ArticleJournal of personalized medicine2022
Discovering Breast Cancer Biomarkers Candidates through mRNA Expression Analysis Based on The Cancer Genome Atlas Database.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Machine learning identifies PYGM as a macrophage polarization-linked metabolic biomarker in rectal cancer prognosis.Frontiers in immunology · 2025Article
- Integration of Bulk and Single-Cell Transcriptomics Reveals BCL2L14 as a Novel IGKC+ T Cell-Associated Therapeutic Target in Breast Cancer.Journal of inflammation research · 2025Article
- Stemness-driven clusters in ovarian cancer: immune characteristics and prognostic implications.Frontiers in oncology · 2025Article
- Integrating network pharmacology and transcriptomics to identify solasonine's anti-osteosarcoma targets and experimental validation.Frontiers in oncology · 2025Article
- An uncertainty-based interpretable deep learning framework for predicting breast cancer outcome.BMC bioinformatics · 2024Article
- The MexTAg collaborative cross: host genetics affects asbestos related disease latency, but has little influence once tumours develop.Frontiers in toxicology · 2024Article
- Article
- tRNA-Uridine Aminocarboxypropyltransferase DTW Domain Containing 2 Suppresses Colon Adenocarcinoma Progression.International journal of genomics · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Research on the discovery of tumor biomarkers based on big data analysis is actively being conducted. This study aimed to secure foundational data for identifying new biomarkers of breast cancer via breast cancer datasets in The Cancer Genome Atlas (TCGA). Methods: The mRNA profiles of 526 breast cancer and 60 adjacent non-cancerous breast tissues collected from TCGA datasets were analyzed via MultiExperiment Viewer and GraphPad Prism. Diagnostic performance was analyzed by identifying the pathological grades of the selected differentially expressed (DE) mRNAs and the expression patterns of molecular subtypes. Results: Via DE mRNA profile analysis, we selected 14 mRNAs with downregulated expression (HADH, CPN2, ADAM33, TDRD10, SNF1LK2, HBA2, KCNIP2, EPB42, PYGM, CEP68, ING3, EMCN, SYF2, and DTWD1) and six mRNAs with upregulated expression (ZNF8, TOMM40, EVPL, EPN3, AP1M2, and SPINT2) in breast cancer tissues compared to that in non-cancerous tissues (p < 0.001). Conclusions: In total, 20 DE mRNAs had an area under cover of 0.9 or higher, demonstrating excellent diagnostic performance in breast cancer. Therefore, the results of this study will provide foundational data for planning preliminary studies to identify new tumor biomarkers.
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