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DNA methylation profiles of cancer-related fatigue associated with markers of inflammation and immunometabolism


Cancer patients are commonly affected by fatigue. Herein, we sought to examine epigenetic modifications (i.e., DNA methylation) related to fatigue in peripheral blood among patients during and after treatment for head and neck cancer (HNC). Further, we determined whether these modifications were associated with gene expression and inflammatory protein markers, which we have previously linked to fatigue in HNC. This prospective, longitudinal study enrolled eligible patients with data collected at pre-radiotherapy, end of radiotherapy, and six months and one-year post-radiotherapy. Fatigue data were reported by patients using the Multidimensional Fatigue Inventory (MFI)-20. DNA methylation (Illumina MethylationEPIC) and gene expression (Applied Biosystems Clariom S) arrays and assays for seven inflammatory markers (R&D Systems multiplex) were performed. Mixed models and enrichment analyses were applied to establish the associations. A total of 386 methylation loci were associated with fatigue among 145 patients (False Discovery Rate [FDR] < 0.05). Enrichment analyses showed the involvement of genes related to immune and inflammatory responses, insulin and lipid metabolism, neuropsychological disorders, and tumors. We further identified 16 methylation-gene expression pairs (FDR < 0.05), which were linked to immune and inflammatory responses and lipid metabolism. Ninety-one percent (351) of the 386 methylation loci were also significantly associated with inflammatory markers (e.g., interleukin 6, c-reactive protein; FDR < 0.05), which further mediated the association between methylation and fatigue (FDR < 0.05). These data suggest that epigenetic modifications associated with inflammation and immunometabolism, in conjunction with relevant gene expression and protein markers, are potential targets for treating fatigue in HNC patients. The findings also merit future prospective studies in other cancer populations as well as interventional investigations.

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Fig. 1
Fig. 2
Fig. 3: Scatter plots of the top 10 significant methylation sites in the promoter region associated with fatigue.
Fig. 4: Scatter plots of the top 10 significant methylation sites in the promoter region associated with inflammatory markers.

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Data availability

The data analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

Codes will be available upon request and communication with the corresponding author.


  1. Goldstein D, Bennett B, Friedlander M, Davenport T, Hickie I, Lloyd A. Fatigue states after cancer treatment occur both in association with, and independent of, mood disorder: a longitudinal study. BMC Cancer. 2006;6:240.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Fang FM, Liu YT, Tang Y, Wang CJ, Ko SF. Quality of life as a survival predictor for patients with advanced head and neck carcinoma treated with radiotherapy. Cancer. 2004;100:425–32.

    Article  PubMed  Google Scholar 

  3. Hickok JT, Morrow GR, Roscoe JA, Mustian K, Okunieff P. Occurrence, severity, and longitudinal course of twelve common symptoms in 1129 consecutive patients during radiotherapy for cancer. J Pain Symptom Manag. 2005;30:433–42.

    Article  Google Scholar 

  4. Collado-Hidalgo A, Bower JE, Ganz PA, Cole SW, Irwin MR. Inflammatory biomarkers for persistent fatigue in breast cancer survivors. Clin Cancer Res. 2006;12:2759–66.

    Article  CAS  PubMed  Google Scholar 

  5. Miller AH, Ancoli-Israel S, Bower JE, Capuron L, Irwin MR. Neuroendocrine-immune mechanisms of behavioral comorbidities in patients with cancer. J Clin Oncol. 2008;26:971–82.

    Article  CAS  PubMed  Google Scholar 

  6. Ren J, Singh BN, Huang Q, Li Z, Gao Y, Mishra P, et al. DNA hypermethylation as a chemotherapy target. Cell Signal. 2011;23:1082–93.

    Article  CAS  PubMed  Google Scholar 

  7. de Vega WC, Herrera S, Vernon SD, McGowan PO. Epigenetic modifications and glucocorticoid sensitivity in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). BMC Med Genomics. 2017;10:11.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Shanmugam MK, Sethi G. Role of epigenetics in inflammation-associated diseases. Subcell Biochem. 2013;61:627–57.

    Article  CAS  PubMed  Google Scholar 

  9. Xiao C, Beitler JJ, Peng G, Levine ME, Conneely KN, Zhao H, et al. Epigenetic age acceleration, fatigue, and inflammation in patients undergoing radiation therapy for head and neck cancer: A longitudinal study. Cancer. 2021;127:3361–71.

    Article  CAS  PubMed  Google Scholar 

  10. Smets EM, Garssen B, Bonke B, De Haes JC. The Multidimensional Fatigue Inventory (MFI) psychometric qualities of an instrument to assess fatigue. J Psychosom Res. 1995;39:315–25.

    Article  CAS  PubMed  Google Scholar 

  11. Fortin JP, Triche TJ Jr., Hansen KD. Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi. Bioinformatics. 2017;33:558–60.

    Article  CAS  PubMed  Google Scholar 

  12. Chen YA, Lemire M, Choufani S, Butcher DT, Grafodatskaya D, Zanke BW, et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics. 2013;8:203–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Fortin JP, Labbe A, Lemire M, Zanke BW, Hudson TJ, Fertig EJ, et al. Functional normalization of 450k methylation array data improves replication in large cancer studies. Genome Biol. 2014;15:503.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Du P, Zhang X, Huang CC, Jafari N, Kibbe WA, Hou L, et al. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinforma. 2010;11:587.

    Article  CAS  Google Scholar 

  15. Carvalho BS, Irizarry RA. A framework for oligonucleotide microarray preprocessing. Bioinformatics. 2010;26:2363–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 2003;31:e15.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Salas LA, Koestler DC, Butler RA, Hansen HM, Wiencke JK, Kelsey KT, et al. An optimized library for reference-based deconvolution of whole-blood biospecimens assayed using the Illumina HumanMethylationEPIC BeadArray. Genome Biol. 2018;19:64–64.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Cox MA, Cox TF. Multidimensional scaling. Handbook of data visualization. Springer; 2008. p. 315–47.

    Chapter  Google Scholar 

  19. Xiao C, Beitler JJ, Higgins KA, Conneely K, Dwivedi B, Felger J, et al. Fatigue is associated with inflammation in patients with head and neck cancer before and after intensity-modulated radiation therapy. Brain Behav Immun. 2016;52:145–52.

    Article  PubMed  Google Scholar 

  20. Tingley D, Yamamoto T, Hirose K, Keele L, Imai K. Mediation: R package for causal mediation analysis. J Stat Softw. 2014;59.

  21. Phipson B, Maksimovic J, Oshlack A. missMethyl: an R package for analyzing data from Illumina’s HumanMethylation450 platform. Bioinformatics. 2016;32:286–8.

    Article  CAS  PubMed  Google Scholar 

  22. Krämer A, Green J, Pollard J Jr., Tugendreich S. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics. 2014;30:523–30.

    Article  PubMed  Google Scholar 

  23. Liu P, Hwang JT. Quick calculation for sample size while controlling false discovery rate with application to microarray analysis. Bioinformatics. 2007;23:739–46.

    Article  CAS  PubMed  Google Scholar 

  24. Ang KK, Zhang Q, Rosenthal DI, Nguyen-Tan PF, Sherman EJ, Weber RS, et al. Randomized phase III trial of concurrent accelerated radiation plus cisplatin with or without cetuximab for stage III to IV head and neck carcinoma: RTOG 0522. J Clin Oncol. 2014;32:2940–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Yang Y, Zhao Y, Hu N, Zhao J, Bai Y. lncRNA KIAA0125 functions as a tumor suppressor modulating growth and metastasis of colorectal cancer via Wnt/β-catenin pathway. Cell Biol Int. 2019;43:1463–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Sandhu S, Sou IF, Hunter JE, Salmon L, Wilson CL, Perkins ND, et al. Centrosome dysfunction associated with somatic expression of the synaptonemal complex protein TEX12. Commun Biol. 2021;4:1371.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Ligthart S, Marzi C, Aslibekyan S, Mendelson MM, Conneely KN, Tanaka T, et al. DNA methylation signatures of chronic low-grade inflammation are associated with complex diseases. Genome Biol. 2016;17:255.

    Article  PubMed  PubMed Central  Google Scholar 

  28. CACNA2DS. 2022.

  29. ZNRF2. 2022.

  30. database Gthg. KCNE1. 2022.

  31. Luciano M, Miyajima F, Lind PA, Bates TC, Horan M, Harris SE, et al. Variation in the dysbindin gene and normal cognitive function in three independent population samples. Genes Brain Behav. 2009;8:218–27.

    Article  CAS  PubMed  Google Scholar 

  32. Dai Y, Yang Y, MacLeod V, Yue X, Rapraeger AC, Shriver Z, et al. HSulf-1 and HSulf-2 are potent inhibitors of myeloma tumor growth in vivo. J Biol Chem. 2005;280:40066–73.

    Article  CAS  PubMed  Google Scholar 

  33. ADGRE3. 2022.

  34. STAT4. 2022.

  35. SDHD. 2022.

  36. TIMM8B. 2022.

  37. database Gthg. LAPTM4A. 2022.

  38. Visone R, Bacalini MG, Di Franco S, Ferracin M, Colorito ML, Pagotto S, et al. DNA methylation of shelf, shore and open sea CpG positions distinguish high microsatellite instability from low or stable microsatellite status colon cancer stem cells. Epigenomics. 2019;11:587–604.

    Article  CAS  PubMed  Google Scholar 

  39. Ollikainen M, Ismail K, Gervin K, Kyllönen A, Hakkarainen A, Lundbom J, et al. Genome-wide blood DNA methylation alterations at regulatory elements and heterochromatic regions in monozygotic twins discordant for obesity and liver fat. Clin Epigenetics. 2015;7:39.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Wagner JR, Busche S, Ge B, Kwan T, Pastinen T, Blanchette M. The relationship between DNA methylation, genetic and expression inter-individual variation in untransformed human fibroblasts. Genome Biol. 2014;15:R37.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Edgar R, Tan PPC, Portales-Casamar E, Pavlidis P. Meta-analysis of human methylomes reveals stably methylated sequences surrounding CpG islands associated with high gene expression. Epigenetics Chromatin. 2014;7:28–28.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Man SM, Karki R, Kanneganti T-D. AIM2 inflammasome in infection, cancer, and autoimmunity: Role in DNA sensing, inflammation, and innate immunity. Eur J Immunol. 2016;46:269–80.

    Article  CAS  PubMed  Google Scholar 

  43. Nkiliza A, Parks M, Cseresznye A, Oberlin S, Evans JE, Darcey T, et al. Sex-specific plasma lipid profiles of ME/CFS patients and their association with pain, fatigue, and cognitive symptoms. J Transl Med. 2021;19:370.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Fellows Maxwell K, Wahls T, Browne RW, Rubenstein L, Bisht B, Chenard CA, et al. Lipid profile is associated with decreased fatigue in individuals with progressive multiple sclerosis following a diet-based intervention: Results from a pilot study. PloS one. 2019;14:e0218075.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Yadav V, Marracci G, Kim E, Spain R, Cameron M, Overs S, et al. Low-fat, plant-based diet in multiple sclerosis: A randomized controlled trial. Mult Scler Relat Disord. 2016;9:80–90.

    Article  PubMed  Google Scholar 

  46. Leuti A, Fazio D, Fava M, Piccoli A, Oddi S, Maccarrone M. Bioactive lipids, inflammation and chronic diseases. Adv Drug Deliv Rev. 2020;159:133–69.

    Article  CAS  PubMed  Google Scholar 

  47. Miller AH, Haroon E, Raison CL, Felger JC. Cytokine targets in the brain: impact on neurotransmitters and neurocircuits. Depression anxiety. 2013;30:297–306.

    Article  CAS  PubMed  Google Scholar 

  48. Capuron L, Pagnoni G, Drake DF, Woolwine BJ, Spivey JR, Crowe RJ, et al. Dopaminergic mechanisms of reduced basal ganglia responses to hedonic reward during interferon-alpha administration. Archives General Psychiatry. 2012;69:1044–53.

  49. Crawford B, Craig Z, Mansell G, White I, Smith A, Spaull S, et al. DNA methylation and inflammation marker profiles associated with a history of depression. Hum Mol Genet. 2018;27:2840–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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The study was supported by NINR at NIH (grant number K99/R00NR014587, R01NR015783) and NCI at NIH (grant number P30CA138292).

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Authors and Affiliations



Conceptualization: CX, GP, KC, DB, AM; Data curation: CX, JF, EW, KH, DS, NS, DB, AM; Formal analysis: CX, GP, HZ, KC; Funding acquisition: CX, KC, DB, AM; Investigation: CX, KH, DS, NS, DB, AM; Methodology: CX, GP, KC, HZ, JF, EW, KH, DS, NS, DB, AM; Project administration: CX, EW; Validation: CX, KC, HZ, JF; Writing – original draft: CX, GP, KC, AM; Writing – review & editing: CX, GP, KC, HZ, JF, EW, KH, DS, NS, DB, AM.

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Correspondence to Canhua Xiao.

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Xiao, C., Peng, G., Conneely, K.N. et al. DNA methylation profiles of cancer-related fatigue associated with markers of inflammation and immunometabolism. Mol Psychiatry (2024).

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