Introduction
Spinal cord injury (SCI) is characterized by damage to the spinal cord resulting from various causes, which can lead to a complete or partial loss of sensory and/or motor function, significantly impacting the patient’s quality of life (Lee et al. 2019 ). As of 2019, there were approximately 20.6 million SCI patients worldwide, with 900,000 new cases reported that year (Collaborators 2023 ). The incidence of SCI in elderly patients is increasing year by year, mainly due to traumatic events such as falls and traffic accidents (Gebeyehu et al. 2024 ). Neuroinflammatory responses following SCI are critical factors influencing recovery (Lin et al. 2019 ). Current treatment options for SCI include surgical decompression (Badhiwala et al. 2021 ), pharmacological interventions (Zhang et al. 2021 ), and rehabilitative physical therapy (Harvey 2016 ). Natural products represent a vital starting point for the development of antimicrobial drugs, often at a lower cost compared to chemically synthesized alternatives (Seiple et al. 2016 ). Most existing drugs are small molecules derived from natural sources (Decherchi and Cavalli 2020 ). Nevertheless, limited progress has been made in exploring therapeutic strategies involving natural small molecules for SCI.
Apigenin (C 15 H 10 O 5 ), a well-known natural small molecule derived from traditional Chinese medicinal herb (Liu et al. 2011 ; Panieri and Saso 2019 ), is one of the most widely distributed flavones (Pang et al. 2018 ). Compared to other commonly studied phytochemicals, Apigenin stands out not only because of its powerful therapeutic effects demonstrated in preclinical studies, especially as a neuroprotective agent for diseases, including multiple myeloma (Adham et al. 2021 ), non-melanoma skin cancer (Kiraly et al. 2016 ), alveolar or bronchial inflammation (Patil et al. 2015 ), Ischemic stroke (Guo et al. 2014 ), and intervertebral disc degeneration (Ding and Li 2020 ), but also due to its ability to cross the blood-brain barrier to reach the brain and spinal cord (Salehi et al. 2019 ). Therefore, selecting apigenin as a strong candidate drug for treating SCI and investigating its related molecular targets is of great significance.
In this research, we integrated network pharmacology, summary data-based Mendelian randomization (SMR) analysis, transcriptome sequencing, and single-cell RNA sequencing (scRNA-seq) to identify the targets of Apigenin, which were subsequently validated through in vitro experiments. This study provides novel insights into the potential of Apigenin as a drug lead for the treatment of SCI and offers a theoretical framework for the discovery of small molecule therapeutics.
Materials and methods
Source data
In the network pharmacology analyses, target genes associated with (SCI) were identified using data from Genecards (Stelzer et al. 2016 ). Information regarding to Apigenin (MOL000008) was obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (Ru et al. 2014 ). For the eQTL Mendelian randomization analyses, data from population-based studies were sourced from genome-wide association studies (GWAS) (Hemani et al. 2018 ), all of which received ethical approval. Gene expression levels specific to spinal cord tissue were accessed through the Genotype-Tissue Expression (GTEx) project (Consortium 2013 ). Transcriptome sequencing data were downloaded from previous study (accession number GSE174549 ) (Li et al. 2022a , b , c ). Additionally, scRNA-seq data were retrieved from another previous study (accession number GSE213240 ) (Li et al. 2022a , b , c ).
Network pharmacology analyses
Network pharmacology is an analytical strategy used to study drug-disease interactions to identify potential therapeutic targets for disease treatment (Zhao et al. 2023 ). After identifying target genes for SCI and Apigenin, key genes were obtained and visualized using R with the ggplot2 (v3.3.6) and VennDiagram (v1.7.3) packages. A protein-protein interaction network for core genes from the STRING (v12.0) database ( https://string-db.org/ ) was constructed (highest confidence STRING score 0.9), with STRING now also fully available for user-submitted genomes (Szklarczyk et al. 2023 ). Disconnected nodes in the network were hidden, and Cytoscape software (v3.9.1) was utilized for visualization. Enrichment analysis was performed using the DAVID tool ( https://david.ncifcrf.gov/tools.jsp ) (Sherman et al. 2022 ), which provides multidimensional functional annotation, including Gene Ontology (GO) enrichment across biological processes (BP), cellular components (CC), and molecular functions (MF), as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment profiling.
SMR analysis
Additional core genes for SNPs that co-localize with SCI were obtained in the screened key genes, and SMR analysis was performed to test for pleiotropic association between gene expression levels and complex trait of interest (Zhu et al. 2016 ; Wu et al. 2018 ). Cis-expression quantitative trait locus (Cis-eQTL) serve as instrumental variables for the transcript levels of these genes. Cis-eQTL SNPs located within 1 MB of a gene can influence the gene’s transcript levels (Võsa et al. 2021 ). The data for SCI were obtained from GWAS summary datasets, which included 254 individuals with SCI, 215,746 controls, and 16,380,463 SNPs. Data analysis used R v4.2.1, and SMR results were visualized as described in Zhu et al. (Zhu et al. 2016 ). The GTEx Project was supported by the Common Fund of the Office of the Director, NIH, and NINDS. The GTEx Portal was accessed on May 7, 2024. Participant consent and ethical approval were obtained in the original studies.
Transcriptome sequencing analyses
Transcriptome sequencing profiles the expression levels of predicted targets at the tissue level (Zhang et al. 2018 ). In previous study (Li et al. 2022a , b , c ), differentially expressed genes were identified by performing transcriptome sequencing on spinal cord tissues from both sham and SCI rats. Data available at GEO, accession number GSE174549 . Here, we screened for core genes to identify differential expression across samples and conducted statistical analysis and data visualization using GraphPad Prism (v8.0.2) software.
Molecular docking validation
The molecular structure of Apigenin was obtained from the PubChem database (Kim et al. 2023 ). The protein structures of the core proteins were sourced from the Protein Data Bank (Baskaran et al. 2024 ). Using AutoDock tools (v1.5.6), we subjected the molecules to hydrogenation, water removal, charge addition, and identification of the docking pocket. We analyzed the docking mode of Apigenin with the proteins through energy minimization calculations, selecting the optimal docking model. Visualization was conducted using PyMOL (v3.0) software.
scRNA-seq analyses
scRNA-seq has facilitated the deconstruction of transcriptomes from spinal cord tissues at the single-cell level (Xiong et al. 2019 ). Another previous study (Li et al. 2022a , b , c ) identified differentially expressed genes through transcriptome sequencing of spinal cord tissues obtained from both sham and SCI rats. The data is accessible in the GEO under accession number GSE213240 . In this study, we performed cell identification, analyses of single gene expression differences, cell counts, Uniform Manifold Approximation and Projection (UMAP), enrichment analysis, differentiation trajectory assessment, and cell communication analysis. The figures were created using the OmicStudio tools ( https://www.omicstudio.cn/tool ) (Lyu et al. 2023 ).
Cell culture and drug
Apigenin, purchased from MedChemExpress (HY-N1201, purity > 99.5%), was dissolved in Dimethyl sulfoxide. Compared to other SCI in vitro models, the H₂O₂-induced PC12 cell model has advantages in pathological mechanism compatibility and experimental quantitative controllability, and has been adopted by multiple studies (Song et al. 2023 ; Hou et al. 2025 ; Xiao et al. 2023 ). PC12 cells, purchased from the Shanghai Cell Research Center (BFN608006423), were cultured in high-glucose Dulbecco’s Modified Eagle Medium (DMEM, 4.5 g/L glucose), supplemented with 10% fetal bovine serum (FBS, Gibco) and 1% penicillin-streptomycin (100 U/mL penicillin, 100 µg/mL streptomycin, Gibco). Cells were maintained at 37 °C in 5% CO2 atmosphere. Refer to the previously extracted primary mouse neuronal cells (Hao et al. 2025 ), in brief, healthy C57 mice within 24 h of birth were sterilized and euthanized by cervical dislocation under sterile conditions. The hippocampal tissue was aseptically dissected, minced, and digested with 0.125% trypsin for 20 min with gentle shaking 2–3 times during digestion. The resulting cell suspension was allowed to settle for 2 min, filtered through a 200-mesh sieve, and cultured in a 37 °C, 5% CO2 incubator for 4–6 h. Ara-C working solution was then added to inhibit the over-proliferation of non-neuronal cells; after 24 h, it was removed. Cells cultured in vitro for 7 days were used for subsequent experiments. The experiment is divided into three groups: Sham group, H₂O₂ group, and Apigenin + H₂O₂ group. All experimental procedures were approved by the Institutional Animal Care and Use Committee of the Ningxia Medical University Experimental Animal Center. All animals were cared for the care of all accordance was conducted for the with the and use of laboratory Care Use Laboratory.
Cell viability assay
Cell viability was assessed using the CCK-8 assay (Cell Counting Kit-8, New Cell & Molecular Biotech), following the manufacturer’s instructions. PC12 cells were plated in 96-well plates at a seeding density of 8 × 10 3 cells per well in 100 µL complete DMEM and incubated for 24 h to allow for cell attachment. Following attachment, cells were treated with experimental conditions (Apigenin or H 2 O 2 ) for 24 h. To determine cell viability, 10 µL CCK-8 reagent was added directly to each well and the plates were incubated at 37 °C for 30 min. Absorbance at 450 nm was measured using a microplate reader (Bio-Rad).
Hoechst 33342 staining
Hoechst 33342 is a blue fluorescent dye that can penetrate cell membranes and exhibits low toxicity to cells, making it ideal for detecting apoptosis (Wu et al. 2024a , b ; Gu et al. 2023 ). In summary, after fixation, the cells were washed twice with phosphate-buffered saline (PBS). Hoechst 33,342 staining was then conducted according to the manufacturer’s instructions (Beyotime). Finally, the cells were observed under a fluorescence microscope.
Real-time reverse transcription PCR (RT-qPCR)
To evaluate the mRNA expression levels of PTGS2, total RNA was isolated using an RNA extraction kit (Nanjing Vazyme Biotech Co., Ltd). Reverse transcription was performed using a High-Capacity cDNA Reverse Transcription kit (ThermoFisher). PCR amplification was then conducted in a two-step process. The PTGS2 primers.
used were as follows: forward primer CATTGACCAGAGCAGAGAGATG and reverse primer GATGTCATGGTAGAGGGCTTTC. The β-actin primers were: forward primer GAGAGGGAAATCGTGCGTGAC and reverse primer CATCTGCTGGAAGGTGGACA. Relative gene expression from RT-qPCR data was calculated using the 2^ (-ΔΔCt) method and normalized to β-actin.
Immunofluorescence staining
PC12 cells were cultured on coverslips and fixed with 4% paraformaldehyde for 15 min at room temperature. After washing with PBS, cells were permeabilized with 0.1% Triton X-100 for 10 min and subsequently blocked with 5% bovine serum albumin in PBS for 1 h at room temperature. Primary antibodies were incubated overnight at 4 °C, followed by a 1-hour incubation with Alexa Fluor™ 594 (Invitrogen) at room temperature. Coverslips were mounted with DAPI (SouthernBiotech). Fluorescence images were captured using a confocal laser scanning microscope (Leica SP8). Immunofluorescence quantification was performed by measuring fluorescence intensity using ImageJ software (version 1.53c), and the average intensity was compared between groups.
JC-1 staining
The mitochondrial membrane potential assay kit with JC-1 (JC-1) was purchased from Beyotime (C2006). All procedures were carried out according to the manufacturer’s instructions. The culture medium was aspirated, and the cells were washed once with PBS. Then, 1 ml of cell culture medium and 1 ml of JC-1 staining working solution were added and mixed thoroughly. The cells were incubated at 37 °C in a cell culture incubator for 20 min. After incubation, the supernatant was aspirated, and the cells were washed twice with 1X JC-1 staining buffer. Finally, 1 ml of cell culture medium was added, and the cells were observed under a fluorescence microscope.
Western blot
The experimental workflow and conditions according to the manufacturer’s instructions. In a nutshell, protein extraction and quantification were performed, followed by the separation of proteins utilizing 12.5% SDS-PAGE. The proteins were transferred to membranes, then incubated with a PTGS2 antibody (Abmart, T58852 ), p53 (Abmart, TA0879), BAX (Abmart, T40051 ), or β-actin (Abmart, P30002 ) for a duration of 4 ℃ overnight. This was followed by a one-hour incubation with a secondary antibody, after which visualization was conducted.
Statistical analysis
All experiments designs incorporated triplicate biological replicates, with experimental values expressed as mean ± standard error of the mean (SEM). Statistical analysis was performed using GraphPad Prism version 9.5 software (GraphPad Software, San Diego, CA). For comparisons between more than two groups, one-way ANOVA followed by Tukey’s post hoc test was used. P-values less than 0.05 were considered statistically significant.
Ethics statement
This study relied on summary-level data that have been made publicly available; ethical approval was obtained for all original studies.
Results
Identify the potential key genes of apigenin in SCI
To investigate potential key genes associated with Apigenin in SCI, 80 target genes for Apigenin and 6,040 for SCI. Among these, 61 co-expressed genes were recognized as key genes (Fig. 1 A). Utilizing the STRING online database, after excluded 12 genes that did not interact, the PPI plots that illustrated interactions among 49 key genes (Fig. 1 B). GO enrichment analysis of the key genes, conducted using the DAVID database revealed BP terms related to positive regulation of apoptotic process, CC terms related to cyclin-dependent protein kinase holoenzyme complex, and MF terms related to protease binding (Fig. 1 C). The p53 signaling pathway was enriched in KEGG pathway analysis (Fig. 1 D).
Identify the potential core genes of apigenin in SCI
To assess whether the association between SNPs and SCI is mediated by gene expression, as inferred from the relationship between SNPs and eQTLs (Jones et al. 2019 ), we conducted a SMR analysis. This analysis ultimately identified four core genes: PTGS2 (Fig. 2 ), BAX (Supplemental Fig. 1 A), CDK6 (Supplemental Fig. 1 SM1B), and MCL1 (Supplemental Fig. 1 C). GO enrichment analysis for these core genes are displayed (Supplemental Fig. 2 A). Consistent with previous findings, the p53 signaling pathway was enriched in KEGG pathway analysis (Supplemental Fig. 2 B).
PTGS2 as a potential target of apigenin in SCI
Previous studies have demonstrated that Apigenin inhibits PTGS2 expression (Adham et al. 2021 ; Kiraly et al. 2016 ; Patil et al. 2015 ; Ding and Li 2020 ) (Fig. 3 A). To elucidate the differential expression of PTGS2 between the sham and SCI groups, and in conjunction with results from earlier studies (Li et al. 2022a , b , c ), the analysis revealed significantly higher PTGS2 expression in the SCI group compared to the sham group ( P = 0.0039) (Fig. 3 B). The expression trends of BAX (Supplemental Fig. 3 A) and CDK6 (Supplemental Fig. 3 B) were consistent with those of PTGS2. Unfortunately, differential expression of MLC1 was not detected. To identify the action sites of Apigenin on PTGS2, we performed molecular docking simulations. The results indicated that Apigenin binds to PTGS2 (Binding energy: -4.38 kcal/mol) at GLU-69, LYS-41 (Fig. 3 C), and ASP-76. Additionally, ALA-42, GLN-32, and PRO-130 were found to bind Apigenin with BAX (Binding energy: -2.72 kcal/mol) (Supplemental Fig. 3 C), while apigenin did not bind to CDK6. These findings suggest that PTGS2 is a potential target gene of Apigenin in the context of SCI.
scRNA-seq analysis of expression differences between PTGS2-high and PTGS2-low groups
Combined with previous scRNA-seq data for the rat sham and SCI groups (Li et al. 2022a , b , c ), we identified ten distinct cell types (Fig. 4 A), including Astrocyte, Endothelial, Ependymal, Microglia, Monocytes/macrophages, Neurons, Oligodendrocytes, Pericytes, Stem_Cell, and T_cells (Supplemental Fig. 5 A). We then examined the differential expression of PTGS2 across these cell types; the results indicated high PTGS2 expression in neurons (Fig. 4 B). Neurons were sub-grouped into seven clusters (Supplemental Fig. 5 B). Clusters 0, 2, 4, 5, and 6 were classified as PTGS2-high, while the remaining clusters were classified as PTGS2-low for UMAP map construction (Fig. 4 C, D). Consistent with the transcriptomic results mentioned above, PTGS2-high expression was enriched in the SCI group compared to the sham group (Fig. 4 E). Differential genes between PTGS2-high and PTGS2-low cells were identified and subjected to GO enrichment analysis (Supplemental Fig. 5 C). The p53 signaling pathway was enriched in KEGG terms (Fig. 4 F). PTGS2-high and PTGS2-low neuronal subsets indicated a transition PTGS2-high to PTGS2-low (Fig. 4 G and Supplemental Fig. 5 D). Top six genes from the PTGS2-high and PTGS2-low subsets on a pseudotime scale are presented (Supplemental Fig. 5 E). Enriched genes from the p53 signaling pathway on a pseudotime scale demonstrated high expression of Igfbp3 expression in PTGS2-high cells (Fig. 4 H). Cell communication analysis revealed strong receptor-ligand interactions for IGFBP3-TMEM219 and ANGPT2-TEK (Fig. 4 I).
Apigenin mitigates H₂O₂-induced damage and Ptgs2 upregulation
Apigenin exhibited a dose-dependent cytotoxicity at concentrations exceeding 20 µM (Fig. 5 A). Exposure to H₂O₂ alone resulted in a significant reduction in cell viability in a concentration-dependent manner, with a notable decrease observed at 200 µM H₂O₂ (Fig. 5 B). When co-treated with Apigenin (20 µM), a marked improvement in cell viability was observed compared to the H₂O₂-only group, indicating the protective effects of Apigenin against H₂O₂-induced damage (Fig. 5 C). Additionally, we assessed cell injury using Hoechst 33342 staining among different groups (Fig. 5 D). The H₂O₂-induced group showed more positive/high staining compared to the sham group, and Apigenin reversed this phenomenon. At the molecular level, RT-qPCR analysis demonstrated that H₂O₂ significantly upregulated PTGS2 mRNA expression (Fig. 5 E). Apigenin also counteracted the effect of H₂O₂ on PTGS2 expression (1.939 ± 0.1403, P < 0.001). Consistently, immunofluorescence staining revealed reducing PTGS2 expression in H₂O₂-treated cells, which was partially reversed by Apigenin (Fig. 5 F). Quantification of PTGS2 fluorescence intensity confirmed these observations, with Apigenin significantly decreasing PTGS2 levels compared to the H₂O₂-only group (Fig. 5 G). These results demonstrate that Apigenin mitigates H₂O₂-induced damage and PTGS2 upregulation in PC12 cells, exhibiting neuroprotective properties.
To further validate the above conclusions, we extracted and cultured primary neuronal cells and performed JC-1 staining and western blot assays. The results showed that compared to the sham group, H₂O₂ significantly activated JC-1 monomers, while Apigenin treatment reversed this phenotype (Fig. 6 A). At the protein expression level, compared to the sham group, H₂O₂ significantly upregulated PTGS2 ( P = 0.0018), BAX ( P = 0.0008) and p53 ( P = 0.0199) protein expression, whereas Apigenin significantly reduced the H₂O₂-induced upregulation of PTGS2 ( P = 0.0022), BAX ( P = 0.0018) and p53 ( P = 0.0251) protein levels (Fig. 6 B-D) (Supplemental Fig. 4 A-B), consistent with the previous results in PC12 cells.
Discussion
Here, we elucidate the therapeutic effects of Apigenin on SCI and explore its feasibility as a therapeutic agent through an integrative approach that combines network pharmacology, SMR analysis, transcriptome sequencing, scRNA-seq, and in vitro experiments. Our study demonstrated the neuroprotective effects of Apigenin in an SCI model and revealed PTGS2 as a target gene. These findings provide valuable insights into the therapeutic potential of Apigenin for SCI and establish a foundation for future research aimed at developing novel small-molecule therapeutics.
PTGS2, also known as Cox-2, converts arachidonic acid into prostaglandins and plays a role in inducing inflammation (Huang et al. 2020 ). Apigenin can further influence the metabolism of arachidonic acid by regulating the expression of COX and lipoxygenase, thereby reducing the production of pro-inflammatory mediators (Javadi and Sobhani 2024 ; Ghanbari-Movahed et al. 2023 ). These mechanisms give apigenin potential applications in the prevention and treatment of inflammation-related diseases. It may also regulate immunity and inflammation in chronic spontaneous urticaria (Chen et al. 2024 ). Published studies indicate a strong involvement of PTGS2 in pain-mediated processes in the spinal cord (Hoffmann 2000 ). Under physiological conditions, PTGS2 is primarily expressed in neurons (Chen et al. 2014 ), and regulates neuronal functions, including excitability and neurotransmission (Toscano et al. 2008 ). This finding is consistent with our observation of elevated PTGS2 expression in SCI, predominantly in neurons. Increased neuronal apoptosis is associated with SCI (Liu et al. 2015 ). PTGS2 has been found to interact with various apoptosis-related proteins, promoting the occurrence of apoptosis (Wu et al. 2024a , b ). PTGS2 is involved in apoptosis inhibition through PGE2 activation (Kunzmann et al. 2013 ). In vivo experiment, after administering apigenin to animals, a significant decrease in serum PGE2 levels was observed, indicating that apigenin can effectively inhibit prostaglandin synthesis (Attallah et al. 2022 ). Overexpression of PTGS2 can lead to mitochondrial dysfunction, characterized by reduced ATP production and increased ROS levels, which may be one of the key mechanisms of neuronal damage (Ghanbari-Movahed et al. 2023 ). Specifically, PTGS2 disrupts normal mitochondrial function by regulating the permeability of the mitochondrial membrane and affecting calcium ion balance within the mitochondria, thereby causing cellular energy metabolism disorders and oxidative stress responses (Li et al. 2022a , b , c ). Apigenin regulates mitochondrial respiration by inhibiting PTGS2, enhancing the mitochondria’s oxidative phosphorylation capacity, promoting ATP synthesis, and simultaneously reducing ROS production. This has important physiological significance for cellular energy metabolism and oxidative stress response (Ononamadu and Seidel 2024 ). Apigenin reduces glucose uptake, increases ROS levels, and causes loss of mitochondrial membrane potential, accompanied by intracellular ATP depletion and downregulation of complexes I, III, and IV in the mitochondrial electron transport chain, leading to the upregulation of molecules that mediate apoptosis and necroptosis (Lee et al. 2023 ; Li et al. 2023 ). Multiple studies demonstrate that Apigenin treatment reduces PTGS2 expression levels (Adham et al. 2021 ; Kiraly et al. 2016 ; Patil et al. 2015 ; Ding and Li 2020 ). These results suggest that PTGS2 is a core gene associated with Apigenin in SCI, holding significant clinical implications for diagnosis and treatment.
Through an integrative approach, this study not only elucidates the molecular mechanism of apigenin in treating SCI (by targeting and inhibiting PTGS2) but also establishes a new paradigm for the clinical translation of natural drugs. The proposed framework of “multiomics causal inference, cellular validation, and precise localization” offers a replicable solution to the challenges associated with the development of drugs for the repair of central nervous system injuries, demonstrating significant clinical translation value and providing industrial guidance. Further investigations of the upstream regulation of PTGS2 by apigenin and the effects of apigenin on PTGS2 and performance of PTGS2 knockout/overexpression experiments to establish causality are highly important.
Conclusion
Here, we identified PTGS2 as the core gene associated with Apigenin in SCI through multitechnique analysis. Apigenin has neuroprotective properties, effectively mitigating H 2 O 2 -induced cell injury and inhibiting PTGS2 expression in PC12 cells. This study provides a theoretical foundation for the application of Apigenin in SCI and may serve as a guide for clinical translation, facilitating the development of natural small-molecule drugs.
Electronic Supplementary Material
Below is the link to the electronic supplementary material.
Supplementary Material 1 (DOCX 29.4 MB)
Abbreviations
Spinal cord injury
Summary-data-based Mendelian Randomization
Protein-protein interaction
Kyoto Encyclopedia of Genes and Genomes enrichment
Single-nucleotide polymorphism
Genome-wide association studies
Two-sample Mendelian randomization
Real-time Reverse transcription
GO, Gene Ontology
Biological process
Cellular components
Molecular function
Cell Counting Kit-8
Author contributions
Zhang Jialin: Conceptualization, Methodology, Software, Data curation, Visualization, Writing- Original draft preparation. Zhao Min: Conceptualization, Methodology, Validation Investigation, Supervision, Writing- Reviewing and Editing, Funding acquisition.
Funding
The Project Supported by Natural Science Foundation of Ningxia Province (Award Number. 2022AAC03497).
Data availability
The raw data supporting the conclusions of this article will be Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform ( https://www.old.tcmsp-e.com/tcmsp.php ), GWAS ( https://www.gwas.mrcieu.ac.uk/ ), GTEx ( https://www.gtexportal.org/ ), GSE174549 , and GSE213240 .
Ethical approval
This study did not involve human or animal subjects, and thus, no ethical approval was required.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
Supplementary Materials
Supplementary Material 1 (DOCX 29.4 MB)
Data Availability Statement
The raw data supporting the conclusions of this article will be Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform ( https://www.old.tcmsp-e.com/tcmsp.php ), GWAS ( https://www.gwas.mrcieu.ac.uk/ ), GTEx ( https://www.gtexportal.org/ ), GSE174549 , and GSE213240 .