Chen Hao
Department of Surgical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Front Genet. 2024 Dec 9;15:1419755. doi: 10.3389/fgene.2024.1419755. eCollection 2024.
Ulcerative colitis has a serious impact on the quality of life of patients and is more likely to progress to colon cancer. Therefore, early diagnosis and timely intervention are of considerable importance.
Gene expression data of active ulcerative colitis were downloaded from the Gene Expression Omnibus (GEO) database, and genes with significant differential expression were identified. Biochemical markers with diagnostic significance were selected through machine learning methods. The expression differences of the selected markers between colon adenocarcinoma (COAD) and healthy control groups in The Cancer Genome Atlas (TCGA) database were analyzed to evaluate their diagnostic value. In addition, the correlation between the selected markers and clinical indicators, as well as their predictive efficacy for the survival of COAD patients, was explored.
Through machine learning and LASSO regression analysis, was finally determined as a diagnostic marker for ulcerative colitis. It demonstrated high diagnostic accuracy in both the training set and the external validation set. Furthermore, was significantly downregulated in COAD tissues compared to normal control tissues. The ROC curve suggested that could serve as a diagnostic marker for COAD with excellent performance, achieving an AUC of 0.969. Immune infiltration analysis indicated a significant negative correlation between the expression of and neutrophils. Correlation analysis suggested a link between and the pathological classification of colon cancer. Survival analysis showed that is negatively correlated with OS, PPS, and RFS in colon cancer.
The author identified as a diagnostic marker for ulcerative colitis through bioinformatics methods, and verified its significant downregulation in colon cancer, as well as its predictive role in the survival of COAD patients. These findings suggest that may serve not only as a diagnostic marker for ulcerative colitis and colon cancer but also as a potential prognostic indicator for colon cancer.
溃疡性结肠炎对患者的生活质量有严重影响，且更易进展为结肠癌。因此，早期诊断和及时干预至关重要。
从基因表达综合数据库（GEO）下载活动期溃疡性结肠炎的基因表达数据，鉴定出具有显著差异表达的基因。通过机器学习方法选择具有诊断意义的生化标志物。分析所选标志物在癌症基因组图谱（TCGA）数据库中结肠癌（COAD）与健康对照组之间的表达差异，以评估其诊断价值。此外，探讨所选标志物与临床指标之间的相关性及其对COAD患者生存的预测效能。
通过机器学习和LASSO回归分析，最终确定[具体基因名称未给出]为溃疡性结肠炎的诊断标志物。它在训练集和外部验证集中均显示出高诊断准确性。此外，与正常对照组织相比，[具体基因名称未给出]在COAD组织中显著下调。ROC曲线表明[具体基因名称未给出]可作为COAD的诊断标志物，性能优异，AUC为0.969。免疫浸润分析表明[具体基因名称未给出]的表达与中性粒细胞之间存在显著负相关。相关性分析表明[具体基因名称未给出]与结肠癌的病理分类之间存在联系。生存分析表明[具体基因名称未给出]与结肠癌的总生存期（OS）、无进展生存期（PPS）和无复发生存期（RFS）呈负相关。
作者通过生物信息学方法鉴定[具体基因名称未给出]为溃疡性结肠炎的诊断标志物，并验证了其在结肠癌中的显著下调及其对COAD患者生存的预测作用。这些发现表明[具体基因名称未给出]不仅可作为溃疡性结肠炎和结肠癌的诊断标志物，还可能作为结肠癌的潜在预后指标。
