Exploring the prognostic and diagnostic value of lactylation-related genes in sepsis.
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 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 for the PUBLIC-DATA validation claims, which reproduce ~1:1. The paper's two signature genes (S100A11, CCNA2) were validated on public GEO sets. Diagnostic ROC AUC on GSE69528 reproduces essentially exactly: S100A11 0.9607 vs reported 0.961 (exact), CCNA2 0.8865 vs reported 0.890 (within-tol); sample split 83 sepsis/55 control matches. 28-day survival on GSE65682 (n=479): S100A11 low-expression-worse reproduces with log-rank p=0.0056 (matches paper, significant); CCNA2 direction matches (high expr = higher mortality) but a plain median split is not significant (p=0.18) vs the paper's p<0.05 from a multi-cohort meta-analysis -> partial. NOT attempted: the discovery DEG chain (4890 DEGs, 55 DE-LRGs, 9 PPI hub genes) runs on the authors' in-house RNA-seq (CNGBdb CNP0002611) plus an unpublished 332-gene lactylation list, neither derivable from public data -> outside the 80% public-data scope, flagged for human auditor, no fabrication claim. No authors' analysis repo exists (cited 'code' is BGI SOAPnuke, a generic read-cleaner); reproduced via standard tools (GEOquery/pROC/survival) per P16.
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
Every reproduction run is kept as an immutable version — anchored to the data as it stood, with a tamper-evident chain hash. A rerun (e.g. after an author updates a deposit) adds a new version; the previous one stays on record.
-
v1 current initial assessment Score 73assessed: 2026-06-14 ⛓ 9e0921a37e5e
✎ I am an author of this paper
Updated or fixed a deposit, or is there an erratum? Ask us to re-run the metrics. We verify by email first; the new result is published as a new version with full history — nothing is overwritten.
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
-
🤖 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: sonnetLactate-derived histone/protein lactylation may play a role in sepsis pathogenesis, and lactylation-related genes identified from differential expression analysis could serve as prognostic and diagnostic markers for sepsis.
- ★ Intersecting sepsis-associated differentially expressed genes with a curated list of 332 lactylation genes yields 55 sepsis-related lactylation genes. finding
- ★ PPI network analysis identifies GAPDH, S100A11, H2BC14, PARP1, TP53, CCNA2, NCL, S100A4, and H2BC13 as core lactylation-related genes in sepsis. finding
- ★ Low S100A11 expression is associated with decreased 28-day survival in sepsis patients. finding
- ★ Low CCNA2 expression is associated with increased 28-day survival in sepsis patients. finding
- ★ S100A11 and CCNA2 have high diagnostic accuracy for sepsis based on ROC/AUC analysis. finding
- ★ Meta-analysis across independent GEO cohorts confirms differential expression patterns of S100A11 and CCNA2 between sepsis survivors and non-survivors. finding
- Single-cell RNA sequencing shows monocyte macrophages, T cells, and B cells have high expression of the sepsis-associated lactylation hub genes. finding
- ★ S100A11 and CCNA2 are proposed as lactylation-related biomarkers for sepsis diagnosis, prognosis, and treatment guidance. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| bulk mRNA-seq | peripheral blood, human (20 sepsis patients, 10 healthy controls) | none (disease state comparison) | differentially expressed genes (DEGs) | BGISEQ-500/MGISEQ-2000 |
| GO and KEGG enrichment analysis | 55 overlapping sepsis-lactylation genes (bioinformatic dataset) | none | functional/pathway enrichment | clusterProfiler (R v4.2.1) |
| protein-protein interaction (PPI) network analysis | 55 overlapping sepsis-lactylation genes | none | hub gene identification | STRING database |
| survival analysis (Kaplan-Meier, log-rank test) | GEO dataset GSE65682, whole blood, human (478 sepsis patients, 365 survivors) | none (gene expression stratification) | 28-day survival rate by gene expression level | GraphPad Prism 8 |
| ROC curve analysis | GEO dataset GSE69528, whole blood, human (83 sepsis, 55 normal controls) | none | diagnostic sensitivity/specificity (AUC) | MedCalc |
| meta-analysis of gene expression (forest plot) | GEO datasets GSE54514, GSE63042, GSE95233, whole blood, human (sepsis survivors vs non-survivors) | none | cross-cohort gene expression levels | — |
| single-cell RNA sequencing (10x Genomics) | PBMCs from 5 peripheral blood samples (2 healthy, 1 SIRS, 2 sepsis) | none (disease state comparison) | cell-type-specific expression of hub genes | 10x Genomics, Cell Ranger, Seurat |
- – 4890 DEmRNAs identified between sepsis and normal groups (2498 upregulated, 2392 downregulated) |FC| ≥ 2, FDR < 0.05
- – 55 sepsis-related lactylation genes obtained by intersecting DEGs with lactylation gene list
- – 9 hub genes identified at core of PPI network
- ▼ Low S100A11 expression linked to lower 28-day survival P < 0.05
- ▼ Low CCNA2 expression linked to higher 28-day survival P < 0.05
- – S100A11 and CCNA2 show high sensitivity/specificity in ROC analysis AUC = 0.961 (S100A11), AUC = 0.890 (CCNA2)
- – Meta-analysis: S100A11 high in survivors/low in non-survivors; CCNA2 low in survivors/high in non-survivors P < 0.05
- ▲ Monocyte macrophages, T cells, and B cells show high expression of hub genes in scRNA-seq
- count 4890 DEmRNAs (2498 up, 2392 down) (DEGs between sepsis (n=20) and healthy (n=10) blood samples)
- fold_change |FC| ≥ 2, FDR < 0.05 (DEG selection criteria)
- count 55 overlapping lactylation genes (intersection of 4890 DEGs and 332 lactylation genes)
- count 332 lactylation genes (background lactylation gene list from prior studies)
- pvalue P < 0.05 (log-rank test, survival difference by S100A11/CCNA2 expression (GSE65682))
- other AUC = 0.961 (ROC diagnostic accuracy of S100A11 (GSE69528: 83 sepsis, 55 normal))
- other AUC = 0.890 (ROC diagnostic accuracy of CCNA2 (GSE69528))
- pvalue P < 0.05 (meta-analysis expression differences across GSE54514, GSE63042, GSE95233)
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 performed bulk RNA sequencing on peripheral blood from 20 sepsis patients and 10 healthy controls, applying fold-change and FDR thresholds to identify differentially expressed genes (DEGs), which were intersected with a curated 332-gene lactylation set to yield 55 candidate genes. Hub genes were prioritized by visual PPI network centrality in STRING, yielding S100A11 and CCNA2, which were then validated in independent GEO cohorts via log-rank survival analysis and ROC curve analysis; a meta-analysis across three additional GEO datasets further assessed expression directionality in survivors vs. non-survivors. Single-cell RNA sequencing on five peripheral blood samples characterized cell-type expression patterns of the hub genes, with results reported as threshold p-values (P < 0.05) and AUC point estimates.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Differential expression analysis with |FC| ≥ 2 and FDR < 0.05 thresholding (via iDEP 2.1; underlying statistical model not explicitly named) | Bulk RNA-seq: sepsis (n=20) vs. healthy controls (n=10) | 20 sepsis, 10 healthy controls | not stated |
| Hypergeometric test (GO functional enrichment, via R/clusterProfiler) | GO enrichment of 55 lactylation–DEG overlapping genes | 55 overlapping genes | not stated |
| Hypergeometric test (KEGG pathway enrichment, via R/clusterProfiler) | KEGG pathway enrichment of 55 overlapping genes | 55 overlapping genes | not stated |
| Log-rank test | 28-day survival analysis for S100A11 and CCNA2 (high vs. low expression groups) in GSE65682 | 802 total samples in GSE65682; paper states 478 patients with sepsis and 365 survivors | not stated |
| ROC curve analysis (AUC) | Diagnostic discrimination of S100A11 and CCNA2 in GSE69528 (sepsis vs. normal control) | 138 (83 sepsis, 55 normal) | na |
| Meta-analysis (specific pooling model not stated; forest plot reported) | Expression of S100A11 and CCNA2 in survivors vs. non-survivors across GSE54514, GSE63042, GSE95233 | 163 + 106 + 124 = 393 combined across three datasets | not stated |
-
Survival analysis dichotomized continuous gene expression into high vs. low groups for log-rank testing↳ Could also: Cox proportional hazards regression using continuous gene expression as a covariate — Cox regression retains the full continuous expression spectrum, avoids an arbitrary split point, and directly yields hazard ratios with confidence intervals as quantitative effect sizes; it is the conventional approach when expression is treated as a prognostic variable
-
Meta-analysis pooled expression data across three GEO cohorts, but the pooling model (fixed vs. random effects) and between-study heterogeneity were not stated↳ Could also: Explicitly specified random-effects meta-analysis (e.g., DerSimonian–Laird) with the heterogeneity statistic I² and Cochran's Q reported alongside the forest plot — Reporting the model choice, I², and individual study weights allows readers to evaluate cross-cohort consistency and is standard practice for meta-analyses of gene expression data; random-effects models are generally preferred when cohort-level differences in platform or population are expected
-
ROC curves were reported as point-estimate AUC values only (0.961 and 0.890)↳ Could also: Report 95% confidence intervals around each AUC (e.g., via DeLong's method) and specify the optimal operating threshold with corresponding sensitivity and specificity — CIs around AUC quantify estimation uncertainty — particularly relevant at n=138 — and an explicit threshold with paired sensitivity/specificity directly supports potential clinical translation of a diagnostic marker
-
Hub genes were identified by visual inspection of centrality in a STRING PPI network↳ Could also: Compute formal network centrality metrics (degree, betweenness, eigenvector centrality) or apply a penalized regression approach (e.g., LASSO) on the 55-gene candidate set — Quantitative centrality metrics or data-driven variable selection provide a reproducible, algorithmically defined ranking that can be reported and re-run, rather than relying on graphical or qualitative prioritization
-
The specific differential expression model used by iDEP 2.1 was not named, though |FC| ≥ 2 and FDR < 0.05 thresholds were stated↳ Could also: Explicitly name and parameterize the DE model (e.g., DESeq2 with negative binomial Wald test and shrinkage estimation, or limma-voom with TMM normalization) — Naming the exact model, normalization strategy, and dispersion estimation approach enables full reproducibility, allows readers to evaluate suitability for the small-n design (n=30 total), and follows community norms for RNA-seq publications
-
Survival and ROC results for two genes were each reported at a nominal P < 0.05 without a stated multiplicity correction↳ Could also: Apply a two-test Bonferroni correction (adjusted α = 0.025) or Benjamini-Hochberg FDR across all hub-gene-level tests — Even a minimal correction for two simultaneous gene-level tests keeps the effective significance threshold interpretable and is straightforward to implement; this is particularly relevant if additional hub genes beyond S100A11 and CCNA2 were also examined at this stage
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.
No assessed neighbours yet — the network grows as more papers are assessed.
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.
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 public-data validation core reproduces ~1:1: S100A11 diagnostic AUC 0.961→0.9607 (exact) and CCNA2 0.890→0.8865 (within-tol) on GSE69528 with matching 83/55 split, and S100A11 28-day survival reproduces with correct direction and significance (p=0.0056). The one substantive deviation — CCNA2 prognostic significance (paper p<0.05 vs reproduced p=0.18) — keeps the correct direction and is explained by our single-cohort median split versus the paper's multi-cohort meta-analysis, i.e. a method choice, not an authors' defect. The discovery DEG chain (4890 DEGs, 55 DE-LRGs) could not be re-derived because it depends on unavailable in-house data and an unpublished gene list, a data-availability limitation rather than fabrication. Overall: solid, with explainable deviations — yellow.
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.
Are you an author? We would genuinely like to hear from you — to clarify the record, add data or code, re-run the pipeline after an accession update, and publish your response right next to the assessment. Everything here is open and auditable.
🚩 Report an error in this record
Spotted something wrong — a verdict you’d contest, a data or value error, or a private detail that slipped through? Tell us, with a short justification. Authors and readers are equally welcome to write in; we review every report.
Prefer email, or the form below not working? Contact us at support@doesitreproduce.com.
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.