PDF(Pubmed)
Abstract：
The nuclear factor kappa B (NFκB) regulatory pathways downstream of tumor necrosis factor (TNF) play a critical role in carcinogenesis. However, the widespread influence of NFκB in cells can result in off-target effects, making it a challenging therapeutic target. Ensemble learning is a machine learning technique where multiple models are combined to improve the performance and robustness of the prediction. Accordingly, an ensemble learning model could uncover more precise targets within the NFκB/TNF signaling pathway for cancer therapy.
In this study, we trained an ensemble learning model on the transcriptome profiles from 16 cancer types in the TCGA database to identify a robust set of genes that are consistently associated with the NFκB/TNF pathway in cancer. Our model uses cancer patients as features to predict the genes involved in the NFκB/TNF signaling pathway and can be adapted to predict the genes for different cancer types by switching the cancer type of patients. We also performed functional analysis, survival analysis, and a case study of triple-negative breast cancer to demonstrate our model\'s potential in translational cancer medicine.
Our model accurately identified genes regulated by NFκB in response to TNF in cancer patients. The downstream analysis showed that the identified genes are typically involved in the canonical NFκB-regulated pathways, particularly in adaptive immunity, anti-apoptosis, and cellular response to cytokine stimuli. These genes were found to have oncogenic properties and detrimental effects on patient survival. Our model also could distinguish patients with a specific cancer subtype, triple-negative breast cancer (TNBC), which is known to be influenced by NFκB-regulated pathways downstream of TNF. Furthermore, a functional module known as mononuclear cell differentiation was identified that accurately predicts TNBC patients and poor short-term survival in non-TNBC patients, providing a potential avenue for developing precision medicine for cancer subtypes.
In conclusion, our approach enables the discovery of genes in NFκB-regulated pathways in response to TNF and their relevance to carcinogenesis. We successfully categorized these genes into functional groups, providing valuable insights for discovering more precise and targeted cancer therapeutics.
In this study, we trained an ensemble learning model on the transcriptome profiles from 16 cancer types in the TCGA database to identify a robust set of genes that are consistently associated with the NFκB/TNF pathway in cancer. Our model uses cancer patients as features to predict the genes involved in the NFκB/TNF signaling pathway and can be adapted to predict the genes for different cancer types by switching the cancer type of patients. We also performed functional analysis, survival analysis, and a case study of triple-negative breast cancer to demonstrate our model\'s potential in translational cancer medicine.
Our model accurately identified genes regulated by NFκB in response to TNF in cancer patients. The downstream analysis showed that the identified genes are typically involved in the canonical NFκB-regulated pathways, particularly in adaptive immunity, anti-apoptosis, and cellular response to cytokine stimuli. These genes were found to have oncogenic properties and detrimental effects on patient survival. Our model also could distinguish patients with a specific cancer subtype, triple-negative breast cancer (TNBC), which is known to be influenced by NFκB-regulated pathways downstream of TNF. Furthermore, a functional module known as mononuclear cell differentiation was identified that accurately predicts TNBC patients and poor short-term survival in non-TNBC patients, providing a potential avenue for developing precision medicine for cancer subtypes.
In conclusion, our approach enables the discovery of genes in NFκB-regulated pathways in response to TNF and their relevance to carcinogenesis. We successfully categorized these genes into functional groups, providing valuable insights for discovering more precise and targeted cancer therapeutics.
摘要：
背景：肿瘤坏死因子（TNF）下游的核因子κB（NFκB）调节途径在癌变中起关键作用。然而,NFκB在细胞中的广泛影响可以导致脱靶效应，使其成为具有挑战性的治疗目标。集成学习是一种机器学习技术，其中组合多个模型以提高预测的性能和鲁棒性。因此，集成学习模型可以揭示肿瘤治疗中NFκB/TNF信号通路内更精确的靶点.
方法：在本研究中，我们对TCGA数据库中16种癌症类型的转录组概况进行了集成学习模型训练,以鉴定出一组与癌症中NFκB/TNF通路始终相关的稳健基因.我们的模型使用癌症患者作为特征来预测NFκB/TNF信号通路中涉及的基因，并且可以通过切换患者的癌症类型来适应预测不同癌症类型的基因。我们还进行了功能分析，生存分析,和三阴性乳腺癌的案例研究，以证明我们的模型在转化癌症医学中的潜力。
结果：我们的模型在癌症患者中准确地鉴定了NFκB对TNF的反应中调节的基因。下游分析表明，鉴定的基因通常参与规范的NFκB调节途径，特别是在适应性免疫方面，抗凋亡，和细胞对细胞因子刺激的反应。发现这些基因具有致癌特性和对患者存活的有害影响。我们的模型还可以区分特定癌症亚型的患者，三阴性乳腺癌（TNBC），已知受TNF下游NFκB调节途径的影响。此外，确定了一个称为单核细胞分化的功能模块，可以准确预测TNBC患者和非TNBC患者的短期生存率。为开发针对癌症亚型的精准医学提供了潜在的途径。
结论：结论：我们的方法能够发现NFκB调节通路中响应TNF的基因及其与癌变的相关性.我们成功地将这些基因分为功能组，为发现更精确和有针对性的癌症疗法提供有价值的见解。
方法：在本研究中，我们对TCGA数据库中16种癌症类型的转录组概况进行了集成学习模型训练,以鉴定出一组与癌症中NFκB/TNF通路始终相关的稳健基因.我们的模型使用癌症患者作为特征来预测NFκB/TNF信号通路中涉及的基因，并且可以通过切换患者的癌症类型来适应预测不同癌症类型的基因。我们还进行了功能分析，生存分析,和三阴性乳腺癌的案例研究，以证明我们的模型在转化癌症医学中的潜力。
结果：我们的模型在癌症患者中准确地鉴定了NFκB对TNF的反应中调节的基因。下游分析表明，鉴定的基因通常参与规范的NFκB调节途径，特别是在适应性免疫方面，抗凋亡，和细胞对细胞因子刺激的反应。发现这些基因具有致癌特性和对患者存活的有害影响。我们的模型还可以区分特定癌症亚型的患者，三阴性乳腺癌（TNBC），已知受TNF下游NFκB调节途径的影响。此外，确定了一个称为单核细胞分化的功能模块，可以准确预测TNBC患者和非TNBC患者的短期生存率。为开发针对癌症亚型的精准医学提供了潜在的途径。
结论：结论：我们的方法能够发现NFκB调节通路中响应TNF的基因及其与癌变的相关性.我们成功地将这些基因分为功能组，为发现更精确和有针对性的癌症疗法提供有价值的见解。
