Department of Hepatobiliary Surgery, Affiliated Hospital of Youjiang Medical University for Nationalities, Guangxi, China.
J Cell Mol Med. 2024 Jan;28(1):e18018. doi: 10.1111/jcmm.18018. Epub 2023 Nov 9.
Metabolic pathways exert a significant influence on the onset and progression of cancer. Public data on hepatocellular carcinoma (HCC) patients were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Analysis was performed in R software using different R packages. Here, we integrated the data from multiple independent HCC cohorts, including TCGA-LIHC, ICGC-FR and ICGC-JP. Then, the enrichment score of 21 metabolism-related pathways was quantified using the ssGSEA algorithm. Next, univariate Cox regression analysis was applied to identify the metabolic terms with significant correlation to patient survival. Finally, a prognosis model based on linoleic acid metabolism, sphingolipid metabolism and regulation of lipolysis in adipocytes was established, which showed good performance in predicting patients' survival. Furthermore, we conducted a biological enrichment analysis to delineate the biological disparities between high- and low-risk patients. Notably, we discerned differences in the microenvironments between these two patient groups. We also found that low-risk patients could potentially respond better to immunotherapy. Drug sensitivity analysis suggested that low-risk patients are more susceptible to bexarotene and erlotinib, yet exhibit resistance to ATRA and bleomycin. Furthermore, through the use of LASSO logistic regression analysis, we identified 19 characteristic genes, which could robustly indicate the risk groups. Our research underscores the role of linoleic acid metabolism, sphingolipid metabolism and the regulation of lipolysis in adipocytes in HCC, pointing towards potential avenues for future research.
代谢途径对癌症的发生和发展有重要影响。我们从癌症基因组图谱（TCGA）和国际癌症基因组联合会（ICGC）数据库中获得了肝细胞癌（HCC）患者的公共数据。在 R 软件中使用不同的 R 包进行了分析。在这里，我们整合了来自多个独立 HCC 队列的数据，包括 TCGA-LIHC、ICGC-FR 和 ICGC-JP。然后，使用 ssGSEA 算法量化了 21 条代谢相关途径的富集评分。接下来，应用单变量 Cox 回归分析来识别与患者生存显著相关的代谢术语。最后，建立了一个基于亚油酸代谢、鞘脂代谢和脂肪细胞脂肪分解调节的预后模型，该模型在预测患者生存方面表现良好。此外，我们进行了生物学富集分析，以描绘高风险和低风险患者之间的生物学差异。值得注意的是，我们发现这两组患者的微环境存在差异。我们还发现低风险患者可能对免疫治疗有更好的反应。药物敏感性分析表明，低风险患者更容易对倍他罗汀和厄洛替尼敏感，但对 ATRA 和博来霉素有抗性。此外，通过使用 LASSO 逻辑回归分析，我们确定了 19 个特征基因，这些基因可以可靠地指示风险组。我们的研究强调了亚油酸代谢、鞘脂代谢和脂肪细胞脂肪分解调节在 HCC 中的作用，为未来的研究指明了方向。
