Identification of a novel 10 immune-related genes signature as a prognostic biomarker panel for gastric cancer.
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”.
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DROP (no_code). Data is available and public (GSE62254), but the publication ships no analysis pipeline code; the single linked GitHub 'Code' URL is the generic vioplot plotting package, not the authors' LASSO/Cox immune-gene-signature pipeline. No pinnable reported value can be reproduced from shipped code, so no claims were graded and no «our HPC» compute was run. Not attempted: reconstructing the 10-gene LASSO-Cox signature, risk score, and survival/nomogram validation from Methods text alone (would be a from-scratch reimplementation, not a reproduction of shipped code; explicitly out of scope per the 80/20 + no-fabrication rules). Verdict is provisional and human-auditable: a reviewer who locates an authors' code supplement not surfaced here could re-screen to 'eligible'.
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v1 current initial assessmentassessed: 2026-06-15 ⛓ 75563584f32b
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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
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Deep full-text extraction
Model: opusBecause immune infiltrating cells in the tumor microenvironment correlate with gastric cancer development and progression, can a prognostic signature based on immune-related genes (IRGs) be developed to predict overall survival in gastric cancer patients?
- ★ A 10 immune-related gene signature (BMPR1B, GHR, IL11RA, INHBB, NPR3, OBP2A, PTN, R3HDML, TAC1, TPM2) was constructed via WGCNA combined with LASSO-Cox and predicts overall survival in gastric cancer resource
- ★ The signature effectively predicts 1-, 3-, and 5-year OS and stratifies patients into high- and low-risk groups with worse prognosis in the high-risk group finding
- ★ The signature is an independent prognostic factor in the training and two external validation datasets by multivariate Cox regression finding
- ★ A nomogram combining the signature with clinical information provides strong discrimination (c-index 0.756) for predicting survival resource
- ★ The risk score correlates with multiple immune infiltrating cell types including CD8 T cells, CD4 memory T cells, NK cells, and macrophages mechanism
- GSEA revealed significant pathways enriched between risk groups, including TGF-beta and Wnt signaling pathways finding
- Combining WGCNA and LASSO-Cox on immune-related genes is an effective method to identify candidate prognostic biomarkers method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Microarray gene expression profiling (WGCNA + LASSO-Cox prognostic modeling) | Gastric cancer patient tumors (training dataset GSE62254, n=300) | none | Co-expression modules, risk score, overall survival prediction | Affymetrix Human Genome U133 Plus 2.0 Array (GPL570) |
| Microarray gene expression profiling (signature validation) | Gastric cancer patient tumors (validation dataset I, GSE15459, n=192) | none | Risk score, time-dependent ROC, Kaplan-Meier OS | GPL570 |
| Microarray gene expression profiling (signature validation) | Gastric cancer patient tumors (validation dataset II, GSE84437, n=433) | none | Risk score, time-dependent ROC, Kaplan-Meier OS | GPL6947 |
| RNA-seq differential expression analysis (DESeq2) | TCGA-STAD tumor (n=342) vs normal (n=30) | none | Differentially expressed genes (log2|FC|≥1, p<0.05) | — |
| Immune infiltration estimation (ESTIMATE and CIBERSORTx deconvolution) | Gastric cancer tumors (GSE62254) | none | Immune/stromal scores, immune cell type fractions vs risk score | CIBERSORTx (100 permutations); ESTIMATE |
| Gene set enrichment analysis (GSEA) | Gastric cancer tumors (GSE62254), high vs low risk score groups | none | Enriched KEGG pathways (nominal p<0.01, FDR<25%) | c2.cp.kegg.v6.2 gene set, 1000 permutations |
- – Signature predicted 1-, 3-, 5-year OS in training dataset (GSE62254) AUC 0.681, 0.741, 0.72
- – Signature predicted 1-, 3-, 5-year OS in validation dataset I (GSE15459) AUC 0.57, 0.619, 0.694
- – Signature predicted 1-, 3-, 5-year OS in validation dataset II (GSE84437) AUC 0.559, 0.624, 0.585
- ▼ High risk score group had significantly worse overall survival in training dataset p<0.0001
- ▼ High risk score group had worse OS in validation datasets I and II GSE15459 p=0.0043; GSE84437 p=0.013
- ▲ Risk score was an independent prognostic factor by multivariate Cox in training dataset HR 2.76 (2.13–3.58), p<0.001
- – Nomogram combining signature and clinical features showed strong discrimination c-index 0.7555
- – Five WGCNA modules correlated with OS; 266 prognostic genes identified in yellow module MEyellow r=-0.23, p=7e-05
- correlation r=0.9 (scale-free R2) (WGCNA soft-threshold power 3 selected)
- other c-index 0.7555135 (Nomogram discrimination ability, training dataset)
- fold_change HR 1.4064 (1.1847–1.67), p=9.75E-05 (INHBB multivariate Cox, strongest individual gene)
- pvalue MEyellow r=-0.23, p=7e-05 (Module-OS correlation, yellow module)
- count 87 candidate genes (Intersection of 4383 TCGA-STAD DEGs and 266 survival-related genes)
- count 1211 overlapping IRGs (GSE62254 and TCGA-STAD intersected with ImmPort IRGs for WGCNA)
- other RS HR 2.72 (2.15–3.44), p<0.001 (Univariate Cox of risk score, training dataset)
- other RS HR 1.72 (1.27–2.33), p<0.001 (Multivariate Cox of risk score, validation dataset II)
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 applied a sequential bioinformatics pipeline — WGCNA (Pearson correlation co-expression networks) followed by LASSO-Cox regression — to derive a 10-gene immune-related risk score (RS) from a training cohort of 300 gastric cancer patients (GSE62254), then validated the RS in two independent GEO datasets (GSE15459, n=192; GSE84437, n=433). Predictive performance was quantified via time-dependent ROC curves (AUC at 1, 3, and 5 years) and a bootstrap-validated nomogram (C-index); survival stratification used Kaplan-Meier analysis with log-rank tests, and RS independence was confirmed by univariate and multivariate Cox regression. Immune infiltration was characterized by ESTIMATE scores and CIBERSORTx deconvolution, and pathway enrichment was assessed by GSEA.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Pearson correlation (WGCNA module-eigengene vs. clinical trait) | Correlation of nine module eigengenes with OS, sex, death, and age in GSE62254 to select survival-correlated modules | 300 | not stated |
| Univariate Cox regression | Screening of all IRGs within five survival-correlated WGCNA modules for OS association; 266 genes retained at p<0.05 | 300 | not stated |
| DESeq2 Wald test | Differentially expressed gene analysis between TCGA-STAD cancer and normal samples (|log2FC|≥1, p<0.05) | 372 (cancer n=342, normal n=30) | not stated |
| LASSO-Cox regression | Feature selection reducing 87 candidate genes to the final 10-gene signature | 300 | not stated |
| Multivariate Cox regression | Independent prognostic factor assessment of RS alongside gender, age, and stage in training and both validation datasets | 300 (training), 192 (validation I), 433 (validation II) | not stated |
| Kaplan-Meier / log-rank test | OS comparison between high RS and low RS groups in training and both validation datasets | 300, 192, 433 respectively | not stated |
| Time-dependent ROC (tROC / AUC) | Predictive accuracy for 1-, 3-, and 5-year OS in training and both validation datasets | 300, 192, 433 respectively | na |
| GSEA permutation test (1000 permutations) | Pathway enrichment between high RS and low RS groups in GSE62254; threshold: nominal p<0.01 and FDR<25% | 300 | not stated |
| CIBERSORTx permutation test (100 permutations) | Immune cell-type fraction estimation from GSE62254 bulk expression data | 300 | not stated |
| Bootstrap resampling (1000 iterations) for C-index | Internal validation of nomogram discrimination ability | 300 | not stated |
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RS was dichotomized at the median to create high and low RS groups for Kaplan-Meier and group-level analyses↳ Could also: Continuous RS could be retained as a linear or spline predictor in Cox regression without dichotomization — Treating RS as continuous avoids information loss inherent in median splitting and yields an HR per unit change in RS that is interpretable across the full prognostic range; restricted cubic splines can additionally reveal whether the RS-hazard relationship is linear
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Univariate Cox regression was applied to all IRGs in WGCNA survival-correlated modules (yielding 266 genes at p<0.05) without a reported multiple-testing correction before LASSO input↳ Could also: Benjamini-Hochberg FDR correction could also be applied to the family of univariate Cox p-values at this screening step — With hundreds of simultaneous tests, an FDR adjustment characterizes which associations exceed a pre-specified false-discovery threshold, providing an additional description of the confidence level of genes entering downstream LASSO selection
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LASSO-Cox was used for feature selection followed by a separate multivariate Cox fit to obtain final coefficients↳ Could also: Elastic net Cox regression (combining L1 and L2 penalties) would also perform simultaneous selection and shrinkage in a single model — Elastic net can be more stable than pure LASSO when predictors are correlated — a likely scenario for co-expressed immune-related genes — potentially producing a more reproducible gene panel across independent datasets
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CIBERSORTx was the sole deconvolution method used to estimate immune cell fractions from bulk expression data↳ Could also: TIMER, xCell, EPIC, or MCP-counter would also estimate immune infiltration from microarray or bulk RNA-seq profiles — Comparing estimates across two or more deconvolution algorithms can characterize which immune-infiltration associations are robust to methodological assumptions, since each tool uses different reference matrices and statistical models
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Predictive accuracy was reported as time-point-specific AUC from time-dependent ROC curves (1, 3, and 5 years)↳ Could also: The integrated Brier score or the concordance index (Harrell's C) over the full follow-up would also summarize discriminative and calibration performance — The integrated Brier score simultaneously captures calibration and discrimination across the entire survival curve rather than at fixed horizons, offering a complementary summary of model accuracy
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Nomogram internal validation used a 1000-resample bootstrap C-index within the single training dataset↳ Could also: K-fold cross-validation would also estimate within-dataset generalization error by holding out folds during model fitting — K-fold cross-validation provides a direct estimate of prediction error on unseen data partitions and is sometimes reported alongside bootstrap validation to describe optimism correction from different perspectives
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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.
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Downstream reach in the literature
240 downstream papers · 3 datasetsHow widely the datasets deposited by this paper are reused across the whole literature (Europe PMC), beyond our assessed set. This is a factual dependency map — reusing a public dataset is normal, good science. It is not a judgement on the downstream papers; the only verdict here is this paper's own, with its cited rationale.
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This is a no-code DROP: the public ACRG cohort (GSE62254) resolves, but the publication ships no runnable analysis pipeline — the only linked 'Code' URL is the generic third-party vioplot plotting package, not the authors' LASSO/Cox immune-gene-signature workflow. Consequently none of the substantive reported values (10-gene signature, risk-score coefficients, KM/HR survival validation, nomogram) were reproduced or compared against any output. The blocker sits on the authors'/deposit side (no code shipped) combined with our scope decision not to reimplement from Methods prose; there is no evidence of fabrication — the values are plausibly derivable in principle from the shared data, just unverifiable here. Overall red because zero claims could be confirmed.
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