Construction of a radiogenomic signature based on endoplasmic reticulum stress for predicting prognosis and systemic combination therapy response in hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is one of the most common tumors worldwide. Various factors in the tumor environment (TME) can lead to the activation of endoplasmic reticulum stress (ERS), thereby affecting the occurrence and development of tumors. The objective of our study was to develop and validate a radiogenomic signature based on ERS to predict prognosis and systemic combination therapy response.

Methods: Using data from The Cancer Genome Atlas Program (TCGA) as a training cohort and data from International cancer genome consortium (ICGC) as a testing cohort. Univariate Cox regression and multivariate Cox regression analysis were used to identify prognostic-related genes and construct a model. HCC single-cell data obtained from Gene Expression Omnibus (GEO) were used to map gene signatures and explore inter-cellular signaling communications. Finally, a radiogenomic signature was used to predict the objective response rate (ORR) and overall survival (OS).

Results: A total of four gene signatures related to ERS, including Stanniocalcin-2 (STC2), Melanoma-Associated Antigen 3 (MAGEA3), BR Serine/Threonine-Protein Kinase 2 (BRSK2), DEAD/H-Box Helicase 11 (DDX11) were identified. Macrophages were significantly different between high-risk and low-risk groups. The high-risk group showed higher targeting programmed cell death-1 (PD-1) and mutated tumor protein p53 (TP53) scores. Drug sensitivity analysis found that most sensitive drugs target the phosphatidylinositol 3-kinase/ mechanistic target of rapamycin (PI3K/mTOR) signaling pathway. Further research revealed the expression of STC2 in the endothelial cells (ECs), particularly plasmalemma vesicle associated protein (PLVAP) + ECs, and may regulate the reprogramming and function of macrophages. Furthermore, we identified nine radiomic features and established a radiogenomic signature based on ERS that can predict prognosis and response to systemic combination therapy. This signature can guide systemic combination therapy for patients with unresectable HCC.

Conclusions: We established an ERS prognostic model that can predict patient prognosis. We also found that ERS is closely related to the TME and is mainly manifested in the interaction between tumor-associated endothelial cells (TAEs) and tumor-associated macrophages (TAMs). Moreover, we constructed a radiogenomic signature based on the ERS. This signature can guide subsequent combination therapy for patients with unresectable HCC.