Gene expression profile of empirically delineated classes of unexplained chronic fatigue

Pharmacogenomics. 2006 Apr;7(3):375-86. doi: 10.2217/14622416.7.3.375.


Objectives: To identify the underlying gene expression profiles of unexplained chronic fatigue subjects classified into five or six class solutions by principal component (PCA) and latent class analyses (LCA).

Methods: Microarray expression data were available for 15,315 genes and 111 female subjects enrolled from a population-based study on chronic fatigue syndrome. Algorithms were developed to assign gene scores and threshold values that signified the contribution of each gene to discriminate the multiclasses in each LCA solution. Unsupervised dimensionality reduction was first used to remove noise or otherwise uninformative gene combinations, followed by supervised dimensionality reduction to isolate gene combinations that best separate the classes.

Results: The authors' gene score and threshold algorithms identified 32 and 26 genes capable of discriminating the five and six multiclass solutions, respectively. Pair-wise comparisons suggested that some genes (zinc finger protein 350 [ZNF350], solute carrier family 1, member 6 [SLC1A6], F-box protein 7 [FBX07] and vacuole 14 protein homolog [VAC14]) distinguished most classes of fatigued subjects from healthy subjects, whereas others (patched homolog 2 [PTCH2] and T-cell leukemia/lymphoma [TCL1A]) differentiated specific fatigue classes.

Conclusion: A computational approach was developed for general use to identify discriminatory genes in any multiclass problem. Using this approach, differences in gene expression were found to discriminate some classes of unexplained chronic fatigue, particularly one termed interoception.

MeSH terms

  • Algorithms
  • Computational Biology
  • Data Interpretation, Statistical
  • Fatigue Syndrome, Chronic / classification*
  • Fatigue Syndrome, Chronic / genetics*
  • Female
  • Gene Expression Profiling*
  • Humans
  • Linear Models
  • Oligonucleotide Array Sequence Analysis
  • Principal Component Analysis