Key Takeaways & Executive Findings
- •• • A 12-gene signature (including TPX2, AURKA, and BIRC5) was identified using LASSO and random forest, achieving a C-index of 0.82 (95% CI: 0.78-0.86) for 5-year survival prediction, outperforming traditional staging (C-index 0.71). • • Knockdown of TPX2 in HCC cell lines (HepG2 and Huh7) reduced proliferation by 45% and migration by 60% (p < 0.01), validating its oncogenic role. • • TPX2 overexpression increased sorafenib IC50 from 2.5 μM to 8.2 μM (3.3-fold), indicating a direct role in drug resistance; combination therapy targeting TPX2 restored sensitivity by 70%. • • The nomogram integrating the gene signature and clinical factors (tumor stage, AFP level) achieved a C-index of 0.82, significantly improving risk stratification compared to AFP alone (C-index 0.65).
Abstract
Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, with high recurrence and drug resistance rates. This study integrates multi-omics data (genomics, transcriptomics, proteomics) and machine learning algorithms to identify novel therapeutic targets and prognostic biomarkers. We analyzed 450 HCC samples from public databases and our cohort, applying LASSO and random forest feature selection to identify a 12-gene signature associated with overall survival (p < 0.001). Functional experiments demonstrated that knockdown of the top candidate gene, TPX2, reduced cell proliferation by 45% and migration by 60% in vitro. Drug resistance analysis revealed that TPX2 overexpression confers resistance to sorafenib (IC50 increased from 2.5 μM to 8.2 μM). Furthermore, we developed a nomogram integrating clinical factors and the gene signature, achieving a C-index of 0.82 (95% CI: 0.78-0.86) for predicting 5-year survival. Our findings provide a robust framework for personalized therapy and risk stratification in HCC.
1. Introduction
Hepatocellular carcinoma (HCC) is the sixth most common cancer and the third leading cause of cancer-related deaths globally. Despite advances in surgical resection and liver transplantation, the 5-year recurrence rate remains high (70%), and systemic therapies such as sorafenib and lenvatinib provide only modest survival benefits due to rapid acquisition of drug resistance. The molecular heterogeneity of HCC, driven by genomic instability and aberrant signaling pathways (e.g., Wnt/β-catenin, PI3K/AKT), complicates the identification of universal therapeutic targets. Current prognostic models rely heavily on clinical parameters (tumor size, vascular invasion) and serum AFP, which lack sensitivity and specificity, leading to suboptimal patient stratification.
To address these bottlenecks, we employed a multi-omics approach integrating genomic, transcriptomic, and proteomic data from 450 HCC samples, coupled with advanced machine learning algorithms (LASSO, random forest) to identify robust biomarkers. Unlike previous studies that focused on single data types, our integrative analysis captures the complex interplay between genetic alterations and protein expression, enabling the discovery of a 12-gene signature with high prognostic accuracy. Furthermore, we experimentally validated the functional role of the top candidate, TPX2, in tumor progression and drug resistance, providing a mechanistic basis for targeted therapy. This study offers a clinically actionable framework for personalized treatment decisions and highlights TPX2 as a promising therapeutic target to overcome sorafenib resistance.
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ZHANG Wei, LI Ming, WANG Fang, CHEN Jie, LIU Yang (2025). Hepatocellular Carcinoma Progression and Drug Resistance: A Multi-Omics and Machine Learning Approach to Identify Novel Therapeutic Targets and Biomarkers. Chinese Journal of New Drugs. https://doi.org/pub_80__articleID_246
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Frequently Asked Questions
What is the mechanistic basis for TPX2-mediated sorafenib resistance?
Our data indicate that TPX2 overexpression activates the PI3K/AKT signaling pathway, leading to upregulation of MDR1 (P-glycoprotein) and increased drug efflux. Specifically, TPX2 knockdown reduced AKT phosphorylation by 50% and MDR1 expression by 40%, restoring sorafenib sensitivity (IC50 decreased from 8.2 μM to 2.8 μM).
How does the 12-gene signature compare to existing prognostic models in terms of predictive accuracy?
The signature achieved a C-index of 0.82 (95% CI: 0.78-0.86) for 5-year overall survival, significantly outperforming the BCLC staging system (C-index 0.71) and AFP-based models (C-index 0.65). In external validation cohorts, the signature maintained a C-index of 0.79, demonstrating generalizability.
What are the scalability and cost implications of implementing this multi-omics approach in clinical settings?
The multi-omics profiling (RNA-seq and proteomics) costs approximately $1,200 per sample, which is higher than standard AFP testing ($20). However, the signature can be measured using qRT-PCR (cost ~$50) after validation, making it cost-effective for routine use. The computational pipeline is open-source and can be run on standard hardware, facilitating adoption.
What are the potential off-target effects of targeting TPX2 in normal tissues?
TPX2 is overexpressed in HCC but has low expression in normal adult tissues except testis. In our preclinical models, TPX2 knockdown using siRNA did not affect viability of normal hepatocytes (cell viability >95%). However, long-term inhibition may affect mitotic spindle dynamics in proliferating cells; thus, careful dosing and tumor-specific delivery are required.
How does the gene signature perform in different etiologies of HCC (e.g., HBV, HCV, NASH)?
Subgroup analysis showed that the signature's prognostic value was consistent across etiologies (HBV: C-index 0.81, HCV: 0.83, NASH: 0.79), indicating that the underlying molecular mechanisms are shared. This is crucial for global applicability, as the prevalence of etiologies varies geographically.
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