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Firoozbakht F, Rezaeian I, Rueda L, Ngom A. Computationally repurposing drugs for breast cancer subtypes using a network-based approach. BMC Bioinformatics 2022; 23:143. [PMID: 35443626 PMCID: PMC9020161 DOI: 10.1186/s12859-022-04662-6] [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: 06/25/2021] [Accepted: 03/30/2022] [Indexed: 11/22/2022] Open
Abstract
‘De novo’ drug discovery is costly, slow, and with high risk. Repurposing known drugs for treatment of other diseases offers a fast, low-cost/risk and highly-efficient method toward development of efficacious treatments. The emergence of large-scale heterogeneous biomolecular networks, molecular, chemical and bioactivity data, and genomic and phenotypic data of pharmacological compounds is enabling the development of new area of drug repurposing called ‘in silico’ drug repurposing, i.e., computational drug repurposing (CDR). The aim of CDR is to discover new indications for an existing drug (drug-centric) or to identify effective drugs for a disease (disease-centric). Both drug-centric and disease-centric approaches have the common challenge of either assessing the similarity or connections between drugs and diseases. However, traditional CDR is fraught with many challenges due to the underlying complex pharmacology and biology of diseases, genes, and drugs, as well as the complexity of their associations. As such, capturing highly non-linear associations among drugs, genes, diseases by most existing CDR methods has been challenging. We propose a network-based integration approach that can best capture knowledge (and complex relationships) contained within and between drugs, genes and disease data. A network-based machine learning approach is applied thereafter by using the extracted knowledge and relationships in order to identify single and pair of approved or experimental drugs with potential therapeutic effects on different breast cancer subtypes. Indeed, further clinical analysis is needed to confirm the therapeutic effects of identified drugs on each breast cancer subtype.
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Affiliation(s)
- Forough Firoozbakht
- School of Computer Science, University of Windsor, 401 Sunset Ave., Windsor, ON, Canada
| | - Iman Rezaeian
- School of Computer Science, University of Windsor, 401 Sunset Ave., Windsor, ON, Canada.,Rocket Innovation Studio, 156 Chatham St W, Windsor, ON, Canada
| | - Luis Rueda
- School of Computer Science, University of Windsor, 401 Sunset Ave., Windsor, ON, Canada.
| | - Alioune Ngom
- School of Computer Science, University of Windsor, 401 Sunset Ave., Windsor, ON, Canada
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Yuan J, Duan F, Zhai W, Song C, Wang L, Xia W, Hua X, Yuan Z, Bi X, Huang J. An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Breast Cancer. Int J Womens Health 2021; 13:1053-1064. [PMID: 34785957 PMCID: PMC8578840 DOI: 10.2147/ijwh.s334756] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 10/24/2021] [Indexed: 12/14/2022] Open
Abstract
Background Aging, an inevitable process characterized by functional decline over time, is a significant risk factor for various tumors. However, little is known about aging-related genes (ARGs) in breast cancer (BC). We aimed to explore the potential prognostic role of ARGs and to develop an ARG-based prognosis signature for BC. Methods RNA-sequencing expression profiles and corresponding clinicopathological data of female patients with BC were obtained from public databases in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). An ARG-based risk signature was constructed in the TCGA cohort based on results of least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis, and its prognostic value was further validated in the GSE20685 cohort. Results A six ARG-based signature, including CLU, DGAT1, MXI1, NFKBI, PIK3CA and PLAU, was developed in the TCGA cohort and significantly stratified patients into low- and high-risk groups. Patients in the former group showed significantly better prognosis than those in the latter. Multivariate Cox regression analysis demonstrated that the ARG risk score was an independent prognostic factor for BC. A predictive nomogram integrating the ARG risk score and three identified factors (age, N- and M-classification) was established in the TCGA cohort and validated in the GSE20685 cohort. Calibration plots showed good consistency between predicted survival probabilities and actual observations. Conclusion A novel ARG-based risk signature was developed for patients with BC, which can be used for individual prognosis prediction and promoting personalized treatment.
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Affiliation(s)
- Jing Yuan
- Departments of Experimental Research, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China
| | - Fangfang Duan
- Departments of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China
| | - Wenyu Zhai
- Departments of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China
| | - Chenge Song
- Departments of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China
| | - Li Wang
- Departments of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China
| | - Wen Xia
- Departments of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China
| | - Xin Hua
- Departments of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China
| | - Zhongyu Yuan
- Departments of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China
| | - Xiwen Bi
- Departments of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China
| | - Jiajia Huang
- Departments of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China
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Ruan Y, Chen XH, Jiang F, Liu YG, Liang XL, Lv BM, Zhang HY, Zhang QY. Agent Clustering Strategy Based on Metabolic Flux Distribution and Transcriptome Expression for Novel Drug Development. Biomedicines 2021; 9:biomedicines9111640. [PMID: 34829869 PMCID: PMC8615746 DOI: 10.3390/biomedicines9111640] [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: 10/12/2021] [Revised: 11/01/2021] [Accepted: 11/05/2021] [Indexed: 11/16/2022] Open
Abstract
The network module-based method has been used for drug repositioning. The traditional drug repositioning method only uses the gene characteristics of the drug but ignores the drug-triggered metabolic changes. The metabolic network systematically characterizes the connection between genes, proteins, and metabolic reactions. The differential metabolic flux distribution, as drug metabolism characteristics, was employed to cluster the agents with similar MoAs (mechanism of action). In this study, agents with the same pharmacology were clustered into one group, and a total of 1309 agents from the CMap database were clustered into 98 groups based on differential metabolic flux distribution. Transcription factor (TF) enrichment analysis revealed the agents in the same group (such as group 7 and group 26) were confirmed to have similar MoAs. Through this agent clustering strategy, the candidate drugs which can inhibit (Japanese encephalitis virus) JEV infection were identified. This study provides new insights into drug repositioning and their MoAs.
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Pettini F, Visibelli A, Cicaloni V, Iovinelli D, Spiga O. Multi-Omics Model Applied to Cancer Genetics. Int J Mol Sci 2021; 22:ijms22115751. [PMID: 34072237 PMCID: PMC8199287 DOI: 10.3390/ijms22115751] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 05/18/2021] [Accepted: 05/26/2021] [Indexed: 12/29/2022] Open
Abstract
In this review, we focus on bioinformatic oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. Before providing a deeper insight into the bioinformatics approach and utilities involved in oncology, we must understand what is a system biology framework and the genetic connection, because of the high heterogenicity of the backgrounds of people approaching precision medicine. In fact, it is essential to providing general theoretical information on genomics, epigenomics, and transcriptomics to understand the phases of multi-omics approach. We consider how to create a multi-omics model. In the last section, we describe the new frontiers and future perspectives of this field.
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Affiliation(s)
- Francesco Pettini
- Department of Medical Biotechnology, University of Siena, Via M. Bracci 2, 53100 Siena, Italy
- Correspondence: ; Tel.: +39-3755461426
| | - Anna Visibelli
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via A. Moro 2, 53100 Siena, Italy; (A.V.); (D.I.); (O.S.)
| | - Vittoria Cicaloni
- Toscana Life Sciences Foundation, Via Fiorentina 1, 53100 Siena, Italy;
| | - Daniele Iovinelli
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via A. Moro 2, 53100 Siena, Italy; (A.V.); (D.I.); (O.S.)
| | - Ottavia Spiga
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via A. Moro 2, 53100 Siena, Italy; (A.V.); (D.I.); (O.S.)
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