Elham Kazemian, Qianxing Mo, Marco Matejcic, Ya-Yu Tsai, Daniel Sobieski, Xiaoyin Li, Aasha I Hoogland, Sylvia L Crowder, Brian D Gonzalez, Laura B Oswald, Alix G Sleight, Nathalie Nguyen, Nicole C Loroña, Victoria Damerell, Khaled R Komrokji, Kathi Mooney, Mary C Playdon, Cornelia M Ulrich, Christopher I Li, David Shibata, Adetunji T Toriola, Jennifer Ose, Anita R Peoples, Sheetal Hardikar, Christoph Kahlert, Erin M Siegel, Julienne E Bower, Stephanie L Schmit, Biljana Gigic, Heather S L Jim, Jane C Figueiredo
{"title":"Genetic predisposition to persistent fatigue after a diagnosis of colorectal cancer","authors":"Elham Kazemian, Qianxing Mo, Marco Matejcic, Ya-Yu Tsai, Daniel Sobieski, Xiaoyin Li, Aasha I Hoogland, Sylvia L Crowder, Brian D Gonzalez, Laura B Oswald, Alix G Sleight, Nathalie Nguyen, Nicole C Loroña, Victoria Damerell, Khaled R Komrokji, Kathi Mooney, Mary C Playdon, Cornelia M Ulrich, Christopher I Li, David Shibata, Adetunji T Toriola, Jennifer Ose, Anita R Peoples, Sheetal Hardikar, Christoph Kahlert, Erin M Siegel, Julienne E Bower, Stephanie L Schmit, Biljana Gigic, Heather S L Jim, Jane C Figueiredo","doi":"10.1093/jnci/djaf140","DOIUrl":null,"url":null,"abstract":"Background Cancer-related fatigue (fatigue) is a common and persistent symptom after cancer treatment, yet the role of genetic susceptibility remains unclear. Methods We leveraged data from a prospective cohort study, ColoCare Study (ie, five U.S. sites and Germany). Fatigue was assessed at five timepoints using the EORTC QLQ-C30 fatigue subscale and analyzed as (1) a binary summary measure of the trajectory from diagnosis into survivorship (defined as severe: yes, no), (2) a mean score across all time points, and (3) the highest (ie, worst) score across all time points. We genotyped samples using Infinium Global Diversity Array with imputation using the TOPMed reference panel to conduct a genome-wide analysis (GWAS). SuSiE was used to identify independent secondary signals. Transcriptome-wide association studies (TWAS) using S-PrediXcan and MultiXcan were conducted to examine genetic regulation of gene expression. COLOC assessed whether variants identified in the GWAS influence gene expression through colocalization analysis. Results Among 1,219 participants, 31.0% experienced severe fatigue over the course of their diagnosis. A locus near LINC02505 on chromosome 4 was associated with severe fatigue (rs6531463, OR = 3.25, p = 3.88 × 10−8). When modeling mean fatigue levels, significantly associated variants were identified in or near NEK10 and SLC4A7. Integrative analyses linked the predicted expression of NEK10 in liver tissue to risk of fatigue (p < 4.36 × 10−6). Colocalization analysis identified genetic loci and gene expression near NEK10 (posterior probabilities > 0.9). Conclusions This study identified novel genetic loci associated with fatigue in CRC patients and may be useful for identifying high-risk individuals for preventative strategies.","PeriodicalId":501635,"journal":{"name":"Journal of the National Cancer Institute","volume":"23 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the National Cancer Institute","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/jnci/djaf140","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Background Cancer-related fatigue (fatigue) is a common and persistent symptom after cancer treatment, yet the role of genetic susceptibility remains unclear. Methods We leveraged data from a prospective cohort study, ColoCare Study (ie, five U.S. sites and Germany). Fatigue was assessed at five timepoints using the EORTC QLQ-C30 fatigue subscale and analyzed as (1) a binary summary measure of the trajectory from diagnosis into survivorship (defined as severe: yes, no), (2) a mean score across all time points, and (3) the highest (ie, worst) score across all time points. We genotyped samples using Infinium Global Diversity Array with imputation using the TOPMed reference panel to conduct a genome-wide analysis (GWAS). SuSiE was used to identify independent secondary signals. Transcriptome-wide association studies (TWAS) using S-PrediXcan and MultiXcan were conducted to examine genetic regulation of gene expression. COLOC assessed whether variants identified in the GWAS influence gene expression through colocalization analysis. Results Among 1,219 participants, 31.0% experienced severe fatigue over the course of their diagnosis. A locus near LINC02505 on chromosome 4 was associated with severe fatigue (rs6531463, OR = 3.25, p = 3.88 × 10−8). When modeling mean fatigue levels, significantly associated variants were identified in or near NEK10 and SLC4A7. Integrative analyses linked the predicted expression of NEK10 in liver tissue to risk of fatigue (p < 4.36 × 10−6). Colocalization analysis identified genetic loci and gene expression near NEK10 (posterior probabilities > 0.9). Conclusions This study identified novel genetic loci associated with fatigue in CRC patients and may be useful for identifying high-risk individuals for preventative strategies.