ArticleGenes2022
Identification of Key Prognostic Genes of Triple Negative Breast Cancer by LASSO-Based Machine Learning and Bioinformatics Analysis.
Article in Genes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed, 69 citations in OpenAlex.
- BUB1 and CDK4/6 Dual Inhibition Increases Radiation Sensitivity in Glioblastoma, Lung Cancer, and Triple-Negative Breast Cancer.Biomedicines · 2026Article
- Biomarker identification of triple negative breast cancer subtypes using machine learning.NPJ systems biology and applications · 2026Article
- Precision Biomarker Identification in Gynecological Cancers Using Coexpression Networks and Attention-Based LSTM in Healthcare 4.0.Diagnostics (Basel, Switzerland) · 2026Article
- Identification of hub genes and construction of a survival prediction model for patients with nasopharyngeal carcinoma.Scientific reports · 2026Article
- PACC1 could serve as a prognostic biomarker for patients with hepatocellular carcinoma.International journal of surgery (London, England) · 2026Article
- BUB1 in cancer genetics and oncogenomics: from chromosomal instability to therapeutic vulnerabilities.Frontiers in genetics · 2026Review
- Identification of the oxidation stress-related gene signatures and functional verification of MINK1 in prostate cancer cells.PloS one · 2026Article
- Article
- Key cell cycle genes in cervical cancer and their potential role in neuromuscular complications: a bioinformatics perspective.European journal of translational myology · 2025Article
- Construction and validation of risk models of prognostic genes associated with parthanatos in papillary thyroid carcinoma based on bioinformatics.Discover oncology · 2025Article
- Machine Learning-Based Prognostic Gene Signature for Early Triple-Negative Breast Cancer.Cancer research and treatment · 2025Article
- Screening and validation of long non-coding RNAs associated with colorectal cancer based on random forest and LASSO regression algorithm.Discover oncology · 2025Article
- Network-Based Integrative Analysis to Identify Key Genes and Corresponding Reporter Biomolecules for Triple-Negative Breast Cancer.Cancer medicine · 2025Article
- The molecular characteristics of DNA damage and repair related to P53 mutation for predicting the recurrence and immunotherapy response in hepatocellular carcinoma.Scientific reports · 2025Article
- Diagnostic potential of CDK1 and STAT1 in acute kidney injury associated with gastrointestinal cancers: a bioinformatics-based study.Frontiers in molecular biosciences · 2025Article
- Statistical and machine learning based platform-independent key genes identification for hepatocellular carcinoma.PloS one · 2025Article
- Identification of Anoikis-related potential biomarkers and therapeutic drugs in chronic thromboembolic pulmonary hypertension via bioinformatics analysis and in vitro experiment.Scientific reports · 2024Article
- From ductal carcinoma in situ to invasive breast cancer: the prognostic value of the extracellular microenvironment.Journal of experimental & clinical cancer research : CR · 2024Review
- Enhancing radiotherapy in triple-negative breast cancer with hesperetin-induced ferroptosis via AURKA targeting nanocomposites.Journal of nanobiotechnology · 2024Article
- Unveiling miRNA-Gene Regulatory Axes as Promising Biomarkers for Liver Cirrhosis and Hepatocellular Carcinoma.ACS omega · 2024Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Improved insight into the molecular mechanisms of triple negative breast cancer (TNBC) is required to predict prognosis and develop a new therapeutic strategy for targeted genes. The aim of this study is to identify key genes which may affect the prognosis of TNBC patients by bioinformatic analysis. In our study, the RNA sequencing (RNA-seq) expression data of 116 breast cancer lacking ER, PR, and HER2 expression and 113 normal tissues were downloaded from The Cancer Genome Atlas (TCGA). We screened out 147 differentially co-expressed genes in TNBC compared to non-cancerous tissue samples by using weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were constructed, revealing that 147 genes were mainly enriched in nuclear division, chromosomal region, ATPase activity, and cell cycle signaling. After using Cytoscape software for protein-protein interaction (PPI) network analysis and LASSO feature selection, a total of fifteen key genes were identified. Among them, BUB1 and CENPF were significantly correlated with the overall survival rate (OS) difference of TNBC patients (p value < 0.05). In addition, BUB1, CCNA2, and PACC1 showed significant poor disease-free survival (DFS) in TNBC patients (p value < 0.05), and may serve as candidate biomarkers in TNBC diagnosis. Thus, our results collectively suggest that BUB1, CCNA2, and PACC1 genes could play important roles in the progression of TNBC and provide attractive therapeutic targets.
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