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用多个机器学习模型预测乳腺癌患者的基因特征

Fangfang Zhu¹, Dafang Xu²

¹First Affiliated Hospital of Huzhou University, No.158, Guangchang Hou Road, Huzhou, 313000, Zhejiang, People's Republic of China.

Discover oncology

|October 1, 2024

概括

这项研究使用机器学习来识别乳腺癌 (BC) 诊断的关键基因特征. 发现9个关键的差异表达基因 (DEG) 是可靠的诊断标记物,为新的生物标记物提供了潜力.

科学领域:

基因组学和生物信息学
癌症研究癌症研究
机器学习在医学中的应用

背景情况:

乳腺癌 (BC) 仍然是全球主要的死亡原因.
准确的诊断和预后生物标志物对于有效的BC管理至关重要.
机器学习为识别复杂的基因表达模式提供了强大的工具.

研究的目的:

用多种机器学习模型预测乳腺癌患者的基因特征.
确定乳腺癌的新型诊断和免疫治疗生物标志物.

主要方法:

合并的数据集 GSE54002 和 GSE22820 用于基因表达分析.
使用基因本体学 (GO),基因和基因组的京都百科全书 (KEGG) 和疾病本体学 (DO) 进行了丰富分析.
使用LASSO回归和SVM-RFE进行特征选择,识别了9个关键差异表达基因 (CF-DEG).

主要成果:

在乳腺癌样本中鉴定了177个差异表达基因 (DEGs).
富化分析揭示了DEGs参与细胞外矩阵组织,神经系统发育,焦点粘附和PI3K-Akt信号通路.
9个已识别的CF-DEGs (ANGPTL7,TSHZ2,SDPR,CLCA4,PAMR1,MME,CXCL2,ADAMTS5,KIT) 通过ROC分析证明了可靠的诊断潜力.

结论:

鉴定的基因特征为乳腺癌分子功能和途径提供了新的见解.
这些发现可能有助于开发乳腺癌的新诊断和免疫治疗生物标志物.
对这些CF-DEG的进一步研究可能会改善乳腺癌患者的治疗结果.

关键词:

乳腺癌是什么? 乳腺癌是什么?丰富分析是一种丰富分析.特性基因是一种特征基因.免疫细胞的透.机器学习是机器学习.

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