Due to the lack of effective screening approaches and early detection biomarkers, ovarian cancer (OC) has the highest mortality rates among gynecological cancers. Herein, we undertook a systematic biomarker discovery and validation approach to identify microRNA biomarkers for the early detection of OC. During the discovery phase, we performed small-RNA sequencing in stage-I high-grade serous OC (n=31), which was validated in multiple, independent datasets (TCGA, n=543; GSE65819, n=87). Subsequently, we performed multivariate logistic regression-based training in a serum dataset (GSE106817, n=640), followed by its independent validation in three datasets (GSE31568, n=85; GSE113486, n=140; Czech Republic cohort, n=192) and one prospective serum cohort (n=95). In addition, we evaluated the specificity of OCaMIR, by comparing its performance in other cancers (GSE31568 cohort, n=369). The OCaMIR demonstrated a robust diagnostic accuracy in the stage-I high-grade serous OC patients in the discovery cohort (area under the curve [AUC)]value=0.99), which was consistently reproducible in both stage-I (AUC=0.96) and all stage patients (AUC=0.89) in the TCGA cohort. Logistic regression-based training and validation of OCaMIR achieved AUC values of 0.89 (GSE106817), 0.85 (GSE31568), 0.86 (GSE113486), and 0.82 (Czech Republic cohort) in the retrospective serum validation cohorts, as well as prospective validation cohort (AUC=0.92). More importantly, OCaMIR demonstrated a significantly superior diagnostic performance compared to CA125 levels, even in stage-I patients, and was more cost effective: highlighting its potential role for the early detection of ovarian cancer. Small RNA sequencing identified a robust non-invasive miRNA signature for early stage serous ovarian cancer detection. Copyright ©2021, American Association for Cancer Research.