A subset of participants from the Hispanic Colorectal Cancer Study (HCCS) provided a blood sample and completed additional self-report measures for this study from 2015–2016. HCCS is a population-based cohort study of individuals self-identified as Hispanic or Latino with a confirmed diagnosis of colorectal cancer (CRC) at any stage.¹⁶ All men and women over 21 years of age with a first-time diagnosis of CRC (International Classification of Diseases for Oncology, Third Edition codes: C18–C20) who were able to communicate in English or Spanish were eligible for participation. Cases were identified from the California Cancer Registry and/or directly from two hospitals in Los Angeles (LAC+USC County Hospital and USC Norris Comprehensive Cancer Center). HCCS participants completed risk factor questionnaires, but only participants who agreed to provide an additional blood sample, which was optional, were eligible to participate in the present study. The University of Southern California Institutional Review Board approved this study, and all procedures contributing to this work complied with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration.

Gene expression values were quantile-normalized,²² and then log2-transformed for analysis using standard linear statistical models to estimate the association of transcript abundance with (z-score transformed) MFSI-SF scores while controlling for age, sex, BMI, history of smoking (1/0), and history of alcohol consumption (1/0). Differentially expressed genes were identified as those showing ≥ 1.5-fold difference in expression over the 4-SD range of normal variation in MFSI-SF scores.

A 2-sample variant of the Transcription Element Listening System (TELiS²³) was applied to the sets of up- and down-regulated genes to test whether the observed differences in gene expression could be driven by specific a priori-hypothesized transcription factors involved in inflammation and immunologic activation (NF-κB, AP-1, CREB/ATF, GR, IRF, and STAT). These analyses tested the (log) ratio of transcription factor-binding motif prevalence for each factor in up- vs. down-regulated promoters, pooled across 9 alternative technical specifications involving parametric variations of promoter length (−300 bp, −600 bp, −1000 to +200 bp relative to the transcription start site) and TFBM detection stringency (MatSim .80, .90, .95).²³

To identify specific cell types that contributed to the empirically observed differences in gene expression, we applied Transcript Origin Analysis (TOA)²⁰ to separate lists of up- and down-regulated genes. TOA tested for significant over-representation of genes preponderantly expressed by a specific subset of PBMCs (i.e., monocytes, dendritic cells, CD4+ T cells, CD8+ T cells, B cells, or Natural Killer cells) using cell-specific reference transcriptomes derived by flow cytometric isolation as described above. We also tested whether MFSI-SF scores were associated with average expression of 19 gene transcripts previously utilized as a generalized index of pro-inflammatory gene expression (IL1A, IL1B, IL6, IL8, TNF, PTGS1, PTGS2, FOS, FOSB, FOSL1, FOSL2, JUN, JUNB, JUND, NFKB1, NFKB2, REL, RELA, RELB) or 34 gene transcripts previously utilized as a generalized index of Type I interferon activity (GBP1, IFI16, IFI27, IFI27L1-2, IFI30, IFI35, IFI44, IFI44L, IFI6, IFIH1, IFIT1-3, IFIT5, IFIT1L, IFITM1-3, IFITM4P, IFITM5, IFNB1, IRF2, IRF7-8, MX1-2, OAS1-3, OASL) and antibody synthesis (IGJ, IGLL1, IGLL3), as previously detailed.¹⁹ In all analyses, standard errors for observed bioinformatic statistics were derived from 200 cycles of bootstrap resampling of linear model residual vectors, while accounting for potential correlation among residuals across transcripts.

The sample comprised 89 colorectal cancer survivors with an average age of 60.5 years, majority male, moderately overweight BMI, and 2.9 years since diagnosis (Table 1). Over half the sample had a history of smoking regularly. Average MFSI-SF scores (M=7.4, SD=6.3) were similar to previous levels found among survivor samples (range=5–12)²⁴ and for population-based norms (M=8.4, SD=3.6)²⁵ but lower than advanced-stage cancer patient samples (range=13–19).²⁶ 18% of our sample showed MFSI-SF global scores >12 which is the national norm for chronically unhealthy individuals.²⁵

Analyses estimating the magnitude of association between leukocyte gene expression and MFSI-SF scores identified 280 gene transcripts showing ≥ 1.5-fold difference in expression over the 4-SD range of normal variation in MFSI-SF scores after controlling for age, sex, BMI, smoking, and alcohol use (155 up-regulated and 120 down-regulated). Up-regulated genes (Supplemental Table 1) included several transcripts associated with fatigue, such as the amyloid subunit synuclein-α (SNCA) and multiple hemoglobin subunits (HBA1, HBA2, HBB, HBG2).^12–15 Other up-regulated transcripts included indicators of immunologic activation (HLA-B, HLA-H, LTB) and related transcriptional regulators (EGR1, ETS1, ATF4, HMGB1, HMGB2, JUND, KLF2, EIF1), as well as several small nucleolar RNAs (SNORA45, SNORA63, SNORD3A, SNORD3C, SNORD3D, SNORD13, SNORD89). Down-regulated genes were difficult to characterize due to the preponderance of unannotated/un-named transcripts (LOC/HS./ORF) but included the lymphocyte differentiation marker LY6E, several keratin-associated proteins and cadherins (KRTAP6-3, KRTAP19-6, KRTAP21-1, KRTAP21-2, CHD5), and indicators of Type I interferon activity (ISG15, IFI6).

To identity the specific cell types mediating fatigue-related transcriptional alterations within the overall leukocyte pool, we conducted Transcript Origin Analysis (TOA) using reference cell transcriptome profiles derived from immunomagnetically isolated leukocyte subsets (CD4+ T cells, CD8+ T cells, B cells, NK cells, monocytes, and DC1, DC2, and DC3 dendritic cells). Results identified B lymphocytes, CD8+ T lymphocytes, and to a lesser extent, CD4+ T lymphocytes as the primary cellular origins of gene transcripts up-regulated in association with MFSI-SF scores (Figure 1a). No individual cell type emerged as a preponderant contributor of gene transcripts that were down-regulated in fatigue.

To identify specific transcription control pathways that might mediate the observed differences in gene expression, we conducted Transcription Element Listening System (TELiS) analyses comparing the prevalence of transcription factor-binding motifs in the promoters of genes that were up- vs. down-regulated in association with MFSI-SF scores. Results indicated up-regulation of NF-κB, CREB/ATF, and STAT family factors, down-regulated activity of IRF factors, and no indication of differential activity for AP-1 or GR (Figure 1b).

Previous studies have linked cancer-related fatigue to increased pro-inflammatory signaling,¹¹ so we also tested whether MFSI-SF scores might be associated with increased expression of a previously defined set of 19 cardinal pro-inflammatory gene transcripts. Results showed a positive association at the trend level (p = .095; Figure 1c). Although not hypothesized a priori, we also tested a previously defined gene set tracking Type I interferon antiviral activity and antibody-related transcription (which is often found to track inversely with pro-inflammatory gene expression²⁷) and did not find a significant association with MFSI-SF scores in this sample.

One limitation involves the cross-sectional study design, which precludes drawing any conclusions regarding the direction of causal relationships among gene expression activity and fatigue. The observed associations may reflect a causal effect of immunologic activation on CNS processes involved in arousal and motivation (e.g., as in the “sickness behavior” literature).^4,34,35 However, the profile of transcription factor alterations observed here (elevated pro-inflammatory and reduced antiviral) is reminiscent of the stress-induced “Conserved Transcriptional Response to Adversity” profile,²⁷ suggesting that at least some of the observed transcriptomic correlates of fatigue could potentially reflect the effects of fatigue-related distress on immune cell gene regulation. Associations may also reflect the effects of stressful life circumstances that causally influence both experienced fatigue and immune cell gene expression. It is also important to note that no assessments of clinical health outcomes or immune function are available in this sample, so the implications of the observed transcriptome differences for health remain to be determined in future studies.

The present findings identify a systematic alteration in lymphocyte gene regulation that may serve as a target for future interventions to reduce cancer-associated fatigue. Based on these findings, therapeutic strategies the reduce CD8+ T cell and/or B cell activation should be considered for potential use in cases of severe or persistent cancer-related fatigue (e.g., monoclonal antibodies targeting cell type-specific activation receptors). Moreover, the RNA-based measures of CD8+ T cell and B cell activation, activation-related transcription factor activity (e.g., NF-κB, STAT, CREB), and specific replicated transcript alterations (SNCA, HBA, HBB) identified in this report may potentially be used as biomarkers to gauge the impact of pharmacologic or behavioral interventions to reduce fatigue.³⁶ The establishment of replicated molecular biomarkers thus opens new horizons for the clinical management of cancer-related fatigue.