Prognostic potential of miR-144 in various cancers: A meta-analysis : Medicine

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Research Article: Systematic Review and Meta-Analysis

Prognostic potential of miR-144 in various cancers: A meta-analysis

Jia, Chong-Yang MDa; He, Yan MDa; Wu, Shi-Nan BDb,c; He, Yan-Ting BDd,e,*; Wang, Ying MDf

Author Information
doi: 10.1097/MD.0000000000031728
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1. Introduction

As the primary public health hazard, cancer is an incurable and multi-factorial disease and is the premier cause of death throughout the world.[1] There will be an estimated 19.3 million new cancer cases and almost 10.0 million cancer deaths last year.[2] Most patients with cancers are diagnosed at an advanced stage and have a poor prognosis. Early detection or diagnosis plays a vital role in the treatment. Thus, it is urgent for us to find effective biomarkers to identify high-risk cancer patients so that clinicians can provide individualized treatment. MicroRNA (miRNA) is a highly conserved endogenous non-coding single-stranded RNA with a length of 18 to 22 nucleotides.[3] They negatively regulate target gene expression by base pairing with the 3′ untranslated region (3′ UTR) of target messenger RNA (mRNA), resulting in degradation or translation that inhibits the target gene expression at the post-transcriptional level.[4] Accumulated evidence have shown that miRNA is involved in many aspects of tumor progression, including cell growth, apoptosis, proliferation, differentiation and migration.[5] MicroRNA has been widely recognized as an effective prognostic biomarker for cancers.

As one of the most attractive miRNAs, the miR-144 is located on chromosome 17q11.2 and plays a vital role in cancer biology.[6] A growing numbers of studies have suggested that miR-144 is involved in tumor biological processes, such as invasion, migration, angiogenesis and cell cycle.[7] MicrorNA-144 (MiR-144) is abnormally expressed in tumors. Some studies found that high miR-144 expression in cancer patients indicated a poor prognosis, while others displayed the different view.[8–10] So far, studies on miR-144 have confirmed that it participated in different metabolic processes of organisms, including promoting apoptosis and anti-apoptosis, and its specific function depended on the physiological status of organisms to a certain extent. However, the purpose of this study is to explore the potential impact of tumor differentiation and malignant degree on the overall survival rate of tumor patients.

Currently, the prognostic values of miR-144 in cancers have still not been fully clear. Therefore, we summarized the available data from published studies and performed a meta-analysis to evaluate the prognostic significance of miR-144 in diverse cancers.

2. Methods

2.1. Search strategy

Eligible studies were identified through searching in PubMed, EMBASE, and Web of Science until April 20, 2022. Using the following words: (microRNA-144 odds ratio (OR) miR-144 OR miRNA-144) AND (tumor OR neoplasm OR cancer OR carcinoma) AND (prognosis OR prognostic OR survival OR outcome). Pooled hazard ratios (HRs) and 95% confidence intervals (95% CIs) were calculated to assess the correlation between miR-144 expression and patient prognosis. In order to avoid omitting the potentially related studies, the reference lists were also screened. Two authors independently conducted this procedure. This study was a meta-analysis that does not require the approval of the ethics committee and the patient consent.

2.2. Selection criteria

The inclusion criteria were: English publications; miR-144 expression was evaluated in the human tissues; association of miR-144 expression with cancer prognosis; studies provided sufficient data. The exclusion criteria: non-English papers; cases, reports, meeting abstracts, reviews, letters or meta-analysis; the prognosis data originated from The Cancer Genome Atlas (TCGA) database; or studies did not provide adequate data to calculate the HRs and 95% CI.

2.3. Data extraction and quality assessment

Two reviewers independently extracted the following information from the included studies: first author’s last name, year of publication, country, study type, tumor type, sample size, detected sample, detected method, analysis type, survival analysis, source of HR, and Newcastle–Ottawa Quality Assessment Scale (NOS) score. If both univariate and multivariate analysis results were offered for survival outcome, we preferred to select the latter ones. If the study provided no HRs and 95% CIs, survival data was extracted from the Kaplan–Meier curves by the software Engauge Digitizer version 4.1.[10] The NOS was used to evaluate the quality of the selected studies.[11]

2.4. Statistical analysis

HRs with 95% CIs were combined to assess the effect of miR-144 expression on prognosis in patients with a variety of cancers. A forest plot was used to estimate the effect of miR-144 expression on survival outcome. Heterogeneity was tested by the Cochran’s Q test and the Higgins I2 statistical method. If the P value of the Q test was < 0.05 and the I2 statistic was > 50% (heterogeneity existed), the random effects model was adopted to calculate pooled HR; otherwise, the fixed effects model was used.[12] The heterogeneity was further explored through subgroup analyses. Sensitivity analysis was performed to test the stabilization of the results. Finally, we assessed the potential publication bias through Begg’s test and Egger’s test.[13,14] All analyses were performed using the STATA 12.0 software (Stata Corporation, College Station, TX). P < .05 was considered statistically significant.

3. Results

3.1. Literature search

A total of 701 studies were initially retrieved from PubMed, Web of Science and EMBASE. The detailed searching process of literature selection was illustrated in Figure 1. After removing literatures, 34 articles remained. Through further browsing, 19 articles were further excluded. Finally, 15 articles were included in this meta-analysis.[9,10,15–25] Characteristics of the included studies was presented in Table 1.

Table 1 - Basic information of eligible studies for miR-144.
Study Year Country Cancer type Sample size Detected sample Detected method Analysis type Survival analysis Source of HR NOS score
Akiyoshi 2012 Japan GC 93 Bone marrow qRT-PCR Multivariate OS Reported 6
Wu 2016a China LUAD 129 Serum qRT-PCR Multivariate OS Reported 6
Wu 2016b China LUSC 54 Serum qRT-PCR Univariate OS Reported 7
Iwaya 2012 Japan CRC 137 Tissue qRT-PCR Multivariate OS Reported 6
Wouter 2020 Netherlands LC 26 Tissue qRT-PCR Univariate OS Reported 6
Cheng 2017 China glioblastoma 111 Tissue qRT-PCR Multivariate OS Reported 7
Brinzan 2019 Romania CRC 82 Tissue qRT-PCR Multivariate OS Reported 7
Zhao 2017 China AML 120 Serum qRT-PCR Univariate OS SC 7
Zheng 2020 China retinoblastoma 50 Tissue qRT-PCR Univariate OS、DFS SC 6
Wang 2015 China osteosarcoma 67 Tissue qRT-PCR Univariate OS SC 7
Liu 2016 China RCC 120 Tissue qRT-PCR Univariate OS SC 7
Liu 2017a China GC 96 Tissue qRT-PCR Univariate OS、DFS SC 7
Liu 2017b China GC 96 Serum qRT-PCR Univariate OS、DFS SC 6
Liu 2015 China PC 40 Tissue qRT-PCR Univariate OS SC 7
Madhavan 2016 Germany BC 265 Tissue qRT-PCR Multivariate OS Reported 6
Li 2021 China CRC 300 Tissue qRT-PCR Multivariate OS, DFS Reported 7
Meng 2021 China RCC 60 Tissue qRT-PCR Multivariate OS, DFS Reported 6
R = retrospective, P = prospective, BC = breast cancer, GC = gastric cancer, RCC = renal cell carcinomas, CRC = colorectal cancer, AML = acute myeloid leukemia, CC = cervical cancer, NOS = Newcastle–Ottawa Quality Assessment Scale, PC = prostate cancer, OS = overall survival, DFS = disease-free survival, SC = survival curve.

Figure 1.:
The flow chart of studies selecting process.

3.2. Study characteristics

The studies were published from 2012 to 2021. Types of tumors included: gastric cancer, lung adenocarcinoma, Lung squamous cell carcinoma, colorectal cancer, non-small-cell lung cancer, glioblastoma, acute myeloid leukemia, retinoblastoma, osteosarcoma, renal cell carcinomas, prostate cancer and breast cancer. The detected sample was different, most with serum or tissue, and 1 study in bone marrow. Most studies were from China, and 5 studies came from Japan, Netherlands, Romania and Germany, respectively. All studies assessed miR-144 expression by quantitative real-time polymerase chain reaction (qRT-PCR). The enrolled studies of the NOS score ranged from 6 to 7.

3.3. Association between miR-144 expression and overall survival

18 studies evaluated the correlation between low miR-144 expression and overall survival (OS). Due to the heterogeneity was significant among these studies (I2 = 73.4%), the random effect model was utilized to calculate the HR and 95% CI. We found that low miR-144 expression was significantly associated with good OS (HR: 0.68, 95% CI: 0.53–0.88) (Fig. 2).

Figure 2.:
Forest plot for the association between miR-144 expression and OS. OS = overall survival.

In order to explore the source of the heterogeneity, we conducted the subgroup analysis based on cancer type, analysis type, race, sample type, source of HR and sample size (Table 2). In the stratified analysis of cancer type, low miR-144 expression had better predictive value in patients with urinary system cancer (HR: 0.48, 95% CI: 0.35–0.64). In addition, there was a significant association between low miR-144 expression and analysis type (univariate analysis), race (Asian), sample type, source of HR (SC) and sample size.

Table 2 - Subgroup analysis for OS.
Stratified analysis No. of studies Pooled HR (95% CI) P value Heterogeneity
I² (%) P value Model
Cancer type
Digestive system 7 0.98 (0.54–1.73) .939 83.9 .00 Random
Urinary system 3 0.48 (0.35–0.64) .00 000 .709 Fixed
Respiratory system 3 0.62 (0.29–1.35) .231 61.6 .074 Random
Others 5 0.66 (0.55–0.78) 0 24..2 .26 Fixed
Analysis type
Univariate analysis 11 0.62 (0.54–0.71) 0 25 .213 Fixed
Multivariate analysis 6 0.67 (0.34–1.34) .261 81.7 0 random
Caucasian 3 1.05 (0.37–2.92) .932 79.8 .007 Random
Asian 14 0.68 (0.45–0.84) .001 73.8 0 Random
Tissue 13 0.70 (0.53–0.97) .033 76.3 0 Random
Serum/bone marrow 5 0.60 (0.37–0.97) .038 69.3 .01 Random
Source of HR
Reported 9 0.72 (0.42–1.23) .228 74.6 0 Random
SC 9 0.61 (0.52–0.70) 0 25 .23 fixed
Sample size
≥100 8 0.61 (0.41–0.64) 0 0 .553 Fixed
<100 10 0.70 (0.52–0.96) .027 68.7 .001 Random
95% CIs = 95% confidence intervals, OS = overall survival.

3.4. Association between miR-144 expression and disease-free survival

As shown in Figure 3, 6 studies reported the relationship between miR-144 expression and disease-free survival (DFS). The random-effect model was performed because of significant obvious heterogeneity in these studies (I2 = 84.5%). The pooled results indicated that low miR-144 expression was no association with DFS (HR: 0.87, 95% CI: 0.59–1.59) (Fig. 3).

Figure 3.:
Forest plot for the association between miR-144 expression and DFS. DFS = disease-free survival.

3.5. Association between miR-144 expression and clinicopathological features

The results were shown in Table 3. Low miR-144 expression showed significant association with tumor diameter (big vs small) (OR: 1.69, 95% CI: 1.08–2.75,), tumor stage (III–IV vs I–II) (OR: 2.52, 95% CI: 3.76–8.14) and invasion depth (T3 + T4 vs T2 + T1) (OR: 3.24, 95% CI: 1.72–4.89). In addition, there was no significance correlation between gender (male vs female) (OR: 1.02, CI: 0.54–1.47), age (old vs young) (OR: 2.21, CI: 0.85–1.63), tumor differentiation (poor vs moderate/well) (OR: 2.43, CI: 0.66–3.2), lymph node status (yes vs no) (OR: 5.68, CI: 0.75–4.84) and distant metastasis (yes vs no) (OR: 2.93, CI: 0.91–5.83) and the low miR-144 expression.

Table 3 - Association between low miR-144 expression and clinicopathological features.
Clinicopathologic features No. of studies Estimate OR (95% CI) P value Heterogeneity
I 2 (%) P value Model
Gender (male vs female) 8 1.02 (0.54–1.47) .51 0 .38 Fixed
Age (old vs young) 9 2.21 (0.85–1.63) .27 0 .15 Fixed
Tumor diameter (big vs small) 8 1.69 (1.08–2.75) .031 48 .048 Random
Tumor stage (III–IV vs I–II) 7 2.52 (3.76–8.14) 0 79 0 Random
Tumor differentiation (poor vs Moderate/Well) 6 2.43 (0.66–3.2) .299 56 .002 Random
Lymph node status (yes vs no) 5 5.68 (0.75–4.84) .401 87 0 Random
Distant metastasis (yes vs no) 6 2.93 (0.91–5.83) .084 75 .037 Random
Invasion depth (T3 + T4 vs T2 + T1) 3 3.24 (1.72–4.89) 0 0 .512 Fixed
OR = odds ratio.

3.6. Sensitivity analyses

In order to examine the stability of the overall results, sensitivity analysis was implemented. Sensitivity analysis showed that the result was not obviously influenced, suggesting the outcomes were stable (Figs. 4 and 5).

Figure 4.:
Sensitivity analysis for OS. OS = overall survival.
Figure 5.:
Sensitivity analysis for DFS. DFS = disease-free survival.

3.7. Publication bias

Begg’s funnel plot and Egger’s test were used to evaluate the potential publication bias.The p values of Begg’s test and Egger’s test for OS were 0.820 and 0.917, respectively (Fig. 6). The P values of Begg test and Egger test for DFS were 1 and 0.651, respectively (Fig. 7). P values were more than 0.05, indicating no significant publication bias.

Figure 6.:
Publication bias for OS. OS = overall survival.
Figure 7.:
Publication bias for DFS. DFS = disease-free survival.

4. Discussion

With the growth of population and aging, the incidence and mortality of cancer is growing rapidly, and it remains a serious threat to human health.[2] Accurate diagnosis and appropriate monitoring of cancer patients remains a major challenge. Therefore, specific therapeutic and prognostic biomarkers are important.[16] MicroRNAs are small, non-coding RNA molecules that can be highly conserved between different organisms. Numerous studies have demonstrated that in peripheral circulation, miRNAs were more stably expressed compared with mRNAs and less easily degradable, and can be detected by high-throughput techniques such as qRT-PCR, microarray and sequencing.[17] Through these ways, a growing number of miRNAs have been found to be associated with diagnostic, prognostic and therapeutic value in a variety of tumors.[18]

Previous studies have been found that a significant relationship between miR-144 expression levels and the prognosis of various malignant tumor patients.[9,10,15–25] However, the results were inconsistent. Akiyoshi et al retrospectively collected the bone marrow of 93 primary gastric cancer patients who underwent surgical treatment, and performed microRNA microarray and gene expression microarray analysis of total RNA in the bone marrow. They found that low miR-144 expression in cancer cells metastasized to the bone marrow was associated with gastric cancer progression, and indicated a poor prognosis.[26] Luo et al reveled that low miR-144 expression in colorectal cancer tissues was associated with enhanced malignant potential, such as venous infiltration, liver metastasis, and liver recurrence. Further multivariate analysis showed that low miR-144 expression was an independent prognostic factor for survival outcome.[19] However, Liu et al displayed that high miR-144 expression could serve as a useful prognostic and clinical target in thyroid cancer (TC).[20] The prognostic value of miR-144 in a variety of cancers was controversial. Thus, in order to determine the prognostic value of miR-144 in various cancers, we conducted the meta-analysis to clarify this question.

A total of 15 articles were eventually included in this meta-analysis. By the pooling strategy, we found that low miR-144 expression was significantly associated with good OS in patients with various cancers. Subsequently, we performed a subgroup analysis for OS. The results showed that cancer type, analysis type, race, sample, type, source of HR and sample size were the sources of heterogeneity. We also found that Low miR-144 expression had better predictive value in patients with urinary system cancer. In addition, low miR-144 expression was associated with tumor diameters (Big vs Small), tumor stage (III–IV vs I–II) and invasion depth (T3 + T4 vs T2 + T1).

MiR-144 plays an important role in tumor growth, invasion and metastasis. For instance, Cao et al reported that miR-144 suppressed the proliferation and metastasis of hepatocellular carcinoma (HCC) cells partially by targeting E2F transcription factor 3 (E2F3). E2F3 is an oncogene located at 6P22 and has a strong proliferative potential.[21]G1 to S-phase transition 1 (GSPT1) protein may be an important factor involving in regulation of the cell cycle and proliferation and apoptosis. GSPT1 was regarded as a potential proto-oncogene involved in multiple tumorigenesis.[22] Xiao et al suggested that miR-144 inhibited cell proliferation and migration by GSPT1 in colorectal cancer. Therefore, miR-144 may be an essential element in colorectal cancer development.[23] MiR-144 inhibited tumor cell growth and metastasis via directly targeting VEGFA and VEGFC.[24] Moreover, miR-144 induced ell cycle arrest and cell apoptosis in pancreatic cancer cells through the mitogen-activated protein kinase signal pathway.[25] In laryngeal squamous cell carcinoma (LSCC), Wu et al observed that miR-144 functioned as a tumor suppressor via targeting IRS1.[27] In addition, related studies have further confirmed that the expression of miR-144 is positively correlated with the expression of apoptosis-related genes such as TP53, FASL, CASP3 and so on.[28] Related studies have further confirmed that the overexpression of miR-144 is related to the mechanism of vascular endothelial injury caused by atherosclerosis, which involves the 7-KC-MiR-144-IDH2 signal pathway.[29] Therefore, the expression level of miR-144 gene is closely related to various metabolic processes, cell proliferation and differentiation, tumor invasiveness and metastatic ability.

There were several inevitable flaws in our study. Firstly, only 13 studies with 1390 patients were included in our study. Secondly, most eligible studies were from China, and the prognostic value of miR-144 needs to be verified by further studies in other countries and region. Thirdly, some HRs with 95% CIs were extracted from the survival curves, which could cause some bias. Finally, there was still significant heterogeneity.

5. Conclusion

Low miR-144 expression was significantly associated with good OS. MiR-144 could be used as a valid biomarker for tumor prognosis, especially in urinary system cancer. However, further larger sample and multi-center prospective studies are needed to confirm these findings before miR-144 can be used in clinical applications.

Author contributions

All authors approved the final version of the manuscript.

Conceptualization: Chong-Yang Jia, Shi-Nan Wu, Ying Wang.

Data curation: Ying Wang.

Formal analysis: Ying Wang.

Funding acquisition: Ying Wang.

Guarantor of the article: Ying Wang.

Investigation: Yan-Ting He.

Methodology: Yan He, Shi-Nan Wu, Yan-Ting He, Ying Wang.

Resources: Yan He, Yan-Ting He.

Software: Yan He, Shi-Nan Wu.

Writing – original draft: Chong-Yang Jia.

Writing – review & editing: Yan He, Shi-Nan Wu, Ying Wang.


    [1]. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin. 2019;69:7–34.
    [2]. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209–49.
    [3]. Felekkis K, Touvana E, Stefanou C. MicroRNAs DC. A newly described class of encoded molecules that play a role in health and disease. Hippokratia. 2010;14:236–40.
    [4]. Garzon R, Fabbri M, Cimmino A, et al. MicroRNA expression and function in cancer. Trends Mol Med. 2006;12:580–7.
    [5]. Ha M, Kim VN. Regulation of microRNA biogenesis. Nat Rev Mol Cell Biol. 2014;15:509–24.
    [6]. Gao Z, Liu R, Liao J, et al. Possible tumor suppressive role of the miR-144/451 cluster in esophageal carcinoma as determined by principal component regression analysis. Mol Med Rep. 2016;14:3805–13.
    [7]. Zhou M, Wu Y, Li H, et al. MicroRNA-144: a novel biological marker and potential therapeutic target in human solid cancers. J Cancer. 2020;11:6716–26.
    [8]. Liang HW, Ye ZH, Yin SY, et al. A comprehensive insight into the clinicopathologic significance of miR-144-3p in hepatocellular carcinoma. Onco Targets Ther. 2017;10:3405–19.
    [9]. Cheng ZX, Song YX, Wang ZY, et al. MiR-144-3p serves as a tumor suppressor by targeting FZD7 and predicts the prognosis of human glioblastoma. Eur Rev Med Pharmacol Sci. 2017;21:4079–86.
    [10]. Tierney JF, Stewart LA, Ghersi D, et al. Practical methods for incorporating summary time-to-event data into meta-analysis. Trials. 2007;8:8–16.
    [11]. 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;25:603–5.
    [12]. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7:177–88.
    [13]. Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994;50:1088–101.
    [14]. Egger M, Davey Smith G, Schneider M, et al. Bias in meta-analysis detected by a simple, graphical test. BM.J 1997;315:629–34.
    [15]. Madhavan D, Peng C, Wallwiener M, et al. Circulating miRNAs with prognostic value in metastatic breast cancer and for early detection of metastasis. Carcinogenesis. 2016;37:461–70.
    [16]. Paul D, Kumar A, Gajbhiye A, et al. Mass spectrometry-based proteomics in molecular diagnostics: discovery of cancer biomarkers using tissue culture. Biomed Res Int. 2013;2013:1–16.
    [17]. Kumar S, Vijayan M, Bhatti JS, et al. MicroRNAs as peripheral biomarkers in aging and age-related diseases. Prog Mol Biol Transl Sci. 2017;146:47–94.
    [18]. Inamura K, Ishikawa Y. MicroRNA in lung cancer: novel biomarkers and potential tools for treatment. J Clin Med. 2016;5:36.
    [19]. Iwaya T, Yokobori T, Nishida N, et al. Downregulation of miR-144 is associated with colorectal cancer progression via activation of mTOR signaling pathway. Carcinogenesis. 2012;33:2391–7.
    [20]. Sun J, Shi R, Zhao S, et al. E2F8, a direct target of miR-144, promotes papillary thyroid cancer progression via regulating cell cycle. J Exp Clin Cancer Res. 2017;36:40.
    [21]. Cao T, Li H, Hu Y, et al. miR-144 suppresses the proliferation and metastasis of hepatocellular carcinoma by targeting E2F3. Tumour Biol. 2014;35:10759–64.
    [22]. Lee JA, Park JE, Lee DH, et al. G1 to S phase transition protein 1 induces apoptosis signal-regulating kinase 1 activation by dissociating 14-3-3 from ASK1. Oncogene. 2008;27:1297–305.
    [23]. Xiao R, Li C, Chai B. MiRNA-144 suppresses proliferation and migration of colorectal cancer cells through GSPT1. Biomed Pharmacother. 2015;74:138–44.
    [24]. Tao P, Wen H, Yang B, et al. MiR-144 inhibits growth and metastasis of cervical cancer cells by targeting VEGFA and VEGFC. Exp Ther Med. 2018;15:562–8.
    [25]. Li J, Sun P, Yue Z, et al. MiR-144-3p induces cell cycle arrest and apoptosis in pancreatic cancer cells by targeting proline-rich protein 11 expression via the mitogen-activated protein kinase signaling pathway. DNA Cell Biol. 2017;36:619–26.
    [26]. Akiyoshi S, Fukagawa T, Ueo H, et al. Clinical significance of miR-144-ZFX axis in disseminated tumour cells in bone marrow in gastric cancer cases. Br J Cancer. 2012;107:1345–53.
    [27]. Wu X, Cui CL, Chen WL, et al. miR-144 suppresses the growth and metastasis of laryngeal squamous cell carcinoma by targeting IRS1. Am J Transl Res. 2016;8:1–11.
    [28]. Guan Y, Liang G, Hawken PA, et al. Roles of small RNAs in the effects of nutrition on apoptosis and spermatogenesis in the adult testis. Sci Rep. 2015;5:10372.
    [29]. Fu X, Huang X, Li P, et al. 7-Ketocholesterol inhibits isocitrate dehydrogenase 2 expression and impairs endothelial function via microRNA-144. Free Radic Biol Med. 2014;71:11–5.

    cancer; meta-analysis; microrNA-144; prognosis

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