Developing a novel immune infiltration-associated mitophagy prediction model for amyotrophic lateral sclerosis using bioinformatics strategies.
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.
- ✓Same input data as the authors
- ✓Reported values were directly 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
Described well enough at the DATA layer, partially at the DEG layer, not at the model layer. The brief's 'code' link is the generic R survival package, not authors' code; no authors' repo exists, so per P16 we ran the named standard Bioconductor pipeline (limma) on the paper's own GEO data (GSE112676/GSE112680) on «our HPC». RESULT: sample composition reproduces 1:1 EXACTLY (233+508=741; 164+137=301 with 75 MIM mimics correctly dropped), confirming correct data+grouping. DEG counts reproduce to the same order of magnitude and same up>down direction but ~2x the reported values (10571 vs 5256 for GSE112676; 3025 vs 2379 for GSE112680) using the paper's stated threshold (adj.P.Val<0.05, no logFC) -> graded partial; the discrepancy is best explained by an unstated additional filter in the paper (pre-filter / logFC cutoff / probe collapse / covariate adjustment), NOT by fabrication. All 22 named mitophagy genes are real and present on the array (exact), but only 14/22 are DEGs in our run (partial). NOT ATTEMPTED (hard 20%): the 18-gene LASSO signature + risk score + time-dependent ROC/AUC and CIBERSORT immune infiltration. Survival data (survival_yr+censored) IS present so the Cox/LASSO model is technically feasible, but we deliberately did not chase it because (a) the LASSO gene names in PMC are OCR-garbled/non-existent as printed, (b) LASSO is seed-dependent, (c) the reported training AUC of exactly 1.000 is a strong overfitting/leakage flag raised for the human auditor. RT-qPCR validation is wet-lab (out of scope).
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v1 current initial assessment Score 71assessed: 2026-06-15 ⛓ 7c3764a7df1f
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- Reproduced
- 2026-06-15
- 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
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Deep full-text extraction
Model: opusThe study tests whether mitophagy-related genes associated with immune infiltration can be used to construct a diagnostic and prognostic prediction model for amyotrophic lateral sclerosis (ALS).
- ★ An 18-gene mitophagy-related prognostic signature was identified by machine learning and used to build a prognostic risk score model for ALS. resource
- ★ 22 mitophagy-related differentially expressed genes and 40 prognostic genes were identified in ALS. finding
- ★ Four mitophagy-related immune infiltration genes (BCKDHA, JTB, KYNU, GTF2H5) show expression in ALS mouse models and patients consistent with bioinformatics predictions, supporting prognostic potential. finding
- ★ High-risk and low-risk groups differ in innate and adaptive immune cell infiltration, particularly T lymphocyte subgroups. finding
- Functional enrichment shows involvement of oxidative phosphorylation, unfolded protein response, KRAS, mTOR signaling, and immune-related pathways. mechanism
- A LASSO-Cox regression pipeline combined with univariate Cox analysis was used to derive prognostic feature genes and risk scores. method
- CIBERSORT was used to estimate proportions of 22 immune cell subsets to link the risk model with immune infiltration. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Bulk gene expression microarray (DEG/limma analysis) | Human peripheral blood, GSE112676 (233 ALS, 508 control) and GSE112680 (164 ALS, 137 control) | none (ALS vs control comparison) | differentially expressed genes (logFC, adjusted p-value) | Illumina HumanHT-12 V3.0 and V4.0 expression bead chip arrays |
| CIBERSORT immune cell deconvolution | Human ALS gene expression data (GSE112676) | none (high-risk vs low-risk groups) | proportions of 22 immune cell subsets | — |
| GSEA hallmark enrichment / GO / KEGG enrichment analysis | Human ALS gene expression (GSE112676 high- vs low-risk groups) | none | pathway enrichment scores | MSigDB 50 hallmark gene sets; clusterProfiler |
| LASSO-Cox regression prognostic modeling / ROC analysis | Human ALS cohorts (GSE112676 training, GSE112680 validation) | none | risk score, survival prognosis, AUC values | glmnet, survival, pROC R packages |
| RT-qPCR | Lumbar spinal cord of B6SJL-Tg(SOD1 G93A) ALS mouse model | SOD1 G93A transgene (ALS model) | relative gene expression (2^-ΔΔCt) | FastKing One-Step RT-PCR Kit; GAPDH internal control |
| RT-qPCR | Peripheral blood of 10 ALS patients vs 10 age/sex-matched healthy individuals | none (disease vs control) | relative gene expression (2^-ΔΔCt) | FastKing One-Step RT-PCR Kit; GAPDH internal control |
- – 5256 DEGs identified between ALS and control in training set GSE112676 (2822 up, 2434 down) 5256 genes
- – 34 ALS-associated mitophagy genes obtained from GeneCards; 25 overlapping genes identified after comparison with training set 34 / 25 genes
- ▲ Eight mitophagy genes (ATG12, ATG5, MAP1LC3B, MFN1, OPTN, SRC, TOMM20, TOMM7) upregulated in ALS
- ▼ Four mitophagy genes (CDC37, MFN2, SQSTM1, TOMM40) downregulated in ALS
- – Validation set GSE112680 DEGs: 1300 upregulated, 1079 downregulated 2379 genes
- – 4405 genes identified by Spearman correlation (P<0.05, |r|>0.3) between DEGs and mitophagy genes 4405 genes
- – BCKDHA, JTB, KYNU, GTF2H5 expression consistent between ALS models/patients and bioinformatics analysis
- count 397 ALS patients with survival information (combined ALS samples with follow-up across both datasets)
- count 233 ALS / 508 control (GSE112676 training cohort composition)
- count 164 ALS / 137 control (GSE112680 validation cohort composition)
- other median survival time 2.42 years [IQR 1.59, 3.52] (survival from onset to death/tracheostomy/NIV)
- count 342 (86.15%) dead, 55 (13.85%) survival (status of overall cohort)
- other C9orf72 repeat expansion 12.8% vs 5.2% (GSE112680 vs GSE112676 cohorts)
- mean mean ages 63.92 and 63.58 (ALS and control groups age of onset)
- count 10 ALS patients and 10 matched healthy controls (RT-qPCR validation cohort)
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.
This bioinformatics study analyzed two GEO microarray datasets (GSE112676, n=741; GSE112680, n=301) to identify mitophagy-related differentially expressed genes in ALS. The main workflow used limma-based DEG analysis with Benjamini-Hochberg FDR correction, Spearman correlation for gene filtering, and univariate Cox followed by LASSO-Cox regression to build an 18-gene prognostic risk score model. Log-rank tests compared survival between median-split high- and low-risk groups; immune infiltration was estimated via CIBERSORT and compared across groups using Wilcoxon tests; and four candidate genes were validated by RT-qPCR in an ALS mouse model and 10 ALS patient blood samples.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| limma moderated t-test with Benjamini-Hochberg FDR correction | Differential expression between ALS and CON groups, training set GSE112676 | 741 (233 ALS + 508 CON) | not stated |
| limma moderated t-test with Benjamini-Hochberg FDR correction | Differential expression between high-risk and low-risk ALS groups, GSE112676 | 233 ALS patients | not stated |
| Spearman correlation | Correlation between 25 mitophagy genes and 5256 DEGs to identify mitophagy-related gene set | 741 (GSE112676 samples) | not stated |
| Univariate Cox proportional hazards regression | Identification of prognostic mitophagy-related candidate genes from training set | 233 ALS patients with survival information | not stated |
| LASSO-Cox regression (penalized Cox proportional hazards) | Feature gene selection and risk score model construction | 233 ALS patients (training set GSE112676) | not stated |
| Log-rank test | Survival comparison between high-risk and low-risk groups (training and validation) | 233 ALS training; 164 ALS validation | not stated |
| Wilcoxon rank sum test | Immune cell infiltration proportions and TMB scores between high- and low-risk groups; feature gene expression ALS vs CON in both datasets; checkpoint gene and HLA family gene expression between risk groups | 233 ALS (training); 164 ALS (validation) | not stated |
| Time-dependent ROC / AUC | Prognostic model accuracy at 5, 7, 10 years (training) and 1, 2, 3 years (validation) | 233 ALS (training); 164 ALS (validation) | na |
| GSEA (gene set enrichment analysis) | Hallmark pathway enrichment between high- and low-risk groups using 50 MSigDB hallmark gene sets | 233 ALS (training set) | na |
| Pearson Chi-squared test; Wilcoxon rank sum test | Baseline characteristic comparisons between training and validation cohorts (Table 1) | 397 total (233 training + 164 validation) | not stated |
| 2^-ΔΔCt method (RT-qPCR relative quantification) | Validation of four feature genes in ALS patient blood and SOD1-G93A mouse lumbar spinal cord | 10 ALS patients + 10 matched healthy controls; mouse n not stated | not stated |
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Multiple Wilcoxon tests were applied simultaneously to 22 immune cell subset proportions, a panel of checkpoint genes, and 17 HLA family genes between risk groups, without a stated multiplicity correction↳ Could also: Apply Benjamini-Hochberg FDR correction across the family of simultaneous comparisons within each panel — When many tests are conducted on the same samples in parallel, FDR adjustment would also control the expected proportion of false discoveries; this is standard practice in high-dimensional immune-profiling comparisons and would align these tests with the correction already applied in the DEG analyses
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The median Risk score of the training cohort was used to dichotomize patients into high- and low-risk groups, and this same threshold was applied to the validation cohort↳ Could also: Use a cutpoint derived by cross-validation within the training set (e.g., maximally selected rank statistics or pre-specified quartile) and report the cutpoint value explicitly — A median split on training data optimizes apparent group separation on that set; cross-validated or pre-specified cutpoints would also partition patients while providing an estimate of how well the threshold generalizes, reducing the risk of optimistic apparent separation
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Univariate Cox regression at P<0.05 was used to pre-screen the mitophagy-related gene candidates before entering them into LASSO-Cox↳ Could also: Apply LASSO-Cox regularization directly to all mitophagy-related candidate genes without a univariate pre-filtering step — Univariate pre-filtering can exclude genes whose marginal effects are small but jointly informative; applying penalized regression to the full candidate set would also allow the regularization itself to determine relevance, which is the conventional approach when the candidate set is not excessively large
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LASSO-Cox was the sole machine learning method used for feature selection and risk model construction↳ Could also: Apply elastic net Cox regression (mixing parameter alpha between 0 and 1) or a random survival forest as an alternative or complementary approach — LASSO tends to select one gene from a correlated cluster; elastic net handles correlated predictors differently, and random survival forests could also capture non-linear gene–survival relationships — comparing concordant features across methods is a common approach to increase confidence in selected biomarkers
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Immune cell deconvolution was performed exclusively with CIBERSORT, estimating 22 immune cell subsets↳ Could also: Apply additional deconvolution methods such as xCell, TIMER2.0, or EPIC in parallel and report concordance across methods — Different deconvolution algorithms use different reference gene sets and assumptions; findings that are consistent across multiple methods would also provide stronger evidence for immune infiltration differences than a single tool alone
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The RT-qPCR small-sample validation (n=10 ALS, n=10 controls) reports relative expression via 2^-ΔΔCt for four genes; no formal between-group statistical test or multiplicity correction is explicitly described for this comparison↳ Could also: Apply a Mann-Whitney U test (or paired test exploiting the age/sex matching) to the ΔCt values for each gene, report exact p-values, and apply a Bonferroni or FDR correction across the four genes — Formal hypothesis testing with stated statistics and multiplicity adjustment would also quantify uncertainty in the small-sample validation and make the validation directly comparable to the bioinformatics significance thresholds
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12 downstream papers · 2 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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What was reproduced
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Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
The data layer reproduces 1:1 (sample counts 741 and 301 exact, MIM mimics correctly dropped), confirming correct data and grouping. The DEG counts deviate ~2x (10571 vs 5256; 3025 vs 2379) with direction/order-of-magnitude preserved — most plausibly an unstated filtering step on the authors' side, an under-specification rather than fabrication. The central prognostic model was not reproducible: the 18-gene LASSO names are OCR-garbled and the reported training AUC=1.000 (vs validation 0.63–0.71) is a clear overfit/leakage flag, so the core claim is only weakly supported. Overall a solid-but-deviating reproduction with the main risk concentrated in an unverifiable, over-optimistic model.
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