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Guo X, Chen L. From G1 to M: a comparative study of methods for identifying cell cycle phases. Brief Bioinform 2024; 25:bbad517. [PMID: 38261342 PMCID: PMC10805071 DOI: 10.1093/bib/bbad517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 11/08/2023] [Accepted: 12/13/2023] [Indexed: 01/24/2024] Open
Abstract
Accurate identification of cell cycle phases in single-cell RNA-sequencing (scRNA-seq) data is crucial for biomedical research. Many methods have been developed to tackle this challenge, employing diverse approaches to predict cell cycle phases. In this review article, we delve into the standard processes in identifying cell cycle phases within scRNA-seq data and present several representative methods for comparison. To rigorously assess the accuracy of these methods, we propose an error function and employ multiple benchmarking datasets encompassing human and mouse data. Our evaluation results reveal a key finding: the fit between the reference data and the dataset being analyzed profoundly impacts the effectiveness of cell cycle phase identification methods. Therefore, researchers must carefully consider the compatibility between the reference data and their dataset to achieve optimal results. Furthermore, we explore the potential benefits of incorporating benchmarking data with multiple known cell cycle phases into the analysis. Merging such data with the target dataset shows promise in enhancing prediction accuracy. By shedding light on the accuracy and performance of cell cycle phase prediction methods across diverse datasets, this review aims to motivate and guide future methodological advancements. Our findings offer valuable insights for researchers seeking to improve their understanding of cellular dynamics through scRNA-seq analysis, ultimately fostering the development of more robust and widely applicable cell cycle identification methods.
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Affiliation(s)
- Xinyu Guo
- Department of Quantitative and Computational Biology, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, United States
| | - Liang Chen
- Department of Quantitative and Computational Biology, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, United States
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Su K, Yu Q, Shen R, Sun SY, Moreno CS, Li X, Qin ZS. Pan-cancer analysis of pathway-based gene expression pattern at the individual level reveals biomarkers of clinical prognosis. Cell Rep Methods 2021; 1:100050. [PMID: 34671755 PMCID: PMC8525796 DOI: 10.1016/j.crmeth.2021.100050] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 05/07/2021] [Accepted: 06/16/2021] [Indexed: 02/08/2023]
Abstract
Identifying biomarkers to predict the clinical outcomes of individual patients is a fundamental problem in clinical oncology. Multiple single-gene biomarkers have already been identified and used in clinics. However, multiple oncogenes or tumor-suppressor genes are involved during the process of tumorigenesis. Additionally, the efficacy of single-gene biomarkers is limited by the extensively variable expression levels measured by high-throughput assays. In this study, we hypothesize that in individual tumor samples, the disruption of transcription homeostasis in key pathways or gene sets plays an important role in tumorigenesis and has profound implications for the patient's clinical outcome. We devised a computational method named iPath to identify, at the individual-sample level, which pathways or gene sets significantly deviate from their norms. We conducted a pan-cancer analysis and demonstrated that iPath is capable of identifying highly predictive biomarkers for clinical outcomes, including overall survival, tumor subtypes, and tumor-stage classifications.
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Affiliation(s)
- Kenong Su
- Department of Computer Science, Emory University, Atlanta, GA 30322, USA
| | - Qi Yu
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA
| | - Ronglai Shen
- Department of Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10017, USA
| | - Shi-Yong Sun
- Department of Hematology & Medical Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Carlos S. Moreno
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Xiaoxian Li
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Zhaohui S. Qin
- Department of Computer Science, Emory University, Atlanta, GA 30322, USA
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA 30322, USA
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Hu S, Xie D, Zhou P, Liu X, Yin X, Huang B, Guan H. LINCS gene expression signature analysis revealed bosutinib as a radiosensitizer of breast cancer cells by targeting eIF4G1. Int J Mol Med 2021; 47:72. [PMID: 33693953 DOI: 10.3892/ijmm.2021.4905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Accepted: 01/22/2021] [Indexed: 11/06/2022] Open
Abstract
Radioresistance is the predominant cause for radiotherapy failure and disease progression, resulting in increased breast cancer‑associated mortality. Using gene expression signature analysis of the Library of Integrated Network‑Based Cellular Signatures (LINCS) and Gene Expression Omnibus (GEO), the aim of the present study was to systematically identify potential candidate radiosensitizers from known drugs. The similarity of integrated gene expression signatures between irradiated eukaryotic translation initiation factor 4 γ 1 (eIF4G1)‑silenced breast cancer cells and known drugs was measured using enrichment scores (ES). Drugs with positive ES were selected as potential radiosensitizers. The radiosensitizing effects of the candidate drugs were analyzed in breast cancer cell lines (MCF‑7, MX‑1 and MDA‑MB‑231) using CCK‑8 and colony formation assays following exposure to ionizing radiation. Cell apoptosis was measured using flow cytometry. The expression levels of eIF4G1 and DNA damage response (DDR) proteins were analyzed by western blotting. Bosutinib was identified as a promising radiosensitizer, as its administration markedly reduced the dosage required both for the drug and for ionizing radiation, which may be associated with fewer treatment‑associated adverse reactions. Moreover, combined treatment of ionizing radiation and bosutinib significantly increased cell killing in all three cell lines, compared with ionizing radiation or bosutinib alone. Among the three cell lines, MX‑1 cells were identified as the most sensitive to both ionizing radiation and bosutinib. Bosutinib markedly downregulated the expression of eIF4G1 in a dose‑dependent manner and also reduced the expression of DDR proteins (including ATM, XRCC4, ATRIP, and GADD45A). Moreover, eIF4G1 was identified as a key target of bosutinib that may regulate DNA damage induced by ionizing radiation. Thus, bosutinib may serve as a potential candidate radiosensitizer for breast cancer therapy.
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Affiliation(s)
- Sai Hu
- Institute for Environmental Medicine and Radiation Hygiene, School of Public Health, University of South China, Hengyang, Hunan 421001, P.R. China
| | - Dafei Xie
- Department of Radiation Biology, Beijing Key Laboratory for Radiobiology, Beijing Institute of Radiation Medicine, Beijing 100850, P.R. China
| | - Pingkun Zhou
- Institute for Environmental Medicine and Radiation Hygiene, School of Public Health, University of South China, Hengyang, Hunan 421001, P.R. China
| | - Xiaodan Liu
- Department of Radiation Biology, Beijing Key Laboratory for Radiobiology, Beijing Institute of Radiation Medicine, Beijing 100850, P.R. China
| | - Xiaoyao Yin
- College of Computer, National University of Defence Technology, Changsha, Hunan 410073, P.R. China
| | - Bo Huang
- Institute for Environmental Medicine and Radiation Hygiene, School of Public Health, University of South China, Hengyang, Hunan 421001, P.R. China
| | - Hua Guan
- Department of Radiation Biology, Beijing Key Laboratory for Radiobiology, Beijing Institute of Radiation Medicine, Beijing 100850, P.R. China
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Lu J, Zhang Y, Wang S, Bi Y, Huang T, Luo X, Cai YD. Analysis of Four Types of Leukemia Using Gene Ontology Term and Kyoto Encyclopedia of Genes and Genomes Pathway Enrichment Scores. Comb Chem High Throughput Screen 2019; 23:295-303. [PMID: 30599106 DOI: 10.2174/1386207322666181231151900] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Revised: 09/24/2018] [Accepted: 12/05/2018] [Indexed: 12/16/2022]
Abstract
AIM AND OBJECTIVE Leukemia is the second common blood cancer after lymphoma, and its incidence rate has an increasing trend in recent years. Leukemia can be classified into four types: acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), and chronic myelogenous leukemia (CML). More than forty drugs are applicable to different types of leukemia based on the discrepant pathogenesis. Therefore, the identification of specific drug-targeted biological processes and pathways is helpful to determinate the underlying pathogenesis among such four types of leukemia. METHODS In this study, the gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that were highly related to drugs for leukemia were investigated for the first time. The enrichment scores for associated GO terms and KEGG pathways were calculated to evaluate the drugs and leukemia. The feature selection method, minimum redundancy maximum relevance (mRMR), was used to analyze and identify important GO terms and KEGG pathways. RESULTS Twenty Go terms and two KEGG pathways with high scores have all been confirmed to effectively distinguish four types of leukemia. CONCLUSION This analysis may provide a useful tool for the discrepant pathogenesis and drug design of different types of leukemia.
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Affiliation(s)
- Jing Lu
- School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, 32 Qingquan Road, Yantai 264005, China
| | - YuHang Zhang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China
| | - ShaoPeng Wang
- School of Life Sciences, Shanghai University, 99 Shangda Road, Shanghai 200444, China
| | - Yi Bi
- School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, 32 Qingquan Road, Yantai 264005, China
| | - Tao Huang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China
| | - Xiaomin Luo
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of MateriaMedica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, 99 Shangda Road, Shanghai 200444, China
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Ding J, Zhang Y. Analysis of key GO terms and KEGG pathways associated with carcinogenic chemicals. Comb Chem High Throughput Screen 2017; 20:CCHTS-EPUB-87434. [PMID: 29256346 DOI: 10.2174/1386207321666171218120133] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Revised: 07/21/2017] [Accepted: 11/15/2017] [Indexed: 11/22/2022]
Abstract
Cancer is one of the serious disease that causes several human deaths every year. Up to now, we have spent lots of time and money to investigate this disease, thereby designing effective treatments. Previous studies mainly focus on studying genetic background of different subtypes of cancer and neglect another important factor, environmental factor. Carcinogenic chemical is one of the type of environmental factor, exposure of such chemical may definitely initiate and promote the tumorigenesis. In this study, we tried to partly describe the differences between carcinogenic and non-carcinogenic chemicals using gene ontology (GO) terms and KEGG pathways. The carcinogenic and non-carcinogenic chemicals that were retrieved from Carcinogenic Potency Database (CPDB) were encoded into numeric vectors using the enrichment theories of GO terms and KEGG pathways. Then, the minimal redundancy maximal relevance (mRMR) method was adopted to analyze all features, resulting in some important GO terms and KEGG pathways. The extensive analysis of the identified GO terms and KEGG pathways indicate that they all play roles during the tumorigenesis, inducing that they can be key indicator for identification of carcinogenic chemicals.
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Affiliation(s)
- Jing Ding
- School of Pharmacy, Zhejiang Pharmaceutical College, Ningbo 315100. China
| | - Ying Zhang
- Center for Certification and Evaluation, Shanghai Municipal Food and Drug Administration Shanghai, Zhejiang. China
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Zheng T, Ni Y, Li J, Chow BKC, Panagiotou G. Designing Dietary Recommendations Using System Level Interactomics Analysis and Network-Based Inference. Front Physiol 2017; 8:753. [PMID: 29033850 PMCID: PMC5625024 DOI: 10.3389/fphys.2017.00753] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Accepted: 09/19/2017] [Indexed: 12/14/2022] Open
Abstract
Background: A range of computational methods that rely on the analysis of genome-wide expression datasets have been developed and successfully used for drug repositioning. The success of these methods is based on the hypothesis that introducing a factor (in this case, a drug molecule) that could reverse the disease gene expression signature will lead to a therapeutic effect. However, it has also been shown that globally reversing the disease expression signature is not a prerequisite for drug activity. On the other hand, the basic idea of significant anti-correlation in expression profiles could have great value for establishing diet-disease associations and could provide new insights into the role of dietary interventions in disease. Methods: We performed an integrated analysis of publicly available gene expression profiles for foods, diseases and drugs, by calculating pairwise similarity scores for diet and disease gene expression signatures and characterizing their topological features in protein-protein interaction networks. Results: We identified 485 diet-disease pairs where diet could positively influence disease development and 472 pairs where specific diets should be avoided in a disease state. Multiple evidence suggests that orange, whey and coconut fat could be beneficial for psoriasis, lung adenocarcinoma and macular degeneration, respectively. On the other hand, fructose-rich diet should be restricted in patients with chronic intermittent hypoxia and ovarian cancer. Since humans normally do not consume foods in isolation, we also applied different algorithms to predict synergism; as a result, 58 food pairs were predicted. Interestingly, the diets identified as anti-correlated with diseases showed a topological proximity to the disease proteins similar to that of the corresponding drugs. Conclusions: In conclusion, we provide a computational framework for establishing diet-disease associations and additional information on the role of diet in disease development. Due to the complexity of analyzing the food composition and eating patterns of individuals our in silico analysis, using large-scale gene expression datasets and network-based topological features, may serve as a proof-of-concept in nutritional systems biology for identifying diet-disease relationships and subsequently designing dietary recommendations.
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Affiliation(s)
- Tingting Zheng
- Systems Biology and Bioinformatics Group, Faculty of Sciences, School of Biological Sciences, The University of HongKong, Hong Kong, Hong Kong
| | - Yueqiong Ni
- Systems Biology and Bioinformatics Group, Faculty of Sciences, School of Biological Sciences, The University of HongKong, Hong Kong, Hong Kong
| | - Jun Li
- Systems Biology and Bioinformatics Group, Faculty of Sciences, School of Biological Sciences, The University of HongKong, Hong Kong, Hong Kong
| | - Billy K C Chow
- Faculty of Science, School of Biological Sciences, The University of Hong Kong, Hong Kong, Hong Kong
| | - Gianni Panagiotou
- Systems Biology and Bioinformatics Group, Faculty of Sciences, School of Biological Sciences, The University of HongKong, Hong Kong, Hong Kong.,Department of Systems Biology and Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Jena, Germany
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