e13587 Background: Cell-free miRNAs (cf-miRNAs) found in bodily fluids like plasma or serum are adept at detecting cancer. When constructing a cancer detection model with a reasonably sized training sample, it seems logical that employing whole genome screening could enhance prediction performance and unveil novel miRNAs. However, screening all cf-miRNAs could be costly and its potential improvement compared to a curated panel remains uncertain. To evaluate the trade-off between accuracy and cost-effectiveness, our study aims to compare the performance of two approaches: a literature-based cf-miRNA panel and a whole genome screening. We utilized real-world datasets to assess the effectiveness of these two methods in cancer detection. Methods: To curate a cf-miRNA panel, we conducted a comprehensive review of published miRNA-related databases, identifying 27 databases, including dbDEMC 2.0, miRCancer, miRStress, HMDD, and others. Of these, 11 databases were both available and downloadable, yielding 196,839 records (database entries). Focusing on cf-miRNAs, we filtered the records to only include those clearly identified as originating from plasma or serum, resulting in 13,595 cf-miRNA records. Next, to ensure robustness, we employed two strategies: requiring multiple records for a given miRNA and confirming its mention in multiple databases to finalize the cf-miRNA panel. For comparison of prediction performance, we obtained a large-scale dataset from the Gene Expression Omnibus, specifically the GPL21263 platform, for modeling experiments. This dataset comprised 8,174 subjects with 2,565 miRNA targets across seven cancer types derived from various cf-miRNA-based cancer studies. We then trained machine learning models on both the curated panel and the full miRNAs from this dataset to facilitate a comparative analysis. Results: Out of the 13,595 cf-miRNA records, 499 miRNAs had over 4 records, and 612 miRNAs were mentioned in multiple databases. There was an overlap of 339 miRNAs, forming our final cf-miRNA panel. The model accuracy using this panel to distinguish between seven cancer types was 67.62%. In contrast, directly employing machine-learning to model whole genome screening resulted in an accuracy of 93.00%, demonstrating a significant improvement compared to the panel-based results. Conclusions: Our study curated a cost-effective cf-miRNA panel of 395 miRNAs, demonstrating the capability to distinguish multiple cancers. However, while the curated panel exhibited reasonable efficacy in cancer detection, the whole genome screening model displayed superior discriminatory power. Hence, for optimizing cancer detection methods, especially in large-scale datasets, employing whole genome screening appears to be a more promising strategy.