Diagnostic Accuracy of miRNA to Identify Colorectal cancer: A Systematic Review and Meta-Analysis

doi:10.21203/rs.3.rs-5278615/v1

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Research Article

Diagnostic Accuracy of miRNA to Identify Colorectal cancer: A Systematic Review and Meta-Analysis

https://doi.org/10.21203/rs.3.rs-5278615/v1

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Background

Colorectal cancer (CRC) is a highly aggressive, high-incidence malignancy. CRC accounted for approximately one out of every ten cancer cases and deaths. Although miRNAs are often used for medical diagnostic purposes, their diagnostic effectiveness in CRC remains uncertain.

Methods

Therefore, from January 2016 to April 2024, we conducted a comprehensive search of China National Knowledge Internet (CNKI), PubMed, Cochrane Library, Web of Science (WoS) and other resources. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), area under the curve (AUC) and Fagan plot analysis were used to assess the overall test performance of machine learning approaches. Moreover, we evaluated the publication bias by the Deeks’funnel plot asymmetry test.

Results

Ultimately, a total of 23 publications were identified and incorporated into this meta-analysis. The aggregated diagnostic data were as follows: The sensitivity of the test was 0.83, with a 95% confidence interval of 0.81–0.84. The specificity was found to be 0.83 with a 95% confidence interval (CI) of 0.81–0.84. The PLR was 4.60 with a 95% CI of 3.77–5.62. The NLR was 0.22 with a 95% CI of 0.17–0.27. The DOR was 23.79 with a 95% CI of 16.26–34.81. The AUC was 0.90 with a 95% CI of 0.87–0.92. The Deek funnel plot suggests that publication bias has no statistical significance. The Fagan plot analysis that the positive probability is 50% and the nagative probability is 5%.

Conclusion

In summary, our results suggest the high accuracy of miRNAs in diagnosing CRC.

miRNAs

Colorectal cancer (CRC)

Accuracy

AUC

Diagnostic

Colorectal cancer (CRC) is one of the most common cancers of the gastrointestinal tract and a leading cause of cancer-related death [1–3]. The implementation of CRC screening programs has improved early detection rates, but many CRC patients are still diagnosed at a later stage, and they often miss the opportunity for curative excision [4]. Despite great improvements, current treatment options and survival rates for advanced colorectal cancer remain limited. Furthermore, CRC also exerts significant impacts on both physical and mental functioning, characterized by elevated stress levels, diminished self-worth, heightened rates of mood disorders [5]. Therefore, timely identification of CRC has the ability to avert these significant consequences.

MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression via recognition of cognate sequences and interference of transcriptional, translational or epigenetic processes [6–8]. In fact, more and more studies have shown that miRNAs are closely related to the occurrence, progression, prognosis, drug resistance and sensitivity of tumors [9, 10]. Therefore, studies are necessary to incorporate miRNAs into preclinical models to develop diagnostic biomarkers.

In recent years, many studies have confirmed that miRNAs can be used as biomarkers for the diagnosis of colorectal cancer [11–13]. A study conducted in Poland confirmed that miR-4316 was used to diagnose CRC in individuals with a diagnostic precision of up to 76.9% [11]. In addition, another Chinese study increased the diagnostic sensitivity of CRC to 88.3% by combining miR-30e-3p, miR-148a-3p and miR-146a-5p, while the diagnosis rate of these three indicators alone was not as high [14]. The study on the use of miRNAs for CRC detection is growing. However, the accuracy of these miRNAs varies greatly in different studies, mostly due to differences in algorithmic techniques and diagnostic criteria .

Based on the above considerations, as well as the lack of systematic reviews and meta-analyses evaluating the accuracy of miRNA for CRC-specific diagnosis, this study attempted to provide evidence on this topic. CRC affects morbidity, mortality, and quality of life among public health service users, and early diagnosis leads to better outcomes. In addition, methods such as CT and MRI imaging (i.e., non-invasive, easy to perform and said to be effective in diagnosing and accurately defining CRC lesions) are also available in public health services. For these reasons, evaluating the accuracy of these techniques in diagnosing CRC through systematic reviews and meta-analyses will provide important evidence for their diagnostic effectiveness.

This study adhered to the reporting elements recommended by the System Review and Meta-Analysis (PRISMA) standards [15]. The protocol was registered on the International Prospective Register of Ongoing Systematic Reviews (PROSPERO number CRD42024570834; https://www.PROSPERO (york.ac.uk)). This study was not subject to institutional review board oversight and patient informed consent requirements because it was based solely on published trials.

2.1 Search strategy

Two autonomous researchers conducted an extensive review of the literature in several databases, starting from January 2016 to April 2024. The databases included in the search were the China National Knowledge Index, PubMed, Cochrane Library, and Web of Science. Only Chinese and English were offered.

Search subject term included "miRNA", "microRNA", "CRC", "Colorectal cancer", " Model", "Sensitivity", "Specificity", "AUC", " Area under the curve ". The search method is adjusted according to the characteristics of database and the search results by combining subject words and free words.

2.2 Inclusion criteria and exclusion criteria

Inclusion criteria: (1) Published articles evaluating the value of miRNAs in the diagnosis of colorectal cancer; (2) Cohort studies with case control studies or diagnostic experiments; (3) The diagnostic criteria for colorectal cancer are clearly described in the literature; Sensitivity, specificity, and optimal diagnostic thresholds are indicated in the literature or can be calculated; (5) Only selected literature from the past 9 years (no earlier than 2015).

Exclusion criteria :(1) Systematic review, review, cases, and articles related to animal tests; (2) incomplete data, such as the necessary data such as the sensitivity and specificity of the item cannot be calculated; (3) Duplication of literature; (4) The full text is not available; (5) Peer-reviewed, challenged, or withdrawn literature.

2.3 Data extraction and quality evaluation

Two researchers independently conducted searches and chose items that were likely to be included in the search results [16, 17]. The research that fulfills the specified criteria for inclusion and exclusion will be meticulously assessed and, if required, approved or rejected.

Subsequently, data pertaining to the diagnostic test under consideration, the index test under consideration, as well as the occurrences of true positives, true negatives, false positives, and false negatives will be extracted from the selected studies. The researchers will contact the authors of any paper that is found to have incomplete information and request them to provide any missing details.

2.4 Quality evaluation

Publication quality was evaluated using the QUADAS-2 quality scoring standard. The scale contains 14 key questions, each of which is judged by a yes, no, or unclear answer. If the two researchers have differences on literature retrieval, screening, data extraction and literature quality evaluation, they will negotiate with the third researcher to resolve the differences.

2.5 Statistical analysis

The statistical analysis was performed using RevMan 5.4 software (RevMan 5.4, Review Manager, Version 5.4, The Cochrane Collaboration), meta-disc software, and Stata version 16.0 (StataCorp LP, USA) [18–20]. The statistical I² and Q tests were used to analyze the heterogeneity. If the heterogeneity was large, the random effect size model was used. If the heterogeneity is small, the fixed effect size model is used. Subgroup analysis and meta-regression analysis were performed to explore the sources of heterogeneity. Summary receiver operating characteristic curve (SROC) was drawn and AUC was calculated. Deek funnel plot was drawn to detect publication bias. Fagan chart was drawn to calculate the pre-test probability and post-test probability to evaluate the clinical value. P < 0.05 was considered statistically significant.

3.1 Search Results

Upon scrutinizing the titles and abstracts of all 1849 papers, it was determined that 1176 of them were redundant and so excluded. Following a comprehensive examination of the entire text, a total of 598 entries were excluded due to their failure to satisfy the specified inclusion criteria. In the end, 23 studies were selected for analysis after fulfilling the eligibility criteria. Figure 1 provides a comprehensive overview of the literature screening process.

3.2 Basic characteristics and quality assessment of included studies

Table 1 provides a concise overview of all the research that have been included. Among them, there are 14 domestic studies and 9 foreign studies. A total of 4398 cases were included in the case group, 2445 in the study group and 1953 in the control group. Supply Fig. 1A, B present the findings of the QUADAS-2 quality evaluation. The majority of the articles included in the current meta-analysis met most of the criteria outlined in the QUADAS-2 assessment, suggesting that the overall quality of the studies included was moderate to high.

Table 1

The main characteristics of the study sets included in the analysis.
Study	Authors	Country	Publication	Study type	Specimen	Assay method	miRNA	Quality	Number of samples/patients	Experimental (percent%)	Control (percent%)	Sensitivity	Specificity	AUC	Precision
1	Nakamura et al. 2022 [35]	USA	Gastroenterology	Non-coding RNA expression analysis data set analysis	Plasma	qRT-PCR	Combine(miR-193a-5p, miR-210, miR-513a-5p and miR-628- 3p)	Good	259	149	110	0.72	0.84	0.82	0.718
2	Asef et al. 2022 [36]	Iran	PersonalizedMedicine	Clinical sample testing	Plasma and Tissue	MethyLight and qPCR	miR-138-5p	Good	80	40	40	0.55	0.825	-	0.550
3	Koopaie et al. 2024 [37]	Iran	Translational Oncology	Clinical sample testing	Saliva	RT-qPCR	miR-92a ☹	Good	75	33	42	0.9524	0.8485	0.947	0.939
3	Koopaie et al. 2024 [37]	Iran	Translational Oncology	Clinical sample testing	Saliva	RT-qPCR	miR-29a☺	Good	75	33	42	0.952	0.878	0.978	0.939
4	He et al. 2017 [38]	China	Oncology Letters	Clinical sample testing	Plasma	RT-PCR	miR-106a	Good	84	42	42	0.742	0.861	0.858	0.738
5	Bilegsaikhan et al. 2018 [39]	China	Journal of Digestive Diseases	Clinical sample testing	Blood	qRT-PCR	miR-338-5p + CEA	Good	290	210	80	0.85	0.88	0.932	0.848
6	Jin et al. 2020 [40]	China	BMC Cancer	Clinical sample testing	Serum	qRT-PCR	miR-4516 ☹	Good	130	80	50	0.944	0.898	0.9584	0.950
							miR-21-5p ☺					0.9063	0.862	0.9278	0.913
							miR-4516 + miR-21-5p ☹					0.9211	0.876	0.9425	0.925
7	Kou et al. 2016 [41]	China	Oncology Letters	Clinical sample testing	Plasma	RT-qPCR	mir-23b	Good	144	96	48	0.8438	0.7708	0.82	0.844
8	Krawczyk et al. 2017 [11]	Poland	International Journal of Colorectal Disease	Clinical sample testing	Plasma	qRT-PCR	miR-506 ☹	Good	135	65	70	0.768	0.607	-	0.769
8	Krawczyk et al. 2017 [11]	Poland	International Journal of Colorectal Disease	Clinical sample testing	Plasma	qRT-PCR	miR-4316 ☺	Good	135	65	70	0.768	0.75	-	0.769
9	Sun et al. 2019 [42]	China	Cancer Biomarkers	Clinical sample testing	Serum	qRT-PCR	miR-30a-5p	Good	198	138	60	0.723	0.75	0.793	0.725
10	Li et al. 2020 [43]	China	Life Sciences	Clinical sample testing	Stool	RT-PCR	miR-135b-5p	Good	106	77	29	0.741	0.965	-	0.740
11	Peng et al. 2020 [14]	China	The International Journal of Biological Markers	Phase III clinical study	Serum	qRT-PCR	Combined (miR-30e-3p 、miR-146a-5p、miR-148a-3p)	Good	232	112	120	0.8	0.78	0.883	0.804
12	González et al. 2019 [44]	Spain	Journal of Clinical Medicine	Clinical sample testing	Saliva	qRT-PCR	Combined (miR-148a-3p、miR-186-5p、miR-29a-3p、miR-29c-3p、miR-491-5p、miR-766-3p)	Good	122	65	47	0.72	0.66	0.754	0.723
13	Tan et al. 2019 [45]	China	Cancer Epidemiology	Clinical sample testing	Plasma and Serum	PCR	Combined (miR-144-3p 、miR-425-5p 、miR-1260b)	Good	235	101	134	0.93	0.91	0.954	0.931
14	Faltejskova et al. 2016 [46]	Czech Republic	Carcinogenesis	Clinical sample testing	Serum	RT-qPCR	Combined (miR-27a-3p 、miR-23a-3p 、miR-376c-3p 、miR-142-5p)	Good	703	427	276	0.89	0.81	0.917	0.890
15	Wikberg et al. 2018 [47]	Sweden	Cancer Medicine	Clinical sample testing	Plasma	PCR	Combined (miR-409-3p、miR-18a、miR-21、miR-22、miR-25)	Good	201	67	134	0.81	0.80	0.930	0.806
16	Zhang et al. 2018 [48]	China	Gene	Clinical sample testing	Plasma	qRT-PCR	Combined (miR-103a-3p、miR-127-3p、miR-151a-5p、miR-17-5p、miR-181a-5p、miR-18a-5p、miR-18b-5p)	Good	271	139	132	0.76	0.86	0.895	0.763
17	Huang et al. 2018 [49]	China	Journal of Cancer Research and Clinical Oncology	Clinical sample testing	Serum	qRT-PCR	miR-16	Good	2915	1458	1457	0.72	0.79	0.85	0.720
18	Shi et al. 2020 [50]	China	Journal of Cellular and Molecular Medicine	Clinical sample testing	Serum	qRT-PCR	miR-92a-1	Good	216	148	68	0.818	0.956	0.914	0.818
19	Yu et al. 2016 [51]	China	Anti-Cancer Agents in Medicinal Chemistry	Clinical sample testing	Serum and Tissue	qRT-PCR	miR-372	Good	195	165	30	0.819	0.733	0.854	0.818
20	Wang et al. 2019 [52]	China	Artificial Cells Nanomedicine and Biotechnology	Clinical sample testing	Serum	qRT-PCR	miR-663	Good	252	126	126	0.831	0.738	0.806	0.833
21	Choi et al. 2019 [53]	Korea	Oncology	Clinical sample testing	Stool	RT-PCR	miR-92a ☹	Good	58	29	29	0.897	0.517	0.76	0.897
21	Choi et al. 2019 [53]	Korea	Oncology	Clinical sample testing	Stool	RT-PCR	miR-144 ☺	Good	58	29	29	0.786	0.667	0.77	0.793
22	Bader et al. 2020 [54]	Egypt	European Journal of Gastroenterology and Hepatology	Clinical sample testing	Serum	RT-PCR	miRNA-223	Good	195	120	75	0.901	0.87	0.914	0.900
23	Wang et al. 2018 [55]	China	European Review for Medical and Pharmacological Sciences	Clinical sample testing	Serum	RT-PCR	miR-103	Good	156	96	60	0.885	0.8	0.857	0.885

3.3 miRNAs can accurately diagnose CRC.

Diagnostic parameters were calculated by pooling data using a random-effects model [21, 22]. This meta-analysis incorporated forest plots displaying the AUC for miRNAs in the detection of CRC. Additionally, it examined the sensitivity, specificity, PLR, NLR, DOR, and SROC curves. The sensitivity of the 23 included investigations varied from 0.81 to 0.84, while their specificity ranged from 0.80 to 0.84, as indicated by the findings of the diagnostic meta-analysis (Fig. 2A, B). These findings indicate that miRNAs are significantly more proficient in accurately detecting diseases than to accurately categorizing non-illness instances. In addition, an evaluation was performed on the combined diagnostic accuracy of the miRNAs in diagnosing CRC. The pooled analysis revealed a Spearman's correlation coefficient with a P value of less than 0.05. The sensitivity and specificity I² values were 77.5% and 68.9%, respectively. The chi-square test P-values were all less than 0.05, indicating a considerable level of heterogeneity in the studies. The median results of the combined diagnostic data were as follows: The pPLR was 4.60 with a 95% CI of 3.77–5.62. The pooled NLR was 0.22 with a 95% CI of 0.17–0.27 (Fig. 3A, B). The pooled DOR was 23.79 with a 95% CI of 16.26–34.81 (Fig. 4A). Figure 4B shows the SROC curve that corresponds to the given data. The AUC was 0.90, demonstrating a substantial level of accuracy in diagnosing CRC utilizing machine learning methodology tests.

3.4 Clinical value analysis

The clinical value disparities across machine learning techniques for diagnosing CRC were assessed by Fagan plot analysis. The probability rose from 20–50% when the machine learning approaches tested yielded good results, and dropped to 5% when the results were negative (Fig. 4C).

3.5 Subgroup analysis

The heterogeneity of the study was strong, and the threshold effect was excluded, indicating that the heterogeneity was caused by other reasons. To further analyze the source of heterogeneity (Table 2), groups were grouped by country (China including Taiwan, outside China), sample size (≥ 100 and < 100 cases), year of publication (before 2019 and after 2020), and test site (serum and tissue/stool). Results The NLR was better in foreign countries than in China, but the specificity, PLR, DOR, AUC were lower. When the sample size was less than 200 cases and whether the substance is a serum specimen, test effectiveness is similar to country differentiation. When the year of publication was before 2019, the diagnostic sensitivity was higher, but the, Specificity, PLR, NLR, DOR is lower.

Table 2

Subgroup analysis of this study.
Variable	Number of studies	Sensitivity	95%CI	Specificity	95%CI	PLR	95%CI	NLR	95%CI	DOR	95%CI	AUC	95%CI	Deek (P value)
Total sample	24	0.83	0.81–0.84	0.83	0.81–0.84	4.6	3.77–5.62	0.22	0.17–0.27	23.79	16.26–34.81	0.9	0.87–0.92	0.46
Publishing country
Home	15	0.83	0.81–0.85	0.84	0.82–0.87	5.46	4.04–7.37	0.2	0.16–0.25	31.47	18.90-52.39	0.92☺	0.89–0.94	1☺
Abroad	9	0.83	0.80–0.85	0.8	0.77–0.83	3.78	2.92–4.90	0.25	0.17–0.38	15.84	8.71–28.80	0.87	0.84–0.90	0.24
Sample size
>200	11	0.84☺	0.82–0.86	0.84	0.82–0.86	5.49	4.11–7.33	0.19	0.15–0.25	30.97	18.73–51.20	0.91	0.89–0.94	0.9
≤ 200	13	0.81	0.79–0.83	0.8	0.77–0.83	3.92	3.00-5.12	0.24	0.17–0.32	18.77	10.69–32.96	0.89	0.86–0.91	0.85
Serum
Yes	20	0.83	0.82–0.85	0.83	0.81–0.85	4.77	3.93–5.79	0.21	0.16–0.26	25.02	17.01–36.81	0.91	0.88–0.93	0.46
No	4	0.77	0.71–0.83	0.78	0.71–0.85	4.04	1.76–9.30	0.28☺	0.17–0.46	19.99	4.41–90.66	0.88	0.85–0.90	0.99
Publication year
2019 ago	16	0.84☺	0.82–0.86	0.81	0.79–0.83	4.21	3.32–5.34	0.21	0.17–0.27	21.01	13.22–33.38	0.9	0.87–0.92	0.2
2020 later	8	0.8	0.77–0.83	0.86☺	0.82–0.88	5.74☺	3.95–8.33	0.22	0.15–0.32	31.93☺	15.33–66.53	0.9	0.88–0.93	0.59
Notes: CI༚confidence interval; PLR: positive likelihood ratio; NLR: negative likelihood ratio; DOR: diagnostic odds ratio; AUC: area under the curve; ☺: Has the best effect size

3.6 Meta-regression analysis

The results of meta-regression analysis showed that publication time, country and specimen type may be the main influencing factors for heterogeneity (P < 0.05), but were not related to sample size. The results of meta regression are shown in Table 3.

Table 3

Possible sources of heterogeneity of covariants in the meta-regression analysis.
	Sensivity applied
	Coef	P	95% CI
Year of publication	−0.006	0.618	−0.033~−0.020
Home or abroad	−0.027	0.592	−0.077~−0.131
Sample size	−0.000	0.663	−0.000 ~ 0.000
Serum or other	0.008	0.905	0.123 ~ 0.138
	DOR applied
	Coef	P	95% CI
Year of publication	0.076	0.557	−0.190 ~ 0.341
Home or abroad	0.776	0.127	−0.243 ~ 1.794
Sample size	−0.000	0.775	−0.001 ~ 0.001
Serum or other	−0.117	0.849	−1.392 ~ 1.158
Notes: Coef, coefficent; CI, confidence interval; DOR, diagnostic odds ratio.

3.7 Publication bias

To evaluate the possibility of publication bias, we conducted the Deeks' funnel plot asymmetry test in our meta-analysis (Fig. 5). A p-value of 0.89 suggests that there is a low probability of publication bias in the research [23, 24].

With the advancement of bioinformatics technology, many prognostic gene markers have been developed [25]. Correct identification is essential to prevent tumor recurrence and therapeutic effect. Features integrated by polygenic profiles, especially miRNAs, have been identified in CRC and validated as candidate biomarkers [26–28].

Researchers have leveraged the power of miRNAs in the field of medicine, focusing primarily on the diagnosis and prognosis of various diseases. Nevertheless, there is a scarcity of research investigating the precision of miRNAs in the diagnosis of CRC. The meta-analysis included 23 studies and using diagnostic meta-analysis technique. The combined sensitivity and specificity of CRC were 0.83 (95%CI0.81-0.84) and 0.83 (95%CI0.81-0.84), respectively. The sensitivity and specificity of miRNAs data analysis for CRC diagnosis were strong. The findings demonstrated that miRNAs exhibited superior accuracy in the diagnosis of CRC. In addition, the area under the curve (AUC) in our meta-analysis results was 0.90 (95% confidence interval: 0.87–0.92), suggesting that the machine learning methods exhibited high accuracy in the diagnosis of CRC.

The PLR and NLR components of the likelihood ratio (LR) can also represent the accuracy of a diagnosis [29, 30]. The likelihood of a disease being diagnosed or ruled out increased significantly when the positive likelihood ratio was greater than 10 or the negative likelihood ratio was less than 0.1 [31]. In our meta-analysis, the pooled positive likelihood ratio (PLR) was 4.60 (95% CI: 3.77–5.62) and the negative likelihood ratio (NLR) was 0.22 (95% CI: 0.17–0.27). These findings indicate that machine learning methods have a much greater rate of correctly diagnosing CRC compared to incorrectly identifying it.

As an independent indicator of morbidity, the DOR reflects the degree of correlation between diagnosis and disease [32–34]. The pooled DOR was 23.79 (95% CI: 16.26–34.81), indicating that machine learning methods have a reliable overall accuracy in identifying CRC. In addition, Fagan diagram was drawn to analyze the use of miRNA to improve the effectiveness of CRC diagnosis. The results showed that the combined PLR of miRNA in the diagnosis of CRC was > 1, and the NLR was > 0.1, also indicating that miRNA was of great value in the clinical diagnosis of CRC, and the diagnostic value of exclusion was limited.

This study was limited in a few ways. First of all, although there are quite a lot of literatures on the diagnosis of CRC by miRNA, most of them lack the necessary data for sex experiments, such as TF, NF, F1, etc. This resulted in only 23 articles being included, which somewhat reduced the viability of the results. Second, after a thorough search of the literature, most studies were small sample and single center studies, which may limit miRNA's ability to reliably assess CRC. Third, 14 of the 23 studies were from China, nine were from abroad, and the languages involved only English and Chinese, which may lead to selection bias and reduce the validity of the research results.

Ultimately, this study demonstrated that miRNAs effectively forecast CRC with precision. In view of the limited number of available tests, it is necessary to further examine the validity and potential applicability of miRNA as a diagnostic indicator of CRC through multi-center, large sample and prospective studies.

CRC

Colorectal cancer

WoS

Web of Science

PLR

positive likelihood ratio

NLR

negative likelihood ratio

DOR

diagnostic odds ratio

AUC

area under the curve

confidence interval

DOR

diagnostic odds ratio

miRNAs

MicroRNAs

PRISMA

Review and Meta-Analysis

SROC

summary receiver operating characteristic

likelihood ratio

CNKI

China National Knowledge Internet.

Author Contributions and Agreement

It was written by Zhigang Chen. The manuscript was revised and subjected to critical discussion by Zhengheng Wu, Haifen Tan and Fuqian Yu. Ideas and themes were created by Dongmei Wang. All authors have read and consented to publish this manuscript.

Patients consent for publication

Not applicable.

Ethical Approval

Not applicable.

Funding

This work was supported by grants from National Natural Science Foundation of China (82273232), Changzhou Sci&Tech Program (LCQYBS202309, 2024CZBJ020), Changzhou Medical Center of Nanjing Medical University Program (CZKY102RC202301).

Author Contribution

Acknowledgments

We thank all authors and reviewers who participated in this work.

Availability of Data and Materials

All data generated or analyzed during this study are included in this article.

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Diagnostic Accuracy of miRNA to Identify Colorectal cancer: A Systematic Review and Meta-Analysis

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Abstract

Background

Methods

Results

Conclusion

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1. Introduction

2. Materials and Methods

2.1 Search strategy

2.2 Inclusion criteria and exclusion criteria

2.3 Data extraction and quality evaluation

2.4 Quality evaluation

2.5 Statistical analysis

3. Result

3.1 Search Results

3.2 Basic characteristics and quality assessment of included studies

3.3 miRNAs can accurately diagnose CRC.

3.4 Clinical value analysis

3.5 Subgroup analysis

3.6 Meta-regression analysis

3.7 Publication bias

4. Discussion

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Ethical Approval

Funding

Author Contribution

Acknowledgments

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References

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