Evidence map›Paper›PMID 34259101›Full record

SynthesisTechnology in cancer research & treatment

The Potential Diagnostic Accuracy of Let-7 Family for Cancer: A Meta-Analysis.

Wen-Ting Zhang, Guo-Xun Zhang, Shuai-Shuai Gao

Abstract readMeta-Analysis
In one paragraph

Synthesis in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
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

13 citing papers in PubMed.

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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

3 authors.

Wen-Ting ZhangXi'an Daxing Hospital, Xi'an, Shaanxi, China.ORCID 0000-0003-4166-9352
Guo-Xun ZhangInternational Doctoral School, University of Seville, Seville, Spain.
Shuai-Shuai GaoXi'an Daxing Hospital, Xi'an, Shaanxi, China.ORCID 0000-0003-3534-6215

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCancer is a global public health problem affecting human health. Early stage of cancer diagnosis, when it is not too large and has not spread is important for successful treatment. Many researchers have proposed that the let-7 microRNA family can be used as a biomarker for cancer diagnosis. The aim of this meta-analysis is to evaluate whether let-7 family can be used as a diagnostic tool for cancer patients.

methodsWe conducted a comprehensive literature search on PubMed, EMBASE, Web of Science, Cochrane Library, Google Scholar, China National Knowledge Infrastructure (CNKI) and Wanfang database, updated to October 23, 2020. A random effects model was used to pool the sensitivity and specificity. Besides, we measured the diagnostic value using positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and area under the curve (AUC) were pooled. In addition, meta-regression and subgroup analysis were performed to explore the possible sources of heterogeneity, and Deeks' funnel chart was used to assess whether there was publication bias.

results31 studies from 15 articles were included in the current meta-analysis. The overall sensitivity, specificity, PLR, NLR, DOR and AUC were 0.80 (95% CI: 0.75-0.85), 0.81 (95% CI: 0.74-0.86), 4.2 (95% CI: 2.9-5.9), 0.24 (95% CI: 0.19-0.32), 17 (95% CI: 10-29) and 0.87 (95% CI: 0.84-0.90), respectively. Subgroup analysis shows that the let-7 family cluster of serum type showed a better diagnostic accuracy of cancer, especially the breast cancer. Although there is no publication bias, it still has some limitations.

conclusionslet-7 family can be considered as a promising non-invasive diagnostic biomarker for cancer.

Indexed as

Biomarkers, TumorMultigene FamilyArea Under CurveHumansMicroRNAsNeoplasmsPrognosisPublication BiasROC CurveBiomarkers, TumorMicroRNAsmirnlet7 microRNA, humancancerdiagnosislet-7 familymeta-analysis

Identifiers

PMID34259101
PMCPMC8283215

What OpenQuestion holds

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Registered trials

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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.