Eight-gene metabolic signature related with tumor-associated macrophages predicting overall survival for hepatocellular carcinoma

• Published in: BMC cancer Vol. 21; no. 1; pp. 31 - 15
• Main Authors: Huo, Junyu, Wu, Liqun, Zang, Yunjin, Dong, Hongjing, Liu, Xiaoqiang, He, Fu, Zhang, Xiao
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 07.01.2021; BioMed Central; BMC
• Subjects: Abundance; Aged; Biomarkers, Tumor - genetics; Biomarkers, Tumor - metabolism; Carcinoma, Hepatocellular - genetics; Carcinoma, Hepatocellular - metabolism; Carcinoma, Hepatocellular - mortality; Carcinoma, Hepatocellular - pathology; Cellular Reprogramming - genetics; Correlation analysis; CTLA-4 protein; Datasets; Female; Follow-Up Studies; Gene expression; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Genetic aspects; Genomes; Health aspects; Hepatocellular carcinoma; Hepatoma; Humans; Immune checkpoint; Liver cancer; Liver Neoplasms - genetics; Liver Neoplasms - metabolism; Liver Neoplasms - mortality; Liver Neoplasms - pathology; Macrophages; Male; Medical prognosis; Metabolic; Metabolic regulation; Metabolism; Metabolites; Metastases; Metastasis; Patients; PD-1 protein; Prognosis; Prognostic; Regression analysis; Risk groups; Signature; Solid tumors; Survival analysis; Survival Rate; Tumor-associated macrophages; Tumor-Associated Macrophages - metabolism; Tumor-Associated Macrophages - pathology; Tumors; China; Hepatocellular carcinoma; Metabolic; Prognostic; Tumor-associated macrophages; Signature
• ISSN: 1471-2407; 1471-2407
• DOI: 10.1186/s12885-020-07734-z

In recent years, the relationship between tumor-associated macrophages (TAMs) and solid tumors has become a research hotspot. This study aims to explore the close relationship of TAMs with metabolic reprogramming genes in hepatocellular carcinoma (HCC) to provide new methods of treatment for HCC. The study selected 343 HCC patients with complete survival information (survival time > = 1 month) in the Cancer Genome Atlas (TCGA) as study subjects. Kaplan-Meier survival analysis assisted in determining the relationship between macrophage infiltration and overall survival (OS), and Pearson correlation tests were used to identify metabolic reprogramming genes (MRGs) associated with tumor macrophage abundance. Lasso regression algorithms were used on prognosis-related MRGs identified by Kaplan-Meier survival analysis and univariate Cox regression analysis to construct a risk score; another independent cohort (including 228 HCC patients) from the International Cancer Genome Consortium (ICGC) was used to verify prognostic signature externally. A risk score composed of 8 metabolic genes could accurately predict the OS of a training cohort (TCGA) and a testing cohort (ICGC). The risk score could be widely used for people with different clinical characteristics, and it is a predictor that is independent of other clinical factors that affect prognosis. As expected, compared with the low-risk group, the high-risk group exhibited an obviously higher macrophage abundance, together with a positive correlation between the risk score and the expression levels of three commonly used immune checkpoints (PD1, PDL1, and CTLA4). Our study constructed and validated a novel eight-gene signature for predicting HCC patient OS, which may contribute to clinical treatment decisions.