CONTEXT: Fatigue is the most common symptom associated with cancer and its treatment. Investigation of molecular mechanisms associated with fatigue may identify new therapeutic targets. OBJECTIVE: The objective of this pilot study was to evaluate the relationships between gene expression and methylation status and evening fatigue severity in women with breast cancer who received chemotherapy. METHODS: Latent class analysis (LCA) was used to identify evening fatigue phenotypes. In this analysis, the lowest (i.e., moderate, n = 7) and highest (i.e., very high, n = 29) fatigue-severity classes identified using LCA were analyzed via two stages. First, a total of 32,609 transcripts from whole blood were evaluated for differences in expression levels between the classes. Next, 637 methylation sites located within the putative transcription factor binding sites for those genes demonstrating differential expression were evaluated for differential methylation state between the classes. RESULTS: A total of 89 transcripts in 75 unique genes were differentially expressed between the moderate (the lowest fatigue-severity class identified) and very high evening fatigue classes. In addition, 23 differentially methylated probes and three differentially methylated regions were found between the moderate and very high evening fatigue classes. CONCLUSIONS: Using a multistaged integrated analysis of gene expression and methylation, differential methylation was identified in the regulatory regions of genes associated with previously hypothesized mechanisms for fatigue, including inflammation, immune function, neurotransmission, circadian rhythm, skeletal muscle energy, carbohydrate metabolism, and renal function as well as core biological processes including gene transcription and the cell-cycle regulation.
CONTEXT: Fatigue is the most common symptom associated with cancer and its treatment. Investigation of molecular mechanisms associated with fatigue may identify new therapeutic targets. OBJECTIVE: The objective of this pilot study was to evaluate the relationships between gene expression and methylation status and evening fatigue severity in women with breast cancer who received chemotherapy. METHODS: Latent class analysis (LCA) was used to identify evening fatigue phenotypes. In this analysis, the lowest (i.e., moderate, n = 7) and highest (i.e., very high, n = 29) fatigue-severity classes identified using LCA were analyzed via two stages. First, a total of 32,609 transcripts from whole blood were evaluated for differences in expression levels between the classes. Next, 637 methylation sites located within the putative transcription factor binding sites for those genes demonstrating differential expression were evaluated for differential methylation state between the classes. RESULTS: A total of 89 transcripts in 75 unique genes were differentially expressed between the moderate (the lowest fatigue-severity class identified) and very high evening fatigue classes. In addition, 23 differentially methylated probes and three differentially methylated regions were found between the moderate and very high evening fatigue classes. CONCLUSIONS: Using a multistaged integrated analysis of gene expression and methylation, differential methylation was identified in the regulatory regions of genes associated with previously hypothesized mechanisms for fatigue, including inflammation, immune function, neurotransmission, circadian rhythm, skeletal muscle energy, carbohydrate metabolism, and renal function as well as core biological processes including gene transcription and the cell-cycle regulation.
Authors: Jens S Andersen; Christopher J Wilkinson; Thibault Mayor; Peter Mortensen; Erich A Nigg; Matthias Mann Journal: Nature Date: 2003-12-04 Impact factor: 49.962
Authors: R S Ames; H M Sarau; J K Chambers; R N Willette; N V Aiyar; A M Romanic; C S Louden; J J Foley; C F Sauermelch; R W Coatney; Z Ao; J Disa; S D Holmes; J M Stadel; J D Martin; W S Liu; G I Glover; S Wilson; D E McNulty; C E Ellis; N A Elshourbagy; U Shabon; J J Trill; D W Hay; E H Ohlstein; D J Bergsma; S A Douglas Journal: Nature Date: 1999-09-16 Impact factor: 49.962
Authors: C Di Pietro; A Rapisarda; V Amico; C Bonaiuto; A Viola; M Scalia; S Motta; A Amato; H Engel; A Messina; G Sichel; K Grzeschik; M Purrello Journal: Cytogenet Cell Genet Date: 2000
Authors: D S Rickman; R Tyagi; X X Zhu; M P Bobek; S Song; M Blaivas; D E Misek; M A Israel; D M Kurnit; D A Ross; P E Kish; S M Hanash Journal: Cancer Res Date: 2001-03-01 Impact factor: 12.701
Authors: Michael L Whitfield; Gavin Sherlock; Alok J Saldanha; John I Murray; Catherine A Ball; Karen E Alexander; John C Matese; Charles M Perou; Myra M Hurt; Patrick O Brown; David Botstein Journal: Mol Biol Cell Date: 2002-06 Impact factor: 4.138
Authors: Kord M Kober; Man-Cheung Lee; Adam Olshen; Yvette P Conley; Marina Sirota; Michael Keiser; Marilyn J Hammer; Gary Abrams; Mark Schumacher; Jon D Levine; Christine Miaskowski Journal: Mol Pain Date: 2020 Jan-Dec Impact factor: 3.395