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<p>Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related mortality worldwide, with limited effective molecularly targeted therapies. This study integrated single-cell transcriptomics, pseudobulk differential analysis, weighted gene co-expression network analysis (WGCNA), immune deconvolution, machine learning, survival modeling, and molecular docking to identify clinically relevant therapeutic targets and candidate phytochemical inhibitors. Tumor-associated myeloid cells exhibited marked transcriptional reprogramming enriched in nuclear receptor signaling, PI3K-Akt pathways, and immune-metabolic regulation. Multi-layer intersection analysis identified core regulatory genes embedded within dense protein-protein interaction and compound-target networks. Machine learning consensus (Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Random Forest) prioritized AHR, TPMT, and LTA4H as candidate hub genes. Survival analysis revealed that high LTA4H expression was significantly associated with poorer overall survival in HCC patients (log-rank <i>p</i> = 0.028; HR = 1.40), whereas AHR and TPMT showed no prognostic significance. Pan-cancer expression profiling confirmed significant LTA4H dysregulation (<i>p</i> &lt; 5e-23). Molecular docking and dynamic simulation (100ns) demonstrated favorable binding of <i>Curcuma longa</i>-derived phytochemicals, particularly quercetin (-6.38&#xa0;kcal/mol), within the LTA4H catalytic pocket. Collectively, these findings identify LTA4H as an immune-associated prognostic biomarker and potential therapeutic target in HCC, supported by multi-omics integration and structural validation.</p> Graphical abstract <p></p>

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Single-Cell Transcriptomic Integration Identifies LTA4H as an Immune-Associated Therapeutic Target in Hepatocellular Carcinoma

  • 期刊论文

  • 发表日期：

    2026年4月7日

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  • Fahad M. Alshabrmi

摘要

Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related mortality worldwide, with limited effective molecularly targeted therapies. This study integrated single-cell transcriptomics, pseudobulk differential analysis, weighted gene co-expression network analysis (WGCNA), immune deconvolution, machine learning, survival modeling, and molecular docking to identify clinically relevant therapeutic targets and candidate phytochemical inhibitors. Tumor-associated myeloid cells exhibited marked transcriptional reprogramming enriched in nuclear receptor signaling, PI3K-Akt pathways, and immune-metabolic regulation. Multi-layer intersection analysis identified core regulatory genes embedded within dense protein-protein interaction and compound-target networks. Machine learning consensus (Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Random Forest) prioritized AHR, TPMT, and LTA4H as candidate hub genes. Survival analysis revealed that high LTA4H expression was significantly associated with poorer overall survival in HCC patients (log-rank p = 0.028; HR = 1.40), whereas AHR and TPMT showed no prognostic significance. Pan-cancer expression profiling confirmed significant LTA4H dysregulation (p < 5e-23). Molecular docking and dynamic simulation (100ns) demonstrated favorable binding of Curcuma longa-derived phytochemicals, particularly quercetin (-6.38 kcal/mol), within the LTA4H catalytic pocket. Collectively, these findings identify LTA4H as an immune-associated prognostic biomarker and potential therapeutic target in HCC, supported by multi-omics integration and structural validation.

Graphical abstract