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A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
Published on: April 6, 2016
Optimization of a modeling platform to predict oncogenes from genome-scale metabolic networks of non-small-cell lung
You-Tyun Wang1, Min-Ru Lin1, Wei-Chen Chen1
1Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan.
Abstract:
Cancer cell dysregulations result in the abnormal regulation of cellular metabolic pathways. By simulating this metabolic reprogramming using constraint-based modeling approaches, oncogenes can be predicted, and this knowledge can be used in prognosis and treatment. We introduced a trilevel optimization problem describing metabolic reprogramming for inferring oncogenes. First, this study used RNA-Seq expression data of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) samples and their healthy counterparts to reconstruct tissue-specific genome-scale metabolic models and subsequently build the flux distribution pattern that provided a measure for the oncogene inference optimization problem for determining tumorigenesis. The platform detected 45 genes for LUAD and 84 genes for LUSC that lead to tumorigenesis. A high level of differentially expressed genes was not an essential factor for determining tumorigenesis. The platform indicated that pyruvate kinase (PKM), a well-known oncogene with a low level of differential gene expression in LUAD and LUSC, had the highest fitness among the predicted oncogenes based on computation. By contrast, pyruvate kinase L/R (PKLR), an isozyme of PKM, had a high level of differential gene expression in both cancers. Phosphatidylserine synthase 1 (PTDSS1), an oncogene in LUAD, was inferred to have a low level of differential gene expression, and overexpression could significantly reduce survival probability. According to the factor analysis, PTDSS1 characteristics were close to those of the template, but they were unobvious in LUSC. Angiotensin-converting enzyme 2 (ACE2) has recently garnered widespread interest as the SARS-CoV-2 virus receptor. Moreover, we determined that ACE2 is an oncogene of LUSC but not of LUAD. The platform developed in this study can identify oncogenes with low levels of differential expression and be used to identify potential therapeutic targets for cancer treatment.
Insights
This study identifies oncogenes in lung cancer by simulating metabolic reprogramming. The platform discovered key genes, including pyruvate kinase (PKM) and angiotensin-converting enzyme 2 (ACE2), crucial for tumorigenesis and potential therapeutic targets.
Area of Science:
- Computational Biology
- Metabolic Engineering
- Cancer Genomics
Background:
- Cancer cells exhibit altered metabolic pathways, a hallmark of tumorigenesis.
- Constraint-based modeling offers a method to simulate metabolic reprogramming and predict oncogenes.
- Understanding these metabolic shifts is vital for cancer prognosis and treatment strategies.
Purpose of the Study:
- To introduce a trilevel optimization problem for inferring oncogenes by modeling metabolic reprogramming.
- To develop a computational platform for identifying oncogenes in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC).
- To investigate the role of differential gene expression in oncogene identification.
Main Methods:
- Reconstruction of tissue-specific genome-scale metabolic models using RNA-Seq data from LUAD and LUSC.
- Application of a trilevel optimization framework to analyze flux distribution patterns and infer oncogenes.
- Comparative analysis of gene expression levels and oncogenic potential for identified genes.
Main Results:
- The platform identified 45 oncogenes for LUAD and 84 for LUSC.
- Pyruvate kinase (PKM), a known oncogene, showed high fitness despite low differential expression.
- Angiotensin-converting enzyme 2 (ACE2) was identified as an oncogene in LUSC but not LUAD.
- Phosphatidylserine synthase 1 (PTDSS1) was identified as an oncogene in LUAD with implications for survival.
Conclusions:
- The developed platform can successfully identify oncogenes, including those with low differential gene expression.
- The findings highlight the importance of metabolic reprogramming in tumorigenesis.
- The identified oncogenes represent potential therapeutic targets for lung cancer treatment.
