Gene expression profile of empirically delineated classes of unexplained chronic fatigue

Author:

Carmel Liran12345,Efroni Sol12345,White Peter D12345,Aslakson Eric12345,Vollmer-Conna Ute12345,Rajeevan Mangalathu S12345

Affiliation:

1. National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA

2. National Cancer Institute Center for Bioinformatics, National Institutes of Health, Bethesda, Maryland, USA

3. University of London, Department of Psychological Medicine, Barts, London and Queen Mary School of Medicine and Dentistry, London, UK

4. Centers for Disease Control and Prevention, 1600 Clifton Road, MSG 41, Atlanta, GA 30333, USA.

5. University of New South Wales, School of Psychiatry, Sydney, Australia

Abstract

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.

Publisher

Future Medicine Ltd

Subject

Pharmacology,Genetics,Molecular Medicine

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