What question did this study set out to answer?

The study aims to identify immune biomarkers associated with colorectal cancer using bioinformatics and statistical methods.

April 16, 2026PeerJ0 citationsOpen Access

Identification of colorectal cancer immune biomarkers via eQTL mapping: a Mendelian randomization and transcriptomic analysis study

GPGenyuan Pu ZYZi Yin XZXi Zhang

Key Points

The study aims to identify immune biomarkers associated with colorectal cancer using bioinformatics and statistical methods.
Analyzed three colorectal cancer datasets from the GEO database.
Performed eQTL analysis to identify instrumental variables for Mendelian randomization analysis.
Intersected Mendelian randomization-associated genes with differentially expressed genes.
Conducted functional enrichment analyses using GO, KEGG, and GSEA.
Validated findings using independent datasets and qRT-PCR assays.
Identified 776 upregulated and 981 downregulated DEGs related to colorectal cancer.
Prioritized nine key genes involved in cytokine receptor interaction pathways.
Revealed changes in immune cell infiltration, including increased CD4+ memory T cells and M0 macrophages.
Demonstrated significant correlation of key genes with immune cell subsets and dysregulation of target genes.

Abstract

Background Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide. The identification of effective molecular targets is crucial for advancing precision medicine and prognostic strategies. This study aims to uncover key CRC biomarkers through integrative bioinformatics analyses, providing mechanistic insights for therapeutic development. Methods We analyzed three CRC datasets from the Gene Expression Omnibus (GEO) database. Expression quantitative trait loci (eQTL) analysis was performed to identify instrumental variables (IVs), which were subsequently used in Mendelian Randomization (MR) analysis with CRC Genome-Wide Association Study (GWAS) data. MR-associated genes were intersected with differentially expressed genes (DEGs) to screen disease-related key genes. Functional enrichment analyses were conducted using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). Additionally, immune cell infiltration and gene-immune correlation analyses were performed. Finally, validation was performed using independent GEO, The Cancer Genome Atlas (TCGA) datasets, summary data-based Mendelian randomization (SMR) and quantitative reverse transcription polymerase chain reaction (qRT-PCR) in CRC cell lines. Results A total of 776 upregulated and 981 downregulated DEGs were identified. Nine key genes were prioritized: ATP13A4, CD1C, METTL7A, SLC18A1, CREB5, CXCR1, GZMB, HECW2, and TEAD2 , predominantly involved in cytokine receptor interaction pathways. CIBERSORT analysis revealed increased activated CD4+ memory T cells and M0 macrophages, alongside decreased plasma cells and natural killer (NK) cells in CRC. Key genes demonstrated significant correlation with immune cell subsets ( e.g. , neutrophils, mast cells), highlighting their role in CRC immunobiology. Validation via SMR and qRT-PCR assays demonstrated significant dysregulation of four target genes ( CXCR1 , HECW2 , ATP13A4 ) ( P < 0.05). Discussion This study suggests that CXCR1 , HECW2 , and ATP13A4 may be involved in CRC development, providing a reference for targeted and immunotherapy research.

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