ArticleExperimental hematology & oncology2020
RDGN-based predictive model for the prognosis of breast cancer.
Article in Experimental hematology & oncology, 2020. 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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13 citing papers in PubMed, 42 citations in OpenAlex.
- Implications of ITCH-mediated ubiquitination of SIX1 on CDC27-cyclinB1 signaling in nasopharyngeal carcinoma.Scientific reports · 2024Article
- All eyes on Eya: A unique transcriptional co-activator and phosphatase in cancer.Biochimica et biophysica acta. Reviews on cancer · 2024Review
- Targeted therapy approaches for epithelial-mesenchymal transition in triple negative breast cancer.Frontiers in oncology · 2024Review
- S100A8/A9 as a risk factor for breast cancer negatively regulated by DACH1.Biomarker research · 2023Article
- Recent advances in targeted strategies for triple-negative breast cancer.Journal of hematology & oncology · 2023Review
- Retinal determination gene networks: from biological functions to therapeutic strategies.Biomarker research · 2023Review
- Breast cancer heterogeneity and its implication in personalized precision therapy.Experimental hematology & oncology · 2023Review
- Editorial: Update on diagnostic and prognostic biomarkers for women's cancers.Frontiers in medicine · 2023Article
- Increased expression of ECT2 predicts the poor prognosis of breast cancer patients.Experimental hematology & oncology · 2022Article
- Tumor organoids: applications in cancer modeling and potentials in precision medicine.Journal of hematology & oncology · 2022Review
- SIX1 Predicts Poor Prognosis and Facilitates the Progression of Non-small Lung Cancer via Activating the Notch Signaling Pathway.Journal of Cancer · 2022Article
- YTHDF1 promotes hepatocellular carcinoma progression via activating PI3K/AKT/mTOR signaling pathway and inducing epithelial-mesenchymal transition.Experimental hematology & oncology · 2021Article
- Tumor mutation burden estimated by a 69-gene-panel is associated with overall survival in patients with diffuse large B-cell lymphoma.Experimental hematology & oncology · 2021Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBreast cancer is the most diagnosed malignancy in females in the United States. The members of retinal determination gene network (RDGN) including DACH, EYA, as well as SIX families participate in the proliferation, apoptosis, and metastasis of multiple tumors including breast cancer. A comprehensive predictive model of RDGN might be helpful to herald the prognosis of breast cancer patients.
methodsIn this study, the Gene Expression Ominibus (GEO) and Gene Set Expression Analysis (GSEA) algorithm were used to investigate the effect of RDGN members on downstream signaling pathways. Besides, based on The Cancer Genome Atlas (TCGA) database, we explored the expression patterns of RDGN members in tumors, normal tissues, and different breast cancer subtypes. Moreover, we estimated the relationship between RDGN members and the outcomes of breast cancer patients. Lastly, we constructed a RDGN-based predictive model by Cox proportional hazard regression and verified the model in two separate GEO datasets.
resultsThe results of GSEA showed that the expression of DACH1 was negatively correlated with cell cycle and DNA replication pathways. On the contrary, the levels of EYA2 and SIX1 were significantly positively correlated with DNA replication, mTOR, and Wnt pathways. Further investigation in TCGA database indicated that DACH1 expression was lower in breast cancers especially basal-like subtype. In the meanwhile, SIX1 was remarkably upregulated in breast cancers while EYA2 level was increased in Basal-like and Her-2 enriched subtypes. Survival analyses demonstrated that DACH1 was a favorable factor while EYA2 and SIX1 were risk factors for breast cancer patients. Given the results of Cox proportional hazard regression analysis, two members of RDGN were involved in the present predictive model and patients with high model index had poorer outcomes.
conclusionThis study showed that aberrant RDGN expression was an unfavorable factor for breast cancer. This RDGN-based comprehensively framework was meaningful for predicting the prognosis of breast cancer patients.
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