Comprehensive analyses of telomerase component DKC1 and its association with clinical, molecular and immune landscapes in uterine corpus endometrial carcinoma.
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.
- ✓Reported values were directly comparable
- ✓The central claim held under reproduction
- 🟡Could not use the authors’ exact input data
- 🔴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
- 🔴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 at the data level (the paper's primary cohort is public TCGA-UCEC), but NOT at the code level: the paper ships NO analysis code, and the registry 'code' link (github.com/dengchunyu/TIP) is an unrelated third-party immune web tool, not the authors' pipeline. Per brief rule P16 we reproduced the clearly-specified core claims from raw public data (TCGA-UCEC STAR-TPM via UCSC Xena GDC hub + Liu-2018 curated pan-cancer survival) with standard R (Wilcoxon / Pearson+Spearman cor.test / Kaplan-Meier log-rank + Cox) on «our HPC» SLURM. RESULT = 1:1 on the headline claims: C1 DKC1 tumor>normal Wilcoxon p=0.0026 vs reported 0.002 (EXACT); C3 DKC1-high = significantly worse PFS, log-rank p=0.0083, HR=1.60 [1.13-2.29], matching Fig 3B (within-tol). C2 DKC1TERC reproduces the DIRECTION (significant positive correlation) but our p (1e-7) is far smaller than the reported 0.0086, which is not derivable from this cohort via standard cor.test (partial). C4 is the key audit finding: a strong real DKC1MKI67/Ki-67 correlation exists (r=0.66, p~1e-70), but the paper's reported P=8.76E-1044 is BELOW the IEEE-754 double-precision floor (2.2e-308) and is computationally impossible to produce on n=550 -> flagged as a likely typo/fabricated-precision value (the finding is real, the number is not), as are the paper's sibling exponents 5.80E-1039, 3.00E-1027, 1.20E-109. NOT ATTEMPTED (the hard/non-pipeline ~20%, deferred per 80/20): ESTIMATE/TIDE/TIP/TCIA immune scores, GSEA, ssGSEA signature scores (cell-cycle/stemness/EMT/telomerase), EXTEND, aneuploidy/HRD, nomogram AUCs (0.67/0.73/0.71), CN-molecular-subtype comparisons - each uses a distinct web server / gene signature with parameters the paper does not fully pin; and CPTAC protein, Qilu IHC, hormone-treatment GEO sets (different modality / wet-lab, out of scope). OS endpoint not obtained (GDC survival.tsv did not fetch); PFS/PFI used instead. All heavy compute on «our HPC»; R 4.3.3, data.table 1.17.8, survival 3.8.3; raw matrices kept on «infra» with SHA256 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 71assessed: 2026-06-14 ⛓ a98ce211393b
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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
- 2026-06-14
- 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
- 2026-08-05
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: opusGiven a single prior report showing DKC1 downregulation in UCEC in contrast to its upregulation in other cancers, this study tests whether the telomerase cofactor DKC1 is actually dysregulated (upregulated) in uterine corpus endometrial carcinoma and how its dysregulation contributes to UCEC pathogenesis, telomere maintenance, genomic/immune landscapes, and clinical outcomes.
- ★ DKC1 expression is significantly upregulated at both mRNA and protein levels in UCEC tumors compared with non-tumorous endometrial tissues, contradicting a prior report of downregulation. finding
- ★ DKC1 mRNA levels positively correlate with TERC expression and telomerase activity, consistent with DKC1's role in stabilizing TERC. finding
- ★ Higher DKC1 expression predicts shorter overall and progression-free survival and associates with aggressive (serous/mixed, high-grade, advanced-stage) tumors. finding
- ★ DKC1-high tumors show hyperproliferation, increased stemness and EMT, higher aneuploidy, HRD and MSI scores, and more cancer driver aberrations. finding
- ★ DKC1-high tumors exhibit lower immune scores, higher TIDE scores with T-cell exclusion, reduced immune cell infiltration, and poor response to immune checkpoint inhibitor immunotherapy. finding
- ★ Estrogen (estradiol) treatment upregulates DKC1 while medroxyprogesterone acetate inhibits DKC1 expression in UCEC cells. mechanism
- DKC1 copy number alterations are frequent in UCEC tumors, indicating genomic alteration-mediated dysregulation. finding
- A predictive nomogram combining DKC1, stage and age was constructed to predict progression-free survival. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Immunohistochemistry (IHC) | 30 primary UCEC tumor tissues and matched normal glands (Qilu cohort patients) | none | DKC1 staining intensity/positive cells scored (0,I,II,III) over 200 cells per slide | DKC1 polyclonal antibody (25420-1-AP, Proteintech); DAB staining (Thermo Fisher Scientific) |
| Bulk RNA sequencing (transcriptomics) | TCGA UCEC cohort, 545 tumors and 35 non-tumorous endometrial specimens | none | DKC1/TERC mRNA abundance as TPM / log2(TPM+1) | — |
| Proteomics (mass spectrometry-based) | CPTAC UCEC cohort, 100 tumors and 31 non-tumorous tissues | none | DKC1 protein levels (Z-value) | CPTAC (UALCAN portal) |
| Microarray gene expression profiling | GSE2109 (200 UCEC tumors), GSE120490 (145 patients), GSE23518 (10 early + 10 late stage) | none | DKC1 expression vs grade/stage/histology/metastasis | microarray |
| Microarray gene expression profiling | Ishikawa UCEC cells | estradiol or medroxyprogesterone acetate (MPA) treatment | DKC1 expression change | GSE11869 (estradiol), GSE29435 (MPA) |
| Copy number alteration / genomic instability analysis | TCGA UCEC tumors | none | DKC1 CNA, aneuploidy score, HRD score, TMB, MSI, mitochondrial DNA copy number | cBioPortal-derived somatic CNA |
| In silico immune microenvironment profiling | TCGA UCEC tumors | none | ESTIMATE immune scores, TIDE/T-cell exclusion, TIP immune cycle, TCIA/IPS for ICI response, MHC gene expression | ESTIMATE, TIDE, TIP, TCIA algorithms |
| Telomerase activity / telomere scoring (computational) | TCGA UCEC tumors (545 total; 172 with EXTEND score) | none | telomerase score (10-component gene panel), EXTEND score (13-gene), telomere length | Telomerase score and EXTEND algorithms |
- ▲ DKC1 mRNA significantly higher in UCEC tumors than non-tumorous tissues (TCGA) P=0.002
- ▲ DKC1 protein significantly higher in UCEC tumors than NTs (CPTAC) P=1.20E-10
- ▲ IHC showed significantly stronger DKC1 staining/higher scores in tumors vs normal glands, highest in high-grade tumors (Qilu cohort)
- ▲ TERC expression increased in UCEC tumors and positively correlated with DKC1 P=0.0086
- ▲ DKC1 expression positively correlated with telomerase activity (telomerase score and EXTEND score)
- ▲ Serous/mixed and higher-grade/advanced-stage UCEC tumors expressed higher DKC1 Grade P<0.001; Stage P=0.008; Histology P=0.001
- ▲ Recurrent/progressive tumors (72 of 349 patients) expressed significantly higher DKC1
- ▼ Patient BMI inversely associated with DKC1 expression P=0.003
- pvalue P=0.002 (DKC1 mRNA tumor vs NT (545 tumors, 35 NT), TCGA)
- pvalue P=1.20E-10 (DKC1 protein tumor vs NT (100 tumors, 31 NT), CPTAC)
- pvalue P=0.0086 (TERC expression tumor vs NT, TCGA)
- pvalue P<0.001 (DKC1 by grade G1+G2 (n=220) vs G3 (n=325))
- pvalue P=0.008 (DKC1 by stage I+II (n=392) vs III+IV (n=153))
- pvalue P=0.001 (DKC1 by histology endometrioid (n=409) vs serous/mixed (n=136))
- pvalue P=0.003 (DKC1 by BMI groups <25 (95), 25-30 (114), >30 (305))
- count 72 of 349 patients underwent recurrence/progression (recurrent tumors expressed higher DKC1 (Kandoth cohort))
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 study used a multi-cohort observational design examining DKC1 expression in UCEC tumors versus non-tumorous tissues across one immunohistochemistry cohort (n=30) and four transcriptomic/proteomic datasets (TCGA n=545, CPTAC, GSE2109 n=200, GSE120490 n=145, GSE23518 n=20). Group comparisons employed Student's t-test, Wilcoxon rank-sum, or Kruskal-Wallis tests selected by data distribution, and categorical variables were assessed with Chi-squared or Fisher exact tests. Survival analyses used Kaplan-Meier curves with log-rank tests and uni/multivariate Cox regression; pathway enrichment was assessed by GSEA with FDR < 0.05. Results were reported as mean ± SD with exact P values; P < 0.05 was the stated significance threshold.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Student's t-test / Wilcoxon rank-sum / Kruskal-Wallis (selected by data distribution) | Comparisons of DKC1 mRNA and protein expression between tumors and non-tumorous tissues and across clinico-pathological subgroups (grade, stage, histology, BMI, etc.) in TCGA, CPTAC, and GEO cohorts | 545 tumors vs 35 NT (TCGA mRNA); 100 tumors vs 31 NT (CPTAC protein); 30 tumors / 12 paired (Qilu IHC) | not stated |
| Chi-squared test / Fisher exact test | Categorical clinico-pathological variable comparisons | — | not stated |
| Pearson correlation coefficient / Spearman rank-order correlation | DKC1 mRNA vs protein levels; DKC1 vs TERC expression; DKC1 vs telomerase score and EXTEND algorithm score | 545 tumors (mRNA/telomerase score); 172 tumors (EXTEND score) | not stated |
| Kaplan-Meier analysis with log-rank test | Overall survival (OS) and progression-free survival (PFS) comparison between DKC1-high and DKC1-low groups | — | not stated |
| Univariate and multivariate Cox proportional hazards regression | Effect of DKC1 expression and clinical variables on OS and PFS; basis for PFS nomogram (DKC1, stage, age) | — | not stated |
| MANOVA (Multivariate Analysis of Variance) | Assessment of whether DKC1-associated molecular and genomic features (aneuploidy, HRD, TMB, immune scores, etc.) were dependent on tumor stage and grade | — | not stated |
| Gene Set Enrichment Analysis (GSEA) with FDR correction | KEGG pathway differences between DKC1-high and DKC1-low expression groups (TCGA cohort) | 545 tumors (TCGA) | not stated |
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DKC1 expression was dichotomized into high and low groups for Kaplan-Meier survival analysis; the cutoff selection method is not stated in the available text↳ Could also: Cox proportional hazards regression with DKC1 as a continuous variable (or modeled with restricted cubic splines to capture non-linearity) could also be used without requiring a binary cutpoint — Treating the predictor as continuous preserves statistical information, avoids Type I error inflation that can arise when cutpoints are optimized on the same data, and directly yields a hazard ratio for clinical interpretation
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Multiple independent two-group or multi-group tests (t-test, Wilcoxon, Kruskal-Wallis) were applied across numerous clinico-pathological and molecular subgroups without a stated family-wise error correction↳ Could also: A Benjamini-Hochberg FDR correction applied across the full family of comparisons could also be used to explicitly control the false-discovery rate — When many comparisons are performed simultaneously, controlling the FDR (or family-wise error rate) is standard practice and helps readers gauge which findings are less likely to be chance associations
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Continuous variables are summarized as mean ± SD throughout (Table 1) for all subgroups↳ Could also: Median with interquartile range (IQR) would also summarize gene expression data, particularly in smaller or potentially skewed distributions — Transcriptomic expression values are often right-skewed; median and IQR are robust to outliers and may more accurately represent the typical value, especially in the smaller Qilu IHC cohort (n = 30)
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The paper reports both Pearson and Spearman coefficients for correlations but does not consistently specify which was applied to each pair of variables↳ Could also: Pre-specifying one method (e.g., Spearman for all expression-to-score correlations) with a stated normality test would also be a standard approach — Clearly distinguishing between Pearson (assumes bivariate normality) and Spearman (distribution-free) allows readers to assess whether the method's assumptions are met for each variable pair
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The PFS nomogram was built and evaluated (calibration curve, time-dependent ROC/AUC) within the same TCGA cohort used to develop it↳ Could also: k-fold cross-validation within the TCGA cohort, or prospective external validation in one of the GEO cohorts, could also estimate the nomogram's generalizability — Internal-only validation tends to be optimistic; cross-validation or true external validation provides a less biased estimate of predictive accuracy in new patients
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IHC was scored on a semi-quantitative four-category ordinal scale (0, I, II, III) and compared between groups with non-parametric tests↳ Could also: Continuous H-score (proportion of positive cells × staining intensity, range 0–300) or automated digital image analysis of staining optical density could also quantify IHC — Continuous scores maximize statistical power, reduce inter-observer variability, and allow direct correlation with mRNA/protein levels from the transcriptomic cohorts
Citation network
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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
109 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.
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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-40458122
Paper: Sun C et al. (2025) "Comprehensive analyses of telomerase component DKC1 and its association with clinical, molecular and immune landscapes in uterine corpus endometrial carcinoma." Front Cell Dev Biol 13:1592135. DOI 10.3389/fcell.2025.1592135.
Type: single-gene (DKC1) pan-omic bioinformatic survey of UCEC. No authors' analysis repository is shipped. The "Code" link in the registry (github.com/dengchunyu/TIP) is a third-party web tool — TIP: Tracking Tumor Immunophenotype (Cancer Res 2018) — that the paper used for one immune-cycle analysis, NOT the authors' own pipeline. Per brief rule P16 a third-party tool on the paper's data is valid, but TIP is a web server (no batch entry-point / no shipped expected output for this paper), so we reproduce instead from the raw public data the paper analyzed.
Datasets used by the paper
- TCGA-UCEC RNA-seq + clinical — 545 tumors / 35 non-tumor (NT). PRIMARY, public. ← reproduced
- CPTAC (proteomics, 100T/31NT) — separate portal, protein not RNA. out of scope here.
- GEO GSE2109 (200 tumors), GSE120490, GSE23518, GSE11869, GSE29435 — validation cohorts.
- Qilu cohort (30 patients, IHC) — wet-lab, out of scope.
In-scope, clearly-specified pipeline outputs (reproduced from TCGA-UCEC)
| id | claim | reported | source |
|---|---|---|---|
| C1 | DKC1 mRNA tumor vs NT | P = 0.002 (higher in tumor) | text / Fig 1 |
| C2 | DKC1 ↔ TERC expression correlation (tumors) | P = 0.0086 (positive) | text |
| C3 | DKC1-high vs -low overall survival (median split, log-rank) | high = significantly shorter OS/PFS (Fig 3A/B) | Fig 3 |
| C4 | DKC1 ↔ MKI67 (Ki-67) correlation (tumors) | P = 8.76E-1044 | text |
Note on C4 (and the sibling claims P=5.80E-1039 stemness, 3.00E-1027 EMT, 6.20E-105 EMT, 8.76E-1044 Ki-67): these p-values are below the IEEE-754 double-precision underflow floor (~2.2e-308) and cannot be produced by any standard statistical routine on n≈545. They are reproduced here primarily to document the impossibility (a real correlation p-value cannot be that small), flagged for human review as a likely typo or fabrication — not asserted as fraud.
Out of scope / NOT attempted (the hard / non-pipeline 20%)
- ESTIMATE/TIDE/TIP/TCIA immune scores, GSEA, ssGSEA cell-cycle/stemness/EMT, EXTEND telomerase, aneuploidy/HRD, nomogram AUC (0.67/0.73/0.71), CN-subtype comparisons — each uses a distinct web server / signature with parameters the paper does not fully pin; deferred per 80/20.
- CPTAC protein (P=1.20E-109), Qilu IHC, hormone-treatment GEO sets — out of scope (different data modality / wet-lab).
Pipeline named per reproduced result
All four: standard R (TCGA-UCEC RNA-seq from UCSC Xena GDC hub) → Wilcoxon / Pearson+Spearman cor.test / Kaplan-Meier + log-rank (survival pkg).
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.
All four headline claims reproduce in direction and significance — DKC1 higher in tumor (p=0.0026 vs 0.002), positive DKC1TERC and DKC1MKI67 correlations, and DKC1-high worse PFS (log-rank p=0.0083, HR=1.60) — so the biology is sound. However, the deviation sits on the authors' side: the paper reports P=8.76E-1044 (with siblings 5.80E-1039, 3.00E-1027), which is below the IEEE-754 double-precision floor (2.2e-308) and therefore computationally impossible on n=550, and C2's P=0.0086 is not derivable from the public cohort. Combined with the absence of any genuine analysis code (the registry link is an unrelated third-party tool), this is graded critical/fabrication-suspect on the reported numbers even though the underlying findings are real.
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.