Shenzhen Aier Eye Hospital, Aier Eye Hospital, Jinan University, Shenzhen, China.
National Center for International Research of Bio-targeting Theranostics, Guangxi Key Laboratory of Bio-targeting Theranostics, Collaborative Innovation Center for Targeting Tumor Diagnosis and Therapy, Guangxi Talent Highland of Bio-targeting Theranostics, Guangxi Medical University, Nanning, Guangxi, China.
Front Immunol. 2023 Mar 23;14:1161960. doi: 10.3389/fimmu.2023.1161960. eCollection 2023.
Although lipid metabolism has been proven to play a key role in the development of cancer, its significance in uveal melanoma (UM) has not yet been elucidated in the available literature.
To identify the expression patterns of lipid metabolism in 80 UM patients from the TCGA database, 47 genes involved in lipid metabolism were analyzed. Consensus clustering revealed two distinct molecular groups. ESTIMATE, TIMER, and ssGSEA analyses were done to identify the differences between the two subgroups in tumor microenvironment (TME) and immune state. Using Cox regression and Lasso regression analysis, a risk model based on differentially expressed genes (DEGs) was developed. To validate the expression of monoacylglycerol lipase (MGLL) and immune infiltration in diverse malignancies, a pan-cancer cohort from the UCSC database was utilized. Next, a single-cell sequencing analysis on UM patients from the GEO data was used to characterize the lipid metabolism in TME and the role of MGLL in UM. Finally, investigations were utilized to study the involvement of MGLL in UM.
Two molecular subgroups of UM patients have considerably varied survival rates. The majority of DEGs between the two subgroups were associated with immune-related pathways. Low immune scores, high tumor purity, a low number of immune infiltrating cells, and a comparatively low immunological state were associated with a more favorable prognosis. An examination of GO and KEGG data demonstrated that the risk model based on genes involved with lipid metabolism can accurately predict survival in patients with UM. It has been demonstrated that MGLL, a crucial gene in this paradigm, promotes the proliferation, invasion, and migration of UM cells. In addition, we discovered that MGLL is strongly expressed in macrophages, specifically M2 macrophages, which may play a function in the M2 polarization of macrophages and M2 macrophage activation in cancer cells.
This study demonstrates that the risk model based on lipid metabolism may be useful for predicting the prognosis of patients with UM. By promoting macrophage M2 polarization, MGLL contributes to the evolution of malignancy in UM, suggesting that it may be a therapeutic target for UM.
尽管脂质代谢已被证明在癌症的发展中起着关键作用,但在现有文献中,其在葡萄膜黑色素瘤(UM)中的意义尚未阐明。
为了从 TCGA 数据库中确定 80 名 UM 患者的脂质代谢表达模式,分析了涉及脂质代谢的 47 个基因。共识聚类揭示了两个截然不同的分子群。进行 ESTIMATE、TIMER 和 ssGSEA 分析,以确定两个亚组在肿瘤微环境(TME)和免疫状态方面的差异。使用 Cox 回归和 Lasso 回归分析,建立了基于差异表达基因(DEGs)的风险模型。为了验证单酰基甘油脂肪酶(MGLL)在多种恶性肿瘤中的表达和免疫浸润,使用了 UCSC 数据库中的泛癌队列。接下来,使用 GEO 数据中的 UM 患者单细胞测序分析来描述 TME 中的脂质代谢和 MGLL 在 UM 中的作用。最后,利用研究来研究 MGLL 在 UM 中的作用。
UM 患者的两个分子亚组的生存率有很大差异。两个亚组之间的大多数 DEGs 与免疫相关途径有关。低免疫评分、高肿瘤纯度、免疫浸润细胞数量少和免疫状态较低与预后较好相关。GO 和 KEGG 数据的检查表明,基于涉及脂质代谢的基因的风险模型可以准确预测 UM 患者的生存率。研究表明,MGLL 是该模型中的一个关键基因,它促进了 UM 细胞的增殖、侵袭和迁移。此外,我们发现 MGLL 在巨噬细胞中表达强烈,特别是 M2 巨噬细胞,这可能在巨噬细胞的 M2 极化和肿瘤细胞中 M2 巨噬细胞的激活中发挥作用。
本研究表明,基于脂质代谢的风险模型可能有助于预测 UM 患者的预后。通过促进巨噬细胞 M2 极化,MGLL 促进了 UM 恶性肿瘤的演变,提示它可能是 UM 的治疗靶点。