解码核心基因和骨髓瘤细胞间通信:生物信息学调查和免疫细胞分析,用于诊断和治疗见解
Kuan Rong1, Haoming Kuang1, Liang Ou1
1Department of Orthopedics, The Affiliated Hospital of Hunan Academy of Traditional Chinese Medicine, Changsha, 410006, China.
Discover oncology
|November 1, 2024
概括
这项研究开发了一个人工神经网络 (ANN) 模型,使用遗传数据来预测骨髓瘤 (OS) 病例. 经验证的ANN模型准确地识别了OS,有助于早期诊断和治疗.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 骨肉瘤 (OS) 诊断依赖于准确的预测模型.
- 遗传特征为OS预测提供了潜在的目标.
研究的目的:
- 开发和验证人工神经网络 (ANN) 模型用于骨髓瘤 (OS) 预测.
- 识别与OS相关的关键基因和免疫细胞相互作用.
主要方法:
- 差异性基因表达分析以确定467个DEGs.
- 基因本体学 (GO) 丰富和途径分析.
- 蛋白与蛋白相互作用 (PPI) 网络分析以找到枢纽DEG.
- 一个ANN模型的构建,验证和性能测试.
- 对免疫细胞透和与DEGs相关性的分析.
主要成果:
- 在OS中确定了467个差异表达基因 (DEGs),其中345个被抑制,122个被刺激.
- 对GO的分析揭示了包括T细胞激活和抗氧化特性在内的功能.
- 通过PPI网络分析确定了前十个枢纽DEG.
- 该ANN模型证明了准确可靠的OS预测.
- 观察到免疫细胞透率的显著变化和与DEGs的相关性.
- 对YES1和MFNG的表达分析显示了与OS临床病理性质和免疫透的相互关系.
结论:
- 开发的基于CDEGs的ANN模型对于预测OS患者是有效的.
- 这种方法促进了骨髓瘤的早期诊断和治疗策略.
- 了解基因表达和免疫细胞相互作用对于OS管理至关重要.
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