STAT3-dependent analysis reveals PDK4 as independent predictor of recurrence in prostate cancer.
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.
- ✓Same input data as the authors
- ✓Reported values were directly comparable
- ✓Reported values are derivable from the shared data
- ✓The central claim held under reproduction
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡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 to reproduce the dataset-specific result 1:1. The RU-assigned accession GSE120741 is the paper's NCI validation cohort, used (Fig EV3) for STAT3-vs-metabolic-signature ssGSEA correlations - NOT for the headline PDK4 recurrence-survival result (that uses MSKCC/GSE21032, a different accession, out of scope here). GEO ships a processed expression matrix (normalized log2 ComBat-corrected read counts, GeneSymbol x 92 tumor samples), so STAR re-alignment was intentionally skipped (80/20); this is the P16 third-party-tool case (ssGSEA via GSVA on the paper's own data). Reproduced on «our HPC» SLURM: ssGSEA (GSVA 2.4.4) of KEGG_OXIDATIVE_PHOSPHORYLATION and KEGG_RIBOSOME (msigdbr 26.1.0), then Pearson correlation of STAT3 expression vs each signature. PRIMARY claims match essentially exactly: OXPHOS rho -0.77 -> -0.749 (|d|=0.021), Ribosome rho -0.82 -> -0.802 (|d|=0.018), same sign and significance. SECONDARY STAT3-target claim: magnitude (0.365 vs 0.39) and p (3.5e-4 vs 1.5e-4) match strikingly but SIGN is opposite using MSigDB AZARE_STAT3_TARGETS; the paper cites two STAT3-target sets (Azare 2007; Carpenter & Lo 2014) and does not pin the exact gene list, so the signature identity is ambiguous - flagged for human review, not a fabrication signal. NOT attempted: PDK4 MSKCC survival (different accession), wet-lab/metabolomics/proteomics/PET (non-pipeline), and exact BH-adjusted p (paper adjusts jointly across all datasets x signatures; we report raw cor.test p, consistent in magnitude). Overall: a clean partial - the two primary GSE120741 correlations reproduce 1:1; one secondary correlation sign-mismatches on an under-specified gene set.
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v1 current initial assessment Score 66assessed: 2026-06-15 ⛓ 97aadbb6eed9
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- Reproduced
- 2026-06-15
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- v1.0
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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: sonnetThe study tests whether STAT3 expression status (low vs. high) in prostate cancer is linked to distinct metabolic programs (OXPHOS/TCA cycle activity) at the transcriptomic and proteomic level, and whether a gene within this STAT3-associated metabolic axis, PDK4, can serve as an independent prognostic biomarker for biochemical recurrence.
- ★ Low STAT3 expression in primary PCa is associated with increased OXPHOS and ribosomal biosynthesis at the transcriptomic level finding
- ★ TCA cycle/OXPHOS is up-regulated at the proteomic level in PCa and is inversely correlated with STAT3 expression finding
- ★ PDK4, a key regulator of the TCA cycle that inhibits pyruvate oxidation and negatively impacts OXPHOS, is down-regulated in low STAT3 patients mechanism
- ★ Low PDK4 expression is significantly associated with a higher risk of biochemical recurrence (BCR) finding
- ★ PDK4 is an independent predictor of biochemical recurrence compared to ISUP grading, clinical/pathological staging, and pre-surgical PSA levels in primary and metastatic tumors finding
- STAT3 gene expression correlates with its transcriptional activity, shown via positive correlation with pY-STAT3 protein levels and STAT3 target gene signatures finding
- ★ WGCNA identifies gene clusters 2 (OXPHOS) and 3 (Ribosomal) as negatively correlated with STAT3 expression, while cluster 11 (Epigenetic) is positively correlated finding
- SOCS3 expression, unlike other STAT3 pathway genes, is significantly associated with Gleason score finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| bulk RNA-seq differential expression / KEGG & Hallmark pathway overexpression analysis | human prostate cancer tissue (TCGA PRAD, n=498) | none (stratified by STAT3 expression: low vs high) | differentially expressed genes and enriched pathways (OXPHOS, Ribosome, JAK-STAT) | — |
| weighted gene co-expression network analysis (WGCNA) | human prostate cancer tissue (TCGA PRAD, n=397 with clinical data; 382 after outlier removal) | none | gene cluster eigengene correlation with STAT3 expression and clinical traits (BCR, GSC, pT, pN) | — |
| Reverse Phase Protein Array (RPPA) | human prostate cancer tissue (TCGA PRAD) | none | tyrosine-phosphorylated (pY) STAT3 protein levels correlated with STAT3 mRNA | RPPA |
| ssGSEA gene signature analysis of RNA-seq data (validation cohorts) | human prostate cancer tissue (NCI n=91, VPC n=43, RAS n=33) | none | correlation of STAT3 expression with KEGG OXPHOS and Ribosome signatures | — |
| multi-way ANOVA of STAT3 pathway gene expression vs clinical variables | human prostate cancer tissue (TCGA PRAD) | none | association of IL6ST, STAT3, SOCS3, JAK1, JAK2, TYK2 expression with GSC/staging | — |
| laser-microdissected shotgun proteomics | human and murine prostate FFPE tissue | none | TCA cycle/OXPHOS protein abundance relative to STAT3 status | — |
- ▼ PDK4 gene expression is significantly down-regulated in low STAT3 patients
- ▼ Low PDK4 expression is significantly associated with higher risk of biochemical recurrence
- – PDK4 predicts disease recurrence independent of ISUP grading, staging, and PSA level
- ▲ TCA cycle/OXPHOS is up-regulated proteomically and inversely correlated with STAT3
- – 1,194 genes significantly differentially expressed between low and high STAT3 groups, with OXPHOS and Ribosome among top up-regulated KEGG pathways
- ▼ WGCNA cluster 2 ("OXPHOS") negatively correlated with STAT3 eigengene ρ=-0.67, adj.P=7e-50
- ▼ WGCNA cluster 3 ("Ribosomal") negatively correlated with STAT3 eigengene ρ=-0.74, adj.P=1e-65
- ▼ STAT3 log cpm negatively correlated with KEGG OXPHOS signature in three independent validation cohorts (NCI, VPC, RAS) NCI ρ=-0.77; VPC ρ=-0.53; RAS ρ=-0.57
- count 498 patients (TCGA PRAD RNA-seq cohort size)
- count 1,194 differentially expressed genes (low vs high STAT3 comparison (logFC≥1, adj.P≤0.05))
- pvalue P=2.5e-05 (STAT3 target gene set up-regulation in high STAT3 group (roast test))
- correlation ρ=0.24, P=7.8e-06 (STAT3 cpm vs pY-STAT3 RPPA correlation)
- correlation ρ=-0.67, adj.P=7e-50 (Cluster 2 (OXPHOS) eigengene vs STAT3)
- correlation ρ=-0.74, adj.P=1e-65 (Cluster 3 (Ribosomal) eigengene vs STAT3)
- correlation ρ=-0.77, adj.P=4.53e-19 (NCI cohort STAT3 vs OXPHOS signature correlation)
- count n=397 (TCGA PRAD samples with matching clinical data used for WGCNA)
Statistical methods review
Model: opusA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
This is an observational, computational study combining transcriptomics (TCGA-PRAD RNA-Seq and three additional public PCa cohorts) with shotgun proteomics of laser-microdissected human and murine FFPE samples. The main analytic approach compared low-versus-high STAT3 patient groups via differential gene expression, built a weighted gene co-expression network (WGCNA), performed gene-set/pathway enrichment (KEGG/GO overexpression, EGSEA, roast, ssGSEA), and used Pearson correlation to relate STAT3 and module eigengenes to molecular signatures and clinical traits, with PDK4 then evaluated as a predictor of biochemical recurrence. Results were reported with correlation coefficients and Benjamini–Hochberg-adjusted P-values.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Differential expression test (thresholds log-FC ≥ 1, adj. P ≤ 0.05) | low STAT3 vs high STAT3 in TCGA PRAD (1,194 DE genes) | n = 100 low STAT3, n = 100 high STAT3 (498 total ranked into tertile-like groups) | not stated |
| KEGG/GO overexpression (enrichment) analysis | DE genes and WGCNA clusters (Fig 1B, Fig 2A,C,D,E) | — | na |
| EGSEA gene set testing | KEGG signaling/metabolic and Hallmark gene sets, low vs high STAT3 (Figs EV1, EV2) | — | not stated |
| roast gene set test | STAT3 TARGETS UP gene set, high vs low STAT3 (P = 2.5e-05) | — | na |
| Pearson correlation | STAT3 log cpm vs pY-STAT3 RPPA, ssGSEA signatures, and module eigengenes/clinical traits across TCGA, NCI, VPC, RAS data sets (Figs 2B, EV3) | TCGA n = 498/397/382; NCI n = 91; VPC n = 43; RAS n = 33 | not stated |
| ssGSEA single-sample enrichment scoring | STAT3 target, OXPHOS, and Ribosome signatures across cohorts | — | na |
| One-way / multi-way ANOVA with Tukey HSD post-hoc | STAT3 pathway gene expression vs clinical traits (e.g., SOCS3 across GSC groups) | — | not stated |
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Patients were dichotomized into low vs high STAT3 by ranking into the top and bottom 20% quantiles (n = 100 each) with the middle group set aside.↳ Could also: STAT3 could also be analyzed as a continuous variable in a regression model alongside the differential-expression or correlation analyses. — A continuous treatment uses all 498 samples and retains information that grouping into quantiles necessarily collapses, which some analysts prefer for power and to avoid threshold dependence.
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Group differences and signature relationships were summarized primarily with correlation coefficients (ρ) and adjusted P-values.↳ Could also: Reporting 95% confidence intervals for the correlation and effect estimates would also be informative. — Confidence intervals convey the precision of an estimate in addition to its point value, complementing the P-value-based reporting.
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Pearson correlation was used to relate STAT3 expression to protein levels, signatures, and module eigengenes.↳ Could also: A rank-based Spearman correlation could also be applied to these relationships. — Spearman captures monotonic associations without assuming linearity or normality, which can be useful when distributions are skewed or relationships are non-linear.
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PDK4 and STAT3 pathway genes were related to clinical traits using ANOVA and correlation, with recurrence framed via group comparisons.↳ Could also: Time-to-event modeling such as Kaplan–Meier with the log-rank test and Cox proportional-hazards regression could also be used for the recurrence endpoint. — Survival models explicitly account for follow-up time and censoring and yield hazard ratios with confidence intervals when assessing independent prognostic value (the visible text does not specify the exact recurrence model used).
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Multiplicity was handled with Benjamini–Hochberg FDR across correlation and enrichment families and Tukey HSD for ANOVA post-hoc tests.↳ Could also: Alternative error-rate controls such as Bonferroni/Holm (family-wise) or storey q-value (FDR) could also be applied. — Different procedures balance sensitivity and stringency differently; the choice depends on whether family-wise error or false-discovery rate is the priority for a given family of tests.
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Differential expression used fixed thresholds (log-FC ≥ 1, adj. P ≤ 0.05) to define significant genes.↳ Could also: Threshold-free gene-set approaches (e.g., GSEA on a ranked list) could also be used to summarize the differential signal. — Ranked, threshold-free methods avoid sensitivity to a specific fold-change cutoff and can detect coordinated shifts in gene sets that individual-gene thresholds may miss.
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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Epigenetic gene co-expression module eigengene positively correlated with STAT3 expressionRNA-seq human prostate adenocarcinoma up 2020×1papers★ This paper is the founder (earliest)
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Gene co-expression modules positively correlated with Gleason score and pT risk, distinct from STAT3-correlated modulesRNA-seq human prostate adenocarcinoma up 2020×1papers★ This paper is the founder (earliest)
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OXPHOS pathway up-regulated in low-STAT3 tumors, i.e. negatively associated with STAT3 expressionRNA-seq human prostate adenocarcinoma down 2020×1papers★ This paper is the founder (earliest)
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Ribosomal gene co-expression module eigengene negatively correlated with STAT3 expressionRNA-seq human prostate adenocarcinoma down 2020×1papers★ This paper is the founder (earliest)
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STAT3 target gene signatures positively correlated with STAT3 expressionRNA-seq human prostate adenocarcinoma up 2020×1papers★ This paper is the founder (earliest)
Citation network
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Data lineage
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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-32323921
Paper: Oberhuber et al. 2020, Mol Syst Biol 16:e9247. "STAT3-dependent analysis reveals PDK4 as independent predictor of recurrence in prostate cancer." PMID 32323921 · PMCID PMC7178451 · DOI 10.15252/msb.20199247
Registry-assigned artifacts for THIS reproduction unit:
- Code: https://github.com/alexdobin/STAR (third-party aligner — P16 case; not authors' own repo)
- Data: GEO GSE120741 (= "The Netherlands Cancer Institute / NCI" cohort, Stelloo et al.; BioProject PRJNA494345). Used by Oberhuber et al. as a public validation cohort.
Key finding from reading the paper (Methods + Results + Fig EV3)
GSE120741 is NOT the dataset behind the headline PDK4-recurrence survival result. The PDK4 biochemical-recurrence survival analysis (Kaplan-Meier / Cox) is done on the MSKCC cohort (GSE21032), which is a different accession not assigned to this RU.
The role of GSE120741 (NCI, n = 91) in the paper is the STAT3-vs-metabolic-signature correlation (Fig EV3B–C and associated text). The paper reports (verbatim values):
- STAT3 log-CPM vs ssGSEA KEGG "OXPHOS" signature — NCI: ρ = −0.77, adj. P = 4.53e-19
- STAT3 log-CPM vs ssGSEA KEGG "Ribosome" signature — NCI: ρ = −0.82, adj. P = 8.33e-23
- STAT3 log-CPM vs STAT3-TARGET signature (AZARE STAT3 targets) — NCI: ρ = −0.39, adj. P = 1.5e-04
Method per paper: "significant negative Pearson correlation of STAT3 log cpm with KEGG 'OXPHOS' / 'Ribosome' signatures derived by ssGSEA (Barbie et al, 2009)"; p-values adjusted by Benjamini–Hochberg. Survival pkgs (survival v3.1-8, survminer v0.4.6) are for the MSKCC survival part — out of scope for this accession.
IN SCOPE (pipeline-derived, tied to GSE120741) — what we reproduce
| id | reported (NCI cohort, Fig EV3 / text) | pipeline |
|---|---|---|
| C1_oxphos_rho | Pearson ρ(STAT3 logCPM, ssGSEA KEGG_OXIDATIVE_PHOSPHORYLATION) = −0.77, adj.P 4.53e-19 | ssGSEA (GSVA) + cor.test on GSE120741 GE table |
| C2_ribosome_rho | Pearson ρ(STAT3 logCPM, ssGSEA KEGG_RIBOSOME) = −0.82, adj.P 8.33e-23 | same |
| C3_stat3target_rho | Pearson ρ(STAT3 logCPM, STAT3-target signature) = −0.39, adj.P 1.5e-04 | same (secondary; signature identity less certain) |
| C0_n | NCI cohort n = 91 | sample count of GE table |
Primary targets: C1, C2 (well-specified KEGG sets, strong effect → robust to method). Secondary: C3 (the exact "AZARE STAT3 targets" gene list is less precisely pinned).
OUT OF SCOPE (not attempted, with reason)
- PDK4 Kaplan-Meier / Cox biochemical-recurrence survival — uses GSE21032 (MSKCC), a different accession not assigned to this RU. Not the GSE120741 result.
- All wet-lab / mouse Stat3-KO / metabolomics / proteomics / PET imaging — non-pipeline.
- STAR read alignment from raw FASTQ — GEO ships a processed gene-expression matrix (GSE120741_Porto_ge_table.txt.gz), so re-aligning from SRA adds no value to reproducing the reported correlation; we use the shipped processed matrix (the standard 80/20 choice).
Reproduction approach
- «our HPC» job: download GSE120741_Porto_ge_table.txt.gz (GEO FTP) to «infra».
- log-CPM normalize (edgeR) if the matrix is raw counts; else use shipped values.
- ssGSEA (GSVA, method="ssgsea") of KEGG_OXIDATIVE_PHOSPHORYLATION and KEGG_RIBOSOME (gene sets from msigdbr/MSigDB) across all samples.
- Pearson correlation: STAT3 expression vs each signature score; BH-adjust.
- Compare ρ to −0.77 / −0.82 (sign + magnitude). within-tol = |Δρ| ≤ 0.10 and same sign.
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
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Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
On the in-scope accession (GSE120741, the NCI validation cohort for the Fig EV3 STAT3-vs-metabolic correlations), the two primary claims reproduce essentially 1:1 from the authors' own shipped matrix: OXPHOS ρ −0.77→−0.749 and Ribosome ρ −0.82→−0.802, same sign and significance. The only conflict is the secondary C3 STAT3-target correlation, which matches in magnitude/p (0.39 vs 0.365; 1.5e-4 vs 3.5e-4) but flips sign — attributable to an under-specified gene set in the Methods (two sets cited, none pinned), i.e. partly an authors-side documentation gap and partly our signature choice, not a fabrication signal. A minor n=92-vs-91 input difference (one unspecified QC drop) does not affect the robust correlations. Overall: solid partial reproduction with explainable deviations on a non-headline claim.
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