TGF-β-dependent reprogramming of amino acid metabolism induces epithelial-mesenchymal transition in non-small cell lung cancers.
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
- 🟡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
- 🟡The deviation was non-trivial in magnitude
- 🟡The central claim did not (fully) hold under reproduction
- 🟡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 survival result 1:1 in DIRECTION + SIGNIFICANCE. Reproduced Fig 6a — the prognostic association of P4HA3 (probe 228703_at) with poor overall survival in all three independent NSCLC cohorts the paper names (GSE3141 [the brief's accession], GSE30219, GSE31210), pulled directly from GEO series matrices on «our HPC» (GEOquery -> survival/survminer; P16 — corrplot is only the paper's plotting pkg for the unrelated correlation figures). Using the paper's stated split method (KM-Plotter 'auto select best cutoff' ~ survminer::surv_cutpoint), all three give HR>1 and log-rank P<0.05 (0.021 / 0.0006 / 0.0026), matching the paper's qualitative claim; KM curve saved for GSE3141. Honest caveat / robustness flag: the paper prints no per-dataset HR or P, and with a pre-specified MEDIAN split all three cohorts are non-significant (P=0.12-0.14) though still directionally consistent (HR>1) — so the reported significance is method-dependent (relies on threshold optimisation, which inflates significance and is what KM-Plotter does). Reproducible and legitimate under the stated method, not a fabrication, but the prognostic signal is borderline under a fixed cutoff. NOT attempted (hard ~20%): the corrplot correlation matrices (Fig 4b/5d; need CCLE metabolome+transcriptome + 76-gene EMT score, plus wet-lab cell-line metabolomics), TCGA Fig 6b, and all wet-lab figures — out of the clean GSE3141 pipeline scope.
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Assessment versions
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v1 current initial assessment Score 85assessed: 2026-06-15 ⛓ 1a9c210b3e6d
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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 paper tests whether and how TGF-β-induced reprogramming of intracellular amino acid metabolism is integrated with and required for epithelial-mesenchymal transition (EMT) in non-small cell lung cancer cells.
- ★ TGF-β induces reprogramming of intracellular amino acid metabolism that is necessary to promote EMT in NSCLC cells finding
- ★ Prolyl 4-hydroxylase α3 (P4HA3) is upregulated during TGF-β stimulation and is required for TGF-β-dependent changes in amino acids, EMT, and tumor metastasis mechanism
- ★ Manipulation (depletion) of extracellular amino acids induces EMT-like responses without TGF-β stimulation finding
- ★ Integrated metabolomic and transcriptomic two-layer omics screening identifies P4HA3 as a key amino acid metabolism enzyme in EMT method
- P4HA3 inhibitor is proposed as a potential therapeutic agent for cancer resource
- P4HA3 overexpression alone is insufficient to induce EMT finding
- P4HA3 expression correlates with EMT markers and EMT score across NSCLC cell lines in CCLE finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| CE-TOFMS metabolomics (polar metabolites/amino acids) | A549, HCC827, H358 NSCLC cell lines | TGF-β treatment (2-5 ng/mL, 3 days to several weeks) | polar metabolite/amino acid levels | capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS) |
| Time-course metabolome analysis | A549 cells | TGF-β 5 ng/mL for 24, 48, 72 h | amino acid levels (fold-change) | CE-TOFMS |
| Real-time PCR (mRNA EMT markers) | A549, SW1573 cells | TGF-β stimulation/withdrawal; amino acid depletion media | mRNA of CDH1, CDH2, FN1, ZEB1, MMP2, MMP9 | — |
| Western blotting | A549, SW1573 cells | amino acid depletion; TGF-β; P4HA3 knockdown | CDH1, CDH2, ZEB1, P4HA3 protein levels | — |
| Amino acid depletion media culture | A549, SW1573 cells | depletion of combined or single amino acids for 72 h | EMT marker expression, cell growth, cell morphology/circularity | — |
| siRNA knockdown | A549, HCC827, H358, SW1573 cells | P4HA3 siRNA +/- TGF-β | EMT marker mRNA, amino acid metabolome | — |
| P4HA3 overexpression | A549, HCC827 cells | P4HA3 overexpression | EMT marker expression | — |
| Bioinformatic correlation analysis | NSCLC cell lines (CCLE; 187/147 lines) and clinical datasets GSE3141, GSE30219, GSE31210 | none | P4HA3 expression vs EMT score, EMT markers, amino acid levels | — |
- – 21 metabolic pathways altered by TGF-β across cell lines, with amino acid metabolism commonly altered
- – TGF-β (72 h) increased Asp, Glu, Lys and decreased Ala, Asn, citrulline, Gln, Gly, His, hydroxyproline, Ile, Leu, Phe, Pro, Thr, Tyr in A549
- – Amino acid changes were reversed after TGF-β withdrawal (MET), except for Asp
- – Amino acid depletion media induced EMT-like responses (down CDH1, up CDH2/ZEB1 mRNA) and elongated morphology
- – P4HA3 knockdown increased CDH1 and decreased CDH2, FN1, MMP9, MMP2, abrogating TGF-β EMT
- – P4HA3 knockdown abrogated TGF-β-mediated amino acid metabolic changes
- – P4HA3 expression and EMT score significantly correlated with citrulline, Arg, ornithine, His, and Lys levels in CCLE
- – P4HA3 overexpression produced insignificant changes in EMT markers
- count 21 pathways altered (metabolic pathways changed by TGF-β across cell lines)
- count 187 lung cancer cell lines (CCLE dataset for P4HA3-EMT correlation)
- count 147 lung cancer cell lines (CCLE metabolome/transcriptome dataset for P4HA3-amino acid correlation)
- count 76 genes (genes used to calculate EMT score in NSCLC)
- other P < 0.05 (FDR <0.05) (pathway significance threshold in MPEA)
- pvalue P < 0.01 (cell circularity change with amino acid depletion in A549)
- other 5-year overall survival <20% (prognosis of NSCLC)
- count n = 4 (replicates for metabolome heat map / PCA)
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.
The study used a multi-omics experimental design (metabolomics via CE-TOFMS combined with transcriptomics) in NSCLC cell lines treated with TGF-β, with comparisons made between stimulated and unstimulated (often paired) conditions and across knockdown/overexpression and amino-acid-depletion conditions. Dimensionality reduction (PCA) and metabolic pathway enrichment analysis (MPEA) were applied to metabolite data, with pathway significance judged by P values and false discovery rates (<0.05), and correlation analyses were used for CCLE datasets. Results were largely reported as heat maps of fold-changes relative to paired controls and as mean ± SD from replicate samples, with significance indicated by thresholds (e.g., **P < 0.01).
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Metabolic pathway enrichment analysis (MPEA) with P values and false discovery rate | Identification of altered metabolic pathways in TGF-β-stimulated A549, HCC827, H358 cells (Fig. 1a) | — | not stated |
| Principal component analysis (PCA) | Metabolomic profiles of TGF-β-stimulated vs unstimulated and P4HA3-knockdown cells (Supplementary Fig. 1g–i; Fig. 5b) | n = 4 | na |
| Correlation analysis (significance by P value, P > 0.05 marked non-significant) | P4HA3 mRNA vs EMT markers/EMT score and amino acid levels in CCLE NSCLC cell lines (Fig. 4b, Fig. 5d, Supplementary Figs. 4e, 5g) | 187 and 147 lung cancer cell lines (as stated) | not stated |
| Unspecified significance test reported as a P-value threshold | Cell circularity after amino acid depletion in A549 cells (Fig. 3e, **P < 0.01) | triplicate samples | not stated |
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Replicate data (e.g., real-time PCR, circularity) were summarized as mean ± SD from triplicate samples.↳ Could also: Reporting could additionally include a 95% confidence interval or showing individual data points alongside the mean. — For small replicate numbers, plotting individual points and a CI can convey both the spread and the precision of the estimate, complementing the SD.
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Significance was conveyed using P-value thresholds such as **P < 0.01 and P < 0.05.↳ Could also: Exact P values together with effect-size estimates (e.g., fold-change with confidence intervals) could also be reported. — Exact P values and effect sizes give readers a continuous sense of evidence strength and practical magnitude beyond a pass/fail threshold.
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Multiple group comparisons (e.g., several amino-acid-depletion conditions, multiple cell lines) were assessed individually.↳ Could also: A single ANOVA model with a post-hoc multiple-comparison correction (e.g., Tukey HSD or Dunnett's against control) could also be applied. — An omnibus model with post-hoc correction controls the family-wise error rate when many conditions are compared simultaneously.
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Pathway significance was assessed with P values and FDR (< 0.05) via MPEA.↳ Could also: The specific multiple-testing method (e.g., Benjamini-Hochberg) and the family of tests it covered could be stated explicitly. — Naming the correction method and its scope helps readers reproduce the enrichment analysis and interpret the FDR threshold.
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Correlations in the CCLE datasets were reported with a significance cutoff (P > 0.05 marked non-significant).↳ Could also: The correlation type (Pearson vs Spearman), the coefficient values, and confidence intervals could also be reported. — Specifying the method and providing coefficients with CIs clarifies the strength and direction of association, especially when relationships may be non-linear.
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The specific hypothesis test underlying threshold annotations (e.g., **P < 0.01 for circularity) is not named in the text.↳ Could also: Stating the exact test used (e.g., two-tailed t-test or Mann-Whitney U) and whether assumptions were checked could also be included. — Naming the test and its assumptions aids reproducibility and helps readers judge fit between the data distribution and the chosen method.
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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TGF-β treatment increases Asp, Glu, and Lys while decreasing Ala, Asn, citrulline, Gln, Gly, His, Ile, Leu, Pro, Thr, and Tyr in A549 NSCLC cells at 72 hmetabolomics a549 mixed 2021×1papers★ This paper is the founder (earliest)
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P4HA3 knockdown abrogates TGF-β-induced amino acid metabolic reprogramming in NSCLC cellsmetabolomics a549 none 2021×1papers★ This paper is the founder (earliest)
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TGF-β treatment commonly alters 21 metabolic pathways, with amino acid metabolism consistently reprogrammed across multiple NSCLC cell linesmetabolomics nsclc-cell-lines mixed 2021×1papers★ This paper is the founder (earliest)
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P4HA3 expression positively correlates with EMT score and with citrulline, Arg, ornithine, His, and Lys levels across NSCLC cell lines in CCLE and clinical gene-expression datasetsother nsclc-cell-lines up 2021×1papers★ This paper is the founder (earliest)
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Combined amino acid depletion induces EMT-like responses in NSCLC cells, with CDH1 downregulation and CDH2/ZEB1 upregulation accompanied by elongated morphologyqPCR a549 mixed 2021×1papers★ This paper is the founder (earliest)
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P4HA3 knockdown reverses TGF-β-induced EMT in NSCLC cells, restoring CDH1 and suppressing CDH2, FN1, MMP9, and MMP2qPCR a549 mixed 2021×1papers★ This paper is the founder (earliest)
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P4HA3 overexpression alone does not significantly alter EMT marker expression in NSCLC cells, indicating P4HA3 is necessary but not sufficient for EMTqPCR a549 none 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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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-34168290
Paper: Nakasuka et al. 2021, Commun Biol 4:782. "TGF-β-dependent reprogramming of amino acid metabolism induces epithelial–mesenchymal transition in non-small cell lung cancers." DOI 10.1038/s42003-021-02323-7 · PMCID PMC8225889.
Brief-listed artifacts: Code = github.com/taiyun/corrplot (generic R plotting package → P16: a third-party tool, not the authors' code; reproduce by running the described pipeline on the paper's own / public data). Data = GEO:GSE3141.
Which reported results are pipeline-derived (in scope) vs not
| Result | Figure | Pipeline | Data | In scope? |
|---|---|---|---|---|
| P4HA3 expression vs NSCLC prognosis (high P4HA3 → poor survival, P<0.05) | Fig 6a | KM survival (KM-Plotter); probe 228703_at; auto-best-cutoff; univariate Cox HR+P | GSE3141, GSE30219, GSE31210 (all GPL570, public) | YES — primary |
| Correlation of P4HA3 / amino acids / EMT score / EMT genes | Fig 4b, 5d | Pearson correlation → corrplot viz |
CCLE metabolome (suppl) + transcriptome; cell-line metabolome (wet-lab) | Partial — needs CCLE download + 76-gene EMT score; secondary |
| P4HA3 in tumor vs non-tumor / TNM | Fig 6b | TCGA expression query | TCGA lung | Out (different data source, not the brief's GSE3141) |
| TGF-β metabolome / EMT wet-lab assays (CE-MS, qPCR, WB, migration) | Figs 1-3,5 | wet-lab | — | OUT (not pipeline) |
What we attempt (80/20)
Primary (clear, low-hanging, uses the brief's GSE3141): reproduce Fig 6a — the Kaplan-Meier association between P4HA3 (probe 228703_at) expression and overall survival in NSCLC, on GSE3141 and (as the other two "independent studies" named in the same sentence) GSE30219 and GSE31210. Method per paper: KM-Plotter with auto-best-cutoff + univariate Cox. We reproduce the equivalent pipeline directly from the GEO series matrices (GEOquery → probe 228703_at → median split AND best-cutoff via survminer::surv_cutpoint, restricted to 25–75% like KM-Plotter → Cox HR + log-rank P). Expected: high expression → worse survival, P<0.05.
Not attempted (the hard ~20%): the corrplot Fig 4b/5d correlation matrices require CCLE metabolome+transcriptome assembly and the 76-gene EMT score, and the cell-line metabolome is wet-lab (CE-TOFMS) — out of the clean GSE3141 scope. TCGA Fig 6b and all wet-lab figures are out of scope. The paper reports only "P<0.05" qualitatively for Fig 6a (no per-dataset HR/P printed), so the comparison is on direction (high→poor) + significance, not an exact numeric match.
Possible-fabrication watch
The reported Fig 6a claim ("significant positive correlation, P<0.05") is checkable against the public GEO survival data; we record the actual HR + log-rank P per cohort so a human can see whether the direction/significance holds in each named dataset.
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.
Fig 6a's qualitative claim — high P4HA3 (228703_at) associates with poor NSCLC overall survival, P<0.05 — reproduces in all three named GEO cohorts under the paper's stated best-cutoff method (HR=1.87/1.84/2.67, P=0.021/0.0006/0.0026), directly from public series matrices, and is not a fabrication. The key caveat is on the methodology side, shared between us and the authors: significance hinges on the threshold-optimising 'auto best cutoff' (a known significance-inflating procedure); under a pre-specified median split all three cohorts are non-significant (P=0.122–0.137) while direction (HR>1) is preserved. Severity is moderate — the central conclusion holds in direction and under the stated method, but the strength of the prognostic signal is borderline and method-sensitive, which the reproduction honestly flags.
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.