通过加权基因共同表达网络分析和机器学习算法识别GJC1作为乳头甲状腺癌的新型诊断标志物
1Department of Endocrinology and Metabolism, The First Hospital of China Medical University, No.155 Nanjing North Street, Heping District, Shenyang, 110001, Liaoning, China.
Discover oncology
|March 17, 2025
概括
缺口连接蛋白玛1 (GJC1) 在甲状腺乳头癌 (PTC) 中表达高. 该基因显示出作为诊断生物标志物和新型PTC基因治疗的目标的潜力.
科学领域:
- 在瘤学瘤学.
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
背景情况:
- 甲状腺 papillary carcinoma (PTC) 的发病率正在上升,增加了患者的负担.
- 对PTC的早期发现和有针对性的干预措施至关重要.
研究的目的:
- 为了确定PTC的新型诊断标记.
- 探索PTC的精确治疗目标.
主要方法:
- 在GEO和TCGA数据集上进行权重基因共表达网络分析 (WGCNA).
- 不同基因表达分析和通路丰富 (GO,DO,KEGG,GSEA).
- 机器学习算法 (LASSO,SVM-RFE,RF) 和ROC曲线分析以识别和验证核心基因.
主要成果:
- 确定了11194个在ECM和炎症通路中丰富的差异表达基因 (DEGs).
- 使用机器学习发现了三个核心基因 (GJC1,KLHL4,NOL4).
- 在GEO (AUC=0.982) 和TCGA (AUC=0.840) 数据库中,GJC1的诊断价值显著.
结论:
- GJC1在PTC中高度表达,并作为潜在的诊断生物标志物.
- GJC1是未来PTC基因治疗的一个有希望的目标.
- 进一步的临床验证是必要的,以支持这些发现.
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