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Jia F, Zhang B, Yu W, Chen Z, Xu W, Zhao W, Wang Z. Exploring the cuproptosis-related molecular clusters in the peripheral blood of patients with amyotrophic lateral sclerosis. Comput Biol Med 2024; 168:107776. [PMID: 38056214 DOI: 10.1016/j.compbiomed.2023.107776] [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: 07/18/2023] [Revised: 11/08/2023] [Accepted: 11/27/2023] [Indexed: 12/08/2023]
Abstract
BACKGROUND Amyotrophic lateral sclerosis (ALS) is a progressive and lethal neurodegenerative disease. Several studies have suggested the involvement of cuproptosis in its pathogenesis. In this research, we intend to explore the cuproptosis-related molecular clusters in ALS and develop a novel cuproptosis-related genes prediction model. METHODS The peripheral blood gene expression data was downloaded from the Gene Expression Omnibus (GEO) online database. Based on the GSE112681 dataset, we investigated the critical cuproptosis-related genes (CuRGs) and pathological clustering of ALS. The immune microenvironment features of the peripheral blood in ALS patients were also examined using the CIBERSORT algorithm. Cluster-specific hub genes were determined by the WGCNA. The most accurate prediction model was selected by comparing the performance of four machine learning techniques. ROC curves and two independent datasets were applied to validate the prediction accuracy. The available compounds targeting these hub genes were filtered to investigate their efficacy in treating ALS. RESULTS We successfully determined four critical cuproptosis-related genes and two pathological clusters with various immune profiles and biological characteristics in ALS. Functional analysis showed that genes in Cluster1 were primarily enriched in pathways closely associated with immunity. The Support Vector Machine (SVM) model exhibited the best discrimination properties with a large area under the curve (AUC = 0.862). Five hub prediction genes (BAP1, SMG1, BCLAF1, DHX15, EIF4G2) were selected to establish a nomogram model, suggesting significant risk prediction potential for ALS. The accuracy of this model in predicting ALS incidence was also demonstrated through calibration curves, nomograms, and decision curve analysis. Finally, three drugs targeting BAP1 were determined through drug-gene interactions. CONCLUSION This study elucidated the complex associations between cuproptosis and ALS and constructed a satisfactory predictive model to explore the pathological characteristics of different clusters in ALS patients.
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Affiliation(s)
- Fang Jia
- Department of Neurosurgery, Xiamen Key Laboratory of Brain Center, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Bingchang Zhang
- Department of Neurosurgery, Xiamen Key Laboratory of Brain Center, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Weijie Yu
- The School of Clinical Medicine, Fujian Medical University, Fuzhou, China
| | - Zheng Chen
- Department of Neurosurgery, Xiamen Key Laboratory of Brain Center, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Wenbin Xu
- Department of Neurosurgery, Xiamen Key Laboratory of Brain Center, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Wenpeng Zhao
- Department of Neurosurgery, Xiamen Key Laboratory of Brain Center, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Zhanxiang Wang
- Department of Neurosurgery, Xiamen Key Laboratory of Brain Center, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
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Wu L, Zhong Y, Wu D, Xu P, Ruan X, Yan J, Liu J, Li X. Immunomodulatory Factor TIM3 of Cytolytic Active Genes Affected the Survival and Prognosis of Lung Adenocarcinoma Patients by Multi-Omics Analysis. Biomedicines 2022; 10:biomedicines10092248. [PMID: 36140350 PMCID: PMC9496572 DOI: 10.3390/biomedicines10092248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 08/31/2022] [Accepted: 09/06/2022] [Indexed: 11/16/2022] Open
Abstract
[Objective] Using multi-omics research methods to explore cytolytic activity-related genes through the immunoregulatory factors HAVCR2 (TIM3) affecting the survival and prognosis of lung adenocarcinoma. [Methods] We combined Cox single factor regression and lasso regression feature selection algorithm to screen out the key genes of cytolytic activity in lung adenocarcinoma, and applied multi-omics research to explore the clinical predictive value of the model, including onset risk, independent prognosis, clinical relevance, signal transduction pathways, drug sensitivity, and the correlation of immune regulatory factors, etc. TCGA data are used as the experimental group, and GEO data is used as the external data control group to verify the stability of the model. The survival curve was generated by the Kaplan–Meier method and compared by log-rank, and the Cox proportional hazard model was used for multivariate analysis. In this study, 10 fresh tissue samples of lung adenocarcinoma were collected for cellular immunohistochemical experiments to analyze the expression of immunoregulatory factors in cancer tissues, and the key immunoregulatory factors were verified and screened out. [Results] A total of 450 genes related to cytolytic activity were differentially expressed, of which 273 genes were up-regulated and 177 genes were down-regulated. A total of 91 key genes related to cytolytic activity related to the prognosis of lung adenocarcinoma were screened through Cox single factor regression. The ROC curve results showed that the AUC values of 1, 3, and 5 years in the training set and test set were all greater than 0.7, indicating that the model has a valid verification. The level of risk score is significantly related to the sensitivity of patients to AKT inhibitor VIII, Lenalidomide, and Tipifarnib. In addition, our study also found that receptor and MHC genes related to immunomodulatory, and chemokines, including HAVCR2, are more highly expressed in the low-risk group. [Conclusions] HAVCR2 (TIM3) immunoregulatory factors affect the expression of key genes that affect cytolytic activity in lung adenocarcinoma cells, and to some extent indirectly affect the survival and prognosis of patients with lung adenocarcinoma.
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Affiliation(s)
- Liusheng Wu
- Peking University Shenzhen Hospital, Clinical College of Anhui Medical University, Tsinghua university, Shenzhen 518036, China
- Thoracic Surgery, Peking University Shenzhen Hospital, Shenzhen 518036, China
| | - Yanfeng Zhong
- Thoracic Surgery, Peking University Shenzhen Hospital, Shenzhen 518036, China
| | - Dingwang Wu
- Thoracic Surgery, Peking University Shenzhen Hospital, Shenzhen 518036, China
| | - Pengcheng Xu
- Thoracic Surgery, Peking University Shenzhen Hospital, Shenzhen 518036, China
| | - Xin Ruan
- Thoracic Surgery, Peking University Shenzhen Hospital, Shenzhen 518036, China
| | - Jun Yan
- Peking University Shenzhen Hospital, Clinical College of Anhui Medical University, Tsinghua university, Shenzhen 518036, China
- Correspondence: (J.Y.); (J.L.); (X.L.)
| | - Jixian Liu
- Thoracic Surgery, Peking University Shenzhen Hospital, Shenzhen 518036, China
- Correspondence: (J.Y.); (J.L.); (X.L.)
| | - Xiaoqiang Li
- Thoracic Surgery, Peking University Shenzhen Hospital, Shenzhen 518036, China
- Correspondence: (J.Y.); (J.L.); (X.L.)
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Semenov O, Daks A, Fedorova O, Shuvalov O, Barlev NA. Opposing Roles of Wild-type and Mutant p53 in the Process of Epithelial to Mesenchymal Transition. Front Mol Biosci 2022; 9:928399. [PMID: 35813818 PMCID: PMC9261265 DOI: 10.3389/fmolb.2022.928399] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 06/01/2022] [Indexed: 12/05/2022] Open
Abstract
The central role of an aberrantly activated EMT program in defining the critical features of aggressive carcinomas is well documented and includes cell plasticity, metastatic dissemination, drug resistance, and cancer stem cell-like phenotypes. The p53 tumor suppressor is critical for leashing off all the features mentioned above. On the molecular level, the suppression of these effects is exerted by p53 via regulation of its target genes, whose products are involved in cell cycle, apoptosis, autophagy, DNA repair, and interactions with immune cells. Importantly, a set of specific mutations in the TP53 gene (named Gain-of-Function mutations) converts this tumor suppressor into an oncogene. In this review, we attempted to contrast different regulatory roles of wild-type and mutant p53 in the multi-faceted process of EMT.
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Affiliation(s)
- Oleg Semenov
- Regulation of Gene Expression Laboratory, Institute of Cytology RAS, Saint-Petersburg, Russia
| | - Alexandra Daks
- Regulation of Gene Expression Laboratory, Institute of Cytology RAS, Saint-Petersburg, Russia
| | - Olga Fedorova
- Regulation of Gene Expression Laboratory, Institute of Cytology RAS, Saint-Petersburg, Russia
| | - Oleg Shuvalov
- Regulation of Gene Expression Laboratory, Institute of Cytology RAS, Saint-Petersburg, Russia
| | - Nickolai A. Barlev
- Regulation of Gene Expression Laboratory, Institute of Cytology RAS, Saint-Petersburg, Russia
- Laboratory of Intracellular Signalling, Moscow Institute of Physics and Technology, Dolgoprudny, Russia
- The Group of Targeted Delivery Mechanisms of Nanosystems, Institute of Biomedical Chemistry, Moscow, Russia
- *Correspondence: Nickolai A. Barlev,
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Elemam NM, Hammoudeh S, Salameh L, Mahboub B, Alsafar H, Talaat IM, Habib P, Siddiqui M, Hassan KO, Al-Assaf OY, Taneera J, Sulaiman N, Hamoudi R, Maghazachi AA, Hamid Q, Saber-Ayad M. Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling. Front Immunol 2022; 13:865845. [PMID: 35529862 PMCID: PMC9067542 DOI: 10.3389/fimmu.2022.865845] [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: 01/30/2022] [Accepted: 03/25/2022] [Indexed: 12/15/2022] Open
Abstract
Since its emergence as a pandemic in March 2020, coronavirus disease (COVID-19) outcome has been explored via several predictive models, using specific clinical or biochemical parameters. In the current study, we developed an integrative non-linear predictive model of COVID-19 outcome, using clinical, biochemical, immunological, and radiological data of patients with different disease severities. Initially, the immunological signature of the disease was investigated through transcriptomics analysis of nasopharyngeal swab samples of patients with different COVID-19 severity versus control subjects (exploratory cohort, n=61), identifying significant differential expression of several cytokines. Accordingly, 24 cytokines were validated using a multiplex assay in the serum of COVID-19 patients and control subjects (validation cohort, n=77). Predictors of severity were Interleukin (IL)-10, Programmed Death-Ligand-1 (PDL-1), Tumor necrosis factors-α, absolute neutrophil count, C-reactive protein, lactate dehydrogenase, blood urea nitrogen, and ferritin; with high predictive efficacy (AUC=0.93 and 0.98 using ROC analysis of the predictive capacity of cytokines and biochemical markers, respectively). Increased IL-6 and granzyme B were found to predict liver injury in COVID-19 patients, whereas interferon-gamma (IFN-γ), IL-1 receptor-a (IL-1Ra) and PD-L1 were predictors of remarkable radiological findings. The model revealed consistent elevation of IL-15 and IL-10 in severe cases. Combining basic biochemical and radiological investigations with a limited number of curated cytokines will likely attain accurate predictive value in COVID-19. The model-derived cytokines highlight critical pathways in the pathophysiology of the COVID-19 with insight towards potential therapeutic targets. Our modeling methodology can be implemented using new datasets to identify key players and predict outcomes in new variants of COVID-19.
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Affiliation(s)
- Noha M Elemam
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates.,Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates
| | - Sarah Hammoudeh
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates.,Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates
| | - Laila Salameh
- Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates.,Dubai Health Authority, Rashid Hospital, Dubai, United Arab Emirates
| | - Bassam Mahboub
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates.,Dubai Health Authority, Rashid Hospital, Dubai, United Arab Emirates
| | - Habiba Alsafar
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.,Department of Biomedical Engineering, College of Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.,Department of Genetics and Molecular Biology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.,Emirates Bio-Research Centre, Ministry of Interior, Abu Dhabi, United Arab Emirates
| | - Iman M Talaat
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates.,Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates.,Pathology Department, Faculty of Medicine, Alexandria University, Alexandria, Egypt
| | - Peter Habib
- School of Information Technology and Computer Science (ITCS), Nile University, Giza, Egypt
| | - Mehmood Siddiqui
- Dubai Health Authority, Rashid Hospital, Dubai, United Arab Emirates
| | | | | | - Jalal Taneera
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates.,Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates
| | - Nabil Sulaiman
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates.,Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates
| | - Rifat Hamoudi
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates.,Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates.,Division of Surgery and Interventional Science, University College London, London, United Kingdom
| | - Azzam A Maghazachi
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates.,Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates
| | - Qutayba Hamid
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates.,Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates.,Meakins-Christie Laboratories, Research Institute of the McGill University Health Center, Montreal, QC, Canada
| | - Maha Saber-Ayad
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates.,Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates.,College of Medicine, Cairo University, Giza, Egypt
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