Evidence map›Paper›PMID 32793728›Full record

ArticleAnnals of translational medicine2020

Identification of critical genes in gastric cancer to predict prognosis using bioinformatics analysis methods.

Jing Liu, Liang Ma, Zhiming Chen, Yao Song, Tinging Gu, Xianchen Liu, Hongyu Zhao, Ninghua Yao

Open access · diamondAbstract read
In one paragraph

Article in Annals of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 29% 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

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Association ofFrontiers in genetics · 2022
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4 · The record

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5 · Who and what money

Authors and funding

8 authors at 4 institutions in 1 country.

Jing LiuDepartment of Oncology, Affiliated Hospital of Nantong University, Nantong, China.
Liang MaDepartment of Chemotherapy, First People's Hospital of Yancheng, Yancheng, China.
Zhiming ChenDepartment of Oncology, Affiliated Hospital of Nantong University, Nantong, China.
Yao SongDepartment of Radiation oncology, Tenth People's Hospital Affiliated to Tongji University, Shanghai, China.
Tinging GuDepartment of Oncology, Affiliated Hospital of Nantong University, Nantong, China.
Xianchen LiuDepartment of Oncology, Affiliated Hospital of Nantong University, Nantong, China.
Hongyu ZhaoDepartment of Oncology, Affiliated Hospital of Nantong University, Nantong, China.
Ninghua YaoDepartment of Oncology, Affiliated Hospital of Nantong University, Nantong, China.
Affiliated Hospital of Nantong University · CNNantong University · CNShanghai Tenth People's Hospital · CNYancheng First People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRanking fourth in the world in tumor incidence and second in cancer-related death worldwide, gastric cancer (GC) is one of the major malignant tumors, and has a very complicated pathogenesis. In the present study, we aimed to identify new biomarkers to predict the survival rate of GC patients.

methodsThe differentially expressed genes (DEGs) between GC tissues and normal stomach tissues were obtained by using GEO2R, and overlapped DEGs were acquired with Venn diagrams. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted with R software. Then, the protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape. Gene Expression Profiling Interactive Analysis (GEPIA) was used to verify the expression differences of hub genes in gastric adenocarcinoma tissues and normal tissues. Overall survival (OS) of hub genes was calculated by Kaplan-Meier plotter.

resultsThere were a total of 128 consistently expressed genes in the two datasets: 85 upregulated genes were enriched in extra-cellular matrix (ECM)-receptor interaction, protein digestion and absorption, focal adhesion, gastric acid secretion, mineral absorption, systemic lupus erythematosus, amoebiasis, and PI3K-Akt signaling pathway, and 43 downregulated genes were enriched in palate development, blood coagulation, positive regulation of transcription from RNA polymerase II promoter, axonogenesis, receptor internalization, negative regulation of transcription from RNA polymerase II promoter, and in no significant signaling pathways. From the PPI network analyzed by Molecular Complex Detection (MCODE) plug-in, all 27 upregulated genes were selected. Furthermore, to analyze the OS among these genes, Kaplan-Meier analysis was conducted, and 25 genes were associated with remarkably worse survival. For validation in GEPIA, 11 of 25 genes were discovered to be highly expressed in GC tissues compared to normal OS tissues. Furthermore, in the re-analysis of the Database for Annotation, Visualization and Integrated Discovery (DAVID), three genes [G2/miotic-specific cyclin B1 (CCNB1), polo-like kinases 1 (PLK1), and pituitary tumor-transforming gene-1 (PTTG1)] were markedly enriched in the cell cycle pathway, particulary the G1-G1/S phase.

conclusionsThree remarkably upregulated DEGs with poor prognosis in GC were identified and may serve as new prognostic biomarkers and targets in GC therapy.

Indexed as

Bioinformatical analysisdifferentially expressed genes (DEGs)G2/miotic-specific cyclin B1 (CCNB1)gastric cancer (GC)pituitary tumor-transforming gene-1 (PTTG1)polo-like kinases 1 (PLK1)survival

Identifiers

PMID32793728
PMCPMC7396774
OpenAlexW3044169631

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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.