This document describes the application of principal component analysis (PCA) based feature extraction to three bioinformatics studies without using class labels. In the first study, PCA was used to select methylation sites that discriminated between healthy and disease samples in twins across three autoimmune diseases, finding many common sites. The second study applied PCA to select stable circulating microRNA biomarkers that classified 14 diseases from controls with high accuracy. The third study used PCA to extract prominent proteins in bacterial cells under different growth conditions without predefined classes. The document discusses the advantages of unsupervised PCA-based feature extraction in providing stability and extracting biologically relevant features even without classification information. It questions when the approach works best and how to evaluate unsupervised feature selection