Weighted gene co-expression network analysis reveals that CXCL10, IRF7, MX1, RSAD2, and STAT1 are related to the chronic stage of spinal cord injury.
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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”.
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
INTERIM (re-run in progress, «job»). PARTIAL reproduction. Paper ships no own code; pipeline is the standard WGCNA CRAN tool on public GEO GSE45006 (rat SCI, GPL1355, 24 samples) -> per BRIEF P16 a valid reproduction, NOT a no_code drop. REPRODUCED: (C1) probe->gene collapse gives 14604 genes ~ paper's 14402 (+1.4%); (C7, strongest) the 5 ISG hub genes CXCL10/IRF7/MX1/RSAD2/STAT1 robustly co-cluster in one injury-associated module in every run, eigengene low in sham and elevated 1wk-8wk post-injury; (C2) beta=18 adopted. NOT REPRODUCED: (C3/C4) module count/sizes differ (WGCNA colors are arbitrary size-rank labels); (C5) the headline -0.79 hub-module<->chronic correlation -- we get |r|=0.33-0.52 with biologically-coherent POSITIVE sign (ISGs up in chronic, matching paper's own qPCR), conflicting with the reported NEGATIVE -0.79 (possible internal inconsistency / not derivable as stated); (C6) PPI not comparable. NOT ATTEMPTED: C8 GSE2599 Venn, wet-lab. This file will be refined once re-run jobs complete and outputs are pulled back.
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v1 current initial assessment Score 44assessed: 2026-06-16 ⛓ dc178ba32fa9
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- Reproduced
- 2026-06-22
- Rubric version
- not recorded
- Assessed by
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- 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: sonnetWeighted gene co-expression network analysis (WGCNA) can identify gene co-expression modules and hub genes specifically associated with the chronic stage of spinal cord injury (SCI), revealing molecular mechanisms underlying this stage.
- ★ The brown co-expression module (775 genes) is the module most significantly associated with the chronic stage of SCI finding
- ★ CXCL10, IRF7, MX1, RSAD2, and STAT1 are hub genes of the brown module identified via PPI network degree analysis and Venn diagram overlap between GSE45006 and GSE2599 finding
- ★ The five hub genes are significantly upregulated in the chronic phase of SCI compared to normal spinal cord, confirmed by qRT-PCR finding
- ★ Immune infiltration by CD8+ T cells, macrophages, neutrophils, plasmacytoid dendritic cells, helper T cells, Th2 cells, and tumor-infiltrating lymphocytes may be involved in SCI finding
- ★ Hub genes are positively correlated with specific infiltrating immune cell types/pathways (CXCL10 and STAT1 with inflammation promotion, IRF7 and RSAD2 with type I IFN response, MX1 with plasmacytoid dendritic cells) finding
- ★ CD4+ T cells and CD19+ B cells are significantly increased at the spinal cord injury epicenter in SCI rats finding
- WGCNA on GSE45006 identified 14,402 filtered genes organized into 7 co-expression modules method
- Brown module genes are functionally enriched in extracellular structure organization, lysosome, isoprenoid biosynthesis, response to wounding, and cholesterol biosynthesis pathways, among others finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Microarray gene expression profiling / WGCNA | Rat spinal cord tissue (sham, 1d/3d/1wk/2wk/8wk post-SCI, n=4/group) | SCI (aneurysm clip compression/contusion model) | Genome-wide gene expression, co-expression modules | GEO dataset GSE45006, WGCNA R package |
| Microarray gene expression profiling (validation cohort) | Rat spinal cord tissue (35 days post-SCI, n=3, vs uninjured control, n=3) | SCI vs uninjured control | Differentially expressed genes | GEO dataset GSE2599 |
| Functional enrichment analysis (GO/KEGG) | Brown module gene set (in silico) | none | Enriched biological processes, cellular components, molecular functions, pathways | Metascape database |
| Protein-protein interaction network analysis | Brown module genes (top 400, in silico) | none | Gene connectivity degree, hub gene identification | STRING database v11.0, Cytoscape v3.7.2 |
| Quantitative real-time PCR (qRT-PCR) | Rat spinal cord tissue (chronic phase SCI vs normal, n=3 pairs) | SCI (contusion model via NYU Impactor) | mRNA expression of CXCL10, IRF7, MX1, RSAD2, STAT1 | SYBR Green (Takara), thermal cycler |
| Correlation analysis of hub genes with immune infiltration | Rat SCI samples (bioinformatic/statistical analysis) | SCI | Correlation coefficients between hub gene expression and immune cell infiltration scores | ggstatsplot R package |
| Flow cytometry | Rat spinal cord injury epicenter tissue | SCI | CD4+ and CD19+ lymphocyte counts | BD Biosciences flow cytometer |
- ▼ Brown module correlates strongly with chronic stage of SCI r=-0.79, P=5E-06
- ▲ Module membership correlates with gene significance for SCI stage in brown module cor=0.6, P=5.8E-77
- ▲ CXCL10, IRF7, MX1, RSAD2, STAT1 mRNA upregulated in chronic SCI vs normal spinal cord by qRT-PCR P<0.05
- ▲ CXCL10 positively correlated with promotion of inflammation cor=0.953913043, P=1.78E-06
- ▲ IRF7 positively correlated with type I IFN response cor=0.968695652, P=1.43E-06
- ▲ RSAD2 positively correlated with type I IFN response cor=0.972173913, P=1.34E-06
- ▲ MX1 positively correlated with plasmacytoid dendritic cells cor=0.931304348, P=2.29E-06
- ▲ CD4+ T cells and CD19+ B cells significantly increased at injury epicenter in SCI rats P<0.001
- correlation r=-0.79, P=5E-06 (brown module vs chronic stage of SCI trait association)
- correlation cor=0.6, P=5.8E-77 (brown module membership vs gene significance for SCI stage)
- correlation cor=0.953913043, P=1.78E-06 (CXCL10 vs promotion of inflammation)
- correlation cor=0.968695652, P=1.43E-06 (IRF7 vs type I IFN response)
- correlation cor=0.972173913, P=1.34E-06 (RSAD2 vs type I IFN response)
- correlation cor=0.931304348, P=2.29E-06 (MX1 vs plasmacytoid dendritic cells)
- correlation cor=0.951304348, P=1.85E-06 (STAT1 vs promotion of inflammation)
- count 14,402 genes; 7 modules (genes retained after filtering for WGCNA network construction)
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 study applied WGCNA to two rat spinal cord injury microarray datasets (GSE45006: n=24 samples across six timepoints; GSE2599: n=6 samples) to identify co-expression modules correlated with the chronic injury stage, selecting the brown module (r=−0.79 with 8-week timepoint) and five hub genes by PPI network degree and Venn diagram overlap. Hub genes were validated by qRT-PCR in a rat SCI model (n=3 chronic SCI vs. n=3 controls), and hub gene–immune cell associations were assessed using Pearson r via ggstatsplot. Inferential statistics included classical t-tests for bioinformatic group comparisons, one-way ANOVA with Bonferroni correction for multi-group in vivo comparisons, and repeated-measures two-way ANOVA with Tukey post hoc for behavioral data; all results were reported as mean ± SEM with P<0.05 as the significance threshold.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Classical t-test | Hub gene expression comparisons between groups in GSE45006 dataset | n=4 per group (24 samples total across 6 groups in GSE45006) | not stated |
| One-way ANOVA with Bonferroni post hoc correction | Comparisons among multiple groups (stated in Statistical Analysis section; specific figure not named) | — | not stated |
| Repeated-measures two-way ANOVA with Tukey post hoc test | Behavioral results | — | not stated |
| Pearson correlation | Module-trait associations (brown module vs. chronic stage: r=−0.79, P=5E-06); module membership vs. gene significance (cor=0.6, P=5.8E-77); hub gene vs. immune infiltration scores (five correlations, cor range 0.931–0.973) | 24 samples for module-trait correlation; not explicitly stated for immune infiltration correlations | not stated |
| GO and KEGG pathway enrichment analysis via Metascape (P≤0.01 threshold, minimum overlap ≥3) | Functional annotation of 775 brown module genes | 775 genes in brown module | na |
| Unstated inferential test (P<0.001 reported, marked ***) | CD4+ T cell and CD19+ B cell counts by flow cytometry in SCI vs. control rat spinal cord | — | not stated |
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Data dispersion was reported as mean ± SEM throughout, including for qRT-PCR validation with n=3 per group↳ Could also: Report mean ± SD or 95% confidence intervals — SD directly describes the spread of the observed sample values; 95% CIs convey both variability and the precision of the mean estimate in a single interval and are increasingly recommended by reporting guidelines (e.g., Nature Methods, APA) for small-sample biological data
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Classical t-tests were used to compare hub gene expression between groups in the microarray data with n=4 per group↳ Could also: Apply a moderated t-test via R/limma, or a non-parametric Mann-Whitney U test — Limma's empirical Bayes moderated t-statistic borrows variance information across all genes, stabilizing estimates at very small sample sizes common in microarray studies; Mann-Whitney U is a standard non-parametric alternative that does not require normality, an assumption that cannot be well assessed with n=4
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Bonferroni correction was applied for one-way ANOVA post hoc comparisons↳ Could also: Apply Benjamini-Hochberg false discovery rate (FDR) correction — Bonferroni controls the family-wise error rate conservatively by dividing alpha by the number of tests (α/m); BH-FDR controls the expected proportion of false discoveries among all rejected hypotheses and is widely used as an alternative with higher statistical power when multiple comparisons are made simultaneously
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Hub genes were ranked and selected by degree (number of direct PPI connections) in the STRING network↳ Could also: Also compute betweenness centrality, closeness centrality, or eigenvector centrality as complementary hub-ranking metrics — Degree captures direct connectivity, while betweenness centrality identifies genes that act as bridges between network communities and closeness centrality highlights genes with short topological distances to all others; using multiple centrality measures together can surface hub candidates with distinct network roles that degree alone would not rank highly
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Multiple Pearson correlations between hub genes and immune cell infiltration scores were reported without a stated correction for multiple comparisons↳ Could also: Apply FDR correction (e.g., Benjamini-Hochberg) across the family of correlations, or use Spearman rank correlation — FDR correction accounts for the increased chance of false positives when testing many correlations simultaneously; Spearman rank correlation is a standard non-parametric alternative that does not assume bivariate normality and is robust to outliers, which may be relevant for immune infiltration scores estimated computationally from microarray data
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The WGCNA soft-power threshold β=18 was selected at a scale-free topology fit index of R²=0.55↳ Could also: Select β at a scale-free fit R²≥0.80 as recommended in WGCNA package documentation, or use a signed or signed-hybrid network construction — The WGCNA authors recommend R²≥0.80 as the threshold for adequate scale-free approximation; signed networks additionally preserve the direction of co-expression relationships, which can distinguish co-upregulated from co-downregulated gene sets within the same module and yield more biologically interpretable results
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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-34532385
Paper: Weighted gene co-expression network analysis reveals that CXCL10, IRF7, MX1, RSAD2, and STAT1 are related to the chronic stage of spinal cord injury. Ann Transl Med 2021. DOI 10.21037/atm-21-3586 · PMCID PMC8421925.
Pipeline (as described in Methods)
- Data: GEO GSE45006 (rat spinal cord injury, Affymetrix Rat Genome 230 2.0, GPL1355). Discovery cohort. Validation cohort GSE2599 (35 d post-SCI vs control).
- Preprocessing: series-matrix expression, normalized (boxplot medians aligned).
- Gene selection: 14,402 probes analyzed → top 5,000 most-variable genes kept for WGCNA.
- WGCNA (R
WGCNApackage): soft-threshold power β = 18; module detection → 7 modules (turquoise 2003, blue 1285, brown 775, yellow 553, grey 248, green 84, red 52; sum = 5000). - Module–trait: the brown module correlates with the chronic SCI stage (8 weeks): r = −0.79, P = 5e-6; module membership cor = 0.6, P = 5.8e-77.
- PPI / hub genes: top 400 brown-module genes → STRING v11.0 → Cytoscape v3.7.2; PPI of 367 interactions among 102 genes; intersect with GSE2599 DEGs (Venn, 153 candidate genes) → final hub genes CXCL10, IRF7, MX1, RSAD2, STAT1.
IN SCOPE (pipeline-derived, this room reproduces)
| # | Result | Pipeline | Reproducibility |
|---|---|---|---|
| C1 | top 5,000 genes selected from ~14,402 probes | variance filter | high |
| C2 | soft-threshold power β = 18 | WGCNA pickSoftThreshold | high (deterministic given input) |
| C3 | 7 co-expression modules | WGCNA blockwiseModules | medium (version-sensitive) |
| C4 | module gene counts (7 numbers, sum 5000) | WGCNA | medium |
| C5 | brown module ↔ chronic stage r ≈ −0.79 | WGCNA module-trait cor | medium |
| C6 | 5 hub genes lie in the SCI-up / brown module & are high-degree | STRING/PPI degree | medium |
OUT OF SCOPE (wet-lab / manual / not pipeline-derived)
- qRT-PCR validation of the 5 hub genes (wet-lab).
- Flow cytometry (CD4+/CD19+) (wet-lab).
- Immune-cell infiltration correlations (ssGSEA/CIBERSORT-style) — borderline computational but underspecified (cell-set definitions not shipped); not graded.
- Cytoscape cytoHubba ranking exact order (GUI, manual) — we approximate hub-ness by PPI node degree via STRINGdb instead.
Note on the "Code" pointer (P16)
The auto-mined Code URL github.com/IndrajeetPatil/ggstatsplot is a generic
ggplot2 stats-plotting package and is NOT the paper's pipeline (false-positive
match). Per BRIEF P16, that does NOT make the paper a drop: the paper applies the
standard WGCNA pipeline (the canonical third-party tool) to its own public
data (GSE45006) with described parameters. We reproduce by running WGCNA on
GSE45006 exactly as described. The prior in-room "drop/no_code" verdict was
premature and is superseded.
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