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Hoseini SH, Enayati P, Nazari M, Babakhanzadeh E, Rastgoo M, Sohrabi NB. Biomarker Profile of Colorectal Cancer: Current Findings and Future Perspective. J Gastrointest Cancer 2024; 55:497-510. [PMID: 38168859 DOI: 10.1007/s12029-023-00990-9] [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] [Accepted: 11/19/2023] [Indexed: 01/05/2024]
Abstract
OBJECTIVE Breakthroughs in omics technology have led to a deeper understanding of the fundamental molecular changes that play a critical role in the development and progression of cancer. This review delves into the hidden molecular drivers of colorectal cancer (CRC), offering potential for clinical translation through novel biomarkers and personalized therapies. METHODS We summarizes recent studies utilizing various omics approaches, including genomics, transcriptomics, proteomics, epigenomics, metabolomics and data integration with computational algorithms, to investigate CRC. RESULTS Integrating multi-omics data in colorectal cancer research unlocks hidden biological insights, revealing new pathways and mechanisms. This powerful approach not only identifies potential biomarkers for personalized prognosis, diagnosis, and treatment, but also predicts patient response to specific therapies, while computational tools illuminate the landscape by deciphering complex datasets. CONCLUSIONS Future research should prioritize validating promising biomarkers and seamlessly translating them into clinical practice, ultimately propelling personalized CRC management to new heights.
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Affiliation(s)
| | - Parisa Enayati
- Biological Sciences Department, Northern Illinois University, DeKalb, IL, USA
| | - Majid Nazari
- Department of Medical Genetics, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
- , P.O. Box, Tehran, 64155-65117, Iran.
| | - Emad Babakhanzadeh
- Department of Medical Genetics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Maryam Rastgoo
- Department of Microbiology, Shiraz Islamic Azad University, Shiraz, Iran
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Tian Z, Jia J, Yin B, Chen W. Constructing the metabolic network of wheat kernels based on structure-guided chemical modification and multi-omics data. J Genet Genomics 2024:S1673-8527(24)00037-7. [PMID: 38458562 DOI: 10.1016/j.jgg.2024.02.008] [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: 11/08/2023] [Revised: 02/27/2024] [Accepted: 02/27/2024] [Indexed: 03/10/2024]
Abstract
Metabolic network construction plays a pivotal role in unraveling the regulatory mechanism of biological activities, although it often proves to be challenging and labor-intensive, particularly with non-model organisms. In this study, we develop a computational approach that employs reaction models based on structure-guided chemical modification and related compounds to construct a metabolic network in wheat. This construction results in a comprehensive structure-guided network, including 625 identified metabolites and additional 333 putative reactions compared with the Kyoto Encyclopedia of Genes and Genomes database. Using a combination of gene annotation, reaction classification, structure similarity, and transcriptome and metabolome analysis correlations, a total of 229 potential genes related to these reactions are identified within this network. To validate the network, the functionality of a hydroxycinnamoyltransferase (TraesCS3D01G314900) for the synthesis of polyphenols and a rhamnosyltransferase (TraesCS2D01G078700) for the modification of flavonoids are verified through in vitro enzymatic studies and wheat mutant tests, respectively. Our research thus supports the utility of structure-guided chemical modification as an effective tool in identifying causal candidate genes for constructing metabolic networks and further in metabolomic genetic studies.
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Affiliation(s)
- Zhitao Tian
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, Hubei 430070, China; Hubei Hongshan Laboratory, Wuhan, Hubei 430070, China
| | - Jingqi Jia
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, Hubei 430070, China; Hubei Hongshan Laboratory, Wuhan, Hubei 430070, China
| | - Bo Yin
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, Hubei 430070, China; Hubei Hongshan Laboratory, Wuhan, Hubei 430070, China
| | - Wei Chen
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, Hubei 430070, China; Hubei Hongshan Laboratory, Wuhan, Hubei 430070, China.
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Wu E, Fan X, Tang T, Li J, Wang J, Liu X, Zungar Z, Ren J, Wu C, Shen B. Biomarkers discovery for endometrial cancer: A graph convolutional sample network method. Comput Biol Med 2022; 150:106200. [PMID: 37859290 DOI: 10.1016/j.compbiomed.2022.106200] [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: 06/04/2022] [Revised: 09/20/2022] [Accepted: 10/09/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND Endometrial carcinoma is the sixth most common cancer in women worldwide. Importantly, endometrial cancer is among the few types of cancers with patient mortality that is still increasing, which indicates that the improvement in its diagnosis and treatment is still urgent. Moreover, biomarker discovery is essential for precise classification and prognostic prediction of endometrial cancer. METHODS A novel graph convolutional sample network method was used to identify and validate biomarkers for the classification of endometrial cancer. The sample networks were first constructed for each sample, and the gene pairs with high frequencies were identified to construct a subtype-specific network. Putative biomarkers were then screened using the highest degrees in the subtype-specific network. Finally, simplified sample networks are constructed using the biomarkers for the graph convolutional network (GCN) training and prediction. RESULTS Putative biomarkers (23) were identified using the novel bioinformatics model. These biomarkers were then rationalised with functional analyses and were found to be correlated to disease survival with network entropy characterisation. These biomarkers will be helpful in future investigations of the molecular mechanisms and therapeutic targets of endometrial cancers. CONCLUSIONS A novel bioinformatics model combining sample network construction with GCN modelling is proposed and validated for biomarker discovery in endometrial cancer. The model can be generalized and applied to biomarker discovery in other complex diseases.
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Affiliation(s)
- Erman Wu
- Institutes for Systems Genetics, Frontiers Science Centre for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Xuemeng Fan
- Institutes for Systems Genetics, Frontiers Science Centre for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Tong Tang
- Institutes for Systems Genetics, Frontiers Science Centre for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China; Department of Computer Science and Information Technologies, Elviña Campus, University of A Coruña, A Coruña, Spain
| | - Jingjing Li
- Institutes for Systems Genetics, Frontiers Science Centre for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Jiao Wang
- Institutes for Systems Genetics, Frontiers Science Centre for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Xingyun Liu
- Institutes for Systems Genetics, Frontiers Science Centre for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Zayatta Zungar
- School of Medicine, University of New England, Armidale, NSW, 2351, Australia
| | - Jiaojiao Ren
- School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu, China
| | - Cong Wu
- Institutes for Systems Genetics, Frontiers Science Centre for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
| | - Bairong Shen
- Institutes for Systems Genetics, Frontiers Science Centre for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
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Gholami N, Haghparast A, Alipourfard I, Nazari M. Prostate cancer in omics era. Cancer Cell Int 2022; 22:274. [PMID: 36064406 PMCID: PMC9442907 DOI: 10.1186/s12935-022-02691-y] [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: 05/25/2022] [Accepted: 08/22/2022] [Indexed: 11/18/2022] Open
Abstract
Recent advances in omics technology have prompted extraordinary attempts to define the molecular changes underlying the onset and progression of a variety of complex human diseases, including cancer. Since the advent of sequencing technology, cancer biology has become increasingly reliant on the generation and integration of data generated at these levels. The availability of multi-omic data has transformed medicine and biology by enabling integrated systems-level approaches. Multivariate signatures are expected to play a role in cancer detection, screening, patient classification, assessment of treatment response, and biomarker identification. This review reports current findings and highlights a number of studies that are both novel and groundbreaking in their application of multi Omics to prostate cancer.
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Affiliation(s)
- Nasrin Gholami
- Hematology and Oncology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | | | - Iraj Alipourfard
- Institutitue of Biology, Biotechnology and Environmental Protection, Faculty of Natural Sciences, University of Silesia, Katowice, Poland
| | - Majid Nazari
- Department of Medical Genetics, Faculty of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
- , P.O. Box 14155-6117, Shiraz, Iran.
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Ren S, Jin Y, Chen Y, Shen B. CRPMKB: a knowledge base of cancer risk prediction models for systematic comparison and personalized applications. Bioinformatics 2022; 38:1669-1676. [PMID: 34927675 DOI: 10.1093/bioinformatics/btab850] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 12/06/2021] [Accepted: 12/15/2021] [Indexed: 02/05/2023] Open
Abstract
MOTIVATION In the era of big data and precision medicine, accurate risk assessment is a prerequisite for the implementation of risk screening and preventive treatment. A large number of studies have focused on the risk of cancer, and related risk prediction models have been constructed, but there is a lack of effective resource integration for systematic comparison and personalized applications. Therefore, the establishment and analysis of the cancer risk prediction model knowledge base (CRPMKB) is of great significance. RESULTS The current knowledge base contains 802 model data. The model comparison indicates that the accuracy of cancer risk prediction was greatly affected by regional differences, cancer types and model types. We divided the model variables into four categories: environment, behavioral lifestyle, biological genetics and clinical examination, and found that there are differences in the distribution of various variables among different cancer types. Taking 50 genes involved in the lung cancer risk prediction models as an example to perform pathway enrichment analyses and the results showed that these genes were significantly enriched in p53 Signaling and Aryl Hydrocarbon Receptor Signaling pathways which are associated with cancer and specific diseases. In addition, we verified the biological significance of overlapping lung cancer genes via STRING database. CRPMKB was established to provide researchers an online tool for the future personalized model application and developing. This study of CRPMKB suggests that developing more targeted models based on specific demographic characteristics and cancer types will further improve the accuracy of cancer risk model predictions. AVAILABILITY AND IMPLEMENTATION CRPMKB is freely available at http://www.sysbio.org.cn/CRPMKB/. The data underlying this article are available in the article and in its online supplementary material. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Shumin Ren
- Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610212, China
| | - Yanwen Jin
- Center for Systems Biology, Soochow University, Suzhou 215006, China
| | - Yalan Chen
- Department of Medical Informatics, School of Medicine, Nantong University, Nantong 226001, China
| | - Bairong Shen
- Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610212, China
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Singla RK, Sharma P, Dubey AK, Gundamaraju R, Kumar D, Kumar S, Madaan R, Shri R, Tsagkaris C, Parisi S, Joon S, Singla S, Kamal MA, Shen B. Natural Product-Based Studies for the Management of Castration-Resistant Prostate Cancer: Computational to Clinical Studies. Front Pharmacol 2021; 12:732266. [PMID: 34737700 PMCID: PMC8560712 DOI: 10.3389/fphar.2021.732266] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 09/06/2021] [Indexed: 02/05/2023] Open
Abstract
Background: With prostate cancer being the fifth-greatest cause of cancer mortality in 2020, there is a dire need to expand the available treatment options. Castration-resistant prostate cancer (CRPC) progresses despite androgen depletion therapy. The mechanisms of resistance are yet to be fully discovered. However, it is hypothesized that androgens depletion enables androgen-independent cells to proliferate and recolonize the tumor. Objectives: Natural bioactive compounds from edible plants and herbal remedies might potentially address this need. This review compiles the available cheminformatics-based studies and the translational studies regarding the use of natural products to manage CRPC. Methods: PubMed and Google Scholar searches for preclinical studies were performed, while ClinicalTrials.gov and PubMed were searched for clinical updates. Studies that were not in English and not available as full text were excluded. The period of literature covered was from 1985 to the present. Results and Conclusion: Our analysis suggested that natural compounds exert beneficial effects due to their broad-spectrum molecular disease-associated targets. In vitro and in vivo studies revealed several bioactive compounds, including rutaecarpine, berberine, curcumin, other flavonoids, pentacyclic triterpenoids, and steroid-based phytochemicals. Molecular modeling tools, including machine and deep learning, have made the analysis more comprehensive. Preclinical and clinical studies on resveratrol, soy isoflavone, lycopene, quercetin, and gossypol have further validated the translational potential of the natural products in the management of prostate cancer.
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Affiliation(s)
- Rajeev K. Singla
- Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- iGlobal Research and Publishing Foundation, New Delhi, India
| | - Pooja Sharma
- Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, India
- Khalsa College of Pharmacy, Amritsar, India
| | | | - Rohit Gundamaraju
- ER Stress and Mucosal Immunology Lab, School of Health Sciences, College of Health and Medicine, University of Tasmania, Launceston, TAS, Australia
| | - Dinesh Kumar
- Department of Pharmaceutical Sciences, Sri Sai College of Pharmacy, Amritsar, India
| | - Suresh Kumar
- Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, India
| | - Reecha Madaan
- Chitkara College of Pharmacy, Chitkara University, Punjab, India
| | - Richa Shri
- Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, India
| | | | - Salvatore Parisi
- Lourdes Matha Institute of Hotel Management and Catering Technology, Thiruvananthapuram, India
| | - Shikha Joon
- Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- iGlobal Research and Publishing Foundation, New Delhi, India
| | - Shailja Singla
- iGlobal Research and Publishing Foundation, New Delhi, India
| | - Mohammad Amjad Kamal
- West China School of Nursing/Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
- Enzymoics; Novel Global Community Educational Foundation, Hebersham, NSW, Australia
| | - Bairong Shen
- Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
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Zhang Y, Zhu L, Wang X. NEM-Tar: A Probabilistic Graphical Model for Cancer Regulatory Network Inference and Prioritization of Potential Therapeutic Targets From Multi-Omics Data. Front Genet 2021; 12:608042. [PMID: 33968127 PMCID: PMC8100334 DOI: 10.3389/fgene.2021.608042] [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: 09/18/2020] [Accepted: 03/22/2021] [Indexed: 11/13/2022] Open
Abstract
Targeted therapy has been widely adopted as an effective treatment strategy to battle against cancer. However, cancers are not single disease entities, but comprising multiple molecularly distinct subtypes, and the heterogeneity nature prevents precise selection of patients for optimized therapy. Dissecting cancer subtype-specific signaling pathways is crucial to pinpointing dysregulated genes for the prioritization of novel therapeutic targets. Nested effects models (NEMs) are a group of graphical models that encode subset relations between observed downstream effects under perturbations to upstream signaling genes, providing a prototype for mapping the inner workings of the cell. In this study, we developed NEM-Tar, which extends the original NEMs to predict drug targets by incorporating causal information of (epi)genetic aberrations for signaling pathway inference. An information theory-based score, weighted information gain (WIG), was proposed to assess the impact of signaling genes on a specific downstream biological process of interest. Subsequently, we conducted simulation studies to compare three inference methods and found that the greedy hill-climbing algorithm demonstrated the highest accuracy and robustness to noise. Furthermore, two case studies were conducted using multi-omics data for colorectal cancer (CRC) and gastric cancer (GC) in the TCGA database. Using NEM-Tar, we inferred signaling networks driving the poor-prognosis subtypes of CRC and GC, respectively. Our model prioritized not only potential individual drug targets such as HER2, for which FDA-approved inhibitors are available but also the combinations of multiple targets potentially useful for the design of combination therapies.
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Affiliation(s)
- Yuchen Zhang
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China
| | - Lina Zhu
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China
| | - Xin Wang
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China.,Key Laboratory of Biochip Technology, Biotech and Health Centre, Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China
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