Robotics & Machine Learning Daily News2024,Issue(Jun.19) :87-88.

First Affiliated Hospital of Harbin Medical University Reports Findings in Adeno carcinoma (Evaluating the predictive value of angiogenesis-related genes for pro gnosis and immunotherapy response in prostate adenocarcinoma using machine learn ing ...)

哈尔滨医科大学第一附属医院报道了腺癌的发现(利用机器学习评价血管生成相关基因对前列腺癌的诊断和免疫治疗反应的预测价值)

Robotics & Machine Learning Daily News2024,Issue(Jun.19) :87-88.

First Affiliated Hospital of Harbin Medical University Reports Findings in Adeno carcinoma (Evaluating the predictive value of angiogenesis-related genes for pro gnosis and immunotherapy response in prostate adenocarcinoma using machine learn ing ...)

哈尔滨医科大学第一附属医院报道了腺癌的发现(利用机器学习评价血管生成相关基因对前列腺癌的诊断和免疫治疗反应的预测价值)

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摘要

一位新闻记者-机器人和机器学习的工作人员新闻编辑每日新闻-肿瘤学的新研究-腺癌素瘤是一篇报道的主题。据NewsRx记者在中国哈尔滨的新闻报道,研究表明:“血管生成是由已有的血管形成新血管的过程,在癌症的发展和进展中起着重要作用。虽然阻断血管生成在治疗不同类型的实体瘤方面已取得成功,但其在前列腺癌(PRAD)中的作用尚未得到深入研究。”本研究利用WGCNA方法识别PRAD患者血管生成相关基因,并通过聚类分析评价其诊断和预后价值,利用多机器学习技术构建诊断模型,利用LASO算法建立预后模型。进一步应用多因素Cox回归分析和多种机器学习算法对PRAD血管生成相关基因中最重要的预后基因MAP7D3进行分析,并探讨MAP7D3与PRAD免疫浸润和药物敏感性的相关性,通过分子对接分析评价MAP7D3与血管生成药物的结合亲和力。60例PRAD组织标本中证实了MAP7D3的表达及其预后价值。本研究通过WGCNA鉴定了10个与PRAD患者血管生成相关的关键基因,并证实了它们在PRAD患者中的潜在预后和免疫相关意义。通过分子对接研究,发现MAP7D3与PRAD的预后及免疫应答密切相关。结果表明,MAP7D3与血管生成药物有很高的结合亲和力,进一步证实MAP7D3在PRAD中上调,与预后不良有关。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology - Adenocarcin oma is the subject of a report. According to news reporting originating in Harbi n, People's Republic of China, by NewsRx journalists, research stated, "Angiogen esis, the process of forming new blood vessels from pre-existing ones, plays a c rucial role in the development and advancement of cancer. Although blocking angi ogenesis has shown success in treating different types of solid tumors, its rele vance in prostate adenocarcinoma (PRAD) has not been thoroughly investigated." The news reporters obtained a quote from the research from the First Affiliated Hospital of Harbin Medical University, "This study utilized the WGCNA method to identify angiogenesis-related genes and assessed their diagnostic and prognostic value in patients with PRAD through cluster analysis. A diagnostic model was co nstructed using multiple machine learning techniques, while a prognostic model w as developed employing the LASSO algorithm, underscoring the relevance of angiog enesis-related genes in PRAD. Further analysis identified MAP7D3 as the most sig nificant prognostic gene among angiogenesisrelated genes using multivariate Cox regression analysis and various machine learning algorithms. The study also inv estigated the correlation between MAP7D3 and immune infiltration as well as drug sensitivity in PRAD. Molecular docking analysis was conducted to assess the bin ding affinity of MAP7D3 to angiogenic drugs. Immunohistochemistry analysis of 60 PRAD tissue samples confirmed the expression and prognostic value of MAP7D3. Ov erall, the study identified 10 key angiogenesis-related genes through WGCNA and demonstrated their potential prognostic and immune-related implications in PRAD patients. MAP7D3 is found to be closely associated with the prognosis of PRAD an d its response to immunotherapy. Through molecular docking studies, it was revea led that MAP7D3 exhibits a high binding affinity to angiogenic drugs. Furthermor e, experimental data confirmed the upregulation of MAP7D3 in PRAD, correlating w ith a poorer prognosis."

Key words

Harbin/People's Republic of China/Asia/Adenocarcinoma/Angiogenesis/Cancer/Cyborgs/Drugs and Therapies/Emerging T echnologies/Genetics/Health and Medicine/Immunology/Immunotherapy/Machine L earning/Oncology

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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