主要乳胶蛋白(major latex protein,MLP)是Bet v 1超家族成员,参与植物的各种应激反应。本研究对桑树主要乳胶蛋白基因MaMLP423的启动子pMaMLP423进行了生物信息学分析和克隆表达分析。结果显示pMaMLP423具有启动子必需的TATA-box和CAAT...主要乳胶蛋白(major latex protein,MLP)是Bet v 1超家族成员,参与植物的各种应激反应。本研究对桑树主要乳胶蛋白基因MaMLP423的启动子pMaMLP423进行了生物信息学分析和克隆表达分析。结果显示pMaMLP423具有启动子必需的TATA-box和CAAT-box等基本顺式作用元件,还有参与光响应(Box 4、TCT-motif、GA-motif等)、脱落酸反应(ABRE、AAGAA-motif)、乙烯响应(ERE)、干旱诱导(MYB、MBS等)、创伤响应(WUN-motif)等顺式作用元件。构建植物表达载体pMaMLP423及缺失部分序列的pMaMLP423ΔS1和pMaMLP423ΔS2,转化烟草瞬时表达,qPCR和组织化学染色检测显示,三个启动子均能驱动下游GUS基因的表达,但缺失S1和S2启动子的活性降低。外源激素脱落酸和乙烯利能显著诱导MaMLP4232基因的表达。本研究为深入了解MaMLP423的功能和作用提供了参考。展开更多
癌症是全球范围内导致死亡的主要疾病之一,尤其是对晚期或发生转移的癌症治疗依然面临巨大的挑战。癌症的精准分期在临床上对治疗方案的选择和患者预后评估至关重要。传统的分期方法主要依赖影像学和临床检查数据,然而随着基因组学和分...癌症是全球范围内导致死亡的主要疾病之一,尤其是对晚期或发生转移的癌症治疗依然面临巨大的挑战。癌症的精准分期在临床上对治疗方案的选择和患者预后评估至关重要。传统的分期方法主要依赖影像学和临床检查数据,然而随着基因组学和分子生物学技术的飞速发展,利用多组学数据进行癌症的早期诊断和分期变得越来越重要。为了提高癌症分类和分期的准确性,本研究提出了一种新的多组学数据分析框架MOGCWMLP。该框架基于图卷积网络(GCN)对不同组学数据进行特征学习,结合加权多层感知机(MLP)网络进行分类决策。具体来说,MOGCWMLP框架集成了RNA-seq、miRNA和lncRNA等三种不同类型的组学数据,通过学习每种数据的特征并进行加权融合,最大化不同组学数据的互补信息。实验结果表明,MOGCWMLP模型在肺鳞癌(LUSC)数据集上的分类精度显著优于现有的单组学模型和多组学模型,尤其是在多组学数据整合的情况下,分类性能得到显著提升。此外,采用可学习的加权融合机制,能够动态调整各视图的贡献,从而进一步优化模型的分类效果。该研究为癌症精准诊断和个性化治疗提供了有效的工具,也为多组学数据的整合提供了新的思路。Cancer remains one of the leading causes of mortality worldwide, particularly in advanced or metastatic cases, where treatment remains a significant challenge. Accurate cancer staging is critical in clinical practice for determining optimal treatment strategies and assessing patient prognosis. Traditional staging methods primarily rely on imaging and clinical examination data. However, with rapid advancements in genomics and molecular biology, lever aging multi-omics data for early cancer diagnosis and staging has become increasingly important. To enhance the accuracy of cancer classification and staging, this study proposes an ovel multi-omics data analysis framework, MOGCWMLP. This framework utilizes graph convolutional networks (GCN) for feature learning across different omics data types and incorporates a weighted multilayer perceptron (MLP) for classification decision-making. Specifically, MOGCWMLP integrates three distinct types of omics data—mRNA, miRNA, and lncRNA—by extracting and fusing their features through a weighted mechanism, there by maximizing the complementary information among different omics modalities. Experimental results demonstrate that the MOGCWMLP model achieves significantly higher classification accuracy on the lung squamous cell carcinoma (LUSC) dataset compared to existing single-omics and multi-omics models. Notably, the integration of multi-omics data leads to substantial improvements in classification performance. Furthermore, the incorporation of a learnable weighted fusion mechanism enables the dynamic adjustment of each modality’s contribution, further optimizing the model’s classification effectiveness. This study provides an effective tool for precise cancer diagnosis and personalized treatment, while also offering new insights into the integration of multi-omics data.展开更多
文摘癌症是全球范围内导致死亡的主要疾病之一,尤其是对晚期或发生转移的癌症治疗依然面临巨大的挑战。癌症的精准分期在临床上对治疗方案的选择和患者预后评估至关重要。传统的分期方法主要依赖影像学和临床检查数据,然而随着基因组学和分子生物学技术的飞速发展,利用多组学数据进行癌症的早期诊断和分期变得越来越重要。为了提高癌症分类和分期的准确性,本研究提出了一种新的多组学数据分析框架MOGCWMLP。该框架基于图卷积网络(GCN)对不同组学数据进行特征学习,结合加权多层感知机(MLP)网络进行分类决策。具体来说,MOGCWMLP框架集成了RNA-seq、miRNA和lncRNA等三种不同类型的组学数据,通过学习每种数据的特征并进行加权融合,最大化不同组学数据的互补信息。实验结果表明,MOGCWMLP模型在肺鳞癌(LUSC)数据集上的分类精度显著优于现有的单组学模型和多组学模型,尤其是在多组学数据整合的情况下,分类性能得到显著提升。此外,采用可学习的加权融合机制,能够动态调整各视图的贡献,从而进一步优化模型的分类效果。该研究为癌症精准诊断和个性化治疗提供了有效的工具,也为多组学数据的整合提供了新的思路。Cancer remains one of the leading causes of mortality worldwide, particularly in advanced or metastatic cases, where treatment remains a significant challenge. Accurate cancer staging is critical in clinical practice for determining optimal treatment strategies and assessing patient prognosis. Traditional staging methods primarily rely on imaging and clinical examination data. However, with rapid advancements in genomics and molecular biology, lever aging multi-omics data for early cancer diagnosis and staging has become increasingly important. To enhance the accuracy of cancer classification and staging, this study proposes an ovel multi-omics data analysis framework, MOGCWMLP. This framework utilizes graph convolutional networks (GCN) for feature learning across different omics data types and incorporates a weighted multilayer perceptron (MLP) for classification decision-making. Specifically, MOGCWMLP integrates three distinct types of omics data—mRNA, miRNA, and lncRNA—by extracting and fusing their features through a weighted mechanism, there by maximizing the complementary information among different omics modalities. Experimental results demonstrate that the MOGCWMLP model achieves significantly higher classification accuracy on the lung squamous cell carcinoma (LUSC) dataset compared to existing single-omics and multi-omics models. Notably, the integration of multi-omics data leads to substantial improvements in classification performance. Furthermore, the incorporation of a learnable weighted fusion mechanism enables the dynamic adjustment of each modality’s contribution, further optimizing the model’s classification effectiveness. This study provides an effective tool for precise cancer diagnosis and personalized treatment, while also offering new insights into the integration of multi-omics data.
