Eleven metabolism‑related genes composed of Stard5 predict prognosis and contribute to EMT phenotype in HCC

Dongping Li; Xiahui Lin; Jiale Li; Xinyi Liu; Feng Zhang; Wenqing Tang; Si Zhang; Ling Dong; Ruyi Xue

doi:10.1186/s12935-023-03097-0

Eleven metabolism‑related genes composed of Stard5 predict prognosis and contribute to EMT phenotype in HCC

Cancer Cell Int. 2023 Nov 17;23(1):277. doi: 10.1186/s12935-023-03097-0.

Authors

Dongping Li^#¹, Xiahui Lin^#¹, Jiale Li^#², Xinyi Liu¹, Feng Zhang¹, Wenqing Tang¹, Si Zhang², Ling Dong³, Ruyi Xue⁴

Affiliations

¹ Department of Gastroenterology and Hepatology, Shanghai Institute of Liver Diseases, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
² Key Laboratory of Glycoconjugate Research Ministry of Public Health, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China.
³ Department of Gastroenterology and Hepatology, Shanghai Institute of Liver Diseases, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China. dong.ling@zs-hospital.sh.cn.
⁴ Department of Gastroenterology and Hepatology, Shanghai Institute of Liver Diseases, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China. xue.ruyi@zs-hospital.sh.cn.

^# Contributed equally.

Abstract

Background: Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide, with a high mortality and poor survival rate. Abnormal tumor metabolism is considered a hallmark of HCC and is a potential therapeutic target. This study aimed to identify metabolism-related biomarkers to evaluate the prognosis of patients with HCC.

Method: The Cancer Genome Atlas (TCGA) database was used to explore differential metabolic pathways based on high and low epithelial-mesenchymal transition (EMT) groupings. Genes in differential metabolic pathways were obtained for HCC metabolism-related molecular subtype analysis. Differentially expressed genes (DEGs) from the three subtypes were subjected to Lasso Cox regression analysis to construct prognostic risk models. Stard5 expression in HCC patients was detected by western blot and immunohistochemistry (IHC), and the role of Stard5 in the metastasis of HCC was investigated by cytological experiments.

Results: Unsupervised clustering analysis based on metabolism-related genes revealed three subtypes in HCC with differential prognosis. A risk prognostic model was constructed based on 11 genes (STARD5, FTCD, SCN4A, ADH4, CFHR3, CYP2C9, CCL14, GADD45G, SOX11, SCIN, and SLC2A1) obtained by LASSO Cox regression analysis of the three subtypes of DEGs. We validated that the model had a good predictive power. In addition, we found that the high-risk group had a poor prognosis, higher proportion of Tregs, and responded poorly to chemotherapy. We also found that Stard5 expression was markedly decreased in HCC tissues, which was associated with poor prognosis and EMT. Knockdown of Stard5 contributed to the invasion and migration of HCC cells. Overexpression of Stard5 inhibited EMT in HCC cells.

Conclusion: We developed a new model based on 11 metabolism-related genes, which predicted the prognosis and response to chemotherapy or immunotherapy for HCC. Notably, we demonstrated for the first time that Stard5 acted as a tumor suppressor by inhibiting metastasis in HCC.

Keywords: EMT; HCC; Metabolic reprogramming; Prognostic model; Stard5.