Genome-scale Metabolic Model Guided Subtyping Lung Cancer towards Personalized Diagnosis. Issue 20 (2022)

• Record Type: Journal Article
• Authors: Tanıl, Ezgi; Kızılilsoley, Nehir; Nikerel, Emrah
• Is Part Of: IFAC-PapersOnLine. Volume 55:Issue 20(2022)
• Page Start: 641
• Page End: 646
• Publication Date: 2022
• Subjects: Subtyping -- staging -- lung cancer -- modeling -- bioinformatics -- genome scale metabolic model -- personalized medicine -- machine learning -- statistical analysis -- biological network models; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2022.09.168
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Mathematical modeling of biological systems are useful in (i) gaining better understanding about the physiological processes in an organism, (ii) simulating alternative scenarios, (iii) finding targets for improved performance within metabolic engineering context (iv) performing several functional analyses, e.g. identify drug targets (v) process scheduling within the context of industrial biotechnology etc. Increasing the predictive capability of these models is of common interest within systems biology studies which allows identification of more effective and personalized treatment strategies for complex metabolic diseases such as cancer by investigation of disease metabolism and providing correct subtyping and staging. By transforming gene-level information to flux/metabolite level information, current disease state can be analyzed and diagnosis of cancer subtype can be performed using a less invasive methods. In this study, subtyping and staging of lung cancer, that is one of the main causes of cancer related deaths, was performed by integrating publicly available RNAseq data of normal, lung adenoma and adenocarcinomas and lung squamous cell neoplasms to human genome scale metabolic model and classification of obtained flux distributions using linear support vector machine (SVM) classifications. Differential flux analysis and pathway enrichment methods showed that model adequately represented tumour metabolism. SVM classification accuracies were calculated as more than 99% for normal and cancer cells and 94% for adenomas and adenocarcinomas and squamous cell neoplasms, indicating high predictive capability of flux distributions.