超越疾病孤岛的思考:系统生物学和机器学习发现的肺结核和肺癌中常见的失调基因》(Thinking Beyond Disease Silos: Dysregulated Genes Common in Tuberculosis and Lung Cancer as Identified by Systems Biology and Machine Learning)。

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
ACS Applied Bio Materials Pub Date : 2024-07-01 Epub Date: 2024-06-10 DOI:10.1089/omi.2024.0116
Sanjukta Dasgupta
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引用次数: 0

摘要

传统的跨临床和狭义表型组学孤岛思考人类疾病的方式往往掩盖了人类疾病的潜在共同分子基质。一体健康 "和 "行星健康 "领域特别关注这种复杂性,并邀请我们跨越传统疾病命名法进行思考。例如,肺结核(TB)和肺癌(LC)是对地球健康有重大影响的主要肺部疾病。尽管病因不同,但它们可以在特定社区或患者中同时存在。这既是预防医学、诊断学和治疗学创新的挑战,也是机遇。本研究报告对公开的基因表达数据进行了生物信息学分析,确定了肺结核和肺癌中重叠的失调基因、下游调控因子和通路。对 NCBI-GEO 数据集(GSE83456 和 GSE103888)的分析揭示了这两种疾病中 CEACAM6、MUC1、ADM、DYSF、PLOD2 和 GAS6 基因的差异表达,通路分析表明它们与赖氨酸降解通路有关。随机森林是一种基于机器学习的分类方法,利用这些特定基因区分肺结核与对照组的准确率为 84%,区分 LC 与对照组的准确率为 83%。此外,还发现了潜在的药物靶点,分子对接证实了华法林与 GAS6 的结合亲和力。总之,本研究表明,迫切需要重新思考人类疾病的临床诊断类别,肺结核和肺结核有可能共享分子底物。展望未来,行星健康和 "同一健康 "学术研究不仅将在医学和生态学领域,而且将在传统诊断常规领域培养新的疾病思维方式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Thinking Beyond Disease Silos: Dysregulated Genes Common in Tuberculosis and Lung Cancer as Identified by Systems Biology and Machine Learning.

The traditional way of thinking about human diseases across clinical and narrow phenomics silos often masks the underlying shared molecular substrates across human diseases. One Health and planetary health fields particularly address such complexities and invite us to think across the conventional disease nosologies. For example, tuberculosis (TB) and lung cancer (LC) are major pulmonary diseases with significant planetary health implications. Despite distinct etiologies, they can coexist in a given community or patient. This is both a challenge and an opportunity for preventive medicine, diagnostics, and therapeutics innovation. This study reports a bioinformatics analysis of publicly available gene expression data, identifying overlapping dysregulated genes, downstream regulators, and pathways in TB and LC. Analysis of NCBI-GEO datasets (GSE83456 and GSE103888) unveiled differential expression of CEACAM6, MUC1, ADM, DYSF, PLOD2, and GAS6 genes in both diseases, with pathway analysis indicating association with lysine degradation pathway. Random forest, a machine-learning-based classification, achieved accuracies of 84% for distinguishing TB from controls and 83% for discriminating LC from controls using these specific genes. Additionally, potential drug targets were identified, with molecular docking confirming the binding affinity of warfarin to GAS6. Taken together, the present study speaks of the pressing need to rethink clinical diagnostic categories of human diseases and that TB and LC might potentially share molecular substrates. Going forward, planetary health and One Health scholarship are poised to cultivate new ways of thinking about diseases not only across medicine and ecology but also across traditional diagnostic conventions.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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