ArticleBioMed research international2021
Genes That Predict Poor Prognosis in Breast Cancer via Bioinformatical Analysis.
Article in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 21 citations in OpenAlex.
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- Targeting RHAMM in Cancer: Crosstalk with Non-Coding RNAs and Emerging Therapeutic Strategies Including Peptides, Oligomers, Antibodies, and Vaccines.International journal of molecular sciences · 2025Review
- Oncogenic activation of SMYD3-SHCBP1 promotes breast cancer development and is coupled with resistance to immune therapy.Cell death & disease · 2025Article
- Identification of key biomarkers in breast cancer based on bioinformatics analysis and experimental verification.Journal of the Egyptian National Cancer Institute · 2025Article
- Identification and validation of a prognostic signature of drug resistance and mitochondrial energy metabolism-related differentially expressed genes for breast cancer.Journal of translational medicine · 2025Article
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- Integrated analysis and validation of ferroptosis-related genes and immune infiltration in acute myocardial infarction.BMC cardiovascular disorders · 2024Article
- Biological functions and therapeutic potential of CKS2 in human cancer.Frontiers in oncology · 2024Review
- Hsa_circ_0005273 acts as a sponge of miR-509-3p to promote the malignant behaviors of breast cancer by regulating HMMR expression.Thoracic cancer · 2023Article
- Breast Reconstruction Does Not Affect the Survival of Patients with Breast Cancer Located in the Central and Nipple Portion: A Surveillance, Epidemiology, and End Results Database Analysis.Frontiers in surgery · 2022Article
- RHAMM Is a Multifunctional Protein That Regulates Cancer Progression.International journal of molecular sciences · 2021Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBreast cancer is one of the most commonly diagnosed cancers all over the world, and it is now the leading cause of cancer death among females. The aim of this study was to find DEGs (differentially expressed genes) which can predict poor prognosis in breast cancer and be effective targets for breast cancer patients via bioinformatical analysis.
methodsGSE86374, GSE5364, and GSE70947 were chosen from the GEO database. DEGs between breast cancer tissues and normal breast tissues were picked out by GEO2R and Venn diagram software. Then, DAVID (Database for Annotation, Visualization, and Integrated Discovery) was used to analyze these DEGs in gene ontology (GO) including molecular function (MF), cellular component (CC), and biological process (BP) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway. Next, STRING (Search Tool for the Retrieval of Interacting Genes) was used to investigate potential protein-protein interaction (PPI) relationships among DEGs and these DEGs were analyzed by Molecular Complex Detection (MCODE) in Cytoscape. After that, UALCAN, GEPIA (gene expression profiling interactive analysis), and KM (Kaplan-Meier plotter) were used for the prognostic information and core genes were qualified.
resultsThere were 96 upregulated genes and 98 downregulated genes in this study. 55 upregulated genes were selected as hub genes in the PPI network. For validation in UALCAN, GEPIA, and KM, 5 core genes (
conclusionThese five genes (
Indexed as
Identifiers
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