Integrating T-cell inflammation features for prognosis in hepatocellular carcinoma: a novel predictive model

Hepatocellular carcinoma (HCC) is the third most common cause of cancer-related death globally and accounts for 75% to 90% of primary liver cancer cases. The high mortality rate of HCC, coupled with the absence of reliable prognostic biomarkers, makes its treatment and prognosis evaluation challenging. The features of the T cell-inflamed microenvironment include active interferon (IFN)-γ signaling and the presence of cytotoxic effector molecules, antigen presentation, and T-cell activating cytokines. Although these features are closely associated with anticancer immunity, their specific roles in HCC remain unclear. This study aimed to investigate the role and prognostic significance of T-cell inflammation (TCI) in HCC patients, providing new insights for clinical diagnosis and treatment strategies.

We integrated single-sample gene set enrichment analysis (ssGSEA) and weighted gene coexpression network analysis (WGCNA) to identify the genes associated with TCI at both the single-cell and bulk-transcriptome levels. The HCC TCI-related score (HTCIRS) was developed and assessed with 10 different machine learning algorithms and their combinations, which was followed by validation of the key gene KLF2 in clinical samples and tissue microarrays (TMAs).

We identified 65 genes associated with TCI, of which 36 were significantly correlated with overall survival (OS). The HTCIRS demonstrated excellent performance in prognostic prediction, revealing differences in biological functions and immune cell infiltration between different risk groups within the tumor microenvironment (TME). Furthermore, KLF2 was identified to be linked to the prognosis of patients with HCC.

The TCI-related score proposed in this study serves as an important tool for prognostic prediction and personalized treatment of patients with HCC, with KLF2 emerging as a potential biomarker for predicting the prognosis of patients with HCC.