Hong Guini, Luo Fengyuan, Chen Zhihong, Ma Liyuan, Lin Guiyang, Wu Tong, Li Na, Cai Hao, Hu Tao, Zhong Haijian, Guo You, Li Hongdong
School of Medical Information Engineering, Gannan Medical University, Ganzhou, China.
Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, China.
Front Med (Lausanne). 2022 Aug 2;9:923275. doi: 10.3389/fmed.2022.923275. eCollection 2022.
The accuracy of CA125 or clinical examination in ovarian cancer (OVC) screening is still facing challenges. Serum miRNAs have been considered as promising biomarkers for clinical applications. Here, we propose a single sample classifier (SSC) method based on within-sample relative expression orderings (REOs) of serum miRNAs for OVC diagnosis.
Based on the stable REOs within 4,965 non-cancer serum samples, we developed the SSC for OVC in the training cohort (GSE106817: OVC = 200, non-cancer = 2,000) by focusing on highly reversed REOs within OVC. The best diagnosis is achieved using a combination of reversed miRNA pairs, considering the largest evaluation index and the lowest number of miRNA pairs possessed according to the voting rule. The SSC was then validated in internal data (GSE106817: OVC = 120, non-cancer = 759) and external data (GSE113486: OVC = 40, non-cancer = 100).
The obtained 13-miRPairs classifier showed high diagnostic accuracy on distinguishing OVC from non-cancer controls in the training set (sensitivity = 98.00%, specificity = 99.60%), which was reproducible in internal data (sensitivity = 98.33%, specificity = 99.21%) and external data (sensitivity = 97.50%, specificity = 100%). Compared with the published models, it stood out in terms of correct positive predictive value (PPV) and negative predictive value (NPV) (PPV = 96.08% and NPV=95.16% in training set, and both above 99% in validation set). In addition, 13-miRPairs demonstrated a classification accuracy of over 97.5% for stage I OVC samples. By integrating other non-OVC serum samples as a control, the obtained 17-miRPairs classifier could distinguish OVC from other cancers (AUC>92% in training and validation set).
The REO-based SSCs performed well in predicting OVC (including early samples) and distinguishing OVC from other cancer types, proving that REOs of serum miRNAs represent a robust and non-invasive biomarker.
CA125或临床检查在卵巢癌（OVC）筛查中的准确性仍面临挑战。血清微小RNA（miRNA）已被视为具有临床应用前景的生物标志物。在此，我们提出一种基于血清miRNA样本内相对表达顺序（REO）的单样本分类器（SSC）方法用于OVC诊断。
基于4965份非癌血清样本中稳定的REO，我们通过关注OVC内高度反转的REO，在训练队列（GSE106817：OVC = 200，非癌 = 2000）中开发了用于OVC的SSC。根据投票规则，考虑最大评估指标和拥有的miRNA对数量最少，使用反转miRNA对的组合实现最佳诊断。然后在内部数据（GSE106817：OVC = 120，非癌 = 759）和外部数据（GSE113486：OVC = 40，非癌 = 100）中对SSC进行验证。
获得的13-miRPairs分类器在训练集中区分OVC与非癌对照时显示出高诊断准确性（敏感性 = 98.00%，特异性 = 99.60%），在内部数据（敏感性 = 98.33%，特异性 = 99.21%）和外部数据（敏感性 = 97.50%，特异性 = 100%）中具有可重复性。与已发表的模型相比，它在正确阳性预测值（PPV）和阴性预测值（NPV）方面表现突出（训练集中PPV = 96.08%，NPV = 95.16%，验证集中两者均高于99%）。此外，13-miRPairs对I期OVC样本的分类准确率超过97.5%。通过将其他非OVC血清样本作为对照进行整合，获得的17-miRPairs分类器可以区分OVC与其他癌症（训练集和验证集中AUC>92%）。
基于REO的SSC在预测OVC（包括早期样本）以及区分OVC与其他癌症类型方面表现良好，证明血清miRNA的REO代表一种强大的非侵入性生物标志物。
