Circulating Non-coding RNAs as Potential Biomarkers for Ischemic Stroke:A Systematic Review

doi:10.21203/rs.3.rs-1244517/v1

Background

Recent studies have demonstrated that dysregulated non-coding RNAs (ncRNAs) are involved in the pathogenesis of ischemic stroke (IS) including neuroinflammation, apoptosis, atherosclerosis and angiogenesis. However, discrepant results make it difficult to apply ncRNAs into clinical practice. Therefore,we performed this meta-analysis to evaluate and elucidate the diagnostic value of ncRNAs in IS.

Methods

We searched the literature in four databases: PubMed, Web of Science, EMBASE and the Cochrane Library up to December 31, 2020 to identify the relationship between differently expressed ncRNAs and IS. Pooled sensitivity, specificity, positive likelihood ratios (PLR), negative likelihood ratios (NLR), diagnostic odds ratio (DOR), and corresponding 95% confidence intervals (95%CIs) were calculated to access diagnostic performance of ncRNAs.

Results

15 studies for miRNAs containing 1687 IS patients and 8 studies for lncRNAs, including 741 IS patients were included in our study. Estimates of the identification efficiency of miRNAs from the 15 studies were: 0.76 (95%CI:0.67-0.84) for SEN, 0.83 (95%CI:0.77-0.88) for SPE, 4.5 (95%CI:3.4-6.1) for PLR, 0.29 (95%CI:0.20-0.40) for NLR, 16 (95%CI:10-26) for DOR. The AUC of the SROC curve showed diagnostic accuracy was 0.87 (95%CI:0.84-0.90), thus the diagnostic value of all the miRNAs was moderate. In addition, the results of lncRNAs were: 0.73 (95%CI: 0.69-0.77) for SEN, 0.75 (95%CI: 0.70-0.79) for SPE, 2.9 (95%CI: 2.4-3.4) for PLR, 0.36 (95%CI: 0.31-0.42) for NLR, 8 (95%CI: 6-11) for DOR, 0.80 (95%CI: 0.76-0.83) for AUC, also a moderate diagnostic value.

Conclusion

Our study revealed that blood circulating ncRNAs could be a moderately effective candidate biomarker for the diagnosis of IS. Furthermore, combined lncRNAs showed more accurate diagnostic properties than single lncRNAs and some single miRNAs (e.g. miR-107) showed better diagnostic performance, which may contribute to IS clinical practice.

ischemic stroke

miRNAs

lncRNAs

diagnosis

biomarker

Stroke is a prevalent disease worldwide, with high disability and mortality. It has caused serious sociodemographic and socioeconomic burden in recent years, and the burden continues to increase (1). Ischemic stroke (IS) is a major subtype of stroke, for which thrombolysis (through the administration of tissue plasminogen), and thrombectomy (the surgical removal of emboli), are the main therapeutic methods. However, the number of patients benefiting from these therapies is relatively low because of the narrow therapeutic window, hemorrhagic transformation, and reperfusion injury. As a medical emergency with high morbidity, there is a lack of serum biomarkers for the diagnosis and prevention of IS (2, 3). Recent studies have found that non-coding RNAs (ncRNAs) show differential expression during IS and play widespread roles in the different phases of the disease (4-6).

Recent studies have demonstrated that ncRNAs are involved in the pathogenesis of IS, including neuroinflammation, apoptosis, atherosclerosis, and angiogenesis (5,6). NcRNAs are defined as non-protein-coding transcripts and are classified into many types according to their length and connection, e.g. microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs. lncRNAs are a type of linear RNA over 200 nucleotides in length, whereas miRNAs are 22 nucleotide long ncRNAs (7). As major members of the ncRNA family, lncRNAs and miRNAs often exhibit cell and tissue specificity. They also play extensive roles in pathophysiological events by regulating the expression of mRNA and protein (8-10). MiRNAs regulate gene expression at the post-transcriptional level by binding to the 3 prime untranslated region of mRNA, whereas lncRNAs exert their effects at the transcriptional and post-transcriptional levels (7, 11). Recently, emerging studies have begun to reveal that ncRNAs can accumulate and be expressed in mammalian brain and in neuronal cell lines and play several roles in physiological and pathological activities in the brain (12).

To date, multiple dysregulated miRNAs and lncRNAs (upregulated or downregulated) have been identified from the middle cerebral artery occlusion model (MACO), the oxygen glucose deprivation/re-oxygenation cell model (OGD/R), and from the blood of IS patients. Stroke significantly alters the expression profile of miRNAs and lncRNAs, indicating that these ncRNAs have the potential to be diagnostic and predictive biomarkers for IS (13-16). However, discrepant reports make it difficult to apply ncRNAs into clinical practice. Moreover, not all ncRNAs are suitable as potential biomarkers for IS. The diagnostic potential of ncRNAs is yet to be determined; we performed this meta-analysis to evaluate and elucidate the diagnostic value of ncRNAs in IS.

2.1Search Strategy

We systematically searched the literature in four databases: PubMed, Web of Science, EMBASE, and the Cochrane Library, up to December 31, 2020. The literature on ncRNAs was retrieved using the following search strategies: (microRNA OR miRNA OR long non-coding RNA OR lncRNA OR non-coding RNA OR ncRNA) AND (diagnosis OR specificity OR sensitivity OR receiver operating characteristics OR ROC) AND (ischemic stroke OR cerebral infarction OR brain infarction OR cerebrovascular disease). We also checked references in these articles to identify further relevant studies. All studies identified were inspected by two independent reviewers, and disagreements, if any, were discussed until a consensus was reached.

2.2Inclusion Criteria

The eligible studies should meet the following criteria: (1) Studies were focused on the diagnostic performance of lncRNAs or miRNAs in IS; (2) Describing peripheral blood detection (plasma, serum, and whole blood); (3) All the patients were diagnosed as IS according to radiological imaging (CT or MRI) and neurological examination; (4) The sufficient data including the sample size of case and controls, sensitivity (SEN), specificity (SPE) and area under curve (AUC) were available. In addition, the exclusion criteria are as follows: (1) Literature not written in English; (2) Reviews, meeting records or letters; (3) Studies not conducted in humans; (4) Not describing peripheral blood detection of lncRNAs and miRNAs.

2.3Data Extraction and Quality Assessment

Two researchers extracted information from included literature covering first author, publication year, ethnicity, method, sample size of case and control, sample source, and necessary data including SPE, SEN and AUC. Quality assessment of these studies were carried out by two independent reviewers according to the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) (17), which contain 4 domains: patient selection, index test, reference standard, and flow and timing. Each domain is used for bias assessment and the first 3 domains were applied to evaluate clinic applicability of these studies.

Data availability statement All data generated or analyzed during this study are included in this published article (and its supplementary information files).

2.4Statistical Analysis

The extracted data were analyzed using STATA 15.0 and p < 0.05 was considered statistically significant. Pooled sensitivity (SEN), specificity (SPE), positive likelihood ratios (PLR), negative likelihood ratios (NLR), diagnostic odds ratios (DOR), and corresponding 95% confidence intervals (95% CIs) were calculated using a random effects model. In addition, the summary receiver operator characteristic (SROC) curve and the area under the SROC curve (AUC) were analyzed to assess the diagnostic performance of lncRNAs and miRNAs for IS. A summary of the PLR and NLR, as well as the Fagan’s nomogram, were used to evaluate the clinical applicability of ncRNAs for the diagnosis of IS. Cochran's-Q and I-squared (I²) tests were applied for assessment of heterogeneity, with p < 0.1 or I² > 50% indicative of significant heterogeneity. ROC plane, sensitivity analysis, bivariate boxplot, and meta-regression were further conducted to explore the potential sources of heterogeneity. A group of analysis with a goodness-of-fit, a bivariate normality, a influence analysis and a outlier detection were used to validate our results. A funnel plot was used to examine publication bias.

3.1Study Characteristics

We searched 1458 articles in total from PubMed (904), Web of Science (389), EMBASE (152), and the Cochrane Library (13), with 634 and 824 articles for miRNAs and lncRNAs, respectively. Of these, 1364 records (566 and 798 for miRNAs and lncRNAs, respectively) were excluded after reading the title and abstract due to: duplication; not describing miRNAs or lncRNAs with IS; not a human model; not describing peripheral blood detection; or studies based on databases. Subsequently, 94 articles remained (66 and 28 for miRNAs and lncRNAs, respectively). After reading the whole text, 71 articles were excluded because they did not provide the necessary data, leaving 23 articles ultimately included for miRNAs and lncRNAs (18-40). A flowchart of the whole selection process is shown in Figure 1.

Of these remaining articles, 15 were miRNA studies and included 1687 IS patients and 1267 controls. There were 27 sets of miRNAs in total, from which single and combined miRNAs (double, triple, and pentuple) were described, in terms of their diagnostic value for IS, respectively in 20 and 7 (3 sets of double, 2 sets of triple, and 2 sets of pentuple miRNAs) studies (Table 1). Meanwhile, 8 studies were conducted for lncRNAs, encompassing 741 qualifying patients and 774 corresponding controls. There were 18 sets of lncRNAs performed in total, from which single and combined lncRNAs (double and triple) were described in 13 and 5 (3 sets of double and 2 sets of triple lncRNAs) studies respectively (Table 2).

3.2Diagnostic Value of NcRNAs

3.2.1 Diagnostic Value of miRNA

The specificity and sensitivity of miRNAs in the diagnosis of IS are shown in forest plots of the 27 sets of miRNAs from the 15 selected studies (Figure 2). These plots indicate significant heterogeneity, with I² values of 95.74 and 89.43 for sensitivity and specificity, respectively, as such we used a random effects model in this meta-analysis. Estimates of the identification efficiency of miRNAs from 15 studies were 0.76 (95% CI: 0.67‒0.84) for SEN, 0.83 (95%CI: 0.77-0.88) for SPE, 4.5 (95%CI: 3.4-6.1) for PLR, 0.29 (95%CI: 0.20-0.40) for NLR, 16 (95%CI: 10-26) for DOR. In addition, the AUC of the SROC curve, indicating diagnostic accuracy, was 0.87 (95% CI: 0.84‒0.90); thus the diagnostic value of all the miRNAs was moderate (Figure 3). In subgroup analysis based on miRNA profile, estimates for the combined miRNAs and single miRNAs were 0.71 (95% CI: 0.53‒0.83) and 0.78 (95% CI: 0.67‒0.86) for SEN, 0.85 (95%CI: 0.71-0.93) and 0.83 (95%CI: 0.75-0.88) for SPE, 4.8 (95%CI: 2.8-8.2) and 4.5 (95%CI: 3.1-6.4) for PLR, 0.35 (95%CI: 0.23-0.53) and 0.26 (95%CI: 0.17-0.41) for NLR, 14 (95%CI:9-21) and 17 (95%CI:9-32) for DOR. In the ROC curve, single miRNA and combined miRNA groups had similar diagnostic values, with AUCs of 0.88 (95% CI: 0.84‒0.90) versus 0.86 (95% CI: 0.82‒0.88), respectively (Figure S1 and Figure S2 in supplementary materials). In summary of the PLR and NLR, the 22nd set of miRNA (miR-107) from the study by Yang et al. (29) had a PLR >10 and NLR <0.1 (figure 4a). Fagan’s nomogram indicated that the positive post-test probability of diagnosing IS would increase to 53%, whereas the negative post-test probability of diagnosing IS would decrease to 7%, with a pretest probability of 20% (Figure 4b).

3.2.2 Diagnostic Value of lncRNA

Similarly, statistics about lncRNAs which were extracted from 8 selected studies were analyzed in the same way. As showed in forest plots (figure 5), significant heterogeneity was found, as I-square value were 56.69 and 74.97 for sensitivity and specificity respectively. The result of lncRNAs were 0.73 (95%CI: 0.69-0.77) for SEN, 0.75 (95%CI: 0.70-0.79) for SPE, 2.9 (95%CI: 2.4-3.4) for PLR, 0.36 (95%CI: 0.31-0.42) for NLR, 8 (95%CI: 6-11) for DOR, 0.80 (95%CI: 0.76-0.83) for AUC (Figure 6). In subgroup analysis based on lncRNA profile, estimates of combined lncRNAs and single lncRNAs were: 0.77 (95%CI: 0.71-0.82) and 0.71 (95%CI: 0.66-0.76) for SEN, 0.75 (95%CI: 0.69-0.80) and 0.75 (95%CI: 0.69-0.80) for SPE, 3.0 (95%CI: 2.4-3.8) and 2.8 (95%CI: 2.2-3.6) for PLR, 0.31 (95%CI: 0.24-0.39) and 0.38 (95%CI: 0.32-0.46) for NLR, 10 (95%CI: 6-15) and 7 (95%CI: 5-11) for DOR. Compared with single lncRNA, combined lncRNA has a higher diagnostic accuracy, with a AUC of 0.83 (95%CI: 0.79-0.86) versus 0.79 (95%CI: 0.75-0.82) as showed in figure S3 and figure S4 (supplementary materials). In summary of the PLR and NLR, no study had a PLR >10 and NLR <0.1, and only the 15th set of lncRNA (lncRNA-AC136007.2) conducted by Li et al. (37) fell on the border line (Figure 7a). In Fagan’s nomogram, the pretest probability was 20%, the PLR and NLR were 3 and 0.36 respectively, and the positive post-test probability and the negative post-test probability of lncRNAs would be 42% and 8% respectively (Figure 7b).

3.3 Sensitivity Analysis, Meta-regression Analysis, and Publication Bias

Significant heterogeneity was observed in the 15 miRNA studies, as shown by the forest plots (SEN: I²=95.74, SPE: I²=89.43). To find the potential source of this heterogeneity, we carried out ROC plane, sensitivity analysis, bivariate boxplot, and meta-regression analysis. In the ROC plane, no typical shoulder arm was observed, indicating that no significant heterogeneity was caused by the threshold effect (Figure 8a). Influence analysis showed the study conducted by Liu et al. (23)may have influence on heterogeneity (Figure 8c). The bivariate boxplot revealed that 4 studies (Liu 2019, Lu 2018, Peng 2015 and Zeng 2011) contributed to this high heterogeneity (Figure 8d). After excluding these 4 studies, the pooled estimates did not change, whereas the sensitivity and specificity heterogeneity decreased (Figure S5). To confirm this result, we conducted further analysis for heterogeneity using a goodness-of-fit, bivariate normality, influence analysis and outlier detection, and found similar results (Figure S6). We read these 4 studies again and carried out further meta-regression analysis on the bias of sample size (≤50 or ＞50), sample source (serum, plasma or whole blood) and publication year (in 5 years or not). And we found that sample size and publication year may accounted for part of heterogeneity, with respectively p < 0.01 and p < 0.05 for sensitivity and specificity in sample size, and p < 0.01 for specificity in publication year (Figure 8b). Publication bias existed across the included studies on miRNAs, as identified by Deek’s funnel plot (p < 0.05) (Figure 9).

As for lncRNAs, significant heterogeneity exist among the 8 lncRNA studies in forest plots (SEN: I2=56.69, SPE: I2=74.97) (Figure 5). Similarly, ROC plane, sensitivity analysis, bivariate boxplot, and meta-regression analysis were carried out. ROC plane showed the significant heterogeneity did not result from threshold effect (Figure 10a). Influence analysis showed no study exert in substantial role in the results (Figure 10c). Three studies (Li 2019, Wang 2017, and Wang 2019) were out of the boxplot and may be the source of heterogeneity (Figure 10d). After excluding the 3 studies, the pooled estimates were similar to the previous results, whereas heterogeneity of sensitivity and specificity decreased (SEN: I²=27.71, SPE: I²=29.26) (Figure S7). Subsequently, a further analysis with a goodness-of-fit, a bivariate normality, a influence analysis and a outlier detection for heterogeneity showed the same result (Figure S8). Other source of heterogeneity indicated by meta-regression analysis may be sample size (>50 or not) with a P<0.01 for sensitivity and a P<0.001 for specificity (Figure 10b). No significant publication bias was found in Deek’s funnel plot with a P of 0.91 (Figure 11).

Several studies have reported aberrant expression of miRNAs and lncRNAs in IS patients (41-50). However, the results of these studies were inconsistent and have not yet reached an consensus. Some are significantly up-regulated, by contrast others are down-regulated in the circulating blood of IS patients. The discrepant results among these studies make it difficult to apply ncRNAs to clinical practice. Therefore, we reviewed 15 studies of miRNAs and 8 studies of lncRNAs and performed a systemic meta-analysis to clarify confusions about the diagnostic value of circulating ncRNAs, which would give suggestions for clinical applicability.

Our meta-analysis revealed a moderate diagnostic performance for blood-based miRNAs for IS. Single miRNAs and combined miRNAs showed no significant difference in diagnostic accuracy as measured by AUC in subgroup analysis (Figure S1 and Figure S2 in supplementary materials). Clinical applicability for diagnosis is dependent on the summary of PLR and NLR and Fagan’s nomogram. In summary of the PLR and NLR, PLR >10 and NLR <0.1 represent for a higher diagnostic accuracy (51). Among the miRNAs,miR-107 from the study by Yang et al. (29) was in this scope (Figure 4a), hence miR-107 could be more beneficial in clinical diagnostics and deserves further research. In their study, circulating miR-107, miR-128b, and miR-153 are higher in IS patients than healthy controls, but miR-107 showed the greatest diagnostic value with the higher specificity and sensitivity. Yang et al. found miRNA-107 promoted excitatory neurotoxicity by regulating glutamate transporter-1 expression in I/R injury rat model (52). Additionally, the study by Cheng et al. has found that miRNA-107 participated in neuronal apoptosis process by p53 pathway in OGD-treated HT-22 hippocampal neuron cell (53). Recent study by Li et al. have demonstrated that miRNA-107 involved in angiogenesis after stroke by targeting Dice (45). In conclusion, miR-107 played an important role in the pathophysiological process of stroke. Hence, miR-107 is a novel candidate biomarker for the differentiation of IS patients from healthy individuals and needed further intensive study. Additionally, the diagnostic odds ratios of miRNAs was 16 , which further confirmed miRNAs as a promising diagnostic tool for IS.

Multiple studies have reported that single lncRNAs possess good diagnostic performance for clinical use (34, 36-40). Whereas Deng et al. (33) found that the combination of linc-DHFRL1-4, SNHG15 and linc-FAM98A-3 showed greater diagnostic value with an AUC value of 0.842, and could precisely distinguish IS patients from healthy control. In our analysis, we summarized lncRNAs and identified that combined lncRNAs yield better diagnostic capability compared to single lncRNAs with a AUC of 0.83 (95%CI: 0.79-0.86). Fangan’s Nomogram (PLR=3) indicated patients with IS are three times more likely to have positive results than health control person (Figure 7b). Only lncRNA-AC136007.2 studied by Li et al. (37) fell on the boundary line of scope with PLR >10 and NLR <0.1 (Figure 7a), whereas the combined lncRNAs were not in this range. Until now the the number of studies about combined lncRNAs is limited, which maybe contributed to the drawbacks. Hence, more and more larger studies are needed to access the diagnostic accuracy of combined lncRNAs for IS.

To our knowledge, this is the first comprehensive meta-analysis to explore the potential value of ncRNAs in the diagnosis of IS. In our study, we not only used ROC plane, sensitivity analysis, bivariate boxplot, and meta-regression to identify the source of heterogeneity but also performed a goodness-of-fit, bivariate normality, influence analysis, and outlier detection to confirm the results. Moreover, both miRNAs and lncRNAs were included in our study, providing new potential biomarkers for clinical practice. Additionally, we also assessed single and combined ncRNAs to explore the best diagnostic biomarkers for IS.

However, several limitations of this meta-analysis should be emphasized. First, significant heterogeneity existed among the involved studies and in the subgroup analysis. ROC plane were carried out to explore whether heterogeneity caused by threshold effect or not. And the results showed atypical shoulder arm, suggesting nil threshold effect in miRNAs and lncRNAs. Bivariate boxplot analysis revealed that there were respectively 4 studies and 3 studies contributed to the significant heterogeneity in miRNAs and lncRNAs analysis. After excluding these studies, the diagnostic efficiency were remain the same, while heterogeneity was decreased. Additionally, meta-regression analysis of miRNAs indicated that sample size and publish year may also account for high heterogeneity. Other factors, such as the variance of the measuring apparatus and the use of different cut-off values between studies may also be responsible for this heterogeneity. Second, nearly all the studies included in this meta-analysis analyzed data from Asian cohorts, and there is a lack of data regarding ncRNAs and IS in other ethnicities. Third, other sample types, such as cerebrospinal fluid, may also be useful as biomarkers, and should be investigated in future studies. Fourth, as previously mentioned, most studies included in this analysis were retrospective case-control studies, as such they were limited by the different sample sizes and inclusion criteria between studies. Lastly, Deek’s funnel plot suggested that publication bias exists for miRNAs. Studies without full texts, such as case reports, conference abstracts, and non-English literature, were excluded from our study, which likely contributed to this publication bias to some degree. Furthermore, negative results are less likely to be published, leading to the exaggeration of results in terms of diagnostic potency. Hence, the results here need to be interpreted with caution.

In conclusion, our study revealed that blood circulating ncRNAs could be a moderately effective candidate biomarker for the diagnosis of IS. Furthermore, while combined lncRNAs showed more accurate diagnostic properties than single lncRNAs, some single miRNAs (e.g. miR-107) showed better diagnostic performance and require more attention. Further research with more extensive statistics should be conducted to confirm our analysis in the future. Studies regarding other types of ncRNAs, such as circular RNAs, are also needed to better understand the diagnostic potential of ncRNAs.

IS: ischemic stroke;ncRNA: non-coding RNA;ncRNA: long non-coding RNA;miRNA: microRNA;circRNA: circular RNA;SEN: sensitivity ;SPE: specificity;PLR: positive likelihood ratios;NLR: negative likelihood ratios ;TP: true positive;FP: false positive ;FN: false negative ;TN: true negative;DOR: diagnostic odds ratio ;95%CIs: 95% confidence intervals;SROC: summary receiver operator characteristic;AUC: area under SROC curve ;MACO: middle cerebral artery occlusion;OGD/R: oxygen and glucose deprivation/reperfusion;qRT-PCR: quantitative real-time PCR;I/R: ischaemia/reperfusion

Ethical Statement

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and materials

All data generated or analyzed during this study are included in this published article (and its supplementary information files).

Conflict of interest

The authors have no conflict of interests.

Funding

This study was supported by Natural Science Foundation of Liaoning province of China(2019-MS-364) and the National Natural Science Foundation of China (81501006).

Authors' contributions

Designed the experiments: RZ, JY. Performed the experiments: QW, Analyzed the data:JZ.Wrote the paper: RZ,JY,JZ.All authors read and approved the final manuscript.

Acknowledgments

We are deeply grateful to all participants of this study.

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Table 1 Summary of single and combined miRNAs from retrieved 15 articles

	Author year	ethnicity	method	miRNA	TP	FP	FN	TN
1	Cheng 2018	Asian	qRT-PCR	miR-148b-3p	40	8	37	34
				miR-151b	33	3	44	39
				miR-27b-3p	39	9	38	33
				miR-148b and miR-151b	51	3	26	39
				miR-148b-3p and miR-27b-3p	52	3	25	39
				miR-151b and miR-27b-3p	21	3	56	39
				miR-148b, miR-151b and miR-27b-3p	40	3	37	39
2	Gao 2019	Asian	qRT-PCR	miR-126	84	13	14	37
2	Gao 2019	Asian	qRT-PCR	miR-143	36	16	62	34
3	Jin 2017	Asian	qRT-PCR	miR-126, miR-130a, miR-222, miR-218, and miR-185	93	50	13	60
4	Jin 2018	Asian	qRT-PCR	miR-126, miR-130a, miR-222, miR-218, and miR-185	124	45	24	103
5	Kim 2015	Asian	qRT-PCR	miRNA-17	31	4	52	33
6	Liu 2019	Asian	qRT-PCR	miR-451	271	10	31	292
7	Long 2013	Asian	qRT-PCR	let-7b	35	8	3	42
				miR-126	35	8	3	42
				miR-30a	36	10	2	40
8	Lu 2018	Asian	qRT-PCR	miR-15a	22	1	57	74
9	Peng 2015	Asian	qRT-PCR	let-7e	60	14	12	37
9	Peng 2015	Asian	qRT-PCR	miR-338	52	24	20	27
10	Tian 2016	Asian	qRT-PCR	miR-16	28	4	12	26
11	Tiedt 2017	Europe	qRT-PCR	miR-125a-5p, miR-125b-5p and miR-143-3p	171	24	29	76
12	Yang 2016	Asian	qRT-PCR	miR-107	107	5	7	53
				miR-128b	83	5	31	53
				miR-153	104	15	10	43
13	Zeng 2011	Asian	qRT-PCR	miRNA-210	99	35	13	25
14	Zhao 2016	Asian	qRT-PCR	miR-335	164	32	4	72
15	Zhou 2018	Asian	qRT-PCR	miR-134	38	14	12	36

Table 2 Summary of single and combined lncRNAs from retrieved 8 studies

	Author year	ethnicity	method	lncRNA	TP	FP	FN	TN
1	Deng 2018	Asian	qRT-PCR	linc-DHFRL1-4	22	9	10	23
				SNHG15	19	5	13	27
				linc-FAM98A-3	19	10	13	22
				linc-DHFRL1-4, SNHG15, linc-FAM98A-3	26	9	6	23
2	Feng 2019	Asian	qRT-PCR	lncRNA ANRIL	91	36	35	89
3	Guo 2018	Asian	qRT-PCR	ENST00000568297	32	18	18	32
				ENST00000568243	35	15	15	35
				NR_046084	31	15	19	35
				ENST00000568297, ENST00000568243	38	12	12	38
				ENST00000568297,NR_046084	35	16	15	34
				ENST00000568243,NR_046084	39	12	11	38
				ENST00000568297, ENST00000568243, and NR_046084	41	10	9	40
4	Li 2020	Asian	qRT-PCR	lncRNA NEAT1	135	36	75	174
5	Li 2019	Asian	qRT-PCR	lncRNA-C14orf64	20	8	12	24
5	Li 2019	Asian	qRT-PCR	lncRNA-AC136007.2	29	3	3	29
6	Wang 2017	Asian	qRT-PCR	H19	29	2	7	23
7	Wang 2019	Asian	qRT-PCR	ZFAS1	59	57	7	54
8	Zhu 2018	Asian	qRT-PCR	MIAT	140	37	49	152

Supplementarymaterials.docx

Circulating Non-coding RNAs as Potential Biomarkers for Ischemic Stroke:A Systematic Review

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Abstract

Figures

1. Introduction

2. Material And Methods

3. Results

4. Discussion

Abbreviations

Declarations

References

Tables

Supplementary Files

Status:

Version 1