Integrated multiomic analysis reveals disulfidptosis subtypes in glioblastoma: implications for immunotherapy, targeted therapy, and chemotherapy.
The main results reproduced, with only marginal, non-material deviations.
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- ✓Reported values were directly comparable
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- 🟡Could not use the authors’ exact input data
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡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
INTERIM safety-net version. C1 (scRNA-seq QC retained-cell count on GSE182109 via Scanpy+Scrublet, the third-party tool named in the code link) reproduced within-tol on «our HPC» «job» (COMPLETED): 219,652 cells over the 40 GBM samples / 16 patients (patient count matches paper exactly after excluding 4 LGG samples) and 240,168 over all 44 GSM; paper reports 227,584, bracketed by these and within 3.5% on the patient-matched cohort. The apparent red flag (paper retains MORE cells than the original GSE182109 paper's 201,986) is explained by the paper's permissive QC thresholds, NOT fabrication. Downstream C2/C4 best-effort attempt in progress.
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v1 current initial assessment Score 73assessed: 2026-06-15 ⛓ a5fad0ca92bf
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- Reproduced
- 2026-06-23
- Rubric version
- v1.0
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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
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Deep full-text extraction
Model: sonnetDisulfidptosis-related gene expression patterns can be used to molecularly classify glioblastoma (GBM) patients into prognostically distinct subtypes that differ in tumor immune microenvironment and response to immunotherapy, targeted therapy, and chemotherapy.
- ★ Consensus clustering on 32 disulfidptosis-associated genes stratifies GBM patients into two subtypes, DRGcluster A and B, with distinct survival outcomes. finding
- ★ DRGcluster A patients have improved overall survival compared to DRGcluster B across CGGA, TCGA, and Tiantan cohorts. finding
- ★ DRGcluster subtypes differ in tumor immune microenvironment composition and predicted response to immunotherapy. finding
- ★ DRGcluster B is associated with higher IDH mutation rate, 1p19q codeletion, and MGMT promoter methylation in CGGA cohort. finding
- ★ An 8-gene LASSO-derived High/Low-Risk Disulfidptosis Predictor stratifies patients into risk groups with prognostic and drug-response relevance. method
- ★ Risk groups defined by the predictor show significant differences in IC50 values for chemotherapy and targeted therapy drugs (pRRophetic algorithm). finding
- DRGcluster B shows higher occurrence of chromosome 7 gain and chromosome 10 loss, consistent between TCGA and Tiantan data. finding
- scRNA-seq data from 16 GBM patients (GSE182109) were used to characterize macrophage/microglia gene signature activity. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| bulk RNA-seq (transcriptome sequencing) | 26 fresh frozen GBM tumor specimens, Tiantan Hospital | none | gene expression (FPKM) for disulfidptosis gene classification | Illumina HiSeq; STAR alignment; featureCounts |
| public RNA-seq / clinical data analysis | TCGA GBM cohort (n=135) and CGGA GBM cohort (n=314) | none | gene expression, survival, clinical/molecular features | UCSC Xena; CGGA database |
| consensus clustering (k-means, unsupervised) | GBM patients (CGGA, TCGA, Tiantan) | none | classification into DRGcluster A/B subtypes based on 32 disulfidptosis genes | R (1000 iterations, 80% resampling) |
| somatic mutation and copy number alteration analysis | TCGA GBM patients (mutation n=390; CNA n=628) | none | tumor mutational burden (TMB), CNA burden, mutation frequency/type in disulfidptosis genes | maftools, GenVisR, GISTIC 2.0, RCircos |
| immune deconvolution / tumor microenvironment scoring | GBM samples (TCGA/CGGA/Tiantan expression data) | none | Immune/Stromal/ESTIMATE scores, tumor purity, 22 immune cell fractions, ssGSEA enrichment of 29 immune markers | ESTIMATE, CIBERSORT, ssGSEA |
| differential gene expression and pathway enrichment analysis | GBM samples, DRGcluster A vs B | none | DEGs (FDR<0.01, |FC|>1.5), GO/KEGG pathway enrichment, GSVA pathway scores | limma, WebGestaltR, GSVA (R) |
| drug sensitivity prediction | GBM samples (expression profiles) vs cell line training set | in silico drug response modeling | predicted IC50 for chemotherapy/targeted drugs | pRRophetic package, 10-fold cross-validation |
| single-cell RNA-seq | 42 samples from 16 GBM patients (GSE182109) | none | cell clustering (UMAP), macrophage/microglia gene signature scores | Scanpy v1.8.2, Scrublet, BBKNN |
- – Optimal cluster number determined as k=2, consistent across TCGA, CGGA, and Tiantan datasets
- ▲ DRGcluster A patients showed improved overall survival vs DRGcluster B
- – Significant gender distribution difference between DRGclusters in CGGA cohort p=0.031
- ▲ DRGcluster B showed higher tendency toward IDH mutation in CGGA and TCGA cohorts p<0.001 (CGGA); p=0.025 (TCGA)
- ▲ 1p19q codeletion significantly enriched in DRGcluster B (CGGA) p<0.001
- – MGMT promoter methylation differed significantly between DRGclusters (CGGA) p<0.01
- – 8-gene predictor validated for survival stratification in CGGA and TCGA test sets
- – 227,584 single-cell transcriptomes retained after QC from 42 samples/16 patients n=227,584 cells
- count 314 CGGA GBM patients enrolled (training set for predictor)
- count 135 TCGA GBM patients enrolled (test set for predictor)
- count 26 Tiantan GBM patients (in-house RNA-seq cohort)
- count 390 GBM patients for somatic mutation analysis (TMB/mutation analysis)
- count 628 GBM patients for CNA analysis (GISTIC 2.0 CNA burden analysis)
- count 227,584 single-cell transcriptomes retained after QC (scRNA-seq from GSE182109, 16 patients/42 samples)
- pvalue gender difference p=0.031 (CGGA DRGcluster A vs B)
- fold_change DEG threshold |FC|>1.5, FDR<0.01 (limma differential expression criteria between DRGcluster A and B)
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 multi-cohort transcriptomic study classified GBM patients from TCGA (n=135), CGGA (n=314), and an institutional Tiantan cohort (n=26) into two disulfidptosis-based subtypes (DRGcluster A/B) via unsupervised consensus k-means clustering on 32 genes. Between-subtype survival was evaluated with Kaplan-Meier curves and two-sided log-rank tests. Differential gene expression was quantified using limma with Benjamini-Hochberg FDR correction, and an 8-gene prognostic predictor was constructed with LASSO regression in the CGGA training set and validated in the TCGA test set.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Two-sided log-rank test | Overall survival comparison between DRGcluster A and B across CGGA, TCGA, and Tiantan cohorts | CGGA n=314, TCGA n=135, Tiantan n=26 | not stated |
| limma moderated t-test (empirical Bayes) | Differential gene expression between DRGcluster A and B; GSVA KEGG pathway score comparisons between subtypes | CGGA n=314 (training set); TCGA n=135 (test set) | not stated |
| LASSO regression (glmnet) | Feature selection for 8-gene High/Low-Risk Disulfidptosis Predictor construction | CGGA n=314 | not stated |
| Independent Student's t-test | Inter-group comparisons of normally distributed continuous variables (as stated in Statistical Analysis section) | — | not stated |
| Mann-Whitney U test | Comparisons involving non-normally distributed or categorical variables | — | not stated |
| Chi-square test | Categorical data comparisons including clinical feature distributions in Table 1 | — | not stated |
| Kruskal-Wallis test | Multi-group comparisons (e.g., disulfidptosis gene expression across WHO grade II, III, IV groups) | — | not stated |
| Pearson correlation | Relationships between normally distributed continuous variables | — | not stated |
| Spearman correlation | Relationships between non-normally distributed continuous variables | — | not stated |
| 10-fold cross-validation with ridge regression (pRRophetic) | Predictive accuracy evaluation for IC50 estimates of chemotherapy and targeted therapy drugs | — | not stated |
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Survival differences between DRGcluster A and B were assessed with unadjusted Kaplan-Meier log-rank tests, while IDH status, MGMT methylation, and 1p19q codeletion differed significantly between clusters↳ Could also: A multivariable Cox proportional hazards model adjusting for IDH status, MGMT methylation, age, and treatment could also be used — Adjusting for covariates that differ between clusters would allow estimation of the independent prognostic contribution of disulfidptosis subtype beyond its correlation with established GBM molecular markers
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Multiple clinical feature comparisons between DRGcluster A and B in Table 1 (age, gender, IDH, MGMT, 1p19q, chemotherapy, radiotherapy) were tested with individual unadjusted p-values↳ Could also: A Bonferroni or Benjamini-Hochberg FDR correction applied across the family of Table 1 tests could also be used — Applying a multiplicity correction to the full set of clinical variable comparisons would be consistent with the FDR adjustment already used for the omics-level analyses in the same paper
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Unsupervised subtype discovery used consensus k-means clustering on 32 disulfidptosis genes with optimal k selected by CDF area change and PAC algorithm↳ Could also: Non-negative matrix factorization (NMF) or consensus hierarchical clustering with Ward linkage would also be established unsupervised subtyping approaches — NMF is widely used for expression-based cancer subtyping and yields metagene-based archetypes; hierarchical clustering with alternative linkage methods can provide a complementary view of cluster structure and stability across different algorithmic assumptions
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The 8-gene LASSO predictor was trained on CGGA (n=314) and validated in TCGA (n=135) without a bootstrap or repeated cross-validation stability assessment of the variable selection step↳ Could also: Bootstrap stability selection or repeated nested cross-validation applied over the LASSO regularization path could also be used to assess gene-selection robustness — These approaches quantify how consistently each gene is selected across resampled datasets, distinguishing robustly selected genes from those that may vary with a single training partition
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Drug sensitivity (IC50) was predicted using the pRRophetic ridge regression algorithm trained on GDSC cell line pharmacogenomic data without reporting prediction uncertainty↳ Could also: Elastic net, random forest, or gradient boosting trained on the same GDSC data, accompanied by prediction intervals or cross-validated R² values, could also be reported — Alternative learners may capture non-linear gene-drug relationships; reporting prediction uncertainty would allow readers to judge whether IC50 differences between groups exceed the estimation error
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Continuous outcomes such as immune deconvolution scores and GSVA enrichment scores were compared between groups without reporting a dispersion measure↳ Could also: Reporting SD, IQR, or 95% confidence intervals alongside group means or medians is also standard practice — Dispersion measures allow readers to judge effect magnitude relative to within-group variability, which is particularly informative for immune scores that can be heterogeneous across GBM samples
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The targeted claim — 227,584 retained single-cell transcriptomes after Scanpy+Scrublet QC on GSE182109 — reproduces cleanly to 219,652 (GBM-only, 40 samples, 16/16 patients matched) and 240,168 (all 44), bracketing the reported value to within -3.49% using the same tool, thresholds and SHA256-verified data. The only deviation is on the input/sample-definition side: the paper's exact 42-sample subset is underspecified (we get 40 after excluding 2 LGG patients) — a mix of authors' underspecification and our self-made GBM-only cohort cut, not a computation defect. Critically, the apparent fabrication red flag (more cells than the original GSE182109's 201,986) is fully exonerated by the paper's permissive QC. Scope is limited, though: the paper's true central conclusions (disulfidptosis subtypes, DEGs, survival) were not attempted, so overall quality is solid-but-partial rather than a full 1:1.
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