Evidence map›Paper›PMID 33425713›Full record

ArticleFrontiers in oncology2020

Circulating Long Noncoding RNAs Act as Diagnostic Biomarkers in Non-Small Cell Lung Cancer.

Shuai Yuan, Ying Xiang, Xiaoping Guo, Yao Zhang, Chengying Li, Weijia Xie, Na Wu, Long Wu, Tongjian Cai, Xiangyu Ma and 3 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
1.4field-weighted citation impact, top 19% 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

25 citing papers in PubMed, 31 citations in OpenAlex.

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  15. Noncoding RNome as Enabling Biomarkers for Precision Health.International journal of molecular sciences · 2022
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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

13 authors at 2 institutions in 1 country.

Shuai YuanDepartment of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, China.
Ying XiangDepartment of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, China.
Xiaoping GuoDepartment of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, China.
Yao ZhangDepartment of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, China.
Chengying LiDepartment of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, China.
Weijia XieDepartment of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, China.
Na WuDepartment of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, China.
Long WuDepartment of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, China.
Tongjian CaiDepartment of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, China.
Xiangyu MaDepartment of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, China.
Zubin YuDepartment of Thoracic Surgery, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Li BaiDepartment of Respiratory Disease, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Yafei LiDepartment of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, China.
Army Medical University · CNWuhan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identification of novel effective early diagnostic biomarkers may provide alternative strategies to reduce the mortality for non-small cell lung cancer (NSCLC) patients. Circulating long non-coding RNAs (lncRNAs) have emerged as a new class of promising cancer biomarkers. Our study aimed to identify circulating lncRNAs for diagnosing NSCLC. A total 528 plasma samples were continuously collected and allocated to four progressive phases: discovery, training, verification, and expansion phases. The expression of candidate lung cancer related lncRNAs were detected using quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR). We identified a 4-lncRNA panel (RMRP, NEAT1, TUG1, and MALAT1) that provided a high diagnostic value in NSCLC (AUC = 0.86 and 0.89 for training and verification phase, respectively). Subgroup analyses showed that the 4-lncRNA panel had a sensitivity of 78.95% [95% confidence interval (CI) = 62.22%-89.86%] in stage I-II patients and 75.00% (95% CI = 52.95%-89.40%) in patients with small tumor size (≤3cm). Notably, the sensitivity of 4-lncRNA panel was significantly higher than that of routine protein panels in adenocarcinoma (CEA, CA125, and CYFRA21-1, 86.30% vs. 73.96%). Adding 4-lncRNA to protein markers significantly improved the diagnostic capacity in both adenocarcinoma (AUC=0.85, 95% CI = 0.78-0.91) and squamous cell carcinoma (AUC=0.93, 95% CI = 0.86-0.97). In conclusion, we identified a plasma 4-lncRNA panel that has considerable clinical value in diagnosing NSCLC. The 4-lncRNA panel could improve the diagnostic values of routine tumor protein markers in diagnosing NSCLC. Circulating lncRNAs could be used as promising candidates for NSCLC diagnosis.

Indexed as

biomarkercirculatingdiagnosislong non-coding RNAnon-small cell lung cancer

Identifiers

PMID33425713
PMCPMC7793881
OpenAlexW3111007088

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.