Diagnostic role of circulating MiR-21 in colorectal cancer: a update meta-analysis

Figures & data

Figure 1. Study flow diagram.

Table 1. Characteristics of included studies in this meta-analysis.

Study (year)	Age (year)		Gender (M/F)		Country	Source of control	Source of miR-21	Samples size		Cancer type	Test method	Reference gene	Diagnosis parameters						QUADAS scores
	P	C	P	C				P	C				TP	FP	FN	TN	Sen	Spe
Sazanov et al. [18] (2017)	NA		NA		Russia	Healthy volunteers	Plasma	31	34	CRC II-IV	qRT-PCR (SYBR Green)	U6 snRNA	20	5	11	29	0.65	0.85	12
							Saliva	34	34				33	3	1	31	0.97	0.91
Basati et al. [19] (2014)	55.35 ± 10.13	55.00 ± 10.35	21/19	22/18	Iran	Healthy volunteers	Serum	40	40	CRC	qRT-PCR (SYBR Green)	RNU6B	31	9	9	31	0.77	0.78	13
Liu et al. [20] (2013)	NA		NA		China	Healthy volunteers	Serum	200	80	CRC	qRT-PCR (TaqMan)	miR-16	130	12	70	68	0.65	0.85	12
Fouad et al. [21] (2017)	49.54 ± 15.37	49.18 ± 16.17	26/24	25/25	Egypt	Healthy volunteers	Serum	50	50	CRC	qRT-PCR(TaqMan)	RNU6B	41	22	9	28	0.82	0.56	12
Sarlinova et al. [22] (2016)	40-86	44-86	46/25	58/22	Slovak	Healthy volunteers	Serum	71	80	CRC	qRT-PCR (TaqMan)	RNU48	51	26	20	54	0.72	0.68	12
Bastaminejad et al. [23] (2017)	NA		21/19	21/19	Iran	Healthy control	Serum	40	40	CRC	qRT-PCR (SYBR Green)	miR-16	34	11	6	29	0.86	0.73	12
							Stool	40	40				34	8	6	32	0.86	0.81
Jin et al. [24] (2020)	50.9 ± 9.5	42.32 ± 9.23	38/42	28/22	China	Healthy control	Serum	80	50	CRC	qRT-PCR (ABI7500)	U6 snRNA	73	7	7	43	0.91	0.86	13
Toiyama et al. [25] (2013)	NA		NA		Japan	Healthy volunteers	Serum	186	53	CRC	qRT-PCR (TaqMan)	cel-miR-39	154	5	32	48	0.83	0.91	12
Sabry et al. [26] (2018)	51.97 ± 12.18	48.47 ± 15.16	19/16	52/49	Egypt	Healthy control	Serum	35	101	CRC	qRT-PCR (SYBR Green)	RNU6B	32	5	3	96	0.91	0.95	13
Hua et al. [27] (2018)	35-86	32-69	30/22	25/20	China	Healthy volunteers	Serum	52	45	CRC	qRT-PCR (SYBR Green)	U6 snRNA	44	9	8	36	0.85	0.80	13
Chen et al. [28] (2019)	58.0 ± 16.5	56.5 ± 16.0	39/33	15/15	China	Healthy control	Plasma	72	30	CRC	qRT-PCR	NA	50	1	22	29	0.69	0.97	12
Song et al. [29] (2013)	59.18 ± 11.1	55.44 ± 12.1	30/10	40/16	China	Healthy control	Serum	40	56	CRC	qRT-PCR(TaqMan)	miR-16	30	17	10	39	0.75	0.69	13
Wang et al. [30] (2012)	63	46	17/15	9/30	China	Healthy individuals	Serum	32	39	CRC	qRT-PCR (SYBR Green)	miR-16	28	10	4	29	0.88	0.74	12
Ogata-Kawata et al. [31] (2014)	35-65	35-65	55/33	3/8	Japan	Physical screening individuals	Serum	88	11	CRC	qRT-PCR (TaqMan)	miR-451	54	1	34	10	0.61	0.91	13
Zhang et al. [32] (2014)	NA		NA		China	Normal control	Serum	41	30	CRC	qRT-PCR	U6 snRNA	21	6	20	24	0.51	0.79	11
Du et al. [33] (2014)	61.1 ± 12.7	61.7 ± 12.6	30/19	30/19	China	Healthy control	Plasma	49	49	CRC	NA	NA	37	3	12	46	0.76	0.93	11
Luo et al. [34] (2013)	67.1 ± 11.0	61.7 ± 6.4	25/25	25/25	Germany	Healthy control	Plasma	80	144	CRC	qRT-PCR (TaqMan)	miR-16	42	27	38	117	0.52	0.81	13
Kanaan et al. [35] (2012)	60 ± 11	61 ± 9	7/13	7/13	America	Healthy control	Plasma	30	30	CRC	qRT-PCR (TaqMan)	U6 snRNA	27	3	3	27	0.9	0.9	13

M: Male; F: Female; P: Patient; C: Control; Sen: Sensitivity; Spe: Specificity; NA: Not Available.

Figure 2. Forest plot of included studies assessing the sensitivity and specificity of circulating miR-21 in CRC.

Figure 3. Forest plot of included studies assessing the PLR of circulating miR-21 in CRC.

Figure 4. Forest plot of included studies assessing the NLR of circulating miR-21 in CRC.

Figure 5. Forest plot of included studies assessing the DOR of circulating miR-21 in CRC.

Figure 6. SROC curve for miR-21 in CRC diagnosis.

Figure 7. HSROC curve for miR-21 in CRC diagnosis.

Figure 8. Deeks’ tests for the assessment of publication bias in miR-21 assays.

Figure 9. Forest plot of included Chinese studies assessing the sensitivity and specificity of circulating miR-21 in CRC.

Figure 10. Forest plot of included Chinese studies assessing the PLR of circulating miR-21 in CRC.

Figure 11. Forest plot of included Chinese studies assessing the NLR of circulating miR-21 in CRC.

Figure 12. Forest plot of included studies using SYBR-Green qRT-PCR as test method assessing the sensitivity and specificity of circulating miR-21 in CRC.

Figure 13. Forest plot of included studies using SYBR-Green qRT-PCR as test method assessing the PLR of circulating miR-21 in CRC.

Figure 14. Forest plot of included studies using SYBR-Green qRT-PCR as test method assessing the NLR of circulating miR-21 in CRC.

Figure 15. Forest plot of included studies using TaqMan qRT-PCR as test method assessing the sensitivity and specificity of circulating miR-21 in CRC.

Figure 16. Forest plot of included studies using TaqMan qRT-PCR as test method assessing the PLR of circulating miR-21 in CRC.

Figure 17. Forest plot of included studies using TaqMan qRT-PCR as test method assessing the NLR of circulating miR-21 in CRC.

Figure 18. Forest plot of included studies using U6 snRNA as internal reference gene assessing the sensitivity and specificity of circulating miR-21 in CRC.

Figure 19. Forest plot of included studies using U6 snRNA as internal reference gene assessing the PLR of circulating miR-21 in CRC.

Figure 20. Forest plot of included studies using U6 snRNA as internal reference gene assessing the NLR of circulating miR-21 in CRC.

Figure 21. Forest plot of included studies using miR-16 as internal reference gene assessing the sensitivity and specificity of circulating miR-21 in CRC.

Figure 22. Forest plot of included studies using miR-16 as internal reference gene assessing the PLR of circulating miR-21 in CRC.

Figure 23. Forest plot of included studies using miR-16 as internal reference gene assessing the NLR of circulating miR-21 in CRC.

Table 2. Possible sources of heterogeneity of covariants in the meta-regression analysis.

	Coef	p	95%CI
Age	−0.08	.2	(−0.2;0.05)
Gender	−0.81	.17	(−2.01;0.4)
Country	−0.1	.48	(−0.42;0.21)

Coef: Coefficent.

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