ArticleOncology letters2019
Integrated bioinformatics analysis to identify 15 hub genes in breast cancer.
Article in Oncology letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Exosomal transfer of p-AKT drives doxorubicin resistance and predicts poor prognosis in breast cancer.Molecular biology reports · 2026Article
- Disrupted circadian control promotes oncogenesis in breast cancer.Bioinformation · 2025Article
- A Bioinformatics Investigation of Hub Genes Involved in Treg Migration and Its Synergistic Effects, Using Immune Checkpoint Inhibitors for Immunotherapies.International journal of molecular sciences · 2024Article
- Identification of Hub of the Hub-Genes From Different Individual Studies for Early Diagnosis, Prognosis, and Therapies of Breast Cancer.Bioinformatics and biology insights · 2024Article
- Article
- Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers.Medical sciences (Basel, Switzerland) · 2023Article
- Demystifying the Role of Prognostic Biomarkers in Breast Cancer through Integrated Transcriptome and Pathway Enrichment Analyses.Diagnostics (Basel, Switzerland) · 2023Article
- Integrative bioinformatics and RNA sequencing based methodology results in the exploration of breast invasive carcinoma biomarkers.American journal of translational research · 2023Article
- Hub genes associated with immune cell infiltration in breast cancer, identified through bioinformatic analyses of multiple datasets.Cancer biology & medicine · 2022Article
- Article
- Identification of hub genes in colorectal cancer based on weighted gene co-expression network analysis and clinical data from The Cancer Genome Atlas.Bioscience reports · 2021Article
- SOCS3 is Related to Cell Proliferation in Neuronal Tissue: An Integrated Analysis of Bioinformatics and Experiments.Frontiers in genetics · 2021Article
- A Multimodal Affinity Fusion Network for Predicting the Survival of Breast Cancer Patients.Frontiers in genetics · 2021Article
- Article
- Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data.Scientific reports · 2020Article
- Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer.Frontiers in genetics · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of the present study was to identify the hub genes and provide insight into the tumorigenesis and development of breast cancer. To examine the hub genes in breast cancer, integrated bioinformatics analysis was performed. Gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database and the differentially expressed genes (DEGs) were identified using the 'limma' package in R. Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis was used to determine the functional annotations and potential pathways of the DEGs. Subsequently, a protein-protein interaction network analysis and weighted correlation network analysis (WGCNA) were conducted to identify hub genes. To confirm the reliability of the identified hub genes, RNA gene expression profiles were obtained from The Cancer Genome Atlas (TCGA)-breast cancer database, and WGCNA was used to screen for genes that were markedly correlated with breast cancer. By combining the results from the GEO and TCGA datasets, 15 hub genes were identified to be associated with breast cancer pathophysiology. Overall survival analysis was performed to examine the association between the expression of hub genes and the overall survival time of patients with breast cancer. Higher expression of all hub genes was associated with significantly shorter overall survival in patients with breast cancer compared with patients with lower levels of expression of the respective gene.
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