Abstract
Breast cancer (BRCA) is the most widespread malignant tumor and the leading cause of death in women. BRCA treatments vary based on the presence of estrogen receptors (ER). Generally, cancer preventive and therapy options for ER-negative BRCA are limited compared to ER-positive BRCA. Therefore, this study investigates the key genes predicting poor prognosis for ER-negative BRCA through the application of LASSO (Least Absolute Shrinkage and Selection Operator) and bioinformatics analysis. These methods are analyzed using a dataset GSE7390 that contains 198 untreated lymph node-negative BRCA patients with gene expressions and clinical information. Differentially Expressed Genes (DEGs) between ER-negative and ER-positive BRCA are found using GEO2R. Here, the regularized regression reduces the dimensionality of DEGs by selecting necessary genes. Later, Support Vector Machines for Survival and Random Survival Forest methods were implemented to construct survival predictive models. Comparatively, LASSO holds higher C-index and AUC values with better prediction accuracy and it selects 28 DEGs consisting of 14 upregulated and 14 downregulated genes. Among them, LCN2 expression is significantly downregulated and ZMYND10 expression is significantly upregulated in tumor tissues compared to normal tissue using GEPIA2. Also, both genes are statistically significant in survival rates for TCGA BRCA patients (p < 0.05). Hence, this study highlights higher LCN2 and low ZMYND10 expression in ER-negative BRCA associated with a poor prognosis. Consequently, LCN2 and ZMYND10 genes can be used to predict and serve as potential biomarkers for future diagnosis and treatment in ER-negative BRCA.
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Divya, P., Suresh, S. Bioinformatics Analysis in the Identification of Prognostic Signatures for ER-Negative Breast Cancer Data. J Indian Soc Probab Stat 25, 1–16 (2024). https://doi.org/10.1007/s41096-024-00187-8
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DOI: https://doi.org/10.1007/s41096-024-00187-8
Keywords
- Breast cancer
- Estrogen receptors
- Survival analysis
- Regularized regression
- Machine learning survival methods
- Bioinformatics analysis