Recent research suggests higher circulating lymphocyte counts may protect against colorectal cancer (CRC). However, the role of specific lymphocyte subtypes and activation states remain unclear. CD4+ T cells-a highly dynamic lymphocyte subtype-undergo gene expression changes upon activation that are critical to their effector function. Previous studies using bulk tissue have limited our understanding of their role in CRC risk to static associations. We applied Mendelian randomization (MR) and genetic colocalisation to evaluate causal relationships of gene expression on CRC risk across multiple CD4+ T cell subtypes and activation states. Genetic proxies were obtained from single-cell transcriptomic data, allowing us to investigate the causal effect of expression of 1,805 genes across CD4+ T cell activation states on CRC risk (78,473 cases; 107,143 controls). Analyses were stratified by CRC anatomical subsites and sex, with sensitivity analyses assessing whether the observed effect estimates were likely to be CD4+ T cell-specific. We identified 6 genes-FADS2, FHL3, HLA-DRB1, HLA-DRB5, RPL28, and TMEM258-with strong evidence for a causal role in CRC development (FDR-P < 0.05; colocalisation H4 > 0.8). Causal estimates varied by CD4+ T cell subtype, activation state, CRC subsite and sex. However, many of genetic proxies used to instrument gene expression in CD4+ T cells also act as eQTLs in other tissues, highlighting the challenges of using genetic proxies to instrument tissue-specific expression changes. We demonstrate the importance of capturing the dynamic nature of CD4+ T cells in understanding CRC risk, and prioritize genes for further investigation in cancer prevention.

Cancer Epidemiology and Prevention Research Unit School of Public Health Imperial College London South Kensington Campus London SW7 2AZ United Kingdom

Center for Cancer Research Medical University of Vienna Borschkegasse 8a Vienna 1090 Austria

Department of Clinical Genetics Karolinska University Hospital Solna Stockholm 171 64 Sweden

Department of Clinical Pathology Colorectal Oncogenomics Group The University of Melbourne VCCC Building Level 10 305 Grattan St Parkville VIC 3010 Australia

Department of Endocrine and Metabolic Diseases Shanghai Institute of Endocrine and Metabolic Diseases Ruijin Hospital Shanghai Jiaotong University School of Medicine 227 South Chongqing Road Shanghai China

Department of Epidemiology and Biostatistics School of Public Health Imperial College London White City Campus 90 Wood Lane London W12 0BZ United Kingdom

Department of Epidemiology Harvard T H Chan School of Public Health 677 Huntington Avenue Boston MA 02115 United States

Department of Epidemiology University of Washington 1959 NE Pacific St F262 Seattle WA 98195 United States

Department of Laboratory Medicine and Pathology Mayo Clinic Arizona Pathology Research Core Mayo Clinic 13400 E Shea Blvd Scottsdale AZ 85259 United States

Department of Molecular Biology of Cancer Institute of Experimental Medicine of the Czech Academy of Sciences Videnska 1083 Prague 142 20 Czech Republic

Department of Molecular Medicine and Surgery Karolinska Institutet Karolinska University Hospital Solna Stockholm SE 171 76 Sweden

Department of Oncologic Pathology Dana Farber Cancer Institute 450 Brookline Avenue Boston MA 02215 United States

Department of Population and Public Health Sciences University of Southern California 1845 N Soto St Los Angeles CA 90032 United States

Division of Medical Oncology and Hematology Princess Margaret Cancer Centre 610 University Ave Toronto Canada ON M5G 2M9

Genomic Medicine and Family Cancer Clinic Royal Melbourne Hospital 300 Grattan St Parkville Melbourne VIC 3000 Australia

Institute of Environmental Medicine Karolinska Institutet Nobels väg 13 Solna Stockholm Sweden

MRC Integrative Epidemiology Unit University of Bristol Oakfield House Oakfield Grove Bristol BS8 2BN United Kingdom

Nutrition and Metabolism Branch International Agency for Research on Cancer WHO 150 cours Alber Thomas Lyon 69372 CEDEX 08 France

Population Health Sciences Bristol Medical School University of Bristol 1st Floor 5 Tyndall Avenue Bristol BS8 1UD United Kingdom

Program in MPE Molecular Pathological Epidemiology Department of Pathology Brigham and Women's Hospital Harvard Medical School 221 Longwood Ave Boston MA 02115 United States

Public Health Sciences Division Fred Hutchinson Cancer Center 1100 Fairview Ave Seattle WA 98109 1024 United States

School of Cellular and Molecular Medicine University of Bristol Biomedical Sciences Building University Walk Bristol BS8 1TD United Kingdom

Shanghai National Clinical Research Center for Metabolic Diseases Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission Shanghai National Center for Translational Medicine Ruijin Hospital Shanghai Jiaotong University School of Medicine 227 South Chongqing Road Shanghai China

Translational Health Sciences Bristol Medical School University of Bristol Dorothy Hodgkin Building Whitson Street Bristol BS1 3NY United Kingdom

University of Melbourne Centre for Cancer Research Victorian Comprehensive Cancer Centre Flemington Road Parkville VIC 3010 Australia

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medRxiv. 2025 Apr 17:2025.04.15.25325863. doi: 10.1101/2025.04.15.25325863. PubMed

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World Health Organization . Colorectal cancer; 2023. https://www.who.int/news-room/fact-sheets/detail/colorectal-cancer

World Cancer Research Fund, American Institute for Cancer Research . Diet, Nutrition, Physical Activity and Colorectal Cancer: a Global Perspective. Continuous Update Project Expert Report 2018; 2018. dietandcancerreport.org

Wu  J, et al.  Values of applying white blood cell counts in the prognostic evaluation of resectable colorectal cancer. Mol Med Rep. 2019:19:2330–2340. 10.3892/mmr.2019.9844 PubMed DOI

Rosman  Y, et al.  Changes in peripheral blood eosinophils may predict colorectal cancer—a retrospective study. World Allergy Organ J.  2022:15:100696. 10.1016/j.waojou.2022.100696 PubMed DOI PMC

Idos  GE, et al.  The prognostic implications of tumor infiltrating lymphocytes in colorectal cancer: a systematic review and meta-analysis. Sci Rep. 2020:10:3360. 10.1038/s41598-020-60255-4 PubMed DOI PMC

Constantinescu  AE, et al.  Circulating white blood cell traits and colorectal cancer risk: a Mendelian randomisation study. Int J Cancer. 2024:154:94–103. 10.1002/ijc.34691 PubMed DOI PMC

Tay  RE, Richardson  EK, Toh  HC. Revisiting the role of CD4+ T cells in cancer immunotherapy—new insights into old paradigms. Cancer Gene Ther. 2021:28:5–17. 10.1038/s41417-020-0183-x PubMed DOI PMC

Soskic  B, et al.  Immune disease risk variants regulate gene expression dynamics during CD4+ T cell activation. Nat Genet. 2022:54:817–826. 10.1038/s41588-022-01066-3 PubMed DOI PMC

Davey Smith  G, Ebrahim  S. Mendelian randomization: can genetic epidemiology contribute to understanding environmental determinants of disease?. Int J Epidemiol. 2003:32:1–22. 10.1093/ije/dyg070 PubMed DOI

Fernandez-Rozadilla  C, et al.  Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries. Nat Genet. 2023:55:89–99. 10.1038/s41588-022-01222-9 PubMed DOI PMC

Huyghe  JR, et al.  Genetic architectures of proximal and distal colorectal cancer are partly distinct. Gut. 2021:70:1325–1334. 10.1136/gutjnl-2020-321534 PubMed DOI PMC

Huyghe  JR, et al.  Discovery of common and rare genetic risk variants for colorectal cancer. Nat Genet. 2019:51:76–87. 10.1038/s41588-018-0286-6 PubMed DOI PMC

Lawlor  DA. Commentary: two-sample Mendelian randomization: opportunities and challenges. Int J Epidemiol. 2016:45:908–915. 10.1093/ije/dyw127 PubMed DOI PMC

Sanderson  E, et al.  Mendelian randomization. Nat Rev Methods Primers.  2022:2:6. 10.1038/s43586-021-00092-5 PubMed DOI PMC

Burgess  S, Thompson  SG. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011:40:755–764. 10.1093/ije/dyr036 PubMed DOI

Hemani  G, Tilling  K, Davey Smith  G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet. 2017:13:e1007081. 10.1371/journal.pgen.1007081 PubMed DOI PMC

Hemani  G, Bowden  J, Davey Smith  G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet. 2018:27:R195–R208. 10.1093/hmg/ddy163 PubMed DOI PMC

Benjamini  Y, Hochberg  Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodol). 1995:57:289–300. 10.1111/j.2517-6161.1995.tb02031.x DOI

Skrivankova  VW, et al.  Strengthening the reporting of observational studies in epidemiology using Mendelian randomization. JAMA. 2021:326:1614. 10.1001/jama.2021.18236 PubMed DOI

Skrivankova  VW, et al.  Strengthening the reporting of observational studies in epidemiology using Mendelian randomisation (STROBE-MR): explanation and elaboration. BMJ. 2021:375:n2233. 10.1136/bmj.n2233 PubMed DOI PMC

Giambartolomei  C, et al.  Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014:10:e1004383. 10.1371/journal.pgen.1004383 PubMed DOI PMC

Rasooly  D, Peloso  GM, Giambartolomei  C. Bayesian genetic colocalization test of two traits using PubMed DOI

Robinson  JW, et al. An efficient and robust tool for colocalisation: Pair-wise Conditional and Colocalisation (PWCoCo) [preprint]. bioRxiv 2022.08.08.503158. 10.1101/2022.08.08.503158 DOI

Lonsdale  J, et al.  The genotype-tissue expression (GTEx) project. Nat Genet. 2013:45:580–585. 10.1038/ng.2653 PubMed DOI PMC

Graham  DB, et al.  TMEM258 is a component of the oligosaccharyltransferase Complex controlling ER stress and intestinal inflammation. Cell Rep. 2016:17:2955–2965. 10.1016/j.celrep.2016.11.042 PubMed DOI PMC

Li  A, Song  N-J, Riesenberg  BP, Li  Z. The emerging roles of endoplasmic Reticulum stress in balancing immunity and tolerance in health and diseases: mechanisms and opportunities. Front Immunol. 2020:10:3154. 10.3389/fimmu.2019.03154 PubMed DOI PMC

Park  HG, Park  WJ, Kothapalli  KSD, Brenna  JT. The fatty acid desaturase 2 ( PubMed DOI PMC

Aglago  EK, et al.  Consumption of fish and long-chain n-3 polyunsaturated fatty acids is associated with reduced risk of colorectal cancer in a large European cohort. Clin Gastroenterol Hepatol. 2020:18:654–666.e6. 10.1016/j.cgh.2019.06.031 PubMed DOI

Kim  M, Park  K. Dietary fat intake and risk of colorectal cancer: a systematic review and meta-analysis of prospective studies. Nutrients. 2018:10:1963. 10.3390/nu10121963 PubMed DOI PMC

Bull  C, et al.  Identifying metabolic features of colorectal cancer liability using Mendelian randomization. Elife. 2023:12:RP87894. 10.7554/eLife.87894.3 PubMed DOI PMC

Haycock  PC, et al.  The association between genetically elevated polyunsaturated fatty acids and risk of cancer. EBioMedicine. 2023:91:104510. 10.1016/j.ebiom.2023.104510 PubMed DOI PMC

Phan  LM, Yeung  S-CJ, Lee  M-H. Cancer metabolic reprogramming: importance, main features, and potentials for precise targeted anti-cancer therapies. Cancer Biol Med. 2014:11:1–19. 10.7497/j.issn.2095-3941.2014.01.001 PubMed DOI PMC

Dumitru  C, Kabat  AM, Maloy  KJ. Metabolic adaptations of CD4+ T cells in inflammatory disease. Front Immunol. 2018:9:540. 10.3389/fimmu.2018.00540 PubMed DOI PMC

Shiina  T, Hosomichi  K, Inoko  H, Kulski  JK. The HLA genomic loci map: expression, interaction, diversity and disease. J Hum Genet. 2009:54:15–39. 10.1038/jhg.2008.5 PubMed DOI

Houtman  M, et al.  Haplotype-specific expression analysis of MHC class II genes in healthy individuals and rheumatoid arthritis patients. Front Immunol. 2021:12:707217. 10.3389/fimmu.2021.707217 PubMed DOI PMC

Dunne  MR, et al.  Characterising the prognostic potential of HLA-DR during colorectal cancer development. Cancer Immunol Immunother.  2020:69:1577–1588. 10.1007/s00262-020-02571-2 PubMed DOI PMC

Barnaba  V. T cell memory in infection, cancer, and autoimmunity. Front Immunol. 2022:12:811968. 10.3389/fimmu.2021.811968 PubMed DOI PMC

Labriet  A, et al.  Germline variability and tumor expression level of ribosomal protein gene RPL28 are associated with survival of metastatic colorectal cancer patients. Sci Rep. 2019:9:13008. 10.1038/s41598-019-49477-3 PubMed DOI PMC

Coghill  ID, et al.  FHL3 is an actin-binding protein that regulates α-actinin-mediated actin bundling. J Biol Chem.  2003:278:24139–24152. 10.1074/jbc.M213259200 PubMed DOI

Huang  Z, Yu  C, Yu  L, Shu  H, Zhu  X. The roles of FHL3 in cancer. Front Oncol. 2022:12:887828. 10.3389/fonc.2022.887828 PubMed DOI PMC

Tan  X, et al.  Immune-related gene expression with colorectal cancer risk: a Mendelian randomization analysis. Discov Oncol.  2025:16:617. 10.1007/s12672-025-02291-y PubMed DOI PMC

Cao  G, et al.  FHL3 contributes to EMT and chemotherapy resistance through up-regulation of slug and activation of TGFβ/Smad-independent pathways in gastric cancer. Front Oncol. 2021:11:649029. 10.3389/fonc.2021.649029 PubMed DOI PMC

Li  P, et al.  FHL3 promotes pancreatic cancer invasion and metastasis through preventing the ubiquitination degradation of EMT associated transcription factors. Aging (Albany NY).  2020:12:53–69. 10.18632/aging.102564 PubMed DOI PMC

Ye  S-B, et al.  High-throughput proteomics profiling-derived signature associated with chemotherapy response and survival for stage II/III colorectal cancer. NPJ Precis Oncol. 2023:7:50. 10.1038/s41698-023-00400-0 PubMed DOI PMC

Blomhoff  A, et al.  Linkage disequilibrium and haplotype blocks in the MHC vary in an HLA haplotype specific manner assessed mainly by DRB1*03 and DRB1*04 haplotypes. Genes Immun. 2006:7:130–140. 10.1038/sj.gene.6364272 PubMed DOI

Davey Smith  G, Hemani  G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014:23:R89–R98. 10.1093/hmg/ddu328 PubMed DOI PMC

Butler-Laporte  G, et al. An accurate genetic colocalization method for the HLA locus [preprint], medRxiv 2024.11.05.24316783. 10.1101/2024.11.05.24316783 DOI

Kjaergaard  AD, Smith  GD, Stewart  P. Mendelian randomization studies in endocrinology: raising the quality bar for submissions and publications in the journal of clinical endocrinology & metabolism. J Clin Endocrinol Metab. 2024:109:1–3. 10.1210/clinem/dgad569 PubMed DOI

Stender  S, Gellert-Kristensen  H, Smith  GD. Reclaiming Mendelian randomization from the deluge of papers and misleading findings. Vol. 23, lipids in health and disease. BioMed Central Ltd; 2024. 10.1186/s12944-024-02284-w PubMed DOI PMC

Smith  GD, Ebrahim  S.  Mendelian Randomisation at 20 years: how can it avoid hubris, while achieving more? Vol. 12, The Lancet Diabetes and Endocrinology. Elsevier Ltd; 2024. p. 14–17. 10.1016/S2213-8587(23)00348-0 PubMed DOI

Sanderson  E, et al.  Mendelian Randomization. Vol. 2, Nature Reviews Methods Primers. Springer Nature; 2022. 10.1038/s43586-021-00092-5 PubMed DOI PMC

R Core Team . R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2023. https://www.R-project.org/

Hemani  G, et al.  The MR-base platform supports systematic causal inference across the human phenome. Elife. 2018:7:34408. 10.7554/eLife.34408 PubMed DOI PMC

Richardson  N, et al. arrow: Integration to ‘Apache’; 2024. https://CRAN.R-project.org/package=arrow

Durinck  S, Spellman  PT, Birney  E, Huber  W. Mapping identifiers for the integration of genomic datasets with the R/bioconductor package biomaRt. Nat Protoc. 2009:4:1184–1191. 10.1038/nprot.2009.97 PubMed DOI PMC

Durinck  S, et al.  BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics. 2005:21:3439–3440. 10.1093/bioinformatics/bti525 PubMed DOI

Barrett  T et al.  et al. data.table: Extension of ‘data.frame’; 2024. https://CRAN.R-project.org/package=data.table

Wickham  H, François  R, Henry  L, Müller  K, Vaughan  D. dplyr: A Grammar of Data Manipulation; 2023. https://CRAN.R-project.org/package=dplyr

Lawrence  M, et al.  Software for computing and annotating genomic ranges. PLoS Comput Biol. 2013:9:e1003118. 10.1371/journal.pcbi.1003118 PubMed DOI PMC

Hemani  G. gwasvcf: Tools for Dealing with GWAS Summary Data in VCF Format; 2024. https://github.com/mrcieu/gwasvcf

Lawrence  M, Gentleman  R, Carey  V. Rtracklayer: an R package for interfacing with genome browsers. Bioinformatics. 2009:25:1841–1842. 10.1093/bioinformatics/btp328 PubMed DOI PMC

Wickham  H. stringr: Simple, Consistent Wrappers for Common String Operations; 2023. https://CRAN.R-project.org/package=stringr

Wickham  H, Vaughan  D, Girlich  M.  tidyr: Tidy Messy Data; 2024. https://CRAN.R-project.org/package=tidyr

Carey  V. gwascat: representing and modeling data in the EMBL-EBI GWAS catalog. doi:10.18129/B9.bioc.gwascat, R package version 2.40.0; 2025. https://bioconductor.org/packages/gwascat.

Slowikowski  K. ggrepel: Automatically Position Non-Overlapping Text Labels with ‘ggplot2'; 2024. https://CRAN.R-project.org/package=ggrepel

Wickham  H. Ggplot2: elegant graphics for data analysis. Springer-Verlag; 2016.

Chang  CC, et al.  Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015:4:7. 10.1186/s13742-015-0047-8 PubMed DOI PMC