Stress Exposures Contribute to Worse Joint Morning and Evening Fatigue Profiles in Patients With Cancer During Chemotherapy.

OBJECTIVES: To evaluate differences among stress, resilience, and coping strategies related to morning and evening fatigue profiles (both low, low morning and moderate evening, both moderate, and both high). SAMPLE & SETTING: Data were collected from 1,334 adult patients with cancer receiving chemotherapy. METHODS & VARIABLES: Morning and evening fatigue severity were rated over two cycles of chemotherapy using the Lee Fatigue Scale. Latent profile analysis was used to identify patient subgroups with distinct joint morning and evening profiles. Data were collected on global, cancer-specific, and cumulative life stress; resilience; and coping strategies. Differences among the latent classes were evaluated using parametric and nonparametric tests. RESULTS: Compared to the other three classes, the both high class reported the highest stress scores, highest occurrence of and effects from a variety of stressful life events, lowest resilience scores, and higher use of disengagement coping strategies. The both high class met the criteria for subsyndromal post-traumatic stress disorder. IMPLICATIONS FOR NURSING: When patients report high levels of fatigue, detailed assessments of stress are warranted to provide tailored interventions.

Stability and consistency of symptom clusters in younger versus older patients receiving chemotherapy.

BACKGROUND: By 2035, the number of newly diagnosed cancer cases will double and over 50% will be in older adults. Given this rapidly growing demographic, a need exists to understand how age influences oncology patients' symptom burden. The study purposes were to evaluate for differences in the occurrence, severity, and distress of 38 symptoms in younger (< 60 years) versus older (≥ 60 years) oncology patients undergoing chemotherapy and to evaluate for differences in the stability and consistency of symptom clusters across the two age groups. METHODS: A total of 1329 patients were dichotomized into the younger and older groups. Patients completed demographic and clinical questionnaires prior to the initiation of their second or third cycle of chemotherapy. A modified version of Memorial Symptom Assessment Scale was used to evaluate the occurrence, severity, and distress of 38 common symptoms associated with cancer and its treatment. Differences between the two age groups in demographic and clinical characteristics and ratings of occurrence, severity, and distress for the 38 symptoms were evaluated using parametric and nonparametric tests. Exploratory factor analyses were done within each age group to identify symptom clusters using symptom occurrence rates. RESULTS: Compared to the younger group (14.8 (± 7.0)), older adults reported a lower mean number of symptoms (12.9 (± 7.2)). Older patients experienced lower occurrence rates for almost 50% of the symptoms. Regarding symptom clusters, an eight-factor solution was selected for both age groups. Across the two age groups, the eight symptom clusters (i.e., physical and cognitive fatigue, respiratory, psychological, hormonal, chemotherapy-related toxicity, weight gain, gastrointestinal, epithelial) were stable. However, symptoms within the physical and cognitive, chemotherapy-related toxicity, and gastrointestinal clusters were not consistent across the age groups. CONCLUSIONS: To be able to provide tailored and effective symptom management interventions to older oncology patients, routine assessments of the core symptoms unique to the symptom clusters identified for this group warrants consideration. The underlying mechanism(s) for these inconsistencies in symptom burden is an important focus for future studies.

Common and distinct risk factors that influence more severe and distressing shortness of breath profiles in oncology outpatients.

BACKGROUND: Shortness of breath occurs in 10%-70% of oncology patients. Very little is known about interindividual variability in its severity and distress and associated risk factors. Using latent profile analyses (LPAs), purpose was to identify subgroups of patients with distinct severity and distress profiles for shortness of breath as single symptom dimensions. In addition, a joint LPA was done using patients' severity AND distress ratings. For each of the three LPAs, differences among the shortness of breath classes in demographic, clinical, symptom, stress, and resilience characteristics were evaluated. METHODS: Patients completed ratings of severity and distress from shortness of breath a total of six times over two cycles of chemotherapy. All of the other measures were completed at enrollment (i.e., prior to the second or third cycle of chemotherapy). Separate LPAs were done using ratings of severity and distress, as well as a joint analysis using severity AND distress ratings. Differences among the latent classes were evaluated using parametric and nonparametric tests. RESULTS: For severity, two classes were identified (Slight to Moderate [91.6%] and Moderate to Severe [8.4%]). For distress, two classes were identified (A Little Bit to Somewhat [83.9%] and Somewhat to Quite a Bit [16.1%]). For the joint LPA, two classes were identified (Lower Severity and Distress [79.9%] and Higher Severity and Distress [20.1%]). While distinct risk factors were associated with each of the LPAs, across the three LPAs, the common risk factors associated with membership in the worse class included: a past or current history of smoking, poorer functional status, and higher comorbidity burden. In addition, these patients had a higher symptom burden and higher levels of cancer-specific stress. CONCLUSIONS: Clinicians can use the information provided in this study to identify high-risk patients and develop individualized interventions.

Torch-eCpG: a fast and scalable eQTM mapper for thousands of molecular phenotypes with graphical processing units.

BACKGROUND: Gene expression may be regulated by the DNA methylation of regulatory elements in cis, distal, and trans regions. One method to evaluate the relationship between DNA methylation and gene expression is the mapping of expression quantitative trait methylation (eQTM) loci (also called expression associated CpG loci, eCpG). However, no open-source tools are available to provide eQTM mapping. In addition, eQTM mapping can involve a large number of comparisons which may prevent the analyses due to limitations of computational resources. Here, we describe Torch-eCpG, an open-source tool to perform eQTM mapping that includes an optimized implementation that can use the graphical processing unit (GPU) to reduce runtime. RESULTS: We demonstrate the analyses using the tool are reproducible, up to 18 × faster using the GPU, and scale linearly with increasing methylation loci. CONCLUSIONS: Torch-eCpG is a fast, reliable, and scalable tool to perform eQTM mapping. Source code for Torch-eCpG is available at https://github.com/kordk/torch-ecpg .

Various types of stress and greater use of disengagement coping are associated with worse sleep disturbance in oncology patients undergoing chemotherapy.

Various types of stress and the choice of coping strategies may be risk factors for higher levels of sleep disturbance in oncology patients. Purposes were to evaluate for differences in global, cancer-specific, and cumulative life stress, as well as resilience and the use of coping strategies among three subgroups of patients with distinct sleep disturbance profiles (i.e., Low, High, Very High). Oncology outpatients (n = 1331) completed measures of global (Perceived Stress Scale), cancer-specific (Impact of Event Scale-Revised), and cumulative life (Life Stressor Checklist-Revised) stress, resilience (Connor-Davidson Resilience Scale) and coping (Brief Cope) prior to their second or third cycle of chemotherapy. Sleep disturbance was assessed six times over two chemotherapy cycles. Differences were evaluated using parametric and non-parametric tests. All stress measures showed a dose response effect (i.e., as the sleep disturbance profile worsened, levels of all types of stress increased). Compared to Low class, the other two classes reported higher levels of global perceived stress and higher occurrence rates and effect from previous stressful life events. Impact of Event Scale-Revised scores for the Very High class indicated post-traumatic symptomatology. Patients in High and Very High classes had resilience scores below the normative score for the United States population and used a higher number of disengagement coping strategies. Our findings suggest that very high levels of sleep disturbance are associated with higher levels of various types of stress, lower levels of resilience, and higher use of disengagement coping strategies. Clinicians need to perform routine assessments and implement symptom management interventions to reduce stress and encourage the use of engagement coping strategies.

Distinct Co-occurring Morning and Evening Fatigue Profiles in Patients With Gastrointestinal Cancers Receiving Chemotherapy.

BACKGROUND: Patients with gastrointestinal cancers experience diurnal variations in fatigue severity during chemotherapy that decrease their functional status and quality of life. OBJECTIVES: Study purposes were to identify subgroups of patients with distinct co-occurring morning and evening fatigue profiles and evaluate for differences among these subgroups in demographic, clinical, stress, and symptom characteristics. METHODS: Patients with gastrointestinal cancers (n = 405) completed questionnaires 6 times over 2 cycles of chemotherapy. The Lee Fatigue Scale was used to evaluate diurnal variations in fatigue severity. Latent profile analysis was used to identify subgroups of patients with distinct co-occurring morning AND evening fatigue profiles. Differences among the subgroups in demographic, clinical, stress, and symptom characteristics at enrollment were evaluated using parametric and nonparametric analyses. RESULTS: Two classes were identified, namely: low morning and moderate evening fatigue (ie, Low-Moderate, 60.0%) and high morning and high evening fatigue (ie, Both High, 40.0%). Compared with the Low-Moderate class, the Both High class was significantly younger, female, unmarried, and unemployed and lacked regular exercise. In addition, they had childcare responsibilities, lower annual income, lower functional status, higher comorbidity burden, and self-reported anemia and depression. Patients in the Both High class reported higher levels of anxiety, depressive symptoms, sleep disturbance, pain, and stress, and lower levels of energy and cognitive function. CONCLUSIONS: Findings provide new insights into the risk factors for higher levels of co-occurring morning and evening fatigue in patients with gastrointestinal cancers. IMPLICATIONS FOR PRACTICE: Clinicians can use this information to identify high-risk patients and develop personalized symptom management interventions.

Adverse Childhood Experiences and Higher Levels of Stress Are Associated With the Co-occurrence of Cancer-Related Cognitive Impairment and Anxiety.

OBJECTIVES: We sought to identify subgroups of patients with distinct joint cancer-related cognitive impairment (CRCI) AND anxiety profiles and evaluate for differences in demographic and clinical characteristics, as well as levels of global stress, cancer-specific stress, cumulative life stress, and resilience. DATA SOURCES: Patients (n = 1332) completed the Attentional Function Index and the Spielberger State Anxiety Inventory six times over two cycles of chemotherapy. Global, cancer-specific, and cumulative life stress and resilience were evaluated using Perceived Stress Scale, Impact of Event Scale-Revised, Life Stressor Checklist-Revised, and Connor-Davidson Resilience Scale, respectively. Latent profile analysis was used to identify subgroups of patients with distinct joint CRCI AND anxiety profiles. Differences were evaluated using parametric and nonparametric tests. RESULTS: Three classes were identified (ie, No CRCI and Low Anxiety [57.3%], Moderate CRCI and Moderate Anxiety [34.5%], and High CRCI and High Anxiety [8.2%]). All of the stress measures showed a dose-response effect (ie, as the CRCI AND anxiety profile worsened, scores for all three types of stress increased). The two highest symptom classes reported higher occurrence rates for six specific stressors (eg, emotional abuse, physical abuse, sexual harassment). CONCLUSIONS: Findings suggest that higher levels of co-occurring CRCI AND anxiety are associated with some common risk factors, as well as higher levels of stress and lower levels of resilience. Increased knowledge of modifiable risk factors and sources of stress associated with the co-occurrence of these two symptoms will assist clinicians to identify high-risk patients and implement individualized interventions.

Anxiety in oncology outpatients is associated with perturbations in pathways identified in anxiety focused network pharmacology research.

PURPOSE: Evaluate for perturbed signaling pathways associated with subgroups of patients with low versus high levels of state anxiety. These pathways were compared to the pathways identified across eight network pharmacology studies of the anxiolytic effect(s) of a variety of compounds. METHODS: Adult outpatients had a diagnosis of breast, gastrointestinal, gynecological, or lung cancer; had received chemotherapy within the preceding four weeks; and were scheduled to receive at least two additional cycles of chemotherapy. Latent profile analysis was used to identify subgroups of patients with distinct anxiety profiles based on Spielberger State Anxiety Inventory scores that were obtained six times over two cycles of chemotherapy. Blood samples were processed using RNA sequencing (i.e., RNA-seq sample, n = 244) and microarray (i.e., microarray sample; n = 256) technologies. Pathway perturbations were assessed using pathway impact analysis. Fisher's combined probability method was used to combine test results using a false discovery rate of 0.01. RESULTS: In the RNA-seq sample, 62.3% and 37.7% of the patients were in the low- and high-anxiety classes, respectively. In the microarray sample, 61.3% and 38.7% were in the low and high-anxiety classes, respectively. Forty-one perturbed signaling pathways were identified. Eight of these pathways were common to those identified in the network pharmacology studies. CONCLUSIONS: Findings increase our knowledge of the molecular mechanisms that underlie anxiety in patients receiving chemotherapy. This study provides initial insights into how anxiety in patients with cancer may share common mechanisms with anxiety in patients with other clinical conditions.

Risk Factors for a Higher Symptom Burden in Patients With Cancer During the COVID-19 Pandemic.

OBJECTIVES: To evaluate for subgroups of patients with distinct symptom profiles and differences in demographic and clinical characteristics and stress and resilience among these subgroups. SAMPLE & SETTING: 1,145 patients with cancer aged 18 years or older completed a survey online. Data were collected between May 2020 and February 2021. METHODS & VARIABLES: Patients completed measures for depression, state anxiety, cognitive function, morning fatigue, evening fatigue, morning energy, evening energy, sleep disturbance, pain, stress, and resilience. Latent class profile analysis was used to identify subgroups of patients with distinct symptom profiles. Differences among the subgroups on study measures were evaluated using parametric and nonparametric tests. RESULTS: Four distinct profiles were identified (none, low, high, and very high). Patients in the high and very high classes reported clinically meaningful levels of all nine symptoms. Differences among the four profiles for stress and resilience exhibited a dose-response effect. IMPLICATIONS FOR NURSING: Findings can serve as benchmark data of the symptom burden of patients with cancer following the COVID-19 pandemic.

The Multifactorial Model of Cancer-Related Cognitive Impairment.

PROBLEM IDENTIFICATION: Cancer-related cognitive impairment (CRCI) is common and is associated with cancer and its treatments. Evidence suggests that the causes are multifactorial, but the field is lacking a comprehensive conceptual model of CRCI to summarize existing knowledge and provide a way to understand and predict causal links, as well as to generate hypotheses. LITERATURE SEARCH: PubMed® and Google Scholar™ were searched, and 130 articles demonstrated several lacking factors needed for a more comprehensive CRCI model. DATA EVALUATION: The new multifactorial model of CRCI includes social determinants of health, patient-specific factors, co-occurring symptoms, treatment factors, and biologic mechanisms. SYNTHESIS: The multifactorial model of CRCI is based on established and emerging evidence. This model is inclusive of all cancer types and associated treatments. IMPLICATIONS FOR NURSING: Although it would be ideal to evaluate all the concepts and components in this model in a comprehensive fashion, investigators with existing datasets could evaluate portions of the model to determine directionality for some of the proposed relationships. The new model can be used to design preclinical and clinical studies of CRCI. Knowledge of the occurrence of CRCI and factors that contribute to this symptom will allow nurses to perform assessments of modifiable and nonmodifiable risk factors.

Palpitations and Co-Occurring Menopausal Symptoms in Women Prior to Breast Cancer Surgery.

OBJECTIVES: To determine the occurrence rate of palpitations in women prior to breast cancer surgery and evaluate for differences in demographic and clinical characteristics and menopausal symptoms in patients with and without palpitations. SAMPLE & SETTING: Presurgery data on palpitations and menopausal symptoms from 398 patients who underwent breast cancer surgery were analyzed. METHODS & VARIABLES: The Menopausal Symptoms Scale was used to evaluate the occurrence, severity, and distress of 46 symptoms, including palpitations. Parametric and nonparametric tests were used to evaluate for differences between patients with and without palpitations. RESULTS: Women with palpitations had lower annual income, lower functional status, higher comorbidity burden, and higher rates of back pain than women without palpitations. Women with palpitations had twice the number of menopausal symptoms and had higher occurrence rates for 39 of the 45 menopausal symptoms. They reported significantly higher severity scores for difficulty concentrating, dizziness, swollen hands/feet, and wake during the night, and higher distress scores for anxiety, hot flashes, swollen hands/feet, and wake during the night. IMPLICATIONS FOR NURSING: Clinicians should perform routine assessments of palpitations and make appropriate referrals to a cardiologist.

Neurotransmitter Gene Polymorphisms Are Associated with Symptom Clusters in Patients Undergoing Radiation Therapy.

OBJECTIVES: Purpose was to evaluate for associations between the severity of three distinct symptom clusters (ie, sickness-behavior, mood-cognitive, treatment-related) and polymorphisms for 16 genes involved in catecholaminergic, GABAergic, and serotonergic neurotransmission. DATA SOURCES: Patients with breast and prostate cancer (n = 157) completed study questionnaires at the completion of radiation therapy. Memorial Symptom Assessment Scale was used to assess the severity of 32 common symptoms. Three distinct symptom clusters were identified using exploratory factor analysis. Associations between the symptom cluster severity scores and neurotransmitter gene polymorphisms were evaluated using regression analyses. CONCLUSION: Severity scores for the sickness-behavior symptom cluster were associated with polymorphisms for solute carrier family 6 (SLC6A) member 2 (SLC6A2), SLC6A3, SLC6A1, and 5-hydroxytryptamine receptor (HTR) 2A (HTR2A) genes. For the mood-cognitive symptom cluster, severity scores were associated with polymorphisms for adrenoreceptor alpha 1D, SLC6A2, SLC6A3, SLC6A1, HTR2A, and HTR3A. Severity scores for the treatment-related symptom cluster were associated with polymorphisms for SLC6A2, SLC6A3, catechol-o-methyltransferase, SLC6A1, HTR2A, SLC6A4, and tryptophan hydroxylase 2. IMPLICATIONS FOR NURSING PRACTICE: Findings suggest that polymorphisms for several neurotransmitter genes are involved in the severity of sickness-behavior, mood-cognitive, and treatment-related symptom clusters in oncology patients at the completion of radiation therapy. Four genes with various associated polymorphisms were common across the three distinct symptom clusters (ie, SLC6A2, SLC6A3, SLC6A1, HTR2A) which suggest that these clusters have common underlying mechanisms.

Multifactorial Model of Dyspnea in Patients With Cancer.

PROBLEM IDENTIFICATION: Dyspnea is a common and distressing symptom for patients with cancer. Although the risk factors for dyspnea in patients with cancer are likely to be multifactorial, a comprehensive description of these risk factors and associated mechanisms is not available in the extant literature. LITERATURE SEARCH: A search of all relevant databases, including Cochrane Library, PubMed®, Embase®, Web of Science, and CINAHL®, was done from January 2009 to May 2022. Case-control and cohort studies that had either a cross-sectional or longitudinal design, as well as randomized controlled trials, were included in the review. Peer-reviewed, full-text articles in English were included. Nineteen studies reported on risk factors for dyspnea. DATA EVALUATION: The methodologic quality of each study was examined using the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. SYNTHESIS: A number of factors can influence the occurrence and severity of dyspnea. Using the Mismatch Theory of Dyspnea as the central core of this Multifactorial Model of Dyspnea in Patients With Cancer, the factors included in this conceptual model are person, clinical, and cancer-related factors, as well as respiratory muscle weakness, co-occurring symptoms, and stress. IMPLICATIONS FOR PRACTICE: The Multifactorial Model of Dyspnea in Patients With Cancer can be used by clinicians to evaluate for multiple factors that contribute to dyspnea and to develop individualized and multilevel interventions for patients experiencing this symptom.

Prediction of morning fatigue severity in outpatients receiving chemotherapy: less may still be more.

INTRODUCTION: Fatigue is the most common and debilitating symptom experienced by cancer patients undergoing chemotherapy (CTX). Prediction of symptom severity can assist clinicians to identify high-risk patients and provide education to decrease symptom severity. The purpose of this study was to predict the severity of morning fatigue in the week following the administration of CTX. METHODS: Outpatients (n = 1217) completed questionnaires 1 week prior to and 1 week following administration of CTX. Morning fatigue was measured using the Lee Fatigue Scale (LFS). Separate prediction models for morning fatigue severity were created using 157 demographic, clinical, symptom, and psychosocial adjustment characteristics and either morning fatigue scores or individual fatigue item scores. Prediction models were created using two regression and five machine learning approaches. RESULTS: Elastic net models provided the best fit across all models. For the EN model using individual LFS item scores, two of the 13 individual LFS items (i.e., "worn out," "exhausted") were the strongest predictors. CONCLUSIONS: This study is the first to use machine learning techniques to accurately predict the severity of morning fatigue from prior to through the week following the administration of CTX using total and individual item scores from the Lee Fatigue Scale (LFS). Our findings suggest that the language used to assess clinical fatigue in oncology patients is important and that two simple questions may be used to predict morning fatigue severity.

An Evaluation of the Multifactorial Model of Cancer-Related Cognitive Impairment.

BACKGROUND: Up to 45% of patients report cancer-related cognitive impairment (CRCI). A variety of characteristics are associated with the occurrence and/or severity of CRCI. However, an important gap in knowledge of risk factors for CRCI is the relative contribution of each factor. The multifactorial model of cancer-related cognitive impairment (MMCRCI) is a conceptual model of CRCI that can be used to evaluate the strength of relationships between various factors and CRCI. OBJECTIVES: The purpose of this study was to use structural regression methods to evaluate the MMCRCI using data from a large sample of outpatients receiving chemotherapy ( n = 1,343). Specifically, the relationships between self-reported CRCI and four MMCRCI concepts (i.e., social determinants of health, patient-specific factors, treatment factors, and co-occurring symptoms) were examined. The goals were to determine how well the four concepts predicted CRCI and determine the relative contribution of each concept to deficits in perceived cognitive function. METHODS: This study is part of a larger, longitudinal study that evaluated the symptom experience of oncology outpatients receiving chemotherapy. Adult patients were diagnosed with breast, gastrointestinal, gynecological, or lung cancer; had received chemotherapy within the preceding 4 weeks; were scheduled to receive at least two additional cycles of chemotherapy; were able to read, write, and understand English; and gave written informed consent. Self-reported CRCI was assessed using the attentional function index. Available study data were used to define the latent variables. RESULTS: On average, patients were 57 years of age, college educated, and with a mean Karnofsky Performance Status score of 80. Of the four concepts evaluated, whereas co-occurring symptoms explained the largest amount of variance in CRCI, treatment factors explained the smallest amount of variance. A simultaneous structural regression model that estimated the joint effect of the four exogenous latent variables on the CRCI latent variable was not significant. DISCUSSION: These findings suggest that testing individual components of the MMCRCI may provide useful information on the relationships among various risk factors, as well as refinements of the model. In terms of risk factors for CRCI, co-occurring symptoms may be more significant than treatment factors, patient-specific factors, and/or social determinants of health in patients receiving chemotherapy.

Distinct Profiles of Morning and Evening Fatigue Co-Occurrence in Patients During Chemotherapy.

BACKGROUND: Morning and evening fatigue are distinct and distressing symptoms experienced during chemotherapy that demonstrate a large amount of interindividual variability. OBJECTIVES: The objectives of this study were to identify subgroups of patients with distinct morning and evening fatigue co-occurrence profiles and evaluate for differences among these subgroups in demographic, clinical, and symptom characteristics and quality of life. METHODS: Oncology patients ( n = 1,334) completed the Lee Fatigue Scale to self-report morning and evening fatigue, six times over two cycles of chemotherapy. Latent profile analysis was used to identify subgroups of patients with distinct morning and evening physical fatigue profiles. RESULTS: Four distinct morning and evening fatigue profiles were identified (i.e., Both Low, Low Morning + Moderate Evening, Both Moderate, and Both High). Compared to the Both Low profile, the Both High profile was significantly younger, less likely to be married or partnered, more likely to live alone, had a higher comorbidity burden, and lower functional status. The Both High profile had higher levels of anxiety, depressive symptoms, sleep disturbance, and pain and lower levels of quality of life. DISCUSSION: The variability in the morning and evening severity scores among the four profiles supports the hypothesis that morning and evening fatigue are distinct but related symptoms. Clinically meaningful levels of both morning and evening fatigue were reported by 50.4% of our sample, which suggests that the co-occurrence of these two symptoms is relatively common. Patients in Both Moderate and Both High profiles experienced an extremely high symptom burden that warrants ongoing assessments and aggressive symptom management interventions.

Big Data in Oncology Nursing Research: State of the Science.

OBJECTIVE: To review the state of oncology nursing science as it pertains to big data. The authors aim to define and characterize big data, describe key considerations for accessing and analyzing big data, provide examples of analyses of big data in oncology nursing science, and highlight ethical considerations related to the collection and analysis of big data. DATA SOURCES: Peer-reviewed articles published by investigators specializing in oncology, nursing, and related disciplines. CONCLUSION: Big data is defined as data that are high in volume, velocity, and variety. To date, oncology nurse scientists have used big data to predict patient outcomes from clinician notes, identify distinct symptom phenotypes, and identify predictors of chemotherapy toxicity, among other applications. Although the emergence of big data and advances in computational methods provide new and exciting opportunities to advance oncology nursing science, several challenges are associated with accessing and using big data. Data security, research participant privacy, and the underrepresentation of minoritized individuals in big data are important concerns. IMPLICATIONS FOR NURSING PRACTICE: With their unique focus on the interplay between the whole person, the environment, and health, nurses bring an indispensable perspective to the interpretation and application of big data research findings. Given the increasing ubiquity of passive data collection, all nurses should be taught the definition, characteristics, applications, and limitations of big data. Nurses who are trained in big data and advanced computational methods will be poised to contribute to guidelines and policies that preserve the rights of human research participants.

Torch-eCpG: A fast and scalable eQTM mapper for thousands of molecular phenotypes with graphical processing units.

Background: Gene expression may be regulated by the DNA methylation of regulatory elements in cis, distal, and trans regions. One method to evaluate the relationship between DNA methylation and gene expression is the mapping of expression quantitative trait methylation (eQTM) loci (also called expression associated CpG loci, eCpG). However, no open-source tools are available to provide eQTM mapping. In addition, eQTM mapping can involve a large number of comparisons which may prevent the analyses due to limitations of computational resources. Here, we describe Torch-eCpG, an open-source tool to perform eQTM mapping that includes an optimized implementation that can use the graphical processing unit (GPU) to reduce runtime. Results: We demonstrate the analyses using the tool are reproducible, up to 18x faster using the GPU, and scale linearly with increasing methylation loci. Conclusions: Torch-eCpG is a fast, reliable, and scalable tool to perform eQTM mapping. Source code for Torch-eCpG is available at https://github.com/kordk/torch-ecpg.

Epigenetic Regulation of Inflammatory Mechanisms and a Psychological Symptom Cluster in Patients Receiving Chemotherapy.

BACKGROUND: A psychological symptom cluster is the most common cluster identified in oncology patients. Although inflammatory mechanisms are hypothesized to underlie this cluster, epigenetic contributions are unknown. OBJECTIVES: This study's purpose was to evaluate associations between the occurrence of a psychological symptom cluster and levels of DNA methylation for inflammatory genes in a heterogeneous sample of patients with cancer receiving chemotherapy. METHODS: Prior to their second or third cycle of chemotherapy, 1,071 patients reported the occurrence of 38 symptoms using the Memorial Symptom Assessment Scale. A psychological cluster was identified using exploratory factor analysis. Differential methylation analyses were performed in two independent samples using Illumina Infinium 450K and EPIC microarrays. Expression-associated CpG (eCpG) loci in the promoter region of 114 inflammatory genes on the 450K and 112 genes on the EPIC microarray were evaluated for associations with the psychological cluster. Robust rank aggregation was used to identify differentially methylated genes across both samples. Significance was assessed using a false discovery rate of 0.05 under the Benjamini-Hochberg procedure. RESULTS: Cluster of differentiation 40 ( CD40 ) was differentially methylated across both samples. All six promoter eCpGs for CD40 that were identified across both samples were hypomethylated in the psychological cluster group. CONCLUSIONS: This study is the first to suggest associations between a psychological symptom cluster and differential DNA methylation of a gene involved in tissue inflammation and cell-mediated immunity. Our findings suggest that increased CD40 expression through hypomethylation of promoter eCpG loci is involved in the occurrence of a psychological symptom cluster in patients receiving chemotherapy. These findings suggest a direction for mechanistic studies.

Perturbations in Neuroinflammatory Pathways Are Associated With a Worst Pain Profile in Oncology Patients Receiving Chemotherapy.

Unrelieved pain occurs in 55% of cancer patients. Identification of molecular mechanisms for pain may provide insights into therapeutic targets. Purpose was to evaluate for perturbations in neuroinflammatory pathways between oncology patients with and without severe pain. Worst pain severity was rated using a 0 to 10 numeric rating scale six times over two cycles of chemotherapy. Latent profile analysis was used to identify subgroups of patients with distinct pain profiles. Pathway impact analyses were performed in two independent samples using gene expression data obtained from RNA sequencing (n = 192) and microarray (n = 197) technologies. Fisher's combined probability test was used to identify significantly perturbed pathways between None versus the Severe pain classes. In the RNA sequencing and microarray samples, 62.5% and 56.3% of patients were in the Severe pain class, respectively. Nine perturbed pathways were related to neuroinflammatory mechanisms (i.e., retrograde endocannabinoid signaling, gamma-aminobutyric acid synapse, glutamatergic synapse, Janus kinase-signal transducer and activator of transcription signaling, phagosome, complement and coagulation cascades, cytokine-cytokine receptor interaction, chemokine signaling, calcium signaling). First study to identify perturbations in neuroinflammatory pathways associated with severe pain in oncology outpatients. Findings suggest that complex neuroimmune interactions are involved in the maintenance of chronic pain conditions. Perspective: In this study that compared oncology patients with none versus severe pain, nine perturbed neuroinflammatory pathways were identified. Findings suggest that complex neuroimmune interactions are involved in the maintenance of persistent pain conditions.