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Updated: Jan 14, 2026
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
DAPL1 Identified as a Novel Prognostic Biomarker in Breast Cancer: Insights from Comprehensive in Silico Analysis
Haichao Zhang1, Xin Qu1, Lu Han1
1Department of Thyroid and Breast Surgery, Tianjin Fourth Central Hospital; The Fourth Center Clinical College of Tianjin Medical University, Tianjin, 300140, China.
Background:
Characterizing tumor-associated gene expression variations in breast cancer is crucial for identifying molecular drivers and therapeutic targets, thereby advancing precision strategies for early detection, prognosis, and treatment optimization.
Objectives:
This study aimed to investigate the oncogenic role of death-associated protein-like 1 (DAPL1) in breast cancer.
Materials And Methods:
We employed a bioinformatics approach to conduct a comprehensive analysis of DAPL1, including DNA methylation, genetic alterations, kinase analysis, immune cell infiltration, and signaling pathways. The breast cancer datasets from TCGA (the cancer genome atlas) and GEO (gene expression omnibus) were utilized.
Results:
DAPL1 is lowly expressed in breast cancer tissues compared to normal tissues. In the TCGA-BRCA (breast invasive carcinoma) cohort, we observed a correlation between low expression of DAPL1 and poor clinical prognosis regarding overall survival. Based on the survival data of GEO, the low DAPL1 expression was associated with a poor prognosis of distant metastasis-free and relapse-free survival. Furthermore, DAPL1 expression was linked to the mutation status or copy number variation of several genes, such as MAP3K1 (mitogen-activated protein kinase kinase kinase 1), NUP98 (nucleoporin 98), and CCDC59 (coiled-coil domain containing 59). The infiltration level of immune cells (e.g., M1 macrophage, B cells, etc.) may be involved in the etiology of breast cancer. Based on the DAPL1-correlated genes, enrichment analysis data indicated the association between DAPL1 expression and a series of biological issues, such as ubiquitin proteasome pathway. Additionally, there were several potential DAPL1-associated phosphorylation kinases, including CDC2 (cell division cycle 2), MAPK (mitogen-activated protein kinases), and PKA (protein kinase A).
Conclusion:
DAPL1 has been recognized as a prognostic biomarker in breast cancer. The molecular mechanisms may involve protein phosphorylation, immune cell infiltration, and several biological issues, especially protein ubiquitination.
