Frontiers in oncology · 2026
Prognostic value and experimental validation of atherosclerosis-derived pathogenic genes in colorectal cancer.
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Li Yuqing, Wei Jinhong, Xu Yuanyuan, Wu Zhenyu, He Saiqi, Zhu Yuhang, Ni Wen, Zhang Di, Xu Huiya, Zhang Chuanjie, Zhou Aijun, Shen Tong, Li Jianming
Аннотация
OBJECTIVES: Colorectal cancer (CRC) and atherosclerosis (AS) share pathological phenotypes and clinical links, but their shared pathogenic mechanisms are unclear. This study aimed to identify shared genetic drivers, construct a CRC risk model using AS-related genes, and validate expression via multi-omics.
METHODS: Transcriptomic data from The Cancer Genome Atlas and Gene Expression Omnibus were analyzed. Core gene modules associated with CRC and AS were screened using weighted gene co-expression network analysis and differentially expressed genes with significant expression differences between CRC tissues and normal tissues were identified through differential analysis. The intersection of these three sets of genes was taken to determine the overlapping genes. A prognostic model with 6 key genes (CDC25C, HMMR, KPNA2, PRR11, PALB2, and TKT) was built via univariate Cox and least absolute shrinkage and selection operator analyses. High/low-risk groups underwent Gene Set Enrichment Analysis (GSEA), immune infiltration, and immune checkpoint analyses. Multi-omics characterized gene expression/localization, validated by reverse transcription-quantitative polymerase chain reaction, Western blotting, and immunohistochemistry.
RESULTS: The model showed reliable predictive performance. Low-risk groups had enriched activated dendritic cells and follicular helper T cells; high-risk groups featured memory B cells and resting mast cells. Most genes overexpressed in lesions, except PRR11 (higher in normal tissues). Experiments confirmed HMMR/PALB2 overexpression in CRC and three AS genes elevated in AS lesions.
CONCLUSION: A CRC risk model based on 6 AS-related genes was developed, identifying 3 novel AS genes. It highlights shared genetic factors, offering prognostic biomarkers for both diseases and insights into their interconnected mechanisms.
METHODS: Transcriptomic data from The Cancer Genome Atlas and Gene Expression Omnibus were analyzed. Core gene modules associated with CRC and AS were screened using weighted gene co-expression network analysis and differentially expressed genes with significant expression differences between CRC tissues and normal tissues were identified through differential analysis. The intersection of these three sets of genes was taken to determine the overlapping genes. A prognostic model with 6 key genes (CDC25C, HMMR, KPNA2, PRR11, PALB2, and TKT) was built via univariate Cox and least absolute shrinkage and selection operator analyses. High/low-risk groups underwent Gene Set Enrichment Analysis (GSEA), immune infiltration, and immune checkpoint analyses. Multi-omics characterized gene expression/localization, validated by reverse transcription-quantitative polymerase chain reaction, Western blotting, and immunohistochemistry.
RESULTS: The model showed reliable predictive performance. Low-risk groups had enriched activated dendritic cells and follicular helper T cells; high-risk groups featured memory B cells and resting mast cells. Most genes overexpressed in lesions, except PRR11 (higher in normal tissues). Experiments confirmed HMMR/PALB2 overexpression in CRC and three AS genes elevated in AS lesions.
CONCLUSION: A CRC risk model based on 6 AS-related genes was developed, identifying 3 novel AS genes. It highlights shared genetic factors, offering prognostic biomarkers for both diseases and insights into their interconnected mechanisms.