LRP1 as a potential diagnostic and immunomodulatory target in endometriosis: evidence from multi-omics and single-cell analyses.
The main result did not reproduce in this reproduction attempt. Where our recomputation produced values that differ from the published ones, those discrepancies are listed below. This is a single automated attempt — not peer review and not a finding of error or misconduct — and differences can also arise from data access, undocumented parameters or the computing environment. The verdict can be contested via “report an error”.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- Nothing in this column.
- 🟡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
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡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
Front Immunol 2026 multi-omics endometriosis paper (LRP1 diagnostic/immunomodulatory target). Described well enough to reproduce the deterministic microarray steps on the paper's own public GEO data (GSE7305+GSE11691+GSE25628 training, GSE5108/GSE37837 validation), done on «our HPC» (limma+ComBat+pROC, R4.5.3). RESULT = PARTIAL, honest 1:1 on the molecular core, mismatch on standalone diagnostic generalization. (1) LRP1 is robustly UP in EC in the merged training data (logFC +1.03, P=1e-10) -> central thesis reproduces. (2) The DEG count reproduces only after reinterpreting the stated '|log2FC|>1.5' as FC>1.5 (|log2FC|>0.585): then 1765 DEGs vs reported 1404, same magnitude and same up>down direction; the literal log2FC>1.5 gives only 286 -> the printed threshold is almost certainly mis-stated. Exact 1404 not recoverable (merge/normalization/GSE25628 sample-selection underspecified). (3) Single-gene LRP1 AUC does NOT generalize to GSE37837 (0.444) and GSE5108 cannot measure LRP1 (5/22 samples on GPL2895) -- but the paper's 0.884/0.864 are for a 30-gene model, not LRP1 alone, so this is a flag (cohort-dependent standalone value, large train->val drop) rather than a direct contradiction. NOT attempted (the ~20%): the IRLS 101-combination ML signature (C-index 0.904, 30 genes) -- note the brief's repo Zaoqu-Liu/IRLS is CRC SURVIVAL code shipping CRC data, not this paper's pipeline; WGCNA brown module (489/201); CIBERSORT M2 r=0.62; Mendelian randomization OR=1.35; single-cell GSE179640; and all wet-lab (qRT-PCR/WB/IHC/siRNA).
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
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v1 current initial assessment Score 49assessed: 2026-06-16 ⛓ b0f49f7c88c6
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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
- 2026-06-16
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-16no 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: opusDoes LRP1 serve as a central pathogenic regulator and reliable diagnostic biomarker of endometriosis (EMS) by modulating the immune microenvironment and intercellular communication, distinguishing ectopic (EC) from eutopic (EU) endometrium?
- ★ LRP1 is the top hub gene with the highest diagnostic performance for distinguishing ectopic from eutopic endometrium in EMS finding
- ★ LRP1 expression is significantly elevated in ectopic endometriotic lesions at both mRNA and protein levels finding
- ★ LRP1 positively correlates with M2 macrophage infiltration and has a causal relationship with EMS established by Mendelian randomization finding
- ★ LRP1 is predominantly expressed in fibroblasts and monocytes and orchestrates cell-cell communication via the MIF signaling pathway (CD74/CXCR4 interactions) mechanism
- ★ LRP1 may promote EMS progression by enhancing cell migration, invasion, and proliferation through the MIF signaling pathway mechanism
- ★ An integrated multi-omics + machine learning pipeline (WGCNA, 101 algorithm combinations, ROC, CIBERSORT, MR, scRNA-seq) was used to build an EMS diagnostic model and identify hub genes method
- A diagnostic model/biomarker resource for distinguishing EC from EU endometrium based on hub genes was established resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Microarray transcriptome / DEG analysis (limma) | Ectopic (EC) and eutopic (EU) endometrial tissue, merged GEO datasets GSE7305, GSE11691, GSE25628 | none | differentially expressed genes (|Log2FC|>1.5, p<0.05) | GEO microarray data; affy/RMA, sva, limma R packages |
| WGCNA co-expression network analysis | 28 EC and 26 EU endometrial samples (training dataset) | none | co-expressed gene modules (brown module) | WGCNA R package |
| Machine learning model construction (10 algorithms, 101 combinations) and ROC diagnostic evaluation | GEO training dataset and validation datasets GSE5108, GSE37837 | none | hub gene selection, C-index, AUC, diagnostic accuracy/precision/recall/F1 | IRLS pipeline, pROC, Enet (alpha=0.3) |
| Immune infiltration deconvolution (CIBERSORT) and correlation analysis | Training set EC/EU endometrial tissue | none | proportion of 22 immune cell types and correlation with hub genes (Spearman) | CIBERSORT, linkET R package |
| eQTL / Mendelian randomization (Two-sample MR, IVW) | GWAS summary data for EMS (FinnGen R10) and eQTL exposure data | genetic variants (SNPs) | causal OR between LRP1 and EMS risk | TwoSampleMR R package; GWAS catalog / FinnGen |
| Single-cell RNA sequencing with cell-cell communication analysis (CellChat) and trajectory analysis (Monocle) | GSE179640 scRNA-seq, eight EU and eight EC endometrium tissues | none | cell type annotation, LRP1 expression per cell type, MIF ligand-receptor signaling, fibroblast differentiation trajectory | Seurat 4.0.4, CellChat 2.2.0, Monocle 2.30.1 |
| mRNA and protein expression validation (e.g., qPCR / Western blot / IHC) | Clinical EU and EC tissue samples from 30 EMS patients | none | LRP1 mRNA and protein expression levels | — |
| Functional cell assays with siRNA knockdown (migration, invasion, proliferation) | Human endometrial stromal cells (12Z cell line) | siRNA knockdown of LRP1, MIF, and LRP1+MIF double knockdown vs negative control siRNA | cell migration, invasion, and proliferation | Lipofectamine 3000; Santa Cruz siRNAs; DMEM/F12 |
- – LRP1 exhibited the highest diagnostic performance among 30 hub genes for distinguishing EC from EU
- ▲ LRP1 positively correlates with M2 macrophage infiltration r=0.62, p<0.001
- ▲ Mendelian randomization confirmed a causal relationship between LRP1 and EMS OR=1.35, 95%CI:1.13-1.61, p=0.001
- ▲ LRP1 mRNA expression significantly elevated in ectopic lesions p<0.01
- ▲ LRP1 protein expression significantly elevated in ectopic lesions
- – LRP1 predominantly expressed in fibroblasts and monocytes, mediating MIF signaling via CD74/CXCR4
- – Merging three GEO datasets identified differentially expressed genes and brown-module co-expressed genes 1,404 DEGs; 489 co-expressed genes
- correlation r=0.62, p<0.001 (LRP1 vs M2 macrophage infiltration)
- other OR=1.35, 95%CI:1.13-1.61, p=0.001 (MR causal estimate, LRP1 effect on EMS risk)
- pvalue p<0.01 (elevated LRP1 mRNA in ectopic lesions)
- count 1,404 DEGs (DEGs from merged GSE7305/GSE11691/GSE25628)
- count 489 co-expressed genes (WGCNA brown module overlap)
- count 30 hub genes (hub genes identified by machine learning)
- count 28 EU and 26 EC (training dataset composition (note: text also states 28 EC and 26 EU in WGCNA section))
- count 30 EMS patients (clinical samples for experimental validation)
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 integrated bulk transcriptomics from three merged GEO datasets (28 EU, 26 EC samples) using limma DEG analysis and WGCNA, then applied 101 machine learning algorithm combinations (C-index ranked) to identify LRP1 as a top hub gene. Diagnostic performance was evaluated by ROC/AUC on three independent cohorts; immune infiltration was estimated by CIBERSORT with Spearman correlation; causal inference used Mendelian randomization (IVW primary, four sensitivity methods); and single-cell RNA-seq (n=16 tissues, GSE179640) was analyzed with Seurat/CellChat. Experimental validation was performed in 30 paired clinical tissue samples and the 12Z endometrial stromal cell line.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| limma moderated t-test (empirical Bayes linear model) | Primary DEG analysis EC vs EU in merged training dataset; secondary LRP1-high vs LRP1-low subgroup DEG analysis | 28 EU + 26 EC for primary; 13 vs 13 for LRP1 subgroup | not stated |
| Pearson correlation (WGCNA, TOM matrix) | Weighted gene co-expression network construction across all EC and EU samples | 54 combined samples | not stated |
| 101 machine learning algorithm combinations (Enet alpha=0.3 highlighted; C-index as evaluation metric) | Ranking and selection of 30 hub genes from 489 candidate hub genes | training dataset n=54 | not stated |
| ROC / AUC (pROC package) | Diagnostic performance of hub genes across training dataset, GSE5108, and GSE37837 | training n=54; GSE5108 n=22; GSE37837 n=36 | na |
| Spearman correlation | Association between hub genes (including LRP1) and CIBERSORT-estimated immune cell fractions | EC samples passing CIBERSORT p<0.05 filter (exact n not stated) | not stated |
| Wilcoxon rank-sum test | Expression comparison of 30 candidate genes between EC and EU in validation datasets GSE5108 and GSE37837 | GSE5108 n=22; GSE37837 n=36 | not stated |
| Inverse variance weighted (IVW) Mendelian randomization; sensitivity analyses: MR Egger, simple mode, weighted median, weighted mode | Causal inference: LRP1 expression (eQTL-derived SNPs, F>10, r2<0.001 LD clumping) → EMS risk (FinnGen R10 GWAS) | GWAS sample sizes not stated in available text; SNP count not stated | stated |
| Hypergeometric test (GO and KEGG enrichment via clusterProfiler and DAVID) | Functional enrichment of up/down-regulated DEGs and LRP1-associated DEGs | 1,404 DEGs; 489 brown-module genes | not stated |
| GSEA (permutation-based enrichment) and GSVA (sample-level scoring) | Pathway activity linked to diagnostic hub genes and LRP1 | — | not stated |
| CIBERSORT deconvolution (support vector regression against LM22 signature matrix) | Estimation of 22 immune cell type proportions in training set; samples with permutation p>0.05 excluded | training dataset samples passing p<0.05 filter (exact n not stated) | not stated |
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Multiple Wilcoxon rank-sum tests were applied across 30 candidate genes in validation datasets with a raw p<0.05 threshold and no stated multiplicity correction↳ Could also: Benjamini-Hochberg FDR correction across the 30 simultaneous tests would also be a standard approach for controlling the expected proportion of false discoveries — When testing many genes simultaneously, FDR control distinguishes signal from chance findings; this is particularly informative in a validation context where the goal is to identify which candidate genes replicate reliably
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CIBERSORT with the LM22 signature matrix was used to estimate 22 immune cell type proportions from bulk expression data↳ Could also: Alternative deconvolution tools such as xCell, MCP-counter, EPIC, or TIMER2.0 could also estimate immune infiltration from the same bulk expression data — Each tool uses a different reference panel and algorithmic framework (marker-based enrichment scores vs. constrained regression); concordance across multiple methods can increase confidence that observed immune associations are robust to methodological assumptions
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t-SNE was used for dimensionality reduction and visualization of single-cell clusters in the scRNA-seq analysis↳ Could also: UMAP (Uniform Manifold Approximation and Projection) is another widely used dimensionality reduction method for scRNA-seq visualization and is now the default in Seurat workflows — UMAP tends to better preserve global inter-cluster relationships alongside local structure; reporting visualization under both methods can confirm that cell-type separation is robust to the choice of algorithm
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IVW was used as the primary Mendelian randomization estimator, with MR Egger, weighted median, simple mode, and weighted mode as sensitivity analyses↳ Could also: The weighted median estimator or MR-PRESSO (which detects and removes outlier pleiotropic instruments) could also be positioned as a co-primary estimator rather than a sensitivity-only method — IVW assumes all genetic instruments are valid; the weighted median is consistent when up to 50% of instruments are invalid, and MR-PRESSO explicitly tests for and corrects horizontal pleiotropy; elevating these methods highlights the degree to which the IVW estimate is robust under different pleiotropy scenarios
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LRP1 expression was dichotomized at the median into LRP1-high and LRP1-low groups in 26 EC samples for downstream DEG and enrichment analysis↳ Could also: Treating LRP1 expression as a continuous predictor in a linear model, or using tertile/quartile groupings, could also characterize the relationship between LRP1 level and downstream gene expression patterns — Median dichotomization reduces statistical power and is sensitive to the chosen threshold; continuous or multi-level analyses retain the full information in the expression data and can reveal non-linear dose-response patterns
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Spearman correlation was used to quantify the association between LRP1 and CIBERSORT-derived immune cell fractions (e.g., r=0.62 with M2 macrophages)↳ Could also: Partial Spearman or partial rank correlation controlling for total immune infiltrate (or stromal fraction) could also quantify the gene-immune-cell association while accounting for compositional dependence — CIBERSORT fractions sum to 1 per sample, making individual cell-type estimates compositionally constrained and not independent of one another; partial correlation or regression-based approaches can account for this interdependence when isolating the specific gene-cell-type relationship
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-42064072
Paper: Xie C, Liu Y. LRP1 as a potential diagnostic and immunomodulatory target in endometriosis: evidence from multi-omics and single-cell analyses. Front Immunol 2026. PMID 42064072 · PMCID PMC13124487 · DOI 10.3389/fimmu.2026.1735221.
Code link in brief: https://github.com/Zaoqu-Liu/IRLS — NOTE: this is NOT the
authors' own repo. It is Zaoqu Liu's CRC immune-lncRNA prognostic signature code
(a survival / C-index 101-combination ML framework, ships CRC model-prepare.rda).
The endometriosis authors borrowed the 101-combination ML integration approach from
it. Per brief rule P16 a third-party tool on the paper's data is valid — but this repo
is survival-oriented and ships no endometriosis data, so re-running it verbatim does
NOT yield the paper's diagnostic numbers. Reproduction is therefore done by
re-implementing the clearly-specified, deterministic microarray pipeline steps on
the paper's own public GEO data.
Datasets (all public GEO microarray; downloaded via GEOquery inside the «our HPC» job)
| GSE | role | n (paper) |
|---|---|---|
| GSE7305 | training/DEG | 10 EU + 10 EC |
| GSE11691 | training/DEG | 9 EU + 9 EC |
| GSE25628 | training/DEG | 9 EU + 7 EC |
| GSE5108 | validation | 11 EU + 11 EC |
| GSE37837 | validation | 18 EU + 18 EC |
(EU = eutopic endometrium; EC = ectopic / endometriotic lesion.)
In scope — pipeline-derived, attempted (clear, deterministic)
- C1 — limma DEG count. Combine GSE7305+GSE11691+GSE25628 (map probes→symbol, intersect common genes, ComBat batch-correct), limma EC-vs-EU, |log2FC|>1.5 & p<0.05. Reported: 1404 DEGs (802 up, 602 down).
- C2 — LRP1 is an upregulated DEG in EC vs EU (direction + significance).
- C3 — LRP1 diagnostic AUC on validation cohorts GSE5108 & GSE37837 (single-gene ROC, pROC). Paper's reported AUCs (GSE5108 0.884, GSE37837 0.864) are for the 30-gene model; the single-gene LRP1 AUC is a directly-comparable, deterministic proxy for LRP1's "diagnostic" claim — labelled as such, not presented as identical.
In scope but harder (the ~20% — attempt only if cheap)
- WGCNA brown module (489 co-expressed, 201 hub) — soft power 6.
- IRLS 101-combination ML signature → Enet(α=0.3), C-index 0.904, 30 hub genes.
Out of scope — not pipeline-derived (not attempted)
- CIBERSORT LRP1↔M2 macrophage r=0.62 (depends on full combined matrix + immune decon).
- Mendelian randomization OR=1.35 (separate GWAS/IVW pipeline + GWAS summary stats).
- Single-cell GSE179640 (9 cell types, MIF signaling) — separate Seurat pipeline.
- All wet-lab: qRT-PCR, Western blot, IHC, siRNA invasion/migration assays.
Assessments & scoring basis
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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 molecular core — LRP1 upregulated in EC — reproduces 1:1 on the paper's own public GEO data (logFC +1.034, P=1.09e-10), so the central thesis holds. The deviations are explainable and lie mostly on input/methodology: the DEG count (1404 reported) only matches in magnitude/direction (1765) after reinterpreting the mis-stated |log2FC|>1.5 as FC>1.5, and the exact value is unrecoverable due to underspecified merge/sample selection. The diagnostic AUCs (0.884/0.864) are for a 30-gene model we did not rebuild; single-gene LRP1 fails to generalize (AUC 0.444 vs 0.912 train), which limits but does not refute the diagnostic claim. No fabrication signal — a solid honest partial with deviations attributable to our method choices and paper underspecification.
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Reproduction footprint
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