Noncoding RNAs (ncRNAs) are RNAs that do not encode proteins and play important roles [
Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), also named nuclear-enriched abundant transcript 2 (NEAT2), is a widely expressed lncRNA that is greater than 8000 nucleotides in length. MALAT1 was first identified as a factor indicating high metastatic potential and poor prognosis in a study of gene expression differences in stage I non-small-cell lung cancer (NSCLC) with/without metastasis [
Electronic searches of PubMed and EMBASE were performed using the following keywords: “MALAT1,” “MALAT-1,” “MALAT1 long non-coding RNA, human,” “metastasis-associated lung adenocarcinoma transcript 1,” “NEAT2,” “NEAT2 long non-coding RNA, human,” “carcinoma,” “neoplasm,” “cancer,” “prognosis,” “prognostic,” and “outcome,” without any limits. The reference lists of the retrieved articles were searched manually. The search ended in February 2015.
The eligibility criteria for the studies were as follows: (1) evaluation of a link between MALAT1 expression and prognosis of patients with any type of cancer; (2) reporting of outcomes, including overall survival (OS), disease-specific survival (DSS), or disease-free survival (DFS); (3) reporting of hazard ratios (HRs) and 95% confidence intervals (CIs) or data that could be used to calculate these values; and (4) full papers in English. Nonhuman research, duplicated studies, reviews, letters, comments, and single case reports were omitted.
Yao Wei and Ben Niu reviewed each eligible study and extracted the data. The following information was collected: author; year of publication; country; cancer type and stage; number of patients; techniques used to assess MALAT1 expression; follow-up period; and cut-off values, HRs, and corresponding 95% CIs for OS, DSS, or DFS. HRs were directly determined by multivariate analysis in some studies, whereas others provided Kaplan-Meier survival curves. For the latter studies, we first extracted several specific points from the survival curves using Engauge Digitizer version 4.1 to obtain two lists of survival rates at specific time points from the two survival curves. We then input the extracted survival rates at specific time points into the spreadsheet developed by Tierney et al. to calculate the HR and 95% CI [
Yao Wei and Ben Niu performed a quality assessment of the included studies according to the guidelines of Hayden et al. [
We evaluated the impact of MALAT1 expression on clinical prognosis by examining the HRs and corresponding 95% CIs. An observed HR of >1 indicated poorer prognosis in patients with elevated MALAT1 expression. The results were considered statistically significant when the 95% CI did not overlap with 1. We used
A flow diagram of the literature search process is presented in Figure
Flow diagram of the meta-analysis.
The clinical characteristics of the 9 included studies are summarized in Table
Characteristics of the studies included in the meta-analysis.
First author | Year | Country | Cancer type | Stage | Sample size ( |
Method | Cut-off | Follow-up (months) | Outcome | Survival analysis |
---|---|---|---|---|---|---|---|---|---|---|
Pang [ |
2015 | China | Pancreatic cancer | I–IV | 126 | RT-qPCR | Median value of 6.23 | 5–60 | OS | Univariate and multivariate |
Zhang [ |
2014 | China | Renal cell carcinoma (clear cell) | I–IV | 106 | RT-qPCR | Mean value of 3.85 | NA | OS | Univariate and multivariate |
Liu [ |
2014 | China | Pancreatic duct adenocarcinoma | I–IV | 45 | RT-qPCR | Mean value (NA) | 24–36 | DSS | Univariate and multivariate |
Zheng [ |
2014 | China | Colorectal cancer | II-III | 146 | RT-qPCR | 6.15 (MALAT1/GAPDH ratio) | 11–72.8 | DFS, OS | Univariate and multivariate |
Schmidt [ |
2011 | Germany | Non-small-cell lung cancer (squamous cell) | I–III | 102 | ISH | A large gene copy cluster in 50% of cells | NA | OS | Univariate and multivariate |
Okugawa [ |
2014 | Japan | Gastric cancer | I–IV | 150 | RT-qPCR | Threshold of 0.985 | 1–78 | OS | Univariate and multivariate |
Shen [ |
2014 | China | Non-small-cell lung cancer | NA | 79 | RT-qPCR | Mean value (NA) | NA | DFS | Univariate |
Lai [ |
2012 | China | Hepatocellular carcinoma | NA | 60 | RT-qPCR | NA | 18.6 (median) | DFS | Univariate and multivariate |
Ma [ |
2015 | China | Glioma | I–IV | 118 | RT-qPCR | Median value of 5.18 | 5 years | OS | Univariate and multivariate |
RT-qPCR: real-time quantitative PCR; ISH:
The results of the quality assessment are presented in Table
Quality assessment of the studies included in the meta-analysis.
First author | Study participation | Study attrition | Prognostic factor measurement | Outcome measurement | Confounding measurements and adjustments | Analysis |
---|---|---|---|---|---|---|
Pang [ |
Yes | Yes | Yes | Yes | Partly | Yes |
Zhang [ |
Yes | Yes | Yes | Partly | Partly | Yes |
Liu [ |
Yes | Yes | Yes | Yes | Partly | Yes |
Zheng [ |
Yes | Yes | Yes | Yes | Partly | Yes |
Schmidt [ |
Yes | Partly | Yes | Partly | Partly | Yes |
Okugawa [ |
Yes | Yes | Partly | Yes | Partly | Yes |
Shen [ |
Partly | Yes | Yes | Partly | Partly | Yes |
Lai [ |
Yes | Yes | Partly | Yes | Partly | Yes |
Ma [ |
Yes | Yes | Yes | Yes | Partly | Yes |
The main results of the meta-analysis are presented in Table
The main results of the pooled analyses.
Survival | Variables | Number of studies | Number of patients | HR | 95% CI |
|
Heterogeneity ( |
---|---|---|---|---|---|---|---|
OS | All |
|
|
|
|
|
|
Tumor type | |||||||
Digestive system | 4 | 467 | 1.86 | 1.37–2.53 | <0.001 | 14.2 | |
Nondigestive system | 3 | 326 | 2.21 | 1.61–3.02 | <0.001 | 0 | |
Histology type | |||||||
Adenocarcinoma | 5 | 573 | 2.03 | 1.53–2.68 | <0.001 | 24.2 | |
Squamous carcinoma | 1 | 102 | 1.78 | 1.08–2.92 | |||
Others | 1 | 118 | 2.29 | 1.37–3.81 | |||
Ethnicity | |||||||
Asian | 6 | 691 | 2.08 | 1.63–2.66 | <0.001 | 8.1 | |
Caucasian | 1 | 102 | 1.78 | 1.08–2.92 | |||
Method | |||||||
RT-qPCR | 6 | 691 | 2.08 | 1.63–2.66 | <0.001 | 8.1 | |
ISH | 1 | 102 | 1.78 | 1.08–2.92 | |||
DFS | All | 3 | 285 | 2.78 | 1.87–4.15 | <0.001 | 0 |
RT-qPCR: real-time quantitative PCR; ISH:
Forest plots of the HRs of elevated MALAT1 expression for overall survival for the included studies.
Forest plots of the HRs of elevated MALAT1 expression for overall survival in different subgroups. (a) Subgroup analysis of HRs for overall survival by tumor type. (b) Subgroup analysis of HRs for overall survival by histology type. (c) Subgroup analysis of HRs for overall survival by region. (d) Subgroup analysis of HRs for overall survival by measurement method.
Sensitivity analysis indicated that the pooled HR was not significantly affected by the exclusion of any of the studies (Figure
Sensitivity analysis of the pooled HRs of MALAT1 expression for overall survival for the included studies.
The funnel plot indicated no significant asymmetry (Figure
Funnel plot for the publication bias test of the included studies for MALAT1 expression and overall survival. SE(
Three studies including 285 participants reported HRs for DFS (Table
Forest plots of the HRs of elevated MALAT1 expression for disease-free survival for the included studies.
The prognostic role of MALAT1 in cancer was evaluated by a meta-analysis of 9 studies including 932 participants. Elevated MALAT1 expression was indicative of poor prognosis in patients with various types of cancer. The pooled HR for OS was 2.02 (95% CI: 1.62–2.52;
The mechanism underlying the relationship between elevated MALAT1 expression and poor prognosis in patients with various types of cancer is uncertain. MALAT1 may regulate alternative splicing. MALAT1 interacts with serine/arginine proteins and influences the distribution of splicing factors in nuclear speckle domains [
The elucidation of prognostic factors is crucial for the identification of high-risk patients who are good candidates for individual therapy. The results of our meta-analysis indicate that elevated MALAT1 expression affects the prognosis of cancer patients, and these findings should promote the development of adequately designed prospective studies. Furthermore, this gene might represent a potential therapeutic target. MALAT1 knockdown strategies may be developed for antimetastatic therapy. Studies of the function of MALAT1 in the vasculature have revealed that its inhibition induces a switch from an endothelial cell phenotype to a promigratory but antiproliferative state, resulting in impaired endothelial cell proliferation
There are several limitations of our study. First, the pooled survival data were calculated based on results reported for patients with various types of cancer because the available studies were heterogeneous. The prognostic role of MALAT1 in each type of cancer could not be evaluated because of the limited data available. Second, there was a bias towards Asian patients because 7 of 9 studies were from China and one study was from Japan. Third, the techniques used to identify MALAT1 expression could have led to possible bias. In most of the studies, MALAT1 expression was detected by real-time quantitative PCR, except in Schmidt LH’s study, which employed
In conclusion, MALAT1 may be a prognostic factor for patients with various types of cancer. Further studies are needed to confirm its precise role among other known prognostic factors for specific types of cancer.
The authors declare that they have no conflict of interests.