几十年来,肺癌研究中的奥米克:从2004年到2024年的图书统计分析和可视化
Xinmeng Wang1, Huijing Dong1, Yumin Zheng1
1Beijing University of Chinese Medicine, Beijing, China.
Journal of thoracic disease
|March 12, 2026
概括
肺癌的OMICS研究正在转向综合的多OMICS方法,专注于瘤微环境和免疫治疗. 人工智能和机器学习对于分析精密瘤学中复杂的omics数据变得越来越重要.
科学领域:
- 生物医学科学 生物医学科学
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
背景情况:
- 奥米克技术 (基因组学,转录组学,蛋白质组学,代谢组学) 对于了解肺癌分子机制和推进精确瘤学至关重要.
- 现有的研究往往侧重于技术方面,缺乏对肺癌全球omics研究格局的全面分析.
- 多omics集成提供了分子交叉通话的系统视图整个肺癌演变过程中,从启动到转移.
研究的目的:
- 系统地评估科学成果,研究趋势和与OMIC相关的肺癌研究中的热点.
- 分析研究重点从单一omics到多omics方法的演变.
- 确定肺癌研究中的新兴主题和方法.
主要方法:
- 从科学网络核心集合 (2004-2024) 中对出版物的图书统计分析.
- 使用VOSviewer,CiteSpace,R和Origin进行文献计量分析和知识域可视化.
- 进行了关键词并发和突发分析,以确定主导和新兴的研究主题.
主要成果:
- 总共有19087个出版物被确定,显示了20多年来的持续增长.
- 中国在出版量方面处于领先地位,而美国则表现出更高的引用影响和协作影响.
- 关键主题包括"表达"",肺癌"",基因表达"",瘤微环境"",突变"和"免疫疗法",表明向整合性多组学,TME和免疫疗法研究的转变.
- 人工智能 (AI) 和机器学习 (ML) 正在成为关键的分析工具.
结论:
- 肺癌奥米克研究正在向整合性,以TME为重点,以免疫治疗为导向的范式发展,得到AI/ML的支持.
- 欧米克斯数据对于早期检测,个性化治疗指导和改善肺癌预后准确性至关重要.
- 加强跨学科的合作和加快多学科数据的临床翻译对于推进精确瘤学至关重要.
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