Principal Component Analysis for the Expression of Angiogenesis-Related Genes in Breast Cancer

Abstract

Objectives: This study aimed to systematically classify angiogenesis-related gene expression profiles in breast cancer using principal component analysis (PCA), with the goal of identifying latent patterns and gene clusters that may underlie distinct angiogenic pathways.

Methods: Tissue samples from 11 Iranian breast cancer patients were analyzed for the expression of eight angiogenesis-related genes (VEGF-A, VEGF-B, VEGF-C, VEGF-D, VEGFR-1, VEGFR-2, VEGFR-3, and PlGF) using real-time PCR. PCA was applied to reduce data dimensionality and uncover underlying patterns, with components retained based on eigenvalues greater than one.

Results: PCA identified two principal components, collectively explaining 70.6% of the total variance. The first component was strongly associated with VEGF-A, VEGF-D, VEGFR-1, VEGFR-2, and PlGF, while the second component was linked to VEGF-B, VEGF-C, and VEGFR-3. This clear separation suggests that the angiogenic signaling network in breast cancer is not monolithic but rather composed of distinct gene clusters, potentially reflecting different biological functions or regulatory mechanisms.

Conclusion: Our findings highlight the utility of PCA in revealing the molecular heterogeneity of angiogenesis-related gene expression in breast cancer. The identification of distinct gene clusters provides valuable insight into the complexity of angiogenic networks and lays the groundwork for future studies aiming to integrate multi-omics data into unified angiogenic indices. Ultimately, this approach could improve patient stratification and inform the development of targeted anti-angiogenic therapies tailored to specific molecular subtypes of breast cancer.