title: Fatigue-related gene networks identified in CD14+ cells isolated from HIV-infected patients: part II: statistical analysis.
abstract: OBJECTIVE
  In limited samples of valuable biological tissues, univariate ranking methods of microarray analyses often fail to show significant differences among expression profiles. In order to allow for hypothesis generation, novel statistical modeling systems can be greatly beneficial. The authors applied new statistical approaches to solve the issue of limited experimental data to generate new hypotheses in CD14(+) cells of patients with HIV-related fatigue (HRF) and healthy controls.
  
  
  METHODS
  We compared gene expression profiles of CD14(+) cells of nucleoside reverse transcriptase inhibitor (NRTI)-treated HIV patients with low versus high fatigue to healthy controls (n = 5 each). With novel Bayesian modeling procedures, the authors identified 32 genes predictive of low versus high fatigue and 33 genes predictive of healthy versus HIV infection. Sparse association and liquid association networks further elucidated the possible biological pathways in which these genes are involved. RELEVANCE FOR NURSING PRACTICE: Genetic networks developed in a comprehensive Bayesian framework from small sample sizes allow nursing researchers to design future research approaches to address such issues as HRF.
  
  
  CONCLUSIONS
  The findings from this pilot study may take us one step closer to the development of useful biomarker targets for fatigue status. Specific and reliable tests are needed to diagnosis, monitor and treat fatigue and mitochondrial dysfunction.
authors.affiliation: Biobehavioral Nursing & Health Systems Department, School of Nursing, University of Washington, Seattle, WA 98195, USA. vossj@u.washington.edu
authors.forename: Joachim G
authors.initials: JG
authors.lastname: Voss
authors.forename: Adrian
authors.initials: A
authors.lastname: Dobra
authors.forename: Caryn
authors.initials: C
authors.lastname: Morse
authors.forename: Joseph A
authors.initials: JA
authors.lastname: Kovacs
authors.forename: Raghavan
authors.initials: R
authors.lastname: Raju
authors.forename: Robert L
authors.initials: RL
authors.lastname: Danner
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Munson
authors.forename: Carolea
authors.initials: C
authors.lastname: Logan
authors.forename: Zoila
authors.initials: Z
authors.lastname: Rangel
authors.forename: Joseph W
authors.initials: JW
authors.lastname: Adelsberger
authors.forename: Mary
authors.initials: M
authors.lastname: McLaughlin
authors.forename: Larry D
authors.initials: LD
authors.lastname: Adams
authors.forename: Marinos C
authors.initials: MC
authors.lastname: Dalakas
mesh_terms: D001499:Bayes Theorem; D053263:Gene Regulatory Networks; D015658:HIV Infections / Q000188:drug therapy / Q000235:genetics* / Q000276:immunology; D006801:Humans; D018950:Lipopolysaccharide Receptors / Q000276:immunology*; D018894:Reverse Transcriptase Inhibitors / Q000627:therapeutic use*
chemical_list: D018950:Lipopolysaccharide Receptors; D018894:Reverse Transcriptase Inhibitors
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