Multiomic machine learning on lactylation for molecular typing and prognosis of lung adenocarcinoma.
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 were directly comparable
- ✓Reported values are derivable from the shared data
- ✓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
- 🟡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 -> 1:1 (within tolerance) on the headline prognostic claim. Reproduced the 9-gene lactylation Random Survival Forest risk model (authors' own code, Script/08_Machine_learning.R, seed=123/ntree=1000/nodesize=5/logrank) trained on TCGA-LUAD and validated on public GEO cohorts. All six reported time-dependent OS AUCs reproduce within +/-0.065 (GSE31210 0.810/0.754/0.756 vs 0.833/0.793/0.783; GSE13213 0.794/0.725/0.720 vs 0.730/0.683/0.668); all 9 hub genes present; RSF gives valid out-of-sample discrimination (C-index 0.736/0.699) and significant risk-group KM separation (GSE31210 p=2.6e-4). Graded partial/within-tol (not exact) for two honest reasons: (1) scope -- only the prognostic-model + validation half was attempted; (2) the TCGA training set is an approximation (the multiomic-complete TCGA subset + its normalization are NOT shipped; README says data 'available upon request'), so the RSF is not byte-identical even at seed=123. NOT attempted (out of scope, needs non-shipped intermediates): the MOVICS 10-algorithm multiomic clustering into 2 cancer subtypes CS1/CS2, and the ~12 downstream analyses (GSVA/ssGSEA, IOBR immune deconvolution, stemness/TMB/MSI, microenvironment, chemokine, oncoPredict drug-resistance, maftools mutation panels). No fabrication flag: validation data is public and the reproduced AUCs (some higher, some lower than reported) corroborate the claim. Data: TCGA-LUAD (UCSC-Xena GDC STAR FPKM-UQ + survival), GEO GSE31210 (GPL570, n=226), GSE13213 (GPL6480, n=117). Repo commit 2bbb5b53c005395e9da45d4f1f54b53283161a8d.
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Assessment versions
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v1 current initial assessment Score 83assessed: 2026-06-14 ⛓ 2cc4883de7d5
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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: opusCan integrating machine learning with multiomic data on lactylation-related genes (LRGs) enable molecular subtyping and prognosis prediction in lung adenocarcinoma (LUAD)?
- ★ Two lactylation cancer subtypes (CS1, CS2) can be identified in LUAD by multiomics ensemble clustering, with CS1 linked to better overall survival than CS2 finding
- ★ Nine hub LRGs (HNRNPC, PPIA, BZW1, GAPDH, H2AFZ, RAN, KIF2C, RACGAP1, WBP11) constitute a machine learning prognostic model that stratifies LUAD patients into high- and low-risk groups resource
- ★ The high-risk group has more advanced stage (T3+4, N1+2+3, M1, III+IV), higher recurrence/metastasis, and lower 1/3/5-year OS finding
- ★ Immune activity is significantly higher in low-risk patients ('hot tumors'), suggesting stronger immunotherapy response, while high-risk are immunosuppressive 'cold tumors' finding
- ★ Low-risk patients show increased sensitivity (lower IC50) to most chemotherapeutics per oncoPredict analysis finding
- ★ An ensemble of 10 multiomics clustering algorithms plus 10 machine learning algorithms (79 combinations) was used, with random survival forest (RSF) yielding the highest mean C-index method
- Most oncogenic mutations were detected in the high-risk group finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Multiomics ensemble clustering (10 algorithms via MOVICS) | TCGA LUAD cohort patient tumors | none | lactylation cancer subtype assignment (CS1/CS2) | MOVICS R package |
| mRNA and lncRNA transcriptome analysis | TCGA LUAD cohort | none | differential LRG expression across subtypes | — |
| DNA methylation (epigenomic) profiling | TCGA/UCSC-Xena LUAD cohort | none | methylation patterns for subtyping | UCSC Xena |
| Somatic mutation analysis | TCGA LUAD cohort | none | mutation frequency/oncoprint, top 5% mutated genes | maftools R package |
| Machine learning prognostic modeling (10 algorithms, 79 combinations) | TCGA LUAD training cohort | none | C-index, risk score | RSF and others (CoxBoost, Lasso, Ridge, Enet, SVM, GBM, SuperPC, plsRcox) |
| External validation (survival/ROC) | GSE31210 (226 patients) and GSE13213 (117 patients) LUAD cohorts | none | Kaplan-Meier OS, AUC | survivalROC R package |
| Immune cell infiltration / TME analysis (8 methods) | TCGA LUAD subtypes and risk groups | none | immune cell infiltration scores, immunotherapy response (TIDE/TIP/subclass mapping) | IOBR R package, GSVA/ssGSEA |
| Drug sensitivity prediction | TCGA LUAD high/low-risk groups | none | IC50 for anti-cancer drugs | oncoPredict R package (GDSC training data) |
- – CS1 associated with better overall survival than CS2 in TCGA p<0.001
- – CS1 better prognosis validated in GSE13213 p=0.002
- – CS1 better prognosis validated in GSE31210 p<0.001
- – RSF model AUC for 1/3/5-year OS in TCGA training set 0.956, 0.975, 0.954
- – Model AUC for 1/3/5-year OS in GSE13213 0.730, 0.683, 0.668
- – Model AUC for 1/3/5-year OS in GSE31210 0.833, 0.793, 0.783
- ▲ Immune cell infiltration elevated in CS1/low-risk relative to CS2/high-risk
- ▼ IC50 lower for most chemotherapeutics in low-risk group
- pvalue p<0.001 (CS1 vs CS2 OS difference in TCGA)
- pvalue p=0.002 (CS1 vs CS2 survival in GSE13213)
- pvalue p<0.001 (CS1 vs CS2 survival in GSE31210)
- other AUC 0.956, 0.975, 0.954 (1/3/5-year OS ROC in TCGA training set)
- other AUC 0.730, 0.683, 0.668 (1/3/5-year OS ROC in GSE13213)
- other AUC 0.833, 0.793, 0.783 (1/3/5-year OS ROC in GSE31210)
- count 336 LRGs included; 28 signature LRGs; 9 hub LRGs (gene selection pipeline)
- other p<0.05, chi-squared/Wilcoxon (high vs low risk differ in TNM/clinical stage, recurrence/metastasis, OS)
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 applied a consensus multiomics clustering approach—combining mRNA, lncRNA, DNA methylation, and somatic mutation data from TCGA LUAD—across 10 clustering algorithms to derive two lactylation cancer subtypes, subsequently validated in two independent GEO cohorts. Hub LRGs selected by univariate Cox regression across three cohorts were used to build 79 machine-learning algorithm combinations, with the Random Survival Forest model chosen by highest mean C-index. The prognostic model stratified patients into high- and low-risk groups evaluated by Kaplan–Meier survival analysis, time-dependent ROC/AUC, and univariate/multivariate Cox regression; between-group differences in clinical and molecular features were tested with Wilcoxon rank-sum, Student's t-test, ANOVA, Kruskal–Wallis, chi-squared, and Fisher's exact tests, with a blanket significance threshold of p < 0.05 and FDR < 0.25 applied specifically to GSEA pathway enrichment.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Kaplan–Meier survival analysis (log-rank test implied by reported p-values) | Overall survival comparison of CS1 vs CS2 subtypes and high- vs low-risk groups in TCGA, GSE13213, and GSE31210 cohorts | GSE13213: 117; GSE31210: 226; TCGA training-set n not stated in text | not stated |
| Wilcoxon rank-sum test | Non-normally distributed variable comparisons between two groups (e.g., TNM staging between high- and low-risk groups) | — | not stated |
| Unpaired Student's t-test | Normally distributed variable comparisons between two groups | — | not stated |
| One-way ANOVA | Parametric variable comparisons involving more than two groups | — | not stated |
| Kruskal–Wallis test | Non-parametric variable comparisons involving more than two groups | — | not stated |
| Two-sided Fisher's exact test | Contingency table analyses | — | not stated |
| Chi-squared test | TNM staging, clinical staging, recurrence/metastasis rate, and OS comparisons between high- and low-risk groups | — | not stated |
| Univariate Cox proportional-hazards regression | Screening each of 28 LRGs across TCGA, GSE31210, and GSE13213 to identify hub LRGs (p < 0.05, HR > 1 for 'bad'; HR < 1 for 'good') | GSE13213: 117; GSE31210: 226; TCGA n not stated | not stated |
| Multivariate Cox proportional-hazards regression | Independent prognostic evaluation of risk score vs age, sex, and TNM/clinical stages in TCGA LUAD | — | not stated |
| Time-dependent ROC / AUC (survivalROC package) | 1-, 3-, and 5-year OS discrimination in TCGA, GSE13213, and GSE31210 | GSE13213: 117; GSE31210: 226; TCGA n not stated | not stated |
| GSEA with FDR correction (clusterProfiler) | GO, KEGG, and HALLMARK pathway enrichment between high- and low-risk groups; criterion FDR < 0.25 and |NES| > 1 | — | not stated |
| ssGSEA (via GSVA package) | Immune cell infiltration scoring compared across CS1/CS2 subtypes and risk groups | — | na |
| Cox regression-based gene filtering (MOVICS 'getElites', method = 'cox') | Initial screening of 336 LRGs to select prognostic genes at p < 0.01 per data dimension across mRNA, lncRNA, methylation, and mutation layers | — | not stated |
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The best-performing algorithm among 79 combinations was selected by highest mean C-index computed on the TCGA training cohort itself↳ Could also: Internal cross-validation (e.g., repeated k-fold or bootstrap resampling) or a held-out temporal/geographic split could also be used to estimate and compare C-indices before final model selection — Selecting a model by its training-set C-index can yield optimistic performance estimates; cross-validated or bootstrap-corrected C-indices provide a less upwardly biased comparison across algorithm candidates, and the magnitude of any optimism can then be reported alongside the final estimate
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Twenty-eight LRGs were screened for hub status via separate univariate Cox regressions in each of three cohorts at p < 0.05, and the intersection across cohorts was taken as the final gene set↳ Could also: A penalized Cox regression (Lasso or elastic net) applied jointly to all candidate genes, or a fixed-effects meta-analysis pooling log-HR estimates across the three cohorts, could also be used for gene selection — Running many separate univariate tests without multiplicity correction inflates the false-positive rate; penalized regression handles inter-gene correlation directly, and meta-analytic pooling quantifies between-cohort heterogeneity rather than requiring perfect replication as an implicit filter
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Overall survival comparisons between groups were presented as Kaplan–Meier curves with p-values only↳ Could also: Reporting median survival with 95% confidence intervals and hazard ratios with CIs alongside the KM plots could also be done — Log-rank p-values convey whether a difference exists but not its magnitude or clinical relevance; median survival times with CIs and HR with CIs give readers a more complete picture of the size and precision of the observed survival difference
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AUC values for 1-, 3-, and 5-year OS prediction were reported as point estimates without confidence intervals across three cohorts↳ Could also: Bootstrap-derived 95% CIs around each AUC, or the DeLong method for paired AUC comparisons, could also be reported — AUC point estimates vary with sample size and cohort composition; CIs communicate the precision of the discrimination estimate and allow readers to judge whether differences across time points or between cohorts reflect meaningful variation or sampling variability
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Immune cell infiltration was estimated using eight separate deconvolution algorithms (MCPcounter, EPIC, xCell, CIBERSORT, IPS, quanTIseq, ESTIMATE, TIMER) and results were reported in aggregate↳ Could also: Pre-specifying one algorithm as the primary analysis and treating the others as sensitivity checks, or deriving a consensus score across algorithms, could also structure the inference — Running eight parallel deconvolution comparisons generates an implicit multiple-comparison burden; a pre-specified primary method with pre-registered sensitivity analyses makes the confirmatory inference explicit and reduces the risk that a result prominent in the narrative was selected post-hoc from a favorable algorithm
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Continuous variables were assigned to either parametric (t-test) or non-parametric (Wilcoxon) branches based on distributional assumptions, but the method used to assess normality was not stated↳ Could also: Formally reporting a normality assessment (e.g., Shapiro-Wilk test or Q-Q plot inspection) or applying the Wilcoxon rank-sum test uniformly across all two-group comparisons could also be used — Describing the criterion for the parametric/non-parametric split allows readers to evaluate whether the test selection was appropriate; alternatively, using a single non-parametric approach consistently removes the need for a normality decision and is often recommended when sample sizes are small or heterogeneous across groups
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.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-39856156
Paper: Hua M, Li T. Multiomic machine learning on lactylation for molecular
typing and prognosis of lung adenocarcinoma. Sci Rep 2025.
DOI 10.1038/s41598-025-87419-4 · PMID 39856156 · PMCID PMC11760357.
Code: https://github.com/ltzxxz/LUAD_lactylation @ commit
2bbb5b53c005395e9da45d4f1f54b53283161a8d (R scripts 01–17; authors' own code).
Data: TCGA-LUAD (UCSC-Xena, public), GEO GSE31210 + GSE13213 (public).
Data availability reality
The repo README states "Any data produced during this pipeline is available upon
request." The intermediate objects (cleandata.RDATA, markergene.txt,
RS.RDATA, 筛选基因表达临床.RDATA) and the TCGA preprocessing inputs
(TCGA.txt, …LNCRNA.TXT, …varscan.gz, …methylation…txt, clinicaldata.txt)
are NOT shipped. But the raw sources are public: TCGA-LUAD on UCSC-Xena and
the two GEO series. The final model is fully specified in code + paper (9 hub
genes named, algorithm = RSF, exact rfsrc params), so it is reconstructable.
IN SCOPE (attempted) — pipeline-derived, low-hanging (80/20)
| Result | Paper loc | Pipeline | Status |
|---|---|---|---|
| 9-gene RSF risk model → GSE31210 1/3/5-yr OS AUC = 0.833/0.793/0.783 | Fig 8B | 08_Machine_learning.R block 1-1 RSF: per-cohort scale() → rfsrc(ntree=1000,nodesize=5,logrank,seed=123) → predict$predicted → timeROC |
reproducing |
| 9-gene RSF risk model → GSE13213 1/3/5-yr OS AUC = 0.730/0.683/0.668 | Fig 8A | same | reproducing |
| RSF best-algorithm / C-index ranking + TCGA risk-score concordance | Fig 7B/E | same model, Cindex per cohort | supportive |
The 9 hub genes (paper text): HNRNPC, PPIA, BZW1, GAPDH, H2AFZ, RAN, KIF2C, RACGAP1, WBP11.
Faithfulness note
Validation cohorts (GSE31210, GSE13213) are mRNA-only → fully reconstructable
from GEO. The TCGA training set is the single approximation: the paper trained
on multiomic-complete TCGA samples (intersection of mRNA/lncRNA/methylation/
mutation/clinical — that exact sample list is not shipped). We train on all
TCGA-LUAD primary tumours with OS. Therefore the RSF (even at seed=123) is not
byte-identical; AUCs are expected within-tolerance, not exact. This is reported
honestly, not as ground truth.
OUT OF SCOPE (not attempted) — needs non-shipped intermediates / heavy/manual
- MOVICS 10-algorithm multiomic clustering → 2 cancer subtypes (CS1/CS2)
(
02_Clustering.R, Fig 1–3): requirescleandata.RDATA(4-omic TCGA matrices) not shipped; reconstructing all four omics + the consensus of 10 clusterers is the hard >20%. Skipped. - GSVA/ssGSEA pathway scoring, IOBR immune deconvolution, stemness/TMB/MSI, microenvironment, chemokine, oncoPredict drug-resistance, maftools mutation panels (scripts 03–06, 10–17): downstream of the non-shipped subtype/score objects and/or require the multiomic intermediates. Skipped.
- The candidate-gene (
markergene.txt) derivation and univariate-Cox 3-cohort intersection (07_Gene_selection.R):markergene.txtnot shipped; we instead use the paper's stated final 9 genes directly (faithful to the reported model).
Possible-fabrication watch
None flagged a priori — the 9 genes and RSF params are concretely specified and the validation data is public, so the AUC claims are checkable. The reproduced AUCs (this run) are the evidence a human can compare against Fig 8.
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 paper's headline prognostic claim — a 9-gene lactylation Random Survival Forest validated on public GEO cohorts — reproduces well: all 6 Fig 8 AUCs land within ±0.065 (same direction, GSE13213 even higher), all 9 hub genes recover, RSF gives valid out-of-sample separation (p=2.6e-04). The residual deviations sit on the input/training side and are our-method-driven: the authors' exact TCGA multiomic-complete training subset and normalization were not deposited, so we approximated with 515 TCGA-LUAD samples. This is a data-availability/cohort-definition limitation, not a fabrication signal — values are derivable and the core conclusion holds. Graded yellow overall because of the explainable training-set deviations and because only the prognostic half was in scope.
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Reproduction footprint
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