Prognosis Value of Low microRNA-34a Expression in Human Gastrointestinal Cancer: A Meta-Analysis

doi:10.21203/rs.3.rs-63455/v1

Download PDF

Research article

Prognosis Value of Low microRNA-34a Expression in Human Gastrointestinal Cancer: A Meta-Analysis

https://doi.org/10.21203/rs.3.rs-63455/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 14 Jan, 2021

Read the published version in BMC Cancer →

You are reading this latest preprint version

Background: Mounting evidence shows that microRNA-34a (miR-34a) is involved in cancer prognosis. Therefore, we summarize the predictive role of miR-34a for survival in patients with gastrointestinal cancers (GICs).

Methods: All the eligible studies were searched by PubMed, Web of Science and EMBASE and survival results were extracted. Then, the hazard ratio (HR) with corresponding 95% confidence intervals (CIs) was calculated to evaluate the prognostic role of miR-34a in GICs. The association between miR-34a expression and clinicopathological characteristics was estimated by odds ratio (OR) and 95%CIs.

Results: A total of 20 studies were included in this meta-analysis. For overall survival (OS), the lower miR-34a expression significantly predicted poorer outcome in GICs, with the pooled HR of 1.86 (95% CI: 1.52-2.28, P<0.01). For disease-free survival (DFS), progressive-free survival (PFS), and recurrence-free survival (RFS), the lower miR-34a expression revealed worse DFS/PFS/RFS with the pooled HR of 1.86 (95% CI: 1.31–2.63, P < 0.01). Significant relation of differentiation/TMN stage/lymphatic metastasis and the expression level of miR-34a was identified.

Conclusion: This meta-analysis reveals that lower miR-34a expression is significantly connected with worse OS and DFS/PFS/RFS of GICs patients. In addition, miR-34a expression level is relatively lower in patients with lymph node metastasis than patients without, and decreased miR-34a expression level is linked to poor tumor differentiation and late TMN stage. MiR-34a may become a new factor for prognosis prediction and progression of GICs.

Cancer Biology

Oncology

microRNA-34a

gastrointestinal cancer

prognosis

meta-analysis

Gastrointestinal cancers (GICs) account for the major cancer-related deaths around the world, especially in developing countries[1]. Previous studies have shown that stomach, esophageal, liver, and colorectal cancers were commonly identified as the leading causes of cancer deaths[2]. Nowadays, common treatments for GICs contain surgeons, neoadjuvant chemoradiotherapy, and adjuvant chemoradiotherapy and immunotherapy, however, the therapeutic effects are limited in patients with advanced stages. Therefore, there is an urgent need for early detection of GICs and recognition of high-risk patients with poor prognosis.

MicroRNAs (miRNAs) are small-molecule RNAs with a length of 19 to 25 nucleotides that regulate post-transcriptional silence of target genes by combining with the 3’-untranslated region (3’-UTR) of target messenger RNA[3]. MiRNAs participate in various biological processes including cell multiplication, differentiation, apoptosis and cell cycle regulation[4]. Studies have reported that miRNAs are abnormally expressed in tumors and have strong diagnostic and prognostic values[5].

MicroRNA-34a (miR-34a), a member of miR-34 family, has been verified abnormally expressed in various tumors, including esophageal cancer (EC) [6], gastric cancer (GC)[7], colorectal cancer (CRC)[8], hepatocellular carcinoma (HCC)[9], pancreatic cancer (PC)[10], gallbladder cancer (GBC)[11], and other cancers[12]. Based on recent studies, miR-34a has been considered closely related to gastrointestinal cancer multiplication[13], invasion[14] and metastasis[15], which beared on the animate biological roles of miR-34a in cellular signal pathways, such as MAPK/Ras pathway[16], Wnt/β-Catenin pathway[17], PI3K/Akt pathway[18], SIRT1/p53 pathway[19], FoxM1/c-Myc pathway[20] and etc. However, the prognostic accuracy of miR-34a in GICs was inconsistent among these studies. Yuxin Hu et al[21], Hui WT et al[22], and Yang B et al[23] reported that the low expression level of miR-34a predicted a worse survival rate in GICs patients. On the contrary, Osawa S et al[24], Zhang X et al[25] and Mojin Wang et al[26] found that GICs patients were benefited from down-regulated miR-34a. Aimed to systematically assess the prognostic value of miR-34a in GICs and discuss the association between miR-34a expression and clinicopathological characteristics, we performed a meta-analysis on the basis of all published relevant studies.

2.1. Literature Search

We searched PubMed, Web of Science and Embase database to identify relevant studies before January 1, 2020. The following keywords were used: ‘microRNA- 34a’, ‘miR-34a’, ‘cancer’, ‘neoplasm’, ‘esophageal’, ‘stomach’, ‘colorectal’, ‘colon’, ‘pancreatic’, ‘hepatocellular’, ‘liver’, ‘gallbladder’, ‘prognosis’, ‘survival’, ‘hazard ratio’, ‘gastrointestinal’, which were combined with ‘AND’ or ‘OR’. The results were limited to papers published in English.

2.2. Selection Criteria

Studies were included in based on the following conditions: (1) the diagnoses of GICs were confirmed by histopathology; (2) measured expression of miR-34a in tissue or blood, and divided into high and low level; (3) reported the survival outcome directly or provided survival data from Kaplan-Meier survival curves. Exclusion criteria are the following: (1) reviews, laboratory studies or letters; (2) lacked key information about survival outcomes or unable to calculate, such as HR or 95% CI.

2.3. Data Extraction and Quality Assessment

Two investigators (Yan-Ling Chen and Xiao-Lin Liu) independently extracted the data from all eligible references, including first author, published time, country, tumor type, sample type, test method, TNM stage, follow-up time and cut-off value, HRs of miR-34a for OS and/or DFS, PFS, RFS, and 95% CIs. In addition, the data of clinical characteristics were collected from studies reported that. All eligible studies were retrospective. The Newcastle-Ottawa Scale (NOS) was used to assess the quality. The range of scores is 0 to 9, and score more than 6 was considered as high quality[27]. Any disagreement achieved consensus finally by discussion.

2.4. Statistical Analysis

We used RevMan 5.3 (Cochrane Collaboration, Oxford, UK) and STATA 12.0 (StataCorp LP, College Station, TX, USA) to conduct the statistical analysis. The pooled HR and corresponding 95%CI were used to evaluate the prognostic value of low miR-34a expression in GICs. The heterogeneity among studies was calculated using Cochran’s Q test and Higgins’s I² statistic. If P > 0.05 or I² ≤ 50%, we considered no significant heterogeneity existed, the fixed-effect model was used; if P ≤ 0.05 or I² > 50%, the random-effect model was used. Some studies didn’t provide the HR and 95% CI directly, we obtained the key points and the relevant data from Kaplan-Meier survival curves by utilizing Engauge Digitizer 4.1 software, then calculated HR and corresponding 95% CI following Tierney’s method [28]. Publication bias was assessed by funnel plots and Egger’s tests. Besides, we performed a sensitivity analysis by removing studies one by one to assess the influence of single study. The association between miR-34a expression and clinicopathological characteristics was evaluated by the pooled OR and 95%CI.

3.1. Literature search

A total of 1196 records were obtained in the beginning. 825 studies were excluded because of duplication. 282 records were excluded after screening the titles and abstracts. According to the selection criteria, 19 studies were identified as eligible finally, including 2 EC, 5 GC, 4 HCC, 4 PC, 3 CRC, 1 GBC. As one of the studies contains two different groups, 20 independent experiments were included to quantitatively analyze. The flow diagram of the study selection is shown in Fig. 1.

3.2. Characteristics of the eligible studies

The main features of eligible studies are summarized in Table 1, and the summary of HRs and their 95%CIs are shown in Table 2. The eligible articles were published between 2011 to 2019, including 1691 participants with OS data and 676 participants with DFS/PFS/RFS data from China, America, Japan, Scotland, Slovakia. The types of GICs included EC, GC, CRC, HCC, PC, and GBC. Quantitative real-time PCR (qRT-PCR) was extensively used in whole studies to assess the expression of miR-34a. Tumor tissues were the most commonly used sample, except for Long L-M’s study [37] in which plasma samples were used. Among included studies, 8 studies have reported HR and the corresponding 95% CI directly, and the HR and 95% CI of remaining 12 studies were calculated by Kaplan-Meier survival curves.

Table 1

The main characteristics of included 20 studies
Study	Year	Country	Tumor type	Design	Sample	Num.	Stage	Cut-off	Follow-up time	Test method	Outcome
Yuxin Hu et al [21]	2011	America	EC	R	Tissue	99	I-IV	Median	> 250	qRT-PCR	OS/DFS
Lin X et al [29]	2015	China	EC	R	Tissue	111	I-IV	Median	NR	qRT-PCR	OS
Osawa S et al [24]	2011	Japan	GC	R	Tissue	37	II-III	70%	60	qRT-PCR	OS
Hui WT et al [22]	2015	China	GC	R	Tissue	76	I-III	Mean	> 60	qRT-PCR	OS
Wei B et al [30]	2015	China	GC	R	Tissue	157	I-IV	NR	> 100	qRT-PCR	OS
Zhang H et al [31]	2015	China	GC	R	Tissue	137	I-IV	2.44	68	qRT-PCR	OS
Yang B et al [23]	2015	China	GC	R	Tissue	50	I-IV	Median	60	qRT-PCR	OS
Li XY et al [32]	2015	China	HCC	R	Tissue	114	I-IV	ROC	90	qRT-PCR	OS/PFS
Yang F et al [33]	2013	China	HCC	R	Tissue	30	NR	Mean	60	qRT-PCR	OS
Cui X et al [34]	2015	China	HCC	R	Tissue	120	NR	Median	60	qRT-PCR	OS/RFS
Xu X et al [20]	2015	China	HCC	R	Tissue	75	I-IV	Median	60	qRT-PCR	OS/RFS
Ohuchida K et al [35]	2011	Japan	PC	R	Tissue	90	NR	NR	༞100	qRT-PCR	OS
Jamieson N.B et al [36]	2012	Scotland	PC	R	Tissue	72	NR	Median	48	qRT-PCR	OS
Long L.-M et al[37]	2016	China	PC	R	plasma	159	I-IV	Mean	24	qRT-PCR	OS
Zhixia Sun et al [38]	2018	China	PC	R	Tissue	139	I-IV	Mean	60	qRT-PCR	OS
Zhang X et al [25]	2017	China	CRC	R	Tissue	84	I-IV	2	36	qRT-PCR	OS
Kristina Hasakova et al [39]	2019	Slovakia	CRC	R	Tissue	64	I-IV	Median	100	qRT-PCR	OS
Gao J et al [40]	2014	China	CRC	R	Tissue	205	II-III	0.307	༞80	qRT-PCR	DFS
Gao J et al [40]	2014	China	CRC	R	Tissue	63	II-III	0.307	༞80	qRT-PCR	DFS
Ke Jin et al [11]	2013	China	GBC	R	Tissue	77	NR	Mean	24	qRT-PCR	OS
Abbreviations: CRC, colorectal cancer; DFS, disease-free survival; EC, esophageal cancer; GBC, gallbladder cancer; GC, gastric cancer; HCC, hepatocellular carcinoma; NR, no report; OS, overall survival; PC, pancreatic cancer; PFS, progressive-free survival; qRT-PCR, quantitative real-time PCR; R, retrospective; RFS, recurrence-free survival.

Table 2

Summary of HRs and their 95% CI
Study	Year	Country	Tumor type	Outcome	HR	95% CI	NOS
Yuxin Hu et al [21]	2011	America	EC	OS DFS	1.41 1.39	0.81–2.44 0.82–2.35	8
Lin X et al [29]	2015	China	EC	OS	3.182	1.273–10.184	6
Osawa S et al [24]	2011	Japan	GC	OS	0.2	0.06–0.68	6
Hui WT et al [22]	2015	China	GC	OS	2.327	1.099–4.927	7
Wei B et al [30]	2015	China	GC	OS	2.31	0.13–40.12	8
Zhang H et al [31]	2015	China	GC	OS	1.33	1.14–1.61	8
Yang B et al [23]	2015	China	GC	OS	3.05	0.6–15.50	8
Li XY et al [32]	2015	China	HCC	OS PFS	1.81 1.22	1.03–3.18 0.92–1.62	6
Yang F et al [33]	2013	China	HCC	OS	3.54	1.67–7.52	7
Cui X et al [34]	2015	China	HCC	OS RFS	1.44 1.49	1.13–1.72 1.15–1.79	8
Xu X et al [20]	2015	China	HCC	OS RFS	1.96 1.96	1.04–3.57 1.10–3.45	8
Ohuchida K et al [35]	2011	Japan	PC	OS	2.92	1.303–6.295	8
Jamieson N.B et al [36]	2012	Scotland	PC	OS	6.67	2.684–16.573	8
Long L.-M et al [37]	2016	China	PC	OS	1.88	1.35–2.64	8
Zhixia Sun et al [38]	2018	China	PC	OS	2.24	1.38–3.36	7
Zhang X et al[25]	2017	China	CRC	OS	1.76	1.01–3.05	6
Kristina Hasakova et al [39]	2019	Slovakia	CRC	OS	1.34	0.65–2.75	8
Gao J et al [40]	2014	China	CRC	DFS DFS	3.819 2.973	2.438–5.983 1.339–6.602	8
Ke Jin et al [11]	2013	China	GBC	OS	2.37	1.11–5.06	8
Abbreviations: 95%CI, 95% confidence interval; CRC, colorectal cancer; DFS, disease-free survival; EC, esophageal cancer; GBC, gallbladder cancer; GC, gastric cancer; HCC, hepatocellular carcinoma; HR, hazard ratio; OS, overall survival; PC, pancreatic cancer; PFS, progressive-free survival; RFS, recurrence-free survival.

3.3. Overall survival is associated with miR-34a expression

We analyzed the association between low expression of miR-34a and OS at first, and remarkable heterogeneity between studies was found (I² = 58.7%, P = 0.001, Table 3). Therefore, a random effects model was used to compute the pooled HR and corresponding 95%CI. The result showed that lower expression level of miR-34a significantly predicted worse OS, with the pooled HR of 1.86 (95% CI: 1.52–2.28; Fig. 2A).

Table 3

Association between miR-34a expression levels and overall survivals
	No.of studies	No.of patients	Pooled HR (95%CI)		Meta regression p-value	Heterogeneity
	No.of studies	No.of patients	Fixed Random			I² p-value
Overall	18	1691	1.600 (1.44–1.77)	1.86 (1.52–2.28)		58.7%	0.001
Ethnicity					0.806
Asian	15	1456	1.58 (1.42–1.76)	1.82 (1.48–2.24)		55.2%	0.005
Caucasian	3	235	1.86 (1.25–2.76)	2.20 (0.90–5.37)		78.6%	0.009
Sample Size					0.979
≥ 100	7	937	1.51 (1.34–1.69)	1.61 (1.35–1.92)		36.1%	0.153
༜100	11	754	1.98 (1.59–2.48)	2.00 (1.37–2.93)		63.2%	0.002
NOS Scores					0.978
≥ 8	11	1100	1.53 (1.36–1.71)	1.75 (1.42–2.16)		49.5%	0.031
< 8	7	591	2.00 (1.56–2.55)	1.87 (1.20–2.93)		65.8%	0.008
Specimen					0.933
tissue	17	1532	1.57 (1.41–1.75)	1.87 (1.50–2.33)		60.2%	0.001
plasma	1	159	1.88 (1.34–2.63)	1.88 (1.34–2.63)		-	-
Cancer Types					0.494
EC	2	210	1.69 (1.04–2.74)	1.87 (0.88-4.00)		45.6%	0.175
GC	5	457	1.33 (1.13–1.57)	1.25 (0.59–2.65)		68.3%	0.013
HCC	4	339	1.60 (1.33–1.92)	1.84 (1.30–2.59)		48.7%	0.119
PC	4	460	2.27 (1.77–2.89)	2.59 (1.69–3.97)		57.1%	0.072
CRC	2	148	1.59 (1.03–2.47)	1.59 (1.03–2.47)		0.0%	0.556
GBC	1	77	2.37 (1.11–5.06)	2.37 (1.11–5.06)		-	-
Abbreviations: 95%CI, 95% confidence interval; CRC, colorectal cancer; EC, esophageal cancer; GBC, gallbladder cancer; GC, gastric cancer; HCC, hepatocellular carcinoma; HR, hazard ratio; NOS, Newcastle-Ottawa Scale; PC, pancreatic cancer.

In order to explicate the heterogeneity in OS, subgroup analysis was conducted by ethnicity (Asian and Caucasian), sample capacity (≥ 100 and < 100), NOS scores (≥ 8 and < 8), specimen (plasma and tissue) and tumor types (EC, GC, CRC, HCC and PC). As a result, the homogeneity was achieved in the CRC group (I² = 0.00%, P = 0.556; Table 3) and the correlation was obvious (HR = 1.59, 95% CI:1.03–2.47, Fig. 2C). What's more, there were significant correlation between the expression level of miR-34a and OS in Asians (HR = 1.82, 95% CI: 1.48–2.24, Fig. 2B), sample capacity greater than or equal to 100 (HR = 1.61, 95% CI: 1.35–1.92, Fig. 2D) or less than 100 (HR = 2.00, 95% CI: 1.37–2.93, Fig. 2D), NOS scores equal to or greater than 8 (HR = 1.75, 95% CI: 1.42–2.16, Fig. 2F) or less than 8 (HR = 1.87, 95% CI: 1.20–2.93, Fig. 2F), specimen removed the plasma (HR = 1.87, 95% CI: 1.50–2.33, Fig. 2E), HCC (HR = 1.84, 95%CI: 1.30–2.59, Fig. 2C), and PC (HR = 2.59, 95% CI:1.69–3.97, Fig. 2C) by random effect model. As shown in Table 3, the significance might be vanished in Caucasian and EC groups when fixed effects model turned into random effect model. Moreover, the heterogeneities were still evident among subgroups, except for the CRC group. To analyse heterogeneity ulteriorly, meta regression was performed, but it made no sense to explain the variation of HRs (p = 0.806 for ethnicity, p = 0.979 for sample capacity, p = 0.978 for NOS scores, p = 0.933 for specimen, p = 0.494 for cancer types, Table 3). Moreover, the sensitivity analysis was performed to assess the contribution of each study and no study seemed to make a difference to the pooled results (Fig. 2G). In addition, publication bias was evaluated by funnel plots and Egger’s tests. As shown in Fig. 2H, the funnel plots showed no obvious asymmetry, and the Egger’s tests revealed no significant publication bias existed (P = 0.058).

3.4. Tumor progression is associated with miR-34a expression

To evaluate the association between miR-34a expression and DFS/PFS/RFS, 6 studies were included in this analysis, and the data revealed that low miR-34a expression predicted a worse outcome with a combined HR of 1.86 (95% CI: 1.31–2.63) via a random effect model (P = 0.001, I² = 76.6%; Fig. 3A). To explain the heterogeneity, we performed subgroup analysis by DFS, PFS and RFS, which showed the significant correlation with the expression of miR-34a (HR = 2.50, 95% CI: 1.27–4.92 for DFS; HR = 1.54, 95% CI: 1.26–1.90 for RFS; Fig. 3B). What’s more, the homogeneity was achieved in the RFS group. Then, the sensitivity analysis was performed by removing studies one by one to assess the influence of single study. As shown in Fig. 3C, the stability of the entire study was not influenced by individual study. Finally, funnel plots and Egger’s tests were implemented to evaluate the publication bias. The funnel plot was roughly symmetric (Fig. 3D) and the P values of Egger’s tests was 0.909. Therefore, no evidence for significant publication bias existed.

3.5. Correlation between miR-34a levels and clinicopathological features in GICs

For obtaining relevant statistics to evaluate the relation between miR-34a expression levels and different clinicopathological characteristics, seven studies containing 647 patients of GICs were screened out. As shown in Table 4, we observed significant association between expression level of miR-34a and lymphatic metastasis (OR = 3.231, 95% CI: 2.237–4.666; Fig. 4A), differentiation degree (OR = 2.228, 95% CI: 1.538–3.228; Fig. 4B) via fixed effects model, and TMN stage (OR = 2.896, 95% CI: 1.302–6.442; Fig. 4C) via a random effect model. There were no significant correlation identified between miR-34a levels and gender (OR = 0.776, 95% CI: 0.566–1.065) or tumor sizes (OR = 0.736, CI: 0.460–1.177). The heterogeneity was disappeared in the group of gender (I² = 0.00%, P = 0.888), lymphatic metastasis (I² = 0.00%, P = 0.754) and medium in the group of tumor size (I² = 20.5%, P = 0.284), differentiation degree (I² = 35.7%, P = 0.169) but obvious in the TNM stage group (I² = 74.4%, P = 0.004). Sensitivity analysis was applied to the clinic characteristic analysis including lymphatic metastasis (Fig. 4D), differentiation degree (Fig. 4E) and TMN stage (Fig. 4F), suggesting no study had significant impacts on the results.

Table 4

Overall analysis of miR-34a expression association with clinicopathologic characteristics
Clinicopathological characteristics	Num. of studies	Num. of patients	Pooled OR (95%CI) Fixed Random		Heterogeneity I² p-value
Gender (male vs. female)	7	647	0.776 (0.566–1.065)	0.777 (0.565–1.067)	0.0%	0.888
Tumor Size (≤ 5 vs ༞ 5 cm)	3	326	0.736 (0.460–1.177)	0.284 (0.433–1.288)	20.5%	0.284
Lymphatic Metastasis (YESvs.NO)	6	571	3.231 (2.237–4.666)	3.200 (2.210–4.635)	0.0%	0.754
TNM stage (III + IV vs. I + II)	5	458	2.468 (1.698–3.588)	2.896 (1.302–6.442)	74.4%	0.004
Differentiation (poor vs. others)	6	597	2.228 (1.538–3.228)	2.373 (1.430–3.938)	35.7%	0.169
Abbreviations: 95%CI, 95% confidence interval; Fixed, fixed effects model; OR, Odds ratio; Random, random pooling model.

In the last few decades, miRNAs have attracted increasing interest among investigators as potential biomarkers for cancer diagnosis and prognosis. Many clinical trials have demonstrated that miRNAs play a pivotal role in tumor development via regulating the expression of target genes and tumor suppressors or directly wielding their functions as oncogenes or tumor suppressors[41] [42]. It has been reported that miR-34a influenced tumor biological activities by targeting several genes or signal pathways, such as CCND1 in EC[43], PDGFR in GC[18], HMGB1 in CRC[44], XIST in PC[45] and etc. Recently, a systemic review has summarized numerous studies in which reported miR-34a has diagnostic and prognostic value in GICs[46]. However, among these studies, two opposing views were presented on whether patients could benefit from the high expression of miR-34a. Hao Wu et al[7], Milad Asadi et al[47] and Yan Zhou et al[48] showed the down-regulation of miR-34a was linked to a poor prognosis in GICs patients, while Hiyoshi Y et al[8], and Mojin Wang[26] reported patients were benefited from down-regulated miR-34a. The prognostic value of miR-34a in GICs has been illustrated in lots of studies, but the particular prognosis role of miR-34a in GICs remains unclear. As far as we know, this is the most overall meta-analysis exploring the clinical value of miR-34a in patients with GICs.

This meta-analysis discussed 20 papers and contained 2367 patients in total. Among these studies, 18 studies including 1691 patients provided the relevant statistics of OS. By the random effect model, the results showed that the decreased miR-34a expression was association with poorer outcome of GICs patients. To explain the potential sources of heterogeneity, subgroup analyses were performed. As a result, the homogeneity was reached in the group of CRC, and the OS of CRC group was found to be greatly associated with the miR-34a expression levels. Though the expression level of miR-34a in CRC patients remains controversial, there are several potential mechanisms suggested how low expression of miR-34a could induce unfavorable outcome of CRC. It has been reported that miR-34a served a key role in suppressing CRC metastasis by targeting and regulating Notch signaling[25]. Also, miR-34a might be an important tumor suppressor of CRC progression by targeting FMNL2 and E2F5[49]. Besides, miR-34a inhibits recurrence of CRC through inhibiting cell growth, migration and invasion, inducing cell apoptosis and cell cycle arrest in a p53-dependent manner [40].

As shown in Table 3, the associations between miR-34a expression levels and OS were also significant in other subgroups. In addition, there is a closer relationship between low miR-34a level and poor OS in patients with PC (HR = 2.59, 95% CI:1.69–3.97). Empirically, HR > 2 is considered as strongly predictive [50]. As for the possible mechanism, Long Li-Min et al reported that miR-34a significantly inhibited the tumor growth of PC tumors by suppressing Notch1, Notch2 and Notch4 expression [37]. Since the heterogeneities within the subgroups were still significant, meta regression was performed to illustrate the influence of different factors including ethnicity, sample capacity, specimen, NOS scores and tumor classification, but there was no factor significantly affect the variation of HR. The analysis of tumor progression and mir-34a expression revealed that low miR-34a expression predicted a worse outcome, especially in DFS (HR = 2.50, 95% CI: 1.27–4.92). According to our research, we could infer that the decreased expression level of miR-34a is closely related to worse prognosis in patients with GICs. But for the EC and GC, the results were still not stable and required more comprehensive studies to further research the miR-34a prognostic value in GICs.

To evaluate the association between miR-34a and the clinical characteristics, seven articles including 647 patients were enrolled. Significant relations were observed between miR-34a expression levels and differentiation/TMN stage/lymphatic metastasis by fixed or random effects model was identified. Appling sensitivity analyses, there was no study had significant impacts on the results. Based on the findings, it suggested that patients with decreased miR-34a expression are more likely to develop lymphatic metastasis, and decreased miR-34a expression level was linked to poor tumor differentiation and late TMN stage.

Though this meta-analysis revealed that miR-34a was a promising biomarker of GICs, several potential limitations of this study should be considered. Firstly, the number of studies included was limited, leading to the relative lack of studies in subgroup analyses, for example, there is only one article reported PFS. Secondly, patients were all Asian and Caucasian, lacking data from other regions, which might result in ethnic bias. Thirdly, the cut-off value among studies were different, we didn’t have absolute criteria to assess whether the expression of miR-34a is low or not, impacting the statistical power of analysis. Finally, some HRs and 95% Cis were calculated according to the data extracted from survival curves, so it’s difficult to exclude the influence of confounding bias.

In conclusion, our study demonstrates that lower miR-34a expression is significantly associated with poorer OS and DFS/PFS/RFS and may be a novel prognostic biomarker in GICs. Moreover, miR-34a expression level is relatively lower in patients with lymph node metastasis, and decreased expression level of miR-34a is related to poor tumor differentiation and late TMN stage. Further multicenter prospective clinical studies are needed to validate the association between miR-34a and prognosis of GICs.

95%CI, 95% confidence interval; CRC, colorectal cancer; DFS, disease-free survival; EC, esophageal cancer; GBC, gallbladder cancer; GC, gastric cancer; HCC, hepatocellular carcinoma; HR, hazard ratio; NOS, Newcastle-Ottawa Scale; NR, no report; OR, Odds ratio; OS, overall survival; PC, pancreatic cancer; PFS, progressive-free survival; qRT-PCR, quantitative real-time PCR; R, retrospective; RFS, recurrence-free survival.

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and materials

The authors declare that all data used or analysed during the current study are available on reasonable request.

Competing interests

The authors have no conflicts of interest to declare.

Funding

This study was supported by the National Natural Science Foundation of China (81900508), and the Natural Science Foundation of Jiangsu Province (BK20190172). The funders have no role in the sudy design, data analysis and manuscript composition.

Authors' contributions

Sudy design: YC. Data collection: YC and XL. Data analysis: YC, XL and LL. Manuscript composition: YC. Manuscript revision: XL. Tables Drafting: YC and LL. Figures Drafting: YC and XL.All authors read and approved the final manuscript.

Acknowledgements

We would like to thank all the people who helped us in this study.

Authors' information

1Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China.

Shams AZ, Haug U. Strategies for prevention of gastrointestinal cancers in developing countries: a systematic review. Journal of global health. 2017;7(2):020405.
Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, Jemal A, Yu XQ, He J. Cancer statistics in China, 2015. Cancer J Clin. 2016;66(2):115–32.
Lu TX, Rothenberg ME: MicroRNA. The Journal of allergy and clinical immunology 2018, 141(4):1202–1207.
Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004;116(2):281–97.
Iorio MV, Croce CM. MicroRNA dysregulation in cancer: diagnostics, monitoring and therapeutics. A comprehensive review. EMBO molecular medicine. 2012;4(3):143–59.
Yang L, Song X, Zhu J, Li M, Ji Y, Wu F, Chen Y, Cui X, Hu J, Wang L, et al. Tumor suppressor microRNA-34a inhibits cell migration and invasion by targeting MMP-2/MMP-9/FNDC3B in esophageal squamous cell carcinoma. Int J Oncol. 2017;51(1):378–88.
Wu H, Huang M, Liu Y, Shu Y, Liu P. Luteolin Induces Apoptosis by Up-regulating miR-34a in Human Gastric Cancer Cells. Technology in cancer research treatment. 2015;14(6):747–55.
Hiyoshi Y, Schetter AJ, Okayama H, Inamura K, Anami K, Nguyen GH, Horikawa I, Hawkes JE, Bowman ED, Leung SY, et al. Increased microRNA-34b and – 34c predominantly expressed in stromal tissues is associated with poor prognosis in human colon cancer. PloS one. 2015;10(4):e0124899.
Sun TY, Xie HJ, Li Z, Kong LF, Gou XN, Li DJ, Shi YJ, Ding YZ. miR-34a regulates HDAC1 expression to affect the proliferation and apoptosis of hepatocellular carcinoma. American journal of translational research. 2017;9(1):103–14.
Long LM, Zhan JK, Wang HQ, Li S, Chen YY, Liu YS. The Clinical Significance of miR-34a in Pancreatic Ductal Carcinoma and Associated Molecular and Cellular Mechanisms. Pathobiol J ImmunoPathol Mol Cell Biol. 2017;84(1):38–48.
Jin K, Xiang Y, Tang J, Wu G, Li J, Xiao H, Li C, Chen Y, Zhao J. miR-34 is associated with poor prognosis of patients with gallbladder cancer through regulating telomere length in tumor stem cells. Tumour Biol. 2014;35(2):1503–10.
Gao H, Zhao H, Xiang W. Expression level of human miR-34a correlates with glioma grade and prognosis. Journal of neuro-oncology. 2013;113(2):221–8.
Shi H, Zhou S, Liu J, Zhu J, Xue J, Gu L, Chen Y. miR-34a inhibits the in vitro cell proliferation and migration in human esophageal cancer. Pathol Res Pract. 2016;212(5):444–9.
Chu J, Li H, Xing Y, Jia J, Sheng J, Yang L, Sun K, Qu Y, Zhang Y, Yin H, et al: LncRNA MNX1-AS1 promotes progression of esophageal squamous cell carcinoma by regulating miR-34a/SIRT1 axis. Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie 2019, 116:109029.
Yoon JH, Choi WS, Kim O, Choi BJ, Nam SW, Lee JY, Park WS. Gastrokine 1 inhibits gastric cancer cell migration and invasion by downregulating RhoA expression. Gastric cancer: official journal of the International Gastric Cancer Association the Japanese Gastric Cancer Association. 2017;20(2):274–85.
Li N, Fu H, Tie Y, Hu Z, Kong W, Wu Y, Zheng X. miR-34a inhibits migration and invasion by down-regulation of c-Met expression in human hepatocellular carcinoma cells. Cancer letters. 2009;275(1):44–53.
Cheng C, Qin Y, Zhi Q, Wang J, Qin C: Knockdown of long non-coding RNA HOTAIR inhibits cisplatin resistance of gastric cancer cells through inhibiting the PI3K/Akt and Wnt/β-catenin signaling pathways by up-regulating miR-34a. International journal of biological macromolecules 2018, 107(Pt B):2620–2629.
Peng Y, Guo JJ, Liu YM, Wu XL. MicroRNA-34A inhibits the growth, invasion and metastasis of gastric cancer by targeting PDGFR and MET expression. Bioscience reports 2014, 34(3).
Lai M, Du G, Shi R, Yao J, Yang G, Wei Y, Zhang D, Xu Z, Zhang R, Li Y, et al. MiR-34a inhibits migration and invasion by regulating the SIRT1/p53 pathway in human SW480 cells. Mol Med Rep. 2015;11(5):3301–7.
Xu X, Chen W, Miao R, Zhou Y, Wang Z, Zhang L, Wan Y, Dong Y, Qu K, Liu C. miR-34a induces cellular senescence via modulation of telomerase activity in human hepatocellular carcinoma by targeting FoxM1/c-Myc pathway. Oncotarget. 2015;6(6):3988–4004.
Hu Y, Correa AM, Hoque A, Guan B, Ye F, Huang J, Swisher SG, Wu TT, Ajani JA, Xu X-C. Prognostic significance of differentially expressed miRNAs in esophageal cancer. Int J Cancer. 2011;128(1):132–43.
Hui W-T, Ma X-B, Zan Y, Wang X-J, Dong L. Prognostic Significance of MiR-34a Expression in Patients with Gastric Cancer after Radical Gastrectomy. Chin Med J. 2015;128(19):2632–7.
Yang B, Huang J, Liu H, Guo W, Li G. miR-335 directly, while miR-34a indirectly modulate survivin expression and regulate growth, apoptosis, and invasion of gastric cancer cells. Tumour Biol. 2016;37(2):1771–9.
Osawa S, Shimada Y, Sekine S, Okumura T, Nagata T, Fukuoka J, Tsukada K. MicroRNA profiling of gastric cancer patients from formalin-fixed paraffin-embedded samples. Oncol Lett. 2011;2(4):613–9.
Zhang X, Ai F, Li X, Tian L, Wang X, Shen S, Liu F. MicroRNA-34a suppresses colorectal cancer metastasis by regulating Notch signaling. Oncol Lett. 2017;14(2):2325–33.
Wang M, Zhang P, Li Y, Liu G, Zhou B, Zhan L, Zhou Z, Sun X. The quantitative analysis by stem-loop real-time PCR revealed the microRNA-34a, microRNA-155 and microRNA-200c overexpression in human colorectal cancer. Med Oncol (Northwood Lond Engl). 2012;29(5):3113–8.
Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol. 2010;25(9):603–5.
Tierney JF, Stewart LA, Ghersi D, Burdett S, Sydes MR. Practical methods for incorporating summary time-to-event data into meta-analysis. Trials. 2007;8:16.
Lin X, Xu XY, Chen QS, Huang C. Clinical significance of microRNA-34a in esophageal squamous cell carcinoma. Genet Mol Res. 2015;14(4):17684–91.
Wei B, Huang QY, Huang SR, Mai W, Zhong XG. MicroRNA–34a attenuates the proliferation, invasion and metastasis of gastric cancer cells via downregulation of MET. Mol Med Rep. 2015;12(4):5255–61.
Zhang H, Li S, Yang J, Liu S, Gong X, Yu X. The prognostic value of miR-34a expression in completely resected gastric cancer: tumor recurrence and overall survival. Int J Clin Exp Med. 2015;8(2):2635–41.
Li X-Y, Wen J-Y, Jia C-C, Wang T-T, Li X, Dong M, Lin QU, Chen Z-H, Ma X-K, Wei LI, et al. MicroRNA-34a-5p enhances sensitivity to chemotherapy by targeting AXL in hepatocellular carcinoma MHCC-97L cells. Oncol Lett. 2015;10(5):2691–8.
Yang F, Li Q-j, Gong Z-b, Zhou L, You N, Wang S, Li X-l, Li J-j, An J-z, Wang D-. MicroRNA-34a targets Bcl-2 and sensitizes human hepatocellular carcinoma cells to sorafenib treatment. Technol Cancer Res Treat. 2014;13(1):77–86. s et al.
Cui X, Wu Y, Wang Z, Liu X, Wang S, Qin C. MicroRNA-34a expression is predictive of recurrence after radiofrequency ablation in early hepatocellular carcinoma. Tumour Biol. 2015;36(5):3887–93.
Ohuchida K, Mizumoto K, Kayashima T, Fujita H, Moriyama T, Ohtsuka T, Ueda J, Nagai E, Hashizume M, Tanaka M. MicroRNA expression as a predictive marker for gemcitabine response after surgical resection of pancreatic cancer. Ann Surg Oncol. 2011;18(8):2381–7.
Jamieson NB, Morran DC, Morton JP, Ali A, Dickson EJ, Carter CR, Sansom OJ, Evans TRJ, McKay CJ, Oien KA. MicroRNA molecular profiles associated with diagnosis, clinicopathologic criteria, and overall survival in patients with resectable pancreatic ductal adenocarcinoma. Clin Cancer Res. 2012;18(2):534–45.
Long L-M, Zhan J-K, Wang H-Q, Li S, Chen Y-Y, Liu Y-S. The Clinical Significance of miR-34a in Pancreatic Ductal Carcinoma and Associated Molecular and Cellular Mechanisms. Pathobiology. 2017;84(1):38–48.
Sun Z, Zhang B, Cui T: Long non-coding RNA XIST exerts oncogenic functions in pancreatic cancer via miR-34a-5p. Oncol Rep 2018, 39(4):1591–1600.
Hasakova K, Reis R, Vician M, Zeman M, Herichova I. Expression of miR-34a-5p is up-regulated in human colorectal cancer and correlates with survival and clock gene PER2 expression. PLoS ONE. 2019;14(10):e0224396.
Gao J, Li N, Dong Y, Li S, Xu L, Li X, Li Y, Li Z, Ng SS, Sung JJ, et al. miR-34a-5p suppresses colorectal cancer metastasis and predicts recurrence in patients with stage II/III colorectal cancer. Oncogene. 2015;34(31):4142–52.
Cho WC. MicroRNAs: potential biomarkers for cancer diagnosis, prognosis and targets for therapy. Int J Biochem Cell Biol. 2010;42(8):1273–81.
Hayes J, Peruzzi PP, Lawler S. MicroRNAs in cancer: biomarkers, functions and therapy. Trends Mol Med. 2014;20(8):460–9.
Sun F, Fu H, Liu Q, Tie Y, Zhu J, Xing R, Sun Z, Zheng X. Downregulation of CCND1 and CDK6 by miR-34a induces cell cycle arrest. FEBS Lett. 2008;582(10):1564–8.
Chandrasekaran KS, Sathyanarayanan A, Karunagaran D. Downregulation of HMGB1 by miR-34a is sufficient to suppress proliferation, migration and invasion of human cervical and colorectal cancer cells. Tumour biology: the journal of the International Society for Oncodevelopmental Biology Medicine. 2016;37(10):13155–66.
Sun Z, Zhang B, Cui T: Long non-coding RNA XIST exerts oncogenic functions in pancreatic cancer via miR-34a-5p. Oncology reports 2018, 39(4):1591–1600.
Kong J, Wang W. A Systemic Review on the Regulatory Roles of miR-34a in Gastrointestinal Cancer. OncoTargets therapy. 2020;13:2855–72.
Asadi M, Shanehbandi D, Mohammadpour H, Hashemzadeh S, Sepehri B. Expression Level of miR-34a in Tumor Tissue from Patients with Esophageal Squamous Cell Carcinoma. Journal of gastrointestinal cancer. 2019;50(2):304–7.
Zhou Y, Ding BZ, Lin YP, Wang HB: MiR-34a, as a suppressor, enhance the susceptibility of gastric cancer cell to luteolin by directly targeting HK1. Gene 2018, 644:56–65.
Lu G, Sun Y, An S, Xin S, Ren X, Zhang D, Wu P, Liao W, Ding Y, Liang L. MicroRNA-34a targets FMNL2 and E2F5 and suppresses the progression of colorectal cancer. Exp Mol Pathol. 2015;99(1):173–9.
Hayes DF, Isaacs C, Stearns V. Prognostic factors in breast cancer: current and new predictors of metastasis. J Mammary Gland Biol Neoplasia. 2001;6(4):375–92.

PRISMAChecklist.doc

Download PDF

Journal Publication

published 14 Jan, 2021

Read the published version in BMC Cancer →

Review #2 received at journal
23 Oct, 2020
Editorial decision: Major revision
23 Oct, 2020
Review #1 received at journal
18 Oct, 2020
Reviewer #3 agreed at journal
10 Oct, 2020
Review #3 received at journal
10 Oct, 2020
Reviewer #2 agreed at journal
24 Sep, 2020
Reviewer #1 agreed at journal
22 Sep, 2020
Editor assigned by journal
21 Sep, 2020
Reviewers invited by journal
21 Sep, 2020
Submission checks completed at journal
15 Sep, 2020
Editor invited by journal
15 Sep, 2020

You are reading this latest preprint version

Prognosis Value of Low microRNA-34a Expression in Human Gastrointestinal Cancer: A Meta-Analysis

Status:

Journal Publication

Version 1

Abstract

Figures

1. Background

2. Methods

2.1. Literature Search

2.2. Selection Criteria

2.3. Data Extraction and Quality Assessment

2.4. Statistical Analysis

3. Results

3.1. Literature search

3.2. Characteristics of the eligible studies

3.3. Overall survival is associated with miR-34a expression

3.4. Tumor progression is associated with miR-34a expression

3.5. Correlation between miR-34a levels and clinicopathological features in GICs

4. Discussion

5. Conclusion

Abbreviations

Declarations

References

Supplementary Files

Status:

Journal Publication

Version 1