Comprehensive analysis of peroxisome proliferator-activated receptors to predict the drug resistance, immune microenvironment, and prognosis in stomach adenocar
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 METHOD, and on substituted public GDC TCGA-STAD STAR-TPM the paper's QUALITATIVE biology reproduces strongly, though the exact reported counts carry a consistent offset. The authors' code loads all expression inputs from a private lab MySQL DB (the GitHub/Zenodo deposit ships only wet-lab data + plotting code), so 1:1 number agreement was never expected. KEY CORRECTION over the prior run: the paper reports PPARG=217 co-expressed genes (a prior run misread it as 1217 and raised a now-WITHDRAWN 'fabrication anomaly' flag); 291 @cutoff0.4 is close, and all three PPAR counts show a coherent ~+23-34% upward offset at the code's cutoff 0.4 (PPARA 1150->1413, PPARD 1244->1569, PPARG 217->291). The 6 named intersection genes (ASAP2,DNM2,EAF1,KIF13B,MFSD9,TMEM164) recover at every cutoff. The paper's headline AUTOPHAGY enrichment recovers in KEGG (PPARA/PPARD). Most strikingly, the immune-checkpoint correlations reproduce almost exactly on independent public data: PPARG-SIGLEC15 0.328 vs reported 0.32, PPARD-SIGLEC15 0.202 vs 0.21, with all three PPARs positively correlated with SIGLEC15 as reported. Minor flags for a human: (1) methods text says |cor|>0.2 but counts only fit the code's 0.4; (2) PPARA-LAG3 |r|=0.15 matches but our sign is negative vs the paper's +0.15. Survival: all three PPARs show the paper's direction (high->better OS) but none reaches significance in GDC OS (log-rank p=0.48-0.81). NOT attempted (secondary, not blocking): CIBERSORT deconvolution, CCLE/GDSC drug-sensitivity, pan-cancer expression, TMB. All compute ran on «our HPC» SLURM compute nodes (jobs above); nothing on front1/«host». Verdicts provisional; a human reviewer signs off (see AUDIT.md + agreement.json).
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 49assessed: 2026-06-20 ⛓ 9d5280014c15
✎ I am an author of this paper
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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
- 2026-06-23
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-20no 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: sonnetThis study tests whether the three PPAR genes (PPARA, PPARD, PPARG) are associated with clinicopathologic features, prognosis, immune microenvironment, genome mutation, and drug sensitivity in stomach adenocarcinoma (STAD), and whether they can serve as prognostic markers and therapeutic targets.
- ★ PPARA, PPARD and PPARG are more abnormally expressed in STAD samples and cell lines compared to most of 32 cancer types in TCGA finding
- ★ High-PPARA expression is associated with longer overall survival (OS) and progression-free interval (PFI) in STAD patients finding
- ★ PPARD expression is higher in Grade 3+4 and male patients; PPARG expression is higher in Grade 3+4 and patients aged >60 finding
- ★ PPAR genes are correlated with tumor immune microenvironment, including immune cell infiltration (NK_cells_resting, T_cells_CD4_memory_resting, macrophages_M0) and immune checkpoint genes (CD274, SIGLEC15) finding
- ★ TTN, MUC16, FAT2 and ANK3 have high mutation frequency in both high and low PPARA/PPARG expression groups, and tumor mutation burden (TMB) correlates with PPARA/PPARG expression finding
- ★ Vinorelbine sensitivity is positively correlated with PPARA, PPARD and PPARG expression, suggesting potential as a treatment drug for STAD finding
- ★ Inhibiting PPARG suppresses viability, migration and invasion of AGS and SGC7901 STAD cell lines finding
- ★ PPAR genes affect STAD development by mediating the immune microenvironment and genome mutation mechanism
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| RNA-seq expression profiling | 32 cancer types (TCGA pan-cancer) including STAD | none | PPARA/PPARD/PPARG expression levels | — |
| Pearson correlation co-expression analysis | TCGA-STAD tumor samples | none | genes correlated with PPARA/PPARD/PPARG expression | — |
| GSVA/ssGSEA pathway enrichment analysis | TCGA-STAD tumor vs normal tissue | none | hallmark pathway activity scores | GSVA package; c2.cp.kegg.v7.0.symbols gene set |
| CIBERSORT immune cell deconvolution and immune checkpoint correlation | TCGA-STAD tumor samples | none | relative abundance of 22 immune cell types; immune checkpoint gene expression | — |
| Somatic mutation calling (Mutect2) with TMB, IPS, TIDE scoring | TCGA-STAD samples (GDC dataset) | none | tumor mutation burden, IPS, TIDE score, mutated gene frequency | maftools package |
| Drug sensitivity prediction (IC50/AUC correlation) | 26 stomach cancer cell lines (GDSC), 175 drugs tested | drug treatment (e.g., Docetaxel, Vinorelbine, Paclitaxel, Cisplatin) | drug response (IC50/AUC) correlated with PPAR expression | GDSC; pRRophetic package |
| qRT-PCR and Western blot | GES1 (normal), AGS and SGC7901 (STAD) cell lines | PPARG siRNA knockdown vs negative control | PPARG mRNA and protein expression | ABI 7500 System with SYBR Green Master Mix (qRT-PCR) |
| CCK-8 viability assay and transwell migration/invasion assay | AGS and SGC7901 STAD cell lines | PPARG siRNA knockdown | cell viability (OD450); number of migrating/invading cells | microplate reader (450 nm) for CCK-8 |
- – PPARA, PPARD and PPARG differentially expressed across 32 primary tumor types and dysregulated in 26 of 29 cancer cell lines
- ▲ High-PPARA expression group had longer OS and PFI than low-PPARA group
- ▲ PPARD higher in Grade 3+4 and male patients; PPARG higher in Grade 3+4 and age >60 patients
- ▲ Six genes (ASAP2, DNM2, EAF1, KIF13B, MFSD9, TMEM164) significantly positively correlated with all three PPAR genes
- – 23 pathways differ significantly between tumor and normal tissue, including MITOTIC_SPINDLE, MYC_TARGETS_V1, E2F_TARGETS (tumor) and XENOBIOTIC_METABOLISM, BILE_ACID_METABOLISM (normal)
- ▲ PPARD and PPARG most strongly correlated with immune checkpoint gene SIGLEC15; PPARA most strongly correlated with LAG3 R=0.21-0.32
- ▲ TMB positively correlated with PPARA and PPARG, and higher in high-PPARA/PPARG expression groups
- ▼ PPARG knockdown suppressed viability, migration and invasion of AGS and SGC7901 cells
- correlation R = 0.32, p = 4.2e-10 (PPARG expression correlated with SIGLEC15 immune checkpoint gene)
- correlation R = 0.21, p = 3.8e-05 (PPARD expression correlated with SIGLEC15 immune checkpoint gene)
- correlation R = 0.15, p = 0.0039 (PPARA expression correlated with LAG3 immune checkpoint gene)
- pvalue p = 0.006 (PPARG expression vs tumor grade in TCGA-STAD cohort)
- pvalue p = 0.005 (PPARG expression vs patient age in TCGA-STAD cohort)
- pvalue p = 0.006 (PPARD expression vs gender in TCGA-STAD cohort)
- pvalue p = 0.046 (PPARA expression vs patient age in TCGA-STAD cohort)
- count 1,150 / 1,244 / 217 genes (genes significantly correlated with PPARA/PPARD/PPARG respectively (|cor|>0.2, FDR<0.05))
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 combined bioinformatic analysis of TCGA/CCLE/GDSC public datasets with in vitro validation in two gastric cancer cell lines. Group comparisons of gene expression and immune/pathway scores were largely performed with the Wilcoxon (Mann-Whitney) test, associations between genes, pathways, drugs and immune features were assessed with Pearson or Spearman correlation, clinicopathological categorical associations were tested with Pearson's chi-squared test, and survival differences were assessed with Kaplan-Meier curves and the log-rank test. Cell-based functional assays (qRT-PCR, Western blot, CCK-8, transwell) were used to validate PPARG's role but were described narratively without an explicitly stated statistical test.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Wilcoxon rank-sum test (Wilcox.test) | expression differences across pan-cancer/tumor-vs-normal, clinical subgroups (stage/grade/age/sex), and high- vs low-PPAR groups (immune cells, checkpoint genes, TMB) | TCGA-STAD cohort (375 total, e.g. 187 vs 188 in high/low splits per Table 1) | not stated |
| Pearson correlation | co-expression of PPARs with other genes, PPARs vs drug sensitivity (GDSC) | not stated precisely (genes/cell lines screened under |cor|>0.2, FDR<0.05) | not stated |
| Spearman correlation | PPARs vs immune cell infiltration and TME-related gene signatures | not stated | not stated |
| Kaplan-Meier estimator with log-rank test | OS and PFI comparison between high- and low-PPARA/D/G expression groups | TCGA-STAD cohort, cutpoint-defined groups (surv_cutpoint) | not stated |
| Pearson's chi-squared test | association between PPAR expression groups and clinicopathological features (Table 1: stage, grade, age, sex, T/N/M stage) | n=187 (high) vs n=188 (low) per gene | not stated |
| GSVA (gene set variation analysis) | pathway enrichment scoring of hallmark gene sets between tumor and normal samples | not stated | na |
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Group differences in gene/pathway/immune-cell scores were assessed mainly with the Wilcoxon (Mann-Whitney) test↳ Could also: A two-sample t-test (with normality checked, e.g. Shapiro-Wilk) or a linear model framework — If expression values approximate a normal distribution, a parametric t-test can offer more statistical power for the same sample size; reporting the normality check would also clarify why the nonparametric test was chosen
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Many separate Wilcoxon and correlation tests were run across clinical subgroups, 22 immune cell types, 7 checkpoint genes, and 23 GSVA pathways, with FDR correction described only for specific correlation-screening steps↳ Could also: A single family-wise correction (e.g., Benjamini-Hochberg FDR or Bonferroni) applied across the full set of comparisons within each figure/analysis — Extending multiplicity correction to every family of repeated tests can help ensure the reported significance calls are comparably controlled across all figures, not only the gene-screening steps
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Survival differences between high/low PPAR groups were assessed with Kaplan-Meier curves and the log-rank test alone↳ Could also: A Cox proportional-hazards regression adjusting for covariates such as age, sex, grade, and stage — A multivariable Cox model would let PPAR expression be evaluated as an independent prognostic factor while quantifying the association as a hazard ratio with a 95% confidence interval
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Correlation strength for gene co-expression and immune/pathway associations was reported using Pearson or Spearman R values without accompanying confidence intervals↳ Could also: Reporting bootstrap or Fisher-transformed 95% confidence intervals alongside each correlation coefficient — A confidence interval conveys the precision of the estimated correlation in addition to its point value and significance
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Cutpoint-based dichotomization (surv_cutpoint) and median-split were used to create high/low expression groups for multiple analyses↳ Could also: Treating PPAR expression as a continuous variable in regression models (e.g., continuous Cox or linear models) — A continuous-variable approach avoids the loss of information and potential cutpoint-dependence that comes with dichotomizing a continuous expression measure, and can be a useful complementary analysis
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In vitro validation experiments (qRT-PCR, Western blot, CCK-8, transwell) are described without a stated number of replicates or a named statistical test for these specific assays↳ Could also: Explicitly reporting the number of independent biological replicates and applying a stated test such as a t-test or one-way ANOVA with post-hoc correction for the cell-based comparisons — Clearly stating replicate numbers and the specific test used for each in vitro comparison would let readers evaluate the precision and multiplicity handling of those functional results alongside the bioinformatic findings
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-38529307
Paper: Jia et al. 2024, PeerJ 12:e17082. "Comprehensive analysis of PPARs to predict drug resistance, immune microenvironment, and prognosis in stomach adenocarcinoma." Code: github.com/1Ligaozhong/Source-data (mirrored on Zenodo 10.5281/zenodo.10076985).
Data availability reality
- Repo + Zenodo = wet-lab source data (AGS cells, WB, PCR, cell numbers, PPARG.xlsx) +
scripts/20221209_STAD_PPARs.R(2815 lines) +_suppl.R. - The R script loads ALL bioinformatic inputs (TCGA-STAD, CCLE, GDSC expression) from a
private lab MySQL database via custom, unshipped helper functions
(
mg_mysql_getGenes_Exp_by_Symbol,getTCGAClinicalBySamples,readMatrix, ...). → The script is NOT runnable as-is; inputs must be obtained from public sources.
In scope (pipeline-derived, attempted)
- Co-expression: Pearson r of PPARA/PPARD/PPARG vs all protein-coding genes in TCGA-STAD tumor (log2 TPM+1), BH-FDR, |cor| cutoff (code=0.4, text=0.2); gene counts per PPAR + 3-way intersection (the named 6 genes). [DONE — see agreement.json C1-C4]
- Survival: median-split KM of each PPAR on overall survival (log-rank). [DONE C5-C7]
In scope but NOT attempted (deprioritised; secondary)
- Pan-cancer PPAR expression across 32 TCGA types (needs pan-cancer pull).
- CCLE (29 cell lines) PPAR expression; GDSC (26 STAD lines × 175 drugs) drug-sensitivity correlations + pRRophetic IC50 (Docetaxel/Cisplatin/Paclitaxel/Vinorelbine).
- Immune infiltration (CIBERSORT 22 cells, ssGSEA hallmark/KEGG), TMB (maftools), GO/KEGG enrichment of co-expressed genes (clusterProfiler).
- Clinicopathologic associations (Table 1).
Out of scope (wet-lab / not pipeline)
- AGS-cell qPCR / Western blot / proliferation assays (the repo's actual deposited data).
Pipelines named
Pearson correlation + BH-FDR (base R / numpy-scipy); survival/survminer (KM + log-rank); clusterProfiler; CIBERSORT; GSVA/ssGSEA; pRRophetic; maftools.
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 named biological core reproduces — all 6 PPAR-shared genes (ASAP2, DNM2, EAF1, KIF13B, MFSD9, TMEM164) recover in the 3-way intersection at every cutoff, and high-PPAR→longer-OS direction is concordant for all three (though non-significant, p=0.48–0.81). The quantitative co-expression counts do not: no single cutoff reproduces C1+C2+C3, and PPARG (reported 1217≈PPARA 1150) is anomalous — public TCGA-STAD gives PPARG ~5× fewer partners (291 vs 1413 @0.4). Part of the discrepancy is on data-availability/our-method (the authors' input matrix was private, so we substituted public GDC TCGA-STAD), but the internal inconsistency is on the authors' side: the stated |cor|>0.2 cutoff implies ~6–7k genes, ~6× the reported ~1200, so the counts are not derivable as described. Net: core finding partial-confirmed, headline numbers not reproducible with a fabrication-suspect PPARG anomaly flagged for human review.
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Reproduction footprint
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