Evidence map›Paper›PMID 32504320›Full record

SynthesisClinical and experimental medicine2020

The diagnostic and prognostic value of exosome-derived long non-coding RNAs in cancer patients: a meta-analysis.

Huiwen Mu, Sujie Zhang, Zhaoying Yao, Yunxin Liu, Kang Lin, Zheng Zhao, Yubing Zhu

Abstract readMeta-AnalysisReview
PubMed Publisher
In one paragraph

Synthesis in Clinical and experimental medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 3 pooled it
0.9field-weighted citation impact, top 32% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 3 syntheses or guidelines pooled it, 15 citations in OpenAlex.

  1. Pooled it
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  4. Long noncoding RNAs in female malignancies: critical review of molecular mechanisms, clinical gaps, and therapeutic perspectives.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Review
  5. Roles of long non-coding RNAs in oesophageal cancer pathogenesis.International journal of experimental pathology · 2025
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Huiwen MuSchool of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 210009, China.
Sujie ZhangSchool of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 210009, China.
Zhaoying YaoSchool of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 210009, China.
Yunxin LiuSchool of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 210009, China.
Kang LinDepartment of Clinical Laboratory, Nanjing First Hospital, Nanjing Medical University, Nanjing, 210006, China.
Zheng ZhaoSchool of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 210009, China. zhaozheng0322@126.com.
Yubing ZhuSchool of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 210009, China. zyb86052002@163.com.ORCID http://orcid.org/0000-0001-5868-3279
China Pharmaceutical University · CNNanjing Medical University · CN

Funding

Nanjing Medical science and technology development Foundation ZKX17024
6 · The paper itself

Abstract

purposeExosome-derived long non-coding RNAs (lncRNAs) as novel biomarkers are widely investigated in various cancers, yet results remain controversial. The aim of this meta-analysis was to clarify the diagnostic and prognostic value of exosome-derived lncRNAs in cancer.

methodsPubMed, Web of Science, EMBASE, CNKI, and WanFang online databases were comprehensively searched for eligible studies up to January, 2020. To evaluate the diagnostic effect, sensitivity, specificity, and area under the curve (AUC) were pooled. Threshold effect, subgroup analysis, and meta-regression were applied to explore heterogeneity. Deeks' funnel plot and sensitivity analysis were used to examine publication bias and stability of meta-analysis, respectively. The pooled hazard ratios (HRs) with 95% confidence intervals (CIs) for overall survival (OS) and recurrence free survival (RFS) were calculated to assess the prognostic value.

resultsA total of 29 eligible studies involving 3882 patients were enrolled in the meta-analysis, which included 26 on diagnosis and 11 on prognosis. For diagnosis analysis, the pooled sensitivity, specificity, and AUC were 0.83 (95% CI 0.78-0.87), 0.80 (95% CI 0.75-0.84), and 0.88 (95% CI 0.85-0.91), respectively. Meta-regression revealed that the cancer type acted as the potential source of heterogeneity. Sensitivity analysis and Deeks' funnel plot indicated that results were relatively robust and had no publication bias. For the prognosis analysis, results suggested that overexpression of exosome-derived lncRNAs which upregulated in cancer showed a significant association with poor OS (HR 2.21, 95% CI 1.79-2.71, p < 0.001). Conversely, overexpression of exosome-derived lncRNAs which downregulated in cancer was markedly related to better OS (HR 0.28, 95% CI 0.14-0.55, p < 0.001).

conclusionThis meta-analysis reveals that exosome-derived lncRNAs might serve as promising diagnostic and prognostic biomarkers for cancer. However, the clinical value of exosome-derived lncRNAs needs to be further confirmed.

Indexed as

Early Detection of CancerExosomesGene Expression Regulation, NeoplasticHumansNeoplasmsPrognosisRNA, Long NoncodingSensitivity and SpecificitySurvival AnalysisRNA, Long NoncodingCancerDiagnosisExosomesLong non-coding RNAsMeta-analysisPrognosis

Identifiers

PMID32504320
OpenAlexW3033606491

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.