Evidence map›Paper›PMID 37069371›Full record

ArticleJournal of molecular medicine (Berlin, Germany)2023

The diagnostic potential of a circRNA-miRNA network in non-small cell lung cancer.

Xinyi Ma, Chengtao Lou, Junjie Pan, Chengwei Zhou, Xiaodong Zhao, Nan Li, Haihua Tian, Xiaodan Meng

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of molecular medicine (Berlin, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
4.6field-weighted citation impact, top 5% 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

9 citing papers in PubMed, 30 citations in OpenAlex.

  1. Article
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  3. Review
  4. Review
  5. Article
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  7. Article
  8. Review
  9. Non-Coding RNAs as Key Regulators in Lung Cancer.International journal of molecular sciences · 2023
    Review
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

8 authors at 2 institutions in 1 country.

Xinyi Ma *Department of Biochemistry and Molecular Biology, Health Science Center, Ningbo University, Ningbo, Zhejiang, 315211, China.
Chengtao Lou *Department of Biochemistry and Molecular Biology, Health Science Center, Ningbo University, Ningbo, Zhejiang, 315211, China.
Junjie PanDepartment of Biochemistry and Molecular Biology, Health Science Center, Ningbo University, Ningbo, Zhejiang, 315211, China.
Chengwei ZhouDepartment of Thoracic Surgery, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, China.
Xiaodong ZhaoDepartment of Thoracic Surgery, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, China.
Nan LiClinic Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China.
Haihua TianDepartment of Laboratory Medicine, The Affiliated Ningbo Kangning Hospital of Health Science Center of Ningbo University, Ningbo, 315201, China.
Xiaodan MengDepartment of Biochemistry and Molecular Biology, Health Science Center, Ningbo University, Ningbo, Zhejiang, 315211, China. mengxiaodan@nbu.edu.cn.ORCID 0000-0002-2254-8072
Ningbo University · CNFirst Affiliated Hospital of Zhengzhou University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Increasing studies demonstrate the significant contributions of circRNA-related competitive endogenous RNA (ceRNA) regulatory networks to tumorigenesis and cancer progression. Here, we aimed to construct a non-small cell lung cancer (NSCLC)-specific circRNA-miRNA network and evaluate its diagnostic potential in NSCLC. MiRNA deep sequencing was performed to screen differentially-expressed serum miRNAs in NSCLC. Four bioinformatics databases (TargetScan, miRanda, starBase, and RNAhybrid) were used to analyze the integrated circRNA-miRNA interaction network. The circRNA-miRNA network, including hsa-miR-4482-3p, hsa-miR-146a-3p, hsa_circ_0008167 and hsa_circ_0003317 was constructed based on their interactions and preliminary testing in NSCLC cells. The relative levels of the selected non-coding RNAs (ncRNAs) were quantified using quantitative real-time polymerase chain reaction (qRT-PCR) in the healthy, pneumonia, benign lung tumor and NSCLC cohorts. The diagnostic power of the circRNA-miRNA network was evaluated using receiver operating characteristic (ROC) analyses. The serum levels of hsa-miR-4482-3p, hsa-miR-146a-3p, hsa_circ_0008167, and hsa_circ_0003317 were dysregulated in NSCLC. The combination of the four ncRNAs showed the highest diagnostic value to discriminate between benign lung tumors and NSCLC. Additionally, the upregulated levels of hsa_circ_0008167 were correlated to more aggressive features of NSCLC, such as lymph node metastasis, distant metastasis, and higher stage. Furthermore, the combination of hsa_circ_0008167 + hsa-miR-4482-3p, and hsa_circ_0008167 + hsa-miR-4482-3p + hsa-miR-146a-3p had the greatest diagnostic power to differentiate between lymph node +/- metastases and higher/lower stages, respectively, compared to circRNAs or miRNAs alone, and traditional tumor markers. In conclusion, we identified a specific circRNA-miRNA network with higher sensitivity and specificity to diagnose NSCLC, thereby providing a new strategy for further development of ceRNA-related tumor markers in other cancers. KEY MESSAGES: Serum miR-4482-3p, miR-146a-3p, circ_0008167 and circ_0003317 are dysregulated in NSCLC. Higher levels of serum circ_0008167 are associated with more malignant NSCLC. Multiple combinations of circRNAs and miRNAs show higher value to diagnose NSCLC.

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsMicroRNAsBiomarkers, TumorHumansRNA, CircularBiomarkers, TumorMicroRNAsRNA, CircularCircular RNACompetitive endogenous RNADiagnosismicroRNANon-small cell lung cancer

Identifiers

PMID37069371
OpenAlexW4366136655

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.