The prognostic value and regulatory mechanisms of microRNA ... · 1/1/2019 · the time between...

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Title page

The prognostic value and regulatory mechanisms of

microRNA-145 in various tumors: a systematic review and

meta-analysis of 50 studies

Liangliang Xu1#

, Yanfang Zhang2#

, Jianwei Tang1, Peng Wang

1, Lian Li

1, Xiaokai

Yan1, Xiaobo Zheng

1, Shengsheng Ren

1, Ming Zhang

1, MingQing Xu

1*

1Department of Liver Surgery, West China Hospital, Sichuan University, Chengdu

610041, Sichuan Province, China.

2Center of Infectious Diseases, West China Hospital, Sichuan University, Chengdu,


# Liangliang Xu and Yanfang Zhang contributed equally to this study and should be

considered as co-first author.

Running title : Prognostic value and regulatory mechanism of miR-145

Abbreviations: miR-145, microRNA-145; HR, hazard ratio; CI, confidence interva

FFPE, formalin fixed and paraffin embedded tissues; TCGA, The Cancer Genome

Altas; qRT-PCR, quantitative real time polymerase chain reaction; ROC, receiver

operating characteristic; SC, survival curve; PFS, progression-free survival, OS,

overall survival; DFS, disease-free survival.

Conflict of interest: All authors declared that they have no conflicts from

commercial interest.

Correspondence to: Mingqing XU, PHD

Department of Liver Surgery, West China Hospital, Sichuan University, Chengdu


E-mail: [email protected]

Financial information: This study was supported by the grants of National Natural

Research. on November 24, 2020. © 2019 American Association for Cancercebp.aacrjournals.org Downloaded from

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 2, 2019; DOI: 10.1158/1055-9965.EPI-18-0570

http://cebp.aacrjournals.org/

2

Science Foundation of China (No. 71673193), the Key Technology Research and

Development Program of the Sichuan Province (2015SZ0131 and 2017FZ0082).




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Abstract

Background: Acting as an important tumor-related miRNA, the clinical significance

and underlying mechanisms of microRNA-145 (miR-145) in various malignant

tumors have been investigated by numerous studies. The present study aimed to

comprehensively estimate the prognostic value and systematically illustrate the

regulatory mechanisms of miR-145 based on all eligible literature.

Methods: Relevant studies were acquired from multiple online databases. Overall

survival (OS) and progression-free survival (PFS) were used as primary endpoints.

Detailed subgroup analyses were performed to decrease the heterogeneity among

studies and recognize the prognostic value of miR-145. All statistical analyses were

performed with RevMan software version 5.3 and STATA software version 14.1.

Results: A total of 48 articles containing 50 studies were included in the

meta-analysis. For OS, the pooled results showed that low miR-145 expression in

tumor tissues was significantly associated with worse OS in patients with various

tumors (hazard ratio (HR) = 1.70, 95% confidence interval (CI) 1.46–1.99, P < 0.001).

Subgroup analysis based on tumor type showed that the downregulation of miR-145

was associated with unfavorable OS in colorectal cancer (HR = 2.17, 95% CI

1.52–3.08, P < 0.001), ovarian cancer (HR = 2.15, 95% CI 1.29–3.59, P = 0.003),

gastric cancer (HR = 1.78, 95% CI 1.35–2.36, P < 0.001), glioma (HR = 1.65, 95% CI

1.30–2.10, P < 0.001), and osteosarcoma (HR = 2.28, 95% CI 1.50–3.47, P < 0.001).

For PFS, the pooled results also showed that the downregulation of miR-145 was

significantly associated with poor PFS in patients with multiple tumors (HR = 1.39,

95% CI 1.16–1.67, P < 0.001) and the subgroup analyses further identified that the

low miR-145 expression was associated with worse PFS in patients with lung cancer

(HR = 1.97, 95% CI 1.25–3.09, P = 0.003) and those of Asian descent (HR = 1.50, 95%

CI 1.23–1.82, P < 0.001). For the regulatory mechanisms, we observed that numerous

tumor-related transcripts could be targeted by miR-145-5p or miR-145-3p, as well as

the expression and function of miR-145-5p could be regulated by multiple molecules.

Conclusions: This meta-analysis indicated that downregulated miR-145 in tumor

tissues or peripheral blood predicted unfavorable prognostic outcomes for patients




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suffering from various malignant tumors. Additionally, miR-145 was involved in

multiple tumor-related pathways and the functioning of significant biological effects.

Impact: MiR-145 is a well-demonstrated tumor suppressor and its expression level is

significantly correlated with the prognosis of patients with multiple malignant tumors.

Key words: MicroRNA-145, cancer, prognosis, mechanism, meta-analysis

Introduction

As an important type of noncoding RNA, microRNA (miRNA) comprises a class

of small endogenous RNAs of ~22 nucleotides in length that play a crucial role in the

regulation of gene expression at the post-transcriptional level(1, 2). Previous studies

have shown that many miRNAs are aberrantly expressed in tumor tissues and play

crucial roles in the growth, differentiation, angiogenesis, metastasis, and drug

resistance of various tumors(3, 4). Moreover, some miRNAs are significantly

associated with the prognosis of patients with various tumors and are potential

prognostic predictors and candidate treatment targets(5). Therefore, the recognition of

the clinical significance and regulatory mechanisms of miRNAs may assist with the

diagnosis, prognosis predicting, and treatment of malignant tumors.

MicroRNA-145 (miR-145) is derived from chromosome 5q32 and contains two

mature subtypes of miR-145-5p and miR-145-3p(6). Based on the deep sequencing

data referred to in miRBase(7), miR-145-5p is much more abundantly expressed than

miR-145-3p. Substantial data obtained from previous studies have demonstrated that

miR-145 is downregulated in various tumors and corresponding cell lines to be

considered a tumor suppressor(8-14). On the contrary, several studies have found that

miR-145 is upregulated in tumor tissues and functions as an oncogene(15, 16). For

example, Naito et al.(15) showed that miR-145 was upregulated in gastric cancer

patients with more advanced tumor stages or with scirrhous type histology, and highly

expressed miR-145 was significantly associated with poor prognosis in gastric cancer

patients. To date, although the correlation between miR-145 expression and the

prognosis of patients with various tumors has been investigated by numerous studies,

the conclusions are not completely consistent. Therefore, we aimed to conduct a




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meta-analysis based on all eligible evidence to evaluate the association between

miR-145 expression and the prognosis of patients with malignant tumors.

Additionally, in response to the need for comprehensive recognition of miR-145,

known regulatory mechanisms of miR-145 will be illustrated in the present study via

systematically reviewing previous studies.

Materials and methods

Identification of relevant studies

A systematic literature search was conducted using online databases including

MEDLINE, Embase, Pubmed, Google Scholar, and China Biology Medicine disc.

The keywords used in the searches were “miR-145 or miRNA-145 or microRNA-145

(all fields).” The categories of diseases and research types were not limited so that the

maximum number of studies was identified. Additionally, the reference lists of

relevant reviews, meta-analyses, and original studies were manually screened to

acquire more studies. The language was not restricted.

Outcomes and definition

In the present study, two primary outcomes, overall survival (OS) and

progression-free survival (PFS), were selected to calculate the association between

miR-145 expression and survival outcome of patients with multiple tumors. OS was

measured from the time at which the baseline blood or tissue sample was obtained to

the date of death from any cause or the date of last follow-up. PFS was recorded as

the time between the baseline blood and tissue sampling for miRNA analysis and

documentation of the first tumor progression, based on clinical or radiological

findings. In different studies, OS was also expressed as disease-specific survival

(DSS)(17) and cancer-specific survival (CSS)(15), while PFS was also described as

recurrence-free interval (RFI)(8), disease-free survival (DFS)(9, 10, 18-26),

biochemical-free survival (BFS)(27), metastasis-free survival (MFS)(28), relapse-free

survival (RFS)(29), and time to relapse (TTR)(11).




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Inclusion and exclusion criteria

The following criteria were used to help select eligible literature: (1) published

studies that could be retrieved from the above-mentioned online databases; (2) the

expression of miR-145 was measured in the tumor tissue, peripheral blood, or body

liquid; (3) the association between miR-145 expression level and survival outcome

was analyzed; and (4) hazard ratio (HR) and 95% confidence interval (CI) were

reported or enough information was provided to calculate such parameters. Studies

were excluded if they included the following items: (1) the patients did not suffer

from malignant tumors; (2) the study was only conducted on an animal model or

tumor cell lines; and (3) no data could be extracted or the studies were published as

abstracts, reviews, conference reports, letters, or editorials.

Study selection and data extraction

To make the management of literature more convenient, all identified citations

were imported into an EndNote library (Thomson Corporation, Stamford, USA).

After removing duplicated studies, two independent investigators (Zhang and Tang)

carefully screened the relevant studies by reading the titles and abstracts. Then, the

entire text of potential eligible studies were evaluated to confirm the final inclusion.

Any discrepancies during the study selection were resolved by discussion with the

corresponding author (Xu) for consensus.

The relevant information was extracted from all included studies by two

independent authors (Wang and Li). The following data elements were sought and

recorded: (1) first author, publication year, and nationality of study population; (2)

miR-145 subtype, tumor type, sample type, and miR-145 assay method; and (3)

sample size, period of follow up, cut-off value, HR, and corresponding 95% CI. When

a study reported the survival results of both univariate and multivariate analyses, only

the latter was extracted because it is more accurate as it accounts for confounding

factors. If a study only reported the survival results using Kaplan-Meier curves, then

the statistical variables were read from the graphical survival plots with the Engauge

Digitizer 4.1 software program, then the HR value and 95% CI were calculated via the




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method reported by Tierney et al.(30). Regarding other missing information, emails

were sent to corresponding authors requesting useful data. Finally, the extracted data

forms were crosschecked between above mentioned two reviewers, and any

disagreements during the process of data extraction were resolved by discussions with

a third author (Zhang).

Quality assessment

In the present study, the quality of included studies was assessed by two

independent investigators (Peng and Lian) using the Newcastle-Ottawa Scale

(NOS)(31). This is an acknowledged tool for assessing the quality of non-randomized

studies via the judgment of three main study characteristics as follows: selection and

definition of the study groups, comparability of the groups, and ascertainment of

outcomes. Then, a possible score of 0–9 was assigned to each study. A study with a

NOS score greater than six was considered to be high quality.

Statistical analysis

The RevMan software version 5.3 (Cochrane Collaboration, Oxford, UK) and

STATA software version 14.1 (StataCorp, College Station, TX, USA) were employed

to perform statistical analyses in the present study. The pooled HR and corresponding

95% CI were used to evaluate the prognostic value of miR-145 for various malignant

neoplasms. The statistical significance of the outcomes was determined by the Z-test

and P values less than 0.05 were considered statistically significant. MiR-145 is a

known tumor suppressor in most tumors; therefore, low expression of miR-145 was

thought as a risk factor and HR greater than one indicated a poor prognosis. The

heterogeneity among studies was assessed using Cochran’s Q test and Higgins

I-square (I2) statistic(32, 33). If a significant heterogeneity was observed, namely P <

0.05 and/or I2 > 50%, the random-effects model (DerSimonian and Laird method)(34)

was used. Alternatively, the fixed-effects model (Mantel-Haenszel method)(35) was

applied to calculate the pooled HR and 95% CI of survival outcomes. To decrease the

heterogeneity among studies and recognize the prognostic value of miR-145 in greater




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detail, subgroup analyses were conducted based on multiple criteria such as miR-145

subtype, tumor type, sample type, and patient ethnicity. Additionally, Begg’s test

(rank correlation test)(36) and Egger’s test (weighted linear regression test)(37) were

employed to evaluate the potential publication bias (P < 0.05 was considered

statistically significant). Furthermore, one-way sensitivity analysis was performed to

identify studies that had a crucial influence on the pooled HR by removing one study

at a time.

Results

Literature selection

A total of 1,262 studies were identified from online databases MEDLINE, Embase,

Pubmed, Google Scholar, and China Biology Medicine disc. Another five studies(17,

38-41) were acquired through manually screening the reference lists of relevant

reviews and meta-analyses. After removing 54 duplicated publications, the remaining

1,213 studies were evaluated by carefully reading the titles and abstracts, after which

1,059 studies were excluded because of the following reasons: not tumor studies,

unpublished, withdrawn articles, letters, abstracts, reviews, or meta-analyses. Next,

the entire text of the remaining 154 studies were assessed. Among them, 106 were

removed because survival analyses were not performed in 92 studies, and HR could

not be extracted or calculated from the other 14 studies. Finally, 48 articles containing

50 studies, which were published between 2010 and 2017, were included in the

meta-analysis for the present study. A detailed flow chart illustrating the process of

literature selection is shown in Figure 1.

Literature characteristics

Among the 50 included studies, 46 studies investigated the prognostic value of

miR-145-5p for malignant tumors and only 4 studies focused on miR-145-3p. A total

of 6,875 patients suffering from 18 different tumors were included in the

meta-analysis, with the sample size in each study ranging from 20 to 1,141 patients

(median 74.5). Quantitative real-time polymerase chain reaction was the method used




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most often to measure the expression of miR-145 (43/50, 86%). A total of 17 out of 50

studies (34%) measured the expression of miR-145 in frozen tissues, 18 (36%) in

formalin fixed and paraffin embedded tissues (FFPE), 6 (12%) in plasma or serum,

while the remaining 8 studies (16%) did not specifically report the tissue type used.

The cut-off value that stratified patients into high and low expression groups varied

among the different studies, with the median value being the most widely used value

(28 out of 50, 56%). For the prognosis, 25 studies reported a correlation between

miR-145 expression and OS, 14 reported PFS, and the remaining 11 reported both OS

and PFS. The length of follow-up ranged from 24 to 310 months with a median of

69.5 months. With respect to HR, 18 studies reported the HR and 95% CI directly,

whereas the remaining 32 studies reflected the survival outcomes using survival

curves, and thus the HR and 95% CI could be calculated. With respect to the quality

of included studies, most included studies (43 out of 50, 86%) were high quality with

the NOS score greater than 6. Other detailed information of enrolled studies is listed

in Table 1.

Meta-analysis of miR-145 expression and overall survival

A total of 36(9, 12-15, 17, 21, 22, 24-26, 29, 39, 40, 42-62) out of the 50 studies

that included 5,074 patients evaluated the relationship between miR-145 expression

and OS of patients suffering from various tumors. Among them, two studies

investigated the expression level and prognostic value of miR-145 in blood samples,

and the results of these two studies were similar with most tissue studies. Therefore, a

pooled analysis containing blood and tissue studies were performed. Owing to a

significant heterogeneity existing among studies (I2

= 62%, P < 0.001), a random

model was employed to calculated the pooled HR and 95% CI of OS. The result

showed that low miR-145 expression was associated with poor OS in multiple tumors,

with a pooled HR of 1.70 (95% CI 1.46–1.99, P < 0.001) (Figure 2). To decrease the

heterogeneity among studies, subgroup analyses were performed based on seven

criteria: miR-145 subtype, tumor type, sample type, HR resource, patient ethnicity,

miR-145 assay method, and cut-off value (Table 2 and supplementary Figure




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S1A-S7A). The results showed that low expression of miR-145 was significantly

associated with worse OS in subgroup analyses of miR-145 subtype, HR resource,

patient ethnicity, miR-145 assay method, and cut-off value. However, the results from

subgroup analysis of tumor type suggested that the downregulation of miR-145 was

obviously associated with poor OS in colorectal cancer (HR = 2.17, 95% CI

1.52–3.08, P < 0.001), ovarian cancer (HR = 2.15, 95% CI 1.29–3.59, P = 0.003),

glioma (HR = 1.65, 95% CI 1.30–2.10, P < 0.001), and osteosarcoma (HR = 2.28, 95%

CI 1.50–3.47, P < 0.001), but not in lung cancer (HR = 1.54, 95% CI 0.70–3.36, P =

0.28), cervical cancer (HR = 1.32, 95% CI 0.64–2.68, P = 0.45), esophageal cancer

(HR = 0.97, 95% CI 0.30–.09, P = 0.95), and breast cancer (HR = 1.19, 95% CI

0.79–1.81, P = 0.41). The study conducted by Naito et al.(15) found that miR-145 was

upregulated in scirrhous type gastric cancer and the downregulation of miR-145 was

significantly associated with better OS in gastric cancer (HR = 0.50, 95% CI

0.26–2.98, P = 0.44)(15). The pathological type and survival result from this previous

study were totally different from other studies focusing on the association between

miR-145 expression and OS in gastric cancer(46, 60). Therefore, further subgroup

analysis for gastric cancer was performed after omitting the study conducted by Naito

et al., and the result without heterogeneity (I2

= 0, P = 0.55) suggested that the low

miR-145 expression also indicated worse OS in gastric cancer (HR = 1.78, 95% CI

1.35–2.36, P < 0.001). For the subgroup analysis of sample type, the downregulated

miR-145 in frozen tissues and serum was significantly associated with poor OS

(frozen tissues: HR = 1.81, 95% CI 1.39–2.35, P < 0.001; serum: HR = 1.74, 95% CI

1.21–2.49, P = 0.003), whereas the association between the downregulation of

miR-145 in FFPE tissues and OS was not statistically significant (HR = 1.35, 95% CI

0.99–1.84, P = 0.06).

Meta-analysis of miR-145 expression and progression-free survival

In the present study, PFS was analyzed along with DFS, FRI, BFS, MFS, TTR and

RFS, because all these indices were used to indicate the tumor recurrence or

deterioration after surgery or treatment. A total of 26 studies(8-11, 16, 18-29, 38, 41,




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49, 52, 53, 56, 61, 63) encompassing 1,971 carcinoma patients evaluated the

correlation between miR-145 expression and PFS. The random-effects model was

employed to estimate the pooled HR owing to an obvious heterogeneity among

studies (I2

= 70%, P < 0.001). Results showed that downregulated miR-145

significantly predicted unfavorable PFS in various cancers, with a HR of 1.39 (95%

CI 1.16–1.67, P < 0.001) (Figure 3). Similar to OS, subgroup analyses of PFS were

also performed. The results showed that the predictive value of miR-145 on PFS in

various cancers was not altered when patients were stratified based on HR resource

and miR-145 assay method (Table 2 and supplementary Figure S1B-S7B).

Nevertheless, the results from the subgroup analysis of tumor type showed that the

low miR-145 expression was only significantly associated with worse PFS in lung

cancer (HR = 1.97, 95% CI 1.25–3.09, P = 0.003). The subgroup analysis based on

patient ethnicity found that the downregulated miR-145 was only associated with poor

PFS in Asian patients (HR = 1.50, 95% CI 1.23–1.82, P < 0.001) and not with the

European (HR = 1.41, 95% CI 0.87–2.29, P = 0.16) or American patients (HR = 0.80,

95% CI 0.32–2.02, P = 0.64). For the subgroup analysis of the cut-off value, the

results showed that the PFS of patients in high and low miR-145 expression groups

that were stratified using the median value was comparable (HR = 1.14, 95% CI

0.86–1.51, P = 0.37). The subgroup analysis found that the low miR-145 expression

in all types of tissues was not associated with the PFS (frozen tissue: HR = 1.51, 95%

CI 0.96–2.37, P = 0.08; FFPE tissue: HR = 1.18, 95% CI 0.94–1.48, P = 0.15; serum:

HR = 1.26, 95% CI 0.86–1.86, P = 0.23).

The assessment of publication bias

In the present study, the funnel plots of Begg’s and Egger’s tests were employed to

evaluate the publication bias of all included studies. No obvious asymmetry was

observed in the funnel plots of Begg’s (OS, P = 0.17, Figure 4A; PFS, P = 0.63,

Figure 4C) and the P values of the Egger’s tests were all higher than 0.05 (OS, P =

0.41, Figure 4B; PFS, P = 0.33, Figure 4D). Therefore, significant publication bias did

not exist in this meta-analysis.




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Sensitivity analysis

Sensitivity analysis of OS and PFS was performed to investigate the influence of

each individual study on the pooled HRs (Figure 5A and 5B). The result showed that

the pooled results were not significantly altered by sequentially omitting any single

data set, demonstrating that the results of this meta-analysis were robust.

MiR-145 can be regulated by multiple factors

Consistent with other mammalian miRNAs, miR-145 was first transcribed from its

parental gene and termed as pri-miRNA, then the pri-miRNA was cleaved into hairpin

intermediates (pre-miRNAs) by the nuclear RNase III Drosha and further processed to

mature miRNAs by cytosolic Dicer, another RNase-III related enzyme (Figure 6)(64,

65). Therefore, the expression level of miR-145 might be regulated in the

transcription and maturation process by various factors. During the transcription step,

Sachdeva et al.(66) first discovered that P53, a well-demonstrated central tumor

suppressor, could induce miR-145 transcription by directly interacting with its

promoter. From then on, other molecules including EWS-FLI-1(67), FOXO(68),

TP53(69), EGFR(70), PPARγ(71), DNMT3b(72), AR(73), and DDX3(74) could also

stimulate the transcription of miR-145 by enhancing promoter activity. On the

contrary, DNA methylation at the miR-145 promoter region(75), C/EBP-b(76),

DCLK1(77), RREB1(78), and ZEB2(79) caused the downregulation of miR-145 by

repressing the activity of the miR-145 promoter. With regard to the

maturation-process, P53(80) and BRCA1(81) could increase the expression of

miR-145 by directly interacting with the Drosha complex, whereas p70S6K(82),

methyltransferase BCDIN3D(83), and TARBP2(84) restrained the maturation of

miR-145 via inhibition of Dicer activity. Moreover, recent studies have shown that the

function of miR-145 could also be repressed by various competing endogenous RNAs

(ceRNAs) including transcribed pseudogenes (e.g., OCT4-pg4(85)), lncRNAs (e.g.,

lncRNA RoR(86-88), TUG1(89), MALAT1(90), UCA1(91), and CRNDE(92)), and

circular RNAs (e.g., circRNA_001569(93) and circBIRC6(94)). The ceRNAs




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identified thus far mainly regulate the function of miR-145-5p instead of miR-145-3p.

The target genes regulated by miR-145-5p/3p in various malignant tumors

After processing with Dicer, pre-miR-145 generated two mature subtypes named

miR-145-5p and miR-145-3p. Then, a RNA-induced silencing complex that silenced

the expression of target transcripts by either facilitating corresponding mRNA

degradation or blocking its translation was formed(64). By systematically reviewing

previous studies, the transcripts that could be targeted by miR-145-5p, miR-145-3p, or

both miR-145-5p and miR-145-3p in different malignant tumors were summarized

(Table 3). Generally, most of them focused on miR-145-5p and it was found to silence

many genes, which participated in almost every aspect of tumor activities, including

tumor growth, metastasis, differentiation, angiogenesis, and drug resistance. Among

these genes, some played important roles in only one aspect of tumor behavior.

Minami et al.(95) revealed that miR-145-5p perturbed the Warburg effect by silencing

KLF4 in bladder cancer cells, resulting in significant cell growth inhibition. Eades et

al.(96) found that the small GTPase ADP-ribosylation factor 6, a target of

miR-145-5p in the triple-negative breast cancer, promoted cell invasion by regulating

E-cadherin localization and impacting cell-cell adhesion. Gao et al.(97) suggested

overexpression of miR-145-5p sensitized breast cancer cells to doxorubicin in vitro

and enhanced the doxorubicin chemotherapy in vivo via inhibition of the multidrug

resistance-associated protein 1. Yet, some targeted genes of miR-145-5p possess

multiple functions in an individual tumor type. For instance, overexpression of

miR-145-5p suppressed esophageal squamous cell carcinoma cell proliferation and

invasion via targeting c-Myc(98), and the knockdown of miR-145-5p responsively

increased both the mRNA and protein levels of Ets1, and thus promoted the

metastasis and angiogenesis of gastric cancer cells(99). Additionally, some genes can

be regulated by miR-145-5p in multiple tumors. For example, FSCN1, one of the

most frequently reported target genes of miR-145-5p, was involved in bladder

cancer(100), esophageal cancer(101), hepatocellular carcinoma (HCC)(102), lung

cancer(103), nasopharyngeal cancer(104), and prostate cancer(105). Compared with




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miR-145-5p, the biological functions of miR-145-3p, which were derived from the

antisense of miR-145-5p, have been reported in few studies. It was reported that three

genes, HMGA2(45), Ang-2(106), and HIF-2α(107), could be regulated by

miR-145-3p in ovarian cancer, pancreatic cancer, and neuroblastoma, respectively, to

inhibit tumor growth or metastasis. Furthermore, previous studies found that

MDTH(108) and UHRF1(109) could be co-regulated by both miR-145-5p and

miR-145-3p in lung cancer and bladder cancer, respectively.

Discussion

Although miRNAs only encompass 19–23 nucleotides and do not have the ability

to encode proteins, they are involved in multiple cellular pathways and play crucial

roles in various diseases(1). Identifying aberrantly expressed miRNAs and illustrating

their underlying mechanisms may assist with early diagnosis, prognosis evaluation,

and treatment development of numerous diseases(110, 111). Among thousands of

known cancer-related miRNAs, miRNA-145 is considered an important one whose

biological function and clinical significance have been investigated by many studies.

To date, only two other meta-analyses have assessed the association between miR-145

expression and the prognosis of patients with malignant tumors(112, 113). Zhang et

al.(112) utilized four prostate cancer studies and evaluated the predicted value of

miR-145 for the DFS. In this study, the author defined the pooled HR < 1 indicated

poor prognosis for the groups with lower miR-145 expression and was considered

statistically significant if the 95% CI did not overlap 1. The pooled result indicated

that the low expression of miR-145 in prostate cancer tissues predicted poor DFS

(HR=0.48, 95% CI 0.30-0.75, P=0.001). However, the result of their study cannot be

applied to other cancers due to the heterogeneity among different tumors and the

small sample size. Yang et al.(113) reported that overexpression of miR-145 was

significantly associated with favorable OS in various carcinomas (HR = 0.47, 95% CI

0.31–0.72, P < 0.01), but not with DFS (HR = 0.87, 95% CI 0.51–1.47, P = 0.569).

However, the conclusion of their study was not robust enough because it was

published 3 years ago and only included 18 studies. In the present study, a




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comprehensive literature search was performed to collect all relevant evidence

available from previous studies, and we performed more detailed subgroup analyses

to recognize the prognostic value of miR-145 in greater detail.

A total of 50 studies that included 6,875 patients evaluated the association between

miR-145 expression and the prognosis of patients with malignant tumors. The pooled

results from 36 studies suggested that low expression of miR-145 significantly

predicted a poor OS, and this result was not altered when the patients were stratified

into different subgroups based on HR resource, patient ethnicity, miR-145 assay

method, and cut-off value. The subgroup analysis based on tumor type suggested that

the downregulation of miR-145 was obviously associated with poor OS in colorectal

cancer, ovarian cancer, gastric cancer, glioma, and osteosarcoma. Additionally, the

subgroup analysis based on tissue type found the prognosis value of frozen tissue and

peripheral blood was better than that of FFPE tissues, which was probably caused by

higher speed of RNA degradation in FFPE tissues than that in the other two types of

tissues(114).

Consistent with OS, the pooled results showed that the downregulation of miR-145

was significantly associated with worse PFS in patients with various cancers. This

result did not change when the patients were assigned to different subgroups based on

miR-145 subtype, HR resource, and miR-145 assay method. However, the subgroup

analysis based on tumor types showed that the downregulation of miR-145 was only

associated with poor PFS in patients suffering from lung cancer, and not those with

prostate cancer, colorectal cancer, esophageal cancer, breast cancer, and glioma.

Meanwhile, the subgroup analysis based on patient ethnicity found that the

downregulation of miR-145 was only associated with poor PFS among Asian patients,

but not with the European and American patients. This discrepancy might arise from

differences in environment or genetic background. Nevertheless, the subgroup

analyses indicated that the aberrantly expressed miR-145 in all types of tissues (e.g.,

blood, frozen, and FFPE tissues) was not significantly associated with PFS.

Additionally, there was no significant difference in PFS between high and low

miR-145 expressed groups, which was classified by median values.




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In response to the need for comprehensive recognition of miR-145, the regulatory

mechanisms of miR-145-5p/3p were elucidated in the present study. Normally,

miR-145 was downregulated in tumor tissues. Chivukula R.R. et al.(115) found that

miR-145 was not expressed in colonic epithelial cells but highly expressed in

mesenchymal cells such as fibroblasts and smooth muscle cells, this result was further

validated by Kent O.A. et al.(116). Hence, they considered that the downregulation of

miR-145 in colorectal tumor tissues was the depletion of mesenchymal cells in tumors

relative to adjacent normal tissues. In addition, the underlying molecular mechanisms

of the downregulation of miR-145 were also investigated in multiple previous studies.

In tumor tissues and cell lines, the factors that prompted the transcription and

maturation of miR-145 were downregulated, whereas the molecules that repressed the

transcription and maturation of miR-145 were upregulated. These contributed to the

downregulation of miR-145. Furthermore, the function of miR-145-5p could also be

restrained by various ceRNAs in the cytoplasm. OCT4-pg4, a pseudogene of OCT4,

holds the common binding sequence with OCT4 for miR-145-5p, and thus functions

as a natural miR-145-5p sponge to protect the OCT4 transcript from being inhibited

by miR-145(85). Long non-coding RNAs (lncRNAs) are an important class of

non-coding RNA and partly serve as molecular sponges to competitively inhibit

miRNAs. Previous studies have demonstrated that the function of miR-145-5p could

be restrained by multiple lncRNAs in various diseases, such as lncRNA-ROR in lung

cancer(117), pancreatic cancer(87), endometrial cancer(86), and colorectal cancer(88);

lncRNA-TUG1 in gastric cancer(118) and bladder cancer(89); and lncRNA-MALAT1

in cervical cancer(90). Additionally, the biological function of miR-145-5p could also

be inhibited by circular RNAs (circRNAs), which was a novel class ceRNA shaped by

a covalently closed loop without 5′-3′ polarity(119). Xie et al.(93) found that

circ-001569 promoted the proliferation and invasion of colorectal cancer cell by

sequestering miR-145-5p. Yu et al.(94) found that circBIRC6 directly interacts with

miR-145-5p and miR-34a to modulate target genes that maintain human pluripotency

and differentiation. The demonstrated target genes of miR-145-5p/3p and their

biological functions in different neoplasms were also illustrated in the present study.




17

It was shown that numerous oncogenes that are involved in almost every aspect of

tumor activity can be regulated by miR-145-5p/3p.

Although the present study provides important information in recognition of the

clinical value and regulatory mechanism of miR-145, many limitations in the study

should be noted. First, despite 50 relevant studies being utilized, the number of studies

belonging to each type of tumor was not sufficient and the sample sizes in most of the

studies were small, with these factors compromising the statistical power of the

meta-analysis. Second, owing to relevant studies being less than two, the prognostic

value of miR-145 for some important solid tumors such as HCC, pancreatic cancer,

and renal cell cancer could not be evaluated during this meta-analysis; therefore, more

well-designed clinical studies with larger sample sizes for these tumors are imperative.

Third, the cut-off value in each study varied, with a golden standard regarding the

cut-off value should be verified to better evaluate the prognostic value of miR-145.

Fourth, owing to several eligible studies not providing the survival data directly,

corresponding HRs and 95% CIs were calculated from survival curves, which might

cause several micro statistical errors. Fifth, although no significant publication bias

was identified in this meta-analysis, potential publication bias might exist owing to

desirable results being published more easily, resulting in over estimation of survival

outcomes. Finally, this study only collected the regulatory mechanisms and biological

functions of miR-145-5p/3p reported thus far, and more novel biological mechanisms

containing miR-145-5p/3p might be unveiled in the future.

In conclusion, based on all eligible evidence, our study demonstrated that the

downregulation of miR-145 was significantly associated with the poor prognosis of

patients with various malignant tumors. And the subgroup analyses indicated that

low expression of miR-145 significantly predicted worse OS in patients with

colorectal cancer, ovarian cancer, glioma, and osteosarcoma. And low expression of

miR-145 was also significantly associated with PFS in patients with lung cancer and

those of Asian descent. In addition, via comprehensively review previous studies, we

found that miR-145 is involved in multiple tumor activities by targeting numerous

genes, and the expression level of miR-145 could also be regulated by multiple




18

factors.

Acknowledgements: None

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26

Table 1 The main characteristics of all included studies in the present meta-analysis

Study (Ref.) Year Country miR-145

subtype Tumor type

Detected

sample Assay method

Expression

in tumor

Cut-off

value

Sample size

(low/high)

Follow-up

(month)

Survival

endpoints HR source

Independent

risk factor

NOS

score

Schaefer (8) 2010 Germany 5p Prostate carcinoma FT qRT-PCR Down Median 75(NR) 93 FRI reported No 8

Chen (38) 2010 China 5p Prostate carcinoma FFPE qRT-PCR Down NR 106（73/33） 82 PFS SC Yes 7

Drebber (39) 2011 Germany 5p Colorectal cancer FFPE qRT-PCR Down ROC curve 50（15/35） 77 OS SC NR 8

Marchini (49) 2011 Italy 5p Ovarian cancer FT qRT-PCR NR Median 89(NR) 143 PFS/OS reported NR 7

Radojicic (9) 2011 Greece 5p Breast cancer FFPE qRT-PCR Down Mean 49（28/21） 118 DFS/OS SC NR 8

Feber (40) 2011 The US 5p Esophageal cancer FFPE qRT-PCR Down Median 100(50/50) 55 OS SC NR 8

Leite (27) 2011 Brazil 5p Prostate carcinoma FT qRT-PCR NR Median 49(NR) 122 BFS SC No 6

Hamano (43) 2011 Japan 5p Esophageal Cancer FFPE qRT-PCR Down Median 98(49/49) 98 OS SC NR 8

Schee (28) 2012 Norway 5p Colorectal cancer FT qRT-PCR NR Median 193(97/96) 63 MFS SC NR 7

Kang (41) 2012 Korea 5p Prostate carcinoma FFPE qRT-PCR NR Median 73(36/37) 55 FRI SC No 7

Huang (44) 2012 China 5p Cervical carcinoma FFPE qRT-PCR NR NR 44（18/26) 70 OS SC No 6

Ko (18) 2012 Canada 5p Esophageal Cancer FFPE qRT-PCR NR Median 25(12/13) 32 DFS SC NR 7

Law (10) 2012 China 5p HCC NR qRT-PCR Down 1.5 fold 47(15/32) 144 DFS SC NR 7

Speranza (52) 2012 Italy 5p Glioblastoma FT qRT-PCR Down Median 20（10/10） 102 PFS/OS SC NR 8

Tanaka (16) 2013 Japan 5p Esophageal cancer Serum qRT-PCR High Median 64（32/32） 40 PFS SC No 8

Saija (50) 2013 Finland 5p Glioma FT Microarray Down Three-fold 268(53/215) 130 OS SC NR 8

Campayo (11) 2013 Spain 5p Lung cancer FT qRT-PCR Down NR 70(14/56) 36 TTR SC Yes 7

Tang (53) 2013 China 5p Osteosarcoma Tissues qRT-PCR Down Median 166(89/77) 152 DFS/OS SC Yes 8

Yu (58) 2013 China 5p HNC Tissues qRT-PCR Down Two-fold 250(125/125) 60 OS SC NR 8

Avgeris (19) 2013 Greece 5p Prostate carcinoma FT qRT-PCR Down NR 62(27/35) 75 DFS reported Yes 8

Muti-1 (17) 2014 Canada 5p Breast Cancer Tissues Mircoarray down Median 740(370/370) 310 DSS SC NR 8

Muti-2 (17) 2014 Canada 3p Breast Cancer Tissues Mircoarray down Median 740(370/370) 310 DSS SC NR 8

Naito (15) 2014 Japan 5p Gastric cancer FFPE qRT-PCR High Median 71(36/35) 67 CSS SC No 8

Xia (55) 2014 Japan 5p TCL FFPE qRT-PCR NR NR 40(10/30) 57 OS reported(m) Yes 7

Slattery (51) 2015 The US 3p Colorectal cancer FFPE Mircoarray NR Expressed or not 1141(1114/28) NR OS reported NR 9

Xia (21) 2015 China 3p Lung Cancer FFPE qRT-PCR Down Median 92(36/46) 106 DFS/OS reported Yes 9

Shen (20) 2015 China 5p Lung cancer FT qRT-PCR Down Median 48(24/24) 24 DFS SC NR 7

Larne (12) 2015 Sweden 5p Prostate carcinoma FFPE qRT-PCR Down Median 49(25/24) 204 OS reported NR 9




27

Liang (48) 2015 China 5p Ovarian cancer Serum qRT-PCR Down Median 84(42/42) 36 OS reported NR 9

Avgeris (22) 2015 Greece 5p Bladder cancer Tissues qRT-PCR Down 1.5 fold 40（22/18） 48 DFS/OS reported(u) NR 9

Wang (54) 2015 China 5p Cervical cancer FT qRT-PCR Down Median 114(63/51) 69 OS reported(m) Yes 9

Ye (57) 2015 China 5p Lung cancer Tissues qRT-PCR Down Median 122(61/61) 60 OS SC NR 8

Kim (45) 2015 Korea 3p Ovarian cancer FT qRT-PCR Down NR 74(48/26) 90 OS reported(m) Yes 8

Li (23) 2015 China 5p Colorectal cancer Serum qRT-PCR Down Median 175(NR) 37 DFS reported(u) No 8

Pecqueux (13) 2016 Germany 5p Colorectal cancer FT qRT-PCR Down Median 25（12/13） 67 OS SC NR 8

Zhang (60) 2016 China 5p Gastric cancer FT qRT-PCR Down NR 145(49/76) 65 OS reported(u) Yes 8

Yang (56) 2016 The US 5p Colorectal cancer FFPE qRT- PCR Down Survival result NR NR PFS/OS reported NR 6

Zhou (61) 2016 China 5p Colorectal cancer FT qRT-PCR Down NR 60（27/33） 80 OS reported(u) No 8

Shi-1 (63) 2016 China 5p Lung cancer Serum qRT-PCR NR NR Pemetrexed 76(31/45) 117 PFS SC NR 6

Shi-2 (63) 2016 China 5p Lung cancer Serum qRT-PCR NR NR Observation 72(50/22) 78 PFS SC NR 6

Li (47) 2016 China 5p Osteosarcoma Tissues qRT-PCR Down NR 39(19/20) 60 OS SC NR 7

Zhan (59) 2016 China 5p Gallbladder cancer FFPE qRT-PCR Down Median 82(41/41) 93 OS SC NR 8

Namkung (25) 2016 Korea 5p Pancreatic cancer FT Mircoarray NR NR 104 NR DFS/OS reported(m) NR 6

Zhao (62) 2016 China 5p Gastric cancer FFPE qRT-PCR Down Mean 63(44/19) 36 OS SC NR 8

Liu (24) 2016 China 5p Breast cancer FT qRT-PCR NR Median 117(NR) 60 DFS/OS SC NR 6

Li (46) 2017 China 5p Gastric cancer TCGA Mircoarray Down NR 361（157/204） 66 OS SC NR 7

Kapodistrias (29) 2017 Greece 5p Liposarcoma FFPE qRT-PCR Down Median 61（31/30） 188 RFS/OS SC No 8

Gan (42) 2017 China 5p Lung Cancer FFPE qRT-PCR Down Mean 101（65/36） 51 OS SC NR 8

Azizmohammadi (14) 2017 Iran 5p Cervical cancer FT qRT-PCR Down Median 35（18/17） 54 OS reported(m) Yes 9

Zhao (26) 2017 The US 5p Glioma Serum Mircoarray NR Median 106(53/53) 24 DFS/OS reported NR 8

Ref.: reference; HR: hazard ratio; NR: not reported; 5P: miR-145-5P; 3P: miR-145-3P; HCC: hepatocellular carcinoma; HNC: Head and Neck Cancer; TCL:

T-cell leukemia/lymphoma; FT: frozen tissues; FFPE: formalin fixed and paraffin embedded tissues; TCGA: The Cancer Genome Altas; qRT-PCR: quantitative

real time polymerase chain reaction; ROC: receiver operating characteristic; FRI: recurrence-free interval; PFS: progression-free survival; OS: overall survival;

DFS: disease-free survival; BFS: biochemical-free survival; MFS: metastasis-free survival; TTR: time to relapse; DSS: disease-specific survival; CSS:

Cancer-specific survival; RFS: relapse-free survival; HR: hazard ratio; SC: survival curve; NOS: Newcastle-Ottawa Scale.




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Table 2 Subgroup analysis of overall survival and progression-free survival in patients with various

cancers

OS (n=36) PFS (n=26)

Subgroup No. of studies

model Pooled HR

(95% CI) P value

HG I2 %

No. of studies

model Pooled HR

(95% CI) P value

HG I2 %

miR-145 type

miR-145-5P 32 Random 1.66(1.40,1.97) <0.001 65

25 Random 1.37(1.14,1.65) <0.001 70

miR-145-3P 4 Fixed 1.99(1.53,2.59) <0.001 0

1 ND 2.18(1.30,3.65) 0.003 ND

Tumor type

Prostate cancer 1 ND 3.00(1.50,6.00) 0.002 ND

5 Random 1.14(0.83,1.56) 0.41 75

Colorectal cancer 5 Fixed 2.17(1.52,3.08) <0.0001 0

4 Random 1.22(0.69,2.16) 0.5 69

Lung cancer 3 Random 1.54(0.70,3.36) 0.28 77

5 Random 1.97(1.25,3.09) 0.003 68

Ovarian cancer 3 Fixed 2.15(1.29,3.59) 0.003 21

1 ND 0.20(0.05,0.87) 0.03 ND

Cervical cancer 3 Random 1.32(0.64,2.68) 0.45 84

ND ND ND ND ND

Esophageal cancer 2 Random 0.97(0.30,3.09) 0.95 83

2 Fixed 0.43(0.19,1.00) 0.05 0

Gastric cancer 4 Random 1.40(0.79,2.48) 0.24 77

ND ND ND ND ND

Breast cancer 4 Random 1.19(0.79,1.81) 0.41 66

2 Fixed 1.28(0.94,1.75) 0.12 47

Glioma 3 Fixed 1.65(1.30,2.10) <0.0001 0

2 Random 2.56(0.86,7.63) 0.09 65

Osteosarcoma 2 Fixed 2.28(1.50,3.47) 0.0001 0

1 ND 1.56(1.12,2.17) 0.008 ND

Others 6 Random 2.36(1.55,3.59) <0.0001 61

4 Random 1.96(1.13,3.37) 0.02 59

Sample type

Frozen tissues 10 Random 1.81(1,39,2.35) <0.0001 51

11 Random 1.51(0.96,2.37) 0.08 75

FFPE 15 Random 1.35(0.99,1.84) 0.06 72

7 Random 1.18(0.94,1.48) 0.15 63

Serum 2 Fixed 1.74(1.21,2.49) 0.003 0

5 Random 1.26(0.86,1.86) 0.23 61

Others 9 Random 2.17(1.64,2.88) <0.0001 57

3 Random 2.33(1.22,4.46) 0.01 62

HR resource

Reported 15 Fixed 2.02(1.74,2.34) <0.0001 0

10 Random 1.62(1.18,2.24) 0.003 54

SC 21 Random 1.43(1.14,1.79) 0.002 72

16 Random 1.28(1.03,1.60) 0.03 73

Ethnicity

Asian 21 Random 1.73(1.38,2.17) <0.0001 72

13 Random 1.50(1.23,1.82) <0.0001 68

European 9 Fixed 1.75(1.40,2.18) <0.0001 26

9 Random 1.41(0.87,2.29) 0.16 69

American 6 Fixed 1.57(1.29,1.90) <0.0001 49

4 Random 0.80(0.32,2.02) 0.64 84

Assay method

qRT-PCR 29 Random 1.68(1.37,2.06) <0.0001 68

24 Random 1.36(1.12,1.66) 0.002 71

Microarray 7 Fixed 1.75(1.49,2.06) <0.0001 0

2 Fixed 1.70(1.22,2.35) 0.001 0

Cut-off value

Median 19 Random 1.59(1.31,1.92) <0.0001 60

15 Random 1.14(0.86,1.51) 0.37 72

Others 17 Random 1.88(1.44,2.45) <0.0001 66

11 Random 1.72(1.34,2.21) <0.0001 64

OS: overall survival; PFS: progression-free survival; HR: hazard ratio; CI: confidence interval; HG:

heterogeneity; ND: no data; SC: survival curve; qRT-PCR: quantitative real time polymerase chain reaction.




29

Table 3. The target genes regulated by miR-145 in various malignant tumors

Tumor type No. of studies

Function Target gene

miR-145-5p

Bladder cancer 7 Growth KLF4/PTBP1,ILK,SOCS7,FSCN1

Metastasis PAK1, PAI-1

Growth and metastasis IGFIR

Breast cancer 7 Growth RTKN, MMP11, Rab27a

Metastasis ARF6, MUC1

Growth and metastasis ERBB3

Drug resistance MRP1

Growth and angiogenesis NRAS, VEGFA

Cervical cancer 5 Growth CDK6

Growth and metastasis SIP1

Radiation resistance HLTF,OCT4

Colorectal cancer 14 Growth NAIP,IGF1R,YES,STAT1,DFF45

Metastasis Paxillin,LASP1,ERG

Growth and metastasis FSCN1,N-RAS,IRS1, PAK4

Growth and angiogenesis p70S6K1

Drug resistance RAD18

Growth and drug resistance FLI-1

Esophageal carcinoma 4 Growth and metastasis PLCE1,c-Myc,FSCN1

Gastric cancer 6 Metastasis CTNND1,N-cadherin,ZEB2,

Growth E2F3

Drug resistance CD44

Metastasis and angiogenesis Ets1

Glioma 4 Metastasis ABCG2,ROCK1,ADAM17

Growth SOX9,ADD3

Hepatocellular carcinoma 6 Growth IRS1,ADAM17,HDAC2,IRS1,IRS2

Metastasis ADAM17

Growth and metastasis FSCN1

Lung cancer 8 Growth ICP27,OCT4

Metastasis OCT4,FSCN1,MTDH,SMAD3,N-cadherin

Growth and metastasis Mucin 1

Melanoma 2 Metastasis FSCN1

Growth and metastasis NRAS

Nasopharyngeal cancer 3 Metastasis SMAD3,FSCN1,ADAM17

Osteosarcoma 7 Metastasis MMP16,Snail,VEGF

Growth and metastasis CDK6,FLI-1,ROCK1

Ovarian cancer 4 Growth c-Myc,

Drug resistance SP1,CDK6

Growth and metastasis TRIM2,p70S6K1,MUC1

Pancreatic cancer 3 Growth and metastasis NEDD9,MUC13

Drug resistance p70S6K1

Prostate cancer 8 Growth SOX2,SENP1,ERG,BNIP3

Metastasis DAB2,HEF1,SWAP70

Growth and metastasis FSCN1

Renal cell carcinoma 2 Growth and metastasis ANGPT2,NEDD9,HK2

Others 10 Growth c-Myc,CDK6,DUSP6,CBFB,PPP3CA,CLINT1

Metastasis CTGF

Growth and metastasis NUAK1,SOX2,AKT3,ADAM19

Drug resistance MRP1

Differentiation OCT4

miR-145-3p

Ovarian cancer 1 Growth and metastasis HMGA2

Pancreatic cancer 1 Metastasis Ang-2

Neuroblastoma 1 Growth and metastasis HIF-2α

miR-145-3p/5p

Lung cancer 1 Growth MTDH

Bladder cancer 1 Growth and metastasis UHRF1

Abbreviation or official full name: KLF4: Kruppel like factor 4; PTBP1: polypyrimidine tract binding protein 1; ILK: integrin linked

kinase; SOCS7: suppressor of cytokine signaling 7; FSCN1: fascin actin-bundling protein 1; PAK1: p21 (RAC1) activated kinase 1;

PAI-1: serpin family E member 1; IGF-IR: insulin like growth factor 1 receptor; RTKN: rhotekin; MMP11: matrix metallopeptidase 11;

Rab27a: RAB27A, member RAS oncogene family; ARF6: ADP ribosylation factor 6; MUC1: mucin 1, cell surface associated; ERBB3:

erb-b2 receptor tyrosine kinase 3; MRP1: mitochondrial 37S ribosomal protein MRP1; NRAS: NRAS proto-oncogene, GTPase; VEGFA:




30

vascular endothelial growth factor A; CDK6: cyclin dependent kinase 6; HLTF: helicase like transcription factor; OCT4: organic

cation/carnitine transporter4; NAIP: NLR family apoptosis inhibitory protein; PAK4: p21 (RAC1) activated kinase 4; YES: YES

proto-oncogene, Src family tyrosine kinase; STAT1: signal transducer and activator of transcription 1; DFF45: DNA fragmentation factor

subunit alpha; LASP1: LIM and SH3 protein 1; ERG: ERG, ETS transcription factor; S6K: Ribosomal protein S6 kinase; RAD18:

RAD18, E3 ubiquitin protein ligase; FLI-1: Fli-1 proto-oncogene, ETS transcription factor; PLCE1: phospholipase C epsilon 1; CTNND1:

catenin delta 1; ZEB2: zinc finger E-box binding homeobox 2; E2F3: E2F transcription factor 3; Ets1: ETS proto-oncogene 1,

transcription factor; ABCG2: ATP binding cassette subfamily G member 2; ROCK1: Rho associated coiled-coil containing protein kinase

1; ADAM17: ADAM metallopeptidase domain 17; SOX2/9: SRY (sex determining region Y)-box 2/9; ADD3: adducin 3; HDAC2:

histone deacetylase 2; IRS1/2: insulin receptor substrate 1/2; MTDH: metadherin; SMAD3: SMAD family member 3; TRIM2: tripartite

motif containing 2; MUC1/13: mucin 1/13, cell surface associated; NEDD9: neural precursor cell expressed, developmentally

down-regulated 9; SENP1: SUMO specific peptidase 1; BNIP3: BCL2 interacting protein 3; DAB2: DAB2, clathrin adaptor protein;

ANGPT2: angiopoietin 2; HK2: hexokinase 2; DUSP6: dual specificity phosphatase 6; CBFB: core-binding factor subunit beta; PPP3CA:

protein phosphatase 3, catalytic subunit, alpha isoform; CLINT1: clathrin interactor 1; CTGF: connective tissue growth factor; NUAK1:

NUAK family kinase 1; AKT3: AKT serine/threonine kinase 3; MRP1: mitochondrial 37S ribosomal protein MRP1; HMGA2: high

mobility group AT-hook 2; Ang-2: angiogenin, ribonuclease A family, member 2; UHRF1: ubiquitin like with PHD and ring finger

domains 1.




31

Figure Legends:

Figure 1. The flow chart illustrating the process of studies selection. CBM: China

Biology Medicine.

Figure 2. Frost plot of the association between the downregulation of miR-145

and overall survival of patients in various tumors.

Figure 3. Frost plot of the association between the downregulation of miR-145

and progression-free survival of patients in various tumors.

Figure 4. Begg’s funnel plot and Egger’s test were used to evaluate the

publication bias. A and C Begg’s funnel plot of overall survival (OS) and

progression-free survival (PFS), B and D Egger’s test of overall survival (OS)

and progression-free survival (PFS).

Figure 5. Sensitivity analysis of the relationship between miR-145 expression

and overall survival (OS) (A) as well as progression-free survival (PFS)

(B).

Figure 6. The expression and function of miR-145 could be regulated by

numerous factors. C/EBP-b: CCAAT/enhancer binding protein beta;

DCLK1: doublecortin like kinase 1; ZEB2: zinc finger E-box binding

homeobox 2; RREB1: ras responsive element binding protein 1;

EWS-FLI-1: EWS-FLI-1 fusion protein; FOXO: forkhead box, sub-group O;

TP53: tumor protein p53; EGFR: epidermal growth factor receptor; PPARγ:

peroxisome proliferator activated receptor gamma; DNMT3b: DNA

methyltransferase 3 beta; AR: androgen receptor; DDX3: DEAD

(Asp-Glu-Ala-Asp) box polypeptide 3; BRCA1: BRCA1: breast cancer 1;

S6K: Ribosomal protein S6 kinase; BCDIN3D: BCDIN3 domain containing

RNA methyltransferase; TARBP2: TARBP2, RISC loading complex RNA

binding subunit; OCT4-pg4: a pseudogene of OCT4;




Published OnlineFirst January 2, 2019.Cancer Epidemiol Biomarkers Prev Liangliang Xu, Yanfang Zhang, Jianwei Tang, et al. meta-analysis of 50 studiesmicroRNA-145 in various tumors: a systematic review and The prognostic value and regulatory mechanisms of

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