Genome-scale metabolic models have provided valuable resources for exploring changes in metabolism under normal and cancer conditions. However, metabolism itself is strongly linked to gene expression, so integration of gene expression data into metabolic models might improve the detection of genes involved in the control of tumor progression. Herein, we considered gene expression data as extra constraints to enhance the predictive powers of metabolic models. We reconstructed genome-scale metabolic models for lung and prostate, under normal and cancer conditions to detect the major genes associated with critical subsystems during tumor development. Furthermore, we utilized gene expression data in combination with an information theory-based approach to reconstruct co-expression networks of the human lung and prostate in both cohorts. Our results revealed 19 genes as candidate biomarkers for lung and prostate cancer cells. This study also revealed that the development of a complementary approach (integration of gene expression and metabolic profiles) could lead to proposing novel biomarkers and suggesting renovated cancer treatment strategies which have not been possible to detect using either of the methods alone.

中文翻译：

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使用基因表达和通量变异性分析探索肺癌和前列腺癌的候选生物标志物

基因组规模的代谢模型为探索正常和癌症条件下的代谢变化提供了宝贵的资源。但是，代谢本身与基因表达密切相关，因此将基因表达数据整合到代谢模型中可能会改善对控制肿瘤进展的基因的检测。在这里，我们认为基因表达数据是额外的限制，以增强代谢模型的预测能力。我们在正常和癌症条件下重建了肺和前列腺的基因组规模代谢模型，以检测与肿瘤发展过程中关键子系统相关的主要基因。此外，我们利用基因表达数据与基于信息论的方法相结合，在两个队列中重建了人肺和前列腺的共表达网络。我们的研究结果揭示了19个基因作为肺癌和前列腺癌细胞的候选生物标记。这项研究还表明，互补方法（基因表达和代谢谱的整合）的发展可能导致提出新的生物标志物，并提出了单独使用这两种方法都无法检测到的改良的癌症治疗策略。