Evidence map›Paper›PMID 30588023›Full record

ArticleOncoTargets and therapy2018

Next-generation sequencing of circulating tumor DNA for detection of gene mutations in lung cancer: implications for precision treatment.

Jinhuo Lai, Bin Du, Yao Wang, Riping Wu, Zongyang Yu

Abstract read
In one paragraph

Article in OncoTargets and therapy, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

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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

12 citing papers in PubMed.

  1. Article
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  5. Review
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  9. Technical and Methodological Aspects of Cell-Free Nucleic Acids Analyzes.International journal of molecular sciences · 2020
    Review
  10. Article
  11. Article
  12. Article
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

5 authors.

Jinhuo LaiDepartment of Oncology, Fujian Medical University Union Hospital, Fuzhou 350001, Fujian Province, People's Republic of China, ljh2252@fjmu.edu.cn.
Bin DuDepartment of Oncology, Fujian Medical University Union Hospital, Fuzhou 350001, Fujian Province, People's Republic of China, ljh2252@fjmu.edu.cn.
Yao WangDepartment of Oncology, Fujian Medical University Union Hospital, Fuzhou 350001, Fujian Province, People's Republic of China, ljh2252@fjmu.edu.cn.
Riping WuDepartment of Oncology, Fujian Medical University Union Hospital, Fuzhou 350001, Fujian Province, People's Republic of China, ljh2252@fjmu.edu.cn.
Zongyang YuDepartment of Medical Oncology, Fuzhou General Hospital of PLA, Fuzhou 350025, Fujian Province, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLung cancer remains a major global health problem, which causes millions of deaths annually. Because the prognosis is mainly determined by the stage of lung cancer, precise early diagnosis is of great significance to improve the survival and prognosis. Circulating tumor DNA (ctDNA) has been recognized as a sensitive and specific biomarker for the detection of early- and late-stage lung cancer, and next-generation sequencing (NGS) of ctDNA has been accepted as a noninvasive tool for early identification and monitoring of cancer mutations. This study aimed to assess the value of NGS-based ctDNA analysis in detecting gene mutations in lung cancer patients.

methodsA total of 101 subjects with pathological diagnosis of lung cancer were enrolled, and blood samples were collected. ctDNA samples were prepared and subjected to NGS assays.

resultsThere were 31 cases harboring 40 gene mutations, and

conclusionOur findings demonstrate that NGS of ctDNA is effective in detecting gene mutations in lung cancer patients, and may be used as a liquid biopsy for lung cancer, which facilitates the development of precision treatment regimens for lung cancer.

Indexed as

circulating tumor DNActDNAgene mutationlung cancernext-generation sequencing

Identifiers

PMID30588023
PMCPMC6299472

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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.