Predict ovarian cancer by pairing serum miRNAs: Construct of single sample classifiers

Abstract

ObjectiveThe 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.MethodsBased 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).ResultsThe 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).ConclusionThe 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.

Construct of qualitative diagnostic biomarkers specific for glioma by pairing serum microRNAs

“…As qualitative biomarkers, they could overcome the drawback of quantitative expression level-based biomarkers. In contrast to the quantitative biomarker, the REO-based biomarkers are insensible to batch effects, data normalization methods, partial RNA degradation, RNA amplification bias, and the proportion of different cancer epithelial cells [13], and thus can be directly applied to individualized clinical diagnosis. The 5-miRPair prediction models would help avoid unnecessary biopsies and could be used in routine screening.…”

“…The relative expression orderings (REOs) of serum miRNAs were employed in developing predictive models, which belonged to the kind of single sample classifier [12]. Compared to the quantitative expression levels, REObased biomarkers are insensitive to batch effects, data normalization methods, partial RNA degradation, and RNA amplification bias [13]. Therefore, we developed two models based on within-sample REOs of serum miRNAs using large samples (n = 15,460).…”

Characteristics of circulating small noncoding RNAs in plasma and serum during human aging

“…By suppressing Wnt signaling inhibitor APC2, overexpression of hsa‐miR‐11,181‐3p can promote Wnt signaling pathway and increase cell viability in colon malignant tumor cell line 52 . For has‐miR‐7845‐5p, its expression in serum has been applied in constructing diagnostic classifier of ovarian cancer, 53 and higher expression was also observed in serum of patients with persistent atrial fibrillation 54 . Some direct targets of core miRNAs have been determined as drivers of age‐related process.…”