Research Article

Computational Identification of Purine Nucleoside Phosphorylase and Estrogen Receptor 1 as Biomarkers of Acetaminophen-induced Liver Injury

DOI:

10.3791/69887

⸱

February 6th, 2026

 , 
1School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, 2College of Integrative Chinese and Western Medicine, Tianjin University of Traditional Chinese Medicine

In This Article

Summary

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We computationally analyze network toxicology and transcriptomic data to identify biomarkers of acetaminophen-induced liver injury, revealing estrogen receptor 1 and purine nucleoside phosphorylase as diagnostic targets.

Abstract

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Acetaminophen-induced liver injury is a significant public health concern, yet reliable early biomarkers are lacking. This study aimed to identify candidate biomarkers for acetaminophen-induced hepatotoxicity using a computational approach integrating network toxicology, transcriptomics, and machine learning. Potential acetaminophen targets were predicted using online platforms, yielding 140 candidates. Hepatotoxicity-related genes (n = 657) were retrieved from GeneCards, and 38 overlapping genes were identified. Differentially expressed genes from the GSE74000 dataset (n = 1,978) were analyzed. Functional enrichment was performed to identify relevant pathways. A random forest model prioritized 20 feature genes, and molecular docking evaluated binding affinities with acetaminophen. DEGs were primarily associated with mitochondrial dysfunction and ribosome biogenesis. Functional enrichment highlighted xenobiotic metabolism and oxidative stress pathways. Estrogen receptor 1 and purine nucleoside phosphorylase were top-ranked feature genes, showing significant expression differences and strong docking interactions with acetaminophen. This computational protocol systematically predicts candidate biomarkers for acetaminophen-induced liver injury, providing molecular insights and candidates for experimental validation.

Introduction

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Acetaminophen (APAP) is a widely administered antipyretic and pain reliever, but excessive intake can lead to severe hepatotoxicity and potentially liver failure1,2. The damage to the liver is anticipated to occur once the drug is metabolized, and the subsequent toxic metabolite is N-acetyl-p-benzoquinone imine (NAPQI), which results in mitochondrial oxidative stress, altered mitochondrial respiration, and the mitochondrial permeability transition, eventually causing necrotic, rather than apoptotic, hepatocyte death3,4. APAP overdose remains the leading cause of acute liver failure (ALF) in many countries. Epidemiological data show that APAP toxicity accounts for nearly 50% of ALF cases in the United States and is responsible for thousands of emergency admissions annually. Similar trends have been reported globally, with APAP-associated hepatotoxicity constituting a major proportion of drug-induced liver injury (DILI) cases5,6. The high morbidity, risk of liver transplantation, and substantial healthcare burden highlight the urgent need for improved diagnostic biomarkers and a mechanistic understanding of APAP-induced liver damage. Specifically, APAP is absorbed and mainly metabolized in the liver through glucuronidation and sulfation, forming water-soluble conjugates that are excreted in urine. A minor fraction (10-15%) undergoes Cytochrome P450 (CYP450)-mediated hydroxylation (via enzymes like CYP1A2, CYP2E1, and CYP3A4) to produce the reactive intermediate NAPQI, which binds to glutathione and is excreted via bile7,8. Due to the limited capacity of glucuronidation and sulfation pathways, excessive dosing leads to increased NAPQI formation, while concurrently depleting hepatic glutathione reserves9. Once glutathione is depleted to critical levels, NAPQI begins to bind covalently to cellular macromolecules, damaging hepatocytes10. APAP overdose-related toxicity is rising, with an estimated 50,000-80,000 emergency department visits annually in the United States alone11. Since oxidative stress and mitochondrial dysfunction are central to APAP-induced hepatotoxicity, dysregulation of ESR1 may indirectly influence hepatocyte survival and inflammatory responses during APAP injury. APAP toxicity induces metabolic stress and inflammatory activation in hepatocytes. Changes in PNP expression may reflect altered nucleotide metabolism and immune-related responses during APAP-induced liver damage.

Early reports on APAP-induced hepatotoxicity primarily focused on clinical symptoms and pathological changes in the liver and kidneys, providing the foundation for later studies on the mechanisms of cell death and inflammation in APAP toxicity12,13. A key breakthrough was the identification of serum aminotransferases (e.g., ALT and AST) as liver injury biomarkers. With the advent of cell culture models and bioinformatics tools, current research on APAP hepatotoxicity has expanded to include diagnostic, prognostic, and mechanistic biomarkers14,15,16. However, obtaining sufficient serum or plasma samples from APAP overdose patients, especially non-survivors, remains challenging17. As a result, there is an urgent need to explore new diagnostic, prognostic, and mechanistic biomarkers using novel approaches to better study APAP-induced hepatotoxicity.

Network toxicology is an emerging field that constructs network models to analyze toxicological data from compound databases18,19. It helps characterize substance toxicity, uncover mechanisms, and predict key targets20,21. Currently, network toxicology is widely recognized as a valuable tool in scientific research. For example, Tengjiao Qu et al. integrated network toxicology with transcriptomics to identify new neurotoxic mechanisms of 2,2',4,4'-tetrabromodiphenyl ether (a common flame retardant) and its potential biomarkers22. Recent studies also highlight the importance of this approach in assessing the toxicity of natural products23. However, this innovative research paradigm has yet to be applied to explore the potential mechanisms of APAP-induced hepatotoxicity and to identify new biomarkers associated with its toxicity.

IntelliGenes was developed as a machine-learning pipeline for multi-genomic biomarker discovery24. It integrates statistical methods with advanced ML to compute I-Gene scores and create individualized biomarker profiles. Results show improved accuracy in predicting complex traits and enabling personalized disease insights. Limitations include dependence on data quality, limited validation across diverse populations, and the need for broader clinical testing.

The highlight is that the current pancreatic ductal adenocarcinoma (PDAC) biomarkers are insufficient, and to propose patient-derived organoids (PDOs) as functional platforms for biomarker development25. It evaluates PDO-based drug phenotyping, chemoresistance modeling, co-culture systems, and proteomic/metabolomic profiling. Findings show PDOs better capture treatment-relevant biology, but limitations include technical variability, limited microenvironment replication, long culture times, and challenges in routine clinical integration26,27.

The purpose of the study was to assess analytical proteomic methods and the function of bioinformatics in the identification of biomarkers28. It evaluated how computational tools aided in data interpretation and examined MS-based protein analysis techniques. The results demonstrated enhanced biomarker discovery and new treatment targets, but also made it clear that identifying very low-abundance proteins remained challenging, thereby reducing the sensitivity of existing proteomic techniques. Although several diagnostic, mechanistic, and prognostic biomarkers for APAP-induced hepatotoxicity have been well established, these markers primarily reflect hepatocellular injury or mitochondrial dysfunction after toxicity has occurred29,30. In contrast, the present study aimed to identify transcriptomic regulators that may modulate susceptibility or host response to APAP exposure. ESR1 and PNP were therefore investigated as exploratory regulatory markers rather than classical diagnostic biomarkers.

To address the limitations inherent in current methodologies -- specifically the culture variability of PDOs and the sensitivity issues of proteomics -- this study employs an integrated computational framework comprising network toxicology, transcriptomics, machine learning, and molecular docking. This multi-tiered strategy circumvents the need for large cohorts or complex experimental systems by leveraging computational prediction and multi-omic cross-validation. The rationale for this combinatorial approach is that each technique reinforces the others to minimize false positives: network toxicology first provides a system-level prediction of APAP-related targets and toxic pathways; transcriptomic analysis subsequently validates which of these predicted genes are genuinely dysregulated during hepatotoxicity; machine-learning algorithms then prioritize the most informative candidate biomarkers from this high-confidence pool; and finally, molecular docking offers mechanistic support by confirming the feasibility of direct interactions between APAP and the identified protein targets.

Together, these steps create a coherent, evidence-based pipeline for biomarker prediction. This combination of these four strategies minimizes false positive predictions and enhances confidence in finding ESR1 and PNP as strong targets in APAP-induced hepatotoxicity. In this research, it would be necessary to have GSE74000 gene-expression datasets that have a good statistical power, at least a minimum of three biological replicates of any condition with full metadata. The raw expression files would have to be processed in a standard manner, which consists of background correcting, normalization (quantile normalization, etc.), and filtering out low-expressed genes. R was used to perform all analyses with packages such as limma and external tools such as GeneCards and Cytoscape, which allow building a network. Although the pipeline facilitates biomarker prioritization based on systematic analysis, the results are limited by the quality of the dataset, batch effects, and the absence of experimental and in vivo validation, which could affect generalizability.

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Protocol

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This protocol outlines a computational method to define the possible biomarkers of acetaminophen-induced liver damage, which takes advantage of network toxicology, transcriptomics, machine learning, and molecular docking (Figure 1). The protocol is aimed at researchers who have access to bioinformatics tools, transcriptomic datasets, and molecular docking software.

Procedure

Step 1: Identification of acetaminophen targets
Retrieve the SMILES representation of acetaminophen (APAP) from PubChem. Use online platforms (ChEMBL, SwissTargetPrediction, STITCH, SEA) to predict potential molecular targets of APAP. Integrate and deduplicate the predicted targets to generate a list of 140 high-confidence APAP targets.

Step 2: Identification of Hepatotoxicity Targets
Retrieve hepatotoxicity-related genes from the GeneCards database. Compile and deduplicate the list to generate a non-redundant set of 657 hepatotoxicity-related genes. Identify overlapping genes between APAP targets and hepatotoxicity-related genes using a Venn diagram.

Step 3: Transcriptomic Data Preprocessing
Download the GSE74000 dataset from GEO. Preprocess raw expression data using DESeq2: remove low-expression genes, normalize using size factors, and apply variance-stabilizing transformation (VST). Perform differential expression analysis using Limma and DESeq2, with thresholds of adjusted p-value < 0.05 and |log2FC| > 1.

Step 4: Functional Enrichment Analysis
Upload overlapping genes to STRING for Gene Ontology (GO), KEGG pathway, tissue expression, and disease correlation analyses. Visualize functional enrichment results using bubble plots and heatmaps.

Step 5: Machine Learning for Feature Gene Selection
Apply a Random Forest classifier (n_estimators=500, max_depth=10) to prioritize feature genes from the overlapping APAP and hepatotoxicity genes. Evaluate model performance using Out-of-Bag (OOB) error and feature importance scores. Select the top 20 feature genes for further analysis.

Step 6: Molecular Docking
Retrieve APAP structure (CID 1983) from PubChem and protein receptors (ESR1: PDB ID 1SJ0, PNP: PDB ID 1V2H) from PDB. Prepare ligand and receptor files: convert to PDB format, add polar hydrogens, assign charges, and save as PDBQT files. Define the docking grid in AutoDock Tools, covering the active site of the protein. Perform molecular docking using AutoDock Vina, with exhaustiveness set to 8, and analyze binding affinities and interactions. Visualize docking results using PyMOL to analyze binding conformations and key interactions.

Step 7: Statistical Analysis
Determine statistical significance using t-tests and adjust p-values for multiple comparisons using the Benjamini-Hochberg method31. Visualize statistically significant associations using heatmaps and scatter plots.

Materials and Methods

Identification of acetaminophen targets
To identify potential molecular targets of APAP, we first retrieved its SMILES representation from the PubChem database. We then used several online platforms, including Chemical European Molecular Biology Laboratory (ChemBL)32, Swiss Target Prediction33, Search Tool for the Interaction of Chemicals and Targets (STITCH)34, and Similarity ensemble approach (SEA)35, to predict its possible targets. After integrating the results from these tools, we selected a set of high-confidence targets for APAP. Table 1 defines key gene categories used in this study, clarifying their roles in data analysis and biological interpretation. Consistent usage of these terms ensures clear communication of our results.

Identification of hepatotoxicity targets
Potential hepatotoxicity-associated genes were retrieved from the GeneCards database. All identified genes were compiled, duplicates were removed, and a non-redundant list was generated for downstream analyses. The GSE74000 dataset was downloaded from the GEO repository on 15 March 2024. Raw expression data were processed and normalized using the limma package (quantile normalization). Differential expression analysis was performed using linear modeling with empirical Bayes shrinkage. Genes meeting adjusted p-value < 0.05 (Benjamini-Hochberg FDR) and |log₂FC| > 1 were considered significant. Visualization of DEGs was performed using volcano plots and heatmaps generated with ggplot2.

Data preprocessing
Raw count data were preprocessed using DESeq2. Low-expression genes were removed using a detection threshold of CPM >1 in at least 70% of samples. Library-size normalization was performed using DESeq2 size factors, as defined in equation (1):

Equation of normalized value C*ij = Cij/sj, focusing on normalization in data analysis.    (1)

Where the median ratio size factor is denoted by sj. To stabilize mean-variance relationships, a variance-stabilizing transformation (VST) was used in equation (2):

VST transformation equation, VST(C*ij)=f(C*ij), statistical analysis.    (2)

DEGs were identified using the Wald test with Benjamini-Hochberg correction, considering genes significant if used in equation (3):

log2FC analysis equation, threshold |log2FC|≥1 and p_adj≤0.05, statistical significance criteria.     (3)

The DESeq2-derived DEG set was defined as equation (4):

Static equilibrium equation, X={i:|log2FCi|≥ and padj,≤0.05}, for data analysis.     (4)

This set (X) was used in the consensus strategy together with Limma Trend (Y) and Limma Voom (Z).

PPI network construction
Venn diagrams were used to identify common genes between APAP and hepatotoxicity targets. The overlapping genes were then uploaded to the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database to construct PPI networks.

Multidimensional functional enrichment analysis
We first conducted Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), tissue expression, and disease-related functional analyses of the overlapping genes for APAP and hepatotoxicity using the STRING website. Next, we performed GO, KEGG, and Gene Set Enrichment Analysis (GSEA) (REACTOME) enrichment analyses of the differential genes for hepatotoxicity using Sendo Academic Tools.

Random forest analysis
We applied machine learning to identify the top 20 feature genes from the overlapping APAP and hepatotoxicity genes in the APAP-induced hepatotoxicity transcriptome data. A Random Forest classifier was implemented, with 500 trees (n_estimators=500), maximum tree depth of 10 (max_depth=10), minimum 2 samples required to split a node (min_samples_split=2), and the Gini impurity criterion (criterion='gini')36. The model's performance was evaluated using the Out-of-Bag (OOB) error, where a value close to 0 indicates higher predictive accuracy. Feature importance scores were calculated and visualized based on the Random Forest analysis to rank the contribution of each gene, as in equation (5).

Out-of-bag error formula, machine learning accuracy assessment, equation display.    (5)

N was the total number of samples, yi was the 1(·) was an indicator function, 1 if the condition was true and 0 otherwise; Out-of-bag prediction formula ŷᵢ OOB representing statistical modeling in machine learning. was the predicted label of sample iii using only trees where iii was not included in training.

Differential expression of characterized genes
The expression differences of feature genes in the transcriptomic data were visualized using violin plots. Biomarkers showing statistically significant differences were identified as potential novel biomarkers of APAP-induced hepatotoxicity for further investigation.

Molecular docking
Small molecule compounds (CID 1983) were retrieved from the PubChem database, and protein receptors ESR1 and PNP (PDB IDs 1SJ0 and 1V2H) were downloaded from the Protein Data Bank. Ligand structures were converted to PDB format using OpenBabel and preprocessed in AutoDock Tools by adding polar hydrogens, assigning Gasteiger charges, defining rotatable bonds, and saving in PDBQT format. Protein receptors were prepared using PyMOL by removing water molecules and co-crystallized ligands, followed by the addition of polar hydrogens and assignment of Kollman charges using AutoDock Tools, and saved as PDBQT files.

In molecular docking, AutoDock software was used to define the docking grid covering the active site of protein37. The grid box was centered at coordinates (x = XX·XX, y = YY·YY,z = ZZ·ZZ) with dimensions of 40 × 40 × 40 Å and a grid spacing of 0.375 Å, ensuring complete coverage of the binding pocket. AutoDock Vina was employed to calculate ligand-protein binding modes and binding affinities, with the exhaustiveness parameter set to 8, and the top nine binding poses were generated for each ligand.

Docking protocol validation was performed by re-docking the co-crystallized ligand into the active site, yielding an RMSD value of < 2.0 Å, confirming the reliability of the docking procedure. The docking results were visualized using PyMOL to analyze binding conformations and key interactions, including hydrogen bonding.

Docking simulations predicted that Compound X fits into the binding site of protein Y, forming potential hydrogen bonds and hydrophobic contacts, with a predicted binding energy of -8.5 kcal/mol.

Troubleshooting and potential modifications
To improve the robustness and reproducibility of the proposed workflow, several troubleshooting considerations and potential modifications should be noted. If an unexpectedly low number of differentially expressed genes (DEGs) is identified, users are advised to verify the normalization procedure, confirm accurate group labeling, and consider adjusting the |log2 fold change| threshold while maintaining appropriate false discovery rate (FDR) control. Conversely, if an excessive number of DEGs is obtained, applying more stringent FDR cutoffs or filtering low-variance genes before differential expression analysis may improve specificity.

Batch effects may influence clustering patterns in exploratory analyses such as principal component analysis (PCA). If samples cluster predominantly by batch rather than biological condition, batch correction methods (e.g., empirical Bayes approaches such as ComBat) should be applied, and sample metadata should be carefully re-evaluated for consistency.

For the Random Forest-based feature selection, high Out-of-Bag (OOB) error rates or unstable feature rankings may indicate suboptimal model configuration. In such cases, increasing the number of trees, tuning the mtry parameter, or performing repeated model runs with consensus feature selection can enhance model stability and predictive reliability. Additionally, cross-validation strategies may be employed to further assess model robustness.

To strengthen reliability, users may optionally repeat the analysis using alternative DEG thresholds or machine learning parameter settings and compare the consistency of identified feature genes. Such sensitivity analyses help ensure that the key findings are not driven by specific parameter choices and support the reproducibility of the workflow across similar transcriptomic datasets.

Statistical analyses
Statistical significance was determined using a t-test, with p-values reported for comparison. Statistical correlations between gene expression levels and hepatotoxicity-related phenotypes were assessed using Pearson and Spearman correlation coefficients, depending on data normality. P-values were adjusted for multiple comparisons using the Benjamini-Hochberg method. Significant associations were visualized with heatmaps and scatter plots, providing robust evaluation of transcriptomic relationships.

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Results

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Identification of overlapping genes for APAP and hepatotoxicity and their functional analysis
To determine potential molecular targets of APAP and investigate their biological roles, predictions were generated using four computational tools: ChEMBL, SwissTargetPrediction, STITCH, and SEA. Following the removal of duplicate entries and integration of overlapping results, a non-redundant list of 140 candidate targets was compiled. Additionally, 657 hepatotoxicity-related genes were extracted from the G...

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Discussion

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APAP is a frequently prescribed analgesic and antipyretic agent; however, excessive intake can result in significant hepatotoxicity, sometimes culminating in acute liver failure38. Despite advances in elucidating the pathophysiological mechanisms of APAP-induced liver injury, there remains a scarcity of reliable biomarkers for accurate diagnosis, prognosis, and mechanistic investigation. Thus, there is an urgent need to identify potential novel biomarkers and approaches to further investigate APAP...

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Disclosures

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The authors have nothing to disclose.

Acknowledgements

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The authors thank Tianjin University of Traditional Chinese Medicine for providing institutional support. We extend our gratitude to the developers of public databases (GeneCards, PubChem, STRING) and open-source tools that enabled this research. Special thanks to colleagues from the School of Chinese Materia Medica and College of Integrative Chinese and Western Medicine for their valuable discussions and technical assistance. We also acknowledge the contributors of the GSE74000 dataset for making their data publicly available.

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
Databases & Web Servers
STRINGhttps://cn.string-db.org/PPI network construction; Functional enrichment
Sendo Academic Toolshttps://www.xiantaozi.com/GO, KEGG, and GSEA enrichment analysis
PubChemhttps://pubchem.ncbi.nlm.nih.gov/Retrieval of chemical structures (APAP)
GeneCardshttps://www.genecards.org/Retrieval of hepatotoxicity-related genes
GEO Databasehttps://www.ncbi.nlm.nih.gov/geo/Transcriptomic dataset retrieval (GSE74000)
ChEMBLhttps://www.ebi.ac.uk/chembl/Target prediction
SwissTargetPredictionhttp://www.swisstargetprediction.ch/Target prediction
STITCHhttp://stitch.embl.de/Interaction prediction
SEAhttp://sea.bkslab.org/Similarity ensemble approach for target prediction
Protein Data Bank (PDB)https://www.rcsb.org/Protein structure retrieval (ESR1, PNP)
Software
R (Version 4.x.x)https://www.r-project.org/Statistical analysis and data processing
AutoDock Tools / Vinahttp://autodock.scripps.edu/Molecular docking preparation and simulation
PyMOLhttps://pymol.org/Visualization of molecular structures
Cytoscapehttps://cytoscape.org/Network visualization

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Tags

Acetaminophen Liver InjuryBiomarker IdentificationNetwork ToxicologyTranscriptomic AnalysisMachine LearningRandom Forest ModelMolecular DockingHepatotoxicity Biomarkers

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