A novel prognostic signature based on immunogenic cell death score predicts outcomes and response to transcatheter arterial chemoembolization and immunotherapy in hepatocellular carcinoma.

Abstract

The phenomenon of immunogenic cell death (ICD) is intricately linked to numerous antitumor treatments and exerts a profound regulatory function in the tumor immune microenvironment (TIME). We aimed to establish a prognostic signature from the ICD-related biomarkers to differentiate the TIME in hepatocellular carcinoma and predict diverse outcomes for patients with liver cancer. ICD score-related genes (ICDSGs) were identified using the weighted gene co-expression network analysis (WGCNA). The ICD score-related signature (ICDSsig) was established by applying LASSO and Cox regression. Model precision was verified using the external datasets. We used independent prognostic variables in clinicopathologic factors to develop a nomogram. Further, clinical characteristics, immune and molecular landscapes, the responses of transcatheter arterial chemoembolization (TACE) and immunotherapy, and chemotherapy sensitivity were analyzed for high- and low-risk patients. ICD score-calculated using the single-sample gene set enrichment analysis (ssGSEA)-displayed strong associations with the TIME in HCC. We identified 34 ICDSGs after integrating the TCGA and GSE104580 datasets. Then, three novel ICDSGs (DNASE1L3, KLRB1, and LILRB1) were screened out to construct the ICDSsig; the prognostic signature performed well in the external databases. The high-risk patients had worse outcomes owing to their advanced pathological state, non-response of TACE, and immune-cold phenotype in the immune landscapes. The immune checkpoint genes, N6-methyladenosine-relevant genes, and microsatellite instability score were increased in the high-risk subgroup, thereby indicating a favorable sensitivity to immunotherapy. Common chemotherapy drugs were more effective in high-risk patients due to low half-maximal inhibitory concentration values. The ICDSsig can potentially predict outcomes and therapeutic responses for patients with liver cancer and may assist clinicians in designing individualized treatment strategies.