BioMed Research International
Volume 2020 (2020), Article ID 6786875, 15 pages
https://doi.org/10.1155/2020/6786875

Research Article

The Potential Diagnostic Value of Exosomal Long Noncoding RNAs in Solid Tumors: A Meta-Analysis and Systematic Review

Zilong Wu,¹ Zihao Xu,² Boyao Yu,³ Jingtao Zhang,⁴ and Bentong Yu⁴

¹The First Clinical Medical College, Nanchang University, Nanchang, Jiangxi, China
²School of Public Health, Nanchang University, Nanchang, Jiangxi, China
³Xiangya School of Medicine, Central South University, Changsha, Hunan 410013, China
⁴Department of Thoracic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China

Correspondence should be addressed to Bentong Yu; yubentong@126.com

Received 6 April 2020; Revised 27 June 2020; Accepted 31 July 2020; Published 17 August 2020

Academic Editor: Kosei Maemura

Copyright © 2020 Zilong Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Background. Exosomes are defined as small membranous vesicles. After RNA content was discovered in exosomes, they emerged as a novel approach for the treatment and diagnosis of cancer. Long noncoding RNAs (lncRNA), a kind of specific RNA transcript, have been reported to function as tumor growth, metastasis, invasion, and prognosis by regulating the tumor microenvironment in exosomes. This study aims at exploring the potential diagnostic of exosomal lncRNA in solid tumors. Methods. A meta-analysis conducted from January 2000 to October 2019 identified publications in the English language. We searched all relevant English literature from the Web of Science, EMBASE, and PubMed databases through October 1, 2019. The articles were strictly screened by our criteria and critiqued using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Results. There were 28 studies with 19 articles (4017 patients) identified, including studies on gastric cancer, laryngeal squamous cell carcinoma, colorectal cancer, cholangiocarcinoma, breast cancer, esophageal squamous cell carcinoma, hepatocellular carcinoma, nonsmall cell lung cancer, and prostate cancer. A meta-analysis showed that the combined value of sensitivity in 29 studies was 0.74 (95% confidence interval [CI], 0.7–0.78), and the combined value of specificity in the studies was 0.81 (95% CI, 0.78–0.83). This suggests the high diagnostic efficacy of liquid exosomes in cancer patients. It is statistically insignificant in terms of sex, ethnicity, and year. The diagnostic power of urinary system tumors was found to be higher than that of digestive system tumors by several subgroup analyses. Conclusions. We performed a meta-analysis and literature review of 28 studies that included 4017 patients with 10 malignant cancer types. Mechanistically, our study demonstrated that lncRNAs in exosomes could be a promising bioindicator for the diagnosis and prognosis of solid tumors. INPLASY Registration Number: INPLASY202060083.

1. Introduction

In the past several decades, cancer therapy has been studied extensively, which has become the most lethal causes of worldwide [1]. During the past century, there has been a dramatic development in cancer treatments, including surgery, radiotherapy, and immunotherapy [2]. However, the overall survival of malignant tumor patients is still unsatisfactory, and the diagnostic efficiency remains lower than desired, especially for advanced tumor patients. With the development of early diagnosis and treatment of cancer, we observed that an ideal biomarker could play a pivotal role [3, 4]. Thus, it is our primary goal to explore a new effective biomarker for early diagnosis, prediction of prognosis, and ideal therapeutic target for cancer patients.

Exosomes are defined as small membranous vesicles, which are 40 to 150 nm in diameter, with specific surface molecular markers such as CD9, CD63, Hsp70, and TSP101 [5, 6]. After RNA content was discovered in exosomes in 2007, they emerged as a novel approach for the treatment and diagnosis of cancer [7]. During the past 2 decades, many researchers have demonstrated that exosomes could contain various kinds of RNAs, including messenger RNAs (mRNA) [8], microRNAs (miRNA) [9], long noncoding RNAs (lncRNA) [10], and circular RNAs (circRNA) [11]. Exosomes in tumor cells have been found to activate specific tumor resistance responses and promote the occurrence and development of tumors [12]. Despite that, a large number of studies have reported that exosomes are involved in angiogenesis and metastasis, drug resistance, immune related, cytokines secretion, apoptosis, cell proliferation, and oncogenic cell transformation [13]. According to an investigation by Mudgapalli et al., exosomes could act as a messenger between the microenvironment and tumor cells that support leukemia growth, inducing oncogenic factors such as c-Myc [14]. In addition, serum-derived exosomes in primary prostate or prostate metastasis increase a high level of PKM2 expression, suggesting that exosomes could be a potential clinical biomarker in prostate cancer [15].

lncRNAs are defined as a type of specific RNA transcript longer than 200 nucleotides, which were once considered unfunctional transcriptional noise [16]. However, it was later found that ncRNAs play a functional role in carcinogenesis, tumor regulation, and gene expression [17]. Furthermore, lncRNA expression always demonstrates poor prognosis in several cancers, including lung cancer [18], osteosarcoma [19], bladder cancer [20], ovarian cancer [21], liver cancer [22], gastric cancer [23], hepatocellular carcinoma [24], and breast cancer [25]. In these studies, there is an obvious connection between high-level expression of lncRNA and worse clinicopathological outcome, such as distant metastasis, tumor size, lymph node metastasis, clinical stage, and drug resistance. For instance, high SNHG11 expression promotes metastasis and proliferation in colorectal cancer by targeting the Hippo pathway [26]. A study by Fang et al. reported that miR-223 could be mediated, as silencing by DLX6-AS1 promotes the progression of bladder cancer through the upregulation of HSP90B1 [27]. In another study, Xu et al. examined lncRNA PVT1, an oncogene that has been found to result in a poor overall survival in esophageal adenocarcinoma though high-level expression [28]. In addition, in our precious research, we demonstrated that the expression of lncRNA SNHG1 was significantly associated with worse clinical outcome [29]. Mechanistically, it has been reported to function as tumor growth, metastasis, invasion, and prognosis by regulating the tumor microenvironment.

Recent studies have shown that biomarkers with tumors are a hot topic, with researchers focusing on the value of ncRNAs, mRNAs, and exosomes in the diagnosis and treatment of cancer. Most of the available literature on these biomarkers was focused mainly on ncRNAs in blood. In addition to these primary data, systematic studies on the prognosis biomarkers are still needed for further exploration. Now that researchers are aware of the significance of ncRNA in exosomes, the function of circRNAs in exosomes has already been reported as a suitable diagnostic biomarker for tumors, especially in hepatocellular carcinoma [30]. However, the diagnostic effectiveness of lncRNAs in exosomes is still not clear.

Therefore, our team is the first to summarize the key role functional lncRNAs play in different types of tumors by performing a meta-analysis. The specificity and sensitivity of exosomal lncRNAs were evaluated to assess their feasibility as biomarkers of cancer diagnosis.

2. Materials and Methods

2.1. Literature Search

Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses criteria (http://prismastatement.org) (Figure S1, Table S1), we conducted a meta-analysis by searching all relevant English-language literature from the Web of Science, EMBASE, and PubMed databases through October 1, 2019. The literature search according to the Population, Intervention, Comparator, and Outcomes (PICO) framework was performed, and the criteria for study eligibility were established. The population was defined as patients with cancer. The intervention was defined as the study that should provide the expression levels of exosomal lncRNA. The Comparator was the clinical histopathological outcome. Outcomes considered for study included sensitivity, specificity, the diagnostic likelihood ratio negative (DLR-), diagnostic likelihood ratio positive (DLR+) with corresponding 95% confidence intervals (CI). Keywords searched were (((((((((((((((((((((((((((RNA, Long Noncoding[Title/Abstract]) OR Noncoding RNA, Long[Title/Abstract]) OR lncRNA[Title/Abstract]) OR Long ncRNA[Title/Abstract]) OR ncRNA, Long[Title/Abstract]) OR RNA, Long Non-Translated[Title/Abstract]) OR Long Non-Translated RNA[Title/Abstract]) OR Non-Translated RNA, Long[Title/Abstract]) OR RNA, Long Non Translated[Title/Abstract]) OR Long Non-Coding RNA[Title/Abstract]) OR Long Non Coding RNA[Title/Abstract]) OR Non-Coding RNA, Long[Title/Abstract]) OR RNA, Long Non-Coding[Title/Abstract]) OR Long Non-Protein-Coding RNA[Title/Abstract]) OR Long Non-Protein-Coding RNA[Title/Abstract]) OR Long Non Protein Coding RNA[Title/Abstract]) OR Non-Protein-Coding RNA, Long[Title/Abstract]) OR RNA, Long Non-Protein-Coding[Title/Abstract]) OR Long Noncoding RNA[Title/Abstract]) OR RNA, Long Untranslated[Title/Abstract]) OR Long Untranslated RNA[Title/Abstract]) OR Untranslated RNA, Long[Title/Abstract]) OR Long ncRNAs[Title/Abstract]) OR ncRNAs, Long[Title/Abstract]) OR Long Intergenic Non-Protein Coding RNA[Title/Abstract]) OR Long Intergenic Non Protein Coding RNA[Title/Abstract]) OR LincRNAs[Title/Abstract]) OR LINC RNA[Title/Abstract]) AND (((((((((((((((((Neoplasms[Title/Abstract]) OR Neoplasia[Title/Abstract]) OR Neoplasias[Title/Abstract]) OR Neoplasm[Title/Abstract]) OR Tumors[Title/Abstract]) OR Tumor[Title/Abstract]) OR Cancer[Title/Abstract]) OR Cancers[Title/Abstract]) OR Malignancy[Title/Abstract]) OR Malignancies[Title/Abstract]) OR Malignant Neoplasms[Title/Abstract]) OR Malignant Neoplasm[Title/Abstract]) OR Neoplasm, Malignant[Title/Abstract]) OR Neoplasms, Malignant[Title/Abstract]) OR Benign Neoplasms[Title/Abstract]) OR Neoplasms, Benign[Title/Abstract]) OR Benign Neoplasm[Title/Abstract]) OR Neoplasm, Benign[Title/Abstract])AND(exosomes[Title/Abstract]) OR exosome[Title/Abstract]). We performed an additional manual search to avoid missing correlatively original literature by using the reference lists of relevant studies.

2.2. Selection of Studies

Studies that met these criteria were included: (1) include all types of solid tumor patients; (2) all solid tumor patients were diagnosed by clinical histopathology; (3) included healthy individuals as a control group; (4) provided the expression levels of exosomal lncRNA; (5) provided the method to testify to the existence of related exosomes; (6) evaluated a liquid sample type; (7) included data about the diagnostic significance of exosomes in all types of cancer patients; and (8) included sample size, control group size, and sensitivity and specificity. Studies with the following characteristics were excluded: (1) duplicate literature; (2) insufficient data; (3) meta-analyses, letters, animal experiments, and reviews; (4) noncancer research; (5) articles that did not verify the presence of exosomes; articles that did not have diagnostic significance data related to the exosomal lncRNA; and (7) studies with a control group that did not meet the requirements.

This study was independently implemented through 2 investigators. In the case of discordance, there was discussion with a third researcher to reach a consensus. The inclusion and exclusion of two investigators was established in Table S2. The -agreement was calculated between two investigators when deciding to include exclude articles. The -agreement was 0.942 (), which indicated the great inter-rater agreement between the two independent investigators and the included studies meet the selective standards.

2.3. Data Extraction

We collected the true-negative, false-negative, true-positive, and false-positive values from the selected literature. For literature that did not provide these data, we calculated these based on the sensitivity and specificity and the number of samples. Moreover, we collected the author name, publication year, country, and ethnicity of the study population, cancer type, lncRNA, sample type, number of the case-patients, and number of control patients, as well as the true positivity, false positivity, true negativity, false negativity, sensitivity, and specificity. Although some articles did not relate clear information, we were able to contact the authors for clarification.

2.4. Quality Assessment

According to the Quality Assessment of Diagnosis Accuracy Studies (QUADAS-2) criteria (http://prisma-statement.org), we evaluated the quality of the included studies using RevMan 5.3 software [31]. The QUADAS-2 included 14 questions about the risk of bias of the included article. Answers included yes, not clear, and no, which corresponded to scores of −1, 0, and 1, respectively. We eliminated low-quality studies from our analysis. The quality of the literature was independently assessed by 2 investigators.

2.5. Statistical Analysis

Statistical analysis was performed by Stata 14.0 software (Stata, College Station, TX). We applied the bivariate random-effects regression model to combine the effect values of all included studies. The analysis included sensitivity, specificity, diagnostic likelihood ratio negative (DLR-), diagnostic likelihood ratio positive (DLR+) with corresponding 95% confidence intervals (CI) [32].The Higgin’s and Cochran’s tests were also included in the analysis [33]. Moreover, we used the kappa statistic to analyse the concordance between diagnosis on exosomal lncRNA and clinical histopathology. Furthermore, we plotted the bivariate boxplot to roughly assess the heterogeneity of the study. We also plotted the summary receiver operating characteristic curve to calculate the pooled under the curve (AUC) value [34].

To further explore the potential heterogeneity between the included studies, we performed a subgroup analysis by Stata software 14.0. The included studies are divided into 6 subgroups based on tumor type, number of samples, and exosome isolation. We performed pooled SEN, SPE, DLR+, DLR-, diagnostic score, and diagnostic odds ratio analysis. Furthermore, Deek’s funnel plot asymmetry test was performed to show the publication bias in the study, with the unequally distributed visual funnel plot or a value less than 0.5 indicating significant publication bias in the study [35].

To explore the more clinical significance of the study, we plotted the Fagan plot showing the relationship between previous probability, likelihood ratio, and posterior probability. We also performed a likelihood ratio scattergram to determine the application value of exosome lncRNA in the clinical diagnosis.

3. Results

3.1. Characteristics and Quality of the Included Studies

The procedure for selecting the included literature in this study is shown in Figure S1. We obtained 863 potential related articles based on the need for the research and the search criteria of the electronic literature database. Because of repetition, 411 articles were excluded. We then excluded 424 articles by manually screening the title and abstract of the article. We screened the full text and excluded 9 articles that did not focus on the topic () or had incomplete data (). Finally, we obtained 19 articles. Several studies have been carried out simultaneously in the literature. Yazarlou et al. [36] showed 4 lncRNAs (UCa1-201, UCa1-203, MalaT1, LinC00355) in exosomes. Zhan et al. [37] reported 3 lncRNAs (MALAT1, PCAT-1, SPRY4-IT1) in exosomes. Zhao et al. [38] illustrated 2 lncRNAs (HOTTIP, PTENP1) in exosomes. Wang et al. [39] demonstrated 2 different lncRNAs (SAP30L-AS1, SChLAP1) in exosomes, and Zhang et al. [40] found 3 lncRNAs (PCAT-1, UBC1, SNHG16). Moreover, Qier et al. [41] reported the lncRNA LINC00152 in exosomes, Wang et al. [42] showed the lncRNA HOTAIR in exosomes, Liu et al. [43] showed the lncRNA CRNDE-h in exosomes, Ge et al. [44] illustrated the lncRNA ENST00000588480.1 in exosomes, Zhang et al. [45] reported the lncRNA MALAT-1 in exosomes, Xue et al. [46] reported the lncRNA UCA-1 in exosomes, Dong et al. [47] reported the lncRNA SNHG14 in exosomes, Kang et al. [48] reported the lncRNA PART1 in exosomes, Sun et al. [49] reported the lncRNA LINC00161 in exosomes, Wang et al. [50] reported the lncRNA H19 in exosomes, Xu et al. [51] reported the lncRNA ENSG00000258332.1 in exosomes, Zhang et al. [52] reported the lncRNA RP11-838N2.4 in exosomes, Zhao et al. [38] reported the lncRNA HOTTIP in exosomes, Zheng et al. [53] reported the lncRNA PTENP1 in exosomes, and Teng et al. [54] reported the lncRNA SOX2-OT in exosomes. Thus, 19 articles were selected that included 28 studies with a total of 2084 cancer patients and 1933 controls.

The basic information of these 28 studies is shown in Table 1. These studies were performed between 2014 and 2019. Four studies were conducted in Iran, and 25 studies were conducted in China. The sample size of the studies ranges from 60 to 320. The sample types were plasma in 5 studies, urine in 10 studies, and serum in 14 studies. In addition, there were 10 types of cancer, including bladder cancer (13 studies), gastric cancer, laryngeal squamous cell carcinoma, colorectal cancer, cholangiocarcinoma, breast cancer, esophageal squamous cell carcinoma, hepatocellular carcinoma, nonsmall cell lung cancer, and prostate cancer. We also collected the information of exosomes that were included in the studies in Table 2. This included the isolation method, exosome content, content number, exosomes diameter range (nm), median diameter (nm), evaluation (TEM and NET), and exosome proteins.

Table 1: Characteristics of the included studies.

Author
Year
Country
Ethnic
Cancer type
lncRNA
Sample
Case
Control
TP
FP
FN
TN
Sensitivity (%)
Specificity (%)
QUADAS score

Qier L
2014
China
Asian
GC
LINC00152
Plasma
79
81
38
12
41
69
0.481
0.852
4

Jiangting Wang
2014
China
Asian
LSCC
HOTAIR
Serum
52
49
49
13
3
36
0.942
0.735
4

Tong Liu
2016
China
Asian
CC
CRNDE-h
Serum
148
160
104
15
44
145
0.703
0.904
4

Xianxiu Ge
2017
China
Asian
CGC
ENST00000588480.1
Serum
35
56
22
15
13
41
0.629
0.732
3

Rui Zhang
2017
China
Asian
NSCLC
MALAT-1
Serum
77
33
46
6
31
27
0.601
0.809
3

Mei Xue
2017
China
Asian
BD
UCA1
Serum
30
30
24
5
6
25
0.800
0.833
4

Huaying Dong
2018
China
Asian
BC
SNHG14
Serum
72
72
58
20
14
52
0.800
0.725
4

Min Kang
2018
China
Asian
ESCC
PART1
Serum
79
79
62
11
17
68
0.786
0.865
2

Li Sun
2018
China
Asian
HCC
LINC00161
Serum
56
56
42
15
14
41
0.750
0.732
5

Jiansong Wang
2018
China
Asian
BC
H19
Serum
52
52
39
11
13
41
0.741
0.781
6

Hong Xu
2018
China
Asian
HCC
ENSG00000258332.1
Serum
60
60
43
10
17
50
0.716
0.834
3

Fatemeh Yazarlou
2018
Iran
Asian
BD
UCa1-201
Urine
59
24
44
4
15
20
0.754
0.812
4

Fatemeh Yazarlou
2018
Iran
Asian
BD
UCa1-203
Urine
59
24
43
6
16
18
0.724
0.750
4

Fatemeh Yazarlou
2018
Iran
Asian
BD
MalaT1
Urine
59
24
38
4
21
20
0.638
0.833
4

Fatemeh Yazarlou
2018
Iran
Asian
BD
LinC00355
Urine
59
24
40
5
19
19
0.680
0.792
4

Yao Zhan
2018
China
Asian
BD
MALAT1
Urine
104
104
75
16
29
88
0.721
0.846
4

Yao Zhan
2018
China
Asian
BD
PCAT-1
Urine
104
104
75
19
29
85
0.721
0.817
4

Yao Zhan
2018
China
Asian
BD
SPRY4-IT1
Urine
104
104
69
24
35
80
0.663
0.769
4

Yao Zhan
2018
China
Asian
BD
MALAT1
Urine
80
80
63
26
17
54
0.787
0.675
4

Yao Zhan
2018
China
Asian
BD
PCAT-1
Urine
80
80
57
16
23
64
0.712
0.800
4

Yao Zhan
2018
China
Asian
BD
SPRY4-IT1
Urine
80
80
70
28
10
52
0.875
0.650
4

Wei Zhang
2018
China
Asian
NSCLC
RP11-838N2.4
Serum
78
78
58
26
20
52
0.745
0.673
3

Rui Zhao
2018
China
Asian
GC
HOTTIP
Serum
126
120
88
18
38
102
0.698
0.85
3

Rui Zheng
2018
China
Asian
BD
PTENP1
Plasma
50
60
33
9
17
51
0.654
0.842
3

Yu-Hui Wang
2018
China
Asian
PSA
SAP30L-AS1
Plasma
34
30
21
5
13
25
0.611
0.821
5

Yu-Hui Wang
2018
China
Asian
PSA
SChLAP1
Plasma
33
30
29
7
4
23
0.879
0.767
5

Yun Teng
2019
China
Asian
LSCC
SOX2-OT
Plasma
75
79
57
21
18
58
0.760
0.732
6

Shujun Zhang
2019
China
Asian
BD
PCAT-1, UBC1, SNHG16
Serum
160
160
128
40
32
120
0.800
0.750
2

GC: gastric cancer; LSCC: laryngeal squamous cell carcinoma; CC: colorectal cancer; CGC: cholangiocarcinoma; NSCLC: nonsmall cell lung cancer; BD: bladder tumor; BC: breast cancer; ESCC: esophageal squamous cell carcinoma; HCC: hepatocellular carcinoma; PSA: prostate cancer; TP: true positivity; FP: false positivity; TN: true negativity; FN: false negativity.

Table 2: The information for the exosome of the included studies.

Author
Year
Isolation method
Exosome content
Content number
Exosomes diameter range(nm)
Median diameter(nm)
Evaluation
Exosome proteins
TEM
NTA

TEM (nM)
NTA (nM)

Qier L
2014
Total exosome isolation kit
lncRNA
1
30-110
None
Yes
None
None
None
None

Jingting Wang
2014
ExoQuick precipitation kit
lncRNA
1
40-100
None
Yes
None
CD63
/
/

Tong Liu
2016
ExoQuick precipitation kit
lncRNA
1
40-100
100
Yes
None
CD63, Hsp70
None
None

Xianxiu Ge
2017
None
lncRNA
1
30-110
None
Yes
Yes
CD63, CD81
30-150
72.2

Rui Zhang
2017
ExoQuick TC
lncRNA
1
30-120
None
Yes
Yes
CD9, CD63
/
/

Mei Xue
2017
ExoQuick precipitation kit
lncRNA
1
50-200
None
Yes
Yes
CD63, TSG101, Hsp70,Hsp90
50-200
None

Huaying Dong
2018
ExoQuick precipitation kit
lncRNA
1
40-120
100
Yes
Yes
CD63, CD81
None
None

Min Kang
2018
ExoQuick precipitation kit
lncRNA
1
30-100
57.89
Yes
Yes
CD63, CD82
20-200
None

Li Sun
2018
Total exosome isolation kit
lncRNA
1
30-150
None
Yes
None
CD63
None
None

Jiansong Wang
2018
ExoQuick precipitation kit
lncRNA
1
30-150
67.52
Yes
None
TSG101, Hsp70
None
None

Hong Xu
2018
Total exosome isolation kit
lncRNA
1
30-100
None
None
None
None
None
None

Fatemeh Yazarlou
2018
Norgen’s urine exosome RNA isolation kit
lncRNA
4
240
None
None
None
CD63
None
None

Yao Zhan
2018
Urine exosome RNA isolation kit
lncRNA
3
60-150
None
Yes
Yes
CD63, CD81
60-150
20-200

Wei Zhang
2018
ExoQuick precipitation kit
lncRNA
1
30-150
57.89
Yes
None
CD9
20-200
None

Rui Zhao
2018
None
lncRNA
1
50-150
None
Yes
Yes
None
/
/

Rui Zheng
2018
ExoQuick exosome precipitation solution
lncRNA
1
50-120
None
Yes
None
CD63, TSG101
None
None

Yu-Hui Wang
2018
None
lncRNA
2
30-120
None
Yes
None
CD63, TSG101
None
None

Yun Teng
2019
None
lncRNA
1
30-100
None
Yes
None
CD9, CD63
None
None

Shujun Zhang
2019
ExoQuick™ solution
lncRNA
3
100
None
Yes
Yes
CD9, CD63
None
None

3.2. Quality Assessments

As shown in Figure 1, the outcomes of the QUADAS-2 study quality assessment indicated that the quality of the included studies was convincing. The QUADAS score is shown in Table 1. The titles of all included documents are provided in Table 3. We assumed that a score greater than 4 indicated a high-quality study and a score lower than 4 indicated a study with low quality. We included 8 poor quality studies and 20 better quality studies.

Figure 1: Overall quality assessment of the included articles using the QUADAS-2 tool.

Table 3: Bibliographic information of the included articles.

Number
Title

1
Exosomal long noncoding RNA HOTTIP as potential novel diagnostic and prognostic biomarker test for gastric cancer

2
Exosome-mediated transfer of lncRNA RP11-838N2.4 promotes erlotinib resistance in nonsmall cell lung cancer

3
Evaluation of serum exosomal lncRNA-based biomarker panel for diagnosis and recurrence prediction of bladder cancer

4
Expression signatures of exosomal long non-coding RNAs in urine serve as novel noninvasive biomarkers for diagnosis and recurrence prediction of bladder cancer

5
Urinary exosomal expression of long noncoding RNAs as diagnostic marker in bladder cancer

6
Hypoxic exosomes facilitate bladder tumor growth and development through transferring long noncoding RNA-UCA1

7
Serum exosomal long noncoding RNAs ENSG00000258332.1 and LINC00635 for the diagnosis and prognosis of hepatocellular carcinoma

8
Combined detection of serum exosomal miR-21 and HOTAIR as diagnostic and prognostic biomarkers for laryngeal squamous cell carcinoma

9
Determination of serum exosomal H19 as a noninvasive biomarker for bladder cancer diagnosis and prognosis

10
Serum and exosome long non coding RNAs as potential biomarkers for hepatocellular carcinoma

11
Exosomal long noncoding RNA CRNDE-h as a novel serum-based biomarker for diagnosis and prognosis of colorectal cancer

12
Plasma long noncoding RNA protected by exosomes as a potential stable biomarker for gastric cancer

13
Exosome-mediated transfer of lncRNA PART1 induces gefitinib resistance in esophageal squamous cell carcinoma via functioning as a competing endogenous RNA

14
The diagnostic/prognostic potential and molecular functions of long noncoding RNAs in the exosomes derived from the bile of human cholangiocarcinoma

15
Exosome-mediated transfer of lncRNA-SNHG14 promotes trastuzumab chemoresistance in breast cancer

16
Exosome–transmitted long noncoding RNA PTENP1 suppresses bladder cancer progression

17
Serum long noncoding RNA MALAT-1 protected by exosomes is upregulated and promotes cell proliferation and migration in nonsmall cell lung cancer

18
Tumor-derived exosomal long noncoding RNAs as promising diagnostic biomarkers for prostate cancer

19
Identification of an exosomal long noncoding RNA SOX2-OT in plasma as a promising biomarker for lung squamous cell carcinoma

3.3. Heterogeneity and Concordance

We assessed heterogeneity by using the value of the Moss model from the threshold effect and Spearman’s correlation. The Cochran and tests were used to evaluate heterogeneity between the included studies. In these studies, the Cochran was 40.775 (), tests were 95 (95% CI, 91–99). The Cochran and tests of sensitivity and specificity (Figure 2) were 77.25 (), 65.05 (95% CI, 51.07–79.03), 57.25 (), 52.83 (95% CI, 32.62–73.15). These outcomes indicated that significant heterogeneity exists in the included studies. Furthermore, we estimated the heterogeneity through graphic methods by bivariate boxplot, as shown in Figure S2. Most of the research fell in the middle, but 4 studies were outside, which also indicated that there may be heterogeneity between the included studies. Furthermore, we assessed the concordance between diagnosis on exosomal lncRNA and clinical histopathology by using the kappa statistic. The kappa statistic of all studies was 0.513, which shows that exosomal lncRNA is moderately accurate to diagnose solid tumors. Moreover, to further explore the concordance between diagnosis on exosomal lncRNA and clinical histopathology, we analyzed the kappa statistic in different types of solid tumors and different sample types. The concordance of blood samples (kappa statistic, 0.523) was better than that of urine samples (kappa statistic, 0.494). The concordance of digestive system tumors (kappa statistic, 0.542) was better than that of urinary system tumors (kappa statistic, 0.511).

Figure 2: Forest plot of sensitivity and specificity for the diagnosis of lncRNA of liquid exosomes in tumor among 28 studies.

3.4. Diagnostic Performance

Sensitivity and specificity are shown in Figure 2 as a forest plot. The combined value of sensitivity in the 29 studies was 0.74 (95% CI, 0.7–0.78), and the combined value of specificity was 0.81 (95% CI, 0.78–0.83). DLR+ and DLR- are shown in Figure 3 as a forest plot. The combined value of DLR+ in the 29 studies was 3.86 (95% CI, 3.31–4.51), and the combined value of DLR-0.32 (95% CI, 0.27–0.37). The pooled odds ratio of the 29 studies was 12.21 (95% CI, 9.31–16), as shown in Figure 4. Furthermore, Figure 5 shows that the AUC was 0.85 (95% CI, 0.81–0.88). These results demonstrated the high diagnostic efficacy of liquid exosomes in cancer patients.

Figure 3: Forest plot of DLR+ and DLR- (DLR: degrees of freedom) for diagnosis of lncRNA of liquid exosomes in tumor among 28 studies.

Figure 4: Forest plot of DOR (DOR: diagnostic OR) for diagnosis of lncRNA of liquid exosomes in tumor among 28 studies.

Figure 5: The SROC of the liquid exosomes lncRNA for the diagnosis of cancer. (AUC: area under the curve; SROC: summary receiver operator characteristic).

3.5. Subgroup Analysis

As shown in Table 4, Figure S3, and Figure S4, we found a difference between digestive system tumors and urinary system tumors in terms of sensitivity (0.73 [95% CI, 0.63–0.81] vs. 0.74 [95% CI, 0.70–0.82]), specificity (0.83 [95% CI, 0.78–0.87] vs. 0.79 [95% CI, 0.75–0.82]), DLR+ (4.21 [95% CI, 3.28–5.41] vs. 3.4 [95% CI, 3.0–4.0]), DLR- (0.33 [95% CI, 0.24–0.45] vs. 0.33 [95% CI, 0.29–0.38]), DOR (12.87 [95% CI, 8.12–20.41] vs. 10 [95% CI, 8–13], and AUC (0.86 [95% CI, 0.82–0.89] vs. 0.83 [95% CI, 0.80–0.86]). In summary, our data illustrated that the diagnostic power is higher in digestive system tumors, the bivariate boxplot (Figure S3E) showed that 6 studies fell in the middle, but 2 studies were outside. The bivariate boxplot (Figure S4E) showed that 14 studies fell in the middle, but 1 study was outside, which also indicated that the heterogeneity between the digestive system tumor group was more significant than that of the urinary system tumor group. We also found a difference between the different sample types in Figure S5, Figure S6, and Table 4. Blood samples had higher sensitivity (0.74), specificity (0.80), and AUC (0.84), indicating that blood samples were more reliable than urine samples. The bivariate boxplot (Figure S5E) showed that 15 studies fell in the middle, but 3 studies were outside. The bivariate boxplot (Figure S6E) showed that 8 studies fell in the middle, but 2 studies were outside. This also indicated that the heterogeneity between the urine groups was more significant than the blood group. Moreover, we found that the group with larger sample sizes (>70) (Figure S7G) and the subgroup with fewer samples (<70) (Figure S8G) had similar capabilities with the same AUC of 0.83 (95% CI, 0.79–0.86). We also performed the subgroup analysis based on other basic information such as age, sex, ethnicity, and country. We found that there were no significant differences between the groups, so they were not discussed.

Table 4: Summary results of subgroup for liquid exosomes in the diagnosis of cancer.

Subgroup
Number of studies
Sensitivity (95% CI)
Specificity (95% CI)
DLR+ (95% CI)
DLR- (95% CI)
DOR (95% CI)
AUC (95% CI)

Type of cancer

    Digestive system tumor
8
0.73 (0.63-0.81)
0.83 (0.78-0.87)
4.21 (3.28-5.41)
0.33 (0.24-0.45)
12.87 (8.12-20.41)
0.86 (0.82-0.89)

    Urinary system tumor
15
0.74 (0.70-0.78)
0.79 (0.75-0.82)
3.4 (3.0-4.0)
0.33 (0.29-0.38)
10 (8-13)
0.83 (0.80-0.86)

Type of specimen

    Blood
18
0.74 (0.69-0.78)
0.80 (0.76-0.83)
3.7 (3.1-4.3)
0.33 (0.28-0.39)
11 (9-14)
0.84 (0.81-0.87)

    Urine
10
0.73 (0.69-0.78)
0.78 (0.73-0.82)
3.3 (2.7-4.0)
0.34 (0.30-0.40)
10 (7-12)
0.82 (0.78-0.85)

Sample size

    >70
15
0.73 (0.69-0.77)
0.79 (0.75-0.83)
3.5 (3.0-4.2)
0.34 (0.29-0.39)
10 (8-13)
0.83 (0.79-0.86)

    <70
13
0.74 (0.68-0.79)
0.79 (0.75-0.83)
3.5 (3.0-4.2)
0.33 (0.27-0.40)
11 (8-15)
0.83 (0.79-0.86)

DLR: degrees of freedom; DOR: diagnostic OR; AUC: area under the curve.

3.6. Publication Bias

As shown in Figure 6, the value of the Deek’s funnel plot asymmetry test was 0.48, which is greater than 0.05, indicating that these studies did not have publication bias.

Figure 6: Deek’s funnel plot asymmetry test for assessing publication bias.

3.7. Clinical Significance of the Study

To explore the clinical significance of this study, the Fagan plot (Figure 7) was performed to show the prior probability of 50%, likelihood ratio of 4, and the post probability of 73%. Furthermore, we plotted the likelihood ratio dot plot (Figure 8). The upper left limit LRP was greater than 10, and the LRN was less than 0.1, indicating that the diagnosis could be diagnosed and excluded. An upper right limit LRP greater than 10 and the LRN greater than 0.1 suggested that the diagnosis could be confirmed. A lower left limit LRP less than 10 and LRN less than 0.1 indicated that the diagnosis could be excluded. A lower right limit LRP less than 10 and LRN greater than 0.1 indicated that neither diagnosis nor diagnosis could be ruled out. In conclusion, it is undeniable that lncRNAs in exosomes could be promising biomarkers in the diagnosis of tumors.

Figure 7: Fagan plot to show the clinical significance in the cancer diagnosis of lncRNA of liquid exosomes.

Figure 8: The dot plot of negative likelihood ratio to show the clinical significance in the cancer diagnosis of lncRNA of liquid exosomes.

4. Discussion

However, there was controversy with respect of the clinical value regarding whether lncRNA could be useful molecules in several cancers. These findings demonstrate that lncRNAs in exosomes can be a compelling indicator of prognosis in human solid tumors, but further confirmation by dependable analyses is required. Therefore, a meta-analysis was performed to account for whether it played a critical role in diagnosis for cancer patients.

This systematic literature review and meta-analysis included 19 articles with a total of 4017 patients with 10 types of malignant tumors. The combined value of sensitivity in 29 studies was 0.74 (95% CI, 0.7–0.78), and the combined value of specificity was 0.81 (95% CI, 0.78–0.83). The combined value of DLR+ in 29 studies was 3.86 (95% CI, 3.31–4.51), and the combined value of DLR- was 0.32 (95% CI, 0.27–0.37). The pooled odds ratio of the 29 studies was 12.21 (95% CI, 9.31–16). The AUC was 0.85 (95% CI, 0.81–0.88). The kappa statistic of all studies was 0.513, which shows that exosomal lncRNA has moderate concordance in diagnosing solid tumors. These results demonstrated the high diagnostic efficacy of liquid exosomes in cancer patients. In our report, the lncRNAs in exosomes were defined by an authoritative method, thus our results are credible. The outcome indicates that there may be heterogeneity between the included studies. And, the results of sensitivity, specificity, and DLR show they can be highly diagnostic efficacy biomarkers. In addition, we conducted subgroup analyses including sex, ethnicity, year, sample type, sample number, and cancer type, which showed no statistical significance in terms of sex, ethnicity, and year. The diagnostic power of digestive system tumors was found to be higher than that of urinary system tumors (sensitivity, 0.73 [95% CI, 0.63–0.81] vs. 0.74 [95% CI, 0.70–0.82]; specificity, 0.83 [95% CI, 0.78–0.87] vs. 0.79 [95% CI, 0.75–0.82]; DLR+, 4.21 [95% CI, 3.28–5.41] vs. 3.4 [95% CI, 3.0–4.0]; DLR-, 0.33 [95% CI, 0.24–0.45] vs. 0.33 [95% CI, 0.29–0.38]; DOR, 12.87 [95% CI, 8.12–20.41] vs. 10 [95% CI, 8–13]; and AUC, 0.86 [95% CI, 0.82–0.89] vs. 0.83 [95% CI, 0.80–0.86]). Thus, the potential biological mechanisms of exosomal lncRNA in the digestive system should be studied further in future studies. The bivariate boxplot indicated that the heterogeneity of the digestive system tumor group was more significant than that of the urinary system group. Moreover, our outcome also suggested that the heterogeneity between the blood group was more significant than that of the urine group, suggesting that we could adopt a blood-exosomes isolation strategy in our further research to collect better data.

With the development of biomarkers in cancer therapy, more specific and high-efficiency molecules have been used clinically, such as circulating tumor DNA (ctDNA), circulating tumor cells, microRNA, lncRNA, and circRNA. For instance, Lee et al. illustrated that patients with detectable ctDNA, a promising prognostic biomarker, showed a trend toward higher risk for disease recurrence than those without detectable ctDNA [55]. Circulating tumor cells can enable early cancer detection, tumor dynamics assessment, minimal residual disease detection, and therapy monitoring [56]. Our study indicated that mir-24-3p, a type of miRNA, can promote cell migration and invasion by targeting TEL2 in nasopharyngeal carcinoma as a biomarker [57]. SNHG1, a novel lncRNA of the SNHG family, indicated that SNHG1 could be a compelling prognosis indicator in human solid tumors [29]. Compared with traditional biomarkers such as AFP [58], CEA [59], and CA199 [60], these next-generation molecules represent high potential in diagnosis value. Nonetheless, the novel biomarkers we mentioned previously still need to be further verified in the future. Therefore, as clinical researchers, it is our duty to explore novel biomarkers and cells that help doctors determine the clinical strategy for cancer patients.

Additionally, our team was also the first to summarize the decade of exosomal long RNA species, suggesting the significant role of exosomal lncRNA in cancer diagnosis and clinical therapy. Accumulating reports have illustrated that exosomal lncRNA is related to many processes of cancer deterioration, such as tumor cell-cell communication, drug resistance, invasion, migration, immune response, and angiogenesis [61]. Wu et al. revealed that the addition of epithelial ovarian cancer-derived exomes in the coculture system, by transferring lncRNAs, restored the migration of endothelial cells which had been inhibited by TAM-derived exosomes through targeting the miR-146b-5p/TRAF6/NF-κB/MMP2 pathway, which suggests that exosomal lncRNAs play a powerful role in the regulation of the tumor microenvironment [62]. Moreover, some studies have reported that exosomal lncRNA can regulate the tumor microenvironment to influence the invasion, growth, metastasis, and prognosis of tumors [63, 64]. Other research has also found that exosomal lncRNA can induce angiogenesis in cancer cells and affect tumor prognosis, growth, and invasion [65, 66]. Pan et al. also found that exosomal lncRNA was additionally related to lymphatic metastasis in gastric cancer patients [67], which showed that tumors can indirectly enhance cellular migration and invasion abilities through exosomal lncRNA. Moreover, some studies have revealed that exosomal lncRNA plays a key role in the acquired drug resistance in some cancers [68, 69]. Takahashi et al. found that LINC-ROR, derived from tumor cells, was enriched in the extracellular vesicles, which can lead to an increased level of chemoresistance in hepatocellular cancer cells. This suggests that hepatocellular cancer cells may use exosomal lncRNA to improve chemoresistance [70]. The potential biological mechanisms of exosomal lncRNA are summarized in Figure 9. Moreover, one of the most significant previous findings in exosomes for our report is that exosomes demonstrated a cargo biological function to deliver a number of molecules, such as drug, ncRNAs, and mRNAs [61]. Therefore, this special biological feature has led to many questions and a need for further investigation. Further work needs to be done to establish whether exosomes are involved in malignant tumor invasion as well as migration. Moreover, because of the specificity [71], stability [72], and accessibility [73], we can not only use exosomes to diagnose solid tumors but we can also apply them to gene therapy.

Figure 9: The potential biological mechanisms of exosomal lncRNA. Exosomes can directly promote tumor growth, invasion, migration, and drug resistance. And indirectly promote tumor growth, invasion, migration by promoting angiogenesis, and regulate microenvironment.

5. Conclusions

As a type of special molecule, lncRNA in exosomes could be a promising bioindicator for the diagnosis and prognosis of cancers. Our study provides convincing evidence through meta-analysis. However, further works are required in the future. Finally, we hope our results encourage more researchers to examine the prognostic and diagnostic role of lncRNA in exosomes as well as explore the underlying biomechanisms in different cancers.

Abbreviation

lncRNAs: | Long noncoding RNAs
DM: | Distant metastasis
LNM: | Lymph node metastasis
TP: | True positivity
FP: | False positivity
TN: | True negativity
FN: | False negativity
DLR-: | Diagnostic likelihood ratio negative
DLR+: | Diagnostic likelihood ratio positive
ctDNA: | Circulating tumor DNA
CTC: | Circulating tumor cell
miRNA: | microRNA
GC: | Gastric cancer
LSCC: | Laryngeal squamous cell carcinoma
CRC: | Colorectal cancer
BC: | Breast cancer
ESCC: | Esophageal squamous cell carcinoma
HCC: | Hepatocellular carcinoma
NSCLC: | Nonsmall cell lung cancer
PSA: | Prostate cancer.

Data Availability

The data supporting the research are included in the article.

Conflicts of Interest

The authors declare no conflicts of interest in this work.

Acknowledgments

Zilong Wu thanks the doctors for fighting the outbreak of new coronavirus infection in China.

Supplementary Materials

Figure S1: the study selection procedure for meta-analysis was conducted to the PRISMA statement. Supplementary 2. Table S1: PRISMA Checklist. Supplementary 3. Table S2: the inclusion and exclusion of two investigators. Supplementary 4. Figure S2: the Bivariate Boxplot of the 28 included study to assess the heterogeneity in the included studies. Supplementary 5. Figure S3: forest plot of sensitivity, specificity, DLR+, DLR-, DOR, and the Bivariate Boxplot an SROC of the liquid exosome lncRNA for the diagnosis of digestive system tumor among 8 studies. (A) Sensitivity, (B) specificity, (C) DLR+, (D) DLR-, (F) Bivariate Boxplot, (G) SROC. Supplementary 6. Figure S4: forest plot of sensitivity, specificity, DLR+, DLR-, DOR, and the Bivariate Boxplot an SROC of the liquid exosomes lncRNA for the diagnosis of urinary system tumor among 15 studies. (A) Sensitivity, (B) specificity, (C) DLR+, (D) DLR-, (F) Bivariate Boxplot, (G) SROC. Supplementary 7. Figure S5: forest plot of sensitivity, specificity, DLR+, DLR-, DOR, and the Bivariate Boxplot an SROC of the liquid exosomes lncRNA for the diagnosis in the group of blood sample type among 18 studies. (A) Sensitivity, (B) specificity, (C) DLR+, (D) DLR-, (F) Bivariate Boxplot, (G) SROC. Supplementary 8. Figure S6: forest plot of sensitivity, specificity, DLR+, DLR-, DOR, and the Bivariate Boxplot an SROC of the liquid exosome lncRNA for the diagnosis in the group of urine sample type among 10 studies. (A) Sensitivity, (B) specificity, (C) DLR+, (D) DLR-, (F) Bivariate Boxplot, (G) SROC. Supplementary 9. Figure S7: forest plot of sensitivity, specificity, DLR+, DLR-, DOR, and the Bivariate Boxplot an SROC of the liquid exosome lncRNA for the diagnosis in the group of sample among 15 studies. (A) Sensitivity, (B) specificity, (C) DLR+, (D) DLR-, (F) Bivariate Boxplot, (G) SROC. Supplementary 10. Figure S8: forest plot of sensitivity, specificity, DLR+, DLR-, DOR, and the Bivariate Boxplot an SROC of the liquid exosome lncRNA for the diagnosis in the group of blood sample type among 13 studies. (A) Sensitivity, (B) specificity, (C) DLR+, (D) DLR-, (F) Bivariate Boxplot, (G) SROC. (Supplementary Materials)

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