Comprehensive analysis of a novel RNA modifications-related model in the prognostic characterization, immune landscape and drug therapy of 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.
- Nothing in this column.
- 🟡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
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 reproduce the MODEL but NOT, 1:1, its headline validation. The registry code link (github.com/ZhengXia/DaPars) is a text-mining false positive (a cited APA tool, not the paper's analysis code); the paper ships no analysis repo, so per P16 we re-ran the described pipeline on the paper's own public data using the published 14-gene LASSO-Cox model (Suppl. Table S10, recovered in full; text betas match exactly). Applying it on «our HPC»: (1) GSE13507 (a meta-GEO training component, microarray) -> RMS significantly stratifies OS, HR=1.70 (p=0.029), correct direction -> the association is real and our pipeline is correct. (2) TCGA-BLCA, the paper's headline independent validation -> reported HR=1.53 (p=0.006) but we obtain only HR=1.18-1.33 (p=0.06-0.28) with continuous C-index ~0.51 (non-discriminative): direction reproduces, the significant effect does NOT. The works-on-microarray-training / fails-on-RNA-seq-validation pattern is the classic signature of cross-platform transfer loss / over-fit validation; combined with the paper's under-specified TCGA normalization and platform-dependent fixed threshold (median 3.344), this is a partial reproduction with the headline TCGA number flagged for human audit (not asserted as fabrication). NOT attempted (hard ~20%): de-novo LASSO re-derivation (non-deterministic), full 8-set meta-GEO sva assembly (HR=3.00), nomogram, immune/GSVA/TISIDB, GDSC drug, IMvigor210 immunotherapy. All raw/intermediate data kept on «infra»; only small results + pointers on «host».
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 71assessed: 2026-06-15 ⛓ 22e9f848054d
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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
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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: opusThe study tests whether a prognostic model built from the 'writer' enzymes of five adenine-related RNA modifications (m6A, m6Am, m1A, APA, and A-to-I editing) can characterize the clinical outcome, immune landscape, and therapeutic efficacy of bladder cancer.
- ★ Two distinct RNA modification patterns exist among BCa samples with radically different clinical outcomes and biological characteristics. finding
- ★ An RNA modification 'writers' score (RMS) model of 14 phenotype-associated prognostic DEGs predicts unfavorable BCa prognosis in both training and validation cohorts. resource
- ★ RMS-high tumors are enriched for immunosuppressive cell infiltration and activation of EMT, angiogenesis, and IL-6/JAK/STAT3 signaling. finding
- ★ The RMS model stratifies responsiveness to chemotherapeutic agents and antibody-drug conjugates between RMS-low and -high groups. finding
- ★ Combining the RMS model with TMB, TNB and PD-L1 improves discrimination of immunotherapy responders from non-responders. finding
- WGCNA identifies hub genes (e.g., KIAA1429/VIRMA) associated with RNA modifications, validated in human BCa specimens. method
- ★ Unsupervised clustering of 34 RNA modification 'writers' plus LASSO regression constructs the RMS signature. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Bulk transcriptomics / unsupervised clustering | 1,410 BCa patients (meta-GEO of 8 GEO datasets) | none | RNA modification cluster assignment from 34 'writer' gene expression profiles | GEO platforms GPL6102, GPL6947, GPL6244, GPL570 |
| RNA-seq based LASSO model construction/validation | meta-GEO training cohort and TCGA-BLCA validation cohort | none | RMS risk score and prognosis (OS) | — |
| Somatic mutation / CNV analysis | TCGA-BLCA (412 mutation, 409 CNV, 411 RNA-seq samples) | none | mutation landscape, copy number variation | VarScan2; maftools R package |
| WGCNA co-expression network analysis | 411 TCGA BCa patients (5,657 prognosis-associated genes) | none | hub genes correlated with clinical traits | WGCNA R package |
| Immunohistochemistry (TMA) | 84 paired human BCa and adjacent non-neoplastic bladder tissues | none | KIAA1429 protein staining score (intensity x proportion) | anti-KIAA1429 antibody (25712-1-AP, Proteintech) |
| RT-qPCR | human BCa tissues | none | KIAA1429 mRNA expression (GAPDH-normalized) | Roche LightCycler 480 II; SYBR Green; HiScript II Q RT SuperMix |
| Drug sensitivity prediction | BCa cohorts | chemotherapeutic agents / antibody-drug conjugates | predicted therapeutic response by RMS group | Genomics of Drug Sensitivity in Cancer (GDSC) database |
| Immune cell infiltration / GSEA | BCa TME (meta-GEO) | none | immune cell infiltration and pathway enrichment by RMS group | — |
- ▲ RMS positively correlated with unsatisfactory outcome in meta-GEO training cohort HR = 3.00, 95% CI = 2.19–4.12
- ▲ RMS associated with poor outcome in TCGA-BLCA validation cohort HR = 1.53, 95% CI = 1.13–2.09
- – Nomogram showed high prognostic prediction accuracy C-index = 0.785
- – Combining RMS with TMB, TNB and PD-L1 distinguished immunotherapy responders from non-responders AUC = 0.828
- ▲ Immunosuppressive cell infiltration and EMT, angiogenesis, IL-6/JAK/STAT3 signaling enriched in RMS-high group
- – Two distinct RNA modification patterns identified with varying clinical outcomes
- other HR = 3.00, 95% CI = 2.19–4.12 (RMS prognostic association in meta-GEO training cohort)
- other HR = 1.53, 95% CI = 1.13–2.09 (RMS prognostic association in TCGA-BLCA validation cohort)
- other C-index = 0.785 (nomogram prognostic prediction accuracy)
- other AUC = 0.828 (RMS + TMB/TNB/PD-L1 immunotherapy response discrimination)
- count 1,410 (BCa samples in meta-GEO cohort)
- count 34 (RNA modification 'writers' used for clustering)
- count 14 (RNA modification phenotype-associated prognostic DEGs in RMS model)
- count 84 (paired BCa samples in tissue microarray)
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 retrospective bioinformatics study merged eight GEO bladder cancer datasets into a meta-GEO training cohort (n = 1,410) and used TCGA-BLCA (n = 411) as a validation cohort. Unsupervised clustering of 34 RNA modification writer gene-expression profiles identified two molecular subtypes, and LASSO regression built a 14-gene RNA modifications-related score (RMS) from subtype-associated DEGs. Cox proportional hazards models quantified the prognostic value of RMS, ROC curves and a nomogram assessed predictive accuracy, and WGCNA identified hub genes that were subsequently validated in 84 paired tumor/adjacent-normal tissue microarray samples by IHC and RT-qPCR.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Unsupervised clustering (specific algorithm not stated) | Classification of 1,410 BCa samples into two RNA modification patterns based on 34 writer gene-expression profiles | 1410 | not stated |
| LASSO (Least Absolute Shrinkage and Selection Operator) regression | Feature selection and construction of the 14-gene RMS prognostic signature from RNA modification phenotype-associated DEGs | 1410 | not stated |
| Cox proportional hazards model | Prognostic evaluation of RMS in meta-GEO training cohort (HR = 3.00, 95% CI 2.19–4.12) and TCGA-BLCA validation cohort (HR = 1.53, 95% CI 1.13–2.09) | 1410 (training); 411 (validation) | not stated |
| ROC curve analysis / AUC | Prediction of immunotherapy response (AUC = 0.828 for RMS combined with TMB, TNB, PD-L1) and nomogram performance assessment | — | not stated |
| Concordance index (C-index) | Evaluation of nomogram prognostic prediction accuracy (C-index = 0.785) | — | na |
| Pearson's correlation coefficient | WGCNA: association between module eigengenes and clinical traits (tumor stage, histological grade, survival status); module membership (MM) and gene significance (GS) calculations | 411 | not stated |
| GSEA (Gene Set Enrichment Analysis) | Biological pathway and immune characteristic analysis between RMS-high and RMS-low groups | — | not stated |
| Decision curve analysis (DCA) | Assessment of clinical net benefit / utility of the nomogram | — | na |
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Unsupervised clustering was used to identify two RNA modification patterns, but the specific clustering algorithm is not named↳ Could also: Consensus clustering (e.g., R/ConsensusClusterPlus) or non-negative matrix factorization (NMF) could also be applied, with explicit reporting of the algorithm, the range of k tested, and stability metrics such as the cophenetic correlation coefficient or silhouette width — Reporting the algorithm and cluster-stability metrics would allow readers to assess whether the two-cluster solution is well-separated and whether additional cluster numbers were considered, which is a standard expectation in molecular subtyping studies
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LASSO regression was used to select 14 prognostic genes from a larger DEG set↳ Could also: Elastic net regression or a random survival forest could also be used for variable selection in a survival context — Elastic net combines LASSO sparsity with ridge grouping and can perform better when predictors are correlated, as co-expressed genes often are; random survival forest makes fewer distributional assumptions and can capture non-linear effects
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Pearson's correlation was used throughout WGCNA for module-trait associations and gene-level correlations↳ Could also: Spearman's rank correlation could also be used for the same purposes — Spearman's correlation is more robust to non-normality and outliers in gene expression data, particularly relevant for TPM-transformed RNA-seq values whose distributions are often right-skewed
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Batch effects across eight GEO platforms were corrected using the ComBat algorithm↳ Could also: limma's removeBatchEffect, surrogate variable analysis (SVA), or PEER factors could also be applied for cross-platform harmonization — SVA estimates latent factors without requiring fully known batch labels and can capture unmeasured sources of systematic variation; reporting post-correction PCA or other diagnostics helps confirm adequate batch removal
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DEGs between the two RNA modification clusters were identified (method and threshold not specified) without an explicitly stated multiplicity correction↳ Could also: Applying a Benjamini-Hochberg FDR correction across all tested genes — and reporting the FDR threshold alongside the number of genes passing it — is a standard approach when screening thousands of genes simultaneously — Stating the FDR threshold and the resulting DEG count allows readers to assess the likely false-discovery burden and to compare the findings with other studies using similar thresholds
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IHC semi-quantitative scores from 84 paired tumor and adjacent-normal samples were used to validate hub gene expression, but the statistical test applied to the paired comparison is not stated↳ Could also: A Wilcoxon signed-rank test (for paired ordinal scores) or a paired t-test on log-transformed continuous H-scores would both be standard for paired IHC data — Paired designs gain power over unpaired analyses by accounting for within-patient variability; naming the test and reporting its p-value and an effect size (e.g., median difference with IQR) supports transparency and reproducibility
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.
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Downstream reach in the literature
336 downstream papers · 8 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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- Inhibition of the CCL2 receptor, CCR2, enhances tumo... 2020 · 141 cites
- The microRNA expression signature of bladder cancer... 2014 · 134 cites
- Tumour-suppressive miRNA-26a-5p and miR-26b-5p inhib... 2016 · 129 cites
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What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
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Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
The 14-gene RMS signature (Suppl. Table S10) reproduces exactly and stratifies OS significantly in the GSE13507 training component (HR=1.70, p=0.029, correct direction), confirming the pipeline is correct and the meta-GEO association is real. However, the paper's headline independent TCGA-BLCA validation (HR=1.53, p=0.006) is not reproducible from public RNA-seq with the published coefficients — we obtain HR=1.18-1.33 (p=0.28/0.057), C-index ~0.51. The deviation is on the input/authors' side (cross-platform microarray->RNA-seq transfer plus under-specified TCGA normalization and a non-transferable fixed threshold), and the value is not cleanly derivable from the shared data — but the surviving direction and the absence of any 'too-perfect' pattern argue against fabrication. Overall a partial reproduction with the headline TCGA number flagged for human audit.
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Reproduction footprint
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