Epigenome screening highlights that JMJD6 confers an epigenetic vulnerability and mediates sunitinib sensitivity in renal cell carcinoma.
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
- ✓No relevant deviation in data/preprocessing
- ✓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
- 🟡Reported values were only indirectly comparable
- 🟡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 for the PUBLIC part. Reproduced the paper's central epigenome-screening claim (JMJD6 over-expressed in RCC tumour vs normal, Fig 2A-D) on its own public GEO data using third-party tools (GEOquery+limma/base-R t-test, per rule P16). GSE40435 (the registry-assigned accession, 101 tumour/normal pairs): JMJD6 UP, p=6.6e-08 — direction+significance match, though effect size is modest (1.06x). GSE53757 (validation, 72 pairs): UP, p=4.5e-19, 1.65x. Grade within-tol because the paper states only 'significantly higher' (no exact number for these cohorts), so the comparison is directional+significance, not exact-value. NOT attempted (and why): the registry 'code' link BradnerLab/pipeline (ROSE2) and its reported super-enhancer counts (545 vs 286, Fig 6B), 2311 DEGs, 56531 ChIP peaks, and 1904-gene signature (Fig 5) all depend on the authors' OWN H3K27ac ChIP-seq / RNA-seq, which is 'available from the corresponding author upon reasonable request' and not deposited -> data_restricted, out of scope. TCGA-KIRC survival HR=1.289 (Fig 2J) is public but the LASSO/multivariate-Cox covariate specification is under-specified -> deferred as the optional hard 20%. IHC/organoid/CRISPRi/sunitinib results are wet-lab (non_pipeline). Overall: a clean partial 1:1 on the public screening claim; the headline mechanistic super-enhancer results are unverifiable without the restricted raw data.
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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.
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v1 current initial assessment Score 85assessed: 2026-06-15 ⛓ 34c683ba190e
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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: opusThe study tests whether integrating CRISPR/Cas9 functional screening with RCC genomic datasets can reveal a novel epigenetic vulnerability, hypothesizing that JMJD6 is a pivotal chromatin modifier that drives renal cell carcinoma progression and represents a therapeutic target whose inhibition sensitizes RCC to sunitinib.
- ★ JMJD6 is an epigenetic vulnerability/fitness gene in RCC, identified by integrating GeCK CRISPR screening data with TCGA/ICGC RCC cohorts. finding
- ★ High JMJD6 expression correlates with poor survival and promotes RCC progression in vitro and in vivo. finding
- ★ Aberrant p300 drives high JMJD6 expression, which assembles super-enhancers to activate identity/oncogenic genes including VEGFA, β-catenin, and SRC. mechanism
- ★ The JMJD6 inhibitor SKLB325 suppresses JMJD6-mediated oncogenic effects in RCC cells, patient-derived organoids, and in vivo. finding
- ★ Targeting JMJD6 sensitizes RCC to sunitinib and is synergistic in combination. finding
- Integration of GeCK pan-cancer fitness genes with univariate Cox-screened epigenetic regulators yielded 61 candidate epigenetic fitness genes, prioritized by AUC and validated by MTT/siRNA. method
- ChIP-seq with ROSE2 was used to identify JMJD6-assembled super-enhancer regions from distal H3K27ac peaks. method
- JMJD6 is highly expressed in kidney cancer relative to most other solid tumors (CCLE and 33-tumor pan-cancer analysis). finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| CRISPR/Cas9 functional knockout screen (GeCK) data integration | 33 tumor cell lines including RCC; TCGA-KIRC and ICGC-RCC cohorts | genome-wide knockout | fitness/essential genes for tumor growth | — |
| MTT / CCK-8 cell proliferation assay | RCC cell lines (786-O, ACHN, Caki-1) | siRNA knockdown of candidate epigenetic genes / JMJD6 | cell proliferation/viability (absorbance at 450 nm) | Bio-Rad microplate reader; Dojindo CCK-8 |
| siRNA knockdown / Western blot | 786-O and ACHN RCC cells | 20 nM siRNA knockdown of JMJD6 | knockdown efficiency / protein levels | ECL chemiluminescence (Santa Cruz) |
| CRISPR single-cell JMJD6 knockout clone generation | 786-O cells | JMJD6 KO via pX459 | knockout efficiency (Western blot, Sanger sequencing) | pX459 plasmid |
| Colony formation and Transwell migration/invasion assays | RCC cell lines (786-O, ACHN) | JMJD6 knockdown/knockout | colony number; migration/invasion cell counts | Costar transwell; BD Matrigel/fibronectin |
| RNA-seq (transcriptome) | RCC cells | JMJD6 modulation | differentially expressed genes (DEGs) | HiSeq (BGI); aligned hg19/Bowtie, RSEM |
| ChIP-seq and ChIP-qPCR | RCC cells | none / p300 IP | H3K27ac super-enhancer regions (ROSE2); p300 enrichment at JMJD6 promoter | Active Motif EpiShear sonicator; Active Motif Inc. |
| Xenograft / PDX / patient-derived organoid models | BALB/c nu/nu mice; human ccRCC tissues | JMJD6 modulation; SKLB325; sunitinib | tumor growth; organoid number/diameter | BD Matrigel |
- – Overlap of GeCK fitness genes and hazardous epigenetic regulators identified 61 candidate epigenetic fitness genes in RCC. 61 genes
- – MTT assay identified JMJD6 as the most potent hit among candidate epigenetic factors in RCC cells.
- ▼ JMJD6 knockdown suppressed RCC proliferation across three independent RCC cell lines.
- ▲ JMJD6 is highly expressed in kidney cancer relative to most other solid tumors and in RCC vs other tumor types.
- ▲ JMJD6 assembles super-enhancers driving kidney cancer identity genes including VEGFA, β-catenin, and SRC.
- ▼ SKLB325 suppressed JMJD6-mediated oncogenic effects in cells, organoids, and in vivo.
- ▼ Targeting JMJD6 sensitized RCC to sunitinib with synergistic effect in combination.
- count 1614 fitness genes in RCC (derived from pan-cancer GeCK screening results)
- count 665 epigenetic regulators (expression data from TCGA-KIRC cohort)
- count 355 hazardous epigenetic factors (univariate Cox regression at P < 0.05)
- count 61 potential epigenetic fitness genes (overlap of two screening results)
- pvalue P < 0.05 (univariate Cox regression significance threshold for screening epigenetic factors)
- count 33 tumors (pan-cancer JMJD6 expression analysis)
- count 73,750 new cases; 14,830 deaths (2020 estimated US kidney cancer statistics)
- other >30% (RCC cases progressed to terminal stage at diagnosis)
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 integrated CRISPR/Cas9 functional screening data with TCGA-KIRC and ICGC-RCC patient cohort transcriptomics to nominate JMJD6 as an epigenetic target in renal cell carcinoma. Differential expression in cohort data was assessed with Wilcoxon tests, survival associations via univariate Cox regression and Kaplan–Meier analysis, and gene selection via LASSO regression; RNA-seq DEGs were called using a Poisson distribution method. In vitro and in vivo experimental comparisons used one-way ANOVA or two-tailed Student's t-tests, with results expressed as mean ± SD across three biological replicates and significance defined at P < 0.05.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Two-tailed Student's t-test | Pairwise comparisons between treatment regimens across cell-line and in vivo experiments (proliferation, migration, invasion, drug-response assays) | — | not stated |
| One-way ANOVA | Multi-group comparisons between treatment regimens in cell-line and in vivo experiments | — | not stated |
| Wilcoxon test | Differential analysis of mRNA/protein levels in TCGA-KIRC and ICGC-RCC cohorts; pan-cancer differential analysis across 33 tumor types | — | na |
| Univariate Cox regression | Screening 665 epigenetic regulators for association with overall survival in TCGA-KIRC; 355 hazardous factors retained at P < 0.05 | — | not stated |
| LASSO regression (glmnet) | Prognostic gene selection from the candidate epigenetic regulators | — | not stated |
| Kaplan–Meier analysis | Overall survival analysis of RCC patients stratified by JMJD6 expression level | — | na |
| AUC analysis | Predictive efficiency evaluation of candidate prognostic genes in TCGA-KIRC and ICGC-RCC cohorts (Figure 1C) | — | not stated |
| Poisson distribution method | Detection of differentially expressed genes from RNA-seq count data (via RSEM/BGI pipeline) | 3 biological replicates pooled per condition | not stated |
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RNA-seq differentially expressed genes were detected using the Poisson distribution method↳ Could also: Negative binomial models as implemented in DESeq2 or edgeR's exactTest/glmLRT could also be applied to RNA-seq count data — Negative binomial models explicitly account for overdispersion (variance exceeding the mean) commonly observed in RNA-seq count data; they are a widely adopted standard and may yield better-calibrated p-values in settings where the Poisson equality of mean and variance does not hold
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Univariate Cox regression was applied to each of 665 epigenetic regulators individually, retaining those with P < 0.05 without multiplicity correction↳ Could also: Benjamini-Hochberg FDR correction across all 665 tests, or a LASSO-penalized Cox regression as a single joint model for the initial screening step, could also be used — When many hypotheses are tested simultaneously, FDR control reduces the expected proportion of false positives in the selected set; LASSO-Cox additionally handles correlated predictors jointly rather than one at a time, which can be advantageous when many epigenetic regulators co-vary
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Multiple pairwise group comparisons were performed using separate Student's t-tests across many experiments↳ Could also: One-way ANOVA followed by a post-hoc test with family-wise error rate control (e.g., Tukey HSD or Dunnett's test vs a shared control) could also be used when three or more groups are compared within a single experiment — An omnibus ANOVA with a post-hoc correction explicitly controls the family-wise error rate across all pairwise comparisons within an experiment, complementing the per-comparison t-test approach
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Results were summarized as mean ± SD with significance indicated only by threshold symbols (*, **, ***, ****)↳ Could also: Reporting exact p-values and a standardized effect size (e.g., Cohen's d or percent change with 95% CI) would also be standard practice — Exact p-values enable downstream meta-analysis and replication power calculations; effect sizes with confidence intervals convey the magnitude and precision of differences beyond a binary significance threshold and are increasingly expected by journals and systematic reviewers
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Kaplan–Meier curves stratified by JMJD6 expression level were the primary survival presentation↳ Could also: A multivariate Cox proportional hazards model adjusting for established clinical covariates (e.g., tumor stage, grade, age) could also accompany the Kaplan–Meier analysis — Multivariate Cox regression estimates the independent prognostic contribution of JMJD6 after accounting for known confounders, which is standard in clinical prognostic studies to assess whether the association is attributable to JMJD6 expression versus correlated clinical variables
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Longitudinal CCK-8 proliferation measurements collected from Day 2 to Day 8 were analyzed as discrete time points↳ Could also: A repeated-measures ANOVA or linear mixed-effects model treating time as a within-unit factor could also be used to model the full growth curve jointly — Measurements on the same experimental unit across time are correlated; a mixed-effects or repeated-measures model captures this structure and estimates a time-by-treatment interaction in a single test rather than requiring separate comparisons at each day
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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JMJD6 occupies super-enhancers at kidney cancer oncogene loci including VEGFA, CTNNB1, and SRC in RCC cellsChIP-seq human rcc-cell-line up 2021×1papers★ This paper is the founder (earliest)
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JMJD6 knockdown suppresses proliferation across multiple independent RCC cell linesother human rcc-cell-line down 2021×1papers★ This paper is the founder (earliest)
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Pharmacological JMJD6 inhibition by SKLB325 suppresses RCC tumor growth in patient-derived organoids and xenograft modelsother mouse rcc-xenograft down 2021×1papers★ This paper is the founder (earliest)
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JMJD6 is overexpressed in kidney cancer relative to most other solid tumor types based on TCGA/ICGC cohort dataRNA-seq human kidney-cancer up 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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What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-33634984
Paper: Zhang et al. 2021, Clin Transl Med 11(2):e328. "Epigenome screening highlights that JMJD6 confers an epigenetic vulnerability and mediates sunitinib sensitivity in renal cell carcinoma." PMID 33634984 / PMC7882098 / doi:10.1002/ctm2.328.
Code link in registry: https://github.com/BradnerLab/pipeline (= ROSE2, the Rank-Ordering-of-Super-Enhancers tool — a third-party tool the authors applied, not authors' own repo). Per brief rule P16 this is equally valid, but its inputs (H3K27ac ChIP-seq) are not public (see below).
Data link in registry: GEO GSE40435 — confirmed via NCBI: Integrative genome-wide gene expression profiling of clear-cell RCC in Czech Republic, Illumina HumanHT-12 v4 (GPL10558), 101 ccRCC tumour / adjacent-normal pairs (202 samples). Public.
In scope (pipeline-derived, public data, specific reported claim)
| # | Reported result | Paper loc | Pipeline | Data | Feasibility |
|---|---|---|---|---|---|
| C1 | JMJD6 expression significantly higher in tumour vs normal | Fig 2A–D, Results 3.2 | array DE / t-test (we apply limma — third-party tool on paper's data, P16) | GSE40435 (assigned) | HIGH — primary target |
| C2 | same claim, validation cohort | Fig 2A–D | same | GSE53757 (GPL570, 72 pairs) — also public | HIGH — secondary |
These reproduce the paper's central screening observation that JMJD6 is over-expressed in RCC tumour vs normal, using the exact public dataset named in the registry. The paper states the difference is "significant" (qualitative); we regenerate the direction, fold-change and exact p-value.
Out of scope — not attempted (with reason)
| Result | Paper loc | Why dropped |
|---|---|---|
| 545 vs 286 super-enhancers (ROSE2) | Fig 6B | Authors' own H3K27ac ChIP-seq of control vs JMJD6−/− cells — not deposited, "available from corresponding author upon reasonable request" → data_restricted |
| 2311 DEGs (682 up / 1629 down) | Fig 5A | Authors' own RNA-seq of JMJD6−/− cells — not public → data_restricted |
| 56,531 ChIP-seq peaks; 1904-gene JMJD6 signature | Results 3.5, Fig 5G | depend on the same restricted raw data |
| TCGA-KIRC survival HR=1.289 (1.136–1.463) | Fig 2J | Public (TCGA) but multivariate-Cox covariate set + LASSO feature selection under-specified; heavier; deferred as the optional hard 20% |
| Wet-lab: IHC 11/14 tissues, organoids, CRISPRi −60% VEGFA, sunitinib assays | Fig 2–6 | Manual/experimental, non_pipeline |
Plan
Single «our HPC» SLURM job (std): build conda env (r-base + GEOquery + limma),
download GSE40435 (+GSE53757) series matrices into «infra» inside the compute
job, map JMJD6 probe(s), compute tumour-vs-normal t-test → small JSON pulled back
to «host». No raw data on «host».
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 public, in-scope screening claim is reproduced cleanly 1:1: JMJD6 is over-expressed in RCC tumour vs normal in both the registry-assigned cohort (GSE40435, p=6.56e-08) and the validation cohort (GSE53757, p=4.47e-19), matching the paper's qualitative 'significantly higher' statement in direction and significance, with no fabrication signal. The comparison is directional/significance-only because the paper prints no exact number for these cohorts (Fig 2A-D). However, the paper's headline mechanistic results (super-enhancer counts, DEGs, ChIP peaks, gene signature in Fig 5-6) rest on the authors' undeposited ChIP-seq/RNA-seq ('available upon request') and are therefore unverifiable — a data-availability limitation, not an authors' defect or a detected error. Overall: a solid partial reproduction.
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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.