Evidence map›Paper›PMID 30112036›Full record

ArticleExperimental and therapeutic medicine2018

Comprehensive integrated analysis of gene expression datasets identifies key anti-cancer targets in different stages of breast cancer.

Meng-Ting Gong, Shou-Dong Ye, Wen-Wen Lv, Kan He, Wen-Xing Li

Open access · diamondAbstract read
In one paragraph

Article in Experimental and therapeutic medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
0.4field-weighted citation impact, top 40% 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, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Key Genes and Prognostic Analysis in HER2+ Breast Cancer.Technology in cancer research & treatment
    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 at 3 institutions in 1 country.

Meng-Ting GongCenter for Stem Cell and Translational Medicine, School of Life Sciences, Anhui University, Hefei, Anhui 230601, P.R. China.
Shou-Dong YeCenter for Stem Cell and Translational Medicine, School of Life Sciences, Anhui University, Hefei, Anhui 230601, P.R. China.
Wen-Wen LvHongqiao International Institute of Medicine, Shanghai Tongren Hospital/Faculty of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, P.R. China.
Kan HeCenter for Stem Cell and Translational Medicine, School of Life Sciences, Anhui University, Hefei, Anhui 230601, P.R. China.
Wen-Xing LiState Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, P.R. China.
Anhui University · CNKunming Institute of Zoology · CNShanghai Jiao Tong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer is one of the primary threats to women's health worldwide. However, the molecular mechanisms underlying the development of breast cancer remain to be fully elucidated. The present study aimed to investigate specific target gene expression profiles in breast cancer tissues in general and in different breast cancer stages, as well as to explore their functions in tumor development. For integrated analysis, a total of 5 gene expression profiling datasets for 3 different stages of breast cancer (stages I-III) were downloaded from the Gene Expression Omnibus of the National Center for Biotechnology Information. Pre-processing of these datasets was performed using the Robust Multi-array Average algorithm and global renormalization was performed for all studies. Differentially expressed genes between breast cancer patients and controls were estimated using the empirical Bayes algorithm. The Database for Annotation, Visualization and Integrated Discovery web server was used for analyzing the enrichment of the differentially expressed genes in Gene Ontology terms of the category biological process and in Kyoto Encyclopedia of Genes and Genomes pathways. Furthermore, breast cancer target genes were downloaded from the Thomson Reuters Integrity Database. We merged these target genes with the genes in breast cancer datasets. Analysis of anti-breast cancer gene networks was performed using the Genome-scale Integrated Analysis of Gene Networks in Tissues web server. The results demonstrated that the normal functions of the cell cycle, cell migration and cell adhesion were altered in all stages of breast cancer. Furthermore, 12 anti-breast cancer genes were identified to be dysregulated in at least one of the three stages. Among all of these genes, ribonucleotide reductase regulatory subunit M2 (RRM2) exhibited the highest degree of interaction with other interacting genes. Analysis of the network interactions revealed that the transcription factor of RRM2 is crucial for cancer development. Other genes, including mucin 1, progesterone receptor and cyclin-dependent kinase 5 regulatory subunit associated protein 3, also exhibited a high degree of interaction with the associated genes. In conclusion, several key anti-breast cancer genes identified in the present study are mainly associated with the regulation of the cell cycle, cell migration, cell adhesion and other cancer-associated cell functions, particularly RRM2.

Indexed as

breast cancergene expressionnetworkribonucleotide reductase regulatory subunit M2target genes

Identifiers

PMID30112036
PMCPMC6090421
OpenAlexW2806702867

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.