From complexity to clarity: development of CHM-FIEFP for predicting effective components in Chinese herbal formulas by using big data
Objective: The presence of complex components in Chinese herbal medicine (CHM) hinders identification of the primary active substances and understanding of pharmacological principles. This study was aimed at developing a big-data-based, knowledge-driven in silico algorithm for predicting central com...
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| Format: | Article |
| Language: | English |
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China Anti-Cancer Association
2024-11-01
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| Series: | Cancer Biology & Medicine |
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| Online Access: | https://www.cancerbiomed.org/content/21/11/1067 |
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| author | Boyu Pan Han Zhu Jiaqi Yang Liangjiao Wang Zizhen Chen Jian Ma Bo Zhang Zhanyu Pan Guoguang Ying Shao Li Liren Liu |
| author_facet | Boyu Pan Han Zhu Jiaqi Yang Liangjiao Wang Zizhen Chen Jian Ma Bo Zhang Zhanyu Pan Guoguang Ying Shao Li Liren Liu |
| author_sort | Boyu Pan |
| collection | DOAJ |
| description | Objective: The presence of complex components in Chinese herbal medicine (CHM) hinders identification of the primary active substances and understanding of pharmacological principles. This study was aimed at developing a big-data-based, knowledge-driven in silico algorithm for predicting central components in complex CHM formulas. Methods: Network pharmacology (TCMSP) and clinical (GEO) databases were searched to retrieve gene targets corresponding to the formula ingredients, herbal components, and specific disease being treated. Intersections were determined to obtain disease-specific core targets, which underwent further GO and KEGG enrichment analyses to generate non-redundant biological processes and molecular targets for the formula and each component. The ratios of the numbers of biological and molecular events associated with a component were calculated with a formula, and entropy weighting was performed to obtain a fitting score to facilitate ranking and improve identification of the key components. The established method was tested on the traditional CHM formula Danggui Sini Decoction (DSD) for gastric cancer. Finally, the effects of the predicted critical component were experimentally validated in gastric cancer cells. Results: An algorithm called Chinese Herb Medicine-Formula vs. Ingredients Efficacy Fitting & Prediction (CHM-FIEFP) was developed. Ferulic acid was identified as having the highest fitting score among all tested DSD components. The pharmacological effects of ferulic acid alone were similar to those of DSD. Conclusions: CHM-FIEFP is a promising in silico method for identifying pharmacological components of CHM formulas with activity against specific diseases. This approach may also be practical for solving other similarly complex problems. The algorithm is available at http://chm-fiefp.net/. |
| format | Article |
| id | doaj-art-3eeb03c922c04627ac467ac9b0a8a2de |
| institution | Kabale University |
| issn | 2095-3941 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | China Anti-Cancer Association |
| record_format | Article |
| series | Cancer Biology & Medicine |
| spelling | doaj-art-3eeb03c922c04627ac467ac9b0a8a2de2024-12-24T12:06:54ZengChina Anti-Cancer AssociationCancer Biology & Medicine2095-39412024-11-0121111067107710.20892/j.issn.2095-3941.2023.0442From complexity to clarity: development of CHM-FIEFP for predicting effective components in Chinese herbal formulas by using big dataBoyu Pan0Han Zhu1Jiaqi Yang2Liangjiao Wang3Zizhen Chen4Jian Ma5Bo Zhang6Zhanyu Pan7Guoguang Ying8Shao Li9Liren Liu10Department of Molecular Pharmacology, Tianjin Medical University Cancer Institute & Hospital, Tianjin 300060, ChinaCollege of Electronic and Information Engineering, Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai 200092, ChinaDepartment of Molecular Pharmacology, Tianjin Medical University Cancer Institute & Hospital, Tianjin 300060, ChinaDepartment of Molecular Pharmacology, Tianjin Medical University Cancer Institute & Hospital, Tianjin 300060, ChinaDepartment of Molecular Pharmacology, Tianjin Medical University Cancer Institute & Hospital, Tianjin 300060, ChinaDepartment of General Surgery, Tianjin Haihe Hospital, Tianjin 300350, ChinaBeijing Intelligent Medicine and Network Pharmacology Co., Ltd, Beijing 100020, ChinaNational Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin’s Clinical Research Center for Cancer, Tianjin 300060, ChinaNational Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin’s Clinical Research Center for Cancer, Tianjin 300060, ChinaInstitute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRist, Department of Automation, Tsinghua University, Beijing 100084, ChinaDepartment of Molecular Pharmacology, Tianjin Medical University Cancer Institute & Hospital, Tianjin 300060, ChinaObjective: The presence of complex components in Chinese herbal medicine (CHM) hinders identification of the primary active substances and understanding of pharmacological principles. This study was aimed at developing a big-data-based, knowledge-driven in silico algorithm for predicting central components in complex CHM formulas. Methods: Network pharmacology (TCMSP) and clinical (GEO) databases were searched to retrieve gene targets corresponding to the formula ingredients, herbal components, and specific disease being treated. Intersections were determined to obtain disease-specific core targets, which underwent further GO and KEGG enrichment analyses to generate non-redundant biological processes and molecular targets for the formula and each component. The ratios of the numbers of biological and molecular events associated with a component were calculated with a formula, and entropy weighting was performed to obtain a fitting score to facilitate ranking and improve identification of the key components. The established method was tested on the traditional CHM formula Danggui Sini Decoction (DSD) for gastric cancer. Finally, the effects of the predicted critical component were experimentally validated in gastric cancer cells. Results: An algorithm called Chinese Herb Medicine-Formula vs. Ingredients Efficacy Fitting & Prediction (CHM-FIEFP) was developed. Ferulic acid was identified as having the highest fitting score among all tested DSD components. The pharmacological effects of ferulic acid alone were similar to those of DSD. Conclusions: CHM-FIEFP is a promising in silico method for identifying pharmacological components of CHM formulas with activity against specific diseases. This approach may also be practical for solving other similarly complex problems. The algorithm is available at http://chm-fiefp.net/.https://www.cancerbiomed.org/content/21/11/1067chinese herbal medicine (chm)chm-fiefpnetwork pharmacologydanggui sini decoctionferulic acid |
| spellingShingle | Boyu Pan Han Zhu Jiaqi Yang Liangjiao Wang Zizhen Chen Jian Ma Bo Zhang Zhanyu Pan Guoguang Ying Shao Li Liren Liu From complexity to clarity: development of CHM-FIEFP for predicting effective components in Chinese herbal formulas by using big data Cancer Biology & Medicine chinese herbal medicine (chm) chm-fiefp network pharmacology danggui sini decoction ferulic acid |
| title | From complexity to clarity: development of CHM-FIEFP for predicting effective components in Chinese herbal formulas by using big data |
| title_full | From complexity to clarity: development of CHM-FIEFP for predicting effective components in Chinese herbal formulas by using big data |
| title_fullStr | From complexity to clarity: development of CHM-FIEFP for predicting effective components in Chinese herbal formulas by using big data |
| title_full_unstemmed | From complexity to clarity: development of CHM-FIEFP for predicting effective components in Chinese herbal formulas by using big data |
| title_short | From complexity to clarity: development of CHM-FIEFP for predicting effective components in Chinese herbal formulas by using big data |
| title_sort | from complexity to clarity development of chm fiefp for predicting effective components in chinese herbal formulas by using big data |
| topic | chinese herbal medicine (chm) chm-fiefp network pharmacology danggui sini decoction ferulic acid |
| url | https://www.cancerbiomed.org/content/21/11/1067 |
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