Deubiquitination enzyme USP35 negatively regulates MAVS signaling to inhibit anti-tumor immunity.
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 are derivable from the shared data
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- 🟡Could not use the authors’ exact input data
- 🟡Reported values were only indirectly comparable
- 🟡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
Wet-lab paper (USP35 deubiquitinates MAVS) with a small bioinformatics garnish. Authors declined to share R code, but TCGA-SKCM + the named third-party tool MCPcounter are public, so reproduction is valid (P16). DESCRIBED WELL ENOUGH? Partially - the survival cutoff and exact TCGA matrix were not stated; we inferred median split on UCSC-Xena TCGA-SKCM HiSeqV2. RESULT: 1:1 on the headline number - reproduced HR=1.322/P=0.041 vs reported HR=1.3/P=0.038 (Fig 1F), essentially exact. Immune-infiltration directions (Suppl Fig 1A) reproduce strongly with MCPcounter: CD8 T, B, dendritic, NK, cytotoxic, monocytic, T cells all negative & significant - matching the paper's thesis. WEAK SPOT: paper's 'positive with neutrophils' not supported by MCPcounter (rho~0, NS) - likely from another algorithm in the 6-tool panel. GSE109485 mouse shows no significant Usp35 change with therapy (no value reported). FABRICATION CONCERN: none-to-low - the reported numbers are independently derivable from public data. NOT ATTEMPTED: all wet-lab assays (out of scope); the non-MCPcounter cell types of Suppl Fig 1A (Treg/CD4/M1-M2, needing xCell/CIBERSORT/EPIC/QUANTISEQ); Fig 1C cBioPortal alteration frequency.
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Assessment versions
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v1 current initial assessment Score 81assessed: 2026-06-14 ⛓ c39f31b1b0a2
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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
- 2026-06-14
- 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: sonnetUSP35 negatively regulates MAVS signaling (the RIG-I/MAVS RNA-sensing pathway) and thereby suppresses anti-tumor immunity in cutaneous melanoma.
- ★ USP35 interacts with MAVS and removes its K63-linked polyubiquitin chains, inhibiting viral-induced MAVS-TBK1-IRF3 activation and downstream inflammatory gene expression mechanism
- ★ USP35 negatively regulates RIG-I/MAVS-mediated type I interferon (IFNβ) signaling finding
- ★ Depletion of USP35 enhances anti-tumor immunity and synergizes with oncolytic virotherapy to suppress xenograft tumor growth of melanoma cells finding
- ★ USP35 is highly expressed in malignant melanoma tissue and is associated with poorer overall survival finding
- ★ The deubiquitinase (catalytic) activity of USP35, dependent on residue C450, is required for its inhibition of MAVS-induced IFNβ reporter activity finding
- USP35 expression correlates with the tumor immune microenvironment, positively with Treg/M2 macrophage/neutrophil infiltration and negatively with B cell/CD8+ T/CD4+ T/M1 macrophage/dendritic cell infiltration finding
- USP35 has the highest proportion of genomic alterations among 26 tumor types in melanoma, mostly gene amplification finding
- USP35 also negatively regulates STING activity in the DNA-sensing pathway (prior literature context, not newly tested here) finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| bioinformatics analysis (TCGA pan-cancer expression, cBioPortal mutation/alteration) | human tumor tissue datasets incl. SKCM (n=363) | none | USP35 mRNA expression and genomic alteration frequency across tumor types | TCGA / cBioPortal |
| GEO dataset differential expression analysis | melanoma patient samples, anti-PD-1/anti-CTLA-4 vs IgG treated | drug (anti-PD-1 or anti-CTLA-4 checkpoint therapy) | USP35 mRNA expression level | GEO database |
| Kaplan-Meier survival analysis | TCGA-SKCM melanoma patients | none | overall survival stratified by USP35 expression | GEPIA database |
| immune infiltration estimation (xCell, QUANTISEQ, MCPcounter, EPIC, CIBERSORTABS, CIBERSORT) | melanoma tumor tissue (TCGA) | none | correlation of USP35 expression with immune cell infiltration | six deconvolution algorithms |
| dual luciferase reporter assay (IFNβ-Luc, ISRE-Luc) | HEK293T cells | overexpression of USP35 WT or USP35-C450A, with MAVS/RIG-I/TBK1 | relative luciferase activity (firefly/Renilla) | Dual-Glo Luciferase Assay System (Promega) |
| RT-qPCR | B16F10, Yummer1.7, HeLa, ID8 cells | USP35 shRNA knockdown + VSV virus or 3pRNA stimulation | mRNA levels of IFNβ, CXCL10, ISG15 | SYBR green master mix, Bio-Rad software |
| ELISA | USP35-knockdown cells | USP35 knockdown | protein levels of IFN-β and CXCL10 | Human IFNβ and CXCL10 ELISA kit (BioResearch) |
| in vivo xenograft tumor growth with intratumoral oncolytic virus injection | B16F10 melanoma cells in C57BL/6 mice | USP35 knockdown combined with oncolytic virus (H101) injection | tumor volume over time | H101 (Oncorine, recombinant Adenovirus Type 5) |
- ▲ USP35 is significantly up-regulated in multiple tumor tissues including melanoma versus adjacent tissue
- – USP35 shows the highest proportion of genomic alterations among 26 tumor types in melanoma, with ~11% of SKCM cases altered 11% (n=40/363)
- ▲ USP35 mRNA expression increased after anti-PD-1 or anti-CTLA-4 treatment compared to IgG
- – High USP35 expression is associated with poorer overall survival in melanoma patients P=0.038, HR=1.3
- – USP35 expression positively correlates with Treg cell, M2 macrophage, and neutrophil infiltration, and negatively correlates with B cell, CD8+ T cell, CD4+ T cell, M1 macrophage, and dendritic cell infiltration
- ▼ Wild-type USP35, but not catalytically inactive USP35-C450A, inhibited MAVS-induced IFNβ-Luc reporter activity
- ▼ USP35 inhibited RIG-I-induced but not TBK1-induced IFNβ-Luc activity, and inhibited RIG-I/MAVS-induced ISRE-Luc activity
- ▲ USP35 knockdown significantly enhanced VSV- and 3pRNA-induced expression of IFNβ, CXCL10, and ISG15 (mRNA and protein) across multiple cell lines
- count n=40/363 (11%) (USP35 genomic alterations in TCGA-SKCM cohort)
- count copy number amplification n=14, multiplex alterations n=14, missense mutations n=10 (breakdown of USP35 genetic alteration types in SKCM)
- pvalue P=0.038, HR=1.3 (association of high USP35 expression with overall survival in melanoma (GEPIA))
- count 324,635 new cases and 57,043 deaths (global melanoma incidence/mortality, GLOBOCAN 2020)
- count 8 mice per group (xenograft tumor growth experiment group size)
- count 5×10^6 cells per mouse (B16F10 tumor cell inoculation dose)
- other H101 1.5×10^9 vp intratumoral injection on d12,15,18,21,23 (oncolytic virus dosing regimen in xenograft experiment)
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 combines bioinformatics analyses of public databases (TCGA, cBioPortal, GEO, GEPIA) with in vitro cell-line experiments and an in vivo syngeneic mouse melanoma model. Pairwise comparisons used t-tests, multi-group comparisons used one-way ANOVA, and tumor growth trajectories were evaluated with two-way repeated-measures ANOVA; immune infiltration correlations used Pearson correlation. All quantitative data are reported as mean ± SEM, and significance is indicated by threshold asterisks (*P < 0.05, **P < 0.01, ***P < 0.001), with exact values reported selectively for the survival analysis.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Two-sample Student's t-test | Pairwise group comparisons throughout in vitro and in vivo experiments (e.g., tumor volume, RT-qPCR, ELISA) | 8 mice per group for in vivo; cell-line n not stated in available text | not stated |
| One-way ANOVA | Comparisons across multiple experimental groups | — | not stated |
| Two-way repeated-measures ANOVA | Tumor growth curves evaluating effect of time and time-by-group interaction | 8 mice per group | not stated |
| Pearson correlation | Correlation between USP35 expression and immune cell infiltration estimates from TCGA-SKCM | 363 (SKCM cohort) | not stated |
| Kaplan-Meier survival analysis (test not explicitly named; log-rank implied by P-value reporting) | Overall survival by USP35 expression level in TCGA-SKCM (GEPIA database) | 363 (SKCM cohort) | not stated |
| Immune deconvolution algorithms (xCell, QUANTISEQ, MCPcounter, EPIC, CIBERSORTABS, CIBERSORT) | Estimation of tumor-infiltrating immune cell fractions correlated with USP35 expression | 363 (SKCM cohort) | na |
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Dispersion is reported exclusively as SEM throughout↳ Could also: SD or 95% confidence intervals could also be used to summarize spread — SEM narrows with larger n and describes precision of the mean estimate, whereas SD describes the variability of individual observations and 95% CIs directly communicate inferential uncertainty; both are often preferred for small-n cell-line experiments to convey biological variability more transparently
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Multiple independent t-tests are used for pairwise comparisons across many figures↳ Could also: A single one-way ANOVA followed by a post-hoc test (e.g., Tukey HSD or Dunnett's vs. control) could also be used when more than two groups are compared simultaneously — A planned post-hoc procedure following ANOVA controls the family-wise error rate within each experiment, which accumulates when multiple t-tests are conducted on the same dataset
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No multiple-testing correction is stated for the many comparisons across figures and conditions↳ Could also: A Benjamini-Hochberg false discovery rate (FDR) adjustment or Bonferroni correction could also be applied across the family of tests within each experiment — When numerous comparisons are made, applying FDR or FWER control provides a declared framework for interpreting which findings meet a pre-specified error threshold, which is standard practice and aids reproducibility
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Pearson correlation was used to associate USP35 expression with immune deconvolution estimates↳ Could also: Spearman rank correlation could also be used for the same association — Spearman correlation makes no distributional assumptions and is robust to outliers and non-linear monotonic relationships; deconvolution scores are often non-normally distributed, so a rank-based approach would also be appropriate
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Kaplan-Meier survival comparison is reported with P = 0.038 and HR = 1.3 but the specific test (e.g., log-rank) is not named, and no CI for the HR is provided↳ Could also: A Cox proportional-hazards model reporting the HR with its 95% CI could also be used, and explicitly naming the log-rank test is standard — Reporting the CI around the HR (here HR = 1.3) conveys the precision of the effect estimate; a multivariable Cox model would also allow adjustment for known prognostic covariates (e.g., stage, age)
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Sample size for in vivo experiments (n = 8 per group) is stated without reference to a power calculation↳ Could also: An a priori power analysis specifying the assumed effect size, alpha, and target power could also be reported — Pre-specified power calculations help readers interpret whether the study was sized to detect the expected effect magnitude and are recommended by many reporting guidelines for animal studies (e.g., ARRIVE)
Citation network
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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-40016186
Paper: Zhang et al. 2025, Cell Death Dis — "Deubiquitination enzyme USP35 negatively regulates MAVS signaling to inhibit anti-tumor immunity." DOI 10.1038/s41419-025-07411-8 · PMCID PMC11868397.
Nature of the paper
Mechanistic wet-lab study (USP35 deubiquitinates/stabilizes MAVS → dampens type-I IFN / anti-tumor immunity). The vast majority of the paper is bench work (co-IP, ubiquitination assays, mouse tumor models, IHC, flow) — out of scope for computational reproduction.
A small bioinformatics garnish uses public datasets + public tools. That is the in-scope part.
Code / data availability (verbatim from paper)
- Code: "The R code required to reproduce these findings cannot be shared at this time as the data also form part of an ongoing study." → no authors' code.
- The only named tool/repo is MCPcounter (github.com/ebecht/MCPcounter), one of six deconvolution algorithms (xCell, QUANTISEQ, MCPcounter, EPIC, CIBERSORTabs, CIBERSORT) used for tumor-infiltrating immune cells.
- Data: TCGA-SKCM (cutaneous melanoma, public) + GEO GSE109485 (mouse B16 melanoma, anti-PD-1/anti-CTLA-4, public).
Per project rule P16, reproducing with the third-party MCPcounter tool on the paper's public data is fully valid.
In-scope pipeline-derived results (what we attempt)
| id | result | paper location | reported | pipeline |
|---|---|---|---|---|
| C1 | USP35-high → poorer overall survival in TCGA-SKCM | Fig 1F | HR=1.3, P=0.038 | Cox PH / log-rank, median split, TCGA-SKCM RNA-seq + OS |
| C2 | USP35 vs immune-cell infiltration in TCGA-SKCM | Supp Fig 1A | pos: Treg, M2-macro, neutrophils; neg: B cells, CD8+ T, CD4+ T, M1-macro, dendritic cells | MCPcounter scores vs USP35 (Spearman) |
| C3 | USP35 expression vs melanoma "before/after in-vivo treatment" | Data-avail / Suppl | direction only (qualitative) | Usp35 FPKM across GSE109485 treatment groups (IgG vs anti-PD-1+CTLA-4) |
C2 gradeable MCPcounter populations (others are non-MCPcounter cell types from the other 5 algorithms): Neutrophils (+), B lineage (−), CD8 T cells (−), Myeloid dendritic cells (−).
Out of scope (not attempted)
- All wet-lab assays (co-IP, K48/K63 ubiquitination, MAVS stability, IFN reporters, mouse implantation/IHC/flow) — not computational.
- Fig 1C cBioPortal alteration frequency (40/363 = 11% SKCM) — portal lookup, not a pipeline run; optionally spot-checkable, not a primary target.
- The full 6-algorithm Supp Fig 1A heatmap (xCell/CIBERSORT/EPIC/QUANTISEQ) — the hard 20%; we reproduce the MCPcounter column (the named code artifact) only.
Data sources used (public)
- TCGA-SKCM expression: UCSC Xena legacy hub
TCGA.SKCM.sampleMap/HiSeqV2(gene-symbol × 474 samples, log2(RSEM norm_count+1)). - TCGA-SKCM survival: Xena
survival/SKCM_survival.txt(OS, OS.time). - GSE109485:
GSE109485_fpkm.txt.gz(12 mouse samples, Ensembl IDs). - MCPcounter: github.com/ebecht/MCPcounter @ b6eac73 (Signatures + Source).
All compute on «our HPC»/«infra»; «host» holds results only.
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 central conclusion reproduces convincingly from public data: Fig 1F survival HR=1.322/P=0.041 vs reported HR=1.3/P=0.038, and USP35 correlates negatively and significantly with CD8 T, B, and dendritic lineages (MCPcounter), matching the paper's thesis. The only deviation — the reported positive neutrophil correlation (rho=+0.014, P=0.76 here) — is on our methodology side (we used MCPcounter alone, not the full 6-tool panel) and is minor. No fabrication concern; the numbers are derivable. Graded yellow overall only because the authors withheld code and left the cutoff/matrix unspecified, so the near-exact agreement rests on inferred methods.
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Reproduction footprint
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