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The diagnostic and prognostic significance of long noncoding RNAs expression in thyroid cancer: A systematic review and meta-analysis
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The diagnostic and prognostic significance of long noncoding RNAs expression in thyroid cancer: A systematic review and meta-analysis Wei Jinga,1, Xiaogai Lia,1, Ruoyu Penga, Shaogang Lva, Yan Zhanga, Zheng Caoa, Jiancheng Tub, ⁎ Liang Minga, a b
Department of Clinical Laboratory, The First Affiliated Hospital of Zhengzhou University, Key Laboratory of Laboratory Medicine of Henan, Zhengzhou 450000, China Department of Laboratory Medicine, Clinical Laboratory Medicine and Center for Gene Diagnosis, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
A R T I C L E I N F O
A B S T R A C T
Keywords: Thyroid cancer LncRNAs Prognosis Diagnosis Meta-analysis
Objective: Thyroid cancer (TC) is the most common malignant endocrine-related cancer with an increasing trend worldwide. Therefore, it’s in urgent need to find new markers for prognosis and diagnosis. Many long noncoding RNAs (lncRNAs) have been reported to be aberrantly expressed in TC, and may serve as biomarkers. Therefore, we performed this meta-analysis to systematically summarize the relationship between lncRNA expressions and TC. Methods: Sources from PubMed, Embase and Web of Science were searched. A total of 16 eligible studies including 15 on clinicopahology, 5 on prognosis and 6 on diagnosis were enrolled in our meta-analysis. Revman5.3 and Stata11.0 Software were used to conduct the meta-analysis. Results: For diagnostic value, lncRNAs could discriminate between TC and the normal, and yield a high overall sensitivity and specificity (0.80, 95% CI: 0.75–0.84; 0.80, 95% CI: 0.70–0.87). Meanwhile, their sensitivity and specificity were 0.74 (95% CI: 0.59–0.85) and 0.81 (95% CI: 0.73–0.88) respectively, when used to differentiate patients with lymph node metastasis (LNM) from without LNM. The summary receiver operator characteristic curve (sROC) showed that lncRNAs could be considered as valuable diagnostic markers for distinguishing TC patients from the normal (AUC = 0.84) and TC patients with LNM from TC patients without LNM (AUC = 0.85). Conclusions: In summary, our meta-analysis suggested that lncRNAs could function as potential diagnostic markers for TC and predict the LNM. In addition, the systematic review elaborated that lncRNAs might be as prognostic indicators in TC.
1. Introduction Thyroid cancer (TC) is the most common type of malignancy in endocrine organs with steadily increasing incidence over the recent years [1]. Based on the histological features, we divide TC into four classes, which are papillary thyroid carcinoma (PTC), follicular thyroid carcinoma (FTC), poorly differentiated carcinoma (PDC) and anaplastic thyroid carcinoma (ATC) [2]. PTC is the predominant form of thyroid cancer, accounting for approximately 80% among all of the cases [3,4]. Conventional surgery has been the main therapy for TC, however, it is often incurable [5]. Adjuvant therapies were depend on patients and disease features [6]. In the high risk patients, external beam radiation therapy (EBRT) may not confer survival advantage [7]. Recently, tyrosine kinase inhibitors becomes a burgeoning treatment for TC patients [8]. Generally, the 5-year survive of PTC was more than 95% [9]. However, increasing numbers of more advanced patients, as well as the ⁎
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TC associated mortality have been seen due to the distant metastasis, genetic and environmental factors [10–12]. In 2016, research reported that 64,300 people were diagnosed with TC in the United States [13]. Moreover, up to 6.6 per 100,000 individuals of the TC rate occurred in China [14]. Investigating the molecular mechanisms related to the occurrence of TC could improve the rate of early diagnosis and treatment [15]. Therefore, to identify sensitive and specific biomarkers for prognosis and diagnosis of patients with TC is in urgently needed. Recently, genomic studies discovered that more than 90% of the mammalian genome can be transcribed into short or long noncoding RNAs [16]. Long non-coding RNAs (lncRNAs) are molecules with a length of longer than 200 nt that cannot code protein products [17]. The role of lncRNAs in cancers has been broadly researched, and commonly agree that lncRNAs can act as oncogenes or tumor suppressor genes during tumorigenisis [18]. Numerous studies have demonstrated that lncRNAs are critical to a wide range of biological
Corresponding author. E-mail address: [email protected] (L. Ming). These authors contributed equally to this work.
https://doi.org/10.1016/j.prp.2018.01.008 Received 23 November 2017; Received in revised form 14 January 2018; Accepted 24 January 2018 0344-0338/ © 2018 Elsevier GmbH. All rights reserved.
Please cite this article as: Jing, W., Pathology - Research and Practice (2018), https://doi.org/10.1016/j.prp.2018.01.008
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value of diagnosis and HR for the prognostic meta-analysis.
progress, including pre-transcription, post-transcription and cell proliferation, differentiation, apoptosis, and migration, which also change the original concept that lncRNA genes are just “noise” [19–22]. Evidence shows that lncRNAs are dysregulated in various types of cancers, such as lung cancer [23], gastric cancer [24], and liver cancer [25]. For example, highly up-regulated in liver cancer (HULC) is a specific gene which is markedly up-regulated both in tissue and plasma in HCC [26]. Yan et al. [24] found that small nucleolar RNA host gene 6 (SNHG6) acted as an oncogene in gastric cancer, and could serve as a prognostic biomarker and a target for novel therapies of gastric cancer patients. Recently, researches suggested that lncRNAs could play crucial roles in the aspects of diagnosis and prognosis for the TC patients. Accumulating studies have confirmed that lncRNAs play functional roles in TC, including MALAT1 [27], PTCSC3 [28], MEG3 [29] and NEAT1 [30]. In 2017, research indicated that papillary thyroid carcinoma susceptibility candidate 3 (PTCSC3)/miR-574-5p regulated the activity of Wnt/β-catenin, and mediated the proliferation and migration of PTC-1 cells, which was vital for the therapy and prognosis of TC [9]. Li et al. [30] revealed that elevated-expression of nuclear enrich abundant transcript 1 (NEAT1) promoted the both onset and the malignant progression of TC through regulating miRNA‐214 expression. Meantime, metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) might function both as an oncogene and a tumor suppressor in different types of TC, and played a key role in epithelial to mesenchymal transition (EMT) in TC [31]. These discoveries suggested that lncRNAs serve as biomarkers of TC. However, single study may be inaccurate and insufficient. Thus, we meta-analyzed the potential value of altered lncRNAs in patients with TC. In our study, by pooling all the available data, we aimed to evaluate the diagnostic and prognostic value of lncRNAs in TC objectively.
2.4. Quality assess The quality assessment is an important component of a thorough meta-analysis. Two investigators independently performed this quality assessment. Newcastle–Ottawa scale (NOS) was used to assess the quality of the studies for prognosis [32]. The NOS criteria assessed quality by three aspects: (1) selection: 0–4; (2) comparability: 0–2; and (3) outcome: 0–3. The total NOS scores were ranged from 0 to 9, and the value of total score ≥7 shows the study has a good quality [33]. Meanwhile, we assessed the quality of all the diagnostic studies according to the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) [34]. 2.5. Statistical analysis We extracted HRs for prognosis using two methods: (1) The HRs were obtained directly from the publication; (2) we estimate the HRs and 95% CI by extracting several survival rates at specified times from the Kaplan–Meier survival curves using Engauge Digitizer version 4.1 [35]. Revman5.3 Software (Revman, the Cochrane Collaboration) was used to conduct the meta-analysis. Heterogeneity among the studies was checked with the Chi-square based on Q statistical test and I2. If heterogeneity was present (I2 ≥ 50% or p ≤ 0.05), random-effect model was used. If not, the fixed-effect model was more appropriate. HRs and 95% CIs were used to assess the association between lncRNAs and survival. The Stata11.0 Software (Stata, College Station) was performed to measure the diagnostic value of lncRNAs in TC. For the diagnostic meta-analysis, sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), summary receiver operator characteristic curve (sROC), and area under the curve (AUC) were used and Deeks’ funnel plot was adopted to evaluate the publication bias of the included studies. Statistical significance was defined when the p-value was less than 0.05.
2. Methods 2.1. Search strategy Electronic searches of PubMed, Embase and Web of Science were performed to obtain relevant articles for the meta-analysis in December 2017. To increase the sensitivity of the search, we used both medical subject heading terms and free-text words including: “lncRNA”, “thyroid cancer”, ”diagnosis”, “survival” and “prognosis”, the detail search strategy was following: “long noncoding RNA”, “lncRNA”, “lincRNA”, “long intergenic non-coding RNA” and “TC”. All searches were limited to human studies and English publications. Additionally, we reviewed related references of the retrieved articles to identify potentially eligible studies.
3. Result 3.1. Literature search and study characteristics As shown in the flow diagram (Fig. 1), 348 articles were retrieved from PubMed, EMBASE, and Web of Science, 272 articles were removed due to duplicated papers, and then only 76 articles were left. We removed 44 after screening the titles and abstracts, and 16 articles were excluded for unavailable data. Subsequently, a total of 16 articles were in the current meta-analysis, including 15 on clinicopahology [14,15,36–48], 5 on prognosis [15,42,44,48,49], and 6 on diagnosis [14,37–39,42,43].
2.2. Inclusion and exclusion criteria The inclusion criteria were as follows: (1) patients in the study were diagnosed with TC; (2) studies investigated the association between lncRNAs and TC; (3) researches provided sufficient data for calculating the rates of true positive (TP), false positive (FP), false negative (FN), and true negative (TN) for diagnostic meta-analysis; (4) sufficient published data were provided to calculate hazard ratios (HR) and 95% confidence interval (95% CI) for prognostic meta-analysis; (5) studies published in English. The major reasons for exclusion criteria were: (1) studies without usable data or incomplete data; (2) reviews, letters, animal studies; (3) duplicate publications.
3.2. Correlation of lncRNAs with clinicopathological features Table 1 summarized the main characteristics of the included 15 studies ranging from 2016 to 2017. 1528 participants were enrolled in these studies, with a maximum sample size of 212 and a minimum sample size of 55 patients. Among them, focally amplified long noncoding on chromosome 1 (FAL1) [36], NR_036575.1 [38], lncRNA activated by TGF-β (ATB) [39], HIT000218960 [40], H19 [42], NONHSAT076754 [43], antisense non-coding RNA in the INK4 locus (ANRIL) [45], n340790 [46] and SPRY4-IT1[48] were up-regulated. Meanwhile, NONHSAT037832 [37], BRAF-activated non-coding RNA (BANCR) [41], cancer susceptibility candidate 2 (CASC2) [44], GAS8AS1 [14], LINC00312 [47] and GAS5 [15] were all down-regulated. In the included studies, there was no correlation between lncRNAs and age. Zhang et al. [14] elaborated that GAS8-AS1 levels were statistically related to the gender. The expression of NR_036575.1 was associated
2.3. Data extraction Two investigators extracted relevant data from the eligible studies independently. We recorded the following information, including the first author, year of publication, country, the lncRNAs, case number, follow-up months, and other features. Meanwhile, we extracted true positive, true negative, false positive and false negative to analyze the 2
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Fig. 1. The flow diagram of this meta-analysis.
with extrathyroidal extension (ETE). The following characteristics level of lncRNAs were generally interrelated, containing focality [15,36,40,41,44], tumor size [37,38,41,42,47], lymph node metastasis (LNM) [14,15,37,39,40,42,43,45–48] and tumor-node-metastasis (TNM) stage [15,40–44,46,47]. Unfortunately, each lncRNA was performed in single study, we could not conducted a meta-analysis on the relationship between one of these lncRNAs and the clinicopathological features of TC patients.
the group with decreased lncRNAs expression. For the overall survival (OS), the decreased expressions of GAS5 and CASC2 were associated with poor prognosis. Similarly, the increased expressions of H19 and SPRY4-IT1 were related to poor prognosis in the forest plot. Meanwhile, we found that the patients with low expression of GAS5 and SLC6A9 had a shorter disease-free survival (DFS) time (Fig. 2). Due to the number of articles, we did not generate the conclusive graph and evaluate publication bias.
3.3. Impact of lncRNAs on prognosis
3.4. Diagnostic accuracy of lncRNAs detection
There were 5 studies exploring the relationship between lncRNA expression levels and prognosis of TC patients (Table 2). All enrolled studies were non-randomized studies, and the quality scores of included studies were from 7 to 8, suggesting that the results of included study were reliable. HRs and 95% CI were extracted from these studies. An observed HR > 1 implied a worse survival for the group with elevated lncRNAs expression. Conversely, HR < 1 implied a worse survival for
The main characteristics of the included studies for the diagnostic meta-analysis were listed in Table 3. A total of 6 publications [14,37–39,42,43] were analyzed. We assessed the quality of all the available studies according to QUADAS-2. Result demonstrated that the overall quality of the enrolled studies were acceptable. The risk of bias and applicability concerns graph for the included studies was showed in Fig. 3.
Table 1 Summary of the comparison for the P values of the association between lncRNAs and clinicopathological features in thyroid cancer. Author
Year
Country
LncRNAs
Tumor type
Total number
Age
Gender
Focality
Tumor size
ETE
LNM
TNM stage
HT
Expression
Jeong Lan Sun Fu Li Liao Liu Xia Xiong Zhang Zhao Li Liu Guo Zhou
2016 2016 2016 2017 2017 2017 2017 2017 2017 2017 2016 2017 2017 2017 2017
Korea China China China China China China China China China China China China China China
FAL1 NONHSAT037832 NR_036575.1 ATB HIT000218960 BANCR H19 NONHSAT076754 CASC2 GAS8-AS1 ANRIL n340790 LINC00312 GAS5 SPRY4-IT1
PTC PTC PTC PTC PTC PTC TC PTC TC PTC TC TC TC TC TC
60 95 83 64 55 92 131 72 86 97 105 85 211 212 80
0.994 0.251 0.435 0.802 0.891 0.214 0.711 0.476 0.175 0.224 0.196 0.414 0.082 0.268 0.230
0.774 0.144 0.273 0.784 0.380 0.711 0.112 0.576 0.152 0.038 0.632 0.077 0.513 0.633 0.626
0.018 0.738 0.734 0.309 0.005 0.038 – 1.000 0.028 1.000 0.498 – – 0.001 0.496
0.696 0.032 0.006 – – 0.046 0.004 – – 0.376 0.202 – < 0.001 0.135 1.000
0.542 0.568 0.011 0.114 – 0.406 – 1.000 0.099 0.535 – – – – –
0.272 0.015 0.754 0.021 0.009 0.992 0.019 0.009 0.268 0.002 0.005 < 0.001 < 0.001 0.028 0.003
0.554 0.771 0.284 0.424 0.022 0.037 0.016 0.022 0.001 0.146 – < 0.001 < 0.001 < 0.001 –
– – – – – – 0.162 – – – 0.486 – – 0.75 0.149
Up-regulation Down-regulation Up-regulation Up-regulation Up-regulation Down-regulation Up-regulation Up-regulation Down-regulation Down-regulation Up-regulation Up-regulation Down-regulation Down-regulation Up-regulation
Abbreviations: LncRNA: Long non-coding RNA; ETE: Extrathyroidal extension; LNM: Lymph node metastasis; TNM: Tumor-node-metastasis; HT: Histological type; PTC: papillary thyroid carcinoma; TC: thyroid carcinoma.
3
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Table 2 Characteristics of studies included in prognosis. Author
Year
Country
LncRNAs
Method
Case number (High/Low)
Outcome
Follow-up months
HR availability
Quality score
Xiang Xiong Guo Liu Zhou
2017 2017 2017 2017 2017
China China China China China
SLC6A9 CASC2 GAS5 H19 SPRY4-IT1
qRT-PCR qRT-PCR qRT-PCR qRT-PCR qRT-PCR
26/26 86 212 95/36 54/26
DFS OS OS, DFS OS OS
30 32 60 60 60
Indirectly Directly Directly Indirectly Indirectly
7 8 8 7 7
Abbreviations: LncRNA: Long non-coding RNA; qRT-PCR: Quantities reverse transcription-polymerase chain reaction; OS: Overall survival; DFS: Disease-free survival; HR, hazard ratio.
Fig. 2. Forest plot for the association between lncRNAs expression levels with prognosis in thyroid cancer. OS = overall survival; DFS = disease free survival.
Table 3 Characteristics of studies included in diagnosis. Author
Year
Country
LncRNAs
Specimen
Case number (Case/Control)
Type of sample (Case/Control)
AUC
TP
FP
TN
FN
Liu Sun Lan Lan Fu Fu Zhang Zhang Xia
2017 2016 2016 2016 2017 2017 2017 2017 2017
China China China China China China China China China
H19 NR_036575.1 NONHSAT037832 NONHSAT037832 ATB ATB GAS8-AS1 GAS8-AS1 NONHSAT076754
FT FT FT FT FT FT Plasma Plasma FT
131/122 83/83 95/87 68/27 64/64 39/25 97/39 47/50 37/35
TC/Normal tissues PTC/Adjacent noncancerous tissues PTC/Adjacent noncancerous tissues PTC with LNM/PTC without LNM PTC/Adjacent noncancerous tissues PTC with LNM/PTC without LNM PTC/NG PTC with LNM/PTC without LNM PTC with LNM/PTC without LNM
0.801 0.915 0.897 0.641 0.916 0.882 0.702 0.746 0.878
95 67 76 40 53 34 82 29 31
30 10 12 8 10 6 17 5 6
92 73 75 19 54 19 22 45 29
36 16 19 28 11 5 15 18 6
Abbreviations;: LncRNA: Long non-coding RNA; FT: Frozen tissue; TC: Thyroid carcinoma; PTC: Papillary thyroid carcinoma; LNM: Tumor-node-metastasis; NG: Nodular goiter; AUC: Area under the curve; TP: True positive; FP: False positive; TN: True negative; FN: False negative.
Fig. 3. Quality assessment of the included studies by QUADAS-2. It summarized “risk of bias” and “applicability concerns” through judging each domain for all enrolling studies.
NLR, and DOR for overall studies were 4.0 (95% CI: 2.6–6.2), 0.32 (95% CI: 0.19–0.54), and 12 (95% CI: 5.0–28.0), respectively. The sROC curve with an area under the curve (AUC) were 0.84 (95% CI: 0.80–0.87) and 0.85 (95% CI: 0.82–0.88) in the two groups (Fig. 5). Deeks’ test was performed to check publication bias. Both of the values of p > 0.05, which indicated that no publication bias were existed (Fig. 6).
The overall sensitivity and specificity of lncRNAs as a diagnostic target for TC in all the studies included were 0.80 (95% CI: 0.75–0.84) and 0.80 (95% CI: 0.70–0.87) respectively with heterogeneity (I2 = 35.34% and 81.59%), when normal tissues or adjacent noncancerous tissues or nodular goiter was used as the control group [14,37–39,42] (Fig. 4A). Additionally, from our calculations, the pooled PLR and NLR were 4 (95% CI: 2.6–6.2) and 0.25 (95% CI: 0.20–0.32). The DOR was 16 (95% CI: 9–28). Simultaneously, we performed another diagnostic meta-analysis between TC with LNM and TC without LNM [14,37,39,43]. The pooled sensitivity and specificity of the lncRNAs were 0.74 (95% CI: 0.59–0.85, I2 = 80.55%) and 0.81 (95% CI: 0.73–0.88, I2 = 43.62%) (Fig. 4B). Besides, the pooled PLR,
4. Discussion TC, originated from follicular or parafollicular thyroid cells, is the most common endocrine cancer, ranking the eighth most frequent 4
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Fig. 4. Forest plots of sensitivities and specificities of lncRNAs for the diagnosis of thyroid cancer. A. thyroid cancer and the normal; B. thyroid cancer with lymph node metastasis and without lymph node metastasis.
functions of lncRNAs in TC [14]. Therefore, the present meta-analysis was the first to systematically analyze the precise prognostic value, diagnostic and clinical significance of lncRNAs in patients with TC. In our study, we investigated the association between the levels of lncRNAs and clinicopathological characteristics. The abnormal expression of NONHSAT037832 [37], ATB [39], HIT000218960 [40], H19 [42], NONHSAT076754 [43], GAS8-AS1 [14], ANRIL [45], n340790 [46], LINC00312 [47], SPRY4-IT1 [48] and GAS5 [15] might be
cancer in China [45,49]. It’s necessary to explore novel biomarkers to help to improve the outcome of TC [36]. According to the ENCODE project, more that 90% of human genome can be transcribed, but only less than 2% transcripts are protein-coding genes, and the rest is noncoding RNA. Recently, mounting evidence demonstrated that lncRNA dysregulation was involved in the progression of cancers [50], including apoptosis, metastasis, migration, and other clinical outcome [51]. Among them, there were a few published studies, focused on the 5
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Fig. 5. The summary receiver operator characteristic (sROC) curve in the diagnosis of thyroid cancer. A. thyroid cancer and the normal; B. thyroid cancer with lymph node metastasis and without lymph node metastasis.
Fig. 6. Deeks’ funnel plots for the assessment of potential bias in the meta-analysis for diagnosis. A. thyroid cancer and the normal; B. thyroid cancer with lymph node metastasis and without lymph node metastasis.
in TC. In the plasma of TC patients, GAS8-AS1 was down-regulated and could function as a potential marker for TC diagnosis and LNM prediction [14]. Recently, studies explored the diagnostic role of lncRNAs in TC which have become a hot spot. Thus, it is important to combine all the relevant studies and carry out a meta-analysis to confirm the vital role of lncRNAs in TC. Regarding the diagnostic significance, the lncRNAs yielded high sensitivity and specificity in TC diagnosis. However, there was heterogeneity in the meta-analysis. We supposed that the sample type, case size and the different cut-off value might be the potential source. The sROC showed that lncRNAs could be thought as a valuable marker for differentiating TC patients from the normal (AUC = 0.84) and TC patients with LNM from the TC patients without LNM (AUC = 0.85). These results indicated that lncRNAs might serve as a novel biomarker and could predict the LNM in TC patients. Up to now, many meta-analysis about lncRNAs have been performed. Our research is the first meta-analysis focused on studying the role of lncRNAs in TC. We summarized the prognostic and clinical role of lncRNAs in TC. Besides, we also evaluated the diagnostic value of lncRNAs in TC, which is a novelty of the study, since no meta-analysis have been done to investigate lncRNAs as a diagnostic biomarker in patients with TC. Nevertheless, there were some limitations in our meta-analysis. Most studies reported were positive results, those with negative results were generally less likely to be published. In addition, our studies were only English researches, no other languages, As a result, the data collection may be incomplete. Then, HR values from 3
considered as biomarkers of LNM. Meanwhile, these lncRNAs were related to TNM stage, including HIT000218960 [40], BANCR [41], H19 [42], NONHSAT076754 [43], CASC2 [44], n340790 [46], LINC00312 [47], and GAS5 [15]. Research elaborated that TC had a good prognosis and low mortality rate, but some features were correlated with poor prognosis, such as LNM and distant metastasis [39].However, results from a single studies is not enough to illustrate the value of lncRNAs completely. Thus, it is in urgently need to combine all the published relevant studies and carry out a meta-analysis to confirm the vital role of lncRNAs in TC. In 2017, Xiang et al. [49] demonstrated that low SLC6A9 expression was associated with a worse prognosis of TC. Meanwhile, overexpression of CASC2 significantly inhibited the proliferation of TC cells and arrested cell cycle at G0/G1 stage, showing that CASC2 might serve as a potential prognostic target [44]. Numerous studies elaborated that the relationship between lncRNAs and prognosis in TC patients. However, we could not perform a meta-analysis because of the small number of the studies enrolled. Thus, more research are necessary to be explore further. Currently, due to lack of susceptible biomarkers for TC, laboratory provide very limited diagnostic tools, for instance biopsy, imaging techniques, and radioiodine scintigraphy [46], indicating improving the success rate of early diagnosis is urgently needed. Fu et al. [39] indicated that ATB was up-regulated in TC tissues, and depletion of ATB could significantly inhibit TC cell proliferation and migration. ROC analyses revealed that ATB could be considered as a diagnostic marker 6
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studies were got from survival curves, which might generate inaccurate results. Finally, most of the population in our studies were Chinese, which might have a bias towards this population. In summary, this meta-analysis investigated the diagnostic and prognostic value of lncRNAs in TC patients. Although these limitations, there was an association between lncRNAs levels and prognosis in TC patients, which demonstrated that lncRNAs could be potential prognostic markers. More importantly, we discovered that lncRNAs might serve as a biomarker to diagnose TC based on the currently available evidence. In the future, more clinical studies with larger-size and better designing need to be conducted to conform our results.
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Disclosure of interest [26]
The authors declare that there are no conflicts of interest.
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This work was supported by the Hospital Fund of The First Affiliated Hospital of Zhengzhou University.
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Contents lists available at ScienceDirect
Pathology - Research and Practice journal homepage: www.elsevier.com/locate/prp
The diagnostic and prognostic significance of long noncoding RNAs expression in thyroid cancer: A systematic review and meta-analysis Wei Jinga,1, Xiaogai Lia,1, Ruoyu Penga, Shaogang Lva, Yan Zhanga, Zheng Caoa, Jiancheng Tub, ⁎ Liang Minga, a b
Department of Clinical Laboratory, The First Affiliated Hospital of Zhengzhou University, Key Laboratory of Laboratory Medicine of Henan, Zhengzhou 450000, China Department of Laboratory Medicine, Clinical Laboratory Medicine and Center for Gene Diagnosis, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
A R T I C L E I N F O
A B S T R A C T
Keywords: Thyroid cancer LncRNAs Prognosis Diagnosis Meta-analysis
Objective: Thyroid cancer (TC) is the most common malignant endocrine-related cancer with an increasing trend worldwide. Therefore, it’s in urgent need to find new markers for prognosis and diagnosis. Many long noncoding RNAs (lncRNAs) have been reported to be aberrantly expressed in TC, and may serve as biomarkers. Therefore, we performed this meta-analysis to systematically summarize the relationship between lncRNA expressions and TC. Methods: Sources from PubMed, Embase and Web of Science were searched. A total of 16 eligible studies including 15 on clinicopahology, 5 on prognosis and 6 on diagnosis were enrolled in our meta-analysis. Revman5.3 and Stata11.0 Software were used to conduct the meta-analysis. Results: For diagnostic value, lncRNAs could discriminate between TC and the normal, and yield a high overall sensitivity and specificity (0.80, 95% CI: 0.75–0.84; 0.80, 95% CI: 0.70–0.87). Meanwhile, their sensitivity and specificity were 0.74 (95% CI: 0.59–0.85) and 0.81 (95% CI: 0.73–0.88) respectively, when used to differentiate patients with lymph node metastasis (LNM) from without LNM. The summary receiver operator characteristic curve (sROC) showed that lncRNAs could be considered as valuable diagnostic markers for distinguishing TC patients from the normal (AUC = 0.84) and TC patients with LNM from TC patients without LNM (AUC = 0.85). Conclusions: In summary, our meta-analysis suggested that lncRNAs could function as potential diagnostic markers for TC and predict the LNM. In addition, the systematic review elaborated that lncRNAs might be as prognostic indicators in TC.
1. Introduction Thyroid cancer (TC) is the most common type of malignancy in endocrine organs with steadily increasing incidence over the recent years [1]. Based on the histological features, we divide TC into four classes, which are papillary thyroid carcinoma (PTC), follicular thyroid carcinoma (FTC), poorly differentiated carcinoma (PDC) and anaplastic thyroid carcinoma (ATC) [2]. PTC is the predominant form of thyroid cancer, accounting for approximately 80% among all of the cases [3,4]. Conventional surgery has been the main therapy for TC, however, it is often incurable [5]. Adjuvant therapies were depend on patients and disease features [6]. In the high risk patients, external beam radiation therapy (EBRT) may not confer survival advantage [7]. Recently, tyrosine kinase inhibitors becomes a burgeoning treatment for TC patients [8]. Generally, the 5-year survive of PTC was more than 95% [9]. However, increasing numbers of more advanced patients, as well as the ⁎
1
TC associated mortality have been seen due to the distant metastasis, genetic and environmental factors [10–12]. In 2016, research reported that 64,300 people were diagnosed with TC in the United States [13]. Moreover, up to 6.6 per 100,000 individuals of the TC rate occurred in China [14]. Investigating the molecular mechanisms related to the occurrence of TC could improve the rate of early diagnosis and treatment [15]. Therefore, to identify sensitive and specific biomarkers for prognosis and diagnosis of patients with TC is in urgently needed. Recently, genomic studies discovered that more than 90% of the mammalian genome can be transcribed into short or long noncoding RNAs [16]. Long non-coding RNAs (lncRNAs) are molecules with a length of longer than 200 nt that cannot code protein products [17]. The role of lncRNAs in cancers has been broadly researched, and commonly agree that lncRNAs can act as oncogenes or tumor suppressor genes during tumorigenisis [18]. Numerous studies have demonstrated that lncRNAs are critical to a wide range of biological
Corresponding author. E-mail address: [email protected] (L. Ming). These authors contributed equally to this work.
https://doi.org/10.1016/j.prp.2018.01.008 Received 23 November 2017; Received in revised form 14 January 2018; Accepted 24 January 2018 0344-0338/ © 2018 Elsevier GmbH. All rights reserved.
Please cite this article as: Jing, W., Pathology - Research and Practice (2018), https://doi.org/10.1016/j.prp.2018.01.008
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value of diagnosis and HR for the prognostic meta-analysis.
progress, including pre-transcription, post-transcription and cell proliferation, differentiation, apoptosis, and migration, which also change the original concept that lncRNA genes are just “noise” [19–22]. Evidence shows that lncRNAs are dysregulated in various types of cancers, such as lung cancer [23], gastric cancer [24], and liver cancer [25]. For example, highly up-regulated in liver cancer (HULC) is a specific gene which is markedly up-regulated both in tissue and plasma in HCC [26]. Yan et al. [24] found that small nucleolar RNA host gene 6 (SNHG6) acted as an oncogene in gastric cancer, and could serve as a prognostic biomarker and a target for novel therapies of gastric cancer patients. Recently, researches suggested that lncRNAs could play crucial roles in the aspects of diagnosis and prognosis for the TC patients. Accumulating studies have confirmed that lncRNAs play functional roles in TC, including MALAT1 [27], PTCSC3 [28], MEG3 [29] and NEAT1 [30]. In 2017, research indicated that papillary thyroid carcinoma susceptibility candidate 3 (PTCSC3)/miR-574-5p regulated the activity of Wnt/β-catenin, and mediated the proliferation and migration of PTC-1 cells, which was vital for the therapy and prognosis of TC [9]. Li et al. [30] revealed that elevated-expression of nuclear enrich abundant transcript 1 (NEAT1) promoted the both onset and the malignant progression of TC through regulating miRNA‐214 expression. Meantime, metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) might function both as an oncogene and a tumor suppressor in different types of TC, and played a key role in epithelial to mesenchymal transition (EMT) in TC [31]. These discoveries suggested that lncRNAs serve as biomarkers of TC. However, single study may be inaccurate and insufficient. Thus, we meta-analyzed the potential value of altered lncRNAs in patients with TC. In our study, by pooling all the available data, we aimed to evaluate the diagnostic and prognostic value of lncRNAs in TC objectively.
2.4. Quality assess The quality assessment is an important component of a thorough meta-analysis. Two investigators independently performed this quality assessment. Newcastle–Ottawa scale (NOS) was used to assess the quality of the studies for prognosis [32]. The NOS criteria assessed quality by three aspects: (1) selection: 0–4; (2) comparability: 0–2; and (3) outcome: 0–3. The total NOS scores were ranged from 0 to 9, and the value of total score ≥7 shows the study has a good quality [33]. Meanwhile, we assessed the quality of all the diagnostic studies according to the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) [34]. 2.5. Statistical analysis We extracted HRs for prognosis using two methods: (1) The HRs were obtained directly from the publication; (2) we estimate the HRs and 95% CI by extracting several survival rates at specified times from the Kaplan–Meier survival curves using Engauge Digitizer version 4.1 [35]. Revman5.3 Software (Revman, the Cochrane Collaboration) was used to conduct the meta-analysis. Heterogeneity among the studies was checked with the Chi-square based on Q statistical test and I2. If heterogeneity was present (I2 ≥ 50% or p ≤ 0.05), random-effect model was used. If not, the fixed-effect model was more appropriate. HRs and 95% CIs were used to assess the association between lncRNAs and survival. The Stata11.0 Software (Stata, College Station) was performed to measure the diagnostic value of lncRNAs in TC. For the diagnostic meta-analysis, sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), summary receiver operator characteristic curve (sROC), and area under the curve (AUC) were used and Deeks’ funnel plot was adopted to evaluate the publication bias of the included studies. Statistical significance was defined when the p-value was less than 0.05.
2. Methods 2.1. Search strategy Electronic searches of PubMed, Embase and Web of Science were performed to obtain relevant articles for the meta-analysis in December 2017. To increase the sensitivity of the search, we used both medical subject heading terms and free-text words including: “lncRNA”, “thyroid cancer”, ”diagnosis”, “survival” and “prognosis”, the detail search strategy was following: “long noncoding RNA”, “lncRNA”, “lincRNA”, “long intergenic non-coding RNA” and “TC”. All searches were limited to human studies and English publications. Additionally, we reviewed related references of the retrieved articles to identify potentially eligible studies.
3. Result 3.1. Literature search and study characteristics As shown in the flow diagram (Fig. 1), 348 articles were retrieved from PubMed, EMBASE, and Web of Science, 272 articles were removed due to duplicated papers, and then only 76 articles were left. We removed 44 after screening the titles and abstracts, and 16 articles were excluded for unavailable data. Subsequently, a total of 16 articles were in the current meta-analysis, including 15 on clinicopahology [14,15,36–48], 5 on prognosis [15,42,44,48,49], and 6 on diagnosis [14,37–39,42,43].
2.2. Inclusion and exclusion criteria The inclusion criteria were as follows: (1) patients in the study were diagnosed with TC; (2) studies investigated the association between lncRNAs and TC; (3) researches provided sufficient data for calculating the rates of true positive (TP), false positive (FP), false negative (FN), and true negative (TN) for diagnostic meta-analysis; (4) sufficient published data were provided to calculate hazard ratios (HR) and 95% confidence interval (95% CI) for prognostic meta-analysis; (5) studies published in English. The major reasons for exclusion criteria were: (1) studies without usable data or incomplete data; (2) reviews, letters, animal studies; (3) duplicate publications.
3.2. Correlation of lncRNAs with clinicopathological features Table 1 summarized the main characteristics of the included 15 studies ranging from 2016 to 2017. 1528 participants were enrolled in these studies, with a maximum sample size of 212 and a minimum sample size of 55 patients. Among them, focally amplified long noncoding on chromosome 1 (FAL1) [36], NR_036575.1 [38], lncRNA activated by TGF-β (ATB) [39], HIT000218960 [40], H19 [42], NONHSAT076754 [43], antisense non-coding RNA in the INK4 locus (ANRIL) [45], n340790 [46] and SPRY4-IT1[48] were up-regulated. Meanwhile, NONHSAT037832 [37], BRAF-activated non-coding RNA (BANCR) [41], cancer susceptibility candidate 2 (CASC2) [44], GAS8AS1 [14], LINC00312 [47] and GAS5 [15] were all down-regulated. In the included studies, there was no correlation between lncRNAs and age. Zhang et al. [14] elaborated that GAS8-AS1 levels were statistically related to the gender. The expression of NR_036575.1 was associated
2.3. Data extraction Two investigators extracted relevant data from the eligible studies independently. We recorded the following information, including the first author, year of publication, country, the lncRNAs, case number, follow-up months, and other features. Meanwhile, we extracted true positive, true negative, false positive and false negative to analyze the 2
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Fig. 1. The flow diagram of this meta-analysis.
with extrathyroidal extension (ETE). The following characteristics level of lncRNAs were generally interrelated, containing focality [15,36,40,41,44], tumor size [37,38,41,42,47], lymph node metastasis (LNM) [14,15,37,39,40,42,43,45–48] and tumor-node-metastasis (TNM) stage [15,40–44,46,47]. Unfortunately, each lncRNA was performed in single study, we could not conducted a meta-analysis on the relationship between one of these lncRNAs and the clinicopathological features of TC patients.
the group with decreased lncRNAs expression. For the overall survival (OS), the decreased expressions of GAS5 and CASC2 were associated with poor prognosis. Similarly, the increased expressions of H19 and SPRY4-IT1 were related to poor prognosis in the forest plot. Meanwhile, we found that the patients with low expression of GAS5 and SLC6A9 had a shorter disease-free survival (DFS) time (Fig. 2). Due to the number of articles, we did not generate the conclusive graph and evaluate publication bias.
3.3. Impact of lncRNAs on prognosis
3.4. Diagnostic accuracy of lncRNAs detection
There were 5 studies exploring the relationship between lncRNA expression levels and prognosis of TC patients (Table 2). All enrolled studies were non-randomized studies, and the quality scores of included studies were from 7 to 8, suggesting that the results of included study were reliable. HRs and 95% CI were extracted from these studies. An observed HR > 1 implied a worse survival for the group with elevated lncRNAs expression. Conversely, HR < 1 implied a worse survival for
The main characteristics of the included studies for the diagnostic meta-analysis were listed in Table 3. A total of 6 publications [14,37–39,42,43] were analyzed. We assessed the quality of all the available studies according to QUADAS-2. Result demonstrated that the overall quality of the enrolled studies were acceptable. The risk of bias and applicability concerns graph for the included studies was showed in Fig. 3.
Table 1 Summary of the comparison for the P values of the association between lncRNAs and clinicopathological features in thyroid cancer. Author
Year
Country
LncRNAs
Tumor type
Total number
Age
Gender
Focality
Tumor size
ETE
LNM
TNM stage
HT
Expression
Jeong Lan Sun Fu Li Liao Liu Xia Xiong Zhang Zhao Li Liu Guo Zhou
2016 2016 2016 2017 2017 2017 2017 2017 2017 2017 2016 2017 2017 2017 2017
Korea China China China China China China China China China China China China China China
FAL1 NONHSAT037832 NR_036575.1 ATB HIT000218960 BANCR H19 NONHSAT076754 CASC2 GAS8-AS1 ANRIL n340790 LINC00312 GAS5 SPRY4-IT1
PTC PTC PTC PTC PTC PTC TC PTC TC PTC TC TC TC TC TC
60 95 83 64 55 92 131 72 86 97 105 85 211 212 80
0.994 0.251 0.435 0.802 0.891 0.214 0.711 0.476 0.175 0.224 0.196 0.414 0.082 0.268 0.230
0.774 0.144 0.273 0.784 0.380 0.711 0.112 0.576 0.152 0.038 0.632 0.077 0.513 0.633 0.626
0.018 0.738 0.734 0.309 0.005 0.038 – 1.000 0.028 1.000 0.498 – – 0.001 0.496
0.696 0.032 0.006 – – 0.046 0.004 – – 0.376 0.202 – < 0.001 0.135 1.000
0.542 0.568 0.011 0.114 – 0.406 – 1.000 0.099 0.535 – – – – –
0.272 0.015 0.754 0.021 0.009 0.992 0.019 0.009 0.268 0.002 0.005 < 0.001 < 0.001 0.028 0.003
0.554 0.771 0.284 0.424 0.022 0.037 0.016 0.022 0.001 0.146 – < 0.001 < 0.001 < 0.001 –
– – – – – – 0.162 – – – 0.486 – – 0.75 0.149
Up-regulation Down-regulation Up-regulation Up-regulation Up-regulation Down-regulation Up-regulation Up-regulation Down-regulation Down-regulation Up-regulation Up-regulation Down-regulation Down-regulation Up-regulation
Abbreviations: LncRNA: Long non-coding RNA; ETE: Extrathyroidal extension; LNM: Lymph node metastasis; TNM: Tumor-node-metastasis; HT: Histological type; PTC: papillary thyroid carcinoma; TC: thyroid carcinoma.
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Table 2 Characteristics of studies included in prognosis. Author
Year
Country
LncRNAs
Method
Case number (High/Low)
Outcome
Follow-up months
HR availability
Quality score
Xiang Xiong Guo Liu Zhou
2017 2017 2017 2017 2017
China China China China China
SLC6A9 CASC2 GAS5 H19 SPRY4-IT1
qRT-PCR qRT-PCR qRT-PCR qRT-PCR qRT-PCR
26/26 86 212 95/36 54/26
DFS OS OS, DFS OS OS
30 32 60 60 60
Indirectly Directly Directly Indirectly Indirectly
7 8 8 7 7
Abbreviations: LncRNA: Long non-coding RNA; qRT-PCR: Quantities reverse transcription-polymerase chain reaction; OS: Overall survival; DFS: Disease-free survival; HR, hazard ratio.
Fig. 2. Forest plot for the association between lncRNAs expression levels with prognosis in thyroid cancer. OS = overall survival; DFS = disease free survival.
Table 3 Characteristics of studies included in diagnosis. Author
Year
Country
LncRNAs
Specimen
Case number (Case/Control)
Type of sample (Case/Control)
AUC
TP
FP
TN
FN
Liu Sun Lan Lan Fu Fu Zhang Zhang Xia
2017 2016 2016 2016 2017 2017 2017 2017 2017
China China China China China China China China China
H19 NR_036575.1 NONHSAT037832 NONHSAT037832 ATB ATB GAS8-AS1 GAS8-AS1 NONHSAT076754
FT FT FT FT FT FT Plasma Plasma FT
131/122 83/83 95/87 68/27 64/64 39/25 97/39 47/50 37/35
TC/Normal tissues PTC/Adjacent noncancerous tissues PTC/Adjacent noncancerous tissues PTC with LNM/PTC without LNM PTC/Adjacent noncancerous tissues PTC with LNM/PTC without LNM PTC/NG PTC with LNM/PTC without LNM PTC with LNM/PTC without LNM
0.801 0.915 0.897 0.641 0.916 0.882 0.702 0.746 0.878
95 67 76 40 53 34 82 29 31
30 10 12 8 10 6 17 5 6
92 73 75 19 54 19 22 45 29
36 16 19 28 11 5 15 18 6
Abbreviations;: LncRNA: Long non-coding RNA; FT: Frozen tissue; TC: Thyroid carcinoma; PTC: Papillary thyroid carcinoma; LNM: Tumor-node-metastasis; NG: Nodular goiter; AUC: Area under the curve; TP: True positive; FP: False positive; TN: True negative; FN: False negative.
Fig. 3. Quality assessment of the included studies by QUADAS-2. It summarized “risk of bias” and “applicability concerns” through judging each domain for all enrolling studies.
NLR, and DOR for overall studies were 4.0 (95% CI: 2.6–6.2), 0.32 (95% CI: 0.19–0.54), and 12 (95% CI: 5.0–28.0), respectively. The sROC curve with an area under the curve (AUC) were 0.84 (95% CI: 0.80–0.87) and 0.85 (95% CI: 0.82–0.88) in the two groups (Fig. 5). Deeks’ test was performed to check publication bias. Both of the values of p > 0.05, which indicated that no publication bias were existed (Fig. 6).
The overall sensitivity and specificity of lncRNAs as a diagnostic target for TC in all the studies included were 0.80 (95% CI: 0.75–0.84) and 0.80 (95% CI: 0.70–0.87) respectively with heterogeneity (I2 = 35.34% and 81.59%), when normal tissues or adjacent noncancerous tissues or nodular goiter was used as the control group [14,37–39,42] (Fig. 4A). Additionally, from our calculations, the pooled PLR and NLR were 4 (95% CI: 2.6–6.2) and 0.25 (95% CI: 0.20–0.32). The DOR was 16 (95% CI: 9–28). Simultaneously, we performed another diagnostic meta-analysis between TC with LNM and TC without LNM [14,37,39,43]. The pooled sensitivity and specificity of the lncRNAs were 0.74 (95% CI: 0.59–0.85, I2 = 80.55%) and 0.81 (95% CI: 0.73–0.88, I2 = 43.62%) (Fig. 4B). Besides, the pooled PLR,
4. Discussion TC, originated from follicular or parafollicular thyroid cells, is the most common endocrine cancer, ranking the eighth most frequent 4
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Fig. 4. Forest plots of sensitivities and specificities of lncRNAs for the diagnosis of thyroid cancer. A. thyroid cancer and the normal; B. thyroid cancer with lymph node metastasis and without lymph node metastasis.
functions of lncRNAs in TC [14]. Therefore, the present meta-analysis was the first to systematically analyze the precise prognostic value, diagnostic and clinical significance of lncRNAs in patients with TC. In our study, we investigated the association between the levels of lncRNAs and clinicopathological characteristics. The abnormal expression of NONHSAT037832 [37], ATB [39], HIT000218960 [40], H19 [42], NONHSAT076754 [43], GAS8-AS1 [14], ANRIL [45], n340790 [46], LINC00312 [47], SPRY4-IT1 [48] and GAS5 [15] might be
cancer in China [45,49]. It’s necessary to explore novel biomarkers to help to improve the outcome of TC [36]. According to the ENCODE project, more that 90% of human genome can be transcribed, but only less than 2% transcripts are protein-coding genes, and the rest is noncoding RNA. Recently, mounting evidence demonstrated that lncRNA dysregulation was involved in the progression of cancers [50], including apoptosis, metastasis, migration, and other clinical outcome [51]. Among them, there were a few published studies, focused on the 5
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Fig. 5. The summary receiver operator characteristic (sROC) curve in the diagnosis of thyroid cancer. A. thyroid cancer and the normal; B. thyroid cancer with lymph node metastasis and without lymph node metastasis.
Fig. 6. Deeks’ funnel plots for the assessment of potential bias in the meta-analysis for diagnosis. A. thyroid cancer and the normal; B. thyroid cancer with lymph node metastasis and without lymph node metastasis.
in TC. In the plasma of TC patients, GAS8-AS1 was down-regulated and could function as a potential marker for TC diagnosis and LNM prediction [14]. Recently, studies explored the diagnostic role of lncRNAs in TC which have become a hot spot. Thus, it is important to combine all the relevant studies and carry out a meta-analysis to confirm the vital role of lncRNAs in TC. Regarding the diagnostic significance, the lncRNAs yielded high sensitivity and specificity in TC diagnosis. However, there was heterogeneity in the meta-analysis. We supposed that the sample type, case size and the different cut-off value might be the potential source. The sROC showed that lncRNAs could be thought as a valuable marker for differentiating TC patients from the normal (AUC = 0.84) and TC patients with LNM from the TC patients without LNM (AUC = 0.85). These results indicated that lncRNAs might serve as a novel biomarker and could predict the LNM in TC patients. Up to now, many meta-analysis about lncRNAs have been performed. Our research is the first meta-analysis focused on studying the role of lncRNAs in TC. We summarized the prognostic and clinical role of lncRNAs in TC. Besides, we also evaluated the diagnostic value of lncRNAs in TC, which is a novelty of the study, since no meta-analysis have been done to investigate lncRNAs as a diagnostic biomarker in patients with TC. Nevertheless, there were some limitations in our meta-analysis. Most studies reported were positive results, those with negative results were generally less likely to be published. In addition, our studies were only English researches, no other languages, As a result, the data collection may be incomplete. Then, HR values from 3
considered as biomarkers of LNM. Meanwhile, these lncRNAs were related to TNM stage, including HIT000218960 [40], BANCR [41], H19 [42], NONHSAT076754 [43], CASC2 [44], n340790 [46], LINC00312 [47], and GAS5 [15]. Research elaborated that TC had a good prognosis and low mortality rate, but some features were correlated with poor prognosis, such as LNM and distant metastasis [39].However, results from a single studies is not enough to illustrate the value of lncRNAs completely. Thus, it is in urgently need to combine all the published relevant studies and carry out a meta-analysis to confirm the vital role of lncRNAs in TC. In 2017, Xiang et al. [49] demonstrated that low SLC6A9 expression was associated with a worse prognosis of TC. Meanwhile, overexpression of CASC2 significantly inhibited the proliferation of TC cells and arrested cell cycle at G0/G1 stage, showing that CASC2 might serve as a potential prognostic target [44]. Numerous studies elaborated that the relationship between lncRNAs and prognosis in TC patients. However, we could not perform a meta-analysis because of the small number of the studies enrolled. Thus, more research are necessary to be explore further. Currently, due to lack of susceptible biomarkers for TC, laboratory provide very limited diagnostic tools, for instance biopsy, imaging techniques, and radioiodine scintigraphy [46], indicating improving the success rate of early diagnosis is urgently needed. Fu et al. [39] indicated that ATB was up-regulated in TC tissues, and depletion of ATB could significantly inhibit TC cell proliferation and migration. ROC analyses revealed that ATB could be considered as a diagnostic marker 6
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studies were got from survival curves, which might generate inaccurate results. Finally, most of the population in our studies were Chinese, which might have a bias towards this population. In summary, this meta-analysis investigated the diagnostic and prognostic value of lncRNAs in TC patients. Although these limitations, there was an association between lncRNAs levels and prognosis in TC patients, which demonstrated that lncRNAs could be potential prognostic markers. More importantly, we discovered that lncRNAs might serve as a biomarker to diagnose TC based on the currently available evidence. In the future, more clinical studies with larger-size and better designing need to be conducted to conform our results.
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[22] [23]
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Disclosure of interest [26]
The authors declare that there are no conflicts of interest.
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Acknowledgment [28]
This work was supported by the Hospital Fund of The First Affiliated Hospital of Zhengzhou University.
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