Analyzing Gene Expression Patterns in Breast Cancer Subtypes Using Multivariate Analysis of Variance (MANOVA)

Abstract

Aims: This study investigated the expression patterns of five crucial genes BRCA1, PALB2, CDK1, CDH1, and CHEK2 across breast cancer subtypes using Multivariate Analysis of Variance (MANOVA). Study Design: A quantitative research design based on MANOVA was employed using gene expression data from 1,139 breast cancer patients representing multiple subtypes. Place and Duration of Study: The study utilized secondary genomic data obtained from publicly available breast cancer databases and was conducted over a six-month period. Methodology: Preliminary diagnostic tests assessed normality, multicollinearity, and multivariate outliers. Shapiro–Wilk tests indicated approximate normality, VIF values below 5 confirmed the absence of multicollinearity, and Mahalanobis distance showed no significant outliers (12.68 < 20.515). Although Levene’s and Box’s M tests indicated heterogeneity of variances and covariance matrices (p < 0.001), Pillai’s trace was adopted due to its robustness. Univariate analyses and Scheffe post hoc tests were subsequently conducted. Results: MANOVA revealed a highly significant overall effect of breast cancer subtype on combined gene expression profiles (Pillai’s Trace = 0.525, F = 48.09, p < 0.001, observed power = 1.000). BRCA1, CDK1, CDH1, and CHEK2 significantly discriminated subtypes (p < 0.001), while PALB2 showed marginal significance (p = 0.003). Luminal A and HER2 demonstrated the strongest gene-specific effects, whereas Luminal B showed the least differentiation. Conclusion: The findings confirm distinct subtype-specific gene expression patterns and demonstrate the effectiveness of MANOVA for genomic studies, highlighting its relevance for precision oncology and targeted breast cancer management.