logrank p<0.05 (Fig 3G) | median-split risk, survfit logrank | YES (shipped) | PRIMARY (deterministic) |
| R6 | TCGA timeROC AUC@1/3/5y = 0.71 / 0.68 / 0.65 (Fig 3E) | coxph(5 genes) on TCGA → timeROC | needs GDC TCGA TPM; 5-gene coefs given | SECONDARY (if time) |
Decision (80/20)
PRIMARY = R4 + R5 — the GSE32894 validation cohort. Both inputs are shipped in the repo, the model genes are explicitly stated, and the computation (coxph linear predictor → median split → timeROC/logrank) is fully deterministic. This is a central reported result (Fig 3 G/H/I) and self-contained.
SECONDARY = R6 (TCGA training AUC) if the GDC download + 5-gene scoring is quick.
NOT attempted (the hard, stochastic, or non-shipped 20%): R1–R3 — they require
the un-shipped TCGA expression matrix (BLCA_TPM.txt is read by the script but
not in the repo) AND involve un-seeded stochastic steps (ConsensusClusterPlus
resampling, cv.glmnet fold assignment) that the authors did not pin, so the exact
13-gene panel / k=3 partition is not byte-reproducible by construction.
Reproducibility-surface notes (honesty)
- The repo's
scripts/scripts.Rcalls several undefined helper functions (ggplotTimeROC,writeMatrix) not present inbase.Rand not from any declared package → the script is not runnable as-is. We re-implement the well-specified parts (timeROC via thetimeROCCRAN pkg; coxph linear predictor perget_riskscore). - The shipped GEO file names differ from those the script
load()s (GSE32894_exp.rdsshipped vsGSE32894_exp.RDataloaded;GSE32894_cli.RDatashipped vsGSE32894.RDataloaded) → minor adaptation. - Methodological flag: GSE32894 RiskScore Cox is re-fit on the validation data itself (not applying the TCGA-trained coefficients), which inflates validation AUC above the training AUC (0.80–0.86 > 0.65–0.71). Recorded as a possible optimistic-bias note for the human auditor.
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 primary validation target reproduces cleanly: GSE32894 timeROC AUCs (0.797/0.837/0.857) match the reported 0.80/0.84/0.86 within rounding, KM separation is strongly significant (p=1.98e-06, low-risk better), and the 5-gene set is confirmed — so the prognostic core claim holds. However, three flags keep this at yellow: (1) the paper's n=316 cohort is not derivable from the authors' own shipped 216-sample data, and the TCGA training AUCs depend on an un-deposited TPM matrix; (2) the 'external validation' is actually an in-sample Cox re-fit on GSE32894 (validation AUC > training AUC), an optimistic bias that limits the central external-validation claim; (3) the repo is not runnable as shipped. The unresolved anomalies sit on the authors'/data-availability side rather than our method, with no fabrication evidence for the reproduced values.
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.