Meta-analysis of gene expression profiles of lean and obese PCOS to identify differentially regulated pathways and risk of comorbidities.
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
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡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 DIFFERENTIAL-EXPRESSION stage 1:1. Ran the authors' own AffyOldLimma.R (faithful port: affy::justRMA + BioMart symbol map + collapse-dup-by-maxSD + limma ModT) on the paper's public GEO CELs for the two unambiguous LEAN GPL570 datasets — GSE98421 (the brief's named accession; subcutaneous adipose, 4v4) and the GSE10946 lean subset (cumulus, 6 nonPCOS-Lean vs 5 PCOS-Lean, exactly matching the paper's 6+5). RESULT: the paper's crisp gene-level claim reproduces EXACTLY — all 7 named commonly-dysregulated lean-PCOS genes (PRRT1, SLITRK4, CRHBP, HAPLN1, SRGN, EREG, WNT5A) are significant DEGs in BOTH lean tissues (intersection of 9, containing precisely those 7). The paper's asymmetric-threshold down-skew (4891 down vs 123 up) also reproduces per tissue (150/2, 892/16). Auditability flag (not fabrication): the reported DEGs are nominal raw-p<0.05 and none survive BH correction in these n=4-6/arm studies. NOT ATTEMPTED (the under-specified / GUI-bound / unshipped-data ~20%): (a) the full 8-dataset aggregate (5014/4224 DEGs, tissue split) — needs GPL6244-ST with ambiguous 16-sample selection (GSE98595), a non-Affy GPL15362 custom array (GSE43264), and GPL96 where the shipped script's BioMart filter is hardcoded to U133-Plus-2; (b) pathway gene-set counts 86/1031/159 and the 4 themes — GSEA v3.0 preranked + interactive Cytoscape 3.6.1 Enrichment Map GUI with the Bader-lab Nov-2018 GMT; (c) the 7 transcription factors (downstream of b); (d) GSVA disease/ICD-11 mapping (222/192/127, 37 visual, 136 PCOS genes) — GSVA-HeatMap.R reads gene-set files not shipped in the repo (hardcoded G:/ paths). Verdict: PARTIAL but high-confidence 1:1 on the deterministic shipped-code DE stage incl. the paper's headline gene signature; no fabrication detected.
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Assessment versions
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v1 current initial assessment Score 78assessed: 2026-06-15 ⛓ 39022467b0aa
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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
- 2026-09-19
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: sonnetLean and obese PCOS may have distinct underlying pathophysiology, so the study systematically compares gene expression profiles, enriched pathways, and comorbidity risk between lean and obese PCOS using a meta-analysis of GEO microarray datasets.
- ★ Meta-analysis of gene expression profiles reveals differentially regulated pathways and comorbidity risk differences between lean and obese PCOS phenotypes finding
- ★ The majority of differentially expressed genes (DEGs) are downregulated in both lean and obese PCOS regardless of tissue type finding
- ★ Ovarian and endometrial tissues share several commonly dysregulated genes, suggesting shared PCOS pathophysiology mechanisms across tissues finding
- ★ Pathways for inflammation/immune response, insulin signaling, steroidogenesis, hormonal and metabolic signaling, gonadotrophic hormone regulation, and cell structure/signaling are enriched in lean and obese PCOS gene expression finding
- ★ The gene-disease network is denser for obese PCOS with a higher comorbidity score compared to lean PCOS finding
- WNT5A is upregulated in cumulus granulosa cells of lean PCOS, consistent with prior qPCR reports of WNT5A overexpression in granulosa cells of lean PCOS women finding
- ★ A meta-analysis approach was used in which each GEO case-control dataset was individually background-corrected, quantile normalized (RMA), annotated (biomaRt) and analyzed for DEGs (limma) before cross-study comparison method
- ★ A gene-disease association score (GDS) was constructed by mapping DEGs to KEGG disease genes categorized by ICD-11 classification to quantify comorbidity risk method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| microarray gene expression profiling | lutein granulosa cells (human, GSE98595) | PCOS (lean) vs control | differentially expressed genes (logFC, p-value) | Affymetrix Human Gene 1.0 ST Array (GPL6244) |
| microarray gene expression profiling | subcutaneous adipose tissue (human, GSE98421) | PCOS (lean) vs control | differentially expressed genes (logFC, p-value) | Affymetrix Human Genome U133 Plus 2.0 Array (GPL570) |
| microarray gene expression profiling | cumulus cells (human, GSE10946) | PCOS (lean and obese) vs control | differentially expressed genes (logFC, p-value) | Affymetrix Human Genome U133 Plus 2.0 Array (GPL570) |
| microarray gene expression profiling | skeletal muscle (human, GSE6798) | PCOS (obese) vs control | differentially expressed genes (logFC, p-value) | Affymetrix Human Genome U133 Plus 2.0 Array (GPL570) |
| microarray gene expression profiling | metaphase II oocyte (human, GSE5850) | PCOS (obese) vs control | differentially expressed genes (logFC, p-value) | Affymetrix Human Genome U133 Plus 2.0 Array (GPL570) |
| microarray gene expression profiling | subcutaneous adipose tissue (human, GSE43264) | PCOS (obese) vs control | differentially expressed genes (logFC, p-value) | NuGO array (human) NuGO_Hs1a520180 (GPL15362) |
| microarray gene expression profiling | endometrial cells - epithelial, endothelial, stromal fibroblasts, mesenchymal stem cells (human, GSE48301) | PCOS (obese) vs control | differentially expressed genes (logFC, p-value) | Affymetrix Human Gene 1.0 ST Array (GPL6244) |
| microarray gene expression profiling | omental adipose tissue (human, GSE5090) | PCOS (obese) vs control | differentially expressed genes (logFC, p-value) | Affymetrix Human Genome U133A Array (GPL96) |
- – 5014 total DEGs (4224 unique) identified across 8 GEO datasets comparing PCOS vs control
- ▼ 123 DEGs (96 unique) upregulated and 4891 DEGs (4101 unique) downregulated in PCOS vs control ~97% of unique DEGs downregulated
- – 86 pathway gene sets (6.7%) commonly enriched in lean and obese PCOS; 1031 (80.8%) unique to lean PCOS; 159 (12.5%) unique to obese PCOS (GPL570 datasets) 6.7% shared
- – Cell-motility/immune response, FAK-related, and ERBB1/PDGFRB signaling pathway clusters were downregulated in lean PCOS but upregulated in obese PCOS; mitochondrial gene expression pathways showed the opposite pattern
- – Seven genes (PRRT1, SLITRK4, CRHBP, HAPLN1, SRGN, EREG, WNT5A) were commonly dysregulated across cumulus cells and subcutaneous adipose tissue (GPL570) in lean PCOS
- – Six genes (GPX7, SERPINI1, TMEM256, SVIP, MAT2A, SRGN) were commonly dysregulated across ovarian, endometrial and adipose tissues
- ▲ The number of DEGs associated with disease categories was much higher in obese PCOS than lean PCOS in the gene-disease network, and endometrial tissue/obese PCOS datasets showed maximum gene-disease association score (GDS)
- ▲ WNT5A expression was upregulated in cumulus granulosa cells of lean PCOS (GSE10946), matching prior qPCR reports of WNT5A overexpression in granulosa cells of lean PCOS women
- count 5014 total DEGs (4224 unique) (DEGs identified across 8 GEO datasets, PCOS vs control)
- count 123 upregulated (96 unique), 4891 downregulated (4101 unique) (direction breakdown of DEGs)
- pvalue p<0.05 (significance threshold for DEG calling (limma))
- fold_change logFC>2 (upregulated), logFC<-0.5 (downregulated) (DEG fold-change thresholds)
- count 86 (6.7%) common, 1031 (80.8%) lean-unique, 159 (12.5%) obese-unique pathway gene sets (GPL570 pathway enrichment comparison)
- count 222 genes developmental anomalies, 192 metabolic disorders, 127 nervous system disorders (DEGs mapped to top ICD-11 disease categories)
- count 37 DEGs mapped to disorders of the visual system; 8 DEGs (incl. FN1, ACTN4, TRPC6) mapped to glomerulus pathology (comorbidity gene-disease mapping)
- count 8 GEO datasets analyzed (e.g., GSE6798 skeletal muscle: 13 control/16 PCOS) (sample sizes of individual GEO datasets (Table 1))
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 paper used a study-level meta-analysis approach in which eight GEO microarray datasets of lean and obese PCOS were each independently preprocessed (RMA normalization) and analyzed for differentially expressed genes (DEGs) via limma moderated t-statistics, then DEG lists were compared across studies by intersection rather than by pooled effect estimation. Pathway enrichment was assessed per dataset with GSEA (permutation-based FDR), and results were visualized and clustered with Cytoscape EnrichmentMap. Comorbidity burden was quantified with a custom gene-disease association score (GDS) mapping DEGs to KEGG disease categories, and GSVA was used to score gene-set variation across tissue types.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| limma moderated t-statistic (empirical Bayes shrinkage of variance estimates) | Identification of DEGs in each of the eight GEO datasets individually (PCOS cases vs. controls) | Varies by dataset: 3–16 controls and 3–16 PCOS subjects per dataset (see Table 1) | not stated |
| GSEA weighted enrichment statistic (permutation-based, 2000 permutations) | Pathway gene-set enrichment analysis for each dataset; FDR q-value < 0.1 and p-value < 1.0 cutoffs applied | Gene sets sized 10–200 genes; based on same per-dataset sample sizes as DEG analysis | not stated |
| Gene Set Variation Analysis (GSVA) | Assessment of ICD-11 disease-associated gene-set variation across ovarian tissue types (GPL570 platform only) for lean and obese PCOS | GPL570 ovarian tissue datasets only; exact combined n not restated at this stage | not stated |
| Custom gene-disease association score (GDS): (DEGs mapped to disease / total genes mapped to disease) × 100 | Comorbidity analysis mapping DEGs to KEGG disease categories by ICD-11 classification | 4224 unique DEGs mapped against KEGG disease database Release 88.2 | na |
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DEGs were filtered using an unadjusted p-value threshold (p < 0.05) combined with logFC cutoffs across tens of thousands of probes per array↳ Could also: Apply Benjamini-Hochberg FDR adjustment (adjusted p < 0.05 or 0.10) to the per-gene limma output before applying logFC filters — With 17,000–54,000 probes tested per array, BH-FDR correction is a widely adopted approach to limit the expected proportion of false discoveries among reported DEGs; reporting both adjusted and unadjusted results would allow readers to evaluate the sensitivity-specificity trade-off, especially important here given small per-dataset sample sizes
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Cross-dataset meta-analysis was performed by comparing lists of significant DEGs across studies (intersection / vote-counting approach)↳ Could also: Use a formal statistical meta-analysis of effect sizes, such as Fisher's combined p-value method, a random-effects model (e.g., metaMA or RankProd), or a combined z-score approach — Pooled effect-size meta-analysis quantifies the direction and magnitude of expression change consistently across studies, propagates per-study uncertainty into a single estimate, and can formally test for heterogeneity between datasets — information that intersection of binary DEG lists does not provide
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Asymmetric logFC thresholds were applied: logFC > 2 for upregulation and logFC < −0.5 for downregulation↳ Could also: Apply a symmetric absolute logFC threshold (e.g., |logFC| ≥ 1) or rank-based filtering that does not require a directionally asymmetric cutoff — Symmetric or |logFC|-based thresholds treat up- and downregulation equivalently, which simplifies interpretation and is the more common convention in the microarray literature; asymmetric thresholds warrant explicit biological justification so readers understand why downregulated genes were captured at a lower magnitude
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Pathway enrichment used the ranked-list GSEA method applied to each individual dataset↳ Could also: Use Over-Representation Analysis (ORA) via Fisher's exact test or a hypergeometric test on the filtered DEG lists, or apply a cross-dataset enrichment aggregation (e.g., combining per-dataset enrichment scores before ranking pathways) — ORA on the intersection DEG lists directly connects the DEG-comparison step to enrichment, and is more interpretable when n is small; cross-dataset aggregation of enrichment scores would make the pathway-level meta-analysis as explicit as the gene-level one
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Comorbidity burden was quantified with a custom GDS formula (proportion of disease-associated genes that appear in the DEG list)↳ Could also: Apply a hypergeometric test or Fisher's exact test to assess whether overlap between DEGs and disease gene sets exceeds chance, using established gene-disease databases such as DisGeNET or the DISEASES resource — A statistical test provides a p-value and confidence interval for each gene-disease association, allowing formal comparison of comorbidity enrichment between lean and obese PCOS rather than a descriptive proportion score; established databases also provide evidence-weighted gene-disease links
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Datasets from different microarray platforms and diverse tissue types were kept fully separate throughout the analysis↳ Could also: Apply cross-platform batch correction (e.g., ComBat from the sva R package) to harmonize datasets sharing the same platform after normalization, then model tissue type as a covariate — Batch correction can reduce platform-driven variance and enable direct cross-dataset comparisons; it is optional but increasingly common when merging microarray studies, and would allow a single integrated DEG or enrichment analysis rather than purely list-level comparisons
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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GPX7, SERPINI1, TMEM256, SVIP, MAT2A, and SRGN are commonly dysregulated across ovarian, endometrial, and adipose tissues in PCOSmicroarray human ovary endometrium adipose pcos mixed 2020×1papers★ This paper is the founder (earliest)
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Ovarian and endometrial PCOS tissues share the largest fraction of commonly dysregulated genes (181 genes, 4.3%) compared to all other tissue-pair combinationsmicroarray human ovary endometrium pcos mixed 2020×1papers★ This paper is the founder (earliest)
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PCOS transcriptome is predominantly downregulated across multiple tissues (4891 downregulated vs 123 upregulated unique DEGs)microarray human pcos multiple-tissue down 2020×1papers★ This paper is the founder (earliest)
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Obese PCOS endometrial DEGs exhibit the highest gene-disease association score among all PCOS tissues, indicating the greatest predicted comorbidity riskother human endometrium pcos-obese up 2020×1papers★ This paper is the founder (earliest)
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PCOS DEGs are significantly enriched in KEGG disease gene sets for developmental anomalies (222 genes), metabolic disorders (192), nervous system disease (127), and visual system disorders (37)other human pcos up 2020×1papers★ This paper is the founder (earliest)
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Lean and obese PCOS have largely distinct enriched pathway gene sets (80.8% lean-specific, 12.5% obese-specific, 6.7% shared), indicating divergent molecular subtypesother human pcos mixed 2020×1papers★ 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.
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.
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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-32695266
Paper: Idicula-Thomas S, Gawde U, Bhaye S, Pokar K, Bader GD. Meta-analysis of gene expression profiles of lean and obese PCOS to identify differentially regulated pathways and risk of comorbidities. Comput Struct Biotechnol J 2020. DOI 10.1016/j.csbj.2020.06.023.
Shipped code: https://github.com/bic-nirrh/pcos-metaanalysis @ 204944eb
(5 R scripts; no data, no driver, hardcoded G:/, C:«path» paths).
The paper's pipeline (per the Methods + repo scripts)
GEO CEL files (8 datasets)
│ AffyOldLimma.R (GPL570/GPL96 → affy::justRMA) | AffySTLimma.R (GPL6244 → oligo::rma)
▼ RMA bg-correct + quantile-norm; probe→gene via BioMart; collapse dup genes by max-SD
limma lmFit + eBayes ModT → topTable → per-dataset DE table + .rnk (gene, t-value)
│ significant DEGs: p<0.05 AND (logFC>2 up | logFC<-0.5 down)
▼
GSEA v3.0 preranked on .rnk (Bader-lab human GMT) → results.edb
▼
Cytoscape 3.6.1 Enrichment Map (Cytoscape-EM.R; p=1.0, q=0.1, sim=0.375 COMBINED)
→ pathway gene-set counts, EM network, top transcription factors
GSVA-HeatMap.R: GSVA of 5 disease gene sets over pooled GPL570 expression → disease/ICD-11 mapping
In scope (attempted) — pipeline-derived, shipped code + public data, deterministic
| Result | Pipeline | Reproducible? |
|---|---|---|
| Per-dataset differential expression (RMA + limma ModT) | AffyOldLimma.R on GEO CEL files |
YES — run on GSE98421 + GSE10946(lean) |
| Significant-DEG identification at paper thresholds (p<0.05, logFC>2 / <-0.5) | limma topTable + threshold | YES |
| "7 genes commonly dysregulated across two lean tissues" (PRRT1, SLITRK4, CRHBP, HAPLN1, SRGN, EREG, WNT5A) | intersection of per-tissue DEGs | YES — adipose (GSE98421) ∩ cumulus (GSE10946-lean), both lean |
Chosen datasets: the brief's named accession GSE98421 (lean, subcutaneous
adipose, GPL570, 4v4 — unambiguous) plus GSE10946 lean subset (cumulus,
GPL570, 6 nonPCOS-Lean vs 5 PCOS-Lean — titles encode group exactly, matches the
paper's 6+5). Both run through the authors' AffyOldLimma.R exactly (GPL570 →
the script's hardcoded affy_hg_u133_plus_2 BioMart filter is correct here).
Out of scope (not attempted) — and why
| Result | Why out of scope |
|---|---|
| Aggregate 5014 DEGs (123 up / 4891 down), 4224 unique, tissue split (1284/2473/202/7/658) | Requires running all 8 datasets incl. GPL6244-ST (ambiguous sample selection in GSE98595: 16 samples vs paper's 3v3), GPL15362 (GSE43264, non-Affy custom array — neither shipped script handles it), GPL96 (GSE5090 — script's BioMart filter is hardcoded to U133-Plus-2, wrong for U133A). The last-20% long tail; not pinnable per-dataset (paper gives only the aggregate). |
| Pathway gene-set counts (86 common / 1031 lean-unique / 159 obese-unique), 4 pathway themes | GSEA preranked + Cytoscape 3.6.1 GUI Enrichment Map; needs the Bader-lab GMT (Nov-2018 snapshot) and an interactive desktop app. Non-deterministic to script headlessly. |
| 7 top transcription factors (ETV3, GABPB1, ELF3, GABPA, ELF1, ELF4, SRF) | Downstream of the Cytoscape EM network (iRegulon/manual). |
| GSVA disease enrichment / ICD-11 mapping (222 developmental / 192 metabolic / 127 nervous; 37 visual; 136 PCOS genes) | GSVA-HeatMap.R reads unshipped files (Allgenes_3tissues_gpl570.txt, GMT_only5_diseases.txt) at hardcoded G:/ paths — gene sets not provided in the repo. |
Verdict framing
This is a partial, faithful reproduction of the differential-expression stage (the deterministic, shipped-code, public-data 80%) on two lean datasets, with an explicit check of the paper's crisp gene-level cross-tissue claim. The downstream GSEA/Cytoscape/GSVA results (the under-specified, GUI-bound, or unshipped-geneset 20%) are deliberately not attempted.
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 paper's headline claim — the 7 commonly-dysregulated lean-PCOS genes across two tissues — reproduces exactly (7/7) by running the authors' own shipped AffyOldLimma.R on the public GEO CELs, with no fabrication. Deviations are minor and on the input/our-method side: the intersection is 9 vs 7 (2 extra genes, plausibly BioMart annotation drift) and the aggregate/pathway/comorbidity counts (C4–C7) were out of scope (GUI-bound GSEA/Cytoscape + unshipped gene sets), so they could not be checked. One honest caveat sits on the authors' side: DEGs are nominal raw-p<0.05 and none survive BH correction in these tiny n=4–6/arm studies, a power limitation — not a data mismatch. Overall a high-confidence partial reproduction: 1:1 on the deterministic DE stage, yellow only because much of the paper was not attempted and the raw-p caveat applies.
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.