乳腺癌预后标志物ANLN和KDR的基因组和表达谱的比较分析。

In silico pharmacology Pub Date : 2025-01-15 eCollection Date: 2025-01-01 DOI:10.1007/s40203-024-00301-5
Aamir Mehmood, Rongpei Li, Aman Chandra Kaushik, Dong-Qing Wei
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引用次数: 0

摘要

乳腺癌(BC)仍然是一种高度异质性的疾病,使诊断和治疗复杂化。本研究利用来自癌症基因组图谱(TCGA)的数据,研究了Anillin (ANLN)和激酶插入结构域受体(KDR)基因在BC中的突变和表达情况,探讨了它们对预后的意义。我们发现,ANLN的高表达与较差的总生存率密切相关,突出了其作为一种强大的预后标志物的潜力。相比之下,KDR尽管突变频率较高,但与生存结果的相关性不太显著。结合转录和翻译数据的机器学习(ML)模型进一步支持ANLN的预后价值,当两个基因一起分析时,显示出更高的生存期预测准确性。功能富集分析显示,ANLN主要参与细胞周期调节,而KDR则与血管生成有关,提示联合靶向这些途径可以提高治疗效果。这些发现强调了ANLN和KDR作为BC预后补充生物标志物的潜力,并强调了在不同队列中进一步验证的必要性。补充信息:在线版本提供补充资料,网址为10.1007/s40203-024-00301-5。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative analysis of the genomic and expression profiles of ANLN and KDR as prognostic markers in breast Cancer.

Breast cancer (BC) remains a highly heterogeneous disease, complicating diagnosis and treatment. This study investigates the prognostic significance of Anillin (ANLN) and Kinase Insert Domain Receptor (KDR) genes, focusing on their mutational and expression landscapes in BC using data from The Cancer Genome Atlas (TCGA). We found that high ANLN's expression is strongly associated with poor overall survival, highlighting its potential as a robust prognostic marker. In contrast, KDR, despite its higher mutation frequency, showed a less significant correlation with survival outcomes. Machine learning (ML) models incorporating transcriptional and translational data further supported ANLN's prognostic value, demonstrating superior accuracy in survival stage prediction when both genes were analyzed together. Functional enrichment analysis revealed that ANLN is primarily involved in cell cycle regulation, while KDR is linked to angiogenesis, suggesting that combined targeting of these pathways could enhance therapeutic efficacy. These findings underscore the potential of ANLN and KDR as complementary biomarkers in BC prognosis and highlight the need for further validation in diverse cohorts.

Supplementary information: The online version contains supplementary material available at 10.1007/s40203-024-00301-5.

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