Unraveling the molecular mechanisms of irisin in liver cancer through network toxicology and molecular docking strategies

Technical Summary: Unraveling the Molecular Mechanisms of Irisin in Liver Cancer

Problem Statement
Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality globally, often diagnosed at advanced stages where curative options are limited. While exercise is known to reduce disease risk and improve quality of life, the specific molecular mechanisms by which exercise-induced myokines, particularly irisin, modulate HCC pathogenesis remain poorly characterized. Existing literature presents conflicting evidence regarding irisin's role in HCC: some studies suggest it enhances proliferation and migration via the PI3K/Akt pathway, while others report no effect or even downregulation in tumor tissues. Furthermore, there is no consensus on irisin expression levels in HCC tissues compared to normal controls. This study aims to resolve these inconsistencies by comprehensively identifying irisin's potential molecular targets and signaling pathways in HCC using a systems biology approach.

Methodology
The study employed a multi-step bioinformatics pipeline integrating network toxicology and molecular docking:

1. Target Collection:

   • Irisin Targets: Potential human target genes modulated by irisin were retrieved from SwissTargetPrediction, ChEMBL, and STITCH databases.
   • HCC Targets: Genes associated with liver cancer were curated from GeneCards and OMIM.
   • Differential Expression: RNA-seq data from the TCGA-LIHC cohort (374 tumor and 50 normal samples) were analyzed using DESeq2 to identify differentially expressed genes (DEGs) (cutoff: adj. P < 0.05, |log₂FC| > 1).
   • Intersection: The intersection of irisin targets, HCC-associated genes, and TCGA-derived DEGs yielded 93 potential targets.

2. Network Construction and Hub Gene Identification:

   • A Protein-Protein Interaction (PPI) network was constructed using the STRING database (confidence score > 0.4) and visualized in Cytoscape.
   • Module Analysis: The MCODE plugin identified the most critical subnetwork.
   • Hub Selection: Five algorithms (Degree, Betweenness, Closeness, MNC, Radiality) within the cytoHubba plugin were used to rank genes. The intersection of the top genes from these algorithms defined the final "hub genes."

3. Prognostic Modeling:

   • A risk score model was constructed based on the expression levels of the identified hub genes using multivariate Cox regression.
   • Patients were stratified into high-risk and low-risk groups. Kaplan-Meier survival analysis and ROC curves were used to evaluate prognostic accuracy.
   • Multivariate Cox regression assessed the risk score as an independent prognostic factor alongside clinical variables (age, stage, gender).

4. Functional and Pathway Enrichment:

   • Gene Ontology (GO), KEGG, Reactome, and Disease Ontology (DO) analyses were performed using the clusterProfiler and ReactomePA packages to elucidate biological processes and pathways.

5. Molecular Docking Validation:

   • Protein structures for the hub genes were predicted using AlphaFold 3.
   • Molecular docking simulations (AutoDock Vina) were conducted to evaluate the binding affinity between irisin and the hub proteins. Binding energies were calculated to assess interaction stability.

Key Results

• Target Identification: The study identified 93 potential targets linking irisin to HCC.
• Hub Genes: Eight core hub genes were identified: RACGAP1, BIRC5, NEK2, UBE2C, TYMS, EZH2, AURKB, and CDK1. All eight genes were significantly upregulated in HCC tissues and associated with poor prognosis (Hazard Ratio > 1). Although AURKA was initially highlighted in the intersection analysis, it was excluded from the final core gene set, while the remaining eight genes were retained.
• Prognostic Value: The irisin-based risk signature served as an independent prognostic factor for HCC patients, distinct from age, stage, and gender. High-risk patients exhibited significantly poorer overall survival (p < 0.001). The model showed moderate predictive capacity with AUC values of 0.76 (1-year), 0.66 (3-year), and 0.582 (5-year).
• Functional Mechanisms: Enrichment analysis revealed that irisin-related targets are potentially involved in four mechanisms of irisin-induced HCC:
  • Mitotic Stability: Regulation of spindle microtubule attachment to kinetochores and sister chromatid segregation.
  • Cell Cycle Dysregulation: Involvement in the G2/M phase transition and APC/C-mediated degradation of cell cycle proteins.
  • Oncogenic Activity: Enhancement of protein serine/threonine kinase activity and chaperone binding.
  • Cell Viability: Promotion of tumor growth and resistance to chemotherapeutic agents (e.g., antifolate resistance).
• Molecular Docking: All eight hub proteins demonstrated strong binding affinities with irisin. Seven proteins exhibited binding energies below −7.2 kcal/mol, while RACGAP1 showed a binding energy of −6.8 kcal/mol, indicating robust binding stability and high affinity for all targets.

Significance and Claims
The authors claim that this study provides a systems-level perspective on the role of irisin in HCC, moving beyond inconsistent single-gene reports to a network-based understanding. The primary contributions are:

1. Identification of Core Targets: The study uncovers eight specific hub genes (RACGAP1, BIRC5, NEK2, UBE2C, TYMS, EZH2, AURKB, CDK1) that are critical in irisin-mediated HCC progression.
2. Mechanistic Insight: It identifies four potential mechanisms: chromosomal instability/aneuploidy during mitosis, cell cycle dysregulation, enhancement of oncogenic protein activity, and increased cancer cell viability.
3. Prognostic Tool: The irisin-related 8-gene signature is validated as an independent prognostic indicator for patient survival.
4. Therapeutic Potential: The strong binding affinities confirmed by molecular docking suggest these hub proteins are viable targets for future therapeutic strategies.

Limitations and Modesty
The authors explicitly acknowledge that this is a computational study. They note that:

• Target datasets from various databases may suffer from heterogeneity due to differing prediction algorithms and standards.
• The binding interactions are predicted in silico and require experimental validation to confirm actual affinity.
• Not all identified mechanisms have been experimentally verified in the context of irisin-induced HCC.
• The study calls for rigorous experimental validation to fully elucidate the precise role of irisin in HCC pathogenesis.

In conclusion, the paper posits that irisin potentially regulates HCC progression through specific mitotic and cell cycle pathways mediated by eight hub genes, offering a new framework for understanding hepatocarcinogenesis and identifying potential targets for prevention and treatment, pending experimental confirmation.