Screening of Diagnostic Biomarkers and Immune Infiltration Characteristics Linking Rheumatoid Arthritis and Rosacea Based on Bioinformatics Analysis.
Provisional — an automated or curator check raised a specific concern and points reviewers here. This is NOT a final assessment and not a determination about the authors.
The main result did not reproduce in this reproduction attempt. Where our recomputation produced values that differ from the published ones, those discrepancies are listed below. This is a single automated attempt — not peer review and not a finding of error or misconduct — and differences can also arise from data access, undocumented parameters or the computing environment. The verdict can be contested via “report an error”.
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
- 🟡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 reconstruct the pipeline and the data; sample-level identification reproduced the paper EXACTLY (GSE12021 9Ctrl/12RA, GSE55457 10Ctrl/13RA dropping OA, GSE65914 20Ctrl/38rosacea). The limma DE step reproduces well for the single-dataset rosacea claim (954 vs 1024 reported DEGs, ~93%, same up>down direction = within-tol). The RA combined-dataset DEG count does NOT reproduce: 720 vs reported 4304 (~6x lower, same down>up direction = mismatch). The discrepancy concentrates in the RA combine/ComBat/normalization step, which the paper underspecifies and which is NOT derivable from the shipped repo: the pipeline input merge.txt is absent and normalize.R's hardcoded group vectors are template residue from an unrelated 'RIF' (recurrent implantation failure) study (54 samples, wrong labels) -- flagged for human review (possible undocumented preprocessing vs error; not asserted fabrication). NOT ATTEMPTED: CIBERSORT immune-fraction values/Fig6 (qualitative, tooling threading bug, dropped per 80/20); WGCNA + cytoHubba/MCODE hub genes (14/17) and 277 co-expressed DEGs (Cytoscape GUI / non-pipeline); ROC of CXCL10/CCL27, ELISA, qRT-PCR, flow cytometry (wet-lab). All heavy compute on «our HPC» SLURM; repo pinned @3329b58; R4.3.3/GEOquery2.70/limma3.58/sva3.50. Large matrices kept on «infra» with SHA256 recorded in data/data.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 48assessed: 2026-06-14 ⛓ 9b7504813542
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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
- 2026-06-14
- 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
- 2026-09-19
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: sonnetGiven epidemiological links between rheumatoid arthritis (RA) and rosacea, this study tests whether the two chronic autoimmune/inflammatory diseases share common molecular regulatory networks and immune cell infiltration patterns that could yield joint diagnostic biomarkers.
- ★ 277 co-expressed DEGs shared between RA and rosacea are enriched in immune processes and chemokine-mediated signaling pathways finding
- ★ 14 and 17 hub genes overlapping between cytoHubba and WGCNA were identified in RA and rosacea, respectively finding
- ★ Macrophages and dendritic cells are the most abundant immune cells in RA and rosacea, respectively finding
- ★ CXCL10 and CCL27 show diagnostic value by ROC analysis and are elevated in serum of RA and rosacea patients finding
- ★ CXCL10/CCL27 regulate the density and function of macrophages/dendritic cells in RA and rosacea, validated by FCM and qRT-PCR mechanism
- CIBERSORT with the LM22 signature matrix was used to quantify 22 immune cell types in RA and rosacea tissue method
- ★ WGCNA identified the turquoise module as most correlated with RA and the pink module as most correlated with rosacea finding
- ★ PPI/cytoHubba analysis identified CXCL10, CCR7, SAA1, and CCR5 as crucial hub genes finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| microarray gene expression profiling / DEG analysis (limma) | human RA synovial membrane tissue (GSE12021, GSE55457; GPL96) | disease vs normal control | differentially expressed genes (|log2FC|>=1, adj. P<0.05) | GPL96 microarray |
| microarray gene expression profiling / DEG analysis (limma) | human rosacea skin tissue (GSE65914; GPL570) | disease vs normal control | differentially expressed genes (|log2FC|>=1, adj. P<0.05) | GPL570 microarray |
| weighted gene co-expression network analysis (WGCNA) | RA and rosacea gene expression matrices | none | module-trait correlation, hub gene modules | — |
| protein-protein interaction (PPI) network / cytoHubba / MCODE | co-expressed DEGs (RA and rosacea) | none | hub gene ranking and functional modules | STRING database, Cytoscape v3.8.1 |
| CIBERSORT immune infiltration analysis | RA and rosacea normalized gene expression profiles | none | relative abundance of 22 immune cell types (LM22) | CIBERSORT (LM22 signature matrix) |
| enzyme-linked immunosorbent assay (ELISA) | serum from RA and rosacea patients | disease vs healthy control | CXCL10 and CCL27 concentration | Invitrogen KAC2361 |
| flow cytometry (FCM) | PBMC-derived macrophages and dendritic cells from human blood | none (immunophenotyping) | CD45, CD86, CD206, CD14, CXCL10, HLA-DR, CD1a, CCL27 marker expression | CytoFLEX flow cytometer, FlowJo v10.07 |
| quantitative real-time PCR (qRT-PCR) | isolated human macrophages (CXCL10-treated) and dendritic cells (CCL27-treated) | recombinant human CXCL10 (0 or 2 ng/mL) or CCL27 (0 or 2 ng/mL), 24h | IL-1β, IL-6, IFN-α, TNF-α mRNA expression (normalized to ACTB) | Applied Biosystems 7500 Real-Time PCR System |
- – 4304 DEGs identified in combined RA dataset (1961 up, 2343 down)
- – 1024 DEGs identified in rosacea dataset (636 up, 388 down)
- – 277 common co-expressed DEGs identified between RA and rosacea, linked to immune and chemokine-mediated signaling
- ▲ Turquoise module (605 genes) showed highest correlation with RA r=0.79
- ▲ Pink module (541 genes) showed highest correlation with rosacea r=0.80
- – 14 and 17 overlapping hub genes identified between cytoHubba and WGCNA in RA and rosacea respectively
- – CXCL10, CCR7, SAA1, CCR5 identified as top hub genes across 5 cytoHubba algorithms
- ▲ Macrophages and dendritic cells identified as most abundant immune cells in RA and rosacea respectively by CIBERSORT
- correlation r=0.79, p=7.9*10^-9 (turquoise module (605 genes) vs RA trait, WGCNA)
- correlation r=0.80, p=2.54*10^-12 (pink module (541 genes) vs rosacea trait, WGCNA)
- count 4304 DEGs (1961 up, 2343 down) (combined RA dataset (GSE12021 + GSE55457) DEGs)
- count 1024 DEGs (636 up, 388 down) (rosacea dataset (GSE65914) DEGs)
- count 277 co-expressed DEGs (overlap between RA and rosacea DEG sets)
- count 14 and 17 overlapping hub genes (cytoHubba ∩ WGCNA hub genes in RA and rosacea)
- other 93% concordance with ground truth phenotypes (CIBERSORT method validation on external datasets)
- correlation |r|>0.4, P<0.05 (threshold for overlapped gene vs leukocyte signature matrix correlation screening)
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 downloaded public microarray datasets (GEO) for rheumatoid arthritis and rosacea, applied SVA batch correction and limma-based differential expression analysis to identify co-expressed DEGs, then used PPI network hub-gene scoring (cytoHubba, 5 topological patterns) and WGCNA module-trait correlation to converge on overlapping hub genes. Immune infiltration was estimated with CIBERSORT and correlated with hub genes via Pearson coefficients. Candidate biomarkers (CXCL10, CCL27) were evaluated with ROC/AUC and validated in a clinical cohort using ELISA, qRT-PCR, and flow cytometry, with Student's t-test or Kruskal-Wallis H-test applied depending on distributional assumptions.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| limma moderated t-statistic (empirical Bayes linear model) | DEG identification: RA combined dataset (GSE12021 + GSE55457) and rosacea dataset (GSE65914) vs respective normal controls | GSE12021: 12 RA + 9 controls; GSE55457: 13 RA + 10 controls; GSE65914: 38 rosacea + 20 controls | not stated |
| Spearman correlation coefficient | WGCNA module-trait relationship heatmaps for RA and rosacea (turquoise module r=0.79, p=7.9e-9; pink module r=0.80, p=2.54e-12) | null | not stated |
| Pearson correlation coefficient (r) | Correlation of overlapped hub genes with 22 CIBERSORT leukocyte gene signature matrix immune cell types; filtered at |r|>0.4 and P<0.05 | null | not stated |
| CIBERSORT (nu-support vector regression deconvolution with LM22 matrix) | Immune cell fraction estimation across all RA and rosacea samples; samples filtered at CIBERSORT P<0.05 | null | not stated |
| ROC curve / AUC (pROC package, AUC>0.7 threshold) | Diagnostic biomarker performance evaluation of CXCL10 and CCL27 | null | na |
| Student's t-test (two-group, presumably two-sided) | Clinical validation cohort: normally distributed continuous variables between disease and control groups (ELISA concentrations, FCM data) | null | stated |
| Kruskal-Wallis H-test with Dunn's multiple-comparisons post-hoc test | Clinical validation cohort: non-normally distributed continuous variables between groups | null | stated |
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Pearson correlation was used to link hub gene expression to CIBERSORT immune cell proportion estimates↳ Could also: Spearman rank correlation could also be applied to these associations — Pearson assumes bivariate normality; deconvolution-derived cell proportion estimates are frequently skewed or zero-inflated, making a rank-based approach a natural complement; both are standard in immune infiltration correlation analyses
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CIBERSORT with the LM22 matrix was the sole method used for bulk-tissue immune cell deconvolution↳ Could also: Additional deconvolution tools such as xCell, MCP-counter, TIMER2.0, or EPIC could also be applied — Different algorithms use distinct reference signatures and optimization frameworks; reporting concordance across two or more methods is one approach to characterizing the robustness of immune infiltration estimates
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Hub genes were prioritized by intersecting cytoHubba rankings (5 topological scoring patterns) with WGCNA key module membership↳ Could also: Other network centrality metrics (betweenness centrality, eigenvector centrality, PageRank) or community detection algorithms (Louvain, InfoMap) could also be used for hub gene identification — Degree-based and betweenness-based centrality capture different structural roles within a network; reporting sensitivity of the final hub gene list to the choice of scoring method is one approach to assessing prioritization robustness
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ROC/AUC alone was used to characterize the diagnostic utility of CXCL10 and CCL27↳ Could also: Decision curve analysis (DCA) or calibration analysis could also accompany ROC evaluation — AUC summarizes discrimination across all possible thresholds equally, whereas DCA estimates net clinical benefit at clinically relevant decision thresholds; calibration assesses agreement between predicted and observed event rates — together these give a more complete characterization of biomarker clinical value
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Batch effects across the two RA GEO datasets were addressed using SVA (surrogate variable analysis) before combined limma analysis↳ Could also: Explicit batch-variable correction methods such as ComBat (within the SVA package) or limma's removeBatchEffect function could also be used, treating known dataset origin as a batch covariate — SVA estimates latent confounders without requiring known batch labels; ComBat and removeBatchEffect allow direct modeling of known batch membership; applying and comparing both approaches is one way to assess sensitivity of the DEG list to the batch correction strategy
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Multiple clinical outcomes (ELISA for CXCL10 and CCL27, multiple FCM markers, qRT-PCR for several genes) were each compared between groups without a stated multiplicity adjustment across this family of tests↳ Could also: A Benjamini-Hochberg FDR or Bonferroni correction applied across the full family of clinical validation comparisons could also be used — Specifying a correction across the set of simultaneously tested outcomes defines the family-wise or false-discovery error rate being controlled; this is a commonly recommended additional step when multiple endpoints are evaluated in a validation cohort
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
159 downstream papers · 2 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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- Three hematologic/immune system-specific expressed g... 2021 · 117 cites
- Exosomal MicroRNA-320a Derived From Mesenchymal Stem... 2020 · 88 cites
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- A novel pathogenic role of the ER chaperone GRP78/Bi... 2012 · 127 cites
- Proteomic analysis of synovial fluid from the osteoa... 2013 · 121 cites
- Gene profiling of Chikungunya virus arthritis in a m... 2012 · 96 cites
- Identification of intra-group, inter-individual, and... 2008 · 85 cites
- Dysregulated integrin αVβ3 and CD47 signaling promot... 2019 · 59 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.
Scope — pmid-39104909
Paper: Wang et al. 2024, J Inflamm Res. "Screening of Diagnostic Biomarkers and Immune Infiltration Characteristics Linking Rheumatoid Arthritis and Rosacea …" PMCID PMC11299729. DOI 10.2147/jir.s467760.
Repo: https://github.com/doctorxia203/FII-2023 (commit pinned at run time). Contents (5 files, 51 KB):
normalize.R—sva::ComBatbatch correction of a pre-merged matrixGSE12021/merge.txt.Diff.R—limmadifferential expression (RA vs Ctrl), thresholds |logFC|>1 & adj.P<0.05.Cibersort.R+sourcecibersort.R— CIBERSORT v1.03 (Newman 2015), usesLM22.txt.LM22.txt— 547-gene × 22-cell-type leukocyte signature matrix (shipped).
Datasets (per Methods + Data Sharing Statement)
- RA: GSE12021 (GPL96: 9 normal + 12 RA = 21) + GSE55457 (GPL96: 10 normal + 13 RA = 23)
→ combined RA dataset, 44 samples (25 RA, 19 control). Batch-corrected with
sva::ComBat. - Rosacea: GSE65914 (GPL570: 20 control + 38 rosacea = 58). (Intro text inconsistently cites "GSE6591"; the Data Sharing Statement and sample counts identify GSE65914.)
IN SCOPE — pipeline-derived, code-backed
| id | result | pipeline | reported location |
|---|---|---|---|
| C1 | RA combined DEGs: 4304 total (1961 up, 2343 down), |log2FC|≥1 & adjP<0.05 | normalize.R (ComBat) + Diff.R (limma) | Results §"Identification and Enrichment Analysis of DEGs" |
| C2 | Rosacea DEGs: 1024 total (636 up, 388 down) | same limma pipeline on GSE65914 | same paragraph |
| C3 | CIBERSORT 22-cell immune fractions; qualitative: macrophages most abundant in RA, DCs in rosacea | Cibersort.R + LM22 | Results §"Immune Infiltration" / Fig 6 |
Primary target: C1 — directly produced by the shipped normalize.R + Diff.R, a
single clear quantitative claim, light compute. C2 is the same pipeline on a different
GEO accession (secondary). C3 is qualitative.
OUT OF SCOPE (not attempted / why)
- Probe-merge input not shipped.
merge.txt(the gene×sample matrix feeding the pipeline) is NOT in the repo; it must be reconstructed from GEO (series matrices + GPL96 probe→symbol collapse by mean, per Methods). This reconstruction is the main reproducibility gap and a source of expected numeric drift. - normalize.R group vectors are template residue. Shipped
batchType=c(rep(1,48),rep(2,6))andmodType=c(rep("Ctrl",24),rep("RIF",24)…)(54 samples, "RIF"=recurrent-implantation- failure labels from an unrelated study) do NOT match the described 44-sample RA design. Faithful reproduction requires correcting these to the documented 21+23 split — recorded as a necessary deviation, not run verbatim. - WGCNA, cytoHubba/MCODE PPI hub genes (Cytoscape, GUI, manual) — non-pipeline / not scriptable here.
- 277 co-expressed DEGs, 14/17 hub genes — depend on WGCNA + Cytoscape steps above.
- ELISA, qRT-PCR, flow cytometry, ROC of CXCL10/CCL27 — wet-lab, out of scope.
Compute plan
All compute on «our HPC» (SLURM, partition=std). Conda env built inside the compute job
(internet on compute nodes): R + Bioconductor limma, sva, GEOquery, hgu133a.db,
e1071, preprocessCore. Data downloaded to «infra» work dir inside the job.
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 rosacea DEG claim reproduces well (954 vs 1024, ~93%, same up>down direction), but the primary RA combined-dataset claim of 4304 DEGs is not derivable from the shipped artifacts — a faithful re-implementation of the described limma+ComBat pipeline yields 720 (~6x lower). The gap localizes to the underspecified RA merge/ComBat/normalization step: the input merge.txt is not deposited and normalize.R hardcodes 'RIF' template residue from an unrelated recurrent-implantation-failure study (54 samples), so the defect sits on the authors'/data-availability side, not our method. Severity is high on the primary count, but the central biomarker/immune-infiltration conclusions were not testable here (CIBERSORT env failure; WGCNA/Cytoscape out of scope), so q7 is limited rather than refuted. Net: a critical reproducibility discrepancy with a real code/data red flag, but not asserted as fabrication.
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.