The most strongly correlated original feature pair must be exactly PC1 and PC2.

Those three coefficients prove that a single component captures all variance in all 30 features.

Several size features carry overlapping variation, so the number of columns is not the same as the number of independent directions of variation.

PCA becomes unnecessary because highly correlated features already give perfect diagnosis predictions.