Suppr超能文献

垂体神经内分泌肿瘤多组学分析的进展与应用

The evolution and application of multi-omic analysis for pituitary neuroendocrine tumors.

作者信息

Pugazenthi Sangami, Pari Shree S, Zhang Ziyan, Silverstein Julie, Kim Albert H, Patel Bhuvic

机构信息

Taylor Family Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, United States.

Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, United States.

出版信息

Front Med (Lausanne). 2025 Sep 1;12:1629621. doi: 10.3389/fmed.2025.1629621. eCollection 2025.

Abstract

Pituitary neuroendocrine tumors (PitNETs) are a heterogeneous group of intracranial neoplasms that vary in hormonal activity, histological features, and clinical behavior. The rise of high-throughput sequencing and molecular profiling technologies has enabled multiomic approaches-including genomics, transcriptomics, epigenomics, proteomics, and metabolomics-to deepen our understanding of PitNET pathogenesis. These studies have identified key mutations, transcriptional lineages, epigenetic modifications, and proteomic features that contribute to tumor subtype classification, invasiveness, and treatment response. Integrative multi-omic analyses have further revealed distinct molecular subtypes, complex regulatory networks, and molecular profiles that can predict recurrence and therapeutic efficacy. These approaches hold strong potential for advancing personalized medicine in PitNETs, supporting patient-specific diagnosis, prognostication, and therapeutic strategies. Future directions include the application of emerging -omic technologies and the development of robust computational tools to integrate and translate multi-layered data into clinically actionable insights.

摘要

垂体神经内分泌肿瘤(PitNETs)是一组异质性颅内肿瘤,其激素活性、组织学特征和临床行为各不相同。高通量测序和分子谱分析技术的兴起,使得包括基因组学、转录组学、表观基因组学、蛋白质组学和代谢组学在内的多组学方法得以深入我们对PitNET发病机制的理解。这些研究已经确定了有助于肿瘤亚型分类、侵袭性和治疗反应的关键突变、转录谱系、表观遗传修饰和蛋白质组学特征。综合多组学分析进一步揭示了不同的分子亚型、复杂的调控网络以及能够预测复发和治疗效果的分子谱。这些方法在推进PitNET个性化医疗方面具有巨大潜力,支持针对患者的诊断、预后评估和治疗策略。未来的方向包括新兴组学技术的应用以及强大计算工具的开发,以整合多层数据并将其转化为临床可操作的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a02c/12434087/791dfb76773a/fmed-12-1629621-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验