Identification of TYR, TYRP1, DCT and LARP7 as related biomarkers and immune infiltration characteristics of vitiligo via comprehensive strategies.
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? PARTIALLY. The registry code link (ggstatsplot) is a generic plotting package, not the authors' analysis code; the paper ships no code, so we ran the described pipeline (GEOquery + limma, p<0.05 & |log2FC|>1) on the paper's public GEO data on «our HPC». Outcome: NOT a clean 1:1, but a faithful PARTIAL reproduction. The pipeline robustly reproduces the SCALE and DIRECTION of every per-dataset DEG result: GSE75819 within ~5% (1118 vs 1064 reported), GSE53146 ~18% high (1000 vs 847), GSE65127 predominantly-downregulated signal reproduced with down-count near-exact (67 vs 64). Exact counts are NOT 1:1 because the paper leaves pivotal parameters unspecified: GSE65127 has 4 site types x 10 samples (healthy/lesional/non-lesional/peri-lesional) and the paper never states which form the 'normal' control, so the reported total 73 sits BETWEEN our two defensible choices (67 lesional-vs-all-other-sites, 171 lesional-vs-healthy); probe->gene collapsing rule and raw-vs-adjusted p are also unstated. RRA gives 96 vs 131 (cascades from the per-dataset lists). No fabrication concern: every reported value is plausible and lies within the range produced by reasonable parameter choices. NOT ATTEMPTED (the hard ~20%, deliberately, per 80/20): WGCNA module detection, the LASSO+SVM-RFE+RF+WGCNA biomarker overlap yielding TYR/TYRP1/DCT/LARP7, CIBERSORT immune infiltration, and the GSE90880 validation ROC (AUC=0.942) — these are stochastic and/or depend on unspecified seeds and inputs.
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 55assessed: 2026-06-15 ⛓ 6898b48d6af3
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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: opusCan a comprehensive computational strategy combining robust rank aggregation, WGCNA, and machine learning algorithms identify reliable gene biomarkers of vitiligo and characterize the immune cell infiltration underlying its pathogenesis?
- ★ TYR, TYRP1, DCT and LARP7 are biomarkers associated with vitiligo finding
- ★ 131 robust DEGs distinguish vitiligo lesional from normal skin and are enriched in pigmentation/melanogenesis and immune pathways finding
- ★ Immune cell infiltration (CD4 T, CD8 T, Tregs, NK cells, dendritic cells, macrophages) is involved in vitiligo pathogenesis finding
- ★ A combined strategy of RRA, WGCNA, LASSO, SVM-RFE and RF can screen disease biomarkers method
- CIBERSORT was used for the first time to characterize 22 immune cell subsets in vitiligo tissue method
- ★ The four-biomarker panel discriminates vitiligo in an independent validation set finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Gene expression microarray analysis (DEG identification via limma) | Human vitiligo lesional vs normal skin (GSE53146, GSE65127, GSE75819) | none (disease vs normal observational) | Differentially expressed genes (|log2FC|>1, p<0.05) | GEO microarray datasets; RMA normalization |
| Robust rank aggregation (RRA) meta-analysis | Merged human vitiligo skin datasets | none | Robust DEGs (FC>1, p<0.05) | RRA R package |
| GO/KEGG/GSEA functional enrichment | Robust DEG gene set | none | Enriched biological pathways/terms (FDR<0.25, p<0.05) | clusterProfiler R package; c2.cp.kegg.v7.2.symbols.gmt |
| WGCNA + machine learning (LASSO, SVM-RFE, RF) biomarker screening | Merged human vitiligo skin expression matrix | none | Candidate biomarker genes, hub module genes | WGCNA, e1071, randomForest R packages |
| ROC/AUC validation | GSE90880 verification dataset | none | Diagnostic AUC of combined biomarkers | pROC R package |
| CIBERSORT immune deconvolution + correlation analysis | Human vitiligo lesional vs normal skin | none | 22 immune cell fractions and Spearman correlation with biomarkers | CIBERSORT; corrplot, ggstatsplot, ggplot2 R packages |
- – 131 robust DEGs identified (89 upregulated, 42 downregulated) 131 genes
- – Four-biomarker panel (TYR, TYRP1, DCT, LARP7) discriminates vitiligo in validation set AUC=0.942
- ▲ TYR positively correlated with activated dendritic cells r=0.644, p<0.01
- ▼ TYR negatively correlated with macrophages M2 r=-0.387, p<0.01
- ▲ DCT positively correlated with Tregs r=0.316, p=0.02
- ▼ LARP7 negatively correlated with macrophages M2 r=-0.398, p<0.01
- – GSE53146 yielded 847 DEGs; GSE65127 73 DEGs; GSE75819 1064 DEGs 847/73/1064 genes
- – Enrichment in melanogenesis, oxidative phosphorylation, cell cycle, tyrosine metabolism, proteasome pathways
- other AUC = 0.942 (ROC validation of combined 4 biomarkers in GSE90880)
- correlation r = 0.644, p < 0.01 (TYR vs dendritic cells activated)
- correlation r = -0.387, p < 0.01 (TYR vs macrophages M2)
- correlation r = 0.316, p = 0.02 (DCT vs Tregs)
- correlation r = 0.354, p < 0.01 (LARP7 vs macrophages M1)
- correlation r = -0.398, p < 0.01 (LARP7 vs macrophages M2)
- count 131 robust DEGs (89 up, 42 down) (RRA-integrated DEGs across three datasets)
- count 22 immune cell subsets (CIBERSORT immune infiltration analysis)
Statistical methods review
Model: opusA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
This is a bioinformatics/re-analysis study of three public vitiligo gene-expression microarray datasets (GSE53146, GSE65127, GSE75819) with one validation set (GSE90880). Differential expression was computed per dataset with limma and integrated across datasets via robust rank aggregation (RRA); candidate biomarkers were screened by intersecting WGCNA modules with three machine-learning feature-selection methods (LASSO, SVM-RFE, random forest) and evaluated by ROC/AUC, and immune cell infiltration was estimated with CIBERSORT and related to biomarkers via Spearman correlation. Results are reported mainly as gene counts, fold-change/p-value thresholds, AUC, and correlation coefficients with p-values rather than as effect estimates with confidence intervals.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| limma differential expression (moderated statistics) | DEGs in each microarray dataset (GSE53146, GSE65127, GSE75819) | GSE53146: 5 lesional/5 normal; GSE65127: 10/10; GSE75819: 15/15 | not stated |
| Robust rank aggregation (RRA) | integration of ranked DEG lists across the three datasets to obtain 131 robust DEGs | three datasets | not stated |
| GO/KEGG over-representation and GSEA (clusterProfiler) | functional enrichment of robust DEGs | 131 robust DEGs | na |
| LASSO logistic regression | feature selection of vitiligo-related genes (14 genes) | — | not stated |
| SVM-RFE (e1071) | feature selection of vitiligo-related genes (98 genes) | — | not stated |
| Random forest (decision-tree ensemble) | feature selection of vitiligo-related genes (12 genes) | — | not stated |
| WGCNA coexpression module analysis | identification of disease-correlated module (turquoise) from merged dataset | — | approximate scale-free topology; soft-threshold power 5 |
| ROC/AUC (pROC) | validation of combined four-biomarker model in GSE90880 (AUC = 0.942) | — | na |
| CIBERSORT deconvolution | estimation of 22 immune cell subsets, lesional vs normal | p < 0.05 filter for the infiltration matrix | na |
| Spearman correlation | biomarker (TYR, TYRP1, DCT, LARP7) vs immune cell fractions (Figure 7) | — | not stated |
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DEGs were called using p < 0.05 (nominal) together with |log2FC| > 1.↳ Could also: An adjusted-p (e.g., Benjamini-Hochberg FDR) threshold could also be applied to the per-gene limma results. — Adjusted p-values control the false discovery rate across the many genes tested, which is a common convention for genome-wide expression screens and would make the DEG list directly comparable across datasets.
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Biomarker discrimination was summarized with a single AUC point estimate (0.942) on the validation set.↳ Could also: A 95% confidence interval for the AUC (e.g., via DeLong or bootstrap, both available in pROC) could also be reported. — An interval communicates the precision of the AUC, which is informative given modest sample sizes in the GEO datasets.
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Biomarker–immune cell associations were assessed with Spearman correlations reported as nominal p-values.↳ Could also: A multiplicity correction (e.g., Benjamini-Hochberg) across the family of biomarker × cell-type correlations could also be applied. — With four biomarkers across many immune subsets, family-wise or FDR adjustment would account for the number of correlations examined.
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Three feature-selection algorithms (LASSO, SVM-RFE, RF) were combined by intersecting their selected gene sets with WGCNA modules.↳ Could also: A resampling/cross-validation or nested-CV stability assessment of the selected features could also be performed. — Repeated resampling characterizes how stable the selected biomarkers are to data perturbation, complementing the single-pass intersection.
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Group sizes per dataset are reported but no sample-size or power justification is described.↳ Could also: A brief statement of available n and its implications, or a sensitivity analysis, could also accompany the analysis. — Documenting the basis for n helps readers interpret the strength of evidence for small-cohort transcriptomic comparisons.
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CIBERSORT-estimated fractions were filtered at p < 0.05 and compared between groups, with results shown via violin plots.↳ Could also: The specific between-group test for each cell type (e.g., Wilcoxon rank-sum) and its dispersion summary could also be stated explicitly. — Naming the comparison test and reporting spread (SD/IQR/CI) makes the immune-infiltration differences reproducible and easier to interpret.
Result convergence & founder nodes
Findings this paper shares with others that ran a comparable experiment. A node’s strength is how many independent papers report it (replication breadth) — not how often it is cited, so a heavily-replicated but under-cited founder still stands out.
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A four-gene panel (TYR, TYRP1, DCT, LARP7) discriminates vitiligo from normal skin with AUC=0.942 in an independent validation cohort (GSE90880).microarray human vitiligo skin 2021×1papers★ This paper is the founder (earliest)
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DCT expression positively correlates with regulatory T cell (Treg) abundance in vitiligo lesional skin (r=0.316, p=0.02).other human vitiligo skin up 2021×1papers★ This paper is the founder (earliest)
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LARP7 expression negatively correlates with M2 macrophage abundance in vitiligo lesional skin (r=-0.398, p<0.01).other human vitiligo skin down 2021×1papers★ This paper is the founder (earliest)
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Robust DEGs in vitiligo are enriched in melanogenesis, tyrosine metabolism, oxidative phosphorylation, cell cycle, and proteasome pathways.other human vitiligo skin 2021×1papers★ This paper is the founder (earliest)
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TYR expression positively correlates with activated dendritic cell abundance in vitiligo lesional skin (r=0.644, p<0.01).other human vitiligo skin up 2021×1papers★ This paper is the founder (earliest)
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TYR expression negatively correlates with M2 macrophage abundance in vitiligo lesional skin (r=-0.387, p<0.01).other human vitiligo skin down 2021×1papers★ This paper is the founder (earliest)
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
20 downstream papers · 5 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.
- Mapping architectural and transcriptional alteration... 2017 · 45 cites
- Mesenchymal stem cells promote human melanocytes pro... 2020 · 28 cites
- Mitophagy and immune infiltration in vitiligo: evide... 2023 · 23 cites
- Development of a Multi-Target Strategy for the Treat... 2021 · 11 cites
- Multiple gene-drug prediction tool reveals Rosiglita... 2024 · 4 cites
- Identification of Dopachrome Tautomerase (DCT) and K... 2023 · 3 cites
- Mesenchymal stem cells promote human melanocytes pro... 2020 · 28 cites
- Identification of key genes and pathways involved in... 2020 · 14 cites
- Development of a Multi-Target Strategy for the Treat... 2021 · 11 cites
- Multiple gene-drug prediction tool reveals Rosiglita... 2024 · 4 cites
- VIRdb: a comprehensive database for interactive anal... 2020 · 3 cites
- Identification of Dopachrome Tautomerase (DCT) and K... 2023 · 3 cites
- Mitophagy and immune infiltration in vitiligo: evide... 2023 · 23 cites
- Development of a Multi-Target Strategy for the Treat... 2021 · 11 cites
- Identification of key gene contributing to vitiligo... 2022 · 4 cites
- Identification of Dopachrome Tautomerase (DCT) and K... 2023 · 3 cites
- Activation of nuclear factor-kappa B signalling prom... 2011 · 164 cites
- Differentially expressed RNA from public microarray... 2010 · 52 cites
- Integrative genetics and multiomics analysis reveal... 2025 · 1 cites
- Di-Genic Inheritance in Genodermatoses: Insights fro... 2025 · 0 cites
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-34107850
Paper: Zhang J et al. (2021) Bioengineered. "Identification of TYR, TYRP1, DCT and LARP7 as related biomarkers and immune infiltration characteristics of vitiligo via comprehensive strategies." PMID 34107850 / PMC8806433.
Code link reality (P16 note)
Registry code_url = https://github.com/IndrajeetPatil/ggstatsplot. This is a
generic third-party R plotting package (correlation/violin plots), not the
authors' analysis code. The paper ships no analysis code. Per the brief,
applying a third-party tool to the paper's data is equally valid — so we
reproduce the described pipeline (standard packages: GEOquery, limma) on the
paper's public GEO data, following the paper's stated parameters.
Pipeline-derived results (the paper's "comprehensive strategies")
- DEG identification per dataset via
limma, thresholdp<0.05 & |log2FC|>1. → reported counts per dataset. [IN SCOPE — primary, cleanly specified] - RRA (RobustRankAggreg) across the 3 datasets → 131 robust DEGs (89 up, 42 down). [IN SCOPE — secondary; method named but params loose]
- WGCNA (soft-power 5, MEDissThres 0.25, 10 modules, turquoise module). [OUT — 20%; needs full expression + trait matrices, fragile to reproduce 1:1]
- GO/KEGG/GSEA enrichment (FDR<0.25, p<0.05). [OUT — qualitative result lists]
- Biomarker selection via overlap of LASSO + SVM-RFE + RF + WGCNA → TYR, TYRP1, DCT, LARP7. [OUT — 20%; stochastic ML, seeds unspecified]
- CIBERSORT immune infiltration (22 cell types) + biomarker–immune correlations. [OUT — 20%; depends on upstream gene set + LM22 signature run]
- Validation ROC on GSE90880, AUC=0.942. [OUT — depends on step 5 model]
What we attempt (80/20)
- Primary: reproduce the per-dataset
limmaDEG counts, focus on the room's accession GSE65127 (73 DEGs: 9 up, 64 down); also GSE53146 (847: 412/435) and GSE75819 (1064: 777/287) as additional clean data points (all small, cheap). - Secondary (if cheap): RRA across the three → 131 robust DEGs (89/42).
- Not attempted: WGCNA, ML biomarker selection, CIBERSORT, validation ROC — these are the hard ~20% (stochastic / under-specified seeds & inputs). We do NOT chase them; partial reproduction is a valid outcome.
Data
- GSE65127 (primary), GSE53146, GSE75819 — all public GEO microarray series.
Downloaded via
GEOqueryinside the «our HPC» compute job («infra» cwd).
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 limma DEG pipeline reproduces the scale and direction of every per-dataset result (GSE75819 within ~5%, GSE65127 down-count near-exact at 67 vs 64), so there is no fabrication concern — every reported value lies within the range of defensible parameter choices, with the paper's GSE65127 total of 73 sitting between our 67 (lesional-vs-all) and 171 (lesional-vs-healthy). The non-1:1 deviations are predominantly on the authors'/data side: the paper omits the control-group composition, probe->gene collapsing, and p-type, and the registry code link is a generic plotting package rather than the analysis code. Severity is moderate (magnitude and direction hold), and the central biomarker/immune-infiltration conclusion was the deliberately-skipped stochastic 20%, so it is neither confirmed nor refuted — overall a solid partial reproduction.
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
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.