Khan Jiyauddin, Bareja Chanchal, Dwivedi Kountay, Mathur Ankit, Kumar Naveen, Saluja Daman
Dr B R Ambedkar Center for Biomedical Research, University of Delhi, Delhi, 110007, India.
Department of Computer Science, FacultyofMathematicalSciences, University of Delhi, Delhi, 110007, India.
Sci Rep. 2025 Jan 8;15(1):1374. doi: 10.1038/s41598-025-85366-8.
Metabolic reprogramming, vital for cancer cells to adapt to the altered microenvironment, remains a topic requiring further investigation for different tumor types. Our study aims to elucidate shared metabolic reprogramming across breast (BRC), colorectal (CRC), and lung (LUC) cancers. Leveraging gene expression data from the Gene Expression Omnibus and various bioinformatics tools like MSigDB, WebGestalt, String, and Cytoscape, we identified key/hub metabolism-related genes (MRGs) and their interactions. The functional characteristics including survival parameters and expression of the key MRGs were analyzed and validated through Gene Expression Profiling Interactive Analysis 2 and qRT-PCR. In addition, we employed machine learning algorithms such as k-nearest neighbours (KNN), support vector regressor (SVR), and extreme gradient boosting (XGBoost) to assess MRGs' effectiveness in predicting overall patient survival. Among 11,384 DEGs analyzed, 540 overlapped across BRC, CRC, and LUC, with 46 MRGs and 20 key/hub MRGs involved in all studied cancer types. Of these, 11 key MRGs were prognostically significant. The qRT-PCR validation of key MRGs in specific cancer cell lines confirmed their expression profiles, with some showing cell-type-specific patterns. SVR exhibited remarkable accuracy in predicting overall survival, emphasizing its clinical utility. Our integrated approach combining bioinformatics analyses and experimental validations underscores the potential of MRGs as biomarkers for metabolic therapies, with machine learning models enhancing predictive capabilities for patient outcomes.
代谢重编程对于癌细胞适应改变的微环境至关重要，仍然是不同肿瘤类型中需要进一步研究的课题。我们的研究旨在阐明乳腺癌（BRC）、结直肠癌（CRC）和肺癌（LUC）之间共享的代谢重编程。利用来自基因表达综合数据库（Gene Expression Omnibus）的基因表达数据以及诸如MSigDB、WebGestalt、String和Cytoscape等各种生物信息学工具，我们确定了关键/核心代谢相关基因（MRG）及其相互作用。通过基因表达谱交互式分析2（Gene Expression Profiling Interactive Analysis 2）和qRT-PCR分析并验证了关键MRG的功能特征，包括生存参数和表达情况。此外，我们采用了诸如k近邻算法（KNN）、支持向量回归（SVR）和极端梯度提升（XGBoost）等机器学习算法来评估MRG在预测患者总体生存方面的有效性。在分析的11384个差异表达基因（DEG）中，有540个在BRC、CRC和LUC中重叠，其中46个MRG和20个关键/核心MRG涉及所有研究的癌症类型。其中，11个关键MRG具有预后意义。在特定癌细胞系中对关键MRG进行的qRT-PCR验证证实了它们的表达谱，有些显示出细胞类型特异性模式。SVR在预测总体生存方面表现出显著的准确性，强调了其临床实用性。我们将生物信息学分析与实验验证相结合的综合方法强调了MRG作为代谢疗法生物标志物的潜力，机器学习模型增强了对患者预后的预测能力。
