Department of Pathology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Burning Rock Biotech, Guangzhou, China.
Pathol Oncol Res. 2023 Feb 2;29:1610819. doi: 10.3389/pore.2023.1610819. eCollection 2023.
The outcomes of patients with diffuse large B-cell lymphoma (DLBCL) vary widely, and about 40% of them could not be cured by the standard first-line treatment, R-CHOP, which could be due to the high heterogeneity of DLBCL. Here, we aim to construct a prognostic model based on the genetic signature of metabolic heterogeneity of DLBCL to explore therapeutic strategies for DLBCL patients. Clinical and transcriptomic data of one training and four validation cohorts of DLBCL were obtained from the GEO database. Metabolic subtypes were identified by PAM clustering of 1,916 metabolic genes in the 7 major metabolic pathways in the training cohort. DEGs among the metabolic clusters were then analyzed. In total, 108 prognosis-related DEGs were identified. Through univariable Cox and LASSO regression analyses, 15 DEGs were used to construct a risk score model. The overall survival (OS) and progression-free survival (PFS) of patients with high risk were significantly worse than those with low risk (OS: HR 2.86, 95%CI 2.04-4.01, < 0.001; PFS: HR 2.42, 95% CI 1.77-3.31, < 0.001). This model was also associated with OS in the four independent validation datasets (GSE10846: HR 1.65, = 0.002; GSE53786: HR 2.05, = 0.02; GSE87371: HR 1.85, = 0.027; GSE23051: HR 6.16, = 0.007) and PFS in the two validation datasets (GSE87371: HR 1.67, = 0.033; GSE23051: HR 2.74, = 0.049). Multivariable Cox analysis showed that in all datasets, the risk model could predict OS independent of clinical prognosis factors ( < 0.05). Compared with the high-risk group, patients in the low-risk group predictively respond to R-CHOP ( = 0.0042), PI3K inhibitor ( < 0.05), and proteasome inhibitor ( < 0.05). Therefore, in this study, we developed a signature model of 15 DEGs among 3 metabolic subtypes, which could predict survival and drug sensitivity in DLBCL patients.
患者患有弥漫性大 B 细胞淋巴瘤（DLBCL）的结果差异很大，其中约有 40%的患者不能通过标准的一线治疗 R-CHOP 治愈，这可能是由于 DLBCL 的高度异质性。在这里，我们旨在构建一个基于 DLBCL 代谢异质性遗传特征的预后模型，以探索 DLBCL 患者的治疗策略。从 GEO 数据库中获得了一个训练和四个验证队列的 DLBCL 的临床和转录组数据。在训练队列中，通过 7 种主要代谢途径的 1916 个代谢基因的 PAM 聚类来识别代谢亚型。然后分析代谢群之间的差异表达基因。总共鉴定出 108 个与预后相关的差异表达基因。通过单变量 Cox 和 LASSO 回归分析，使用 15 个 DEG 构建风险评分模型。高危患者的总生存期（OS）和无进展生存期（PFS）明显差于低危患者（OS：HR 2.86，95%CI 2.04-4.01，<0.001；PFS：HR 2.42，95%CI 1.77-3.31，<0.001）。该模型与四个独立验证数据集的 OS 也相关（GSE10846：HR 1.65，= 0.002；GSE53786：HR 2.05，= 0.02；GSE87371：HR 1.85，= 0.027；GSE23051：HR 6.16，= 0.007）和两个验证数据集的 PFS（GSE87371：HR 1.67，= 0.033；GSE23051：HR 2.74，= 0.049）。多变量 Cox 分析表明，在所有数据集，风险模型可以独立于临床预后因素预测 OS（<0.05）。与高危组相比，低危组患者预测性地对 R-CHOP（= 0.0042）、PI3K 抑制剂（<0.05）和蛋白酶体抑制剂（<0.05）有反应。因此，在这项研究中，我们开发了一种基于 3 种代谢亚型中 15 个 DEG 的特征模型，该模型可以预测 DLBCL 患者的生存和药物敏感性。
