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Jin W, Zhang J, Chen X, Yin S, Yu H, Gao F, Yao D. Unraveling the complexity of histone-arginine methyltransferase CARM1 in cancer: From underlying mechanisms to targeted therapeutics. Biochim Biophys Acta Rev Cancer 2023; 1878:188916. [PMID: 37196782 DOI: 10.1016/j.bbcan.2023.188916] [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: 03/09/2023] [Revised: 04/28/2023] [Accepted: 05/12/2023] [Indexed: 05/19/2023]
Abstract
Coactivator-associated arginine methyltransferase 1 (CARM1), a type I protein arginine methyltransferase (PRMT), has been widely reported to catalyze arginine methylation of histone and non-histone substrates, which is closely associated with the occurrence and progression of cancer. Recently, accumulating studies have demonstrated the oncogenic role of CARM1 in many types of human cancers. More importantly, CARM1 has been emerging as an attractive therapeutic target for discovery of new candidate anti-tumor drugs. Therefore, in this review, we summarize the molecular structure of CARM1 and its key regulatory pathways, as well as further discuss the rapid progress in better understanding of the oncogenic functions of CARM1. Moreover, we further demonstrate several representative targeted CARM1 inhibitors, especially focusing on demonstrating their designing strategies and potential therapeutic applications. Together, these inspiring findings would shed new light on elucidating the underlying mechanisms of CARM1 and provide a clue on discovery of more potent and selective CARM1 inhibitors for the future targeted cancer therapy.
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Affiliation(s)
- Wenke Jin
- Sichuan Engineering Research Center for Biomimetic Synthesis of Natural Drugs, School of Life Science and Engineering, Southwest Jiaotong University, Chengdu 610031, China; School of Pharmaceutical Sciences, Shenzhen Technology University, Shenzhen 518118, China; Key Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, and State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Jin Zhang
- School of Pharmacy, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong 518055, China
| | - Xiya Chen
- School of Pharmaceutical Sciences, Shenzhen Technology University, Shenzhen 518118, China; School of Pharmacy, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong 518055, China
| | - Siwen Yin
- School of Nursing, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Haiyang Yu
- Key Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, and State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China.
| | - Feng Gao
- Sichuan Engineering Research Center for Biomimetic Synthesis of Natural Drugs, School of Life Science and Engineering, Southwest Jiaotong University, Chengdu 610031, China.
| | - Dahong Yao
- School of Pharmaceutical Sciences, Shenzhen Technology University, Shenzhen 518118, China.
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Yang F, Zhou LQ, Yang HW, Wang YJ. Nine-gene signature and nomogram for predicting survival in patients with head and neck squamous cell carcinoma. Front Genet 2022; 13:927614. [PMID: 36092911 PMCID: PMC9449318 DOI: 10.3389/fgene.2022.927614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 07/25/2022] [Indexed: 12/24/2022] Open
Abstract
Background: Head and neck squamous cell carcinomas (HNSCCs) are derived from the mucosal linings of the upper aerodigestive tract, salivary glands, thyroid, oropharynx, larynx, and hypopharynx. The present study aimed to identify the novel genes and pathways underlying HNSCC. Despite the advances in HNSCC research, diagnosis, and treatment, its incidence continues to rise, and the mortality of advanced HNSCC is expected to increase by 50%. Therefore, there is an urgent need for effective biomarkers to predict HNSCC patients’ prognosis and provide guidance to the personalized treatment.Methods: Both HNSCC clinical and gene expression data were abstracted from The Cancer Genome Atlas (TCGA) database. Intersecting analysis was adopted between the gene expression matrix of HNSCC patients from TCGA database to extract TME-related genes. Differential gene expression analysis between HNSCC tissue samples and normal tissue samples was performed by R software. Then, HNSCC patients were categorized into clusters 1 and 2 via NMF. Next, TME-related prognosis genes (p < 0.05) were analyzed by univariate Cox regression analysis, LASSO Cox regression analysis, and multivariate Cox regression analysis. Finally, nine genes were selected to construct a prognostic risk model and a prognostic gene signature. We also established a nomogram using relevant clinical parameters and a risk score. The Kaplan–Meier curve, survival analysis, time-dependent receiver operating characteristic (ROC) analysis, decision curve analysis (DCA), and the concordance index (C-index) were carried out to assess the accuracy of the prognostic risk model and nomogram. Potential molecular mechanisms were revealed by gene set enrichment analysis (GSEA). Additionally, gene correlation analysis and immune cell correlation analysis were conducted for further enriching our results.Results: A novel HNSCC prognostic model was established based on the nine genes (GTSE1, LRRN4CL, CRYAB, SHOX2, ASNS, KRT23, ANGPT2, HOXA9, and CARD11). The value of area under the ROC curves (AUCs) (0.769, 0.841, and 0.816) in TCGA whole set showed that the model effectively predicted the 1-, 3-, and 5-year overall survival (OS). Results of the Cox regression assessment confirmed the nine-gene signature as a reliable independent prognostic factor in HNSCC patients. The prognostic nomogram developed using multivariate Cox regression analysis showed a superior C-index over other clinical signatures. Also, the calibration curve had a high level of concordance between estimated OS and the observed OS. This showed that its clinical net can precisely estimate the one-, three-, and five-year OS in HNSCC patients. The gene set enrichment analysis (GSEA) to some extent revealed the immune- and tumor-linked cascades.Conclusion: In conclusion, the TME-related nine-gene signature and nomogram can effectively improve the estimation of prognosis in patients with HNSCC.
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