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Luthfi M, Pandey RB, Su YC, Sompornpisut P. Deciphering molecular basis of pesticide-induced recurrent pregnancy loss: insights from transcriptomics analysis. Toxicol Mech Methods 2024; 34:527-544. [PMID: 38294000 DOI: 10.1080/15376516.2024.2307975] [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: 10/15/2023] [Accepted: 01/15/2024] [Indexed: 02/01/2024]
Abstract
Recent studies have revealed a notable connection between pesticide exposure and Recurrent Pregnancy Loss (RPL), yet the precise molecular underpinning of this toxicity remains elusive. Through the alignment of Differentially Expressed Genes (DEGs) of healthy and RPL patients with the target genes of 9 pesticide components, we identified a set of 12 genes responsible for RPL etiology. Interestingly, biological process showed that besides RPL, those 12 genes also associated with preeclampsia and cardiovascular disease. Enrichment analysis showed the engagement of these genes associated with essential roles in the molecular transport of small molecules, as well as the aldosterone-regulated sodium reabsorption, endocrine and other factor-regulated calcium reabsorption, mineral absorption, ion homeostasis, and ion transport by P-type ATPases. Notably, the crosstalk targets between pesticide components played crucial roles in influencing RPL results, suggesting a role in attenuating pesticide agents that contribute to RPL. It is important to note that non-significant concentration of the pesticide components observed in both control and RPL samples should not prematurely undermine the potential for pesticides to induce RPL in humans. This study emphasizes the complexity of pesticide induced RPL and highlights avenues for further research and precautionary measures.
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Affiliation(s)
- Muhammad Luthfi
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, Thailand
- Center of Excellence in Computational Chemistry, Department of Chemistry, Chulalongkorn University, Bangkok, Thailand
| | - R B Pandey
- School of Mathematics and Natural Sciences, University of Southern Mississippi, Hattiesburg, MS, USA
| | - Yong-Chao Su
- Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Pornthep Sompornpisut
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, Thailand
- Center of Excellence in Computational Chemistry, Department of Chemistry, Chulalongkorn University, Bangkok, Thailand
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2
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Zhu G, Wang L, Wang X, Dong X, Yang S, Wang J, Xu S, Zeng Y. Comparative Proteomics Identified EXOSC1 as a Target Protein of Anticancer Peptide LVTX-8 in Nasopharyngeal Carcinoma Cells. J Proteome Res 2024. [PMID: 38700954 DOI: 10.1021/acs.jproteome.4c00031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Nasopharyngeal carcinoma (NPC) is a prevalent malignancy that usually occurs among the nose and throat. Due to mild initial symptoms, most patients are diagnosed in the late stage, and the recurrence rate of tumors is high, resulting in many deaths every year. Traditional radiotherapy and chemotherapy are prone to causing drug resistance and significant side effects. Therefore, searching for new bioactive drugs including anticancer peptides is necessary and urgent. LVTX-8 is a peptide toxin synthesized from the cDNA library of the spider Lycosa vittata, which is consisting of 25 amino acids. In this study, a series of in vitro cell experiments such as cell toxicity, colony formation, and cell migration assays were performed to exam the anticancer activity of LVTX-8 in NPC cells (5-8F and CNE-2). The results suggested that LVTX-8 significantly inhibited cell proliferation and migration of NPC cells. To find the potential molecular targets for the anticancer capability of LVTX-8, high-throughput proteomic and bioinformatics analysis were conducted on NPC cells. The results identified EXOSC1 as a potential target protein with significantly differential expression levels under LVTX-8+/LVTX-8- conditions. The results in this research indicate that spider peptide toxin LVTX-8 exhibits significant anticancer activity in NPC, and EXOSC1 may serve as a target protein for its anticancer activity. These findings provide a reference for the development of new therapeutic drugs for NPC and offer new ideas for the discovery of biomarkers related to NPC diagnosis. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (https://proteomecentral.proteomexchange.org) via the iProX partner repository with the data set identifier PXD050542.
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Affiliation(s)
- Ganghua Zhu
- Department of Otolaryngology-Head and Neck Surgery, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Lingxiang Wang
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha 410081, Hunan, China
| | - Xingyao Wang
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha 410081, Hunan, China
| | - Xiaoping Dong
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha 410081, Hunan, China
| | - Shu Yang
- Department of Otolaryngology-Head and Neck Surgery, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Jiaqi Wang
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha 410081, Hunan, China
| | - Siyuan Xu
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha 410081, Hunan, China
| | - Yong Zeng
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha 410081, Hunan, China
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3
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Yao X, Ouyang S, Lian Y, Peng Q, Zhou X, Huang F, Hu X, Shi F, Xia J. PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies. Genome Med 2024; 16:56. [PMID: 38627848 PMCID: PMC11020195 DOI: 10.1186/s13073-024-01330-7] [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: 07/26/2023] [Accepted: 04/02/2024] [Indexed: 04/19/2024] Open
Abstract
Despite the abundance of genotype-phenotype association studies, the resulting association outcomes often lack robustness and interpretations. To address these challenges, we introduce PheSeq, a Bayesian deep learning model that enhances and interprets association studies through the integration and perception of phenotype descriptions. By implementing the PheSeq model in three case studies on Alzheimer's disease, breast cancer, and lung cancer, we identify 1024 priority genes for Alzheimer's disease and 818 and 566 genes for breast cancer and lung cancer, respectively. Benefiting from data fusion, these findings represent moderate positive rates, high recall rates, and interpretation in gene-disease association studies.
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Affiliation(s)
- Xinzhi Yao
- College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
| | - Sizhuo Ouyang
- College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
| | - Yulong Lian
- College of Science, Huazhong Agricultural University, Wuhan, China
| | - Qianqian Peng
- College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
| | - Xionghui Zhou
- College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
| | - Feier Huang
- College of Life Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Xuehai Hu
- College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
| | - Feng Shi
- College of Science, Huazhong Agricultural University, Wuhan, China
| | - Jingbo Xia
- College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China.
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China.
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4
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Buzzao D, Castresana-Aguirre M, Guala D, Sonnhammer ELL. Benchmarking enrichment analysis methods with the disease pathway network. Brief Bioinform 2024; 25:bbae069. [PMID: 38436561 PMCID: PMC10939300 DOI: 10.1093/bib/bbae069] [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: 09/29/2023] [Revised: 01/10/2024] [Accepted: 02/03/2024] [Indexed: 03/05/2024] Open
Abstract
Enrichment analysis (EA) is a common approach to gain functional insights from genome-scale experiments. As a consequence, a large number of EA methods have been developed, yet it is unclear from previous studies which method is the best for a given dataset. The main issues with previous benchmarks include the complexity of correctly assigning true pathways to a test dataset, and lack of generality of the evaluation metrics, for which the rank of a single target pathway is commonly used. We here provide a generalized EA benchmark and apply it to the most widely used EA methods, representing all four categories of current approaches. The benchmark employs a new set of 82 curated gene expression datasets from DNA microarray and RNA-Seq experiments for 26 diseases, of which only 13 are cancers. In order to address the shortcomings of the single target pathway approach and to enhance the sensitivity evaluation, we present the Disease Pathway Network, in which related Kyoto Encyclopedia of Genes and Genomes pathways are linked. We introduce a novel approach to evaluate pathway EA by combining sensitivity and specificity to provide a balanced evaluation of EA methods. This approach identifies Network Enrichment Analysis methods as the overall top performers compared with overlap-based methods. By using randomized gene expression datasets, we explore the null hypothesis bias of each method, revealing that most of them produce skewed P-values.
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Affiliation(s)
- Davide Buzzao
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 171 21 Solna, Sweden
| | | | - Dimitri Guala
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 171 21 Solna, Sweden
| | - Erik L L Sonnhammer
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 171 21 Solna, Sweden
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Li S, Hu G, Kuang L, Zhou T, Jiang H, Pang F, Li J, Chen X, Bao J, Li W, Li C, Li M, Wang L, Zhang D, Zhang J, Yang Z, Jin H. Unraveling the mechanism of ethyl acetate extract from Prismatomeris connata Y. Z. Ruan root in treating pulmonary fibrosis: insights from bioinformatics, network pharmacology, and experimental validation. Front Immunol 2024; 14:1330055. [PMID: 38259493 PMCID: PMC10801734 DOI: 10.3389/fimmu.2023.1330055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024] Open
Abstract
Introduction Pulmonary fibrosis is a terminal lung disease characterized by fibroblast proliferation, extracellular matrix accumulation, inflammatory damage, and tissue structure destruction. The pathogenesis of this disease, particularly idiopathic pulmonary fibrosis (IPF), remains unknown. Macrophages play major roles in organ fibrosis diseases, including pulmonary fibrosis. The phenotype and polarization of macrophages are closely associated with pulmonary fibrosis. A new direction in research on anti-pulmonary fibrosis is focused on developing drugs that maintain the stability of the pulmonary microenvironment. Methods We obtained gene sequencing data and clinical information for patients with IPF from the GEO datasets GSE110147, GSE15197, GSE24988, GSE31934, GSE32537, GSE35145, GSE53845, GSE49072, GSE70864, and GSE90010. We performed GO, KEGG enrichment analysis and GSEA analysis, and conducted weighted gene co-expression network analysis. In addition, we performed proteomic analysis of mouse lung tissue. To verify the results of bioinformatics analysis and proteomic analysis, mice were induced by intratracheal instillation of bleomycin (BLM), and gavaged for 14 days after modeling. Respiratory function of mice in different groups was measured. Lung tissues were retained for histopathological examination, Western Blot and real-time quantitative PCR, etc. In addition, lipopolysaccharide, interferon-γ and interleukin-4 were used to induce RAW264.7 cells for 12h in vitro to establish macrophage inflammation and polarization model. At the same time, HG2 intervention was given. The phenotype transformation and cytokine secretion of macrophages were investigated by Western Blot, RT-qPCR and flow cytometry, etc. Results Through bioinformatics analysis and experiments involving bleomycin-induced pulmonary fibrosis in mice, we confirmed the importance of macrophage polarization in IPF. The analysis revealed that macrophage polarization in IPF involves a change in the phenotypic spectrum. Furthermore, experiments demonstrated high expression of M2-type macrophage-associated biomarkers and inducible nitric oxide synthase, thus indicating an imbalance in M1/M2 polarization of pulmonary macrophages in mice with pulmonary fibrosis. Discussion Our investigation revealed that the ethyl acetate extract (HG2) obtained from the roots of Prismatomeris connata Y. Z. Ruan exhibits therapeutic efficacy against bleomycin-induced pulmonary fibrosis. HG2 modulates macrophage polarization, alterations in the TGF-β/Smad pathway, and downstream protein expression in the context of pulmonary fibrosis. On the basis of our findings, we believe that HG2 has potential as a novel traditional Chinese medicine component for treating pulmonary fibrosis.
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Affiliation(s)
- Sizheng Li
- New Drug Safety Evaluation Center, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Guang Hu
- New Drug Safety Evaluation Center, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- School of Biomedical Sciences, Hunan University, Changsha, Hunan, China
| | - Lian Kuang
- New Drug Safety Evaluation Center, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Tianyu Zhou
- New Drug Safety Evaluation Center, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Haiyan Jiang
- New Drug Safety Evaluation Center, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Fei Pang
- New Drug Safety Evaluation Center, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jie Li
- New Drug Safety Evaluation Center, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xinyi Chen
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Jie Bao
- New Drug Safety Evaluation Center, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- National Medical Products Administration (NMPA) Key Laboratory of Safety Research and Evaluation of Innovative Drug, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- R&D Department, Beijing Union-Genius Pharmaceutical Technology Development Co. Ltd., Beijing, China
| | - Wanfang Li
- New Drug Safety Evaluation Center, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- National Medical Products Administration (NMPA) Key Laboratory of Safety Research and Evaluation of Innovative Drug, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- R&D Department, Beijing Union-Genius Pharmaceutical Technology Development Co. Ltd., Beijing, China
| | - Chuangjun Li
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Menglin Li
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Lulu Wang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Dongming Zhang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Jinlan Zhang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Zengyan Yang
- Section of Science & Technology, Guangxi International Zhuang Medicine Hospital, Nanning, Guangxi, China
| | - Hongtao Jin
- New Drug Safety Evaluation Center, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- National Medical Products Administration (NMPA) Key Laboratory of Safety Research and Evaluation of Innovative Drug, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- R&D Department, Beijing Union-Genius Pharmaceutical Technology Development Co. Ltd., Beijing, China
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Li P, Wei J, Zhu Y. CellGO: a novel deep learning-based framework and webserver for cell-type-specific gene function interpretation. Brief Bioinform 2023; 25:bbad417. [PMID: 37995133 PMCID: PMC10790717 DOI: 10.1093/bib/bbad417] [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: 08/07/2023] [Revised: 10/09/2023] [Accepted: 10/29/2023] [Indexed: 11/25/2023] Open
Abstract
Interpreting the function of genes and gene sets identified from omics experiments remains a challenge, as current pathway analysis tools often fail to consider the critical biological context, such as tissue or cell-type specificity. To address this limitation, we introduced CellGO. CellGO tackles this challenge by leveraging the visible neural network (VNN) and single-cell gene expressions to mimic cell-type-specific signaling propagation along the Gene Ontology tree within a cell. This design enables a novel scoring system to calculate the cell-type-specific gene-pathway paired active scores, based on which, CellGO is able to identify cell-type-specific active pathways associated with single genes. In addition, by aggregating the activities of single genes, CellGO extends its capability to identify cell-type-specific active pathways for a given gene set. To enhance biological interpretation, CellGO offers additional features, including the identification of significantly active cell types and driver genes and community analysis of pathways. To validate its performance, CellGO was assessed using a gene set comprising mixed cell-type markers, confirming its ability to discern active pathways across distinct cell types. Subsequent benchmarking analyses demonstrated CellGO's superiority in effectively identifying cell types and their corresponding cell-type-specific pathways affected by gene knockouts, using either single genes or sets of genes differentially expressed between knockout and control samples. Moreover, CellGO demonstrated its ability to infer cell-type-specific pathogenesis for disease risk genes. Accessible as a Python package, CellGO also provides a user-friendly web interface, making it a versatile and accessible tool for researchers in the field.
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Affiliation(s)
- Peilong Li
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science and Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200032, China
| | - Junfeng Wei
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science and Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200032, China
| | - Ying Zhu
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science and Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200032, China
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7
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Lin G, Dong L, Cheng KK, Xu X, Wang Y, Deng L, Raftery D, Dong J. Differential Correlations Informed Metabolite Set Enrichment Analysis to Decipher Metabolic Heterogeneity of Disease. Anal Chem 2023; 95:12505-12513. [PMID: 37557184 DOI: 10.1021/acs.analchem.3c02246] [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] [Indexed: 08/11/2023]
Abstract
Metabolic pathways are regarded as functional and basic components of the biological system. In metabolomics, metabolite set enrichment analysis (MSEA) is often used to identify the altered metabolic pathways (metabolite sets) associated with phenotypes of interest (POI), e.g., disease. However, in most studies, MSEA suffers from the limitation of low metabolite coverage. Random walk (RW)-based algorithms can be used to propagate the perturbation of detected metabolites to the undetected metabolites through a metabolite network model prior to MSEA. Nevertheless, most of the existing RW-based algorithms run on a general metabolite network constructed based on public databases, such as KEGG, without taking into consideration the potential influence of POI on the metabolite network, which may reduce the phenotypic specificities of the MSEA results. To solve this problem, a novel pathway analysis strategy, namely, differential correlation-informed MSEA (dci-MSEA), is proposed in this paper. Statistically, differential correlations between metabolites are used to evaluate the influence of POI on the metabolite network, so that a phenotype-specific metabolite network is constructed for RW-based propagation. The experimental results show that dci-MSEA outperforms the conventional RW-based MSEA in identifying the altered metabolic pathways associated with colorectal cancer. In addition, by incorporating the individual-specific metabolite network, the dci-MSEA strategy is easily extended to disease heterogeneity analysis. Here, dci-MSEA was used to decipher the heterogeneity of colorectal cancer. The present results highlight the clustering of colorectal cancer samples with their cluster-specific selection of differential pathways and demonstrate the feasibility of dci-MSEA in heterogeneity analysis. Taken together, the proposed dci-MSEA may provide insights into disease mechanisms and determination of disease heterogeneity.
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Affiliation(s)
- Genjin Lin
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
| | - Liheng Dong
- School of Computing and Data Science, Xiamen University Malaysia, 43600 Sepang, Malaysia
| | - Kian-Kai Cheng
- Faculty of Chemical and Energy Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, Malaysia
| | - Xiangnan Xu
- School of Business and Economics, Humboldt-Universität zu Berlin, Berlin 10099, Germany
| | - Yongpei Wang
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
| | - Lingli Deng
- Department of Information Engineering, East China University of Technology, Nanchang 330013, China
| | - Daniel Raftery
- Northwest Metabolomics Research Center, University of Washington, Seattle, Washington 98109, United States
| | - Jiyang Dong
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
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Ahmed F, Samantasinghar A, Manzoor Soomro A, Kim S, Hyun Choi K. A systematic review of computational approaches to understand cancer biology for informed drug repurposing. J Biomed Inform 2023; 142:104373. [PMID: 37120047 DOI: 10.1016/j.jbi.2023.104373] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/25/2023] [Accepted: 04/23/2023] [Indexed: 05/01/2023]
Abstract
Cancer is the second leading cause of death globally, trailing only heart disease. In the United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, the success rate for new cancer drug development remains less than 10%, making the disease particularly challenging. This low success rate is largely attributed to the complex and poorly understood nature of cancer etiology. Therefore, it is critical to find alternative approaches to understanding cancer biology and developing effective treatments. One such approach is drug repurposing, which offers a shorter drug development timeline and lower costs while increasing the likelihood of success. In this review, we provide a comprehensive analysis of computational approaches for understanding cancer biology, including systems biology, multi-omics, and pathway analysis. Additionally, we examine the use of these methods for drug repurposing in cancer, including the databases and tools that are used for cancer research. Finally, we present case studies of drug repurposing, discussing their limitations and offering recommendations for future research in this area.
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Affiliation(s)
- Faheem Ahmed
- Department of Mechatronics Engineering, Jeju National University, Republic of Korea
| | | | | | - Sejong Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea; Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.
| | - Kyung Hyun Choi
- Department of Mechatronics Engineering, Jeju National University, Republic of Korea.
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Slater K, Williams JA, Schofield PN, Russell S, Pendleton SC, Karwath A, Fanning H, Ball S, Hoehndorf R, Gkoutos GV. Klarigi: Characteristic explanations for semantic biomedical data. Comput Biol Med 2023; 153:106425. [PMID: 36638616 DOI: 10.1016/j.compbiomed.2022.106425] [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: 08/30/2022] [Revised: 12/04/2022] [Accepted: 12/13/2022] [Indexed: 12/24/2022]
Abstract
Annotation of biomedical entities with ontology classes provides for formal semantic analysis and mobilisation of background knowledge in determining their relationships. To date, enrichment analysis has been routinely employed to identify classes that are over-represented in annotations across sets of groups, such as biosample gene expression profiles or patient phenotypes, and is useful for a range of tasks including differential diagnosis and causative variant prioritisation. These approaches, however, usually consider only univariate relationships, make limited use of the semantic features of ontologies, and provide limited information and evaluation of the explanatory power of both singular and grouped candidate classes. Moreover, they are not designed to solve the problem of deriving cohesive, characteristic, and discriminatory sets of classes for entity groups. We have developed a new tool, called Klarigi, which introduces multiple scoring heuristics for identification of classes that are both compositional and discriminatory for groups of entities annotated with ontology classes. The tool includes a novel algorithm for derivation of multivariable semantic explanations for entity groups, makes use of semantic inference through live use of an ontology reasoner, and includes a classification method for identifying the discriminatory power of candidate sets, in addition to significance testing apposite to traditional enrichment approaches. We describe the design and implementation of Klarigi, including its scoring and explanation determination methods, and evaluate its use in application to two test cases with clinical significance, comparing and contrasting methods and results with literature-based and enrichment analysis methods. We demonstrate that Klarigi produces characteristic and discriminatory explanations for groups of biomedical entities in two settings. We also show that these explanations recapitulate and extend the knowledge held in existing biomedical databases and literature for several diseases. We conclude that Klarigi provides a distinct and valuable perspective on biomedical datasets when compared with traditional enrichment methods, and therefore constitutes a new method by which biomedical datasets can be explored, contributing to improved insight into semantic data.
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Affiliation(s)
- Karin Slater
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, UK; Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, UK; MRC Health Data Research UK (HDR UK), Midlands, UK; University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK.
| | - John A Williams
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, UK; Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, UK; University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK
| | - Paul N Schofield
- Department of Physiology, Development, and Neuroscience, University of Cambridge, UK
| | - Sophie Russell
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, UK
| | - Samantha C Pendleton
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, UK
| | - Andreas Karwath
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, UK; Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, UK; MRC Health Data Research UK (HDR UK), Midlands, UK; University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK
| | - Hilary Fanning
- Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, UK; University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK
| | - Simon Ball
- Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, UK; University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK
| | - Robert Hoehndorf
- Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, UK
| | - Georgios V Gkoutos
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, UK; Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, UK; NIHR Experimental Cancer Medicine Centre, UK; NIHR Surgical Reconstruction and Microbiology Research Centre, UK; NIHR Biomedical Research Centre, UK; MRC Health Data Research UK (HDR UK), Midlands, UK; University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK
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