#AI reads Urine# A Multiomic Approach Integrating Genomic and Metabolomic Data Highlights Colorectal Cancer Pathways

This study integrated genomic and metabolomic multi-omics techniques to reveal the associations between colorectal cancer (CRC)-related genetic variants and urinary metabolites, as well as the underlying pathogenic mechanisms. A metabolome-wide association study (MWAS) was conducted on 1,951 participants, identifying significant correlations between 7 CRC-associated single nucleotide polymorphisms (SNPs) and urinary metabolites. Among these, the intronic variant **rs10411210** in the *RHPN2* gene showed the strongest association with urinary sucrose excretion, the variant in the *SLC6A18* gene was linked to amino acids such as tyrosine and leucine, and variants in *MAP2K5* and *BMP2* were associated with gut microbial metabolites. Functional experiments targeting the *RHPN2* locus demonstrated that CRISPR/Cas9-mediated knockout of the 48-nt intronic region containing rs10411210 inhibited the proliferation of Caco-2 colorectal cancer cells. RNA sequencing further revealed significant alterations in the expression of genes involved in cell division and metabolic regulation. Collectively, these findings confirm that integrating multi-omics data can elucidate the functional mechanisms of CRC genetic variants, providing novel targets for disease prevention and treatment.

J Proteome Res. 2026 Jan 28. doi: 10.1021/acs.jproteome.5c00459.

Youhe Gao

Statement: During the preparation of this work the author(s) used Doubao / AI reading for summarizing the content. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

For earlier AI Reads Urine articles：

https://www.keaipublishing.com/en/journals/advances-in-biomarker-sciences-and-technology/ai-reads-urine/