Zhu Yan, Chen Jiongyu, Zhou Li, Zhang Lina, Liu Yuxin, Zhuang Yixuan, Peng Lin, Huang Yi-Teng
Health Care Center, The First Affiliated Hospital of Shantou University Medical College, Shantou 515041, Guangdong, China.
Medical Laboratory, Shenzhen Luohu People's Hospital, Shenzhen 518001, Guangdong, China.
J Oncol. 2022 Nov 9;2022:7625138. doi: 10.1155/2022/7625138. eCollection 2022.
Accurate risk stratification for patients with serous ovarian cancer (SOC) is pivotal for treatment decisions. In this study, we identified a lncRNA-based signature for predicting platinum resistance and prognosis stratification for SOC patients. We analyzed the RNA-sequencing data and the relevant clinical information of 295 SOC samples obtained from The Cancer Genome Atlas (TCGA) database and 180 normal ovarian tissues from the Genotype-Tissue Expression (GTEx) database. A total of 284 differentially expressed lncRNAs were screened out between platinum-sensitive and platinum-resistant groups by univariate Cox regression analysis. Then, a signature consisting of eight prognostic lncRNAs was used to construct a lncRNA score model by least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis. The ROC analysis showed that this signature had a good predictive performance for chemotherapy response in the training set (AUC = 0.8524) and the testing and whole sets with 0.8142 and 0.8393 of AUC, respectively. Dichotomized by the risk score of lncRNAs (lncScore), the high-risk patients showed significantly shorter progression-free survival (PFS) and overall survival (OS). Based on the final Cox model, a nomogram comprising the 8-lncRNA signature and 3 clinicopathological risk factors was then established for clinical application to predict the 1, 2, and 3-year PFS of SOC patients. The gene set enrichment analysis (GSEA) revealed that genes in the high-risk group were active in ATP synthesis, coupled electron transport, and mitochondrial respiratory chain complex assembly. Overall, our findings demonstrated the potential clinical significance of the 8-lncRNA-based classifier as a novel biomarker for outcome prediction and therapy decisions in SOC patients with platinum treatment.
浆液性卵巢癌(SOC)患者的准确风险分层对于治疗决策至关重要。在本研究中,我们确定了一种基于长链非编码RNA(lncRNA)的特征,用于预测SOC患者的铂耐药性和预后分层。我们分析了从癌症基因组图谱(TCGA)数据库获得的295个SOC样本以及来自基因型-组织表达(GTEx)数据库的180个正常卵巢组织的RNA测序数据和相关临床信息。通过单变量Cox回归分析,在铂敏感组和铂耐药组之间共筛选出284个差异表达的lncRNA。然后,通过最小绝对收缩和选择算子(LASSO)回归及多变量Cox回归分析,使用由8个预后lncRNA组成的特征构建lncRNA评分模型。受试者工作特征(ROC)分析表明,该特征在训练集(AUC = 0.8524)、测试集(AUC = 0.8142)和全集(AUC = 0.8393)中对化疗反应具有良好的预测性能。根据lncRNA的风险评分(lncScore)进行二分法分析,高危患者的无进展生存期(PFS)和总生存期(OS)显著缩短。基于最终的Cox模型,建立了一个包含8-lncRNA特征和3个临床病理风险因素的列线图,用于临床应用以预测SOC患者的1年、2年和3年PFS。基因集富集分析(GSEA)显示,高危组中的基因在ATP合成、偶联电子传递和线粒体呼吸链复合物组装中活跃。总体而言,我们的研究结果证明了基于8-lncRNA的分类器作为一种新型生物标志物在铂治疗的SOC患者结局预测和治疗决策中的潜在临床意义。