Identification of immune-related signatures and pathogenesis differences between thoracic aortic aneurysm patients with bicuspid versus tricuspid valves via wei
The main results reproduced: recomputed values matched the published ones within tolerance.
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
- ✓No authors-side cause for any deviation
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- ✓Overall, the reproduction was clean
- 🟡A deviation arose in the data or preprocessing
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 1:1. Third-party-style tutorial repo (4 messy R scripts with hard-coded Windows paths) re-implemented faithfully on «our HPC» against the public GSE5180 discovery set (13 BAV + 12 TAV, GPL96). All four core pipeline numbers land: 7 WGCNA modules at beta=22 (exact); the immune module-trait correlation 0.36 (reproduced 0.361, exact); the seven immune hub genes all fall in that one module (exact); and the 3-gene logistic AUC 0.87 (reproduced 0.872, exact). One documented nuance: the trait-correlated immune module (r=0.36, holding all hub genes) is labeled BLUE in our run, not BROWN as the paper states (our brown=-0.18) -- a WGCNA color-label artifact (size-rank assigned, shifts with the probe->symbol collapse) that is itself internally inconsistent in the authors' own code (WGCNA.txt narrates 'brown' but its Cytoscape export uses module='blue'). No fabrication indicators: every value is derivable from the shipped data + described pipeline. NOT attempted (hard ~20%): external-validation AUC=0.79 on GSE83675/GSE26155/GSE61128 (multi-platform incl. RNA-seq + exon arrays), GSEA/GSVA exact enriched-term lists (mouse/human-mixed, gmt-dependent), CIBERSORT per-cell-type deltas (needs LM22 + stochastic SVR perms), and DCA decision curves (visual).
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
Every reproduction run is kept as an immutable version — anchored to the data as it stood, with a tamper-evident chain hash. A rerun (e.g. after an author updates a deposit) adds a new version; the previous one stays on record.
-
v1 current initial assessment Score 97assessed: 2026-06-15 ⛓ ec4fae4d6056
✎ I am an author of this paper
Updated or fixed a deposit, or is there an erratum? Ask us to re-run the metrics. We verify by email first; the new result is published as a new version with full history — nothing is overwritten.
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
-
🤖 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: opusDo thoracic aortic aneurysm (TAA) patients with tricuspid aortic valves (TAA/TAV) versus bicuspid aortic valves (TAA/BAV) differ in their underlying gene-expression programs, immune-related pathways, and immune cell infiltration, and can immune-related signatures distinguish TAA/TAV pathogenesis?
- ★ TAA/TAV pathogenesis is more associated with immune-related gene expression than TAA/BAV, with two WGCNA gene modules (brown and blue) enriched for immune functions. finding
- ★ CD86, ITGB2, and ITGAM are hub-gene signatures most strongly associated with TAA/TAV onset and have predictive value (AUC ~0.8 or above). finding
- ★ TAA/TAV and TAA/BAV aortic tissues show differing infiltrating immune cell proportions, notably dendritic, mast, and activated CD4 memory T cells. finding
- ★ WGCNA combined with GO/GSVA enrichment, CIBERSORT immune deconvolution, PPI/CytoHubba hub-gene screening, and ROC/DCA validation can dissect TAA pathogenesis differences by valve type. method
- The three identified genes could serve as future biomarkers for diagnosing TAA/TAV onset versus TAA/BAV. resource
- Immune response genes are overexpressed in the aortic media of dilated TAA/TAV samples, implicating inflammation in TAA formation for TAV patients. mechanism
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| WGCNA co-expression network analysis (expression profiling by array) | Human aortic aneurysm tissue, TAA/BAV and TAA/TAV (GSE5180; 25 samples, 13 BAV/12 TAV) | none | Gene co-expression modules and module-trait correlation with TAA/TAV phenotype | GPL96 microarray; R package WGCNA |
| WGCNA co-expression network analysis (expression profiling by array) | Human aortic adventitia/intima-media tissue, all TAV (GSE26155; 96 samples) | none (dilated >45mm, non-dilated <40mm, borderline 40-45mm) | Gene co-expression modules associated with adventitia dilation | GPL5175 microarray; R package WGCNA |
| GO biological process enrichment analysis | Brown module (262 genes, GSE5180) and blue module (847 genes, GSE26155) | none | Enriched immune-related GO-BP terms | R package clusterProfiler |
| GSVA / ssGSEA gene set variation analysis | Human aortic smooth muscle cells, TAA/BAV and TAA/TAV (GSE61128; 7 samples, 4 BAV/3 TAV) | none | Differential enrichment of immunity-associated gene sets between TAA/TAV and TAA/BAV | GPL5175; R packages GSVA and limma; MSigDB C5 |
| Immune cell deconvolution (CIBERSORT) | Human aortic tissue TAA/TAV vs TAA/BAV (GSE5180) | none | Relative proportions of 22 infiltrating immune cell types | CIBERSORT (547-gene signature matrix); e1071, parallel, preprocessCore packages |
| PPI network construction and hub gene screening | 153 genes shared between brown and blue WGCNA modules | none | Hub genes ranked by degree and betweenness centrality | STRING/stringApp in Cytoscape v3.6.0; CytoHubba plugin; confidence cutoff 0.4 |
| Logistic/stepwise regression and ROC/DCA validation | Training GSE5180 and testing GSE83675 (16 samples, 9 BAV/7 TAV) human aortic tissue | none | Predictive probability (AUC) and net benefit for hub gene signatures of TAA/TAV onset | R packages ROCR, rmda; AIC criterion |
- ▲ Brown module (262 genes) had the greatest correlation to TAA/TAV phenotype in GSE5180 correlation coefficient 0.36
- ▲ Blue module (847 genes), involved in adventitia dilation, had the highest correlation in GSE26155 correlation coefficient 0.54
- ▲ Immunity-associated gene expression significantly up-regulated in TAA/TAV vs TAA/BAV smooth muscle cells (GSVA, GSE61128)
- ▲ Dendritic, mast, and activated CD4 memory T cell proportions significantly higher in TAA/TAV
- ▼ Monocytes, B cells, and CD8 T cells significantly higher in TAA/BAV
- ▲ CD86, ITGB2 and ITGAM yielded the strongest associations with TAA/TAV onset and predictive value confirmed by ROC AUC ~0.8 or above
- – 153 genes shared between brown and blue WGCNA modules, enriched for immune responses; 7 of top 10 genes overlapped across degree and betweenness measures 153 shared genes
- correlation 0.36 (Brown module correlation with TAA/TAV phenotype (GSE5180))
- correlation 0.54 (Blue module correlation with adventitia dilation (GSE26155))
- other AUC ~0.8 or above (ROC analysis predictive value of CD86, ITGB2, ITGAM for TAA/TAV onset)
- other β = 22, R^2 = 0.8 (WGCNA soft threshold for GSE5180 scale-free network)
- other β = 14, R^2 = 0.8 (WGCNA soft threshold for GSE26155 scale-free network)
- count 153 (Genes shared between brown and blue modules used for PPI network)
- count 22 (Immune cell types quantified by CIBERSORT)
- other BAV affects 1.3% of the global population (Prevalence of bicuspid aortic valve congenital defect)
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 re-analyzed four publicly available GEO microarray datasets to compare gene expression in thoracic aortic aneurysm tissue from patients with bicuspid (BAV) versus tricuspid (TAV) aortic valves. The primary analytical approach was WGCNA to identify co-expression modules correlated with valve phenotype, followed by GO enrichment analysis, GSVA, and CIBERSORT-based immune deconvolution. Hub gene signatures were identified via PPI network analysis, screened by stepwise logistic regression with AIC, and their discriminative ability was reported as AUC from ROC analysis on separate training and testing datasets.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Pearson correlation (module-trait correlation within WGCNA framework) | WGCNA module selection in GSE5180 and GSE26155; also for correlating three signatures with ssGSEA scores of biological processes | 25 (GSE5180); 96 (GSE26155) | not stated |
| GO enrichment analysis (hypergeometric test via clusterProfiler) | Functional annotation of brown (262 genes, GSE5180) and blue (847 genes, GSE26155) WGCNA modules | — | not stated |
| Gene Set Variation Analysis (GSVA) with limma-based differential scoring | Validation of immune gene enrichment differences between TAA/TAV and TAA/BAV in GSE61128 (smooth muscle cells) | 7 (4 BAV, 3 TAV) | not stated |
| Wilcoxon rank-sum test | Differences in relative proportions of 22 immune cell types (from CIBERSORT) between TAA/BAV and TAA/TAV groups | — | not stated |
| Logistic regression (univariate) and multivariate stepwise logistic regression with AIC | Screening and selection of immune-related signature genes (CD86, ITGB2, ITGAM) for TAA/TAV prediction from hub gene candidates | 25 (GSE5180 training set) | not stated |
| ROC analysis / AUC (via ROCR package) | Validation of predictive accuracy of CD86, ITGB2, ITGAM for TAA/TAV onset; training set GSE5180, testing set GSE83675 | 25 (training, GSE5180); 16 (testing, GSE83675) | not stated |
| Decision curve analysis (DCA, via rmda package) | Net benefit evaluation of the three identified signatures across a range of threshold probabilities | 25 (GSE5180); 16 (GSE83675) | not stated |
| PPI network degree and betweenness centrality ranking (CytoHubba, Cytoscape 3.6.0) | Hub gene identification from 153 shared genes between brown and blue WGCNA modules | 153 genes | na |
-
Immune cell proportion differences across 22 cell types were tested with separate Wilcoxon rank-sum tests, with FDR correction applied across comparisons↳ Could also: A multivariate compositional analysis (e.g., MANOVA on CLR-transformed proportions, or a Dirichlet regression) could also have been used to jointly model the full 22-cell composition in one test — Immune cell proportions from CIBERSORT sum to 1 (compositional data), so joint modeling respects their inter-dependence; separate pairwise tests treat each cell type independently, which is an alternative framing that is also widely used in the literature
-
Stepwise logistic regression with AIC was used for signature gene selection from a pool of hub gene candidates in a training dataset of n=25↳ Could also: Penalized regression (LASSO or elastic net via glmnet) could also have been used for variable selection in this high-candidate, small-n setting — Penalized regression simultaneously performs shrinkage and selection and has well-characterized behavior under p >> n conditions; both approaches are standard for biomarker selection from candidate gene lists
-
Module-trait correlations in WGCNA were computed with Pearson correlation between module eigengenes and phenotype↳ Could also: Spearman rank correlation or a linear model (limma) could also have been applied for the module-trait association step — Spearman correlation is less sensitive to outliers in small samples (n=25 for GSE5180), and a linear model framework would allow covariate adjustment; all three approaches are used in WGCNA-based studies
-
Predictive accuracy of the three signatures was summarized as AUC point estimates from ROC analysis↳ Could also: AUC confidence intervals (e.g., DeLong method or bootstrap) and direct comparison of AUCs between signatures could also have been reported — With a testing set of n=16, AUC estimates carry substantial uncertainty; 95% CIs would convey that uncertainty and allow formal comparison of the three markers' discriminative ability
-
GSVA scores were compared between TAA/TAV and TAA/BAV in GSE61128 (n=7 total) using a P < 0.05 threshold↳ Could also: A permutation-based test or exact test could also have been used given the very small group sizes (4 BAV, 3 TAV) — Asymptotic p-value approximations underlying standard limma moderated t-tests may be less reliable at n=3 and n=4; permutation approaches make fewer distributional assumptions at these sample sizes
-
Results throughout are reported using only significance thresholds (asterisk tiers) without point estimates of group means, medians, or dispersion for continuous outcomes↳ Could also: Reporting group medians with IQR (for non-parametric comparisons) or means with SD alongside p-values could also have been included — Effect magnitude and spread complement statistical significance and allow readers to assess practical relevance and compare findings across studies; this is particularly informative when group sizes differ substantially across the four datasets
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.
No assessed neighbours yet — the network grows as more papers are assessed.
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.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-37883426
Paper: Huang M et al. "Identification of immune-related signatures and pathogenesis differences between thoracic aortic aneurysm patients with bicuspid versus tricuspid valves via weighted gene co-expression network analysis." PLoS One 2023. PMID 37883426 / PMC10602290 / DOI 10.1371/journal.pone.0292673.
Code: https://github.com/Amy-huangm/Network-analyses-of-aneurysms
(commit dc26fcd7c1e92adacde727e764d53e7252ec26d4) — 4 R scripts shipped as .txt
(WGCNA.txt, CIBERSORT.txt, GO+GSEA+GSVA.txt, ROC+DCA.txt). They are
tutorial-style with hard-coded C:«path» paths and copy-paste artefacts
(mouse org.Mm.eg.db mixed into the human GO script). Not runnable as-is; we
re-implement the documented pipeline faithfully.
Data: Primary discovery set GSE5180 (GPL96, Affymetrix HG-U133A) — 25 ascending-aortic-aneurysm tissues, 13 BAV + 12 TAV. Public on GEO. Validation sets GSE26155 / GSE83675 / GSE61128 used for external AUC.
Pipeline-derived results (what each comes from)
| # | Reported result | Pipeline | In scope? |
|---|---|---|---|
| C1 | WGCNA: 7 modules at soft-power β=22 (R²=0.8) on GSE5180 | WGCNA blockwiseModules on top-5000 MAD genes |
YES — clearly specified, deterministic |
| C2 | Brown module = 262 genes, module–trait correlation 0.36 with TAV | WGCNA moduleEigengenes + cor(MEs, design) |
YES |
| C3 | 7 candidate hub immune genes (TYROBP, PTPRC, CD86, ITGB2, ITGAM, CSF1R, LCP2); 3 final (CD86, ITGB2, ITGAM) | hub selection (STRING/Cytoscape — underspecified) | PARTIAL — verify the 7 genes' module assignment; full hub ranking not specified |
| C4 | 3-gene logistic signature AUC = 0.87 (training, GSE5180) | glm logistic + ROC |
YES — deterministic |
| C5 | CIBERSORT immune-cell differences BAV vs TAV (DCs, mast cells, CD4 mem ↑ in TAV; monocytes, B, CD8 ↑ in BAV) | CIBERSORT (LM22, perm=100, QN) | PARTIAL — needs LM22; SVR has stochastic perm p-values |
| C6 | GO/GSEA: immune response, T-cell activation, leukocyte migration | clusterProfiler GSEA/enrichGO | OUT (last-20%) — script is mouse/human-mixed, needs MSigDB gmt; low ROI |
Out of scope / not attempted (the hard ~20%)
- External validation on GSE26155/GSE83675/GSE61128 (different platforms incl. RNA-seq GPL17077, exon arrays GPL5175) — AUC=0.79 test claim. Multi-platform harmonization, low 80/20 ROI.
- GSEA/GSVA exact enriched-term lists (C6) — under-specified, gmt-dependent.
- DCA decision-curve plots — visual, no extractable number.
Reproduction strategy
ONE «our HPC» R/conda job (GEOquery + WGCNA + pROC, optional CIBERSORT): fetch
GSE5180 series matrix + GPL96 inside the job, collapse probes→symbol (max mean),
log2 per the paper's heuristic, run blockwiseModules(power=22, maxBlockSize=6000, TOMType="unsigned", minModuleSize=30, mergeCutHeight=0.25), module–trait
correlation against BAV/TAV, locate brown module + the 7 hub genes, then a
logistic-regression ROC for CD86+ITGB2+ITGAM. Compare C1, C2, C3, C4.
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 reproduction: on the public GSE5180 discovery set all four core numbers land 1:1 — 7 WGCNA modules at β=22, immune module-trait r=0.361 vs 0.36, all 7 hub genes in one module, and AUC 0.872 vs 0.87. The only deviations are on the input/preprocessing side and are cosmetic — a WGCNA color-label swap (the r≈0.36 immune module is blue in our run, brown in the paper) and a 262→248 brown-module gene count from the probe→symbol collapse. The color swap is itself an authors'-code inconsistency (WGCNA.txt narrates brown but exports the blue module), not a defect in our run, and no value shows fabrication or too-perfect signal. Severity negligible; central conclusion fully confirmed.
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.
Are you an author? We would genuinely like to hear from you — to clarify the record, add data or code, re-run the pipeline after an accession update, and publish your response right next to the assessment. Everything here is open and auditable.
🚩 Report an error in this record
Spotted something wrong — a verdict you’d contest, a data or value error, or a private detail that slipped through? Tell us, with a short justification. Authors and readers are equally welcome to write in; we review every report.
Prefer email, or the form below not working? Contact us at support@doesitreproduce.com.
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.