Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No. 119 Nansihuan West Road, Fengtai District, Beijing, 100070, China.
Beijing Fuzheng Cancer Hospital, No. 20 Jinghai 3rd road, Yizhuang Economic and Technological Development Zone, Beijing, 100070, China.
Lipids Health Dis. 2023 Apr 1;22(1):46. doi: 10.1186/s12944-023-01780-9.
Up to 85% of hepatocellular carcinoma (HCC) cases in China can be attributed to infection of hepatitis B virus (HBV). Lipid metabolism performs important function in hepatocarcinogenesis of HBV-related liver carcinoma. However, limited studies have explored the prognostic role of lipid metabolism in HBV-related HCC. This study established a prognostic model to stratify HBV-related HCC based on lipid metabolisms.
Based on The Cancer Genome Atlas HBV-related HCC samples, this study selected prognosis-related lipid metabolism genes and established a prognosis risk model by performing uni- and multi-variate Cox regression methods. The final markers used to establish the model were selected through the least absolute shrinkage and selection operator method. Analysis of functional enrichment, immune landscape, and genomic alteration was utilized to investigate the inner molecular mechanism involved in prognosis.
The risk model independently stratified HBV-infected patients with liver cancer into two risk groups. The low-risk groups harbored longer survival times (with P < 0.05, log-rank test). TP53, LRP1B, TTN, and DNAH8 mutations and high genomic instability occurred in high-risk groups. Low-risk groups harbored higher CD8 T cell infiltration and BTLA expression. Lipid-metabolism (including "Fatty acid metabolism") and immune pathways were significantly enriched (P < 0.05) in the low-risk groups.
This study established a robust model to stratify HBV-related HCC effectively. Analysis results decode in part the heterogeneity of HBV-related liver cancer and highlight perturbation of lipid metabolism in HBV-related HCC. This study's findings could facilitate patients' clinical classification and give hints for treatment selection.
在中国，高达 85%的肝细胞癌（HCC）病例可归因于乙型肝炎病毒（HBV）感染。脂代谢在 HBV 相关肝癌的发生中起着重要作用。然而，有限的研究探讨了脂代谢在 HBV 相关 HCC 中的预后作用。本研究建立了一个基于脂代谢的预后模型，对 HBV 相关 HCC 进行分层。
基于癌症基因组图谱 HBV 相关 HCC 样本，本研究选择了与预后相关的脂代谢基因，并通过单变量和多变量 Cox 回归方法建立了预后风险模型。通过最小绝对收缩和选择算子（LASSO）方法选择最终用于建立模型的标记物。分析功能富集、免疫景观和基因组改变，以研究涉及预后的内在分子机制。
该风险模型独立地将 HBV 感染的肝癌患者分为两个风险组。低危组的生存时间更长（P<0.05，对数秩检验）。TP53、LRP1B、TTN 和 DNAH8 突变和高基因组不稳定性发生在高危组中。低危组具有更高的 CD8 T 细胞浸润和 BTLA 表达。脂代谢（包括“脂肪酸代谢”）和免疫途径在低危组中显著富集（P<0.05）。
本研究建立了一个有效的模型，能够有效地对 HBV 相关 HCC 进行分层。分析结果部分解码了 HBV 相关肝癌的异质性，并强调了 HBV 相关 HCC 中脂代谢的紊乱。本研究的发现可以促进患者的临床分类，并为治疗选择提供提示。
