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. 2020 Jun 19;13:5807–5817. doi: 10.2147/OTT.S250796

Long Non-Coding RNA MALAT1 as a Detection and Diagnostic Molecular Marker in Various Human Cancers: A Pooled Analysis Based on 3255 Subjects

Yue Zhao 1,2,*, Ya-qi Yu 1,*, Song You 3, Chang-mao Zhang 4, Liang Wu 5, Wenxiu Zhao 1, Xiao-min Wang 1,✉
PMCID: PMC7311208  PMID: 32606791

Abstract

Purpose

Accumulating studies have explored the potential diagnostic value of lncRNA MALAT1 in various cancers. However, there are still inconsistent results in diagnostic accuracy and reliability in individual studies. The aim of this pooled study was to summarize the overall diagnostic capacity of lncRNA MALAT1 in cancer detection and diagnosis.

Methods

Eligible studies satisfying the inclusion criteria were screened and selected from the online database. All statistical analyses were performed using Stata 14.0.

Results

A total of 17 eligible studies were included in this pooled analysis, with 1777 cases and 1478 controls. The overall results were shown as follows: sensitivity, 0.74 (95% CI=0.65–0.81), specificity, 0.79 (95% CI=0.73–0.84), positive likelihood ratio (PLR), 3.48 (95% CI=2.79–4.32), negative likelihood, 0.33 (95% CI=0.25–0.44), diagnostic score, 2.34 (95% CI=1.99–2.69), diagnostic odds ratio, 10.41 (95% CI=7.33–14.78) and area under the curve, 0.83 (95% CI=0.80–0.86). Deeks’ funnel plot asymmetry test (p = 0.66) suggested no potential publication bias.

Conclusion

All these results indicate that lncRNA MALAT1 achieves a relatively moderate accuracy in cancer detection and diagnosis, and could serve as a diagnostic biomarker for cancers.

Keywords: lncRNA, MALAT1, cancer, pooled analysis, detection, diagnosis

Introduction

With a leading cause of morbidity and mortality in recent years, cancer has become a significant public health problem that threatening peoples’ life quality all over the world.1,2 Despite the rapid development of cancer treatments, such as surgery, chemotherapy, radiotherapy, and targeted therapy, the prognosis of patients with cancers still remains unsatisfactory.3–5 The lack of early diagnostic techniques and Delay in the diagnosis of cancer are the major obstacles in the current situation, contributing to missing the optimal opportunity for treatment, less chance of surviving, and more expensive costs.6–8 Thus, it is urgent to identify a novel diagnostic marker with good sensitivity and specificity for the early detection of cancer.

LncRNAs are often described as non-coding transcripts more than 200 nucleotides in length, lacking functional open reading frames (ORFs) and protein-coding capability.9,10 Previous studies have shown that lncRNAs perform multiple physiological and pathological biological functions.11 Aberrant expression of lncRNAs is widely observed in many cancers, involving in cancer initiation, progression, and metastasis.12–14 Furthermore, increasing evidence indicates that lncRNAs could serve as potential biomarkers in cancer detection and diagnosis, with high sensitivity and specificity.15,16

MALAT1, located at 11q13, was firstly characterized by Ji et al17 in a study of early-stage non-small-cell lung cancer, with a total 8.5 kb full length. A subsequent research study has demonstrated that MALAT1 is widely expressed in normal tissues, involving in cell viability and normal development.18 While MALAT1 was initially described as a prognostic marker of lung cancer metastasis, emerging evidence has shown that this lncRNA contributes greatly to other types of cancer’s development and progression.19–22 Furthermore, an increasing number of studies suggest that the aberrant expression of MALAT1 in tumor tissues or body fluids may serve as a biomarker for tumor diagnosis and prognosis.

However, the diagnostic accuracy of MALAT1 in individual studies is still inconsistent and controversial. For instance, Liu et al23 revealed that MALAT1 achieve a moderate-high sensitivity and specificity of 86.0% and 75.0% in the diagnosis of gastric cancer, respectively, whereas Yazarlou et al24 demonstrated that a relatively low sensitivity and specificity of 62.7% and 69.4%, respectively, in bladder cancer detection. The agreement is hard to reach among these results because of ethnicity, study design, types of tumors, stage of cancer, and the small sample size. Previously, Mei et al performed a meta-analysis of MALAT1 expression in cancer detection for the first time, revealing that MALAT1 might be an effective cancer diagnostic marker. However, a limited number of cancer cases and small sample size in single tumor type make it unpersuasive enough.25 Thus, we conducted this pooled analysis to summarize the overall diagnostic performance of MALAT1 in cancer detection and diagnosis and further explored its clinical value.

Methods

Search Strategy and Study Eligibility Criteria

Literature research was performed in the database including PubMed, Cochrane library, CNKI and Wanfang library, up to December 20, 2019, by the following searching strategy: “cancer” or “tumor” or “carcinoma” or “neoplasm” or “malignancy” or “neoplasm” and “MALAT1” and “sensitivity” or “specificity” or “ROC curve” or “accuracy”. Two investigators (YZ and YQY) check the titles and abstract information of the studies, and then scanned the full articles to delete irrelevant studies by using the following inclusion criteria: (1) human clinical studies; (2) the diagnostic value of lncRNA MALAT1 for detecting cancer evaluated in studies; (3) the number of true positive (TP), false positive (FP), false negative (FN) and true negative (TN) in cancer patients and controls could be drawn or calculated from the original included studies and (4) being published in English or Chinese. Accordingly, the articles were excluded on the basis of the following exclusion criteria: (1) laboratory studies on animal models or cell lines; (2) reviews, meta-analysis, case reports, commentaries and (3) lack of sufficient data to calculate TP, FP, FN and TN.

Data Extraction and Quality Assessment

The data and primary information from each included study, including first author, year of publication, country, ethnicity, cancer type, Normalizer, sample type, test method, cutoff value, sample size, true positive (TP), false positive (FP), true negative (TN), and false negative (FN) were extracted by two reviewers (YZ and YQY) independently and cross-checked. Divergences will come to an agreement by another two authors (SY and CMZ).

Quality Assessment

The QUADAS-2 was applied to evaluate the quality of the studies included in this pooled analysis systematically. With the maximum QUADAS-2 score of 7, we can judge the quality of the included studies based on the score. Studies with four or more scores were defined as moderate-high quality. All of these were carried out independently by two authors (YZ and SY); Any disagreement was resolved in the conference by two authors (LW and WXZ).

Statistical Analysis

All statistical analysis were performed using Stata 14.0 (Stata Corporation, College Station, TX, USA). The pooled sensitivity, specificity, diagnostic score (DS), diagnostic odds ratio (DOR), positive likelihood ratio (PLR) and negative likelihood ratio (NLR) and other parameters were calculated by the bivariate model. Then, summary receiver operator characteristic (SROC) curves are applied to analyze and calculated the area under the ROC curves (AUC), to assess the overall diagnostic value of lncRNA MALAT1 in cancer detection and diagnosis. These data were confirmed by a hierarchical summary receiver operating characteristics (HSROC) model. Cochran-Q and Inconsistency index (I2) test were applied in order to evaluate the statistical heterogeneity across the included publications. A P value less than 0.10 for the Q test or I2 value higher than 50% indicated obvious heterogeneity between the studies.26 In addition, Fagan’s Nomogram was used to confirm relationships between prior-test probability, likelihood ratio, and posttest probability. Deeks’ funnel plot was applied for publication bias evaluation.

Results

Studies Selection and General Features of Included Studies

A total of 17 eligible studies23,24,27–41 including 1777 cases and 1478 controls were finally included in the pooled analysis after a systematic search of PubMed, Cochrane library, CNKI and Wanfang library database from 2013 to 2019, according to inclusion and exclusion criteria (Figure 1). The main characteristics of the included studies were displayed in Table 1. In total, there were studies on breast cancer (N=4), lung cancer (N=4), bladder cancer (N=3), gastric cancer (N=1), oral squamous cell carcinoma (N=1), osteosarcoma (N=1), endometrial cancer (N=1), nasopharyngeal carcinoma (N=1) and prostate cancer (N=1). The expression of lncRNA MALAT1 was measured by qRT-PCR methods based on serum (N=8), plasma (N=4), tissue (N=2), urine (N=2) and Pleural effusion (N=1). In all included studies, the expression of MALAT1 showed an up-regulation trend in cancer samples when compared with controls.

Figure 1.

Figure 1

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The flow diagram of relevant studies from the electronic databases.

Abbreviations: CNKI, China National Knowledge Infrastructure; MALAT1, metastasis-associated lung adenocarcinoma transcript 1.

Table 1.

Characteristics of the Included Studies

Author Year Country Ethnicity Cancer Type Normalizer Sample Type Test Method Cutoff Case/Control TP FP FN TN
Liu23 2019 China Asian GC GAPDH Serum qRT-PCR N/A 100/100 86 25 14 75
Wu27 2018 China Asian BRC GAPDH Plasma qRT-PCR 1.69 102/50 55 7 47 43
Wang.J28 2018 China Asian OSCC GAPDH Plasma qRT-PCR 1.18 70/50 61 14 9 36
Li29 2018 China Asian BRC GAPDH Tissue qRT-PCR N/A 64/64 50 22 14 42
Yazarlou24 2018 Iran Asian BC 5s rRNA Urine qRT-PCR 6.86 59/49 37 15 22 34
Huang30 2018 China Asian BRC GAPDH Serum qRT-PCR 0.6200 (Training set) 158/107 140 25 18 82
2.1345 (Validation set)
Zhan31 2018 China Asian BC GAPDH Urine qRT-PCR N/A 184/184 138 42 46 142
Wang.W32 2018 China Asian LC GAPDH Pleural effusion qRT-PCR 6.975 217/132 161 12 56 120
He33 2017 China Asian NPC GAPDH Serum qRT-PCR N/A 101/101 67 11 34 90
Huo34 2017 China Asian OS GAPDH Serum qRT-PCR 3.68 46/40 37 11 9 29
Zidan35 2017 Egypt African BRC β-actin Serum qRT-PCR 2.6 80/80 67 15 13 65
Han36 2016 China Asian EC GAPDH Tissue qRT-PCR 6.445 104/104 47 18 57 86
Wen37 2016 China Asian LC GAPDH Serum qRT-PCR 0.03 84/60 49 11 35 49
Duan38 2016 China Asian BC GAPDH Serum qRT-PCR N/A 120/120 68 39 52 81
Peng39 2016 China Asian LC GAPDH Serum qRT-PCR 1.096 (Training set) 156/107 151 63 5 44
2.0845 (Validation set)
Ren40 2013 China Asian PC GAPDH Plasma qRT-PCR 867.8 copies/mL 87/105 51 16 36 89
Weber41 2013 Germany Caucasian LC GAPDH Plasma qRT-PCR −0.41 45/25 21 0 24 25
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Abbreviations: GC, gastric cancer; BRC, breast cancer; OSCC, oral squamous cell carcinoma; BC, bladder cancer; LC, lung cancer; NPC, nasopharyngeal carcinoma; OS, osteosarcoma; EC, endometrial cancer; PC, prostate cancer; NA, not available; TP, true positive; FP, false positive; FN, false negative; TN, true negative.

Quality Assessment

The scores of the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) study quality assessment were shown in Table 2. Among the 17 studies, seven studies achieved 6 stars, four studies achieved 5 stars, and six studies achieved 4 stars, indicating a moderate-high quality for most of the studies.

Table 2.

Study Quality of the Diagnostic Studies Judged by the QUADAS II Checklist

Author Year Risk of Bias Concerns Regarding Applicability Total Stars
Patient Selection Index Test Reference Standard Flow and Timing Patient Selection Index Test Reference Standard
Liu23 2019 Low Low Low Unclear Low Low Low 6
Wu27 2018 Low Low Low Unclear Low Low Low 6
Wang.J28 2018 Low Low Low Unclear High Low Low 5
Li29 2018 Low High Low Unclear Unclear Low Low 4
Yazarlou24 2018 Unclear Unclear Low Low Unclear Low Low 4
Huang30 2018 Low Low Low Low Unclear Low Low 6
Zhan31 2018 Low Low Low Unclear Unclear Low Low 5
Wang.W32 2018 Low Unclear Low Unclear Low Low Low 5
He33 2017 Low Unclear Low Low Unclear Low Low 5
Huo34 2017 Unclear Low Low Unclear Unclear Low Low 4
Zidan35 2017 Unclear Low Low Unclear Unclear Low Low 4
Han36 2016 Low High Low Unclear Unclear Low Low 4
Wen37 2016 Low Low Low Unclear Low Low Low 6
Duan38 2016 Low Unclear Low Unclear Unclear Low Low 4
Peng39 2016 Low Low Low Low High Low Low 6
Ren40 2013 Low Low Low Low Unclear Low Low 6
Weber41 2013 Unclear Low Low Low Low Low Low 6
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Pooled Results

The forest plot of data from 17 included articles on sensitivity and specificity for MALAT1 in diagnosing cancer was shown in Figure 2. The sensitivity and specificity for the pooled data were 0.74 [95% CI=0.65–0.81] and 0.79 [95% CI=0.73–0.84], respectively. Significant heterogeneity was found for both sensitivity [I2=91.92%, 95% CI=89.14–94.70%] and specificity [I2=87.62%, 95% CI=82.80–92.45%]. Meanwhile, we found that the pooled PLR was 3.48 [95% CI=2.79–4.32], the NLR was 0.33 [95% CI=0.25–0.44] (Figure 3). The DS and DOR were 2.34 [95% CI=1.99–2.69] and 10.41 [95% CI=7.33–14.78], respectively (Figure 4).

Figure 2.

Figure 2

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Forest plots of pooled sensitivity and specificity of 17 included studies.

Figure 3.

Figure 3

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Forest plots of positive likelihood ratio (PLR) and negative likelihood ratio (NLR) for MALAT1 in the diagnosis of cancer.

Abbreviation: DLR, diagnostic likelihood ratio.

Figure 4.

Figure 4

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Forest plots of pooled diagnostic score (DS) and diagnostic odds ratio (DOR) for MALAT1 in the diagnosis of cancer.

Pooled SROC and HSROC

The SROC curve for the 17 included studies was 0.83 [95% CI=0.80–0.86], as shown in Figure 5, indicating a relatively moderate-high diagnostic value. Besides, The HSROC curve of these included studies was consistent with the results of the bivariate model. The calculated β value was −0.29 (95% CI=−0.76–0.17), and the P value was 0.210, implying that the HSROC was symmetrical (Figure 6).

Figure 5.

Figure 5

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Summary receiver operating characteristic (SROC) graph of 17 included studies.

Abbreviations: SENS, sensitivity; SPEC, specificity; AUC, area under curve.

Figure 6.

Figure 6

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Hierarchical summary receiver operating characteristics (HSROC) curve for MALAT1 in the diagnosis of cancer.

Clinical Utility of the Index Test

We performed Fagan’s Nomogram to predict the increasing inerrability about a positive diagnosis by using the value of the test, and it is used for estimating posttest probabilities. When MALAT1 assays were tested for all individuals with a pretest probability of 55% to have cancer, a positive result would improve posttest probability having cancer to 81%, whereas a negative result would drop the posttest probability to 29% (Figure 7).

Figure 7.

Figure 7

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Fagan’s Nomogram for calculation of posttest probabilities.

Abbreviation: LR, likelihood ratio.

Influence Analysis and Robustness Tests

God-of-ft and bivariate normality analysis (Figure 8A and B) showed that the bivariate model was moderately robust. And then, we performed sensitivity analysis after further excluded 3 outliers found by influence analysis and outlier detection in Figure 8C and D. The sensitivity dropped from 0.74 to 0.73, the specificity increased from 0.79 to 0.80, the PLR increased from 3.48 to 3.69, the NLR remain the same as 0.33, the DS increased from 2.34 to 2.40, the DOR increased from 10.41 to 11.04, and the AUC increased from 0.83 to 0.84, showing no significant change after the exclusion of the outliers (Supplementary Figures 1–3).

Figure 8.

Figure 8

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Graphs for sensitivity analysis: (A) goodness of fit, (B) bivariate normality, (C) influence analysis, and (D) outlier detection.

Publication Bias Evaluation

The publication bias was evaluated by Deeks’ funnel plot in this pooled analysis and indicated no significant publication bias for MALAT1 (P=0.66) (Figure 9).

Figure 9.

Figure 9

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Graph of Deeks’ funnel plot asymmetry test.

Subgroup Analysis

According to the cancer type, we performed the subgroup analysis for lung cancer and breast cancer (Supplementary Table 1 and Supplementary Figure 4). The pooled sensitivity, specificity, PLR, DLR, DS, DOR and AUC for lung cancer were 0.76, 0.85, 5.02, 0.28, 2.88, 17.73 and 0.88, respectively. Whereas for breast cancer, the pooled sensitivity, specificity, PLR, DLR, DS, DOR and AUC were 0.78, 0.78, 3.56, 0.28, 2.55, 12.86 and 0.84, respectively. Meanwhile, subgroup analysis, according to different sample types, were also performed (Supplementary Table 2 and Supplementary Figure 5). The pooled results from studies based on serum sample detection were as follows: sensitivity, 0.81; specificity, 0.75; PLR, 3.18; NLR, 0.26; DS 2.52; DOR 12.43 and AUC, 0.84. As for studies based on plasma sample detection, the pooled results were as follows: sensitivity, 0.63; specificity, 0.85; PLR, 4.29; NLR, 0.43; DS 2.29; DOR 9.90 and AUC, 0.84.

Discussion

In recent years, lncRNA MALAT1 has been reported to participate in the prognosis of tumors by regulating a variety of biological processes, such as proliferation, apoptosis, invasion and angiogenesis.22,42–44 Furthermore, several studies demonstrated that aberrant expression of MALAT1 in tumor tissues or body fluids might serve as a biomarker for tumor diagnosis and prognosis.28,29,31,32 An ideal biomarker should be easily obtained with minimal risk and discomfort to patients, as well as detectable and reproducible in standard clinical laboratories. Luckily, lncRNA MALAT1 possesses all these features as a biomarker.45 MALAT1 is not only expressed in tissues but also detectable in body fluids, such as blood and urine,24,38 which are easily obtainable with minimal damage to the patient. However, the diagnostic accuracy of MALAT1 still remains inconsistent and controversial. Thus, in our analysis, 17 studies with 3255 subjects were pooled to evaluate the diagnostic value of lncRNA MALAT1 in human cancers detection.

In the present pooled analysis, the results showed that the sensitivity, specificity and AUC of MALAT1 were 0.74, 0.79 and 0.83, respectively, indicating a capacity to distinguish cancer patients from normal people. Meanwhile, a pooled PLR of 3.48 and NLR of 0.33 implied that patients with cancer have a 3.48-fold higher possibility of being MALAT1 positive for patients with cancer compared with controls, and 33% of all individuals have negative results, suggesting that the diagnostic value of MALAT1 is relatively moderate. Moreover, the pooled DS and DOR were 2.34 and 10.41, reflecting a moderate level of diagnostic accuracy. In addition, Fagan’s nomogram showed MALAT1 could raise the probability of cancer detection by 26% (post-test probability 81%, pre-test probability 55%).

Similar analysis were also performed in lung cancer and breast cancer subgroup. The pooled AUC, DS and DOR were 0.88, 2.88 and 17.73 in lung cancer, and 0.84, 2.55 and 12.86 in breast cancer, indicating that MALAT1 could act as an effective diagnostic biomarker in both types of these two cancers. Meanwhile, MALAT1 achieved a high diagnostic value in circulating blood for cancer detection, with a pooled AUC of 0.84, DS of 2.52 and DOR of 12.43 in serum sample detection, and an AUC of 0.84, DS of 2.29 and DOR of 9.90 in plasma sample detection. Furthermore, a pooled sensitivity of 0.81 and specificity of 0.75 in serum, and a pooled sensitivity of 0.63 and specificity of 0.85 in plasma, demonstrated that circulating MALAT1 had relatively moderate accuracy in human cancer detection.

The pooled analysis in our study also had its limitations. First, a very high ratio of data in Chinese populations was included in our analysis, which might contribute to inevitable publication bias. Second, not all of the studies reported the cutoff values of lncRNA MALAT1. Third, only studies published in English or Chinese were screened and included in the pooled analysis, studies published in other languages should not be ignored.

Conclusion

In summary, these pooled results demonstrated that lncRNA MALAT1 could serve as a detection and diagnosis biomarker in various human cancers, especially in serum/plasma, with a relatively moderate accuracy in distinguishing cancer patients from all individuals. We also proved that the moderate diagnostic value of MALAT1 in lung cancer and breast cancer subgroup; However, more cancer types studies with a large sample size are needed to strengthen our conclusion.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (grant numbers 81871963, 81572335) and the China Scholarship Council (No:201906310088 to Yue Zhao).

Disclosure

All authors declare that there are no conflicts of interest.

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