Evidence map›Paper›PMID 32775256›Full record

ArticleGland surgery2020

Identification of key differentially expressed genes between ER-positive/HER2-negative breast cancer and ER-negative/HER2-negative breast cancer using integrated bioinformatics analysis.

Siyuan Gan, Haixia Dai, Rujia Li, Wang Liu, Ruifang Ye, Yanping Ha, Xiaoqing Di, Wenhua Hu, Zhi Zhang, Yanqin Sun

Open access · diamondAbstract read
In one paragraph

Article in Gland surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.2field-weighted citation impact, top 51% 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

5 citing papers in PubMed, 5 citations in OpenAlex.

  1. Pharmaceuticals (Basel, Switzerland) · 2024
    Article
  2. Article
  3. Article
  4. Article
  5. Veterinary sciences · 2022
    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 1 institution in 1 country.

Siyuan GanDepartment of Pathology, Guangdong Medical University, Zhanjiang 524023, China.
Haixia DaiDepartment of Ultrasound, The Affiliated Hospital of Guangdong Medical University, Zhanjiang 524023, China.
Rujia LiDepartment of Pathology, Guangdong Medical University, Zhanjiang 524023, China.
Wang LiuDepartment of Respiratory, The Second Affiliated Hospital of Guangdong Medical University, Zhanjiang 524023, China.
Ruifang YeDepartment of Pathology, Guangdong Medical University, Zhanjiang 524023, China.
Yanping HaDepartment of Pathology, Guangdong Medical University, Zhanjiang 524023, China.
Xiaoqing DiDepartment of Pathology, The Affiliated Hospital of Guangdong Medical University, Zhanjiang 524023, China.
Wenhua HuDepartment of Pathology, The Affiliated Hospital of Guangdong Medical University, Zhanjiang 524023, China.
Zhi ZhangDepartment of Thyroid and Mammary Vascular Surgery, The Affiliated Hospital of Guangdong Medical University, Zhanjiang 524023, China.
Yanqin SunDepartment of Pathology, Guangdong Medical University, Zhanjiang 524023, China.
Guangdong Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTreatment strategies for various subtypes of breast cancer (BC) are different based on their distinct molecular characteristics. Therefore, it is very important to identify key differentially expressed genes (DEGs) between ER-positive/HER2-negative BC and ER-negative/HER2-negative BC.

methodsGene expression profiles of GSE22093 and GSE23988 were obtained from the Gene Expression Omnibus database. There were 74 ER-positive/HER2-negative BC tissues and 85 ER-negative/HER2-negative BC tissues in the two profile datasets. DEGs between ER-positive/HER2-negative tissues and ER-negative/HER2-negative BC tissues were identified by the GEO2R tool. The common DEGs among the two datasets were detected with Venn software online. Next, we made use of the Database for Annotation, Visualization and Integrated Discovery to analyze enriched Kyoto Encyclopedia of Gene and Genome (KEGG) pathways and gene ontology terms. Then, the protein-protein interactions (PPIs) of these DEGs were visualized by Cytoscape with the Search Tool for the Retrieval of Interacting Genes. Of the proteins in the PPI network, Molecular Complex Detection plug-in analysis identified nine core upregulated genes and one core downregulated gene. UALCAN and Gene Expression Profiling Interactive Analysis were applied to determine the expression of these 10 genes in BC. Furthermore, for the analysis of overall survival among those genes, the Kaplan-Meier method was implemented.

resultsNinety-three common DEGs (63 upregulated and 30 downregulated) were identified. KEGG pathway enrichment analysis showed that upregulated DEGs were particularly enriched in the progesterone-mediated oocyte maturation pathway. In addition, PGR might be a prognostic biomarker for ER-positive/HER2-negative BC. CCND1 is a poor prognostic biomarker for ER-positive/HER2-negative BC and ER-negative/HER2-negative BC. Moreover, TFF1, AGR2 and EGFR might be predictive biomarkers of node metastasis in ER-positive/HER2-negative BC and ER-negative/HER2-negative BC.

conclusionsCCND1, AGR2, PGR, TFF1 and EGFR are the key DEGs between ER-positive/HER2-negative BC and ER-negative/HER2-negative BC. Further studies are required to confirm the functions of the identified genes.

Indexed as

bioinformatics analysisBreast cancer (BC)estrogen receptor (ER)human epidermal growth factor receptor 2 (HER2)key differentially expressed genes

Identifiers

PMID32775256
PMCPMC7347802
OpenAlexW3021749216

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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.