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Cancer Survival Analysis

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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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[基于权重基因共同表达网络分析和机器学习的基因组预测模型]

Zhengyu Li¹, Baohua Tian¹, Haixia Liang¹

¹College of Biomedical Engineering, Taiyuan University of Technology, Taiyuan 030024, P. R. China.

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi

|September 4, 2023

概括

这项研究确定了四个关键基因 (HGF,SDC4,ENPP2,RND3) 作为 keloids 的诊断标记. 为创伤患者的早期 keloid 风险评估开发了一个 nomogram 预测模型.

关键词:

凯洛伊德是什么意思凯洛伊德是什么意思最小绝对收缩和操作员的选择.诺莫格拉姆预测模型支持向量的机器递归特征消除.权重基因共同表达网络分析.

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科学领域:

皮肤病学和生物信息学
在瘤学瘤学.
分子生物学分子生物学

背景情况:

质瘤是良性皮肤瘤,由于伤口愈合后连接组织过度扩散而引起的.
准确的预测和早期诊断胆固醇风险对于有效的管理和进展控制至关重要.

研究的目的:

通过基因表达数据,识别基洛因的诊断标记物.
开发和验证用于早期 keloid 诊断的 nomogram 预测模型.

主要方法:

来自基因表达综合 (GEO) 数据库的四个基洛因数据集的分析.
利用权重基因共同表达网络分析 (WGCNA),差异表达分析和蛋白质-蛋白质相互作用网络的中心性.
采用机器学习算法 (LASSO,SVM-RFE) 来选诊断标记,并构建了一个名ogram模型.

主要成果:

确定了四种核心诊断标记物:肝细胞生长因子 (HGF),Syndecan-4 (SDC4),ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2),以及Rho家族的关诺辛三聚酶3 (RND3).
开发的诺莫格拉姆模型显示了高校准度,临床效用和预测能力 (AUC>控制模型).
基因组丰富分析 (GSEA) 探索了潜在的生物途径,涉及到 keloid 发育.

结论:

鉴定到的标记物和名谱模型对早期临床诊断状体有很大的前景.
这种预测模型可以帮助管理创伤患者的 keloid 风险.
需要进一步验证和临床应用这个名ogram.