Evidence map›Paper›PMID 37930873›Full record

ArticleAnnals of medicine2023

Screening microRNAs as potential prognostic biomarkers for lung adenocarcinoma.

Hongshuang Dai, Lin Li, Yikun Yang, Huang Chen, Xin Dong, Yousheng Mao, Yanning Gao

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
4.1field-weighted citation impact, top 6% 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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Liquid biopsy techniques and lung cancer: diagnosis, monitoring and evaluation.Journal of experimental & clinical cancer research : CR · 2024
    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

7 authors at 4 institutions in 2 countries.

Hongshuang DaiState Key Laboratory of Molecular Oncology, Department of Etiology and Carcinogenesis, National Cancer Center; National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Lin LiDepartment of Pathology, National Cancer Center;National Clinical Research Center for Cancer, Chinese Academy of Medical Sciences and Peking Union Medical College Cancer Hospital, Beijing, China.
Yikun YangDepartment of Thoracic Surgical Oncology, National Cancer Center; National Clinical Research Center for Cancer, Chinese Academy of Medical Sciences and Peking Union Medical College Cancer Hospital, Beijing, China.
Huang ChenDepartment of Pathology, China-Japan Friendship Hospital, Beijing, China.
Xin DongDepartment of Clinical Laboratory, National Cancer Center; National Clinical Research Center for Cancer, Chinese Academy of Medical Sciences and Peking Union Medical College Cancer Hospital, Beijing, China.
Yousheng MaoDepartment of Thoracic Surgical Oncology, National Cancer Center; National Clinical Research Center for Cancer, Chinese Academy of Medical Sciences and Peking Union Medical College Cancer Hospital, Beijing, China.
Yanning GaoState Key Laboratory of Molecular Oncology, Department of Etiology and Carcinogenesis, National Cancer Center; National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Chinese Academy of Medical Sciences & Peking Union Medical College · CNNational Cancer Center · USChina-Japan Friendship Hospital · CNState Key Laboratory of Molecular Oncology

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo screen and identify microRNAs (miRNAs) associated with the prognosis of lung adenocarcinoma (LUAD) using clinical samples and construct a prediction model for the prognosis of LUAD.

methods160 patient samples were used to screen and identify miRNAs associated with the prognosis of LUAD. Differentially expressed miRNAs were analyzed using gene chip technology. The selected miRNAs were validated using samples from the validation sample group. Cox proportional hazards regression was used to construct the model and Kaplan-Meier was used to plot survival curves. Model power was assessed by testing the prognosis of the constructed model using real-time polymerase chain reaction (RT-PCR) data.

resultsThe data showed that miR-1260b, miR-21-3p and miR-92a-3p were highly expressed in the early recurrence and metastasis group, while miR-2467-3p, miR-4659a-3p, miR-4514, miR-1471 and miR-3621 were lowly expressed. It was further confirmed that miR-21-3p was significantly highly expressed in the early recurrence and metastasis group (

conclusionmiRNAs associated with the prognosis of patients with stage I LUAD were screened and validated, and a risk model for predicting the prognosis of patients was constructed. This model has good consistency with the actual prognosis of patients.

Indexed as

Adenocarcinoma of LungLung NeoplasmsMicroRNAsBiomarkers, TumorGene Expression ProfilingHumansPrognosisROC CurveBiomarkers, TumorMicroRNAsLung adenocarcinomamicroRNApredictive modelprognostic signature

Identifiers

PMID37930873
PMCPMC10629414
OpenAlexW4388410051

What OpenQuestion holds

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LicenceCC BY-NC
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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.