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Article Open Access

Exploring the role of cytochrome P450 family 1 subfamily B member 1 and quercetin in modulating neuropathic pain after spinal cord injury

  • Authors:
    • Pengyu Zhou
    • Lu Li
    • Yu Cao
    • Jiahao Chen
    • Chuyin Chen
    • Xiangsheng Zhang
    • Jiurong Chen
    • Yingdong Deng
    • Ziqiang Lin
    • Yupei Lai
    • Suo Wang
    • Simin Tang
    • Wenqi Zhang
    • Peng Sun
    • Jun Zhou
  • View Affiliations / Copyright

    Affiliations: Department of Anesthesiology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, Guangdong 510630, P.R. China, Department of Anesthesiology, Shunde Hospital of Guangzhou University of Chinese Medicine, Foshan, Guangdong 528300, P.R. China, Department of Anesthesiology, Hainan General Hospital (Affiliated Hospital of Hainan Medical University), Haikou, Hainan 570311, P.R. China, Department of Anesthesiology, Sun Yat‑Sen University Cancer Center, Guangzhou, Guangdong 510060, P.R. China
    Copyright: © Zhou et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 7
    |
    Published online on: October 15, 2025
       https://doi.org/10.3892/mmr.2025.13717
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Abstract

Spinal cord injury (SCI) represents a notable global health challenge, with neuropathic pain (NP) being a common complication that intensifies patient suffering. Existing research tends to overlook the temporal aspects of NP and fails to offer targeted treatment options. To tackle this issue, the present study initially examined genome‑wide association study summaries related to NP, incorporating expression quantitative trait locus (eQTL) from blood samples through summary‑based Mendelian randomization. This allowed the investigation of the association between NP and eQTL, facilitating the identification of genes linked to the risk of NP. Following this, weighted gene co‑expression network analysis of a Gene Expression Omnibus dataset was utilized to identify SCI‑related module genes, resulting in the detection of 218 shared genes across these analyses. Subsequent functional enrichment assessments, protein‑protein interaction evaluations and machine learning technique analyses, including least absolute shrinkage and selection operator regression, random forest and support vector machine recursive feature elimination analyses, highlighted three central genes: Glycerol‑3‑phosphate dehydrogenase 1‑like, epoxide hydrolase 2 and cytochrome P450 family 1 subfamily B member 1 (CYP1B1). Additionally, network pharmacology and molecular docking analyses confirmed CYP1B1 as a viable therapeutic target. A analysis of single‑cell RNA sequencing datasets demonstrated an increase in CYP1B1 expression within spinal cord fibroblasts following SCI. Furthermore, quercetin (Que) was shown to inhibit CYP1B1 expression and reduce NP (based on mechanical paw withdrawal threshold and thermal paw withdrawal latency) in murine models. The results of the present study highlight the important role of spinal cord fibroblast CYP1B1 as a notable contributor to NP following SCI and suggest that Que may serve as a promising mechanism‑based therapeutic option.
View Figures

Figure 1

Research workflow. CYP1B1, cytochrome
P450 family 1 subfamily B member 1; eQTL, expression-quantitative
trait loci; HEIDI, heterogeneity in dependent instruments; NP,
neuropathic pain; PPI, protein-protein interaction; Que, quercetin;
SCI, spinal cord injury; SMR, summary-based Mendelian
randomization; WGCNA, weighted gene co-expression network
analysis.

Figure 2

(A) Volcano plot showing 610 genes
associated with trigeminal neuralgia. (B) Volcano plot showing 619
genes associated with post-herpetic neuralgia. Genes positively
correlated with the disease are marked in red and genes negatively
correlated with the disease are marked in blue. (C) Co-expression
modules of genes represented by different colors under the gene
tree. (D) The scale-free topology model fit (y-axis, signed
R2) against different soft threshold (power) values
(x-axis). (E) The mean connectivity (average number of connections
per gene, y-axis) vs. the soft threshold (power) (x-axis). (F)
Heatmap of module-trait relationships in SCI, with each unit of a
different module containing the corresponding correlation and
P-value. (G) Green Module: Correlation between module membership
and gene significance for treatment. (H) Pink module: Correlation
between module membership and gene significance for treatment. (I)
Light green module: Correlation between module membership and gene
significance for treatment. SCI, spinal cord injury; SHAM, sham
surgery group; SMR, summary-based Mendelian randomization; SNP,
short nucleotide polymorphism.

Figure 3

(A) Venn diagram obtained by crossing
NP-related candidate genes with SCI key module genes, with a total
of 218 common genes. (B) Upset plot depicting the intersection of
the top 45 genes produced by each of the seven algorithms to get 21
genes. (C) Results of GO and KEGG enrichment analysis of 218 common
genes. Different colors represent the classification of different
enrichment pathways, including KEGG, and BP, CC and MF in GO, with
P<0.05 for all enriched pathways, and the number of each bar
cell represents the Count. (D) Protein-protein interaction network
diagram of the 21 core genes (there are only 20 gene interactions
in the image as the gene ALOX5AP cannot be identified by the SRING
database). (E) Proportions of 28 immune cell types in different
samples are shown in the bar graph. (F) Comparison of the
proportions of 28 immune cell types in SCI and control groups.
*P<0.05, **P<0.01, ***P<0.001 and ****P<0.0001 vs.
control. BP, biological processes; CC, cellular component; GO, Gene
Ontology; KEGG, Kyoto Encylopedia of Genes and Genomes; MF,
molecular function; NP, neuropathic pain; SCI, spinal cord injury;
TME, tumor microenvironment; MCC, maximal clique centrality; MNC,
measurement coefficient network; EPC, embedding pathway
components.

Figure 4

(A) Screening of diagnostic marker
genes in the LASSO model. This panel shows the cross-validated
mean-squared error plotted against Log(λ). The number of genes
corresponding to the lowest point of the curve (n=10) was most
suitable for the diagnosis of SCI. (B) This panel displays the
number of features (variables) retained in the model at different
values of Log(λ). (C) Characteristic gene selection using the
SVM-RFE technique. A total of 11 characteristic genes were
identified from the 21 core genes. (D) The 15 trait genes were
ranked according to the importance score. (E) Venn diagram of
diagnostic candidate genes identified by three machine algorithms.
(F) Predicted nomograms of candidate genes for SCI diagnosis. To
use it, locate the value for each biomarker (GPD1L, EPHX2, CYP1B1)
on its respective scale and draw a line upward to the ‘Points’ axis
to determine the score for each variable. Sum all the points to get
the ‘Total points’. (G) ROC curve of CYP1B1 in GSE151371 with an
AUC value of 0.953. (H) ROC curve of EPHX2 in GSE151371 with an AUC
value of 0.989. (I) ROC curve of GPD1L in GSE151371 with an AUC
value of 0.957. (J) Visualization of SCI diagnostic candidate genes
based on CTD pain inference scores. AUC, area under the curve; CTD,
Comparative Toxicogenomics Database; CYP1B1, cytochrome P450 family
1 subfamily B member 1; EPHX2, epoxide hydrolase 2; GPD1L,
glycerol-3-phosphate dehydrogenase 1-like; LASSO, least absolute
shrinkage and selection operator; RMSE, root mean square error;
ROC, receiver operating characteristic; SCI, spinal cord injury;
SVM-RFE, support vector machine recursive feature elimination.

Figure 5

(A) MR association between CYP1B1
gene expression and trigeminal neuralgia in blood. All SNPs
available in GWAS and eQTL data are shown in. (A) Grey dots
represent the P-values of SNPs for GWAS, and diamonds represent
P-values of probes for SMR tests. (B) P-values for eQTL analyses of
CYP1B1 SNPs from GWAS (used in the heterogeneity in dependent
instruments test) are plotted against effect sizes of SNPs from
eQTL studies. The orange dashed line indicates the effect size
estimate for the MR association at the top cis-eQTL. The error line
is the standard error of the SNP effect. (C) Chemical structure of
Que. (D) Interaction of the CYP1B1 receptor with Que ligands. The
protein is depicted as a blue cartoon (α-helices and β-sheets) with
its molecular surface shown in transparent gray. Que is shown as
red sticks. (E) Detailed view of the local interactions within the
Que-CYP1B1 binding pocket Protein residues are shown as blue
sticks. The ligand, Que, is shown as red sticks. (F) Venn diagram
of potential targets of Que and 21 core genes. (G) Rg of the
Que-CYP1B1 complex. (H) RMSD of the Que-CYP1B1 complex. (I) RMSF of
CYP1B1 protein residues. (J) Solvent accessible surface of the
Que-CYP1B1 complex. (K) Free energy landscape of the Que-CYP1B1
complex. CYP1B1, cytochrome P450 family 1 subfamily B member 1;
eQTL, expression-quantitative trait loci; GWAS, genome wide
association study; MR, Mendelian randomization; PC, principal
component; Que, quercetin; Rg, radius of gyration; RMSD, root mean
square deviation; RMSF, root mean square fluctuation; SMR,
summary-based Mendelian randomization.

Figure 6

(A) A clustering heat map of the
samples with the optimal clustering number set to k=2. (B) CDF
curve for various values of k, constrained within the minimum range
of the consensus index. (C) Principal component analysis graphs
with the optimal clustering number set to k=2. (D) Differential
expression of CYP1B1 across two distinct clusters. (E) CYP1B1
expression in the GSE171441 dataset. (F) CYP1B1 expression in the
GSE236754 dataset. (G) A total of 13 cell populations were
identified in the single-cell sequencing data from the GSE162610
dataset. (H) Proportion of the 13 cell populations in the two
groups. (I) CYP1B1 expression in 13 cell populations. (J)
Comparison of changes in CYP1B1 levels in fibroblasts of two
groups. *P<0.05, **P<0.01, ***P<0.001 and ****P<0.0001.
CDF, cumulative distribution function; CYP1B1, cytochrome P450
family 1 subfamily B member 1; NK, natural killer; OPC,
oligodendrocyte precursor cells; SCI, spinal cord injury; SHAM,
sham surgery group; umap, uniform manifold approximation and
projection; Y1, Principal Component 1; Y2, Principal Component
2.

Figure 7

(A) Flow chart of the animal
experiment. (B) MWT measurements for the SHAM, SCI and SCI + Que
groups were recorded on day 14. (C) TWL for the SHAM, SCI and SCI +
Que groups was also measured on day 14. (D) BMS scores for the
SHAM, SCI and SCI + Que groups were assessed at baseline and on
postoperative days 1, 3, 5, 7, 10 and 14. A statistically
significant difference was observed (***P<0.001) between the SCI
and SHAM groups of mice (n=6 each). Additionally, a significant
difference was noted between the SCI and SCI + Que groups
(*P<0.05), with n=6. (E) RT-qPCR assessment of CYP1B1 mRNA
levels in spinal cord tissues from SHAM, SCI and SCI + Que groups,
with expression levels normalized to GAPDH. (F) Semi-quantitative
analysis of CYP1B1 protein levels in spinal cord tissues from the
SHAM, SCI and SCI + Que groups, with fold-changes normalized to
GAPDH. (G) Western blot analysis was conducted to assess CYP1B1
expression in spinal cord tissues from the SHAM, SCI and SCI + Que
groups on day 14 post-modeling. (H) Immunofluorescence analysis was
performed to assess the expression levels of CYP1B1 and the
fibroblast marker PDGF-D in spinal cord tissues from the SHAM, SCI
and SCI + Que groups, with n=6. Scale bar, 50 µm. (I) Quantitative
fluorescence analysis of CYP1B1 expression was conducted on spinal
cord tissues from the SHAM, SCI and SCI + Que groups. (J) RT-qPCR
evaluation of CYP1B1 mRNA expression in fibroblasts treated under
control conditions, with 10 ng/ml TGF-β and with 10 ng/ml TGF-β +
10 µmol/ml Que, with mRNA levels normalized to GAPDH. (K) Western
blot analysis was performed to evaluate CYP1B1 expression in
fibroblasts subjected to control conditions, 10 ng/ml TGF-β
stimulation or co-treatment with 10 ng/ml TGF-β and 10 µmol/ml Que.
(L) Semi-quantitative evaluation of CYP1B1 protein expression in
fibroblasts subjected to control conditions, treated with 10 ng/ml
TGF-β or treated with 10 ng/ml TGF-β combined with 10 µmol/ml Que,
with protein levels normalized to GAPDH. (M) A semi-quantitative
analysis was conducted on the Fn protein levels in fibroblasts
subjected to control conditions, 10 ng/ml TGF-β treatment or
treatment with a combination of 10 ng/ml TGF-β and 10 µmol/ml Que,
with the resulting fold-changes normalized against GAPDH. (N)
Western blot analysis was utilized to investigate Fn expression in
fibroblasts under control conditions, after treatment with 10 ng/ml
TGF-β or after co-treatment with 10 ng/ml TGF-β and 10 µmol/ml Que.
The data are presented as the mean ± SEM, with sample sizes of n=6
per group for (B, C, D, E, F, G, H and I), n=3 per group for (J)
and n=4 per group for (K, L, M and N). *P<0.05, **P<0.01,
***P<0.001 and ****P<0.0001. BMS, Basso Mouse Scale; Con,
control; CYP1B1, cytochrome P450 family 1 subfamily B member 1;
PDGF-D, platelet-derived growth factor D; dpi, days post-injury;
Fn, Fibronectin; MWT, mechanical withdrawal threshold; Que,
quercetin; RT-qPCR, reverse transcription-quantitative PCR; SCI,
spinal cord injury; SHAM, sham surgery group; TWL, thermal
withdrawal latency.

Figure 8

Mechanism diagram. CYP1B1, cytochrome
P450 family 1 subfamily B member 1; SCI, spinal cord injury.
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Copy and paste a formatted citation
Spandidos Publications style
Zhou P, Li L, Cao Y, Chen J, Chen C, Zhang X, Chen J, Deng Y, Lin Z, Lai Y, Lai Y, et al: Exploring the role of cytochrome P450 family 1 subfamily B member 1 and quercetin in modulating neuropathic pain after spinal cord injury. Mol Med Rep 33: 7, 2026.
APA
Zhou, P., Li, L., Cao, Y., Chen, J., Chen, C., Zhang, X. ... Zhou, J. (2026). Exploring the role of cytochrome P450 family 1 subfamily B member 1 and quercetin in modulating neuropathic pain after spinal cord injury. Molecular Medicine Reports, 33, 7. https://doi.org/10.3892/mmr.2025.13717
MLA
Zhou, P., Li, L., Cao, Y., Chen, J., Chen, C., Zhang, X., Chen, J., Deng, Y., Lin, Z., Lai, Y., Wang, S., Tang, S., Zhang, W., Sun, P., Zhou, J."Exploring the role of cytochrome P450 family 1 subfamily B member 1 and quercetin in modulating neuropathic pain after spinal cord injury". Molecular Medicine Reports 33.1 (2026): 7.
Chicago
Zhou, P., Li, L., Cao, Y., Chen, J., Chen, C., Zhang, X., Chen, J., Deng, Y., Lin, Z., Lai, Y., Wang, S., Tang, S., Zhang, W., Sun, P., Zhou, J."Exploring the role of cytochrome P450 family 1 subfamily B member 1 and quercetin in modulating neuropathic pain after spinal cord injury". Molecular Medicine Reports 33, no. 1 (2026): 7. https://doi.org/10.3892/mmr.2025.13717
Copy and paste a formatted citation
x
Spandidos Publications style
Zhou P, Li L, Cao Y, Chen J, Chen C, Zhang X, Chen J, Deng Y, Lin Z, Lai Y, Lai Y, et al: Exploring the role of cytochrome P450 family 1 subfamily B member 1 and quercetin in modulating neuropathic pain after spinal cord injury. Mol Med Rep 33: 7, 2026.
APA
Zhou, P., Li, L., Cao, Y., Chen, J., Chen, C., Zhang, X. ... Zhou, J. (2026). Exploring the role of cytochrome P450 family 1 subfamily B member 1 and quercetin in modulating neuropathic pain after spinal cord injury. Molecular Medicine Reports, 33, 7. https://doi.org/10.3892/mmr.2025.13717
MLA
Zhou, P., Li, L., Cao, Y., Chen, J., Chen, C., Zhang, X., Chen, J., Deng, Y., Lin, Z., Lai, Y., Wang, S., Tang, S., Zhang, W., Sun, P., Zhou, J."Exploring the role of cytochrome P450 family 1 subfamily B member 1 and quercetin in modulating neuropathic pain after spinal cord injury". Molecular Medicine Reports 33.1 (2026): 7.
Chicago
Zhou, P., Li, L., Cao, Y., Chen, J., Chen, C., Zhang, X., Chen, J., Deng, Y., Lin, Z., Lai, Y., Wang, S., Tang, S., Zhang, W., Sun, P., Zhou, J."Exploring the role of cytochrome P450 family 1 subfamily B member 1 and quercetin in modulating neuropathic pain after spinal cord injury". Molecular Medicine Reports 33, no. 1 (2026): 7. https://doi.org/10.3892/mmr.2025.13717
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