Key Takeaways & Executive Findings
- •• Distinct tumor microenvironment subtypes in hepatocellular carcinoma are associated with differential prognosis and immune infiltration. • A novel TME-related gene signature accurately predicts overall survival in HCC patients across multiple cohorts. • Integrated multi-omics analysis reveals potential biomarkers for immunotherapy response in HCC. • The study provides a comprehensive resource for understanding TME heterogeneity and its clinical implications in HCC.
Abstract
Hepatocellular carcinoma (HCC) is a highly heterogeneous malignancy with a complex tumor microenvironment (TME) that profoundly influences disease progression and therapeutic response. In this study, we performed an integrated multi-omics analysis of HCC using transcriptomic, genomic, and epigenetic data from public databases and our own cohort. We characterized the immune cell infiltration patterns and identified distinct TME subtypes associated with differential prognosis and immunotherapy outcomes. Through weighted gene co-expression network analysis (WGCNA) and machine learning, we constructed a prognostic signature based on TME-related genes, which robustly predicted overall survival in multiple independent cohorts. Furthermore, we explored the interplay between TME, somatic mutations, and copy number variations, revealing potential biomarkers for immune checkpoint blockade. Our findings highlight the clinical significance of TME heterogeneity in HCC and provide a foundation for personalized treatment strategies. The prognostic model and immune-related biomarkers may facilitate risk stratification and guide immunotherapeutic decisions in HCC patients.
1. Introduction
Hepatocellular carcinoma (HCC) is one of the most common and lethal malignancies worldwide, with a rising incidence and poor prognosis. Despite advances in surgical resection, liver transplantation, and systemic therapies, the overall survival of HCC patients remains unsatisfactory. The tumor microenvironment (TME) plays a critical role in tumor initiation, progression, and response to therapy. It comprises a complex ecosystem of immune cells, stromal cells, extracellular matrix, and signaling molecules that interact dynamically with cancer cells. Understanding the composition and functional state of the TME is essential for developing effective immunotherapies and prognostic biomarkers.
Recent high-throughput technologies have enabled comprehensive profiling of the TME at the molecular level. Integrated multi-omics approaches, combining genomics, transcriptomics, and epigenomics, offer a powerful means to dissect TME heterogeneity and identify clinically relevant subtypes. In this study, we systematically analyzed HCC samples from multiple cohorts to characterize immune infiltration patterns, construct a prognostic model based on TME-related genes, and explore the genomic and epigenetic alterations associated with distinct TME phenotypes. Our findings provide novel insights into the molecular underpinnings of HCC TME and may inform personalized treatment strategies.
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ZHANG Wei, LI Ming, WANG Fang, CHEN Jie, LIU Yang, ZHAO Lei, SUN Hong, ZHOU Peng (2026). Integrated Multi-Omics Analysis of Tumor Microenvironment and Immune Infiltration in Hepatocellular Carcinoma: Implications for Prognosis and Immunotherapy. Journal of Shanghai Jiao Tong University (Science) (上海交通大学学报). https://doi.org/10.16183/j.cnki.jsjtu.2026.066
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Frequently Asked Questions
What is the tumor microenvironment (TME) and why is it important in hepatocellular carcinoma?
The tumor microenvironment (TME) consists of immune cells, stromal cells, and extracellular components surrounding tumor cells. In hepatocellular carcinoma (HCC), the TME influences tumor growth, metastasis, and response to therapies. Understanding TME composition helps in predicting prognosis and guiding immunotherapy.
How was the prognostic signature developed in this study?
We used weighted gene co-expression network analysis (WGCNA) and machine learning algorithms on transcriptomic data from HCC cohorts to identify TME-related genes. A prognostic signature was constructed based on these genes and validated in independent cohorts, showing robust predictive performance for overall survival.
What are the clinical implications of the TME subtypes identified?
The distinct TME subtypes are associated with different immune infiltration levels and survival outcomes. These subtypes may help stratify patients for immunotherapy, as certain subtypes may respond better to immune checkpoint inhibitors.
Which biomarkers were found to be associated with immunotherapy response?
Our integrated analysis revealed that specific immune-related genes and genomic alterations, such as mutations in certain genes and copy number variations, correlate with immunotherapy response. These biomarkers could be used to select patients who are likely to benefit from immunotherapy.
How can this research impact clinical practice?
The prognostic model and biomarkers identified in this study can assist clinicians in risk stratification and treatment planning for HCC patients. By identifying patients with high-risk TME profiles, personalized therapeutic strategies, including immunotherapy, can be optimized to improve outcomes.
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