In Silico Screening of Circulating MicroRNAs as Potential Biomarkers for the Diagnosis of Ovarian Cancer

Table 1

Main characteristics of the studies included in the meta-analysis.

Author/publication year	Country	Sample	Method	Patients	Controls	miRNAs studied	AUC	Sensitivity	Specificity
Kan et al. [6]	Australia	Serum	Taqman	28 serous epithelial ovarian cancers	28 healthy women	MIR200A; MIR200B; MIR200C	0.675; 0.722; 0.727	0.821; 0.893; 0.750	0.357; 0.321; 0.536
Gao and Wu [13]	China	Serum	Taqman	74/19 epithelial/borderline ovarian cancers	50 healthy women	MIR200C	0.79	0.720	0.700
Meng et al. [11]	Germany	Serum	Taqman	180 epithelial ovarian cancers	66 healthy women	MIR25; MIR429	0.834; 0.704	0.924; 0.594	0.639; 0.955
Meng et al. [12]	Germany	Serum exosomes	Taqman	163 epithelial ovarian cancers	20 benign ovarian diseases	MIR200A; MIR200B; MIR200C	0.914; 0.815; 0.655	0.900; 1.000; 1.000	0.839; 0.528; 0.311
Elias et al. [8]	United States	Serum	Next-generation sequencing	98 epithelial ovarian cancers	15 healthy women	MIR200A; MIR200B; MIR200C; MIR429; MIR25	0.649; 0.737; 0.779; 0.703; 0.875	0.265; 0.500; 0.571; 0.469; 0.602	1.000; 0.933; 0.933; 0.867; 1.000
				98 epithelial ovarian cancers	45 benign ovarian diseases	MIR200A; MIR200B; MIR200C; MIR429	0.693; 0.783; 0.762; 0.692	0.439; 0.643; 0.673; 0.694	0.911; 0.778; 0.756; 0.622