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Updated: Jun 13, 2026
06:01
A Genetically Engineered Mouse Model of Sporadic Colorectal Cancer
Published on: July 6, 2017
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Transcriptome-wide Mendelian randomisation exploring dynamic CD4+ T cell gene expression in colorectal cancer
Benedita Deslandes1,2, Xueyan Wu3,4, Matthew A Lee5
1MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
Summary
Investigating CD4+ T cell gene expression reveals six genes causally linked to colorectal cancer (CRC) risk. Understanding dynamic immune cell changes is crucial for cancer prevention research.
Area of Science:
- Immunology
- Genetics
- Cancer Research
Background:
- Lymphocytes may protect against colorectal cancer (CRC).
- The role of specific lymphocyte subtypes and activation states in CRC development is unclear.
- Previous studies on CD4+ T cells in CRC risk used bulk tissue, missing dynamic cellular changes.
Purpose of the Study:
- To investigate causal relationships between CD4+ T cell gene expression and CRC risk.
- To explore these relationships across different CD4+ T cell subtypes and activation states.
- To identify specific genes involved in CRC development influenced by CD4+ T cell activity.
Main Methods:
- Utilized Mendelian randomization (MR) and genetic colocalization analyses.
- Employed single-cell transcriptomic data to derive genetic proxies for gene expression.
- Analyzed gene expression of 1,805 genes across five CD4+ T cell activation states in a large cohort (78,473 cases, 107,143 controls).
Main Results:
- Identified six genes (FADS2, FHL3, HLA-DRB1, HLA-DRB5, RPL28, TMEM258) with evidence of causal CD4+ T cell expression in CRC development.
- Observed variations in causal effects across CD4+ T cell subtypes, activation states, CRC subsites, and sex.
- Sensitivity analysis indicated that genetic proxies for CD4+ T cell expression also function as eQTLs in other tissues.
Conclusions:
- Capturing the dynamic nature of CD4+ T cells is essential for understanding disease risk.
- Prioritizes specific genes for further research in cancer prevention.
- Highlights challenges in achieving tissue-specific genetic instrumental variable analysis.
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