Prognostic value of microRNAs in colorectal cancer: a meta-analysis

Abstract

Background

Numerous studies have shown that miRNA levels are closely related to the survival time of patients with colon, rectal, or colorectal cancer (CRC). However, the outcomes of different investigations have been inconsistent. Accordingly, a meta-analysis was conducted to study associations among the three types of cancers.

Materials and methods

Studies published in English that estimated the expression levels of miRNAs with survival curves in CRC were identified until May 20, 2017 by online searches in PubMed, Embase, Web of Science, and the Cochrane Library by two independent authors. Pooled HRs with 95% CIs were used to estimate the correlation between miRNA expression and overall survival.

Results

A total of 63 relevant articles regarding 13 different miRNAs, with 10,254 patients were ultimately included. CRC patients with high expression of blood miR141 (HR 2.52, 95% CI 1.68–3.77), tissue miR21 (HR 1.31, 95% CI 1.12–1.53), miR181a (HR 1.52, 95% CI 1.26–1.83), or miR224 (HR 2.12, 95% CI 1.04–4.34), or low expression of tissue miR126 (HR 1.55, 95% CI 1.24–1.93) had significantly poor overall survival (P<0.05).

Conclusion

In general, blood miR141 and tissue miR21, miR181a, miR224, and miR126 had significant prognostic value. Among these, blood miR141 and tissue miR224 were strong biomarkers of prognosis for CRC.

Keywords:

• microRNA
• colorectal cancer
• prognosis
• meta-analysis

Introduction

Numerous researchers have studied the associations between miRNA expression and the survival outcomes of colorectal cancer (CRC) patients.^1–258 CRC has a 10% cancer incidence and mortality worldwide,²⁵⁹ and thus, it is one of the most serious diseases threatening human health. Despite great success in the treatment of CRC, the prognosis of CRC patients is still poor. Therefore, it is fundamental for the diagnosis, treatment, and prognosis of CRC patients to understand its emphasized molecular origin.²⁶⁰ Despite a comprehensive study about the mechanisms of CRC, there are still some challenges that require recognizing prognostic biomarkers with minimal invasion and sensitivity. Accordingly, it is of vital significance to improve the survival rate of CRC patients, utilizing rapid and reliable tumor-prognosis biomarkers.

miRNAs, small noncoding RNA gene products of approximately 22 nucleotides, are found in various types of organisms. They account for 2%–5% of the entire genome, number about 1,000, and regulate the expression of ≥20% of human genes.²⁶¹ In addition, they play crucial roles in regulating the translation and degradation of mRNAs via base pairing to partially complementary sites, predominantly in the 3′-untranslated areas of mRNAs.^262–264

In the study of CRC, a large number of articles have covered the fact that miRNAs are closely related to the survival time of patients.^1–258 There were relatively small samples in these papers, and the present work aims to estimate the most accurate prognostic value between miRNA level and survival outcome of CRC patients, better to comprehend the miRNAs with prognostic pertinence that are potential candidates for clinical verification in the future.

Materials and methods

Search strategy

We used four online databases – PubMed, Embase, Web of Science, and the Cochrane Library – to find pertinent literature published until May 20, 2017. The combination term “miR and colorectal cancer” was employed for the literature search. Two authors (S Gao and ZY Zhao) independently performed this comprehensive online search.

Inclusion criteria

Articles qualified if they satisfied the following criteria: patients with colon/rectal cancer or CRC; miRNA levels in tissue, plasma, or serum and survival results were measured; at least one survival curve was measured of overall survival (OS), cause-specific survival (CSS), disease-free survival (DFS), recurrence-free survival (RFS), progression-free survival (PFS), and metastasis-free survival (MFS), with or without HRs/95% CIs; and full text published in English.

Exclusion criteria

Exclusion criteria were experimental studies, reviews, or letters without primary data and retracted papers; frequency of research evaluating prognostic value of miRNAs in tissue of four or less. Only the most comprehensive study was included for this meta-analysis if more than one paper had been published in the same research group.

Quality assessment

SG and ZY Zhao identified all qualifying studies analyzing the prognostic value of miRNAs in CRC, and YZ reevaluated uncertain data.

Study selection

A flow diagram of the study selection process is presented in Figure 1. Our study found 1,843 articles for consideration within this meta-analysis, and 322 articles suitable for assessment of prognostic miRNA signatures in CRC and full-text papers were acquired by evaluating titles and abstracts. On elaborate review of research methodologies, 64 investigations were excluded, the details of which are shown in Figure 1. On the basis of the exclusion criteria, 63 studies were finally included in this meta-analysis.

Figure 1 Flow diagram of literature search and selection.

Study frequency

The frequency of studies estimating the prognostic value of miRNAs in CRC are shown in Tables 1 (blood) and 2 (tissue), including miRNA name, number of studies estimating prognostic value, and references.

Table 1 Frequency of studies estimating prognostic value of blood miRNA expression in colorectal cancer

miR	n	Reference(s)	miR	n	Reference(s)	miR	n	Reference(s)
15b	1	^Citation1	122	1	^Citation16	221	1	^Citation26
17-3p	1	^Citation2	124-5p	1	^Citation9	324-3p	1	^Citation12
19a	1	^Citation3	135	1	^Citation17	345	1	^Citation12
21	4	^Citation4^–^Citation7	139-5p	1	^Citation18	372	1	^Citation27
23b	1	^Citation8	141	2	^Citation14, ^Citation19	628-5p	1	^Citation12
26a	1	^Citation9	143	1	^Citation12	885-5p	1	^Citation28
29a	1	^Citation10	155	1	^Citation20	886-3p	1	^Citation12
29b	1	^Citation11	183	1	^Citation21	1290	1	^Citation29
34a*	1	^Citation12	194	1	^Citation11	4772-3p	1	^Citation30
92a	2	^Citation10, ^Citation13	196b	1	^Citation22	6826	1	^Citation17
96	1	^Citation14	200b	2	^Citation14, ^Citation16	6875	1	^Citation17
103	1	^Citation15	200c	1	^Citation23
106a	1	^Citation2	203	2	^Citation24, ^Citation25

Note: Highlighted studies were included in the present meta-analysis.

Study characteristics

Literature with Kaplan–Meier survival curves for CRC are detailed in Table 3. If data were not provided visually and merely as curves, they were extracted from the curves, and estimated HRs with 95% CIs were subsequently calculated using the method of Tierney et al²⁶⁵ with Engauge Digitizer version 4.1 software. In addition, if outcomes of both univariate and multiple covariates were covered, only the latter was chosen, because of adjustment for confounders.

Table 2 Frequency of studies estimating prognostic value of tissue-miRNA expression in colorectal cancer

miR	n	Reference(s)	miR	n	Reference(s)	miR	n	Reference(s)	miR	n	Reference(s)	miR	n	Reference(s)
let7a-5p	1	^Citation31	34a-5p	1	^Citation99	143	6	^Citation59, ^Citation112, ^Citation150^–^Citation153	211	1	^Citation197	487b	1	^Citation233
let7a-2	1	^Citation32	34a	1	^Citation100	144	1	^Citation154	212	1	^Citation198	490-3p	1	^Citation234
let7a	1	^Citation33	92a	3	^Citation64, ^Citation101, ^Citation102	145	5	^Citation33, ^Citation64, ^Citation150, ^Citation155, ^Citation156	214	1	^Citation199	491-5p	1	^Citation205
let7b	1	^Citation34	93	2	^Citation34, ^Citation103	148a*	1	^Citation157	215	4	^Citation58, ^Citation155, ^Citation179, ^Citation200	494	2	^Citation34, ^Citation235
let7c	1	^Citation35	96-5p	1	^Citation104	148a	3	^Citation81, ^Citation158, ^Citation159	217	2	^Citation201, ^Citation202	498	1	^Citation215
let7e	1	^Citation18	96	1	^Citation105	149	2	^Citation160, ^Citation161	218	2	^Citation203, ^Citation204	503	3	^Citation227, ^Citation236, ^Citation237
let7g*	1	^Citation36	99a-3p	1	^Citation36	150	1	^Citation162	221-3p	1	^Citation205	505*	1	^Citation36
let7g	1	^Citation37	99a	2	^Citation35, ^Citation106	153	1	^Citation163	221*	1	^Citation206	505	1	^Citation32
let7i	1	^Citation28	99b-5p	1	^Citation107	154	1	^Citation164	221	2	^Citation28, ^Citation207	506	2	^Citation238, ^Citation239
7	3	^Citation34, ^Citation39, ^Citation40	100	2	^Citation106, ^Citation108	155	1	^Citation60	223	1	^Citation208	515-5p	1	^Citation134
9	1	^Citation41	101	1	^Citation64	181a-1	1	^Citation32	224	5	^Citation206, ^Citation209^–^Citation212	517a	1	^Citation240
10b	4	^Citation28, ^Citation42^–^Citation44	103a	1	^Citation58	181a	5	^Citation18, ^Citation165^–^Citation168	229-5p	1	^Citation36	542-3p	2	^Citation241, ^Citation242
15a-5p	1	^Citation45	103-1	1	^Citation81	181b	2	^Citation18, ^Citation169	296	1	^Citation213	556	1	^Citation67
15a	1	^Citation46	103	1	^Citation109	181 c	1	^Citation170	320a	1	^Citation28	570	1	^Citation157
16	3	^Citation46^–^Citation48	106a-5p	1	^Citation110	182	3	^Citation171^–^Citation173	320e	1	^Citation214	573	1	^Citation134
17-5p	3	^Citation49^–^Citation51	106a	7	^Citation49, ^Citation64, ^Citation67, ^Citation111^–^Citation114	183	1	^Citation174	320	1	^Citation215	579	1	^Citation134
17	1	^Citation52	106b	3	^Citation58, ^Citation115, ^Citation116	185	1	^Citation133	326	1	^Citation216	590-5p	2	^Citation243, ^Citation244
18a	2	^Citation53, ^Citation54	107	1	^Citation36	187	2	^Citation175, ^Citation176	328	1	^Citation32	592	2	^Citation186, ^Citation245
19b	1	^Citation38	124-5 p	1	^Citation9	188-3p	1	^Citation177	335	1	^Citation217	610	1	^Citation246
20a-5p	2	^Citation55, ^Citation56	124	2	^Citation117, ^Citation118	191	1	^Citation178	337-5p	1	^Citation36	625-3p	1	^Citation247
20a	2	^Citation57, ^Citation58	125b	5	^Citation33, ^Citation35, ^Citation106, ^Citation112, ^Citation119	192	2	^Citation179, ^Citation180	338-3p	1	^Citation218	625	1	^Citation248
21	17	^Citation5, ^Citation58^–^Citation73	126	6	^Citation120^–^Citation125	193a-5p	1	^Citation181	340	1	^Citation219	630	1	^Citation249
22	2	^Citation74, ^Citation75	128	2	^Citation126, ^Citation127	193b	1	^Citation182	342-3p	1	^Citation205	638	1	^Citation250
23b	2	^Citation76, ^Citation77	130a	1	^Citation81	194	3	^Citation38, ^Citation183, ^Citation184	361-5p	1	^Citation220	652	1	^Citation32
24-3p	2	^Citation78, ^Citation79	130b	1	^Citation128	195-5p	1	^Citation33	362-3p	1	^Citation157	664-3p	1	^Citation186
25	1	^Citation80	132	2	^Citation129, ^Citation130	195	2	^Citation33, ^Citation34	365-1	1	^Citation76	720	1	^Citation251
26a-2	1	^Citation81	133a	2	^Citation131, ^Citation132	196a	1	^Citation185	365-2	1	^Citation76	802	1	^Citation134
26b	1	^Citation82	133b	2	^Citation133, ^Citation134	196b-5p	1	^Citation186	365	1	^Citation221	875-5p	1	^Citation252
29a	2	^Citation83, ^Citation84	134	1	^Citation135	196b	1	^Citation185	370	1	^Citation36	885-5p	1	^Citation28
29b	1	^Citation85	135b	3	^Citation136^–^Citation138	197	1	^Citation32	372	1	^Citation222	889	1	^Citation36
30a-5p	1	^Citation86	137	2	^Citation139, ^Citation140	198	1	^Citation187	376a	1	^Citation223	944	1	^Citation157
30a	1	^Citation87	138-5p	2	^Citation141, ^Citation142	199a-3p	1	^Citation188	378a-3p	1	^Citation224	1260b	1	^Citation253
30b	1	^Citation88	138	1	^Citation143	199b	1	^Citation189	378a-5p	1	^Citation224	1288	1	^Citation254
30d	1	^Citation89	139-3p	1	^Citation144	200a	3	^Citation82, ^Citation149, ^Citation190	378	1	^Citation225	1290	1	^Citation29
31-3p	2	^Citation90, ^Citation91	139-5p	2	^Citation18, ^Citation145	200c	3	^Citation23, ^Citation149, ^Citation191	422a	2	^Citation141, ^Citation226	1292	1	^Citation227
31-5p	3	^Citation91^–^Citation93	139	1	^Citation146	203	2	^Citation24, ^Citation192	424-3p	1	^Citation227	1297	1	^Citation255
31	4	^Citation64, ^Citation94^–^Citation96	140-5p	2	^Citation147, ^Citation148	204-5p	2	^Citation193, ^Citation194	429	5	^Citation149, ^Citation228^–^Citation231	1826	1	^Citation256
32	2	^Citation32, ^Citation97	141	2	^Citation34, ^Citation149	206	1	^Citation195	450b-5p	1	^Citation232	4500	1	^Citation257
33b	1	^Citation98	143-5p	1	^Citation58	210	1	^Citation196	455-5p	1	^Citation186	4775	1	^Citation258

Note: Highlighted studies were included in the present meta-analysis.

Table 3 Characteristics of studies included on colorectal cancer

miRNA	Study	Country/source	Design	Sample	Number	Stage	Cutoff	Method	Follow-up (months)	Result	HR (L/H)	HR (H/L)	95% CI
21	Menéndez et al^Citation4	Spain	P	Serum	102	I–IV	1.00	qRT-PCR	36	OSa		0.50	0.25–1.02
										DFSa		0.51	0.25–1.06
21	Toiyama et al^Citation5	Japan	R	Serum	188	I–IV	<0.01	RT-qPCR	84	OSa		4.12	1.10–15.40
21	Monzo et al^Citation6	Spain	R	Plasma	52	I–IV	Median	TaqMan	48	DFSb		2.32	0.80–6.71
21	Tsukamoto et al^Citation7	Japan	R	Plasma	326	I–IV	Median	qRT-PCR	84	OSa		2.28	1.81–5.74
					259					DFSa		2.34	1.87–4.60
92a	Wang and Gu^Citation10	China	R	Serum	74	II–IV	<0.06	RT-qPCR	35	OSb		1.17	0.70–1.97
92a	Liu et al^Citation13	China	R	Serum	166	I–IV	<0.01	RT-qPCR	53	OSa		4.36	1.64–11.57
141	Cheng et al^Citation19	China, USA	R	Plasma	258	I–IV	Median	RT-qPCR	96	OSa		2.40	1.18–4.86
141	Sun et al^Citation14	USA	R	Plasma	168	I–IV	Mean	RT-qPCR	96	OSb		2.58	1.58—4.21
200b	Maierthaler et al^Citation16	Germany I	R	Plasma	308	I–IV	Median	RT-qPCR	>72	OSa		0.77	0.57–1.05
		Germany II			219					OSa		1.21	0.98–1.50
200b	Sun et al^Citation14	USA	R	Plasma	169	I–IV	Mean	RT-qPCR	96	OSb		2.46	1.57–3.85
203	Hur et al^Citation24	Japan	R	Serum	186	I–IV	ROC	RT-qPCR	70	OSa		2.14	1.09–4.21
203	Shi et al^Citation25	China	R	Serum	180	II–IV	Median	RT-qPCR	60	OSb		0.47	0.27–0.81
21	Kulda et al^Citation59	Czech Republic	R	Frozen	46	I–IV	8.10	RT-qPCR	56	DFSb		1.80	0.05–65.37
21	Shibuya et al^Citation60	Japan	R	Frozen	156	I–IV	Mean	TaqMan	84	OSa		1.95	1.05–4.48
					116					DFSa		2.53	1.15–5.59
21	Nielsen et al^Citation61	Denmark I	R	FFPE	129	II	65%	ISH	>60	OSb		1.17	1.02–1.34
										DFSa		1.29	1.06–1.56
		Denmark II			67					OSb		0.97	0.83–1.13
										DFSa		0.85	0.73–1.01
21	Faltejskova et al^Citation62	Czech Republic	R	Tissue	44	I–IV	Median	RT-qPCR	86	OSb		2.72	0.63–11.83
21	Kjaer-Frifeldt et al^Citation63	Denmark	P	FFPE	520	II	Mean	ISH	84	OSa		1.08	0.97–1.22
										RF-CSSa		1.41	1.19–1.67
21	Schee et al^Citation64	Norway	P	Frozen	193	I–III	Median	qRT-PCR	>60	MFSb		1.17	0.59–2.32
21	Chen et al^Citation65	China	R	Tissue	195	I–IV	Mean	RT-qPCR	>100	OSa		2.56	1.43–4.57
21	Toiyama et al^Citation5	Japan	R	FFPE	166	I–IV	3.70	RT-qPCR	84	OSa		0.59	0.21–1.63
21	Oue et al^Citation66	Japan	R	FFPE	87	II–III	None	qRT-PCR	60	OSa		3.13	1.20–8.17
		Germany			145	II				OSa		2.65	1.06–6.66
21	Bullock et al^Citation67	UK	P	Frozen, FFPE	50	II	Mean	qRT-PCR	96	OSb		2.47	1.19–5.55
										DFSb		2.68	1.21–5.93
21	Fukushima et al^Citation68	Japan	R	Frozen	306	I–IV	Mean	RT-qPCR	90	OSa		2.88	1.70–5.08
					244					DFSa		2.94	1.68–5.36
21	Kang et al^Citation69	South Korea	R	FFPE	277	IIA–IIIC	Median	ISH	80	RFSa		2.24	1.25–4.02
21	Caritg et al^Citation58	Spain	R	Frozen	69	II	2.04	TaqMan	>140	DFSb		1.33	0.14–12.47
21	Feiersinger et al^Citation70	Germany	R	FFPE	29	I–IV	Median	qRT-PCR	205.15	OSb		1.45	0.39–5.43
										DFSb		1.76	0.75–4.11
21	Iseki et al^Citation71	Japan	R	FFPE	32	None	8.10	qRT-PCR	63.2	OSa		2.52	0.65–8.34
										PFSa		4.93	1.08–20.81
21	Lee et al^Citation72	South Korea	R	FFPE	170	I–IV	Median	ISH	105	OSb		0.93	0.54–1.60
21	Mima et al^Citation73	USA I	P	FFPE	190/192	I–IV	25%	RT-qPCR	207.6	OSa		0.99	0.75–1.31
										CSSa		0.88	0.58–1.31
		USA II			192/192		50%			OSa		1.03	0.78–1.35
										CSSa		1.10	0.75–1.60
		USA III			191/192		75%			OSa		1.40	1.07–1.84
										CSSa		1.42	0.98–2.04
106a	Díaz et al^Citation49	Spain	R	Frozen	110	I–IV	Median	RT-qPCR	99	OSa		0.53	0.26–1.08
										DFSa		0.36	0.17–0.78
106a	Feng et al^Citation111	China	R	Frozen	28	MB–NIB	Median	qRT-PCR	60	MFSb		3.63	0.56–23.68
106a	Schee et al^Citation64	Norway	P	Frozen	193	I–III	Median	qRT-PCR	>60	MFSb		0.81	0.41–1.59
106a	Ak et al^Citation112	Turkey	R	FFPE	40	I–IV	None	qRT-PCR	>200	OSb		0.94	0.35–2.56
106a	Bullock et al^Citation67	UK	P	Frozen, FFPE	50	II	Mean	qRT-PCR	96	OSb		2.25	1.00–5.04
										DFSb		2.91	1.32–6.42
106a	Hao et al^Citation113	China	R	Tissue	138	I–IV	66%	RT-qPCR	>60	OSa		1.87	1.13–3.09
										DFSa		1.22	0.70–2.12
106a	Hao et al^Citation114	China	R	FFPE	65	I–IV	Median	qRT-PCR	>60	OSb		2.00	0.51–7.85
125b	Nishida et al^Citation119	Japan	R	Frozen	89	None	Median	RT-qPCR	>96	OSb		2.42	0.99–5.91
125b	Ak et al^Citation112	Turkey	R	FFPE	40	I–IV	None	qRT-PCR	>200	OSb		0.90	0.32–2.56
125b	Cappuzzo et al^Citation35	Italy	R	FFPE	183	None	None	None	48	OSa		0.58	0.32–1.05
125b	Rokavec et al^Citation106	TCGA	R	Tissue	438	I–IV	None	Downloaded	>133	OSb		1.88	1.36–2.60
125b	Sun et al^Citation33	TCGA	R	Tissue	107	I–IV	Median	Downloaded	141.1	OSb		2.29	1.33–3.92
126	Hansen et al^Citation120	Denmark	R	FFPE	89	None	Median	ISH	58	OSb	1.93		1.13–3.29
					83					PFSb	2.69		1.42–5.08
126	Hansen et al^Citation121	Sweden, Denmark	P	FFPE	89	None	Median	qRT-PCR	>30	PFSa	2.04		1.19–3.45
126	Hansen et al^Citation122	DCCG	P	FFPE	560	II	Median	qRT-PCR	84	OSa	1.32		1.00–1.72
										RF-CSSa	1.04		0.71–1.52
126	Liu et al^Citation123	China	R	Frozen	92	I–IV	None	qRT-PCR	92	OSb	2.65		1.00–6.98
126	Ebrahimi et al^Citation124	Australia	R	FFPE	132	I–IV	<l/>2	qRT-PCR	>100	OSb	1.81		0.82–4.00
126	Yuan et al^Citation125	China	R	Tissue	75	I–IV	0/>0	ISH	68	OSb	2.35		0.91–6.06
143	Kulda et al^Citation59	Czech Republic	R	Frozen	46	I–IV	11.40	RT-qPCR	56	DFSb	0.45		0.07–2.78
143	Drebber et al^Citation150	Germany	R	FFPE	40	I–IV	1.00	RT-qPCR	76.8	OSb	1.52		0.32–7.22
143	Pichler et al^Citation151	Austria	R	FFPE	77	II–IV	None	qRT-PCR	>125	CSSa	1.86		1.06–3.25
					52					PFSb	1.55		0.91–2.66
143	Guo et al^Citation152	China	R	Tissue	79	I–IV	Median	qRT-PCR	122	OSb	1.45		0.69–3.07
143	Ak et al^Citation112	Turkey	R	FFPE	40	I–IV	1.76	qRT-PCR	>200	OSb	2.69		0.80–9.08
143	Simmer et al^Citation153	DCCG	P	FFPE	55	I–IV	Median	TaqMan	42	PFSa	0.45		0.24–0.85
145	Drebber et al^Citation150	Germany	R	FFPE	40	I–IV	0.10	RT-qPCR	76.8	OSb	1.95		0.43–8.79
145	Schee et al^Citation64	Norway	P	Frozen	193	I–III	Median	qRT-PCR	>60	MFSb	0.61		0.30–1.22
145	Pecqueux et al^Citation155	Germany	R	Frozen	47	None	Median	RT-qPCR	>60	OSb	3.73		1.45–9.55
145	Zhou et al^Citation156	China	R	Frozen	60	I–IV	Median	qRT-PCR	80	OSb	2.57		1.12–5.90
										DFSb	2.58		1.12–5.94
145	Sun et al^Citation33	TCGA	R	Tissue	107	I–IV	Median	Downloaded	>144	OSb	0.52		0.30–0.77
181a	Nishimura et al^Citation165	Japan	R	Frozen	162	I–IV	Median	qRT-PCR	>144	OSb		2.00	1.05–3.80
										DFSb		2.26	1.10–4.61
181a	Ji et al^Citation166	China I	R	Tissue	137	I–IV	Median	RT-qPCR	100	OSa		1.87	1.08–3.25
		China II		FFPE	294		1.00	ISH		OSa		1.38	1.11–1.72
181a	Pichler et al^Citation167	Austria	R	FFPE	80	II–IV	None	qRT-PCR	>125	CSSa		0.63	0.37–1.21
					54					PFSb		0.57	0.36–0.91
181a	Li et al^Citation168	China	R	Frozen	72	I–IV	None	RT-qPCR	>60	OSb		2.06	1.00–4.23
181a	Miyoshi et al^Citation18	TCGA	R	Tissue	93	II–III	None	Downloaded	135	RFSb		2.85	1.24–6.55
224	Liao et al^Citation209	China	R	Frozen	110	I–IV	Median	qRT-PCR	87	OSb		1.82	0.88–3.79
224	Yuan et al^Citation206	China	R	Tissue	108	I–III	None	qRT-PCR	60	OSa		0.27	0.14–0.51
					54					DFSa		0.07	0.02–0.25
224	Zhang et al^Citation210	China	R	Frozen	108	I–II	25.72	qRT-PCR	62.5	DFSb		1.87	0.79–4.41
224	Adamopoulos et al^Citation211	Greece	R	Frozen	104	I–IV	56%	qRT-PCR	120	OSa		4.41	1.72–11.34
					91					DFSa		4.61	1.41–15.09
224	Ling et al^Citation212	TCGA	R	Tissue	143	I–IV	None	Downloaded	72	OSa		2.88	0.97–8.56
		Italy I			54			qRT-PCR	115	OSa		2.77	0.95–8.11
		Italy II			68			qRT-PCR	115	OSa		4.14	0.96–17.76
		Romania			38			qRT-PCR	70	OSa		1.76	0.36–8.64
		Austria			74			qRT-PCR	130	OSa		2.36	1.32–4.21
		UK			41			qRT-PCR	60	OSb		4.92	1.31–18.46
										MFSb		6.51	1.97–21.51
429	Li et al^Citation228	China	R	Frozen	107	I–III	Median	qRT-PCR	82	OSb		2.09	0.84–5.17
429	Diaz et al^Citation149	Spain	R	Frozen	127	I–III	None	TaqMan	113	OSb		0.35	0.16–0.77
429	Sun et al^Citation229	China	R	Frozen	84	I–IV	None	qRT-PCR	96	OSb		0.29	0.16–0.55
429	Dong et al^Citation230	China	R	Frozen	78	I–IV	Median	qRT-PCR	60	OSb		2.66	1.25–5.68
429	Han et al^Citation231	China	R	Frozen	71	I–IV	Median	qRT-PCR	60	OSa		1.85	1.02–3.33

Notes:

a multiple-covariate analysis;

b Univariate analysis;

Abbreviations: L/H, low versus high miRNA expression; H/L, high versus low miRNA expression; P, prospective; qRT-PCR, quantitative real-time polymerase chain reaction; OS, overall survival; DFS, disease-free survival; R, retrospective; RT-qPCR, reverse transcription qRT-PCR; RF-CSS, recurrence-free cause-specific survival; ROC, receiver-operating characteristic; FFPE, formalin-fixed, paraffin-embedded; ISH, in situ hybridization; MFS, metastasis-free survival; RFS, recurrence-free survival; PFS, progression-free survival; CSS, cause-specific survival; TCGA, the Cancer Genome Atlas; DCCG, Dutch Colorectal Cancer Group.

Statistical analyses

All analyses were performed utilizing Stata version 13.0 (StataCorp, College Station, TX, USA). Merged HRs were regarded as significant at the P<0.05 level if 95% CIs did not contain the value 1. Effect values for HRs were regarded as large if ≥2. HRs for OS were regarded as the prime reference standard if OS P-values were inconsistent with other survival outcomes with respect to the associated miRNA level. All analyses employed random-effect models instead of fixed-effect models, because there existed differences among the studies, including tissue detected (frozen or formalin-fixed, paraffin-embedded), blood (plasma or serum), tumor stage (I–IV), cutoff values, and miRNA-analysis methods. Publication bias was measured by Begg’s funnel plot, and a two-tailed P-value <0.05 was regarded as significant. The trim-and-fill method was performed if publication bias occurred. Sensitivity analysis was employed to weigh how powerful merged HRs were after a single study had been removed. An individual study was suspected of having excess of influence if the point estimation was outside the 95% CI after removal from the analysis.

Results

Meta-analysis

An overview of HRs appraised from comprehensive analysis of all the miRNAs is given in Table 4. Thirteen miRNAs were involved in this meta-analysis: miR21, miR92a, miR106a, miR125b, miR126, miR141, miR143, miR145, miR181a, miR200b, miR203, miR224, and miR429. Results of survival analyses of these miRNAs are given in Figures 2–8.

Table 4 Meta-analysis results for miRNA expression in colorectal cancer

miRNA	Survival analysis	Articles	Studies included	HR	95% CI	Figure	P-value	Heterogeneity (Higgins’s I^2)	Patients, n
High miR21	OS	3	4, 5, 7	1.56	0.47–5.23	2	0.47	85.2%, P<0.01	616
High miR21	DFS	3	4, 6, 7	1.39	0.49–3.96	2	0.53	84.4%, P<0.01	480
High miR92a	OS	2	10, 13	2.11	0.59–7.61	2	0.25	81.6%, P=0.02	240
High miR141	OS	2	14, 19	2.52	1.68–3.77	2	<0.01	0.0%, P=0.87	426
High miR200b	OS	2	14, 16	1.28	0.75–2.19	2	0.36	88.8%, P<0.01	696
High miR203	OS	2	24, 25	0.99	0.22–4.37	2	0.99	91.4%, P<0.01	366
High miR21	OS	13	5, 60–68, 70–73	1.31	1.12–1.53	3A	<0.01	65.3%, P<0.01	2,861
High miR21	OSa	8	5, 60, 63, 65, 66, 68, 71, 73	1.47	1.16–1.87	3A	<0.01	71.7%, P<0.01	2,372
High miR21	DFS	7	58–61, 67, 68, 70	1.64	1.11–2.41	3D	0.01	79.2%, P<0.01	554
High miR21	RFS/CSS/MFS/PFS	5	63, 64, 69, 71, 73	1.33	1.06–1.67	3D	0.01	48.6%, P=0.07	1,787
High miR21	OS, adjustedb			1.13	0.96–1.34	4B	0.15	71.6%, P<0.01
High miR106a	OS	5	49, 67, 112–114	1.31	0.72–2.36	5	0.38	62.2%, P=0.03	403
High miR106a	DFS/MFS	5	49, 64, 67, 11, 113	1.14	0.55–2.36	5	0.72	75.8%, P<0.01	519
High miR125b	OS	5	33, 35, 106, 112, 119	1.43	0.83–2.47	5	0.19	74.6%, P<0.01	857
Low miR126	OS	5	120, 122–125	1.55	1.24–1.93	6	<0.01	1.2%, P=0.40	948
Low miR126	PFS/RFS/CSS	3	120–122	1.72	0.95–3.10	6	0.07	75.2%, P=0.02	732
Low miR143	DFS/CSS/PFS	3	59, 151, 153	1.00	0.47–2.13	6	1.00	77.7%, P<0.01	230
Low miR143	OS	3	112, 150, 152	1.69	0.94–3.04	6	0.08	0.0%, P=0.69	159
Low miR145	OS	4	33, 150, 155, 156	1.68	0.55–5.12	7	0.36	85.4%, P<0.01	254
Low miR145	MFS/DFS	2	64, 156	1.23	0.30–5.06	7	0.77	85.1%, P<0.01	253
High miR181a	OS	3	165, 166, 168	1.52	1.26–1.83	7	<0.01	0.0%, P=0.45	665
High miR181a	DFS/CSS/PFS/RFS	3	18, 165, 67	1.17	0.53–2.59	7	0.69	84.0%, P<0.01	309
High miR224	OS	4	206, 209, 211, 212	2.12	1.04–4.34	8	0.04	80.9%, P<0.01	740
High miR224	DFS/MFS	4	206, 210–212	1.43	0.23–8.77	8	0.70	90.6%, P<0.01	294
High miR429	OS	5	146, 228–231	1.00	0.39–2.58	8	1.00	88.7%, P<0.01	467

Notes:

a Multiple-covariate analysis;

b adjusted with trim-and-fill method.

Abbreviations: OS, overall survival; DFS, disease-free survival; RFS, recurrence-free survival; CSS, cause-specific survival; MFS, metastasis-free survival; PFS, progression-free survival.

Figure 2 Pooled analyses of OS or DFS in association with high blood miR21-, miR92a-, miR141-, miR200b-, and miR203-expression levels.

Note: Weights are from random-effect analysis.

Abbreviations: OS, overall survival; DFS, disease-free survival.

Figure 3 (A) Forest plots of pooled analyses of OS or OS (multiple-covariate analysis) in association with high tissue miR21-expression levels; (B) Begg’s funnel plot of publication bias for pooled analysis of OS in association with high tissue miR21-expression levels; (C) sensitivity analysis of pooled analysis of OS in association with high tissue miR21-expression levels; (D) forest plots of pooled analyses of DFS or RFS/CSS/MFS/PFS in association with high tissue miR21-expression levels. Weights are from random-effects analysis in A and D. ^aMultiple-covariate analysis; ^bunivariate analysis.

Abbreviations: OS, overall survival; DFS, disease-free survival; RFS, recurrence-free survival; CSS, cause-specific survival; MFS, metastasis-free survival; PFS, progression-free survival.

Figure 4 (A) Funnel plot of pooled analysis adjusted with the trim-and-fill method of OS in association with high tissue miR21-expression levels. Circles, included studies; diamonds, presumed missing studies. (B) Forest plot of pooled analysis adjusted with the trim-and-fill method of OS in association with high tissue miR21-expression levels. (C) Sensitivity analysis of pooled analysis adjusted with the trim-and-fill method of OS in association with high tissue miR21-expression levels. Weights are from random-effects analysis. ^aMultiple-covariate analysis; ^bunivariate analysis.

Abbreviation: OS, overall survival.

Figure 5 Pooled analyses of OS or DFS/MFS in association with high tissue miR106a- and miR125b-expression levels. Weights are from random-effects analysis.

Abbreviations: OS, overall survival; DFS, disease-free survival; MFS, metastasis-free survival; TCGA, the Cancer Genome Atlas.

Figure 6 Pooled analyses of OS, PFS/RFS/CSS, or DFS/CSS/PFS in association with low tissue miR126- and miR143-expression levels. Weights are from random-effects analysis.

Abbreviations: OS, overall survival; DCCG, Dutch Colorectal Cancer Group; PFS, progression-free survival; RFS, recurrence-free survival; CSS, cause-specific survival; DFS, disease-free survival.

Figure 7 Pooled analyses of OS, MFS/DFS or DFS/CSS/PFS/RFS in association with high tissue miR145-expression levels or low tissue miR181a-expression levels. Weights are from random-effects analysis.

Abbreviations: OS, overall survival; TCGA, the Cancer Genome Atlas; MFS, metastasis-free survival; DFS, disease-free survival; CSS, cause-specific survival; PFS, progression-free survival; RFS, recurrence-free survival.

Figure 8 Pooled analyses of OS or DFS/MFS in association with high tissue miR224- and miR429-expression levels. Weights are from random-effects analysis.

Abbreviations: OS, overall survival; TCGA, the Cancer Genome Atlas; DFS, disease-free survival; MFS, metastasis-free survival.

CRC patients with high blood miR141, high tissue miR181a and miR224, or low tissue miR126 expression have significantly shorter OS

Two studies^14,19 focused on associations between high blood miR141 levels and OS, indicating that CRC patients with high blood miR141 levels had significantly shorter OS than those with low miR141 expression (HR 2.52, 95% CI 1.68–3.77, P<0.01; Figure 2). Five papers^{120,122–125} stressed connections between low tissue miR126 levels and OS, suggesting that CRC patients with low expression of tissue miR126 levels had significantly poorer OS than those with high miR126 expression (HR 1.55, 95% CI 1.24–1.93, P<0.01; Figure 6).

Three articles concentrated on the relationship between high tissue miR181a levels and OS, demonstrating that CRC patients with high miR181a levels had significantly worse OS than those with low miR181a expression (HR 1.52, 95% CI 1.26–1.83, P<0.01; Figure 7). Four studies paid attention to correlations between high expression of tissue miR224 levels and OS, showing that CRC patients with high tissue miR224 levels had significantly shorter OS than those with low miR224 expression (HR 2.12, 95% CI 1.04–4.34, P=0.04; Figure 8).

There was no significant relationship between high expression levels of blood miR21, miR92a, miR200b, miR203, tissue miR106a, miR125b, or miR429 or low expression levels of tissue miR143 or miR145 and OS

Details are given in Table 4 and Figures 2 and 5–8.

High tissue miR21 expression forecasts poor OS

Thirteen investigations^{5,60–68,70–73} analyzed the connection between high tissue miR21 levels and OS, showing that CRC patients with high tissue miR21 levels had significantly worse OS than those with low miR21 expression (HR 1.31, 95% CI 1.12–1.53, P<0.01; Figure 3A).

Publication bias

To assess publications showing some degree of bias for OS of CRC patients with high tissue miR21 levels, our study used Begg’s funnel plot (Figure 3B). The P-value was less than 0.01, indicating the presence of publication bias. As such, the trim-and-fill method was performed and the pooled HR recalculated with presumed missing studies to estimate asymmetry in the funnel plot (Figure 4A), indicating no publication bias (P=0.73). The recalculated HR changed significance for OS (HR 1.13, 95% CI 0.96–1.34, P=0.15; Figure 4B).

Sensitivity analysis

For research on OS of CRC patients with high tissue miR21 levels, the sensitivity analysis did not manifest alterations during outcomes on the basis of the exclusion of any single investigation (Figure 3C), showing that no sole study significantly affected the merged HR or 95% CI. This also proved true for the outcome of OS adjusted with the trim-and-fill method (Figure 4C).

Key findings

We carried out a meta-analysis of 13 miRNAs and OS. Serving as the most investigated miRNA, miR21 (high tissue levels) in CRC showed significantly shorter OS than low tissue miR21 levels (P<0.05). However, there was no significant relationship between high blood miR21 levels and OS (P=0.47). The different detected sample types and relatively small sample capacity of the miR21 blood group (only three studies analyzing the relationship between blood miR21 levels and OS) may have been potential clinical reasons and caused the statistical significance between tissue and blood miR21 levels.

Encouragingly, the HR from analysis of the association between high tissue miR21 levels and OS (multiple-covariate analysis)^{5,60,63,65,66,68,71,73} was 1.47, which was greater than that reported in any of the 13 articles.^{5,60–78,70–73} Nevertheless, the significance did not remain in accordance with the forest plot, which was adjusted with the trim-and-fill method because publication bias existed (P<0.01; Figure 3B). This result indicated that the prognostic value of tissue miR21 was not stable in CRC patients. There were other miRNAs with significant prognostic value in CRC, including blood miR141 and tissue miR21, miR181a, miR224, and miR126 (P<0.05). Among these, blood miR141 and tissue miR224 were powerful prognostic candidates in CRC (HR ≥2).

Discussion

Present situation

Increasing numbers of studies have indicated that diverse miRNAs are connected with survival results in CRC patients.^1–258 Nevertheless, no systematic review or meta-analysis has evaluated HRs between miRNA levels and survival outcomes of CRC patients. Therefore, it was of vital significance to launch a meta-analysis to comprehend the relationship between expression levels of miRNAs and prognoses of CRC patients.

Molecular mechanisms for miRNAs researched

An overview of miRNAs with dysregulated expression and their potential targets and pathways of entry is detailed in Figure 9. There was noticeable functional overlap and relationships among the miRNAs. Seven miRNAs (miR21, miR106a, miR126, miR143, miR181a, miR224, and miR429) touched upon cell functions, including cell apoptosis, cell cycle, and death. To sum up, these associations may refer to CRC progression.

Figure 9 Summary of microRNAs with altered expression and potential targets and pathways entered in this study.

Abbreviations: ATG7, autophagy related 7; E2F1, E2F transcription factor 1; TGFBR2, transforming growth factor beta receptor 2; CDKN1A, cyclin dependent kinase inhibitor 1A; TP53, tumor protein p53; BCL2, apoptosis regulator; CXCR4, C-X-C motif chemokine receptor 4; TLR2, toll like receptor 2; PTEN, phosphatase and tensin homolog; WIF1, WNT inhibitory factor 1; CDH1, cadherin 1; PHLPP1, PH domain and leucine rich repeat protein phosphatase 1; PHLPP2, PH domain and leucine rich repeat protein phosphatase 2; MBD2, methyl-CpG binding domain protein 2; SMAD4, SMAD family member 4; SOX2, SRY-box 2; HOXA5, homeobox A5; AKT1, AKT serine/threonine kinase 1; FOXO3, forkhead box O3; RHOA, ras homolog family member A.

Other CRC molecular pathways

In addition to miRNAs, there are some other molecular data that can be confounders, related to mortalities, such as the chromosomal instability pathway, the DNA mismatch repair system, and microsatellite instability (MSI). Features of distinctive pathways are different models of genetic instability, succeeding clinical presentations, and features of pathological behavior. A majority of CRC follows the chromosomal instability pathway, features of which are extensive loss of heterozygosis and gross chromosomal abnormalities.^266,267 Second, about 15% of CRC is due to the derangement of the DNA mismatch repair system and consequential MSI. The former is in charge of protein production, which identifies and directly repairs mononucleotide mismatches at MS sequences that escape the proofreading system of DNA polymerase. Furthermore, a previous meta-analysis indicated that MSI-high CRC patients had a 40% better OS rate compared with MS-stable CRC patients.²⁶⁸

Molecular pathological epidemiology (MPE)

MPE is a multidisciplinary research field of associations between endogenous and exogenous ingredients, molecular cancer biomarkers, and cancer progression and also a comprehensive interdisciplinary science on the strength of the characteristic principal and continuum theory of diseases.^269,270 Other than miRNAs, DNA mutation and methylation and other diagnostics, such as blood tests, also play crucial roles in cancer prognosis and MPE, which deeply investigates environmental exposure, intermediate phenotypes, such as blood biomarkers, and molecular changes in cancer using molecular pathologic analyses. MPE helps precision medicine by providing robust evidence for exposure–outcome associations, such as with drugs.

Strengths

This study has some strengths. Almost all the articles with survival consequences in CRC patients with disparate miR-NAs were searched. Furthermore, the current expression profile of miRNAs is explicitly detailed in Tables 1 and 2 according to miRNAs and types of detected samples (blood or tissue). Papers assessing at least one of the survival curves of OS, CSS, DFS, RFS, PFS, and MFS were eventually included, and papers covering merely HRs or 95% CIs without any of the survival curves were excluded. Meta-analyses were performed on miRNAs investigated five or more times in CRC tissues. Virtually all the studies included had sample sizes ≥30 (except two),^70,111 reinforcing the usability and enlarging the feasibility of consequences to CRC patients.

Limitations

Nevertheless, we cannot overemphasize the following limitations. There was much heterogeneity in designs of studies, and most of the outcomes from our meta-analyses contained high heterogeneity (I²≥50%). Statistical assessment of publication bias was suboptimal. There existed differences among the studies, including tissue-detected (frozen or formalin-fixed, paraffin-embedded), blood (plasma or serum), tumor stage (I–IV), cutoff values, and miRNA methods. The present meta-analysis simply included papers published in English, perhaps excluding potential studies published in other languages with respect to miRNA level and prognosis of CRC patients. Papers covering only HRs or 95% CIs without survival curves were excluded, lowering the sample sizes of the papers included. Because of the massive interrelation between papers and data about CRC, we subjectively and selectively included specific studies on the basis of the inclusion and exclusion criteria, bringing about the omission of several possible miRNAs with prognostic value and a relatively small number of included studies. The studies included contained three types of cancers (colon and rectal cancer and CRC), which blurred the division between tumor types. Some blood miRNAs were from cell-free RNA, while others were from exosome isolates. These were considered the same to some degree and may have caused some deviations in the final results.

Implications for prospective clinical and scientific study

It should be mentioned that the current meta-analysis is the first system assessment of the pertinence of miRNA level to the prognosis of CRC patients. This study presents foundations for prospective clinical and scientific study with respect to clinical staff and other health care providers, for whom simultaneous determination of miRNA expression is able greatly to reinforce assessment of life expectancy of CRC patients, thus enabling prompt therapy, and for scientific researchers. Current research progress and trends in connections between miRNAs and prognosis of CRC patients are shown in Tables 1 and 2. Selectively basic experiments can be conducted using these details (Figure 9). Conflicting results on the prognosis of miRNAs may be addressed based on the present meta-analysis.

Conclusion

In general, blood miR141 and tissue miR21, miR181a, miR224, and miR126 have significant prognostic value. Among these, blood miR141 and tissue miR224 are strong biomarkers of prognosis in CRC.

Author contributions

All authors contributed toward data analysis, drafting and critically revising the paper and agree to be accountable for all aspects of the work.

Disclosure

The authors report no conflicts of interest in this work.