Oncogene inference optimization using constraint-based modelling incorporated with protein expression in normal and tumour tissues
| العنوان: | Oncogene inference optimization using constraint-based modelling incorporated with protein expression in normal and tumour tissues |
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| المؤلفون: | Yi Chen Shu, Jin Mei Lai, Peter Mu Hsin Chang, Feng Sheng Wang, Wu Hsiung Wu, Fan Yu Li, Chi Ying F. Huang |
| المصدر: | Royal Society Open Science Royal Society Open Science, Vol 7, Iss 3 (2020) |
| بيانات النشر: | The Royal Society, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | flux balance analysis, Metabolic network, Computational biology, cancer cell metabolism, head and neck squamous cell carcinoma, 03 medical and health sciences, 0302 clinical medicine, medicine, PTEN, Epigenetics, lcsh:Science, Biochemistry, Cellular and Molecular Biology, PI3K/AKT/mTOR pathway, 030304 developmental biology, 0303 health sciences, Multidisciplinary, biology, Cancer, medicine.disease, Flux balance analysis, 030220 oncology & carcinogenesis, Cancer cell, biology.protein, lcsh:Q, Literature survey, Research Article, multiple-level optimization |
| الوصف: | Cancer cells are known to exhibit unusual metabolic activity, and yet few metabolic cancer driver genes are known. Genetic alterations and epigenetic modifications of cancer cells result in the abnormal regulation of cellular metabolic pathways that are different when compared with normal cells. Such a metabolic reprogramming can be simulated using constraint-based modelling approaches towards predicting oncogenes. We introduced the tri-level optimization problem to use the metabolic reprogramming towards inferring oncogenes. The algorithm incorporated Recon 2.2 network with the Human Protein Atlas to reconstruct genome-scale metabolic network models of the tissue-specific cells at normal and cancer states, respectively. Such reconstructed models were applied to build the templates of the metabolic reprogramming between normal and cancer cell metabolism. The inference optimization problem was formulated to use the templates as a measure towards predicting oncogenes. The nested hybrid differential evolution algorithm was applied to solve the problem to overcome solving difficulty for transferring the inner optimization problem into the single one. Head and neck squamous cells were applied as a case study to evaluate the algorithm. We detected 13 of the top-ranked one-hit dysregulations and 17 of the top-ranked two-hit oncogenes with high similarity ratios to the templates. According to the literature survey, most inferred oncogenes are consistent with the observation in various tissues. Furthermore, the inferred oncogenes were highly connected with the TP53/AKT/IGF/MTOR signalling pathway through PTEN, which is one of the most frequently detected tumour suppressor genes in human cancer. |
| اللغة: | English |
| تدمد: | 2054-5703 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f17556134b985d128fb8ec5db134aeb9Test http://europepmc.org/articles/PMC7137941Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....f17556134b985d128fb8ec5db134aeb9 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20545703 |
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