Investigating the Role of Obesity in Prostate Cancer and Identifying Biomarkers for Drug Discovery: Systems Biology and Deep Learning Approaches
Prostate cancer (PCa) is the second most frequently diagnosed cancer for men and is viewed as the fifth leading cause of death worldwide. The body mass index (BMI) is taken as a vital criterion to elucidate the association between obesity and PCa. In this study, systematic methods are employed to in...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/27/3/900 |
| _version_ | 1797486045813538816 |
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| author | Shan-Ju Yeh Yun-Chen Chung Bor-Sen Chen |
| author_facet | Shan-Ju Yeh Yun-Chen Chung Bor-Sen Chen |
| author_sort | Shan-Ju Yeh |
| collection | DOAJ |
| description | Prostate cancer (PCa) is the second most frequently diagnosed cancer for men and is viewed as the fifth leading cause of death worldwide. The body mass index (BMI) is taken as a vital criterion to elucidate the association between obesity and PCa. In this study, systematic methods are employed to investigate how obesity influences the noncutaneous malignancies of PCa. By comparing the core signaling pathways of lean and obese patients with PCa, we are able to investigate the relationships between obesity and pathogenic mechanisms and identify significant biomarkers as drug targets for drug discovery. Regarding drug design specifications, we take drug–target interaction, drug regulation ability, and drug toxicity into account. One deep neural network (DNN)-based drug–target interaction (DTI) model is trained in advance for predicting drug candidates based on the identified biomarkers. In terms of the application of the DNN-based DTI model and the consideration of drug design specifications, we suggest two potential multiple-molecule drugs to prevent PCa (covering lean and obese PCa) and obesity-specific PCa, respectively. The proposed multiple-molecule drugs (apigenin, digoxin, and orlistat) not only help to prevent PCa, suppressing malignant metastasis, but also result in lower production of fatty acids and cholesterol, especially for obesity-specific PCa. |
| first_indexed | 2024-03-09T23:28:33Z |
| format | Article |
| id | doaj.art-b23f3c58a1704a00ad6e8fa44bab5d55 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-09T23:28:33Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-b23f3c58a1704a00ad6e8fa44bab5d552023-11-23T17:14:21ZengMDPI AGMolecules1420-30492022-01-0127390010.3390/molecules27030900Investigating the Role of Obesity in Prostate Cancer and Identifying Biomarkers for Drug Discovery: Systems Biology and Deep Learning ApproachesShan-Ju Yeh0Yun-Chen Chung1Bor-Sen Chen2Laboratory of Automatic Control, Signal Processing and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, TaiwanLaboratory of Automatic Control, Signal Processing and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, TaiwanLaboratory of Automatic Control, Signal Processing and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, TaiwanProstate cancer (PCa) is the second most frequently diagnosed cancer for men and is viewed as the fifth leading cause of death worldwide. The body mass index (BMI) is taken as a vital criterion to elucidate the association between obesity and PCa. In this study, systematic methods are employed to investigate how obesity influences the noncutaneous malignancies of PCa. By comparing the core signaling pathways of lean and obese patients with PCa, we are able to investigate the relationships between obesity and pathogenic mechanisms and identify significant biomarkers as drug targets for drug discovery. Regarding drug design specifications, we take drug–target interaction, drug regulation ability, and drug toxicity into account. One deep neural network (DNN)-based drug–target interaction (DTI) model is trained in advance for predicting drug candidates based on the identified biomarkers. In terms of the application of the DNN-based DTI model and the consideration of drug design specifications, we suggest two potential multiple-molecule drugs to prevent PCa (covering lean and obese PCa) and obesity-specific PCa, respectively. The proposed multiple-molecule drugs (apigenin, digoxin, and orlistat) not only help to prevent PCa, suppressing malignant metastasis, but also result in lower production of fatty acids and cholesterol, especially for obesity-specific PCa.https://www.mdpi.com/1420-3049/27/3/900prostate cancer (PCa)lean PCaobese PCamultiple-molecule drugcarcinogenic mechanismdeep neural network (DNN)-based DTI model |
| spellingShingle | Shan-Ju Yeh Yun-Chen Chung Bor-Sen Chen Investigating the Role of Obesity in Prostate Cancer and Identifying Biomarkers for Drug Discovery: Systems Biology and Deep Learning Approaches Molecules prostate cancer (PCa) lean PCa obese PCa multiple-molecule drug carcinogenic mechanism deep neural network (DNN)-based DTI model |
| title | Investigating the Role of Obesity in Prostate Cancer and Identifying Biomarkers for Drug Discovery: Systems Biology and Deep Learning Approaches |
| title_full | Investigating the Role of Obesity in Prostate Cancer and Identifying Biomarkers for Drug Discovery: Systems Biology and Deep Learning Approaches |
| title_fullStr | Investigating the Role of Obesity in Prostate Cancer and Identifying Biomarkers for Drug Discovery: Systems Biology and Deep Learning Approaches |
| title_full_unstemmed | Investigating the Role of Obesity in Prostate Cancer and Identifying Biomarkers for Drug Discovery: Systems Biology and Deep Learning Approaches |
| title_short | Investigating the Role of Obesity in Prostate Cancer and Identifying Biomarkers for Drug Discovery: Systems Biology and Deep Learning Approaches |
| title_sort | investigating the role of obesity in prostate cancer and identifying biomarkers for drug discovery systems biology and deep learning approaches |
| topic | prostate cancer (PCa) lean PCa obese PCa multiple-molecule drug carcinogenic mechanism deep neural network (DNN)-based DTI model |
| url | https://www.mdpi.com/1420-3049/27/3/900 |
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