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Dallavilla T, Galiè S, Sambruni G, Borin S, Fazio N, Fumagalli-Romario U, Manzo T, Nezi L, Schaefer MH. Differences in the molecular organisation of tumours along the colon are linked to interactions within the tumour ecosystem. Biochim Biophys Acta Mol Basis Dis 2024; 1870:167311. [PMID: 38909851 DOI: 10.1016/j.bbadis.2024.167311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 05/22/2024] [Accepted: 06/14/2024] [Indexed: 06/25/2024]
Abstract
Tumours exhibit significant heterogeneity in their molecular profiles across patients, largely influenced by the tissue of origin, where certain driver gene mutations are predominantly associated with specific cancer types. Here, we unveil an additional layer of complexity: some cancer types display anatomic location-specific mutation profiles akin to tissue-specificity. To better understand this phenomenon, we concentrate on colon cancer. While prior studies have noted changes of the frequency of molecular alterations along the colon, the underlying reasons and whether those changes occur rather gradual or are distinct between the left and right colon, remain unclear. Developing and leveraging stringent statistical models on molecular data from 522 colorectal tumours from The Cancer Genome Atlas, we reveal disparities in molecular properties between the left and right colon affecting many genes. Interestingly, alterations in genes responsive to environmental cues and properties of the tumour ecosystem, including metabolites which we quantify in a cohort of 27 colorectal cancer patients, exhibit continuous trends along the colon. Employing network methodologies, we uncover close interactions between metabolites and genes, including drivers of colon cancer, showing continuous abundance or alteration profiles. This underscores how anatomic biases in the composition and interactions within the tumour ecosystem help explaining gradients of carcinogenesis along the colon.
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Affiliation(s)
- Tiziano Dallavilla
- Department of Experimental Oncology, European Institute of Oncology-IRCCS, Milano, Italy
| | - Serena Galiè
- Department of Experimental Oncology, European Institute of Oncology-IRCCS, Milano, Italy
| | - Gaia Sambruni
- Department of Experimental Oncology, European Institute of Oncology-IRCCS, Milano, Italy
| | - Simona Borin
- Digestive Surgery, European Institute of Oncology-IRCCS, Milano, Italy
| | - Nicola Fazio
- Division of Gastrointestinal Medical Oncology and Neuroendocrine Tumors, European Institute of Oncology-IRCCS, Milano, Italy
| | | | - Teresa Manzo
- Department of Experimental Oncology, European Institute of Oncology-IRCCS, Milano, Italy
| | - Luigi Nezi
- Department of Experimental Oncology, European Institute of Oncology-IRCCS, Milano, Italy
| | - Martin H Schaefer
- Department of Experimental Oncology, European Institute of Oncology-IRCCS, Milano, Italy.
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2
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Zhang Y, Zhou Y, Zhou Y, Yu X, Shen X, Hong Y, Zhang Y, Wang S, Mou M, Zhang J, Tao L, Gao J, Qiu Y, Chen Y, Zhu F. TheMarker: a comprehensive database of therapeutic biomarkers. Nucleic Acids Res 2024; 52:D1450-D1464. [PMID: 37850638 PMCID: PMC10767989 DOI: 10.1093/nar/gkad862] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 09/21/2023] [Accepted: 09/29/2023] [Indexed: 10/19/2023] Open
Abstract
Distinct from the traditional diagnostic/prognostic biomarker (adopted as the indicator of disease state/process), the therapeutic biomarker (ThMAR) has emerged to be very crucial in the clinical development and clinical practice of all therapies. There are five types of ThMAR that have been found to play indispensable roles in various stages of drug discovery, such as: Pharmacodynamic Biomarker essential for guaranteeing the pharmacological effects of a therapy, Safety Biomarker critical for assessing the extent or likelihood of therapy-induced toxicity, Monitoring Biomarker indispensable for guiding clinical management by serially measuring patients' status, Predictive Biomarker crucial for maximizing the clinical outcome of a therapy for specific individuals, and Surrogate Endpoint fundamental for accelerating the approval of a therapy. However, these data of ThMARs has not been comprehensively described by any of the existing databases. Herein, a database, named 'TheMarker', was therefore constructed to (a) systematically offer all five types of ThMAR used at different stages of drug development, (b) comprehensively describe ThMAR information for the largest number of drugs among available databases, (c) extensively cover the widest disease classes by not just focusing on anticancer therapies. These data in TheMarker are expected to have great implication and significant impact on drug discovery and clinical practice, and it is freely accessible without any login requirement at: https://idrblab.org/themarker.
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Affiliation(s)
- Yintao Zhang
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Ying Zhou
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Diagnosis and Treatment of Severe Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China
| | - Yuan Zhou
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xinyuan Yu
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xinyi Shen
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven 06510, USA
| | - Yanfeng Hong
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Yuxin Zhang
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Shanshan Wang
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jinsong Zhang
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Jianqing Gao
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yunqing Qiu
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Diagnosis and Treatment of Severe Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China
| | - Yuzong Chen
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, The Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
- Institute of Biomedical Health Technology and Engineering, Shenzhen Bay Laboratory, Shenzhen 518000, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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Singla RK, Wang X, Gundamaraju R, Joon S, Tsagkaris C, Behzad S, Khan J, Gautam R, Goyal R, Rakmai J, Dubey AK, Simal-Gandara J, Shen B. Natural products derived from medicinal plants and microbes might act as a game-changer in breast cancer: a comprehensive review of preclinical and clinical studies. Crit Rev Food Sci Nutr 2023; 63:11880-11924. [PMID: 35838143 DOI: 10.1080/10408398.2022.2097196] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Breast cancer (BC) is the most prevalent neoplasm among women. Genetic and environmental factors lead to BC development and on this basis, several preventive - screening and therapeutic interventions have been developed. Hormones, both in the form of endogenous hormonal signaling or hormonal contraceptives, play an important role in BC pathogenesis and progression. On top of these, breast microbiota includes both species with an immunomodulatory activity enhancing the host's response against cancer cells and species producing proinflammatory cytokines associated with BC development. Identification of novel multitargeted therapeutic agents with poly-pharmacological potential is a dire need to combat advanced and metastatic BC. A growing body of research has emphasized the potential of natural compounds derived from medicinal plants and microbial species as complementary BC treatment regimens, including dietary supplements and probiotics. In particular, extracts from plants such as Artemisia monosperma Delile, Origanum dayi Post, Urtica membranacea Poir. ex Savigny, Krameria lappacea (Dombey) Burdet & B.B. Simpson and metabolites extracted from microbes such as Deinococcus radiodurans and Streptomycetes strains as well as probiotics like Bacillus coagulans and Lactobacillus brevis MK05 have exhibited antitumor effects in the form of antiproliferative and cytotoxic activity, increase in tumors' chemosensitivity, antioxidant activity and modulation of BC - associated molecular pathways. Further, bioactive compounds like 3,3'-diindolylmethane, epigallocatechin gallate, genistein, rutin, resveratrol, lycopene, sulforaphane, silibinin, rosmarinic acid, and shikonin are of special interest for the researchers and clinicians because these natural agents have multimodal action and act via multiple ways in managing the BC and most of these agents are regularly available in our food and fruit diets. Evidence from clinical trials suggests that such products had major potential in enhancing the effectiveness of conventional antitumor agents and decreasing their side effects. We here provide a comprehensive review of the therapeutic effects and mechanistic underpinnings of medicinal plants and microbial metabolites in BC management. The future perspectives on the translation of these findings to the personalized treatment of BC are provided and discussed.
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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, Sichuan, China
- iGlobal Research and Publishing Foundation, New Delhi, India
| | - Xiaoyan Wang
- Department of Pathology, Clinical Medical College and the First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, China
| | - Rohit Gundamaraju
- ER Stress and Mucosal Immunology Lab, School of Health Sciences, College of Health and Medicine, University of Tasmania, Launceston, Tasmania, Australia
| | - Shikha Joon
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- iGlobal Research and Publishing Foundation, New Delhi, India
| | | | - Sahar Behzad
- Evidence-based Phytotherapy and Complementary Medicine Research Center, Alborz University of Medical Sciences, Karaj, Iran
- Department of Pharmacognosy, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Johra Khan
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Al Majmaah, Saudi Arabia
- Health and Basic Sciences Research Center, Majmaah University, Majmaah, Saudi Arabia
| | - Rupesh Gautam
- Department of Pharmacology, MM School of Pharmacy, MM University, Sadopur, Haryana, India
| | - Rajat Goyal
- Department of Pharmacology, MM School of Pharmacy, MM University, Sadopur, Haryana, India
| | - Jaruporn Rakmai
- Kasetsart Agricultural and Agro-Industrial Product Improvement Institute (KAPI), Kasetsart University, Bangkok, Thailand
| | | | - Jesus Simal-Gandara
- Nutrition and Bromatology Group, Department of Analytical Chemistry and Food Science, Faculty of Science, Universidade de Vigo, Ourense, Spain
| | - Bairong Shen
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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Zhang G, Zhang Z, Pei Y, Hu W, Xue Y, Ning R, Guo X, Sun Y, Zhang Q. Biological and clinical significance of radiomics features obtained from magnetic resonance imaging preceding pre-carbon ion radiotherapy in prostate cancer based on radiometabolomics. Front Endocrinol (Lausanne) 2023; 14:1272806. [PMID: 38027108 PMCID: PMC10644841 DOI: 10.3389/fendo.2023.1272806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 09/27/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction We aimed to investigate the feasibility of metabolomics to explain the underlying biological implications of radiomics features obtained from magnetic resonance imaging (MRI) preceding carbon ion radiotherapy (CIRT) in patients with prostate cancer and to further explore the clinical significance of radiomics features on the prognosis of patients, based on their biochemical recurrence (BCR) status. Methods Metabolomic results obtained using high-performance liquid chromatography coupled with tandem mass spectrometry of urine samples, combined with pre-RT radiomic features extracted from MRI images, were evaluated to investigate their biological significance. Receiver operating characteristic (ROC) curve analysis was subsequently conducted to examine the correlation between these biological implications and clinical BCR status. Statistical and metabolic pathway analyses were performed using MetaboAnalyst and R software. Results Correlation analysis revealed that methionine alteration extent was significantly related to four radiomic features (Contrast, Difference Variance, Small Dependence High Gray Level Emphasis, and Mean Absolute Deviation), which were significantly correlated with BCR status. The area under the curve (AUC) for BCR prediction of these four radiomic features ranged from 0.704 to 0.769, suggesting that the higher the value of these four radiomic features, the greater the decrease in methionine levels after CIRT and the lower the probability of BCR. Pre-CIRT MRI radiomic features were associated with CIRT-suppressed metabolites. Discussion These radiomic features can be used to predict the alteration in the amplitude of methionine after CIRT and the BCR status, which may contribute to the optimization of the CIRT strategy and deepen the understanding of PCa.
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Affiliation(s)
- Guangyuan Zhang
- Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, China
- Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
- Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China
| | - Zhenshan Zhang
- Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, China
- Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
- Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China
| | - Yulei Pei
- Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, China
- Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
- Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China
| | - Wei Hu
- Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, China
- Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
- Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China
| | - Yushan Xue
- Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, China
- Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
- Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China
| | - Renli Ning
- Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
- Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China
- Department of Research and Development, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, China
| | - Xiaomao Guo
- Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, China
- Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
- Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China
| | - Yun Sun
- Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
- Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China
- Department of Research and Development, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, China
| | - Qing Zhang
- Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, China
- Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
- Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China
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Zhang Z, Zhu H, Dang P, Wang J, Chang W, Wang X, Alghamdi N, Lu A, Zang Y, Wu W, Wang Y, Zhang Y, Cao S, Zhang C. FLUXestimator: a webserver for predicting metabolic flux and variations using transcriptomics data. Nucleic Acids Res 2023; 51:W180-W190. [PMID: 37216602 PMCID: PMC10320190 DOI: 10.1093/nar/gkad444] [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: 03/28/2023] [Revised: 04/29/2023] [Accepted: 05/11/2023] [Indexed: 05/24/2023] Open
Abstract
Quantitative assessment of single cell fluxome is critical for understanding the metabolic heterogeneity in diseases. Unfortunately, laboratory-based single cell fluxomics is currently impractical, and the current computational tools for flux estimation are not designed for single cell-level prediction. Given the well-established link between transcriptomic and metabolomic profiles, leveraging single cell transcriptomics data to predict single cell fluxome is not only feasible but also an urgent task. In this study, we present FLUXestimator, an online platform for predicting metabolic fluxome and variations using single cell or general transcriptomics data of large sample-size. The FLUXestimator webserver implements a recently developed unsupervised approach called single cell flux estimation analysis (scFEA), which uses a new neural network architecture to estimate reaction rates from transcriptomics data. To the best of our knowledge, FLUXestimator is the first web-based tool dedicated to predicting cell-/sample-wise metabolic flux and metabolite variations using transcriptomics data of human, mouse and 15 other common experimental organisms. The FLUXestimator webserver is available at http://scFLUX.org/, and stand-alone tools for local use are available at https://github.com/changwn/scFEA. Our tool provides a new avenue for studying metabolic heterogeneity in diseases and has the potential to facilitate the development of new therapeutic strategies.
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Affiliation(s)
- Zixuan Zhang
- College of Software, College of Computer Science and Technology, Jilin University, Changchun 130012, China
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Haiqi Zhu
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Department of Computer Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Pengtao Dang
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Department of Electric Computer Engineering, Purdue University, Indianapolis, IN 46202, USA
| | - Jia Wang
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Department of Computer Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Wennan Chang
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Xiao Wang
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Department of Computer Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Norah Alghamdi
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Alex Lu
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Yong Zang
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Wenzhuo Wu
- Department of Industrial Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Yijie Wang
- Department of Computer Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Yu Zhang
- College of Software, College of Computer Science and Technology, Jilin University, Changchun 130012, China
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Sha Cao
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Chi Zhang
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
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Yang B, Jiang Y, Yang J, Zhou W, Yang T, Zhang R, Xu J, Guo H. Characterization of metabolism-associated molecular patterns in prostate cancer. BMC Urol 2023; 23:104. [PMID: 37280589 DOI: 10.1186/s12894-023-01275-w] [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: 12/27/2022] [Accepted: 05/18/2023] [Indexed: 06/08/2023] Open
Abstract
BACKGROUND Metabolism is a hallmark of cancer and it involves in resistance to antitumor treatment. Therefore, the purposes of this study are to classify metabolism-related molecular pattern and to explore the molecular and tumor microenvironment characteristics for prognosis predicting in prostate cancer. METHODS The mRNA expression profiles and the corresponding clinical information for prostate cancer patients from TCGA, cBioPortal, and GEO databases. Samples were classified using unsupervised non-negative matrix factorization (NMF) clustering based on differentially expressed metabolism-related genes (MAGs). The characteristics of disease-free survival (DFS), clinicopathological characteristics, pathways, TME, immune cell infiltration, response to immunotherapy, and sensitivity to chemotherapy between subclusters were explored. A prognostic signature was constructed by LASSO cox regression analysis based on differentially expressed MAGs and followed by the development for prognostic prediction. RESULTS A total of 76 MAGs between prostate cancer samples and non-tumorous samples were found, then 489 patients were divided into two metabolism-related subclusters for prostate cancer. The significant differences in clinical characteristics (age, T/N stage, Gleason) and DFS between two subclusters. Cluster 1 was associated with cell cycle and metabolism-related pathways, and epithelial-mesenchymal transition (EMT), etc., involved in cluster 2. Moreover, lower ESTIMATE/immune/stromal scores, lower expression of HLAs and immune checkpoint-related genes, and lower half-maximal inhibitory concentration (IC50) values in cluster 1 compared with cluster 2. The 10 MAG signature was identified and constructed a risk model for DFS predicting. The patients with high-risk scores showed poorer DFS. The area under the curve (AUC) values for 1-, 3-, 5-year DFS were 0.744, 0.731, 0.735 in TCGA-PRAD dataset, and 0.668, 0.712, 0.809 in GSE70768 dataset, 0.763, 0.802, 0.772 in GSE70769 dataset. Besides, risk score and Gleason score were identified as independent factors for DFS predicting, and the AUC values of risk score and Gleason score were respectively 0.743 and 0.738. The nomogram showed a favorable performance in DFS predicting. CONCLUSION Our data identified two metabolism-related molecular subclusters for prostate cancer that were distinctly characterized in prostate cancer. Metabolism-related risk profiles were also constructed for prognostic prediction.
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Affiliation(s)
- Bowei Yang
- Department of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yongming Jiang
- Department of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Jun Yang
- Department of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Wenbo Zhou
- Department of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Tongxin Yang
- Department of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Rongchang Zhang
- Department of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Jinming Xu
- Department of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Haixiang Guo
- Department of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China.
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Yang S, Huan R, Yue J, Guo J, Deng M, Wang L, Peng S, Lin X, Liu L, Wang J, Han G, Zha Y, Liu J, Zhang J, Tan Y. Multiomics integration reveals the effect of Orexin A on glioblastoma. Front Pharmacol 2023; 14:1096159. [PMID: 36744263 PMCID: PMC9894894 DOI: 10.3389/fphar.2023.1096159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 01/10/2023] [Indexed: 01/22/2023] Open
Abstract
Objectives: This study involved a multi-omics analysis of glioblastoma (GBM) samples to elaborate the potential mechanism of drug treatment. Methods: The GBM cells treated with or without orexin A were acquired from sequencing analysis. Differentially expressed genes/proteins/metabolites (DEGs/ DEPs/ DEMs) were screened. Next, combination analyses were conducted to investigate the common pathways and correlations between the two groups. Lastly, transcriptome-proteome-metabolome association analysis was carried out to determine the common pathways, and the genes in these pathways were analyzed through Kaplan-Meier (K-M) survival analysis in public databases. Cell and animal experiments were performed to investigate the anti-glioma activity of orexin A. Results: A total of 1,527 DEGs, 52 DEPs, and 153 DEMs were found. Moreover, the combination analyses revealed that 6, 4, and 1 common pathways were present in the transcriptome-proteome, proteome-metabolome, and transcriptome-metabolome, respectively. Certain correlations were observed between the two data sets. Finally, 11 common pathways were discovered in association analysis, and 138 common genes were screened out in these common pathways. Six genes showed significant differences in terms of survival in both TCGA and CGGA. In addition, orexin A inhibited the proliferation, migration, and invasion of glioma in vitro and in vivo. Conclusion: Eleven common KEGG pathways with six common genes were found among different omics participations, revealing the underlying mechanisms in different omics and providing theoretical basis and reference for multi-omics research on drug treatment.
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Affiliation(s)
- Sha Yang
- Guizhou University Medical College, Guiyang, Guizhou Province, China
| | - Renzheng Huan
- Department of Neurosurgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jianhe Yue
- Department of Neurosurgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jin Guo
- Guizhou University Medical College, Guiyang, Guizhou Province, China
| | - Mei Deng
- Department of Neurosurgery, Guizhou Provincial People’s Hospital, Guiyang, China
| | - Liya Wang
- Department of Neurosurgery, Guizhou Provincial People’s Hospital, Guiyang, China
| | - Shuo Peng
- Department of Neurosurgery, Guizhou Provincial People’s Hospital, Guiyang, China
| | - Xin Lin
- Department of Nephrology, Guizhou Provincial People’s Hospital, Guiyang, China
| | - Lin Liu
- Department of Respiratory and Critical Care Medicine, Guizhou Provincial People’s Hospital, Guiyang, China
| | - Jia Wang
- Department of Neurosurgery, Chongqing Emergency Medical Center, Chongqing, China
| | - Guoqiang Han
- Department of Neurosurgery, Guizhou Provincial People’s Hospital, Guiyang, China
| | - Yan Zha
- Department of Nephrology, Guizhou Provincial People’s Hospital, Guiyang, China
| | - Jian Liu
- Guizhou University Medical College, Guiyang, Guizhou Province, China,Department of Neurosurgery, Guizhou Provincial People’s Hospital, Guiyang, China,*Correspondence: Jian Liu, ; Jiqin Zhang, ; Ying Tan,
| | - Jiqin Zhang
- Department of Anesthesiology, Guizhou Provincial People’s Hospital, Guiyang, China,*Correspondence: Jian Liu, ; Jiqin Zhang, ; Ying Tan,
| | - Ying Tan
- Department of Neurosurgery, Guizhou Provincial People’s Hospital, Guiyang, China,*Correspondence: Jian Liu, ; Jiqin Zhang, ; Ying Tan,
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Metabolic Phenotyping in Prostate Cancer Using Multi-Omics Approaches. Cancers (Basel) 2022; 14:cancers14030596. [PMID: 35158864 PMCID: PMC8833769 DOI: 10.3390/cancers14030596] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 01/14/2022] [Accepted: 01/20/2022] [Indexed: 12/17/2022] Open
Abstract
Prostate cancer (PCa), one of the most frequently diagnosed cancers among men worldwide, is characterized by a diverse biological heterogeneity. It is well known that PCa cells rewire their cellular metabolism to meet the higher demands required for survival, proliferation, and invasion. In this context, a deeper understanding of metabolic reprogramming, an emerging hallmark of cancer, could provide novel opportunities for cancer diagnosis, prognosis, and treatment. In this setting, multi-omics data integration approaches, including genomics, epigenomics, transcriptomics, proteomics, lipidomics, and metabolomics, could offer unprecedented opportunities for uncovering the molecular changes underlying metabolic rewiring in complex diseases, such as PCa. Recent studies, focused on the integrated analysis of multi-omics data derived from PCa patients, have in fact revealed new insights into specific metabolic reprogramming events and vulnerabilities that have the potential to better guide therapy and improve outcomes for patients. This review aims to provide an up-to-date summary of multi-omics studies focused on the characterization of the metabolomic phenotype of PCa, as well as an in-depth analysis of the correlation between changes identified in the multi-omics studies and the metabolic profile of PCa tumors.
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