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Zhang L, Li Y, Hu W, Gao S, Tang Y, Sun L, Jiang N, Xiao Z, Han L, Zhou W. Computational identification of mitochondrial dysfunction biomarkers in severe SARS-CoV-2 infection: Facilitating therapeutic applications of phytomedicine. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2024; 131:155784. [PMID: 38878325 DOI: 10.1016/j.phymed.2024.155784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 03/18/2024] [Accepted: 04/13/2024] [Indexed: 06/25/2024]
Abstract
BACKGROUND Currently, SARS-CoV-2 has not disappeared and continues to prevail worldwide, with the ongoing risk of mutations and the potential for severe COVID-19. The impairment of monocyte mitochondrial function caused by SARS-CoV-2, leading to a metabolic and immune dysregulation, is a crucial factor in the development of severe COVID-19. PURPOSE Discover effective phytomedicines based on mitochondrial-related biomarkers in severe SARS-CoV-2 infection. METHODS Firstly, differential gene analysis and gene set enrichment analysis (GSEA) were conducted on monocytes datasets to identify genes and pathways distinguishing severe patients from uninfected individuals. Then, GO and KEGG enrichment analysis on the differentially expressed genes (DEGs) obtained. Take the DEGs and intersect them with the MitoCarta 3.0 gene set to obtain the differentially expressed mitochondrial-related genes (DE-MRGs). Subsequently, machine learning algorithms were employed to screen potential mitochondrial dysfunction biomarkers for severe COVID-19 based on score values. ROC curves were then plotted to assess the distinguish capability of the biomarkers, followed by validation using two additional independent datasets. Next, the effects of the identified biomarkers on metabolic pathways and immune cells were explored through Gene Set Variation Analysis (GSVA) and CIBERSORT. Finally, potential nature products for severe COVID-19 were screened from the expression profile dataset based on dysregulated mitochondrial-related genes, followed by in vitro experimental validation. RESULTS There are 1812 DEGs and 17 dysregulated mitochondrial processes between severe COVID-19 patients and uninfected individuals. A total of 77 DE-MRGs were identified, and the potential biomarkers were identified as RECQL4, PYCR1, PIF1, POLQ, and GLDC. In both the training and validation sets, the area under the ROC curve (AUC) for these five biomarkers was greater than 0.9. And they did not show significant changes in mild to moderate patients (p > 0.05), indicating their ability to effectively distinguish severe COVID-19. These biomarkers exhibit a highly significant correlation with the dysregulated metabolic processes (p < 0.05) and immune cell imbalance (p < 0.05) in severe patients, as demonstrated by GSVA and CIBERSORT algorithms. Curcumin has the highest score in the predictive model based on transcriptomic data from 496 natural compounds (p = 0.02; ES = 0.90). Pre-treatment with curcumin for 8 h has been shown to alleviate mitochondrial membrane potential damage caused by the SARS-CoV-2 S1 protein (p < 0.05) and reduce elevated levels of reactive oxygen species (ROS) (p < 0.01). CONCLUSION The results of this study indicate a significant correlation between severe SARS-CoV-2 infection and mitochondrial dysfunction. The proposed mitochondrial dysfunction biomarkers identified in this study are associated with the disease progression, metabolic and immune changes in severe SARS-CoV-2 infected patients. Curcumin has a potential role in preventing severe COVID-19 by protecting mitochondrial function. Our findings provide new strategies for predicting the prognosis and enabling early intervention in SARS-CoV-2 infection.
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Affiliation(s)
- Lihui Zhang
- Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Beijing Institute of Pharmacology & Toxicology, Beijing 100850, China; State Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology & Toxicology, Beijing 100850, China
| | - Yuehan Li
- Beijing Institute of Pharmacology & Toxicology, Beijing 100850, China; State Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology & Toxicology, Beijing 100850, China
| | - Wanting Hu
- Beijing Institute of Pharmacology & Toxicology, Beijing 100850, China; State Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology & Toxicology, Beijing 100850, China
| | - Shengqiao Gao
- Beijing Institute of Pharmacology & Toxicology, Beijing 100850, China; State Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology & Toxicology, Beijing 100850, China
| | - Yiran Tang
- Beijing Institute of Pharmacology & Toxicology, Beijing 100850, China; State Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology & Toxicology, Beijing 100850, China
| | - Lei Sun
- Beijing Institute of Pharmacology & Toxicology, Beijing 100850, China; State Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology & Toxicology, Beijing 100850, China
| | - Ning Jiang
- Beijing Institute of Pharmacology & Toxicology, Beijing 100850, China; State Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology & Toxicology, Beijing 100850, China
| | - Zhiyong Xiao
- Beijing Institute of Pharmacology & Toxicology, Beijing 100850, China; State Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology & Toxicology, Beijing 100850, China
| | - Lu Han
- Beijing Institute of Pharmacology & Toxicology, Beijing 100850, China; State Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology & Toxicology, Beijing 100850, China.
| | - Wenxia Zhou
- Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Beijing Institute of Pharmacology & Toxicology, Beijing 100850, China; State Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology & Toxicology, Beijing 100850, China.
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He Z, Xu X, Chen Y, Huang Y, Wu B, Xu Z, Du J, Zhou Q, Cheng X. Integrated network pharmacology and bioinformatics to identify therapeutic targets and molecular mechanisms of Huangkui Lianchang Decoction for ulcerative colitis treatment. BMC Complement Med Ther 2024; 24:280. [PMID: 39044211 PMCID: PMC11267728 DOI: 10.1186/s12906-024-04590-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 07/16/2024] [Indexed: 07/25/2024] Open
Abstract
BACKGROUND Huangkui Lianchang Decoction (HLD) is a traditional Chinese herbal formula for treating ulcerative colitis (UC). However, its mechanism of action remains poorly understood. The Study aims to validate the therapeutic effect of HLD on UC and its mechanism by integrating network pharmacology, bioinformatics, and experimental validation. METHODS UC targets were collected by databases and GSE19101. The active ingredients in HLD were detected by ultra-performance liquid chromatography-tandem mass spectrometry. PubChem collected targets of active ingredients. Protein-protein interaction (PPI) networks were established with UC-related targets. Gene Ontology and Kyoto Encyclopedia (KEGG) of Genes and Genomes enrichment were analyzed for the mechanism of HLD treatment of UC and validated by the signaling pathways of HLD. Effects of HLD on UC were verified using dextran sulfate sodium (DDS)-induced UC mice experiments. RESULTS A total of 1883 UC-related targets were obtained from the GSE10191 dataset, 1589 from the database, and 1313 matching HLD-related targets, for a total of 94 key targets. Combined with PPI, GO, and KEGG network analyses, the signaling pathways were enriched to obtain IL-17, Toll-like receptor, NF-κB, and tumor necrosis factor signaling pathways. In animal experiments, HLD improved the inflammatory response of UC and reduced UC-induced pro-inflammatory factors such as Tumor Necrosis Factor Alpha (TNF-α), interleukin 1β (IL-1β), and interleukin 6 (IL-6). HLD suppressed proteins TLR4, MyD88, and NF-κB expression. CONCLUSIONS This study systematically dissected the molecular mechanism of HLD for the treatment of UC using a network pharmacology approach. Further animal verification experiments revealed that HLD inhibited inflammatory responses and improved intestinal barrier function through the TLR4/MyD88/NF-κB pathway.
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Affiliation(s)
- Zongqi He
- Kunshan Hospital of Chinese Medicine, Kunshan, 215300, PR China
| | - Xiang Xu
- Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Zhangjiagang, 215600, PR China
| | - Yugen Chen
- Department of Colorectal Surgery, Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155, Hanzhong Road, Nanjing, Jiangsu Province, 210004, PR China
| | - Yuyu Huang
- Kunshan Hospital of Chinese Medicine, Kunshan, 215300, PR China
| | - Bensheng Wu
- Kunshan Hospital of Chinese Medicine, Kunshan, 215300, PR China
| | - Zhizhong Xu
- Kunshan Hospital of Chinese Medicine, Kunshan, 215300, PR China
| | - Jun Du
- Kunshan Hospital of Chinese Medicine, Kunshan, 215300, PR China
| | - Qing Zhou
- Department of Colorectal Surgery, Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155, Hanzhong Road, Nanjing, Jiangsu Province, 210004, PR China.
| | - Xudong Cheng
- Kunshan Hospital of Chinese Medicine, Kunshan, 215300, PR China.
- Pharmacy Department, Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine, No. 18, Yang Su Road, Suzhou, Jiangsu Province, 215009, PR China.
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Ma M, Huang M, He Y, Fang J, Li J, Li X, Liu M, Zhou M, Cui G, Fan Q. Network Medicine: A Potential Approach for Virtual Drug Screening. Pharmaceuticals (Basel) 2024; 17:899. [PMID: 39065749 PMCID: PMC11280361 DOI: 10.3390/ph17070899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 06/27/2024] [Accepted: 07/04/2024] [Indexed: 07/28/2024] Open
Abstract
Traditional drug screening methods typically focus on a single protein target and exhibit limited efficiency due to the multifactorial nature of most diseases, which result from disturbances within complex networks of protein-protein interactions rather than single gene abnormalities. Addressing this limitation requires a comprehensive drug screening strategy. Network medicine is rooted in systems biology and provides a comprehensive framework for understanding disease mechanisms, prevention, and therapeutic innovations. This approach not only explores the associations between various diseases but also quantifies the relationships between disease genes and drug targets within interactome networks, thus facilitating the prediction of drug-disease relationships and enabling the screening of therapeutic drugs for specific complex diseases. An increasing body of research supports the efficiency and utility of network-based strategies in drug screening. This review highlights the transformative potential of network medicine in virtual therapeutic screening for complex diseases, offering novel insights and a robust foundation for future drug discovery endeavors.
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Affiliation(s)
- Mingxuan Ma
- School of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai 519000, China; (M.M.); (M.H.); (Y.H.); (J.L.); (M.L.); (M.Z.)
| | - Mei Huang
- School of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai 519000, China; (M.M.); (M.H.); (Y.H.); (J.L.); (M.L.); (M.Z.)
| | - Yinting He
- School of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai 519000, China; (M.M.); (M.H.); (Y.H.); (J.L.); (M.L.); (M.Z.)
| | - Jiansong Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 570000, China;
| | - Jiachao Li
- School of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai 519000, China; (M.M.); (M.H.); (Y.H.); (J.L.); (M.L.); (M.Z.)
| | - Xiaohan Li
- School of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai 519000, China; (M.M.); (M.H.); (Y.H.); (J.L.); (M.L.); (M.Z.)
| | - Mengchen Liu
- School of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai 519000, China; (M.M.); (M.H.); (Y.H.); (J.L.); (M.L.); (M.Z.)
| | - Mei Zhou
- School of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai 519000, China; (M.M.); (M.H.); (Y.H.); (J.L.); (M.L.); (M.Z.)
| | - Guozhen Cui
- School of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai 519000, China; (M.M.); (M.H.); (Y.H.); (J.L.); (M.L.); (M.Z.)
| | - Qing Fan
- Basic Medical Science Department, Zhuhai Campus of Zunyi Medical University, Zhuhai 519041, China
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Zhang W, Huang RS. Computer-aided drug discovery strategies for novel therapeutics for prostate cancer leveraging next-generating sequencing data. Expert Opin Drug Discov 2024; 19:841-853. [PMID: 38860709 DOI: 10.1080/17460441.2024.2365370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 06/04/2024] [Indexed: 06/12/2024]
Abstract
INTRODUCTION Prostate cancer (PC) is the most common malignancy and accounts for a significant proportion of cancer deaths among men. Although initial therapy success can often be observed in patients diagnosed with localized PC, many patients eventually develop disease recurrence and metastasis. Without effective treatments, patients with aggressive PC display very poor survival. To curb the current high mortality rate, many investigations have been carried out to identify efficacious therapeutics. Compared to de novo drug designs, computational methods have been widely employed to offer actionable drug predictions in a fast and cost-efficient way. Particularly, powered by an increasing availability of next-generation sequencing molecular profiles from PC patients, computer-aided approaches can be tailored to screen for candidate drugs. AREAS COVERED Herein, the authors review the recent advances in computational methods for drug discovery utilizing molecular profiles from PC patients. Given the uniqueness in PC therapeutic needs, they discuss in detail the drug discovery goals of these studies, highlighting their translational values for clinically impactful drug nomination. EXPERT OPINION Evolving molecular profiling techniques may enable new perspectives for computer-aided approaches to offer drug candidates for different tumor microenvironments. With ongoing efforts to incorporate new compounds into large-scale high-throughput screens, the authors envision continued expansion of drug candidate pools.
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Affiliation(s)
- Weijie Zhang
- Department of Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN, USA
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, USA
| | - R Stephanie Huang
- Department of Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN, USA
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, USA
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Li S, Zheng Z, Wang B. Machine learning survival prediction using tumor lipid metabolism genes for osteosarcoma. Sci Rep 2024; 14:12934. [PMID: 38839983 PMCID: PMC11153634 DOI: 10.1038/s41598-024-63736-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 05/31/2024] [Indexed: 06/07/2024] Open
Abstract
Osteosarcoma is a primary malignant tumor that commonly affects children and adolescents, with a poor prognosis. The existence of tumor heterogeneity leads to different molecular subtypes and survival outcomes. Recently, lipid metabolism has been identified as a critical characteristic of cancer. Therefore, our study aims to identify osteosarcoma's lipid metabolism molecular subtype and develop a signature for survival outcome prediction. Four multicenter cohorts-TARGET-OS, GSE21257, GSE39058, and GSE16091-were amalgamated into a unified Meta-Cohort. Through consensus clustering, novel molecular subtypes within Meta-Cohort patients were delineated. Subsequent feature selection processes, encompassing analyses of differentially expressed genes between subtypes, univariate Cox analysis, and StepAIC, were employed to pinpoint biomarkers related to lipid metabolism in TARGET-OS. We selected the most effective algorithm for constructing a Lipid Metabolism-Related Signature (LMRS) by utilizing four machine-learning algorithms reconfigured into ten unique combinations. This selection was based on achieving the highest concordance index (C-index) in the test cohort of GSE21257, GSE39058, and GSE16091. We identified two distinct lipid metabolism molecular subtypes in osteosarcoma patients, C1 and C2, with significantly different survival rates. C1 is characterized by increased cholesterol, fatty acid synthesis, and ketone metabolism. In contrast, C2 focuses on steroid hormone biosynthesis, arachidonic acid, and glycerolipid and linoleic acid metabolism. Feature selection in the TARGET-OS identified 12 lipid metabolism genes, leading to a model predicting osteosarcoma patient survival. The LMRS, based on the 12 identified genes, consistently accurately predicted prognosis across TARGET-OS, testing cohorts, and Meta-Cohort. Incorporating 12 published signatures, LMRS showed robust and significantly superior predictive capability. Our results offer a promising tool to enhance the clinical management of osteosarcoma, potentially leading to improved clinical outcomes.
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Affiliation(s)
- Shuai Li
- Department of Spine Surgery, The Second Xiangya Hospital, Central South University, Renmin Middle Road 139, Changsha, 410011, Hunan, China
| | - Zhenzhong Zheng
- Department of Spine Surgery, The Second Xiangya Hospital, Central South University, Renmin Middle Road 139, Changsha, 410011, Hunan, China
| | - Bing Wang
- Department of Spine Surgery, The Second Xiangya Hospital, Central South University, Renmin Middle Road 139, Changsha, 410011, Hunan, China.
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Wang Y, Li Q, Yang X, Guo H, Ren T, Zhang T, Ghadakpour P, Ren F. Exosome-Mediated Communication in Thyroid Cancer: Implications for Prognosis and Therapeutic Targets. Biochem Genet 2024:10.1007/s10528-024-10833-2. [PMID: 38839646 DOI: 10.1007/s10528-024-10833-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 05/08/2024] [Indexed: 06/07/2024]
Abstract
Thyroid cancer (THCA) is one of the most common malignancies of the endocrine system. Exosomes have significant value in performing molecular treatments, evaluating the diagnosis and determining tumor prognosis. Thus, the identification of exosome-related genes could be valuable for the diagnosis and potential treatment of THCA. In this study, we examined a set of exosome-related differentially expressed genes (DEGs) (BIRC5, POSTN, TGFBR1, DUSP1, BID, and FGFR2) by taking the intersection between the DEGs of the TCGA-THCA and GeneCards datasets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of the exosome-related DEGs indicated that these genes were involved in certain biological functions and pathways. Protein‒protein interaction (PPI), mRNA‒miRNA, and mRNA-TF interaction networks were constructed using the 6 exosome-related DEGs as hub genes. Furthermore, we analyzed the correlation between the 6 exosome-related DEGs and immune infiltration. The Genomics of Drug Sensitivity in Cancer (GDSC), the Cancer Cell Line Encyclopedia (CCLE), and the CellMiner database were used to elucidate the relationship between the exosome-related DEGs and drug sensitivity. In addition, we verified that both POSTN and BID were upregulated in papillary thyroid cancer (PTC) patients and that their expression was correlated with cancer progression. The POSTN and BID protein expression levels were further examined in THCA cell lines. These findings provide insights into exosome-related clinical trials and drug development.
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Affiliation(s)
- Yiwei Wang
- Department of Anatomy, College of Basic Medical Sciences of Shenyang Medical College, Shenyang, Liaoning, People's Republic of China
- Molecular Morphology Laboratory, College of Basic Medical Sciences, Liaoning, Shenyang Medical College, Shenyang, People's Republic of China
- Key Laboratory of Human Ethnic Specificity and Phenomics of Critical Illness in Liaoning Province, Shenyang Medical College, Shenyang, Liaoning, People's Republic of China
| | - Qiang Li
- Department of Orthopedics, Liaoning, Fuxin Central Hospital, Fuxin, People's Republic of China
| | - Xinrui Yang
- Molecular Morphology Laboratory, College of Basic Medical Sciences, Liaoning, Shenyang Medical College, Shenyang, People's Republic of China
| | - Hanyu Guo
- Department of Anatomy, College of Basic Medical Sciences of Shenyang Medical College, Shenyang, Liaoning, People's Republic of China
| | - Tian Ren
- Emergency Medical Center, Liaoning, Affiliated Central Hospital of Shenyang Medical College, Shenyang, People's Republic of China
| | - Tianchi Zhang
- Department of Computer and Information Technology, University of Pennsylvania, Philadelphia, PA, United States
| | | | - Fu Ren
- Department of Anatomy, College of Basic Medical Sciences of Shenyang Medical College, Shenyang, Liaoning, People's Republic of China.
- Key Laboratory of Human Ethnic Specificity and Phenomics of Critical Illness in Liaoning Province, Shenyang Medical College, Shenyang, Liaoning, People's Republic of China.
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Chen B, Liu Y, He Y, Shen C. Pan-cancer analysis of prognostic and immunological role of IL4I1 in human tumors: a bulk omics research and single cell sequencing validation. Discov Oncol 2024; 15:139. [PMID: 38691253 PMCID: PMC11063023 DOI: 10.1007/s12672-024-01000-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 04/29/2024] [Indexed: 05/03/2024] Open
Abstract
BACKGROUND Interleukin-4 inducible gene 1 (IL4I1) regulates tumor progression in numerous tumor types. However, its correlation with immune infiltration and prognosis of patients in a pan-cancer setting remains unclear. METHODS Data from the Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), UALCAN, Clinical Proteomic Tumor Analysis Consortium (CPTAC), Gene Expression Omnibus (GEO), cBioPortal, Cancer Single-cell State Atlas (CancerSEA), and Tumor IMmune Estimation Resource(TIMER) databases were used to evaluate IL4I1 expression, clinical features and prognostic effects, gene set enrichment, and correlation with immune cell infiltration, as well as the relationship between IL4I1 methylation and expression and survival prognosis. Correlations with 192 anticancer drugs were also analyzed. RESULTS IL4I1 was significantly overexpressed in the majority of tumors, and the imbalance of IL4I1 was significantly correlated with overall survival and pathological stage. Moreover, total IL4I1 protein was increased in cancer. Therefore, IL4I1 may be used as a prognostic biomarker or protective factor in numerous types of cancer. The methylation level of IL4I1 may also be used as a prognostic marker. The functional enrichment of IL4I1 was closely related to the immunomodulatory pathway. In addition, the level of tumor-associated macrophage infiltration was positively correlated with the expression of IL4I1 in pan-cancerous tissues. scRNA-seq analysis suggested that IL4I1 differ significantly among different cells in the tumor microenvironment and was most enriched in macrophages. Various immune checkpoint genes were positively correlated with IL4I1 expression in most tumors. In addition, patients with high IL4I1 expression may be resistant to BMS-754807 and docetaxel, but sensitive to temozolomide. CONCLUSION IL4I1 may play a role as promoter of cancer and prognostic indicator in patients. High expression of IL4I1 is associated with the state of tumor immunosuppression and may contribute to tumor-associated macrophage invasion. Therefore, IL4I1 may be a new therapeutic target for the treatment and prognosis of patients with cancer.
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Affiliation(s)
- Bin Chen
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yi Liu
- Emergency Department, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yuping He
- Health Management Center, Xiangya Hospital, Central South University, Changsha, Hunan, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China.
| | - Chenfu Shen
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China.
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Jiang T, Zhang M, Hao S, Huang S, Zheng X, Sun Z. Revealing the role of the gut microbiota in enhancing targeted therapy efficacy for lung adenocarcinoma. Exp Hematol Oncol 2024; 13:15. [PMID: 38336927 PMCID: PMC10854116 DOI: 10.1186/s40164-024-00478-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 01/16/2024] [Indexed: 02/12/2024] Open
Abstract
Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death globally. Although the gut microbiota's role in the antitumor efficacy of many cancers has been revealed, its involvement in the response to gefitinib therapy for LUAD remains unclear. To fill this gap, we conducted a longitudinal study that profiled gut microbiota changes in PC-9 tumor-bearing mice under different treatments, including gefitinib monotherapy and combination therapies with probiotics, antibiotics, or Traditional Chinese Medicine (TCM). Our findings demonstrated that combining probiotics or TCM with gefitinib therapy outperformed gefitinib monotherapy, as evidenced by tumor volume, body weight, and tumor marker tests. By contrast, antibiotic intervention suppressed the antitumor efficacy of gefitinib. Notably, the temporal changes in gut microbiota were strongly correlated with the different treatments, prompting us to investigate whether there is a causal relationship between gut microbiota and the antitumor efficacy of gefitinib using Mediation Analysis (MA). Finally, our research revealed that thirteen mediators (Amplicon Sequence Variants, ASVs) regulate the antitumor effect of gefitinib, regardless of treatment. Our study provides robust evidence supporting the gut microbiota's significant and potentially causal role in mediating gefitinib treatment efficacy in mice. Our findings shed light on a novel strategy for antitumor drug development by targeting the gut microbiota.
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Affiliation(s)
- Ting Jiang
- Department of Scientific Research, Qingdao Municipal Hospital of Traditional Chinese Medicine (Qingdao Hiser Medical Group), Qingdao, China
| | - Meng Zhang
- Key Laboratory of Dairy Biotechnology and Engineering, Ministry of Education, Inner Mongolia Agricultural University, Hohhot, China
| | - Shaoyu Hao
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Shi Huang
- Faculty of Dentistry, The University of Hong Kong, Hong Kong, SAR, China.
| | - Xin Zheng
- Department of Scientific Research, Qingdao Municipal Hospital of Traditional Chinese Medicine (Qingdao Hiser Medical Group), Qingdao, China.
| | - Zheng Sun
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA.
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Zou D, Xu T. Construction and validation of a colon cancer prognostic model based on tumor mutation burden-related genes. Sci Rep 2024; 14:2867. [PMID: 38311637 PMCID: PMC10838917 DOI: 10.1038/s41598-024-53257-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 01/30/2024] [Indexed: 02/06/2024] Open
Abstract
Currently, immunotherapy has entered the clinical diagnosis and treatment guidelines for colon cancer, but existing immunotherapy markers cannot predict the effectiveness of immunotherapy well. This study utilized the TCGA-COAD queue to perform differential gene analysis on high and low-mutation burden samples, and screen differentially expressed genes (DEGs). To explore new molecular markers or predictive models of immunotherapy by using DEGs for NMF classification and prognostic model construction. Through systematic bioinformatics analysis, the TCGA-COAD cohort was successfully divided into high mutation burden subtypes and low mutation burden subtypes by NMF typing using DEGs. The proportion of MSI-H between high mutation burden subtypes was significantly higher than that of low mutation burden subtypes, but there was no significant difference in immunotherapy efficacy between the two subtypes. Drug sensitivity analysis showed significant differences in drug sensitivity between the two subtypes. Subsequently, we constructed a prognostic model using DEGs, which can effectively predict patient survival and immunotherapy outcomes. The prognosis and immunotherapy outcomes of the low-risk group were significantly better than those of the high-risk group. The external dataset validation of the constructed prognostic model using the GSE39582 dataset from the GEO database yielded consistent results. At the same time, we also analyzed the TMB and MSI situation between the high and low-risk groups, and the results showed that there was no significant difference in TMB between the high and low-risk groups, but the proportion of MSI-H in the high-risk group was significantly higher than that in the low-risk group. Finally, we conclude that TMB is not a suitable molecular marker for predicting the efficacy of immunotherapy in colon cancer. The newly constructed prognostic model can effectively differentiate the prognosis of colon cancer patients and predict their immunotherapy efficacy.
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Affiliation(s)
- Daoyang Zou
- The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Tianwen Xu
- The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
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Liu Z, Chen R, Yang L, Jiang J, Ma S, Chen L, He M, Mao Y, Guo C, Kong X, Zhang X, Qi Y, Liu F, He F, Li D. CDS-DB, an omnibus for patient-derived gene expression signatures induced by cancer treatment. Nucleic Acids Res 2024; 52:D1163-D1179. [PMID: 37889038 PMCID: PMC10767794 DOI: 10.1093/nar/gkad888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/25/2023] [Accepted: 10/05/2023] [Indexed: 10/28/2023] Open
Abstract
Patient-derived gene expression signatures induced by cancer treatment, obtained from paired pre- and post-treatment clinical transcriptomes, can help reveal drug mechanisms of action (MOAs) in cancer patients and understand the molecular response mechanism of tumor sensitivity or resistance. Their integration and reuse may bring new insights. Paired pre- and post-treatment clinical transcriptomic data are rapidly accumulating. However, a lack of systematic collection makes data access, integration, and reuse challenging. We therefore present the Cancer Drug-induced gene expression Signature DataBase (CDS-DB). CDS-DB has collected 78 patient-derived, paired pre- and post-treatment transcriptomic source datasets with uniformly reprocessed expression profiles and manually curated metadata such as drug administration dosage, sampling time and location, and intrinsic drug response status. From these source datasets, 2012 patient-level gene perturbation signatures were obtained, covering 85 therapeutic regimens, 39 cancer subtypes and 3628 patient samples. Besides data browsing, download and search, CDS-DB also supports single signature analysis (including differential gene expression, functional enrichment, tumor microenvironment and correlation analyses), signature comparative analysis and signature connectivity analysis. This provides insights into drug MOA and its heterogeneity in patients, drug resistance mechanisms, drug repositioning and drug (combination) discovery, etc. CDS-DB is available at http://cdsdb.ncpsb.org.cn/.
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Affiliation(s)
- Zhongyang Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
- College of Chemistry and Materials Science, Key Laboratory of Medicinal Chemistry and Molecular Diagnosis (Hebei University), Hebei University, Baoding 071002, China
- College of Life Sciences, Hebei University, Baoding 071002, China
| | - Ruzhen Chen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Lele Yang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
- College of Chemistry and Materials Science, Key Laboratory of Medicinal Chemistry and Molecular Diagnosis (Hebei University), Hebei University, Baoding 071002, China
| | - Jianzhou Jiang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
- College of Life Sciences, Hebei University, Baoding 071002, China
| | - Shurui Ma
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
- School of Basic Medicine, Anhui Medical University, Hefei 230032, China
| | - Lanhui Chen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Mengqi He
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Yichao Mao
- College of Life Sciences, Hebei University, Baoding 071002, China
| | - Congcong Guo
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Xiangya Kong
- Beijing Cloudna Technology Company, Limited, Beijing 100029, China
| | - Xinlei Zhang
- Beijing Cloudna Technology Company, Limited, Beijing 100029, China
| | - Yaning Qi
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
- College of Chemistry and Materials Science, Key Laboratory of Medicinal Chemistry and Molecular Diagnosis (Hebei University), Hebei University, Baoding 071002, China
| | - Fengsong Liu
- College of Life Sciences, Hebei University, Baoding 071002, China
| | - Fuchu He
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Dong Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
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11
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Yang D, Zhu Z, Yao Q, Chen C, Chen F, Gu L, Jiang Y, Chen L, Zhang J, Wu J, Gao X, Wang J, Li G, Zhao Y. ccTCM: A quantitative component and compound platform for promoting the research of traditional Chinese medicine. Comput Struct Biotechnol J 2023; 21:5807-5817. [PMID: 38213899 PMCID: PMC10781882 DOI: 10.1016/j.csbj.2023.11.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/14/2023] [Accepted: 11/15/2023] [Indexed: 01/13/2024] Open
Abstract
Traditional Chinese medicine (TCM) databases play a vital role in bridging the gap between TCM and modern medicine, as well as in promoting the popularity of TCM. Elucidating the bioactive ingredients of Chinese medicinal materials is key to TCM modernization and new drug discovery. However, one drawback of current TCM databases is the lack of quantitative data on the constituents of Chinese medicinal materials. Herein, we present ccTCM, a web-based platform designed to provide a component and compound-content-based resource on TCM and analysis services for medical experts. In terms of design features, ccTCM combines resource distribution, similarity analysis, and molecular-mechanism analysis to accelerate the discovery of bioactive ingredients in TCM. ccTCM contains 273 Chinese medicinal materials commonly used in clinical settings, covering 29 functional classifications. By searching and comparing, we finally adopted 2043 studies, from which we collected the compounds contained in each TCM with content greater than 0.001 %, and a total of 1449 were extracted. Subsequently, we collected 40,767 compound-target pairs by integrating multiple databases. Taken together, ccTCM is a versatile platform that can be used by TCM scientists to perform scientific and clinical TCM studies based on quantified ingredients of Chinese medicinal materials. ccTCM is freely accessible at http://www.cctcm.org.cn.
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Affiliation(s)
- Dongqing Yang
- Department of Public Health, School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Zhu Zhu
- Department of Pathology and Pathophysiology, School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Qi Yao
- Department of Pathology and Pathophysiology, School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Cuihua Chen
- Research and Innovation Center, College of Traditional Chinese Medicine·Integrated Chinese and Western Medicine College, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Feiyan Chen
- Research and Innovation Center, College of Traditional Chinese Medicine·Integrated Chinese and Western Medicine College, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Ling Gu
- Research and Innovation Center, College of Traditional Chinese Medicine·Integrated Chinese and Western Medicine College, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Yucui Jiang
- Research and Innovation Center, College of Traditional Chinese Medicine·Integrated Chinese and Western Medicine College, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Lin Chen
- Department of Physiology, School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Jingyuan Zhang
- Department of Treatise on Febrile Diseases, School of Traditional Chinese Medicine & Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Juan Wu
- Department of Public Health, School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Xingsu Gao
- Department of Public Health, School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Junqin Wang
- Department of Public Health, School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Guochun Li
- Department of Public Health, School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Yunan Zhao
- Department of Pathology and Pathophysiology, School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China
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12
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Lv Q, Chen G, He H, Yang Z, Zhao L, Chen HY, Chen CYC. TCMBank: bridges between the largest herbal medicines, chemical ingredients, target proteins, and associated diseases with intelligence text mining. Chem Sci 2023; 14:10684-10701. [PMID: 37829020 PMCID: PMC10566508 DOI: 10.1039/d3sc02139d] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 07/30/2023] [Indexed: 10/14/2023] Open
Abstract
Traditional Chinese Medicine (TCM) has long been viewed as a precious source of modern drug discovery. AI-assisted drug discovery (AIDD) has been investigated extensively. However, there are still two challenges in applying AIDD to guide TCM drug discovery: the lack of a large amount of standardized TCM-related information and AIDD is prone to pathological failures in out-of-domain data. We have released TCM Database@Taiwan in 2011, and it has been widely disseminated and used. Now, we developed TCMBank, the largest systematic free TCM database, which is an extension of TCM Database@Taiwan. TCMBank contains 9192 herbs, 61 966 ingredients (unduplicated), 15 179 targets, 32 529 diseases, and their pairwise relationships. By integrating multiple data sources, TCMBank provides 3D structure information of ingredients and provides a standard list and detailed information on herbs, ingredients, targets and diseases. TCMBank has an intelligent document identification module that continuously adds TCM-related information retrieved from the literature in PubChem. In addition, driven by TCMBank big data, we developed an ensemble learning-based drug discovery protocol for identifying potential leads and drug repurposing. We take colorectal cancer and Alzheimer's disease as examples to demonstrate how to accelerate drug discovery by artificial intelligence. Using TCMBank, researchers can view literature-driven relationship mapping between herbs/ingredients and genes/diseases, allowing the understanding of molecular action mechanisms for ingredients and identification of new potentially effective treatments. TCMBank is available at https://TCMBank.CN/.
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Affiliation(s)
- Qiujie Lv
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University Shenzhen Guangdong 518107 P. R. China
| | - Guanxing Chen
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University Shenzhen Guangdong 518107 P. R. China
| | - Haohuai He
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University Shenzhen Guangdong 518107 P. R. China
| | - Ziduo Yang
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University Shenzhen Guangdong 518107 P. R. China
| | - Lu Zhao
- Department of Clinical Laboratory, The Sixth Affiliated Hospital, Sun Yat-sen University Guangzhou Guangdong 510655 P. R. China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University Guangzhou Guangdong 510655 P. R. China
| | - Hsin-Yi Chen
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University Shenzhen Guangdong 518107 P. R. China
| | - Calvin Yu-Chian Chen
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University Shenzhen Guangdong 518107 P. R. China
- Department of Medical Research, China Medical University Hospital Taichung 40447 Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University Taichung 41354 Taiwan
- Guangdong L-Med Medicine Biotechnology Co., Ltd Meizhou Guangdong 514699 P. R. China
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13
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Tian S, Li Y, Xu J, Zhang L, Zhang J, Lu J, Xu X, Luan X, Zhao J, Zhang W. COIMMR: a computational framework to reveal the contribution of herbal ingredients against human cancer via immune microenvironment and metabolic reprogramming. Brief Bioinform 2023; 24:bbad346. [PMID: 37816138 PMCID: PMC10564268 DOI: 10.1093/bib/bbad346] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 08/16/2023] [Accepted: 09/13/2023] [Indexed: 10/12/2023] Open
Abstract
Immune evasion and metabolism reprogramming have been regarded as two vital hallmarks of the mechanism of carcinogenesis. Thus, targeting the immune microenvironment and the reprogrammed metabolic processes will aid in developing novel anti-cancer drugs. In recent decades, herbal medicine has been widely utilized to treat cancer through the modulation of the immune microenvironment and reprogrammed metabolic processes. However, labor-based herbal ingredient screening is time consuming, laborious and costly. Luckily, some computational approaches have been proposed to screen candidates for drug discovery rapidly. Yet, it has been challenging to develop methods to screen drug candidates exclusively targeting specific pathways, especially for herbal ingredients which exert anti-cancer effects by multiple targets, multiple pathways and synergistic ways. Meanwhile, currently employed approaches cannot quantify the contribution of the specific pathway to the overall curative effect of herbal ingredients. Hence, to address this problem, this study proposes a new computational framework to infer the contribution of the immune microenvironment and metabolic reprogramming (COIMMR) in herbal ingredients against human cancer and specifically screen herbal ingredients targeting the immune microenvironment and metabolic reprogramming. Finally, COIMMR was applied to identify isoliquiritigenin that specifically regulates the T cells in stomach adenocarcinoma and cephaelin hydrochloride that specifically targets metabolic reprogramming in low-grade glioma. The in silico results were further verified using in vitro experiments. Taken together, our approach opens new possibilities for repositioning drugs targeting immune and metabolic dysfunction in human cancer and provides new insights for drug development in other diseases. COIMMR is available at https://github.com/LYN2323/COIMMR.
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Affiliation(s)
- Saisai Tian
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Yanan Li
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Jia Xu
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
- College of Pharmacy, Henan University, Kaifeng 475000, China
| | - Lijun Zhang
- The Research Center for Traditional Chinese Medicine, Shanghai Institute of Infectious Diseases and Biosafety, Institute of Interdisciplinary Integrative Medicine
| | - Jinbo Zhang
- Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China Department of Pharmacy, Tianjin Rehabilitation Center of Joint Logistics Support Force, Tianjin, 300110, China
| | - Jinyuan Lu
- College of Pharmacy, Anhui University of Chinese Medicine, Anhui 230012, China
| | - Xike Xu
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Xin Luan
- The Research Center for Traditional Chinese Medicine, Shanghai Institute of Infectious Diseases and Biosafety, Institute of Interdisciplinary Integrative Medicine
| | - Jing Zhao
- The Research Center for Traditional Chinese Medicine, Shanghai Institute of Infectious Diseases and Biosafety, Institute of Interdisciplinary Integrative Medicine
| | - Weidong Zhang
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
- The Research Center for Traditional Chinese Medicine, Shanghai Institute of Infectious Diseases and Biosafety, Institute of Interdisciplinary Integrative Medicine
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14
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Ren A, Wu T, Wang Y, Fan Q, Yang Z, Zhang S, Cao Y, Cui G. Integrating animal experiments, mass spectrometry and network-based approach to reveal the sleep-improving effects of Ziziphi Spinosae Semen and γ-aminobutyric acid mixture. Chin Med 2023; 18:99. [PMID: 37573423 PMCID: PMC10422734 DOI: 10.1186/s13020-023-00814-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 07/30/2023] [Indexed: 08/14/2023] Open
Abstract
BACKGROUND Ziziphi Spinosae Semen (ZSS) is a plant widely used as medicine and food in Asian countries due to its numerous health benefits. γ-aminobutyric acid (GABA), a non-proteinaceous amino acid, is one of the major inhibitory neurotransmitters with a relaxant function. In this study, a system pharmacology approach was employed to assess the effects of a mixture composed of ZSS and GABA (ZSSG) on sleep improvement. METHODS Mice were divided into five groups (n = 10) and received either no treatment, sodium pentobarbital, or sodium barbital with diazepam or ZSSG. The effects of ZSSG on sleep quality were evaluated in mice, and differential metabolites associated with sleep were identified among the control, ZSS, GABA, and ZSSG groups. Additionally, network-based ingredient-insomnia proximity analysis was applied to explore the major ingredients. RESULTS ZSSG significantly improved sleep quality by decreasing sleep latency and prolonging sleep duration in sodium pentobarbital-induced sleeping mouse model (P < 0.05). ZSSG significantly enhanced the brain content of GABA in mice. Furthermore, ZSSG also significantly decreased sleep latency-induced by sodium barbital in mice (P < 0.05). Metabolic analysis revealed significant differences in 10 metabolites between ZSSG group and the groups administering ZSS or GABA. Lastly, using the network-based ingredient screening model, we discovered potential four active ingredients and three pairwise ingredient combinations with synergistic effect on insomnia from ZSSG among 85 ingredients identified by UPLC-Q/TOF-MS. Also, we have constructed an online computation platform. CONCLUSION Our data demonstrated that ZSSG improved the sleeping quality of mice and helped to balance metabolic disorders-associated with sleep disorders. Moreover, based on the network-based prediction method, the four potential active ingredients in ZSSG could serve as quality markers-associated with insomnia. The network-based framework may open up a new avenue for the discovery of active ingredients of herbal medicine for treating complex chronic diseases or symptoms, such as insomnia.
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Affiliation(s)
- Airong Ren
- Department of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519000, Guangdong, China
| | - Tingbiao Wu
- Department of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519000, Guangdong, China
| | - Yarong Wang
- Department of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519000, Guangdong, China
| | - Qing Fan
- Basic Medical Science Department, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519000, Guangdong, China
| | - Zhenhao Yang
- Department of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519000, Guangdong, China
| | - Shixun Zhang
- Department of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519000, Guangdong, China
| | - Yongjun Cao
- Department of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519000, Guangdong, China
| | - Guozhen Cui
- Department of Bioengineering, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519000, Guangdong, China.
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