Evidence map›Paper›PMID 35726219›Full record

ArticleJournal of oncology2022

Integrated Bioinformatics Analysis for Identifying the Significant Genes as Poor Prognostic Markers in Gastric Adenocarcinoma.

Yamei Li, Yan Luo, Qiang Tian, Yu Lai, Lei Xu, Hailong Yun, Yang Liang, Dandan Liao, Rui Gu, Liye Liu and 6 more

Open access · hybridAbstract read
In one paragraph

Article in Journal of oncology, 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
0.3field-weighted citation impact, top 46% 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, 4 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  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

16 authors at 2 institutions in 1 country.

Yamei LiDepartment of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu 610041, China.ORCID https://orcid.org/0000-0002-1059-7950
Yan LuoDepartment of Pathology, General Hospital of Western Theater Command, Chengdu 610083, China.
Qiang TianDepartment of Pathology, General Hospital of Western Theater Command, Chengdu 610083, China.
Yu LaiDepartment of Oncology, The Fifth People's Hospital of Nanchong (People's Hospital of Gaoping District Nanchong), Nanchong 637000, China.
Lei XuDepartment of Pathology, General Hospital of Western Theater Command, Chengdu 610083, China.
Hailong YunDepartment of Pathology, General Hospital of Western Theater Command, Chengdu 610083, China.
Yang LiangDepartment of Pathology, General Hospital of Western Theater Command, Chengdu 610083, China.
Dandan LiaoDepartment of Pathology, General Hospital of Western Theater Command, Chengdu 610083, China.
Rui GuLaboratory of Basic Medicine, General Hospital of Western Theater Command, Chengdu 610083, China.
Liye LiuDepartment of General Surgery, General Hospital of Western Theater Command, Chengdu 610083, China.
Mu YuanDepartment of Scientific Research & Training, General Hospital of Western Theater Command, Chengdu 610083, China.
Yijiao LiDepartment of Anesthesiology, The People's Hospital of Leshan, Leshan 614000, China.
Yufan LiLaboratory of Basic Medicine, General Hospital of Western Theater Command, Chengdu 610083, China.
Mingze LuDepartment of Human Resources, General Hospital of Western Theater Command, Chengdu 610083, China.
Xin YongDepartment of Gastroenterology, General Hospital of Western Theater Command, Chengdu 610083, China.
Hua ZhangDepartment of Pathology, General Hospital of Western Theater Command, Chengdu 610083, China.ORCID https://orcid.org/0000-0001-6071-0651
Sichuan University · CNNanchong Central Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gastric adenocarcinoma (GAC) is the most common histological type of gastric cancer and imposes a considerable health burden globally. The purpose of this study was to identify significant genes and key pathways participated in the initiation and progression of GAC. Four datasets (GSE13911, GSE19826, GSE54129, and GSE79973) including 171 GAC and 77 normal tissues from Gene Expression Omnibus (GEO) database were collected and analyzed. Through integrated bioinformatics analysis, we obtained 69 commonly differentially expressed genes (DEGs) among the four datasets, including 20 upregulated and 49 downregulated genes. The prime module in protein-protein interaction network of DEGs, including ADAMTS2, COL10A1, COL1A1, COL1A2, COL8A1, BGN, and SPP1, was enriched in protein digestion and absorption, ECM-receptor interaction, focal adhesion, PI3K-Akt signaling pathway, and amoebiasis. Furthermore, expression and survival analysis found that all seven hub genes were highly expressed in GAC tissues and 6 of them (except for SPP1) were able to predict poor prognosis of GAC. Finally, we verified the 6 high-expressed hub genes in GAC tissues via immunohistochemistry, Western blot, and RNA quantification analysis. Altogether, we identified six significantly upregulated DEGs as poor prognostic markers in GAC based on integrated bioinformatical methods, which could be potential molecular markers and therapeutic targets for GAC patients.

Identifiers

PMID35726219
PMCPMC9206555
OpenAlexW4285744377

What OpenQuestion holds

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LicenceCC BY
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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.