For a disease that I am interested in, five or six published works of research have shown an association of changes in gene expression with presence of the disease. However, all these diagnostic biomarker studies were performed with small numbers of cases (15-30) and controls (15-30), and the overlaps between their lists of potential biomarkers are minimal. The cases and controls are defined using clinically relevant criteria.
We recently completed a relatively large, multi-institutional study with 80 cases and 80 controls to look into this. All clinical specimens were processed together as a batch at one of the institutions. Analyses of our data suggests that there is no association of gene expression with presence of the disease. Using data from one of the published studies that used the same gene expression measurement technology as this study, to calculate effect-size with the SSPA R package based on moderated t statistics as per the limma R package, this study has a power of ~92% at alpha=0.10.
Analyses that we have performed are:
unsupervised hierarchical clustering based on various kinds of distance measurements (subjective evaluation)
principal component analysis; traditional and matrix plots (subjective evaluation)
various kinds of two-group tests for differential expression of genes (significance threshold of 0.05 for P values adjusted for false discovery)
classification/prediction analyses with leave-one-out and Monte Carlo cross-validations using linear kernel support vector machines (SVM) and top-scoring pair (TSP) methods; for SVM, variable selection is based on differential expression measures; TSP does not require differential expression (accuracy rates in these analyses are in the 45%-55% range)
confirming absence of differential expression for some of the genes, identified in the previous studies as putative disease markers, after quantifying gene expression using a different technology (reverse transcription-PCR instead of hybridization microarray)
Are these enough to have confidence in the negative finding of our study, that gene expression measurements cannot be used for disease diagnosis? Is there any other type of subjective or objective evaluation of the expression data-set that I should do?
I understand the usual caveats for such conclusions. E.g., there might be sub-groups of cases where disease markers may exist, or that the sensitivity or specificity of the measurement technology may not have been adequate enough for some true marker genes to be picked up.