Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Multicenter Study
. 2017 Mar:91:187-210.
doi: 10.1016/j.cyto.2016.12.023. Epub 2017 Jan 19.

Inflammatory pathway genes associated with inter-individual variability in the trajectories of morning and evening fatigue in patients receiving chemotherapy

Fay Wright  1 , Marilyn Hammer  2 , Steven M Paul  3 , Bradley E Aouizerat  4 , Kord M Kober  3 , Yvette P Conley  5 , Bruce A Cooper  3 , Laura B Dunn  6 , Jon D Levine  7 , Gail DEramo Melkus  8 , Christine Miaskowski  9
Affiliations
Multicenter Study

Inflammatory pathway genes associated with inter-individual variability in the trajectories of morning and evening fatigue in patients receiving chemotherapy

Fay Wright et al. Cytokine. 2017 Mar.

Abstract

Fatigue, a highly prevalent and distressing symptom during chemotherapy (CTX), demonstrates diurnal and interindividual variability in severity. Little is known about the associations between variations in genes involved in inflammatory processes and morning and evening fatigue severity during CTX. The purposes of this study, in a sample of oncology patients (N=543) with breast, gastrointestinal (GI), gynecological (GYN), or lung cancer who received two cycles of CTX, were to determine whether variations in genes involved in inflammatory processes were associated with inter-individual variability in initial levels as well as in the trajectories of morning and evening fatigue. Patients completed the Lee Fatigue Scale to determine morning and evening fatigue severity a total of six times over two cycles of CTX. Using a whole exome array, 309 single nucleotide polymorphisms SNPs among the 64 candidate genes that passed all quality control filters were evaluated using hierarchical linear modeling (HLM). Based on the results of the HLM analyses, the final SNPs were evaluated for their potential impact on protein function using two bioinformational tools. The following inflammatory pathways were represented: chemokines (3 genes); cytokines (12 genes); inflammasome (11 genes); Janus kinase/signal transducers and activators of transcription (JAK/STAT, 10 genes); mitogen-activated protein kinase/jun amino-terminal kinases (MAPK/JNK, 3 genes); nuclear factor-kappa beta (NFkB, 18 genes); and NFkB and MAP/JNK (7 genes). After controlling for self-reported and genomic estimates of race and ethnicity, polymorphisms in six genes from the cytokine (2 genes); inflammasome (2 genes); and NFkB (2 genes) pathways were associated with both morning and evening fatigue. Polymorphisms in six genes from the inflammasome (1 gene); JAK/STAT (1 gene); and NFkB (4 genes) pathways were associated with only morning fatigue. Polymorphisms in three genes from the inflammasome (2 genes) and the NFkB (1 gene) pathways were associated with only evening fatigue. Taken together, these findings add to the growing body of evidence that suggests that morning and evening fatigue are distinct symptoms.

Keywords: Cancer; Chemotherapy; Diurnal variability; Fatigue; Genes; Hierarchical linear modeling; Inflammation.

PubMed Disclaimer

Conflict of interest statement

Conflicts of interest: The authors have no conflicts of interest to declare.

Figures

Figures 1
Figures 1
A–F – Unconditional piecewise model of mean morning fatigue scores for six assessment points over two cycles of chemotherapy (A). Influence of the recessive model (TT+TC vs. CC) of the rare C allele in IL12B rs3213094 on the inter-individual differences in the intercept for morning fatigue (B). Influence of the dominant model (CC vs. CA+AA) of the rare A allele in TNFA rs1041981 on the slope parameters for morning fatigue (C). Influence of the recessive model (AA+AG vs. GG) of the rare G allele in NOD2 rs2076756 on the inter-individual differences in the intercept for morning fatigue (D). Influence of the recessive model (GG+GC vs. CC) of the rare C allele in NLRP5 rs471979 on the slope parameters for morning fatigue (E). Influence of the dominant model (TT vs. TC+CC) of the rare C allele in NLRP6 rs74044411 on the slope parameters for morning fatigue (F).
Figures 2
Figures 2
A–D – Influence of the liability score for the sum of the occurrence of the rare alleles in IL4R on the slope parameters for morning fatigue (A). Influence of the dominant model (AA vs. AG+GG) of the rare G allele for TNFRSF14 rs2234163 on the inter-individual differences in the intercept for morning fatigue (B). Influence of the liability score for the sum of the occurrence of the rare alleles in IL17RB, on the inter-individual differences in the intercept for morning fatigue (C). Influence of the liability score for the sum of the occurrence of the rare alleles in TNFRSF21 on the inter-individual differences in the intercept for morning fatigue (D).
Figures 3
Figures 3
A–C – Influence of the additive model (TT vs. TC vs. CC) of the rare C allele for TNFRSF10A rs17620 on the slope parameters for morning fatigue (A). Influence of the liability score for the sum of the occurrence of the rare alleles in TNFRSF10D on the slope parameters for morning fatigue (B). Influence of the liability score for the sum of the occurrence of the rare alleles in TNFRSF11A on the slope parameters for morning fatigue (C).
Figures 4
Figures 4
A–D – Unconditional piecewise model of mean evening fatigue scores for six assessment points over two cycles of chemotherapy (A). Influence of the recessive model (TT+TC vs. CC) of the rare C allele in IL12B rs3213094 on the inter-individual differences in the intercept for evening fatigue (B). Influence of the liability score for the sum of the occurrence of the rare alleles in IL12B on the inter-individual differences in the intercept for evening fatigue (C). Influence of the dominant model (CC vs. CA+AA) of the rare A allele in TNFA rs1041981 on the slope parameters for evening fatigue (D).
Figures 5
Figures 5
A–E – Influence of the additive model (TT vs. TC vs. CC) of the rare C allele in CARD6 rs10512747 on the inter-individual differences in the intercept for evening fatigue (A). Influence of the dominant model (CC vs. CG+GG) of the rare G allele for NLRP4 rs17857373 on the inter-individual differences in the intercept for evening fatigue (B). Influence of the recessive model (AA+AG vs. GG) of the rare G allele for NOD2 rs2076756 on the inter-individual differences in the intercept for evening fatigue (C). Influence of the dominant model (TT vs. TC+CC) of the rare C allele for NLRP6 rs74044411 on the slope parameters for evening fatigue (D). Influence of the dominant model (AA vs. AG+GG) for the rare G allele for IL17RD rs61742267 on the inter-individual differences in the intercept for evening fatigue (E).
Figures 6
Figures 6
A–C – Influence of the dominant model (TT vs. TC+CC) of the rare C allele for IL17RB rs2232346 and the recessive model (TT+TC vs. CC) of the rare C allele for IL17RB rs1043261 on the inter-individual differences in the intercept for evening fatigue (A). Influence of the dominant model (AA vs. AG+GG) of the rare G allele for TNFRSF14 rs2234163 on the inter-individual differences in the intercept for evening fatigue (B). Influence of the liability score for the sum of the occurrence of the rare alleles in LTBR on the slope parameters for evening fatigue (C).
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Berger AM, Mitchell SA, Jacobsen PB, Pirl WF. Screening, evaluation, and management of cancer-related fatigue: Ready for implementation to practice? CA Cancer J Clin. 2015;65(3):190–211. - PubMed
    1. Fisch MJ, Zhao F, O'Mara AM, Wang XS, Cella D, Cleeland CS. Predictors of significant worsening of patient-reported fatigue over a 1-month timeframe in ambulatory patients with common solid tumors. Cancer. 2014;120(3):442–450. - PMC - PubMed
    1. Huang HP, Chen ML, Liang J, Miaskowski C. Changes in and predictors of severity of fatigue in women with breast cancer: A longitudinal study. Int J Nurs Stud. 2014;51(4):582–592. - PubMed
    1. Ancoli-Israel S, Liu L, Rissling M, Natarajan L, Neikrug AB, Palmer BW, Mills PJ, Parker BA, Sadler GR, Maglione J. Sleep, fatigue, depression, and circadian activity rhythms in women with breast cancer before and after treatment: a 1-year longitudinal study. Support Care Cancer. 2014;22(9):2535–2545. - PMC - PubMed
    1. Trudel-Fitzgerald C, Savard J, Ivers H. Evolution of cancer-related symptoms over an 18-month period. J Pain Symptom Manage. 2013;45(6):1007–1018. - PubMed

Publication types