Zhou Zhongbao, Chai Yumeng, Li Yulong, Zhang Yong, Wang Tao
Department of Urology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Discov Oncol. 2024 Dec 18;15(1):796. doi: 10.1007/s12672-024-01631-8.
Lipid metabolism is crucial in tumor formation and progression. However, the role of lipid metabolism genes (LMGs) in bladder cancer (BLCA) are unknown. The purpose of this study was to construct a LMGs-related subtypes that predicted the treatment and prognosis of BLCA patients.
The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used for this study. The gene set enrichment analysis (GSEA) was utilized to distinguish functional differences between high-risk (HR) and low-risk (LR) groups. Single-sample GSEA (ssGSEA) was employed to determine potential associations between prognostic outcomes and immune status.
First, BLCA patients were divided into two subtypes by non-negative matrix factorization (NMF) clustering, and there were substantial variations in survival status, immune cell infiltration and immune classification between the two subtypes. Next, a prognostic signature involving 8 LMGs was identified (AKR1B1, SCD, CYP27B1, UGCG, SGPL1, FASN, TNFAIP8L3, PLA2G2A). HR patients exhibited worse outcome than LR patients. Multivariate Cox regression analysis confirmed that LMGs-related signature was an independent prognostic indicator of BLCA patients' survival. Compared with clinicopathological variables, LMGs-related signature showed higher prognostic predictive ability, with an area under curve of 0.720 at 5 years of follow-up. Through immunotherapy analysis, drug sensitivity analysis, TIDE score and immune cell infiltration characteristics, LMGs-related signature was confirmed to accurately predict the prognosis and treatment response of BLCA patients.
Our newly established prognostic signature, which involved eight LMGs, can give prognostic distinction for BLCA and may eventually lead to novel targets for treatment.
脂质代谢在肿瘤的形成和进展中至关重要。然而，脂质代谢基因（LMGs）在膀胱癌（BLCA）中的作用尚不清楚。本研究的目的是构建一种与LMGs相关的亚型，以预测BLCA患者的治疗和预后。
本研究使用了癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）。基因集富集分析（GSEA）用于区分高危（HR）组和低危（LR）组之间的功能差异。采用单样本GSEA（ssGSEA）来确定预后结果与免疫状态之间的潜在关联。
首先，通过非负矩阵分解（NMF）聚类将BLCA患者分为两个亚型，两个亚型在生存状态、免疫细胞浸润和免疫分类方面存在显著差异。接下来，鉴定出一个包含8个LMGs的预后特征（AKR1B1、SCD、CYP27B1、UGCG、SGPL1、FASN、TNFAIP8L3、PLA2G2A）。HR患者的预后比LR患者差。多变量Cox回归分析证实，与LMGs相关的特征是BLCA患者生存的独立预后指标。与临床病理变量相比，与LMGs相关的特征显示出更高的预后预测能力，在5年随访时曲线下面积为0.720。通过免疫治疗分析、药物敏感性分析、TIDE评分和免疫细胞浸润特征，证实与LMGs相关的特征能够准确预测BLCA患者的预后和治疗反应。
我们新建立的涉及8个LMGs的预后特征能够对BLCA进行预后区分，并最终可能带来新的治疗靶点。
