Xiang Jiangdong, Su Rongjia, Wu Sufang, Zhou Lina
Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Department of Gynecologic Oncology, International Peace Maternity and Child Health Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Front Oncol. 2022 Sep 15;12:967342. doi: 10.3389/fonc.2022.967342. eCollection 2022.
The key biochemical feature of malignant tumor is the conversion of energy metabolism from oxidative phosphorylation to glycolysis, which provides sufficient capacity and raw materials for tumor cell rapid growth. Our study aims to construct a prognostic signature for ovarian cancer based on lactate metabolism-related genes (LMRGs).
Data of ovarian cancer and non-diseased ovarian data were downloaded from TCGA and the GTEx database, respectively. LMRGs were obtained from GeneCards and MSigDB databases, and the differentially expressed LMRGs were identified using limma and DESeq2 R packages. Cox regression analysis and LASSO were performed to determine the LMRGs associated with OS and develop the prognostic signature. Then, clinical significance of the prognostic signature in ovarian cancer was assessed.
A total of 485 differentially expressed LMRGs in ovarian tissue were selected for subsequent analysis, of which 324 were up-regulated and 161 were down regulated. We found that 22 LMRGs were most significantly associated with OS by using the univariate regression analysis. The prognostic scoring model was consisted of 12 LMRGs (SLCO1B3, ERBB4, SLC28A1, PDSS1, BDH1, AIFM1, TSFM, PPARGC1A, HGF, FGFR1, ABCC8, TH). Kaplan-Meier survival analysis indicated that poorer overall survival (OS) in the high-risk group patients (P<0.0001). This prognostic signature could be an independent prognostic indicator after adjusting to other clinical factors. The calibration curves of nomogram for the signature at 1, 2, and 3 years and the ROC curve demonstrated good agreement between the predicted and observed survival rates of ovarian cancer patients. Furthermore, the high-risk group patients have much lower expression level of immune checkpoint-TDO2 compared with the low-risk group (P=0.024).
We established a prognostic signature based on LMRGs for ovarian cancer, and highlighted emerging evidence indicating that this prognostic signature is a promising approach for predicting ovarian cancer prognosis and guiding clinical therapy.
恶性肿瘤的关键生化特征是能量代谢从氧化磷酸化转变为糖酵解，这为肿瘤细胞的快速生长提供了充足的能力和原料。我们的研究旨在基于乳酸代谢相关基因（LMRGs）构建卵巢癌的预后标志物。
分别从TCGA和GTEx数据库下载卵巢癌数据和非疾病卵巢数据。从GeneCards和MSigDB数据库中获取LMRGs，并使用limma和DESeq2 R包鉴定差异表达的LMRGs。进行Cox回归分析和LASSO分析以确定与总生存期（OS）相关的LMRGs并开发预后标志物。然后，评估预后标志物在卵巢癌中的临床意义。
总共选择了485个在卵巢组织中差异表达的LMRGs进行后续分析，其中324个上调，161个下调。通过单变量回归分析，我们发现22个LMRGs与OS最显著相关。预后评分模型由12个LMRGs组成（SLCO1B3、ERBB4、SLC28A1、PDSS1、BDH1、AIFM1、TSFM、PPARGC1A、HGF、FGFR1、ABCC8、TH）。Kaplan-Meier生存分析表明，高危组患者的总生存期（OS）较差（P<0.0001）。在调整其他临床因素后，该预后标志物可能是一个独立的预后指标。该标志物在1年、2年和3年的列线图校准曲线以及ROC曲线表明，卵巢癌患者的预测生存率和观察生存率之间具有良好的一致性。此外，高危组患者的免疫检查点TDO2表达水平明显低于低危组（P=0.024）。
我们建立了基于LMRGs的卵巢癌预后标志物，并强调了新出现的证据表明该预后标志物是预测卵巢癌预后和指导临床治疗的一种有前景的方法。
