Kucarov Marianna Dimitrova, Szakállas Niklolett, Molnár Béla, Kozlovszky Miklos
Doctoral School of Applied Informatics and Applied Mathematics, Óbuda University, 1034 Budapest, Hungary.
BioTech Research Center, Óbuda University, 1034 Budapest, Hungary.
Sensors (Basel). 2025 Jul 17;25(14):4465. doi: 10.3390/s25144465.
The rapid advancement of genomic technologies has significantly transformed biomedical research and clinical applications, particularly in oncology. Identifying patient-specific genetic mutations has become a crucial tool for early cancer detection and personalized treatment strategies. Detecting tumors at the earliest possible stage provides critical insights beyond traditional tissue analysis. This paper presents a novel cyber-physical system that combines high-resolution tissue scanning, laser microdissection, next-generation sequencing, and genomic analysis to offer a comprehensive solution for early cancer detection. We describe the methodologies for scanning tissue samples, image processing of the morphology of single cells, quantifying morphometric parameters, and generating and analyzing real-time genomic metadata. Additionally, the intelligent system integrates data from open-access genomic databases for gene-specific molecular pathways and drug targets. The developed platform also includes powerful visualization tools, such as colon-specific gene filtering and heatmap generation, to provide detailed insights into genomic heterogeneity and tumor foci. The integration and visualization of multimodal single-cell genomic metadata alongside tissue morphology and morphometry offer a promising approach to precision oncology.
基因组技术的快速发展显著改变了生物医学研究和临床应用，尤其是在肿瘤学领域。识别患者特异性基因突变已成为早期癌症检测和个性化治疗策略的关键工具。在尽可能早的阶段检测肿瘤提供了超越传统组织分析的关键见解。本文提出了一种新型的网络物理系统，该系统结合了高分辨率组织扫描、激光显微切割、下一代测序和基因组分析，为早期癌症检测提供了全面的解决方案。我们描述了扫描组织样本的方法、单细胞形态的图像处理、形态计量参数的量化以及实时基因组元数据的生成和分析。此外，该智能系统整合了来自开放获取基因组数据库的数据，用于特定基因的分子途径和药物靶点。所开发的平台还包括强大的可视化工具，如结肠特异性基因筛选和热图生成，以提供对基因组异质性和肿瘤病灶的详细见解。多模态单细胞基因组元数据与组织形态学和形态计量学的整合与可视化提供了一种有前景的精准肿瘤学方法。
