Identification and analysis of genes associated with epithelial ovarian cancer by integrated bioinformatics methods.
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.
- ✓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
- 🟡Could not use the authors’ exact input data
- 🟡A deviation arose in the data or preprocessing
- 🟡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 1:1 with the named tools, despite the repo shipping only output images (no code) -> reproduced via described methods on the paper's own 3 public GEO datasets (BRIEF P16). HEADLINE: GEO2R/limma DEG intersection across GSE119056+GSE54388+GSE66957 gives 297 overlapping DEGs (259 up / 38 down) vs reported 306 (265 / 41) = within-tol (97%). The authors' loosely-stated 'p<0.05' is GEO2R's ADJUSTED p (raw-p gives 621, far over). STRING PPI on the reproduced set = 978 edges / 297 nodes at default conf 0.400 vs reported 1105 / 306 = within-tol. 19 of 20 reported MCC hub genes fall directly out of the reproduced up-DEG set (CENPF only just below the adj-p cutoff); all 20 are cell-cycle/mitosis genes, matching the reported GO 'cell division' / KEGG 'cell cycle' enrichment qualitatively. NO fabrication detected: every checked number is derivable from the public data via the described pipeline. NOT attempted (80/20 tail / out of scope): cytoHubba MCC ranking algorithm itself, formal GO/KEGG p-values, Kaplan-Meier survival (UCSC Xena/TCGA), GEPIA validation, clinical-stage ANOVA - all manual web tools. Main reproducibility friction = custom GSE119056 platform GPL19615 has no gene-symbol column (mapped GB_ACC->symbol via org.Hs.eg.db).
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Assessment versions
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v1 current initial assessment Score 75assessed: 2026-06-15 ⛓ 4c167ce7bb20
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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: opusThe study tests whether integrated bioinformatics analysis of multiple GEO datasets can identify reliable differentially expressed genes (DEGs) and hub genes that drive epithelial ovarian cancer (EOC) progression and serve as novel biomarkers or therapeutic targets.
- ★ 306 overlapping DEGs (265 up-regulated, 41 down-regulated) were identified across three independent GEO datasets in EOC vs normal ovarian tissue. finding
- ★ Four under-researched hub genes (CDC45, CDCA5, KIF4A, ESPL1) are up-regulated in EOC tissues compared with normal tissues. finding
- ★ CDCA5 and ESPL1 likely play tumor-promotive roles and have potential as novel therapeutic targets for EOC. finding
- ★ Higher expression of CDCA5 and ESPL1 is associated with poorer overall survival and progression-free survival in EOC patients. finding
- ★ Expression of the four hub genes decreases gradually with continuous progression (advancing stage) of EOC despite overall elevation versus normal tissue. finding
- Integrated analysis of multiple GEO datasets via DEG screening, GO/KEGG enrichment, STRING-based PPI network, and survival analysis identifies reliable candidate genes. method
- DEGs are enriched in cancer-related biological processes (cell division, proliferation, adhesion) and KEGG pathways (pathways in cancer, cell cycle, carbon metabolism). finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| microarray gene expression profiling / DEG identification (GEO2R) | human EOC tissues vs adjacent normal ovarian tissues (GSE119056, GSE54388, GSE66957) | none | differentially expressed genes (p<0.05, |logFC|>1) | GEO2R web tool |
| GO and KEGG pathway enrichment analysis | overlapping DEGs from three GEO datasets | none | enriched biological processes, cellular components, molecular functions, pathways | — |
| protein-protein interaction network construction / hub gene identification | 306 overlapping DEGs | none | network nodes/edges, top 20 hub genes by MCC | STRING database, Cytoscape (MCODE, CytoHubba) |
| hub gene expression validation | EOC tissues vs normal ovarian tissues (TCGA/GTEx) | none | relative mRNA expression (fold change 2, p<0.05) | GEO and GEPIA databases |
| tumor stage expression analysis (ANOVA) | TCGA EOC samples at different stages | none | hub gene expression by tumor stage | GEPIA2 |
| overall survival and progression-free survival analysis | TCGA EOC patient samples stratified by high/low hub gene expression | none | OS and PFS survival curves | UCSC Xena |
- – 306 overlapping DEGs identified (265 up-regulated, 41 down-regulated) 306 DEGs (265 up, 41 down)
- – PPI network: 306 nodes, 1105 edges, average node degree 7.22, average local clustering coefficient 0.424 PPI enrichment p<1.0e-16
- ▲ CDC45, CDCA5, KIF4A, ESPL1 significantly up-regulated in EOC vs normal tissues in GEO and GEPIA
- ▼ Hub gene expression decreases gradually with EOC progression (stage) CDC45 Pr(>F)=0.000554; CDCA5 Pr(>F)=0.00668; KIF4A Pr(>F)=0.0217; ESPL1 Pr(>F)=0.00966
- – Higher CDCA5 and ESPL1 expression associated with poor OS and PFS; CDC45 and KIF4A had no statistical influence on survival
- – 16 of top 20 hub genes already studied in EOC; CDC45, CDCA5, KIF4A, ESPL1 had only 1-2 prior papers 4 of 20 genes
- count 306 DEGs (265 up-regulated, 41 down-regulated) (overlapping DEGs from three GEO datasets)
- pvalue < 1.0e-16 (PPI network enrichment p-value)
- other average node degree 7.22; clustering coefficient 0.424; 1105 edges (PPI network metrics)
- pvalue Pr(>F)=0.000554 (CDC45), 0.00668 (CDCA5), 0.0217 (KIF4A), 0.00966 (ESPL1) (ANOVA across tumor stages)
- fold_change log2FC ranging ~1.39-6.03 across genes/datasets (e.g. MKI67 GSE119056 log2FC=6.025098) (DEG log2 fold changes in Table 2)
- pvalue p<0.05 and |logFC|>1 (DEG screening threshold)
- other 5-year survival rate ~30% (advanced ovarian cancer prognosis (background))
- count module 1: 30 nodes/416 edges; module 2: 12 nodes/46 edges (top two MCODE modules in PPI network)
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 integrated three GEO microarray datasets to identify overlapping DEGs in epithelial ovarian cancer (EOC) versus normal ovarian tissue using GEO2R (p<0.05, |log2FC|>1). GO/KEGG enrichment and STRING-based PPI network analysis were used to select hub genes, which were then validated in GEPIA and an independent GEO dataset. ANOVA was used to assess hub gene expression across tumor stages, and log-rank survival analysis (OS and PFS) from TCGA/UCSC Xena was used to evaluate prognostic associations.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| GEO2R differential expression (limma-based moderated t-test with Benjamini-Hochberg adjustment, per GEO2R default) | DEG identification in each of three GEO datasets (GSE119056, GSE54388, GSE66957) | not stated per dataset | not stated |
| One-way ANOVA | Hub gene expression across EOC tumor stages (Fig 6; CDC45, CDCA5, KIF4A, ESPL1) | TCGA EOC samples via GEPIA2; exact n not stated | not stated |
| Log-rank test (implied by survival curve presentation) | Overall survival and progression-free survival of EOC patients stratified by hub gene expression (Fig 7, TCGA/UCSC Xena) | TCGA EOC cohort; exact n not stated | not stated |
| GO and KEGG enrichment (hypergeometric or Fisher's exact test, per standard tools) | Functional annotation of 306 overlapping DEGs (Fig 2) | 306 DEGs | not stated |
| GEPIA expression comparison (Wilcoxon or ANOVA, per GEPIA default) | Validation of hub gene expression in EOC vs. normal tissues (Fig 5) | TCGA + GTEx via GEPIA; exact n not stated | not stated |
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Survival associations were assessed by splitting patients into high vs. low expression groups and applying log-rank tests for each of four genes independently, yielding eight comparisons (OS and PFS × 4 genes)↳ Could also: A Cox proportional hazards regression including each hub gene as a continuous variable, with adjustment for stage and other covariates, could also be used; or a single multivariate model incorporating all four genes — Cox regression uses expression as a continuous predictor (avoiding arbitrary dichotomization), estimates hazard ratios with confidence intervals as effect sizes, and can adjust for clinical confounders such as tumor stage, which was shown to associate with expression in this same paper
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Four separate one-way ANOVAs were performed to test hub gene expression across tumor stages without mention of post-hoc pairwise correction↳ Could also: ANOVA followed by a Tukey HSD or Dunnett post-hoc test with family-wise error rate control could also be used to identify which stage pairs differ — Post-hoc correction identifies which specific stage-to-stage differences are significant after the omnibus ANOVA, providing more granular and interpretable conclusions about the stage-gradient trend described in the text
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The eight survival comparisons (OS and PFS for four genes) were not described as receiving multiplicity correction↳ Could also: A Benjamini-Hochberg FDR correction applied across the family of survival p-values could also be used — With eight related hypothesis tests on the same cohort, an FDR correction would quantify the expected proportion of false discoveries, which is standard practice when reporting multiple survival comparisons from the same dataset
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Hub gene expression validation in GEO (Fig 4) reports significance with p<0.05 (*) without specifying the underlying test↳ Could also: Explicit reporting of the test used (e.g., Wilcoxon rank-sum, Student's t-test, or limma moderated t-test), the exact statistic, and sample sizes for each comparison could also be provided — Stating the test, its assumptions, and sample sizes allows readers to assess the appropriateness of the method for the data distribution and group sizes, and aids reproducibility
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Hub genes were selected by intersecting DEGs across three datasets (requiring overlap in all three) as the primary cross-dataset integration method↳ Could also: A meta-analysis approach (e.g., combining effect sizes or p-values via Fisher's method or a random-effects model) could also integrate the three datasets — Meta-analysis uses all available effect-size information from each study rather than requiring nominal significance in all three, potentially recovering genes with consistent but modest effects that fail the intersection criterion in one dataset
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Results throughout are summarized without dispersion measures or confidence intervals; figures show point estimates of expression differences↳ Could also: Reporting SD, IQR, or 95% confidence intervals alongside central tendency estimates could also be used — Dispersion measures and confidence intervals convey the precision and variability of estimates, helping readers gauge biological variability and the uncertainty around reported differences, which is especially informative given that sample sizes are not stated in the text
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.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-34143800
Paper: Gui T, Yao C, Jia B, Shen K. Identification and analysis of genes associated with epithelial ovarian cancer by integrated bioinformatics methods. PLoS One 2021. DOI 10.1371/journal.pone.0253136 · PMCID PMC8213194.
Repo: https://github.com/gtguiting/GEO-ovarian-cancer — contains only output images (Volcano Plot-GSE119056/54388/66957, Bubble Chart-BP/CC/MF/KEGG). No analysis scripts shipped. Per BRIEF rule P16 we reproduce with the described tools (GEO2R/limma, STRING) on the paper's own public GEO data — equally valid.
Pipeline-derived results (IN SCOPE)
| id | reported result | pipeline | feasibility |
|---|---|---|---|
| C1 | 306 overlapping DEGs (265 up / 41 down), intersection of 3 datasets, GEO2R p<0.05 & |logFC|>1 | GEO2R = limma on series matrices | HIGH — primary anchor |
| C2 | PPI network: 306 nodes, 1105 edges (STRING) | STRINGdb on the DEG set | MEDIUM |
| C3 | GO BP/CC/MF + KEGG top terms (cell division, cell cycle, pathways in cancer, carbon metabolism) | enrichment (DAVID-equiv: clusterProfiler) | qualitative |
| C4 | Top-20 hub genes by cytoHubba MCC (BUB1, CDK1, CCNB2, TPX2, KIF11, CDC45, CENPF, DLGAP5, CDCA5, UBE2C, TOP2A, ASPM, MELK, KIF4A, SPAG5, MKI67, CEP55, ESPL1, KIF14, NEK2) | cytoHubba MCC | hard-20% (approx by degree) |
OUT OF SCOPE (wet-lab / external / manual web)
- Survival analysis (Kaplan-Meier OS/PFS) via UCSC Xena / TCGA — manual web tool.
- GEPIA expression validation, clinical-stage ANOVA (Pr(>F) per gene) — manual web (GEPIA).
- Literature-based "focus gene" selection (CDC45/CDCA5/KIF4A/ESPL1) — manual.
Plan
Primary: nail C1 (the 306/265/41 counts) on «our HPC» via getGEO+limma, testing both adj.P and raw-P thresholds to find which the authors used. Then C2 (STRINGdb edges) and C3 (enrichment, qualitative). C4 hub MCC = optional 80/20 tail.
Assessments & scoring basis
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Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
This is a solid, honest reproduction: the headline DEG intersection (297 vs reported 306; 259/38 up/down vs 265/41), PPI size (978 vs 1105 edges), and hub-gene set (19/20 recovered) all reproduce within ~3–12% from the paper's own public GEO data, and the central cell-cycle/mitosis EOC signature is confirmed. The small gaps sit on our side / preprocessing, driven by probe→gene collapse choices, GEO2R/limma version, and the symbol-less custom platform GPL19615 requiring a self-defined GB_ACC→symbol mapping. The only authors'-side weakness is methodological thinness — a loosely-stated 'p<0.05' (actually adjusted p) and no shipped code — but no fabrication: every checked value is derivable from the public data. Overall yellow because it is not an exact 1:1 match but the deviations are minor and fully explainable.
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Reproduction footprint
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