Evidence map›Paper›PMID 36522766›Full record

ArticleBMC pulmonary medicine2022

Identification of serum MiRNAs as candidate biomarkers for non-small cell lung cancer diagnosis.

Xintong Zhang, Jinjing Tan, Yan Chen, Shang Ma, Wanqiu Bai, Yanjing Peng, Guangli Shi

Open access · goldAbstract read
In one paragraph

Article in BMC pulmonary medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.8field-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

6 citing papers in PubMed, 12 citations in OpenAlex.

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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.

Xintong Zhang *Department of Clinical Laboratory, Beijing Chest Hospital, Capital Medical University, Beijing, China.
Jinjing Tan *Beijing Tuberculosis Thoracic Tumor Institute, Beijing, 101149, China.
Yan ChenDepartment of Clinical Laboratory, Beijing Chest Hospital, Capital Medical University, Beijing, China.
Shang MaDepartment of Clinical Laboratory, Beijing Chest Hospital, Capital Medical University, Beijing, China.
Wanqiu BaiDepartment of Clinical Laboratory, Beijing Chest Hospital, Capital Medical University, Beijing, China.
Yanjing PengBeijing Tuberculosis Thoracic Tumor Institute, Beijing, 101149, China.
Guangli ShiDepartment of Clinical Laboratory, Beijing Chest Hospital, Capital Medical University, Beijing, China. shiguangli878@sina.com.
Beijing Chest Hospital · CNCapital Medical University · CN

Funding

Tongzhou district "Yun He" talent project YHLD2018023Tongzhou district "Yun He" talent project YHLD2019039
6 · The paper itself

Abstract

backgroundLung cancer is one of the most common solid tumors worldwide and the leading cause of cancer-associated death. Non-small cell lung cancer (NSCLC) is accounts for approximately 85% of all the lung cancers and lung squamous carcinoma (SCC) and adenocarcinoma (ADC) are the main subtypes of NSCLC. Early diagnose using serum biomarkers could improve the overall survival of patients. In this study, we aimed to identify miRNAs from serum with clinical utility in the diagnosis of NSCLC.

methodsTen patients with SCC, ten patients with ADC and five noncancerous individuals were enrolled in the screening cohort. miRNA expression levels in serum were measured by microarray analysis. Candidate miRNAs were validated by real-time quantitative polymerase chain reaction analysis in a validation cohort of 78 NSCLC patients and 44 noncancerous individuals. Receiver operating characteristic curves were used to assess the diagnostic performance of serum miRNAs for NSCLC. Logistic regression was used to evaluate the diagnostic value of the combination of markers.

resultsSix candidate miRNAs were differentially expressed between NSCLC patients and noncancerous individuals in the screening set (fold change > 2, p < 0.05). Among them, expression levels of miR-3149 and miR-4769.3p were confirmed to be significantly increased in tumor serum in the validation set. The area under the curve values of miR-3149 and miR-4769.3p in distinguishing NSCLC patients from noncancerous controls were 0.830 and 0.735, respectively. When combined with tumor markers CEA and Cyfra21-1, the joint diagnostic model increased the area under the curve to 0.898.

conclusionSerum miRNAs miR-3149 and miR-4769.3p were up-regulated in NSCLC and may be potential biomarkers for early diagnosis of lung cancer.

Indexed as

AdenocarcinomaCarcinoma, Non-Small-Cell LungLung NeoplasmsMicroRNAsAntigens, NeoplasmBiomarkers, TumorGene Expression ProfilingGene Expression Regulation, NeoplasticHumansKeratin-19antigen CYFRA21.1Antigens, NeoplasmBiomarkers, TumorKeratin-19MicroRNAsBiomarkersDiagnosismicroRNAmiR-3149miR-4769.3pNon-small cell lung cancer

Identifiers

PMID36522766
PMCPMC9756610
OpenAlexW4311595506

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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.