Digestive diseases and sciences · 2025
Shared Biomarkers LCN2 and CXCL11 for Ulcerative Colitis and Colon Cancer: Bioinformatics Analysis and Diagnostic Model Construction.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Wu Zhihao, Sun Xiaofen, Li Binglong, Xie Zhiyuan, Zheng Jing, Sun Nianqing, Guan Ying, Chen Jianqiang
Аннотация
BACKGROUND: Long-term chronic inflammation is an important risk factor for colon cancer. Ulcerative colitis is a complex chronic inflammatory disease. Related studies have shown that the risk of colon cancer in patients with ulcerative colitis is 2-3 times higher than that in the general population.
METHOD: Transcriptome data from GEO and TCGA databases were analyzed using RStudio. Differential expression was analyzed with "limma" and disease-related genes were identified via WGCNA. Core genes were screened by GO, KEGG, and PPI analyses, and further refined using MCC, LASSO, and RF algorithms. Expression levels and diagnostic value were evaluated via ROC curves; a disease diagnosis model was constructed. Immune cell infiltration was assessed with CIBERSORT, and GSEA analysis was performed based on gene expression.
RESULT: Eighty-seven common genes were identified through differential analysis and WGCNA. Using these genes, a PPI network was built, and the top 15 genes were selected by the MCC algorithm. LASSO and RF algorithms identified LCN2 and CXCL11 as characteristic genes, highly expressed in the disease group with AUC > 0.7. The diagnosis model performed well. GO, KEGG, and GSEA analyses showed immune and inflammatory responses were important in the disease, with characteristic genes enriched in immune response and cell proliferation pathways.
CONCLUSION: It was found that ulcerative colitis and colon cancer have common diagnostic markers and similar pathogenic pathways and also show similarities in the immune cell infiltration microenvironment. The disease diagnosis model constructed by combining genes is superior to the diagnostic effect of a single gene on disease.
METHOD: Transcriptome data from GEO and TCGA databases were analyzed using RStudio. Differential expression was analyzed with "limma" and disease-related genes were identified via WGCNA. Core genes were screened by GO, KEGG, and PPI analyses, and further refined using MCC, LASSO, and RF algorithms. Expression levels and diagnostic value were evaluated via ROC curves; a disease diagnosis model was constructed. Immune cell infiltration was assessed with CIBERSORT, and GSEA analysis was performed based on gene expression.
RESULT: Eighty-seven common genes were identified through differential analysis and WGCNA. Using these genes, a PPI network was built, and the top 15 genes were selected by the MCC algorithm. LASSO and RF algorithms identified LCN2 and CXCL11 as characteristic genes, highly expressed in the disease group with AUC > 0.7. The diagnosis model performed well. GO, KEGG, and GSEA analyses showed immune and inflammatory responses were important in the disease, with characteristic genes enriched in immune response and cell proliferation pathways.
CONCLUSION: It was found that ulcerative colitis and colon cancer have common diagnostic markers and similar pathogenic pathways and also show similarities in the immune cell infiltration microenvironment. The disease diagnosis model constructed by combining genes is superior to the diagnostic effect of a single gene on disease.