使用权重基因表达网络分析和机器学习来识别子宫内膜癌生物标志物
Hui Wang1, Yanwen Gong2, Xiu Han3
1Department of Gynaecology, Guangyuan Central Hospital, Jingxiangzi, No. 16, Guangyuan Sichuan, 628000, China.
BMC cancer
|January 21, 2026
概括
在子宫内膜癌中高MAL表达与预后不佳和瘤细胞增多和入侵相关. 虽然MAL显示出作为生物标志物的潜力,但在考虑临床因素时,其独立的预后价值是有限的.
科学领域:
- 在瘤学瘤学.
- 分子生物学分子生物学
- 生物标志物发现发现
背景情况:
- 子宫内膜癌 (UCEC) 发病率正在上升,需要新的生物标志物用于诊断和治疗.
- 确定可靠的生物标志物对于管理UCEC负担至关重要.
研究的目的:
- 通过综合生物信息学和实验方法识别UCEC的潜在生物标志物.
- 研究MAL在UCEC中的预后和功能作用.
主要方法:
- RNA-seq数据分析 (GEO,TCGA) 包括DEG分析和WGCNA.
- 机器学习算法用于基因查和生存分析 (OS,PFS).
- 在体外测试 (EdU,Transwell) 来评估扩散和入侵,以及药物敏感性概况.
主要成果:
- 高MAL表达与UCEC患者的较短的OS和PFS有关.
- 升高的MAL表达在体外促进了UCEC细胞的增殖和入侵.
- 马尔表达与免疫特征相关,可能反映瘤进展.
结论:
- 高MAL表达与子宫内膜癌的预后不佳有关,支持其在扩散和入侵中的作用.
- 在对临床因素进行调整后,MAL的独立预后值是有限的.
- 马尔作为一种与疾病进展和生存相关的分子标志物,有助于风险分层.
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