Evidence map›Paper›PMID 36400805›Full record

ArticleScientific reports2022

Identification of key biomarkers for STAD using filter feature selection approaches.

Yangyang Wang, Jihan Wang, Ya Hu, Jingbo Shangguan, Qiying Song, Jing Xu, Hanping Wang, Mengju Xue, Liping Wang, Yuanyuan Zhang

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed, 8 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. 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

10 authors at 5 institutions in 1 country.

Yangyang WangSchool of Electronics and Information, Northwestern Polytechnical University, Xi'an, Shaanxi, China. wangyang2154@mail.nwpu.edu.cn.
Jihan WangInstitute of Medical Research, Northwestern Polytechnical University, Xi'an, Shaanxi, China.
Ya HuDepartment of Medical College, Hunan Polytechnic of Environment and Biology, Hengyang, Hunan, China.
Jingbo ShangguanDepartment of Basic Medicine, School of Medicine, Xi'an International University, Xi'an, Shaanxi, China.
Qiying SongDepartment of Basic Medicine, School of Medicine, Xi'an International University, Xi'an, Shaanxi, China.
Jing XuDepartment of Basic Medicine, School of Medicine, Xi'an International University, Xi'an, Shaanxi, China.
Hanping WangEngineering Research Center of Personalized Anti-Aging Health Product Development and Transformation, Universities of Shaanxi Province, Xi'an, Shaanxi, China.
Mengju XueDepartment of Basic Medicine, School of Medicine, Xi'an International University, Xi'an, Shaanxi, China.
Liping WangHonghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Yuanyuan ZhangHonghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China. zhangyuanyuan_8811@126.com.
Xi’an International University · CNNorthwestern Polytechnical University · CNHunan Institute of Technology · CNXi'an Honghui Hospital · CNXi'an Jiaotong University · CN

Funding

Key Research and Development Program of Shaanxi Province 2019NY-156
6 · The paper itself

Abstract

Gastric cancer (GC) is the fifth most common cancer and the third leading cause of cancer death worldwide. Discovery of diagnostic biomarkers prompts the early detection of GC. In this study, we used limma method combined with joint mutual information (JMI), a machine learning algorithm, to identify a signature of 11 genes that performed well in distinguishing tumor and normal samples in a stomach adenocarcinoma cohort. Other two GC datasets were used to validate the classifying performances. Several of the candidate genes were correlated with GC tumor progression and survival. Overall, we highlight the application of feature selection approaches in the analysis of high-dimensional biological data, which will improve study accuracies and reduce workloads for the researchers when identifying potential tumor biomarkers.

Indexed as

AdenocarcinomaStomach NeoplasmsAlgorithmsBiomarkers, TumorComputational BiologyHumansBiomarkers, Tumor

Identifiers

PMID36400805
PMCPMC9674689
OpenAlexW4309472063

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.