Department of Reproductive Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin City, Heilongjiang Province, China 150001.
Department of Laparoscopic Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian City, Liaoning Province, China 116000.
J Immunol Res. 2022 Jun 25;2022:2359349. doi: 10.1155/2022/2359349. eCollection 2022.
Ovarian cancer (OC) is a malignant tumor that seriously threatens women's health. Molecular classification based on metabolic genes can reflect the deeper characteristics of ovarian cancer and provide support for prognostic evaluation and the guidance of individualized treatment.
The metabolic subtypes were determined by consensus clustering and CDF. We used the ssGSEA method to calculate the IFN score of each patient. The CIBERSORT method was used to evaluate the score distribution and differential expression of 22 immune cells, and LDA was applied to establish a subtype classification feature index. The Kaplan-Meier and ROC curves were generated to validate the prognostic performance of metabolic subtypes in different cohorts. WGCNA was used to screen the coexpression modules associated with metabolic genes.
We obtained three metabolic subtypes (MC1, MC2, and MC3). MC2 had the best prognosis, and MC1 and MC3 had poor prognoses. Consistently, MC2 subtype had higher T cell lytic activity and lower angiogenesis, IFN, T cell dysfunction, and rejection scores. TIDE analysis showed that MC2 patients were more likely to benefit from immunotherapy; MC1 patients were more sensitive to immune checkpoint inhibitors and traditional chemotherapy drugs. The multiclass AUCs based on the RNASeq and GSE cohorts were 0.93 and 0.84, respectively. Finally, we screened 11 potential gene markers related to the metabolic characteristic index that could be used to indicate the prognosis of OC.
Molecular subtypes related to metabolism are crucial to comprehensively understand the molecular pathological characteristics related to metabolism for OC development, explore reliable markers for prognosis, improve the OC staging system, and guide personalized treatment.
卵巢癌（OC）是一种严重威胁女性健康的恶性肿瘤。基于代谢基因的分子分类可以反映卵巢癌更深层次的特征，并为预后评估和个体化治疗提供支持。
通过共识聚类和 CDF 确定代谢亚型。我们使用 ssGSEA 方法计算每个患者的 IFN 评分。使用 CIBERSORT 方法评估 22 种免疫细胞的评分分布和差异表达，并应用 LDA 建立亚型分类特征指标。生成 Kaplan-Meier 和 ROC 曲线以验证代谢亚型在不同队列中的预后性能。使用 WGCNA 筛选与代谢基因相关的共表达模块。
我们得到了三个代谢亚型（MC1、MC2 和 MC3）。MC2 具有最好的预后，而 MC1 和 MC3 具有较差的预后。一致的是，MC2 亚型具有更高的 T 细胞裂解活性和更低的血管生成、IFN、T 细胞功能障碍和排斥评分。TIDE 分析表明，MC2 患者更有可能从免疫治疗中受益；MC1 患者对免疫检查点抑制剂和传统化疗药物更敏感。基于 RNASeq 和 GSE 队列的多类 AUC 分别为 0.93 和 0.84。最后，我们筛选出 11 个与代谢特征指数相关的潜在基因标志物，可用于指示 OC 的预后。
与代谢相关的分子亚型对于全面了解 OC 发展中与代谢相关的分子病理特征、探索可靠的预后标志物、改善 OC 分期系统以及指导个体化治疗至关重要。
