A platelet-related signature for predicting the prognosis and immunotherapy benefit in bladder cancer based on machine learning combinations.
Provisional — an automated or curator check raised a specific concern and points reviewers here. This is NOT a final assessment and not a determination about the authors.
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.
- ✓Reported values were directly comparable
- 🟡Could not use the authors’ exact input data
- 🔴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 to attempt a clean 1:1 check. The paper re-uses the third-party IRLS ML-combination framework (github.com/Zaoqu-Liu/IRLS @7951eac), which ships only colorectal lncRNA demo data, so I reproduced the paper's OWN published 10-gene Elastic-Net platelet risk score on its OWN named accession GSE31684 (GPL570, n=93) on «our HPC»: all 10 signature genes mapped and the printed coefficients were applied verbatim, then I recomputed the 1/3/5-yr time-dependent AUC + Harrell C-index. RESULT = PARTIAL: the signature is prognostic in the correct direction (AUC>0.5, C 0.55-0.58) and the 1-yr AUC roughly reproduces (best 0.611 vs reported 0.631), BUT the reported 3-yr (0.694) and 5-yr (0.707) AUCs are ~0.09-0.12 higher than anything the published formula yields on GSE31684, and that shortfall is ROBUST across 4 preprocessing variants (overall vs disease-specific survival x z-score vs raw log2). This is logged as a possible-optimism flag for human review (not an accusation; benign explanations exist: probe-collapse choice, the exact n=90 subset, a different time-AUC estimator, or per-cohort coefficient refitting). NOT ATTEMPTED (hard 20%): the full 101-combination IRLS rerun selecting Enet(alpha=0.4) at C=0.73 - it needs unshipped inputs (the 480-PRG list, the 5084 DEGs, the 19-gene Cox panel, and 5 harmonized cohorts incl. TCGA-BLCA); and the wet-lab TUBA1C knockdown / immune-TME / IMvigor210 downstream (out of computational scope).
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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v1 current initial assessment Score 51assessed: 2026-06-14 ⛓ b85722ba422d
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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 aims to determine whether platelet-related genes can be used to construct a machine-learning-based signature (PRS) that predicts prognosis and immunotherapy benefit in bladder cancer.
- ★ An Enet (alpha=0.4) machine-learning algorithm built from 10 platelet-related genes yields the optimal platelet-related signature (PRS) for bladder cancer prognosis, with average C-index 0.73 resource
- ★ High PRS score is associated with worse overall survival across TCGA and multiple GEO cohorts finding
- ★ PRS is an independent risk factor for bladder cancer overall survival and outperforms clinical characteristics and 52 previously published prognostic signatures in C-index finding
- ★ Low PRS score correlates with a more immune-activated tumor microenvironment (higher CD8+ T cell, NK cell, macrophage M1, cytolytic activity, T cell co-stimulation) finding
- ★ Low PRS score is associated with higher TMB, higher PD-1/CTLA4 immunophenoscore, and lower TIDE/ITH/immune escape scores, indicating better predicted immunotherapy benefit finding
- ★ In three independent immunotherapy cohorts (IMvigor210, GSE91061, GSE78220), low PRS score patients show better immunotherapy response and survival finding
- Low PRS score is associated with lower IC50 (greater predicted sensitivity) to several chemotherapy and targeted therapy drugs finding
- ★ TUBA1C, the gene with the highest coefficient in the PRS, is upregulated in bladder cancer and its knockdown suppresses bladder cancer cell proliferation finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| bulk transcriptome differential expression analysis (limma) | bladder cancer tissue vs normal tissue (TCGA, GSE13507, GSE31684, GSE32894, GSE48276) | none | differentially expressed genes (|log2FC|>1.5, P<0.05) | limma R package |
| univariate/multivariate Cox regression | bladder cancer patient cohorts (TCGA + GEO) | none | prognostic significance of platelet-related genes and PRS | R software 3.5.0 |
| integrative machine learning modeling (101 algorithm combinations, 10 ML methods, LOOCV) | TCGA and GEO bladder cancer cohorts | none | C-index of prognostic models | R scripts (IRLS framework, https://github.com/Zaoqu-Liu/IRLS) |
| immune infiltration deconvolution (TIMER, xCell, MCP-counter, CIBERSORT, CIBERSORT-ABS, EPIC, quanTIseq, ESTIMATE, GSVA) | bladder cancer tissue (TCGA/GEO) | none | immune cell abundance, immune/stromal/ESTIMATE scores, immune function gene set scores | — |
| drug sensitivity/immunotherapy benefit prediction (TMB, immunophenoscore, DEPTH2 ITH score, TIDE score, oncoPredict IC50) | TCGA bladder cancer cases and immunotherapy cohorts (IMvigor210, GSE91061, GSE78220) | none | predicted immunotherapy response/benefit and drug IC50 | TCIA, DEPTH2, TIDE website, oncoPredict R package |
| GSEA / hallmark pathway functional enrichment | bladder cancer, stratified by PRS score group | none | differentially enriched signaling pathways | GSEA |
| immunohistochemistry | human tissue (Human Protein Atlas) | none | TUBA1C protein expression | Human Protein Atlas |
| RT-qPCR, CCK-8 proliferation assay, wound healing assay | bladder cancer cell lines (RT4, T24, J82, UM-UC-3, 5637) and normal SV-HUC-1 cells; TUBA1C siRNA knockdown in UM-UC-3 and 5637 | siRNA knockdown of TUBA1C | TUBA1C mRNA expression, cell proliferation, migration | ABI 7900HT (RT-qPCR), CCK-8 kit (Beyotime) |
- – Enet (alpha=0.4)-based 10-gene PRS achieved the highest average C-index of 0.73 among 101 model combinations C-index=0.73
- ▲ High PRS score predicted worse overall survival with 1-, 3-, 5-year AUC of 0.754, 0.779, 0.806 in TCGA (similar trends in GSE13507, GSE31684, GSE32894, GSE48276) AUC 0.754/0.779/0.806
- – PRS confirmed as independent risk factor by univariate/multivariate Cox analysis in TCGA and all GEO datasets P<0.05
- ▼ PRS score negatively correlated with CD8+ T cell, NK cell, and macrophage M1 abundance
- – Low PRS group had higher TMB and PD-1/CTLA4 immunophenoscore, lower TIDE, ITH and immune escape scores P<0.05
- ▲ High PRS score patients were non-responders to anti-PD-1 therapy and had worse survival in IMvigor210 (P=0.002) and similar findings in GSE91061 and GSE78220 P=0.002; P<0.01 for response rate
- ▼ Low PRS score associated with lower IC50 for docetaxel, cisplatin, 5-fluorouracil, paclitaxel, axitinib, crizotinib, foretinib, and lapatinib P<0.05
- ▼ TUBA1C knockdown suppressed bladder cancer cell proliferation
- count 480 PRGs identified from GSEA gene sets (platelet-related gene collection)
- count 5,084 DEGs identified (bladder cancer vs normal, |log2FC|>1.5, P<0.05)
- count 19 genes (prognostic biomarkers from univariate Cox analysis among PRGs)
- other C-index = 0.73 (average C-index of optimal Enet (alpha=0.4) PRS model)
- fold_change AUC 0.754, 0.779, 0.806 (1-,3-,5-year, TCGA) (PRS prognostic performance in TCGA cohort)
- pvalue P<0.05 (univariate/multivariate Cox PRS independent risk factor; immune correlations; drug IC50 comparisons)
- pvalue P=0.002 (overall survival difference by PRS score in IMvigor210 cohort)
- count cohort sizes: TCGA n=396, GSE13507 n=165, GSE31684 n=90, GSE32894 n=223, GSE48276 n=73, IMvigor210 n=298, GSE91061 n=98, GSE78220 n=28 (dataset sample sizes used)
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 developed a platelet-related prognostic signature (PRS) for bladder cancer by fitting 101 machine learning algorithm combinations to TCGA training data and selecting the optimal model (elastic net, alpha=0.4) by average C-index across four independent GEO validation cohorts. The signature was evaluated for independent prognostic value by univariate and multivariate Cox regression, for survival discrimination by Kaplan-Meier curves and time-dependent ROC analysis, and for immunotherapy predictive utility by comparing immune infiltration scores, checkpoint expression, TMB, TIDE, ITH, and drug IC50 values between PRS-stratified groups across seven additional datasets. In vitro siRNA knockdown of TUBA1C—the gene with the highest PRS coefficient—was conducted in two bladder cancer cell lines and assessed by CCK-8 proliferation assay.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| limma-based moderated t-test (differential expression) | DEG identification between bladder cancer and normal tissue in TCGA; threshold |log2FC|≥1.5 and P<0.05 | TCGA cancer n=396; number of normal samples not stated | not stated |
| Univariate Cox proportional hazards regression | Screening 480 PRGs for prognostic relevance; evaluating PRS and clinical covariates as risk factors across all cohorts | TCGA n=396; GSE13507 n=165; GSE31684 n=90; GSE32894 n=223; GSE48276 n=73 | not stated |
| Elastic net regularized Cox regression (Enet, alpha=0.4), LOOCV framework across 101 algorithm combinations | PRS model construction in TCGA training cohort; model selection by average C-index across GEO cohorts | TCGA n=396 | not stated |
| Kaplan-Meier survival analysis (log-rank test implied) | Overall survival comparison between high and low PRS groups in five prognostic cohorts and three immunotherapy cohorts | TCGA n=396; GSE13507 n=165; GSE31684 n=90; GSE32894 n=223; GSE48276 n=73; IMvigor210 n=298; GSE91061 n=98; GSE78220 n=28 | not stated |
| Time-dependent ROC / AUC | 1-, 3-, and 5-year survival prediction performance across all prognostic cohorts | Per cohort as above | na |
| Multivariate Cox proportional hazards regression | Confirming PRS as independent risk factor after adjusting for age, gender, tumor grade, and clinical stage | TCGA n=396 and all GEO cohorts | not stated |
| Unpaired Student's t-test, one-way ANOVA, Chi-squared test, or Fisher's exact test (as appropriate) | Between-group comparisons of immune cell abundance, ESTIMATE/immune/stromal scores, TMB, immunophenoscore, TIDE, ITH, IC50 drug values, and immunotherapy response rates (high vs. low PRS) | null | not stated |
| GSEA (gene set enrichment analysis) and GSVA (gene set variation analysis) | Functional pathway enrichment and hallmark gene set scores across PRS groups | null | na |
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The PRS stratification cutoff was determined using the surv_cutpoint function, which selects the threshold that maximizes the log-rank statistic within the same dataset↳ Could also: A pre-specified cutoff (e.g., median, tertiles, or a clinically motivated value) could also be applied, or the continuous PRS score could be modeled directly in a Cox model — A data-driven cutpoint optimized on the test sample inflates type I error and may reduce reproducibility across external cohorts; a fixed or pre-specified threshold would be more transportable and avoids the statistical penalty of implicit multiple testing over possible cut values
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Model selection from 101 algorithm combinations was based on average C-index evaluated in the same GEO datasets used for comparative performance reporting throughout the paper↳ Could also: Nested cross-validation or bootstrap resampling, with a completely separate held-out test set reserved exclusively for final performance estimation, could also be used — When the same external cohorts serve both as the selection criterion and as the reported benchmark, the reported C-index may reflect the best of many candidates rather than unbiased generalization; a nested design or fully independent test set would provide a less optimistic performance estimate
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Dozens of simultaneous between-group comparisons (immune cell types, drug IC50 values, biomarker scores, checkpoint genes) were each tested at an unadjusted P<0.05 threshold↳ Could also: Applying a Benjamini-Hochberg FDR correction across each family of related comparisons would also be a standard approach — With many simultaneous tests, the expected number of false positives at P<0.05 increases proportionally; FDR adjustment quantifies and controls the expected proportion of false discoveries among declared significant results
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DEG filtering used an unadjusted P<0.05 threshold alongside |log2FC|≥1.5 across thousands of transcripts↳ Could also: Applying a Benjamini-Hochberg FDR-adjusted q-value threshold (e.g., q<0.05 or q<0.1) as the primary filter is also standard with limma for large-scale differential expression — At the scale of a transcriptome-wide test, unadjusted P<0.05 is expected to yield a substantial proportion of false-positive DEGs; FDR adjustment would reduce the number of spurious genes entering the PRG candidate pool
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Results from seven independent immune deconvolution algorithms (TIMER, xCell, MCP-counter, CIBERSORT, CIBERSORT-ABS, EPIC, quanTIseq) were reported in parallel without formal synthesis↳ Could also: A consensus or rank-aggregation approach across algorithms could also produce a single, integrated estimate per immune cell type — Individual deconvolution methods use different reference matrices and statistical assumptions and do not always agree; a consensus score reduces algorithm-specific noise and facilitates a cleaner, single interpretation per cell type
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Survival outcomes were summarized by dichotomizing patients into high vs. low PRS score groups for Kaplan-Meier display↳ Could also: Modeling the continuous PRS score with restricted cubic splines in a Cox model could also characterize the survival-risk relationship — Dichotomization discards the prognostic information within each half of the distribution and can obscure dose-response non-linearity; a continuous spline model preserves the full distribution and allows visualization of whether the PRS-hazard relationship is monotonic or has inflection points
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.
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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
170 downstream papers · 2 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.
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What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-39280688
Paper: Chen et al. (2024) Transl Androl Urol. "A platelet-related signature for predicting the prognosis and immunotherapy benefit in bladder cancer based on machine learning combinations." PMID 39280688 · PMCID PMC11399026 · DOI 10.21037/tau-24-80.
Code referenced by paper: https://github.com/Zaoqu-Liu/IRLS — this is the
Zaoqu-Liu lab's generic "integrative machine-learning + LOOCV" framework (the
Machine Learning based intergration.R script that builds 101 model combinations
from 10 algorithms). The repo ships the lab's colorectal lncRNA demo data
(model-prepare.rda), not the bladder-cancer inputs of this paper. So the
repo is a third-party tool re-used by the paper — applying it (or the model it
produced) to the paper's own data is a valid reproduction (BRIEF rule P16).
The pipeline (paper Methods)
- Collect 480 platelet-related genes (PRGs) from GSEA "platelet" gene sets.
- DEGs (|log2FC|≥1.5, p<0.05) → 5084; ∩ PRGs + univariate Cox → 19 prognostic PRGs.
- Feed the 19 genes into the IRLS framework: 10 ML algorithms → 101 combinations, LOOCV in TCGA-BLCA (n=396), scored by mean C-index across 5 cohorts (TCGA, GSE13507, GSE31684, GSE32894, GSE48276).
- Winner: Elastic Net (Enet, alpha=0.4), 10-gene signature, mean C-index 0.73.
- Published model (Fig 2 / Results):
RS = 0.2011·P2RY1 + 0.1512·PDPN − 0.1285·DGKQ + 0.1692·MMRN1 + 0.1568·RSU1 + 0.2870·GNB3 + 0.7020·TUBA1C + 0.0880·MFN2 − 0.0941·RABGAP1L + 0.0006·KIF1B - Downstream: TME/immune-checkpoint/TMB/IPS comparisons, IMvigor210 immunotherapy response, TUBA1C knockdown CCK-8 (wet-lab).
In scope (attempted)
- R1 — Validate the published 10-gene Enet signature on GSE31684 (the paper's
named data accession; Affymetrix GPL570, n≈93, has
survival.months+last known status). Recompute the time-dependent AUC at 1/3/5 years and Harrell's C-index, compare to the paper's reported GSE31684 values (AUC 0.631 / 0.694 / 0.707). This is the cleanest 1:1 check: paper's own model formula → paper's own data → same metric. Uses the standard IRLS preprocessing (per-gene z-score within cohort).
Out of scope / not attempted (the hard ~20%) — and why
- Full 101-combination IRLS rerun selecting Enet(alpha=0.4) at C=0.73. Requires the unshipped inputs: the exact 480-PRG list, the 5084 DEGs, the 19-gene Cox panel, and 5 harmonized cohorts (incl. TCGA-BLCA RNA + survival). The repo ships only colorectal demo data; the bladder inputs are not provided. Reconstructing the gene-selection cascade would not be a faithful reproduction of their run.
- Other cohorts' AUCs (TCGA/GSE13507/GSE32894/GSE48276) — could be added with more per-cohort clinical wrangling; GSE31684 is the named accession and suffices for a clear data point. Optionally added if the primary run is clean.
- Wet-lab (TUBA1C knockdown, CCK-8) — not computational, out of scope.
- Immune/TME/IMvigor210 downstream — depends on the constructed risk groups; secondary, not attempted in the 80%.
Caveat affecting exactness
The reported per-cohort AUC depends on (a) probe→gene collapse, (b) expression normalization (z-score vs raw log), and (c) the OS event definition. We fix these to the conventional IRLS choices and report them explicitly; small deviations from 0.631/0.694/0.707 are expected and graded honestly (within-tol / partial).
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.
This is a clean 1:1 check — the paper's published 10-gene Elastic-Net platelet signature (all 10 genes + coefficients transcribed exactly, C5 exact) applied to its own named GEO accession GSE31684. The 1-year AUC reproduces closely (0.611 vs reported 0.631) and the signature is prognostic in the correct direction, but the reported 3yr (0.694) and 5yr (0.707) AUCs are not derivable from the published formula — our best is 0.604/0.587, a ~0.09-0.17 shortfall that is robust across 4 preprocessing variants. The deviation sits in the output statistic rather than the input model; cause is genuinely ambiguous between our methodological choices (probe-collapse, exact n=90 subset, time-AUC estimator) and optimistic authors'-side reporting, so it is flagged for human review as possible-optimism, not fabrication. Overall: a solid reproduction with real, explainable but unresolved mid/long-term discrepancies — yellow.
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
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