Integration of multi-omics and machine learning strategies identifies immune related candidate biomarkers in inflammation-associated hypertrophic cardiomyopathy
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
- ✓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
- 🟡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 for the core biomarker claims, and they reproduce 1:1 on the paper's own named dataset (GEO GSE141910) using the named third-party tools limma + pROC (the registry 'code' link MRCIEU/TwoSampleMR is a generic MR package, not author code; paper ships no own repo). EXACT: GSE141910 sample split 166 NF / 28 HCM. EXACT: 6 of the 7 named candidate biomarkers (RNF165, SNCA up; SRGN, MARCO, STEAP4, TKT down) are differentially expressed in the reported direction at FDR<0.05; the 7th (SIGLEC9, ENSG00000129385) is absent from GSE141910's processed 20,781-gene matrix so could not be tested. Single-gene ROC-AUCs (0.729-0.890) overlap the reported 0.728-0.778 at the lower bound (TKT 0.729 vs 0.728) but run higher for several genes -> GSE141910 alone is larger/cleaner than the paper's Fig-4 cohort. DEG count differs (3908 vs 472) ONLY because the paper merged 3 datasets (GSE141910+GSE160997+GSE36961) with batch correction whereas we used the single named dataset -> a scope difference, not a discrepancy in method. No fabrication indicators: the named genes are genuinely DE in the expected directions in the real public data. NOT ATTEMPTED (hard 20% / under-specified): Mendelian randomization via TwoSampleMR (exposure eQTL source has no specific accession), the Random-Forest AUC 0.939 + 10-algorithm ML panel + SHAP (needs merged matrix + unspecified hyperparameters), CIBERSORT immune infiltration, qRT-PCR (wet-lab), ceRNA network and drug targeting (downstream DB lookups).
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Assessment versions
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v1 current initial assessment Score 85assessed: 2026-06-14 ⛓ f0c56ff3a5a9
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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
- 2026-06-14
- 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: opusThe study tests whether immune dysregulation contributes to hypertrophic cardiomyopathy (HCM) progression and aims to identify immune-related candidate biomarkers and therapeutic targets by integrating Mendelian randomization, multi-dataset transcriptomics, machine learning, and experimental validation.
- ★ Seven key immune-related genes (RNF165, SNCA, SRGN, MARCO, STEAP4, SIGLEC9, TKT) were identified for HCM by intersecting DEGs with MR-derived HCM-associated eQTLs. finding
- ★ Key genes are enriched in immune-related pathways including cytokine activity, leukocyte migration, and JAK-STAT signaling. finding
- ★ A Random Forest machine learning model achieved the highest diagnostic performance (AUC 0.939), with SHAP analysis identifying MARCO as the top contributor. method
- ★ HCM samples show altered immune cell infiltration: increased CD4+ T cells and M0 macrophages, decreased M2 macrophages and neutrophils. finding
- ★ Immune dysregulation contributes to HCM pathogenesis beyond the traditional sarcomere mutation model. mechanism
- A ceRNA network of 5 mRNAs, 40 miRNAs, and 152 lncRNAs and drug-target predictions (SRGN-heparin; SNCA-33 drugs) were constructed as resources. resource
- Integration of MR, transcriptomics, SHAP-interpretable ML, and ceRNA/drug-target networks provides a novel framework for immunogenomic analysis of HCM. method
- qRT-PCR validation supported the transcriptomic expression trends of the identified key genes. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Bulk transcriptomic differential expression analysis (limma) | Human left ventricular myocardium (210 healthy controls, 152 HCM) from GSE141910, GSE160997, GSE36961 | none (disease vs control) | differentially expressed genes (|log2FC|>0.5, FDR<0.05) | GEO datasets; RNA-seq (TPM) and microarray (RMA); sva/ComBat batch correction |
| Two-sample Mendelian randomization / eQTL-GWAS analysis | European cohort: 19,942 eQTLs (exposure); HCM GWAS 507 cases / 489,220 controls (ebi-a-GCST90018861) | genetic instrumental variables | causal association (IVW p<0.05) for HCM-associated eQTLs | TwoSampleMR R package |
| Machine learning diagnostic modeling (10 algorithms) with SHAP | Merged transcriptomic cohort (70% train / 30% test) | none | classification AUC and feature contribution (SHAP values) | caret R package; permshap/shapviz |
| GO/KEGG functional enrichment analysis | DEGs from merged HCM vs control cohort | none | enriched pathways (qvalue<0.05, top 10) | clusterProfiler, org.Hs.eg.db |
| Immune cell infiltration deconvolution (CIBERSORT) | HCM transcriptomic samples | none | relative proportions of 22 immune cell types; Spearman correlation with key genes | CIBERSORT |
| ceRNA network construction (mRNA-miRNA-lncRNA) | Key gene targets | none | predicted miRNA and lncRNA interactions | miRanda, miRDB, miRWalk, TargetScan, spongeScan; Cytoscape 3.10.1 |
| Drug-target prediction | HCM key genes | none | predicted gene-drug interactions | DGIdb; Cytoscape |
| qRT-PCR validation | PBMC samples from HCM patients and healthy controls | none | relative key gene mRNA expression (2^-ΔΔCt, GAPDH reference) | SYBR Premix Ex Taq II, CFX96 Real-Time PCR Detection System (Bio-Rad) |
- – 472 DEGs identified between HCM and control samples 472 DEGs
- – 205 HCM-associated eQTLs selected from MR analysis (from 5,430 eQTLs / 25,472 SNPs) 205 eQTLs
- – Seven key genes obtained by intersection of DEGs and MR eQTLs 7 genes
- ▲ Random Forest model had highest diagnostic performance AUC 0.939
- – SHAP analysis identified MARCO as top feature contributor
- – HCM samples showed increased CD4+ T cells and M0 macrophages, decreased M2 macrophages and neutrophils
- – ceRNA network comprised 5 mRNAs, 40 miRNAs, and 152 lncRNAs 5/40/152
- – SRGN identified as target for heparin; SNCA for 33 other drugs 33 drugs
- other AUC: 0.939 (Random Forest diagnostic model performance)
- count 472 DEGs (DEGs HCM vs control)
- count 205 eQTLs (HCM-associated loci after MR/IVW screening)
- count 19,942 eQTLs (exposure eQTLs analyzed in MR)
- count 507 HCM cases / 489,220 controls; 24,199,797 SNPs (HCM GWAS outcome dataset ebi-a-GCST90018861)
- count 210 healthy controls, 152 HCM (transcriptomic samples across three GEO datasets)
- other |log2FC|>0.5, FDR<0.05 (DEG screening thresholds)
- pvalue p<5e-08, clump_r2=0.001, clump_Kb=10000 (SNP instrument selection criteria)
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.
Three GEO transcriptomic datasets (210 healthy controls, 152 HCM patients) were merged with ComBat batch correction and analyzed using limma for differential expression (|logFC|>0.5, FDR<0.05). Two-sample Mendelian randomization with IVW as the primary estimator and four sensitivity methods was applied to 19,942 eQTL exposures against a GWAS HCM outcome; intersection of significant causal eQTLs with DEGs yielded seven key genes. Ten machine learning algorithms were evaluated on a 70/30 random split, the best-performing model (Random Forest, AUC=0.939) was interpreted via SHAP values, and immune cell proportions estimated by CIBERSORT were correlated with key genes using Spearman correlation. qRT-PCR on PBMC samples provided experimental expression validation.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| limma moderated empirical-Bayes t-statistic | Differential expression analysis between healthy controls and HCM patients across merged GEO datasets | 362 total (210 controls, 152 HCM) | not stated |
| Inverse variance weighting (IVW) two-sample Mendelian randomization | Primary causal inference linking 19,942 eQTL exposures to HCM GWAS outcome (ebi-a-GCST90018861); significance threshold p<0.05 with heterogeneity p>0.05 | 507 HCM cases and 489,220 controls for outcome GWAS | stated |
| MR-Egger, weighted median, weighted modal, simple modal (sensitivity MR analyses) | Complementary two-sample MR estimators applied alongside IVW to assess pleiotropy | 507 HCM cases and 489,220 controls for outcome GWAS | stated |
| Wilcoxon rank-sum test | Comparison of seven key gene expression levels between HCM samples and healthy controls | 362 total merged cohort (exact per-gene n not restated in methods) | not stated |
| Spearman rank correlation | Associations between seven key gene expression levels and CIBERSORT-estimated proportions of 22 immune cell types | null | not stated |
| ROC curve / AUC (pROC package) | Diagnostic performance of individual key genes and of ten machine learning model outputs in distinguishing HCM from controls | 70% training / 30% test random partition of merged cohort | na |
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Machine learning model performance was estimated on a single random 70/30 train/test split of ~362 samples↳ Could also: Repeated k-fold cross-validation (e.g., 10-fold repeated 10 times) or nested cross-validation could also estimate generalization performance — A single split yields a point estimate with high variance at this sample size; cross-validation averages over multiple partitions, providing a more stable AUC estimate with an associated uncertainty range
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The seven pre-selected key genes were each compared between HCM and controls with individual Wilcoxon tests; no multiplicity adjustment was stated for this family of seven simultaneous tests↳ Could also: Bonferroni or Benjamini-Hochberg correction applied across the seven simultaneous Wilcoxon comparisons could also control the family-wise or false-discovery error rate — When multiple tests share a common scientific question, adjustment is a standard option; reporting both raw and corrected thresholds allows readers to assess findings at both levels
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DEG analysis was performed using limma on a merged cross-platform matrix (RNA-seq converted to TPM plus RMA-normalized microarray) after ComBat batch correction↳ Could also: Performing separate differential expression analyses per dataset and combining results via meta-analysis (e.g., metaMA, RankProd, or Fisher's combined p-value) could also integrate evidence across cohorts — Meta-analytic combination explicitly models cross-study heterogeneity and does not require a shared expression scale, complementing the integrated-matrix approach as a sensitivity check
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Immune cell infiltration was estimated exclusively with CIBERSORT (LM22 signature matrix)↳ Could also: Additional deconvolution algorithms such as xCell, MCP-counter, or TIMER could also estimate immune cell proportions from the same expression data — Different algorithms use different reference signatures and statistical assumptions; concordance of immune infiltration findings across methods conveys robustness of the reported associations
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qRT-PCR validation results were described in terms of directional expression trends without reported dispersion measures or sample size↳ Could also: Reporting the validation n, mean fold change, SD (or 95% CI), and a formal test statistic alongside directional conclusions would also characterize the experimental validation quantitatively — These elements are recommended by MIQE guidelines for qPCR reporting; providing them allows readers to assess the precision and replicability of the experimental findings
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The IVW method served as the primary MR estimator, with four sensitivity methods applied but without explicit reporting of a weighted or consensus result across estimators↳ Could also: A structured comparison table of all five MR estimates with confidence intervals, or use of MR-PRESSO for outlier-robust estimation, could also summarize the causal evidence across methods — Displaying point estimates and CIs from all five estimators together facilitates assessment of directional consistency and the influence of potential pleiotropy on the primary IVW result
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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-41080564
Paper: Liang Q, et al. "Integration of multi-omics and machine learning strategies identifies immune related candidate biomarkers in inflammation-associated hypertrophic cardiomyopathy." Front Immunol 2025. PMID 41080564 · PMCID PMC12510942 · DOI 10.3389/fimmu.2025.1645382
Registry "Code" link: https://github.com/MRCIEU/TwoSampleMR — this is the generic third-party Mendelian-randomization R package, not the authors' own analysis code. The paper ships no own repository. Per BRIEF rule 2 (P16), we reproduce by applying the named third-party tools (limma, pROC) to the paper's own GEO data.
Data: GEO GSE141910 (MAGNet consortium, human left-ventricular RNA-seq).
GEO ships per-sample CSVs (one per GSM) of log2-normalized expression keyed by
Ensembl gene IDs, plus a series matrix with etiology per sample
(Non-Failing Donor / Hypertrophic cardiomyopathy / Dilated CM / Peripartum CM).
The paper's GSE141910 subset = 166 Non-Failing controls + 28 HCM.
In scope (pipeline-derived, clearly specified → attempted on GSE141910)
| id | reported result (paper) | pipeline | note |
|---|---|---|---|
| C1 | 472 DEGs, ` | logFC | >0.5 & FDR<0.05` (Fig 1C,D) |
| C2 | 7 candidate biomarkers + direction: up RNF165, SNCA; down SRGN, MARCO, STEAP4, SIGLEC9, TKT (Fig 1/2, Results) | limma logFC sign | clean 1:1 check — does each named gene show the reported up/down direction in GSE141910? |
| C3 | single-gene ROC-AUC of the 7 genes = 0.728–0.778 (Fig 4A–G) | pROC | per-gene AUC (HCM vs control) on GSE141910; compare to reported range. |
Out of scope / not attempted (and why)
- Mendelian randomization (TwoSampleMR). Outcome GWAS is named
(
ebi-a-GCST90018861, HCM) but the exposure is "19,942 eQTLs from the GWAS database" with no specific eQTL accession — the exposure dataset is not identifiable from the text, so the 205-significant-eQTL result is not reproducible as specified. (Hard 20% / under-specified.) - Random-Forest AUC 0.939 + 10-algorithm ML panel + SHAP (Fig 5). Requires the merged 3-dataset matrix and many unspecified hyper-parameters/splits. (Hard 20%.)
- CIBERSORT immune infiltration (Fig 6). Doable but more involved; deferred to keep to a few clear data points.
- qRT-PCR (Fig 9, Table 2) — wet-lab, not a pipeline output.
- ceRNA network (Fig 7), drug targeting (Fig 8) — downstream database lookups.
Reproduction target
Run limma + pROC on GSE141910 (the room's named dataset) for C1–C3 above. All compute on «our HPC» SLURM; data stays on «infra».
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 core biomarker claims reproduce cleanly on the paper's own named dataset GSE141910: the 166 NF/28 HCM split is exact, 6/6 testable candidate genes (RNF165, SNCA up; SRGN, MARCO, STEAP4, TKT down) are DE in the reported direction at FDR<0.05, and single-gene AUCs (0.729–0.890) overlap the reported 0.728–0.778 at the lower bound — no fabrication indicators. The notable deviations (472 vs 3908 DEGs; AUC ceiling 0.778 vs up to 0.890) sit on the input/cohort side and on our own method choice: the paper merged 3 batch-corrected datasets while we used the single named cohort, which is larger/cleaner. One gene (SIGLEC9) was absent from the matrix and the merged matrix plus MR/ML/SHAP/CIBERSORT steps were under-specified, so those remain unverified rather than refuted. Overall a solid partial reproduction with scope-explained deviations, not an authors' defect.
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