Influence of Sociodemographic Characteristics and Inflammation-Related Gene Variants on the Comfort Level of Caregivers of Patients With Head and Neck Cancer

Background: Sociodemographic characteristics and inflammatory cytokines, such as interleukin (IL)-1β, IL-1 cytokine receptor type 2 (IL1R2), IL-6, and triggering receptor expressed on myeloid cells like 2 (TREML2), may influence psychological disorders, including discomfort. Single-nucleotide variants (SNVs) determine individual differences for the modulation of cytokines and indicate that genetics may also influence the comfort levels. However, the relationship between sociodemographic characteristics, holistic comfort, and the roles played by IL1B rs16944, IL1R2 rs4141134, IL6 rs1800795, and TREML2 rs3747742 SNVs on the comfort levels of family caregivers (FCGs) of head and neck cancer (HNC) patients in palliative care (PC) is unknown. Thus, its investigation consisted in the aim of the present study. Methods: A questionnaire was applied to obtain sociodemographic information on 95 FCGs. The genotypes were identified using TaqMan assays. The Holistic Comfort Questionnaire for the Caregiver, which consists of 49 questions, was used to measure comfort levels. Differences between groups were assessed by the t test and linear regression. Results: Employed FCGs ( p = .04), those youngest ( p = .04), smokers ( p = .04), and those with IL1R2 GA or AA genotypes ( p = .03) presented lower comfort regarding the overall, environmental, sociocultural, and psychospiritual domains, respectively. Conclusions: Employment status, smoking habit, young age, and SNV IL1R2 rs4141134 could influence the comfort levels of FCGs of patients with HNC in PC.

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The mediating effects of social support and self-efficacy on the relationship between social distress and emotional distress in head and neck cancer outpatients with facial disfigurement

Psycho-Oncology ◽

10.1002/pon.1877 ◽

2010 ◽

Vol 21 (2) ◽

pp. 144-152 ◽

Cited By ~ 24

Author(s):

Minako Deno ◽

Mie Tashiro ◽

Mitsunori Miyashita ◽

Takahiro Asakage ◽

Koji Takahashi ◽

...

Keyword(s):

Social Support ◽

Head And Neck Cancer ◽

Head And Neck ◽

Emotional Distress ◽

Neck Cancer ◽

Self Efficacy ◽

Mediating Effects ◽

Facial Disfigurement ◽

Social Distress ◽

The Relationship

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Decomposing the subclonal structure of tumors with two-way mixture models on copy number aberrations

10.1101/278887 ◽

2018 ◽

Author(s):

An-Shun Tai ◽

Chien-Hua Peng ◽

Shih-Chi Peng ◽

Wen-Ping Hsieh

Keyword(s):

Head And Neck Cancer ◽

Head And Neck ◽

Neck Cancer ◽

Copy Number ◽

Tumor Heterogeneity ◽

Tumor Evolution ◽

Depth Information ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Copy Number Aberrations

AbstractMultistage tumorigenesis is a dynamic process characterized by the accumulation of mutations. Thus, a tumor mass is composed of genetically divergent cell subclones. With the advancement of next-generation sequencing (NGS), mathematical models have been recently developed to decompose tumor subclonal architecture from a collective genome sequencing data. Most of the methods focused on single-nucleotide variants (SNVs). However, somatic copy number aberrations (CNAs) also play critical roles in carcinogenesis. Therefore, further modeling subclonal CNAs composition would hold the promise to improve the analysis of tumor heterogeneity and cancer evolution. To address this issue, we developed a two-way mixture Poisson model, named CloneDeMix for the deconvolution of read-depth information. It can infer the subclonal copy number, mutational cellular prevalence (MCP), subclone composition, and the order in which mutations occurred in the evolutionary hierarchy. The performance of CloneDeMix was systematically assessed in simulations. As a result, the accuracy of CNA inference was nearly 93% and the MCP was also accurately restored. Furthermore, we also demonstrated its applicability using head and neck cancer samples from TCGA. Our results inform about the extent of subclonal CNA diversity, and a group of candidate genes that probably initiate lymph node metastasis during tumor evolution was also discovered. Most importantly, these driver genes are located at 11q13.3 which is highly susceptible to copy number change in head and neck cancer genomes. This study successfully estimates subclonal CNAs and exhibit the evolutionary relationships of mutation events. By doing so, we can track tumor heterogeneity and identify crucial mutations during evolution process. Hence, it facilitates not only understanding the cancer development but finding potential therapeutic targets. Briefly, this framework has implications for improved modeling of tumor evolution and the importance of inclusion of subclonal CNAs.

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A Bayesian Sensitivity Analysis to Partition Body Mass Index Into Components of Body Composition: An Application to Head and Neck Cancer Survival

American Journal of Epidemiology ◽

10.1093/aje/kwz188 ◽

2019 ◽

Vol 188 (11) ◽

pp. 2031-2039

Author(s):

Patrick T Bradshaw ◽

Jose P Zevallos ◽

Kathy Wisniewski ◽

Andrew F Olshan

Keyword(s):

Body Mass Index ◽

Sensitivity Analysis ◽

Body Composition ◽

Head And Neck Cancer ◽

Head And Neck ◽

Body Mass ◽

Neck Cancer ◽

Hazard Ratio ◽

Unit Increase ◽

The Relationship

Abstract Previous studies have suggested a “J-shaped” relationship between body mass index (BMI, calculated as weight (kg)/height (m)2) and survival among head and neck cancer (HNC) patients. However, BMI is a vague measure of body composition. To provide greater resolution, we used Bayesian sensitivity analysis, informed by external data, to model the relationship between predicted fat mass index (FMI, adipose tissue (kg)/height (m)2), lean mass index (LMI, lean tissue (kg)/height (m)2), and survival. We estimated posterior median hazard ratios and 95% credible intervals for the BMI-mortality relationship in a Bayesian framework using data from 1,180 adults in North Carolina with HNC diagnosed between 2002 and 2006. Risk factors were assessed by interview shortly after diagnosis and vital status through 2013 via the National Death Index. The relationship between BMI and all-cause mortality was convex, with a nadir at 28.6, with greater risk observed throughout the normal weight range. The sensitivity analysis indicated that this was consistent with opposing increases in risk with FMI (per unit increase, hazard ratio = 1.04 (1.00, 1.08)) and decreases with LMI (per unit increase, hazard ratio = 0.90 (0.85, 0.95)). Patterns were similar for HNC-specific mortality but associations were stronger. Measures of body composition, rather than BMI, should be considered in relation to mortality risk.

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