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Szadai L, Bartha A, Parada IP, Lakatos A, Pál D, Lengyel AS, de Almeida NP, Jánosi ÁJ, Nogueira F, Szeitz B, Doma V, Woldmar N, Guedes J, Ujfaludi Z, Pahi ZG, Pankotai T, Kim Y, Győrffy B, Baldetorp B, Welinder C, Szasz AM, Betancourt L, Gil J, Appelqvist R, Kwon HJ, Kárpáti S, Kuras M, Murillo JR, Németh IB, Malm J, Fenyö D, Pawłowski K, Horvatovich P, Wieslander E, Kemény LV, Domont G, MarkoVarga G, Sanchez A. Predicting immune checkpoint therapy response in three independent metastatic melanoma cohorts. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.01.592032. [PMID: 38746333 PMCID: PMC11092593 DOI: 10.1101/2024.05.01.592032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
While Immune checkpoint inhibition (ICI) therapy shows significant efficacy in metastatic melanoma, only about 50% respond, lacking reliable predictive methods. We introduce a panel of six proteins aimed at predicting response to ICI therapy. Evaluating previously reported proteins in two untreated melanoma cohorts, we used a published predictive model (EaSIeR score) to identify potential proteins distinguishing responders and non-responders. Six proteins initially identified in the ICI cohort correlated with predicted response in the untreated cohort. Additionally, three proteins correlated with patient survival, both at the protein, and at the transcript levels, in an independent immunotherapy treated cohort. Our study identifies predictive biomarkers across three melanoma cohorts, suggesting their use in therapeutic decision-making. Abstract Figure
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Chen W, Geng D, Chen J, Han X, Xie Q, Guo G, Chen X, Zhang W, Tang S, Zhong X. Roles and mechanisms of aberrant alternative splicing in melanoma - implications for targeted therapy and immunotherapy resistance. Cancer Cell Int 2024; 24:101. [PMID: 38462618 PMCID: PMC10926661 DOI: 10.1186/s12935-024-03280-x] [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: 10/29/2023] [Accepted: 02/22/2024] [Indexed: 03/12/2024] Open
Abstract
BACKGROUND Despite advances in therapeutic strategies, resistance to immunotherapy and the off-target effects of targeted therapy have significantly weakened the benefits for patients with melanoma. MAIN BODY Alternative splicing plays a crucial role in transcriptional reprogramming during melanoma development. In particular, aberrant alternative splicing is involved in the efficacy of immunotherapy, targeted therapy, and melanoma metastasis. Abnormal expression of splicing factors and variants may serve as biomarkers or therapeutic targets for the diagnosis and prognosis of melanoma. Therefore, comprehensively integrating their roles and related mechanisms is essential. This review provides the first detailed summary of the splicing process in melanoma and the changes occurring in this pathway. CONCLUSION The focus of this review is to provide strategies for developing novel diagnostic biomarkers and summarize their potential to alter resistance to targeted therapies and immunotherapy.
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Affiliation(s)
- Wanxian Chen
- Department of Plastic and Burns Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, 515000, P. R. China
- Plastic Surgery Research Institute, Ear Deformities Treatment Center and Cleft Lip and Palate Treatment Center, Shantou University Medical College, Shantou, China
| | - Deyi Geng
- Department of Plastic and Burns Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, 515000, P. R. China
- Plastic Surgery Research Institute, Ear Deformities Treatment Center and Cleft Lip and Palate Treatment Center, Shantou University Medical College, Shantou, China
| | - Jiasheng Chen
- Department of Plastic and Burns Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, 515000, P. R. China
- Plastic Surgery Research Institute, Ear Deformities Treatment Center and Cleft Lip and Palate Treatment Center, Shantou University Medical College, Shantou, China
| | - Xiaosha Han
- Department of Plastic and Burns Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, 515000, P. R. China
- Plastic Surgery Research Institute, Ear Deformities Treatment Center and Cleft Lip and Palate Treatment Center, Shantou University Medical College, Shantou, China
| | - Qihu Xie
- Department of Plastic and Burns Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, 515000, P. R. China
- Plastic Surgery Research Institute, Ear Deformities Treatment Center and Cleft Lip and Palate Treatment Center, Shantou University Medical College, Shantou, China
| | - Genghong Guo
- Department of Plastic and Burns Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, 515000, P. R. China
- Plastic Surgery Research Institute, Ear Deformities Treatment Center and Cleft Lip and Palate Treatment Center, Shantou University Medical College, Shantou, China
| | - Xuefen Chen
- Department of Plastic and Burns Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, 515000, P. R. China
- Plastic Surgery Research Institute, Ear Deformities Treatment Center and Cleft Lip and Palate Treatment Center, Shantou University Medical College, Shantou, China
| | - Wancong Zhang
- Department of Plastic and Burns Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, 515000, P. R. China
- Plastic Surgery Research Institute, Ear Deformities Treatment Center and Cleft Lip and Palate Treatment Center, Shantou University Medical College, Shantou, China
| | - Shijie Tang
- Department of Plastic and Burns Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, 515000, P. R. China
- Plastic Surgery Research Institute, Ear Deformities Treatment Center and Cleft Lip and Palate Treatment Center, Shantou University Medical College, Shantou, China
| | - Xiaoping Zhong
- Department of Plastic and Burns Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, 515000, P. R. China.
- Plastic Surgery Research Institute, Ear Deformities Treatment Center and Cleft Lip and Palate Treatment Center, Shantou University Medical College, Shantou, China.
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Xu M, Li S. Nano-drug delivery system targeting tumor microenvironment: A prospective strategy for melanoma treatment. Cancer Lett 2023; 574:216397. [PMID: 37730105 DOI: 10.1016/j.canlet.2023.216397] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 08/30/2023] [Accepted: 09/11/2023] [Indexed: 09/22/2023]
Abstract
Melanoma, the most aggressive form of cutaneous malignancy arising from melanocytes, is frequently characterized by metastasis. Despite considerable progress in melanoma therapies, patients with advanced-stage disease often have a poor prognosis due to the limited efficacy, off-target effects, and toxicity associated with conventional drugs. Nanotechnology has emerged as a promising approach to address these challenges with nanoparticles capable of delivering therapeutic agents specifically to the tumor microenvironment (TME). However, the clinical approval of nanomedicines for melanoma treatment remains limited, necessitating further research to develop nanoparticles with improved biocompatibility and precise targeting capabilities. This comprehensive review provides an overview of the current research on nano-drug delivery systems for melanoma treatment, focusing on liposomes, polymeric nanoparticles, and inorganic nanoparticles. It discusses the potential of these nanoparticles for targeted drug delivery, as well as their ability to enhance the efficacy of conventional drugs while minimizing toxicity. Furthermore, this review emphasizes the significance of interdisciplinary collaboration between researchers from various fields to advance the development of nanomedicines. Overall, this review serves as a valuable resource for researchers and clinicians interested in the potential of nano-drug delivery systems for melanoma treatment and offers insights into future directions for research in this field.
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Affiliation(s)
- Mengdan Xu
- Department of Hematology and Breast Cancer, Cancer Hospital of Dalian University of Technology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang 110042, China
| | - Shenglong Li
- Second Ward of Bone and Soft Tissue Tumor Surgery, Cancer Hospital of Dalian University of Technology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang 110042, China; The Liaoning Provincial Key Laboratory of Interdisciplinary Research on Gastrointestinal Tumor Combining Medicine with Engineering, China.
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Jantsch MH, Doleski PH, Viana AR, da Silva JLG, Passos DF, Cabral FL, Manzoni AG, Ebone RDS, Soares ABU, de Andrade CM, Schetinger MRC, Leal DBR. Effects of clopidogrel bisulfate on B16-F10 cells and tumor development in a murine model of melanoma. Biochem Cell Biol 2023; 101:443-455. [PMID: 37163764 DOI: 10.1139/bcb-2022-0249] [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] [Indexed: 05/12/2023] Open
Abstract
Metastatic melanoma is a very aggressive skin cancer. Platelets are constituents of the tumor microenvironment and, when activated, contribute to cancer progression, especially metastasis and inflammation. P2Y12 is an adenosine diphosphate receptor that triggers platelet activation. Inhibition of P2Y12 by clopidogrel bisulfate (CB) decreases platelet activation, which is also controlled by the extracellular concentration and the metabolism of purines by purinergic enzymes. We evaluated the effects of CB on the viability and proliferation of cultured B16-F10 cells. We also used a metastatic melanoma model with C57BL-6 mice to evaluate cancer development and purine metabolism modulation in platelets. B16-F10 cells were administered intraperitoneally to the mice. Two days later, the animals underwent a 12-day treatment with CB (30 mg/kg by gavage). We have found that CB reduced cell viability and proliferation in B16-F10 culture in 72 h at concentrations above 30 µm. In vivo, CB decreased tumor nodule counts and lactate dehydrogenase levels and increased platelet purine metabolism. Our results showed that CB has significant effects on melanoma progression.
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Affiliation(s)
- Matheus Henrique Jantsch
- Laboratório de Imunobiologia Experimental e Aplicada (LABIBIO), Departamento de Microbiologia e Parasitologia, Centro de Ciências da Saúde, Universidade Federal de Santa Maria, Santa Maria, RS, Brazil
- Programa de Pós-graduação em Ciências Biológicas: Bioquímica Toxicológica, Centro de Ciências Naturais e Exatas, Universidade Federal de Santa Maria, Santa Maria, RS, Brazil
- Instituto Federal Farroupilha, Campus Santo Ângelo, Santo Ângelo, RS, Brazil
| | - Pedro Henrique Doleski
- Laboratório de Imunobiologia Experimental e Aplicada (LABIBIO), Departamento de Microbiologia e Parasitologia, Centro de Ciências da Saúde, Universidade Federal de Santa Maria, Santa Maria, RS, Brazil
- Programa de Pós-graduação em Ciências Farmacêuticas, Centro de Ciências da Saúde, Universidade Federal de Santa Maria, Santa Maria, RS, Brazil
| | - Altevir Rossato Viana
- Programa de Pós-graduação em Nanociências; Laboratório de Biociências. Universidade Franciscana, Santa Maria, RS, Brazil
| | - Jean Lucas Gutknecht da Silva
- Programa de Pós-graduação em Ciências Biológicas: Bioquímica Toxicológica, Centro de Ciências Naturais e Exatas, Universidade Federal de Santa Maria, Santa Maria, RS, Brazil
| | - Daniela Ferreira Passos
- Laboratório de Imunobiologia Experimental e Aplicada (LABIBIO), Departamento de Microbiologia e Parasitologia, Centro de Ciências da Saúde, Universidade Federal de Santa Maria, Santa Maria, RS, Brazil
- Programa de Pós-graduação em Ciências Biológicas: Bioquímica Toxicológica, Centro de Ciências Naturais e Exatas, Universidade Federal de Santa Maria, Santa Maria, RS, Brazil
| | - Fernanda Licker Cabral
- Laboratório de Imunobiologia Experimental e Aplicada (LABIBIO), Departamento de Microbiologia e Parasitologia, Centro de Ciências da Saúde, Universidade Federal de Santa Maria, Santa Maria, RS, Brazil
- Programa de Pós-graduação em Ciências Farmacêuticas, Centro de Ciências da Saúde, Universidade Federal de Santa Maria, Santa Maria, RS, Brazil
| | - Alessandra Guedes Manzoni
- Programa de Pós-graduação em Ciências Biológicas: Bioquímica Toxicológica, Centro de Ciências Naturais e Exatas, Universidade Federal de Santa Maria, Santa Maria, RS, Brazil
| | - Renan da Silva Ebone
- Laboratório de Imunobiologia Experimental e Aplicada (LABIBIO), Departamento de Microbiologia e Parasitologia, Centro de Ciências da Saúde, Universidade Federal de Santa Maria, Santa Maria, RS, Brazil
- Programa de Pós-graduação em Ciências Biológicas: Bioquímica Toxicológica, Centro de Ciências Naturais e Exatas, Universidade Federal de Santa Maria, Santa Maria, RS, Brazil
| | | | - Cínthia Melazzo de Andrade
- Laboratório de Imunobiologia Experimental e Aplicada (LABIBIO), Departamento de Microbiologia e Parasitologia, Centro de Ciências da Saúde, Universidade Federal de Santa Maria, Santa Maria, RS, Brazil
- Hospital Veterinário, Universidade Federal de Santa Maria, Santa Maria, RS, Brazil
| | - Maria Rosa Chitolina Schetinger
- Programa de Pós-graduação em Ciências Biológicas: Bioquímica Toxicológica, Centro de Ciências Naturais e Exatas, Universidade Federal de Santa Maria, Santa Maria, RS, Brazil
| | - Daniela Bitencourt Rosa Leal
- Laboratório de Imunobiologia Experimental e Aplicada (LABIBIO), Departamento de Microbiologia e Parasitologia, Centro de Ciências da Saúde, Universidade Federal de Santa Maria, Santa Maria, RS, Brazil
- Programa de Pós-graduação em Ciências Biológicas: Bioquímica Toxicológica, Centro de Ciências Naturais e Exatas, Universidade Federal de Santa Maria, Santa Maria, RS, Brazil
- Programa de Pós-graduação em Ciências Farmacêuticas, Centro de Ciências da Saúde, Universidade Federal de Santa Maria, Santa Maria, RS, Brazil
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Xu P, Zhang B. Multiscale network modeling reveals the gene regulatory landscape driving cancer prognosis in 32 cancer types. Genome Res 2023; 33:1806-1817. [PMID: 37907329 PMCID: PMC10691533 DOI: 10.1101/gr.278063.123] [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: 05/03/2023] [Accepted: 09/22/2023] [Indexed: 11/02/2023]
Abstract
Cancer is a complex disease with diverse molecular mechanisms that affect patient prognosis. Network-based approaches are effective in revealing a holistic picture of cancer prognosis and gene interactions. However, a comprehensive landscape of coexpression networks and prognostic gene modules across multiple cancer types remains elusive. In this study, we performed a systematic analysis of coexpression networks in 32 cancer types. Our analysis identified 4749 prognostic modules that play a vital role in regulating cancer progression. Integrative epigenomic analyses revealed that these modules were regulated by interactions between gene expression and methylation. Coregulated genes of network modules were enriched in chromosome cytobands and preferentially localized in open chromatin regions. The preserved network modules formed 330 module clusters that resided in chromosome hot spots. The cancer-type-specific prognostic modules participated in unique essential biological processes in different cancer types. Overall, our study provides rich resources of prevalent gene networks and underlying multiscale regulatory mechanisms driving cancer prognosis, which lay a foundation for biomarker discovery and therapeutic target development.
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Affiliation(s)
- Peng Xu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA;
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA;
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
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Hounye AH, Hu B, Wang Z, Wang J, Cao C, Zhang J, Hou M, Qi M. Evaluation of drug sensitivity, immunological characteristics, and prognosis in melanoma patients using an endoplasmic reticulum stress-associated signature based on bioinformatics and pan-cancer analysis. J Mol Med (Berl) 2023; 101:1267-1287. [PMID: 37653150 DOI: 10.1007/s00109-023-02365-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: 07/17/2022] [Revised: 05/27/2023] [Accepted: 08/21/2023] [Indexed: 09/02/2023]
Abstract
We aimed to develop endoplasmic reticulum (ER) stress-related risk signature to predict the prognosis of melanoma and elucidate the immune characteristics and benefit of immunotherapy in ER-related risk score-defined subgroups of melanoma based on a machine learning algorithm. Based on The Cancer Genome Atlas (TCGA) melanoma dataset (n = 471) and GTEx database (n = 813), 365 differentially expressed ER-associated genes were selected using the univariate Cox model and LASSO penalty Cox model. Ten genes impacting OS were identified to construct an ER-related signature by using the multivariate Cox regression method and validated with the Gene Expression Omnibus (GEO) dataset. Thereafter, the immune features, CNV, methylation, drug sensitivity, and the clinical benefit of anticancer immune checkpoint inhibitor (ICI) therapy in risk score subgroups, were analyzed. We further validated the gene signature using pan-cancer analysis by comparing it to other tumor types. The ER-related risk score was constructed based on the ARNTL, AGO1, TXN, SORL1, CHD7, EGFR, KIT, HLA-DRB1 KCNA2, and EDNRB genes. The high ER stress-related risk score group patients had a poorer overall survival (OS) than the low-risk score group patients, consistent with the results in the GEO cohort. The combined results suggested that a high ER stress-related risk score was associated with cell adhesion, gamma phagocytosis, cation transport, cell surface cell adhesion, KRAS signalling, CD4 T cells, M1 macrophages, naive B cells, natural killer (NK) cells, and eosinophils and less benefitted from ICI therapy. Based on the expression patterns of ER stress-related genes, we created an appropriate predictive model, which can also help distinguish the immune characteristics, CNV, methylation, and the clinical benefit of ICI therapy. KEY MESSAGES: Melanoma is the cutaneous tumor with a high degree of malignancy, the highest fatality rate, and extremely poor prognosis. Model usefulness should be considered when using models that contained more features. We constructed the Endoplasmic Reticulum stress-associated signature using TCGA and GEO database based on machine learning algorithm. ER stress-associated signature has excellent ability for predicting prognosis for melanoma.
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Affiliation(s)
| | - Bingqian Hu
- Department of Dermatology, Xiangya Hospital of Central South University, Changsha, 410000, China
| | - Zheng Wang
- School of Computer Science, Hunan First Normal University, Changsha, 410205, China
| | - Jiaoju Wang
- School of Mathematics and Statistics, Central South University, Changsha, 410083, China
| | - Cong Cao
- School of Mathematics and Statistics, Central South University, Changsha, 410083, China
| | - Jianglin Zhang
- Department of Dermatology, The Second Clinical Medical College, Shenzhen People's Hospital Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, Guangdong, 518020, China
| | - Muzhou Hou
- School of Mathematics and Statistics, Central South University, Changsha, 410083, China.
| | - Min Qi
- Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, 410008, China.
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Guo L, Cao J, Hou J, Li Y, Huang M, Zhu L, Zhang L, Lee Y, Duarte ML, Zhou X, Wang M, Liu CC, Martens Y, Chao M, Goate A, Bu G, Haroutunian V, Cai D, Zhang B. Sex specific molecular networks and key drivers of Alzheimer's disease. Mol Neurodegener 2023; 18:39. [PMID: 37340466 PMCID: PMC10280841 DOI: 10.1186/s13024-023-00624-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 05/08/2023] [Indexed: 06/22/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a progressive and age-associated neurodegenerative disorder that affects women disproportionally. However, the underlying mechanisms are poorly characterized. Moreover, while the interplay between sex and ApoE genotype in AD has been investigated, multi-omics studies to understand this interaction are limited. Therefore, we applied systems biology approaches to investigate sex-specific molecular networks of AD. METHODS We integrated large-scale human postmortem brain transcriptomic data of AD from two cohorts (MSBB and ROSMAP) via multiscale network analysis and identified key drivers with sexually dimorphic expression patterns and/or different responses to APOE genotypes between sexes. The expression patterns and functional relevance of the top sex-specific network driver of AD were further investigated using postmortem human brain samples and gene perturbation experiments in AD mouse models. RESULTS Gene expression changes in AD versus control were identified for each sex. Gene co-expression networks were constructed for each sex to identify AD-associated co-expressed gene modules shared by males and females or specific to each sex. Key network regulators were further identified as potential drivers of sex differences in AD development. LRP10 was identified as a top driver of the sex differences in AD pathogenesis and manifestation. Changes of LRP10 expression at the mRNA and protein levels were further validated in human AD brain samples. Gene perturbation experiments in EFAD mouse models demonstrated that LRP10 differentially affected cognitive function and AD pathology in sex- and APOE genotype-specific manners. A comprehensive mapping of brain cells in LRP10 over-expressed (OE) female E4FAD mice suggested neurons and microglia as the most affected cell populations. The female-specific targets of LRP10 identified from the single cell RNA-sequencing (scRNA-seq) data of the LRP10 OE E4FAD mouse brains were significantly enriched in the LRP10-centered subnetworks in female AD subjects, validating LRP10 as a key network regulator of AD in females. Eight LRP10 binding partners were identified by the yeast two-hybrid system screening, and LRP10 over-expression reduced the association of LRP10 with one binding partner CD34. CONCLUSIONS These findings provide insights into key mechanisms mediating sex differences in AD pathogenesis and will facilitate the development of sex- and APOE genotype-specific therapies for AD.
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Affiliation(s)
- Lei Guo
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Jiqing Cao
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- James J Peters VA Medical Center, Research & Development, Bronx, NY, 10468, USA
| | - Jianwei Hou
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- James J Peters VA Medical Center, Research & Development, Bronx, NY, 10468, USA
| | - Yonghe Li
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, 32224, USA
| | - Min Huang
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- James J Peters VA Medical Center, Research & Development, Bronx, NY, 10468, USA
| | - Li Zhu
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- James J Peters VA Medical Center, Research & Development, Bronx, NY, 10468, USA
| | - Larry Zhang
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- James J Peters VA Medical Center, Research & Development, Bronx, NY, 10468, USA
| | - Yeji Lee
- James J Peters VA Medical Center, Research & Development, Bronx, NY, 10468, USA
- Department of Neuroscience, Yale University, New Haven, CT, 06510, USA
| | - Mariana Lemos Duarte
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- James J Peters VA Medical Center, Research & Development, Bronx, NY, 10468, USA
| | - Xianxiao Zhou
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Minghui Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Chia-Chen Liu
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, 32224, USA
| | - Yuka Martens
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, 32224, USA
| | - Michael Chao
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Alison Goate
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Guojun Bu
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, 32224, USA
| | - Vahram Haroutunian
- James J Peters VA Medical Center, Research & Development, Bronx, NY, 10468, USA
- Alzheimer Disease Research Center Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- James J Peters VA Medical Center, MIRECC, Bronx, NY, 10468, USA
| | - Dongming Cai
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- James J Peters VA Medical Center, Research & Development, Bronx, NY, 10468, USA.
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Alzheimer Disease Research Center Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
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Liu S, Ye Z, Xue VW, Sun Q, Li H, Lu D. KIF2C is a prognostic biomarker associated with immune cell infiltration in breast cancer. BMC Cancer 2023; 23:307. [PMID: 37016301 PMCID: PMC10071625 DOI: 10.1186/s12885-023-10788-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 03/29/2023] [Indexed: 04/06/2023] Open
Abstract
BACKGROUND The kinesin-13 family member 2C (KIF2C) is a versatile protein participating in many biological processes. KIF2C is frequently up-regulated in multiple types of cancer and is associated with cancer development. However, the role of KIF2C in immune cell infiltration of tumor microenvironment and immunotherapy in breast cancer remains unclear. METHODS The expression of KIF2C was analyzed using Tumor Immune Estimation Resource (TIMER) database and further verified by immunohistochemical staining in human breast cancer tissues. The correlation between KIF2C expression and clinical parameters, the impact of KIF2C on clinical prognosis and independent prognostic factors were analyzed by using TCGA database, the Kaplan-Meier plotter, and Univariate and multivariate Cox analyses, respectively. The nomograms were constructed according to independent prognostic factors and validated with C-index, calibration curves, ROC curves, and decision curve analysis. A gene set enrichment analysis (GSEA) was performed to explore the underlying molecular mechanisms of KIF2C. The degree of immune infiltration was assessed by the Estimation of Stromal and Immune cells in Malignant Tumor tissues using the Expression (ESTIMATE) algorithm and the single sample GSEA (ssGSEA). The Tumor mutational burden and Tumor Immune Dysfunction and Rejection (TIDE) were used to analyze immunotherapeutic efficiency. Finally, the KIF2C-related competing endogenous RNA (ceRNA) network was constructed to predict the putative regulatory mechanisms of KIF2C. RESULTS KIF2C was remarkably up-regulated in 18 different types of cancers, including breast cancer. Kaplan-Meier survival analysis showed that high KIF2C expression was associated with poor overall survival (OS). KIF2C expression was associated with clinical parameters such as age, TMN stage, T status, and molecular subtypes. We identified age, stage, estrogen receptor (ER) and KIF2C expression as OS-related independent prognosis factors for breast cancer. An OS-related nomogram was developed based on these independent prognosis factors and displayed good predicting ability for OS of breast cancer patients. Finally, our results revealed that KIF2C was significantly related to immune cell infiltration, tumor mutational burden, and immunotherapy in patients with breast cancer. CONCLUSION KIF2C was overexpressed in breast cancer and was positively correlated with immune cell infiltration and immunotherapy response. Therefore, KIF2C can serve as a potential biomarker for prognosis and immunotherapy in breast cancer.
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Affiliation(s)
- Shanshan Liu
- Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Department of Pharmacology, Carson International Cancer Center, Shenzhen University Medical School, Shenzhen, Guangdong, 518055, China
| | - Ziwei Ye
- Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Department of Pharmacology, Carson International Cancer Center, Shenzhen University Medical School, Shenzhen, Guangdong, 518055, China
| | - Vivian Weiwen Xue
- Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Department of Pharmacology, Carson International Cancer Center, Shenzhen University Medical School, Shenzhen, Guangdong, 518055, China
| | - Qi Sun
- Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Department of Pharmacology, Carson International Cancer Center, Shenzhen University Medical School, Shenzhen, Guangdong, 518055, China
| | - Huan Li
- Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Department of Pharmacology, Carson International Cancer Center, Shenzhen University Medical School, Shenzhen, Guangdong, 518055, China
| | - Desheng Lu
- Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Department of Pharmacology, Carson International Cancer Center, Shenzhen University Medical School, Shenzhen, Guangdong, 518055, China.
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9
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Xu X, Ju Y, Zhao X, Yang P, Zhu F, Fang B. SMG7-AS1 as a prognostic biomarker and predictor of immunotherapy responses for skin cutaneous melanoma. Genomics 2023; 115:110614. [PMID: 36931476 DOI: 10.1016/j.ygeno.2023.110614] [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: 11/04/2022] [Revised: 02/14/2023] [Accepted: 03/13/2023] [Indexed: 03/17/2023]
Abstract
Skin cutaneous melanoma (SKCM) is the most life-threatening skin cancer and lacks early detection and effective treatment strategies. Many long noncoding RNAs are associated with the development of tumors and may serve as potential immunotherapeutic targets. In this study, microarray analysis was performed to screen for differentially expressed lncRNAs between SKCM and normal tissues, and SMG7-AS1 was identified as an upregulated lncRNA in SKCM. Subsequently, bioinformatic analysis revealed that dysregulation of SMG7-AS1 influences metastasis and immune infiltration. qRT-PCR of clinical samples demonstrated that the expression of SMG7-AS1 was higher in melanoma tissues. Flow cytometry showed that SMG7-AS1 plays a vital role in the cell cycle. Additionally, SMG7-AS1 was found to be associated with immunotherapy responses. To the best of our knowledge, this study is the first to report that SMG7-AS1 is associated with SKCM and may serve as a prognostic biomarker and predictor of immunotherapy responses in SKCM.
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Affiliation(s)
- Xuezheng Xu
- Department of Orthopaedics, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, People's Republic of China
| | - Yikun Ju
- Department of Plastic and Aesthetic (Burn) Surgery, The Second Xiangya Hospital, Central South University, Changsha 410011, People's Republic of China
| | - Xueheng Zhao
- NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, Institute of Reproductive and Stem Cell Engineering, Central South University, Changsha 410078, People's Republic of China
| | - Pu Yang
- Department of Plastic and Aesthetic (Burn) Surgery, The Second Xiangya Hospital, Central South University, Changsha 410011, People's Republic of China
| | - Fang Zhu
- NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, Institute of Reproductive and Stem Cell Engineering, Central South University, Changsha 410078, People's Republic of China
| | - Bairong Fang
- Department of Plastic and Aesthetic (Burn) Surgery, The Second Xiangya Hospital, Central South University, Changsha 410011, People's Republic of China.
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10
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Shi ZD, Pang K, Wu ZX, Dong Y, Hao L, Qin JX, Wang W, Chen ZS, Han CH. Tumor cell plasticity in targeted therapy-induced resistance: mechanisms and new strategies. Signal Transduct Target Ther 2023; 8:113. [PMID: 36906600 PMCID: PMC10008648 DOI: 10.1038/s41392-023-01383-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 12/07/2022] [Accepted: 02/20/2023] [Indexed: 03/13/2023] Open
Abstract
Despite the success of targeted therapies in cancer treatment, therapy-induced resistance remains a major obstacle to a complete cure. Tumor cells evade treatments and relapse via phenotypic switching driven by intrinsic or induced cell plasticity. Several reversible mechanisms have been proposed to circumvent tumor cell plasticity, including epigenetic modifications, regulation of transcription factors, activation or suppression of key signaling pathways, as well as modification of the tumor environment. Epithelial-to-mesenchymal transition, tumor cell and cancer stem cell formation also serve as roads towards tumor cell plasticity. Corresponding treatment strategies have recently been developed that either target plasticity-related mechanisms or employ combination treatments. In this review, we delineate the formation of tumor cell plasticity and its manipulation of tumor evasion from targeted therapy. We discuss the non-genetic mechanisms of targeted drug-induced tumor cell plasticity in various types of tumors and provide insights into the contribution of tumor cell plasticity to acquired drug resistance. New therapeutic strategies such as inhibition or reversal of tumor cell plasticity are also presented. We also discuss the multitude of clinical trials that are ongoing worldwide with the intention of improving clinical outcomes. These advances provide a direction for developing novel therapeutic strategies and combination therapy regimens that target tumor cell plasticity.
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Affiliation(s)
- Zhen-Duo Shi
- Department of Urology, Xuzhou Clinical School of Xuzhou Medical University, Jiangsu, China.,Department of Urology, Xuzhou Central Hospital, Xuzhou, Jiangsu, China.,School of Life Sciences, Jiangsu Normal University, Jiangsu, China.,Department of Urology, Heilongjiang Provincial Hospital, Heilongjiang, China
| | - Kun Pang
- Department of Urology, Xuzhou Clinical School of Xuzhou Medical University, Jiangsu, China.,Department of Urology, Xuzhou Central Hospital, Xuzhou, Jiangsu, China
| | - Zhuo-Xun Wu
- Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, St. John's University, Queens, NY, 11439, USA
| | - Yang Dong
- Department of Urology, Xuzhou Clinical School of Xuzhou Medical University, Jiangsu, China.,Department of Urology, Xuzhou Central Hospital, Xuzhou, Jiangsu, China
| | - Lin Hao
- Department of Urology, Xuzhou Clinical School of Xuzhou Medical University, Jiangsu, China.,Department of Urology, Xuzhou Central Hospital, Xuzhou, Jiangsu, China
| | - Jia-Xin Qin
- Department of Urology, Xuzhou Clinical School of Xuzhou Medical University, Jiangsu, China.,Department of Urology, Xuzhou Central Hospital, Xuzhou, Jiangsu, China
| | - Wei Wang
- Department of Medical College, Southeast University, Nanjing, China
| | - Zhe-Sheng Chen
- Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, St. John's University, Queens, NY, 11439, USA.
| | - Cong-Hui Han
- Department of Urology, Xuzhou Clinical School of Xuzhou Medical University, Jiangsu, China. .,Department of Urology, Xuzhou Central Hospital, Xuzhou, Jiangsu, China. .,School of Life Sciences, Jiangsu Normal University, Jiangsu, China. .,Department of Urology, Heilongjiang Provincial Hospital, Heilongjiang, China.
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11
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Xu P, Wang M, Sharma NK, Comeau ME, Wabitsch M, Langefeld CD, Civelek M, Zhang B, Das SK. Multi-omic integration reveals cell-type-specific regulatory networks of insulin resistance in distinct ancestry populations. Cell Syst 2023; 14:41-57.e8. [PMID: 36630956 PMCID: PMC9852073 DOI: 10.1016/j.cels.2022.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 09/26/2022] [Accepted: 12/13/2022] [Indexed: 01/12/2023]
Abstract
Our knowledge of the cell-type-specific mechanisms of insulin resistance remains limited. To dissect the cell-type-specific molecular signatures of insulin resistance, we performed a multiscale gene network analysis of adipose and muscle tissues in African and European ancestry populations. In adipose tissues, a comparative analysis revealed ethnically conserved cell-type signatures and two adipocyte subtype-enriched modules with opposite insulin sensitivity responses. The modules enriched for adipose stem and progenitor cells as well as immune cells showed negative correlations with insulin sensitivity. In muscle tissues, the modules enriched for stem cells and fibro-adipogenic progenitors responded to insulin sensitivity oppositely. The adipocyte and muscle fiber-enriched modules shared cellular-respiration-related genes but had tissue-specific rearrangements of gene regulations in response to insulin sensitivity. Integration of the gene co-expression and causal networks further pinpointed key drivers of insulin resistance. Together, this study revealed the cell-type-specific transcriptomic networks and signaling maps underlying insulin resistance in major glucose-responsive tissues. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Peng Xu
- Department of Genetics & Genomic Sciences, Mount Sinai Center for Transformative Disease Modeling, Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Minghui Wang
- Department of Genetics & Genomic Sciences, Mount Sinai Center for Transformative Disease Modeling, Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Neeraj K Sharma
- Department of Internal Medicine, Section of Endocrinology and Metabolism, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
| | - Mary E Comeau
- Department of Biostatistics and Data Science, Division of Public Health Sciences, and Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
| | - Martin Wabitsch
- Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics and Adolescent Medicine, University Medical Center Ulm, Eythstr. 24, D-89075 Ulm, Germany
| | - Carl D Langefeld
- Department of Biostatistics and Data Science, Division of Public Health Sciences, and Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
| | - Mete Civelek
- Center for Public Health Genomics, Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Bin Zhang
- Department of Genetics & Genomic Sciences, Mount Sinai Center for Transformative Disease Modeling, Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Swapan K Das
- Department of Internal Medicine, Section of Endocrinology and Metabolism, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA.
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12
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Bai E, Dong M, Lin X, Sun D, Dong L. Expressional and functional characteristics of checkpoint kinase 1 as a prognostic biomarker in hepatocellular carcinoma. Transl Cancer Res 2022; 11:4272-4288. [PMID: 36644193 PMCID: PMC9834594 DOI: 10.21037/tcr-22-1701] [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: 06/16/2022] [Accepted: 10/17/2022] [Indexed: 12/28/2022]
Abstract
Background Hepatocellular carcinoma (HCC) is the most common pathological subtype of liver cancer and is the third leading cause of cancer death worldwide. Checkpoint kinase 1 (CHEK1), an essential serine/threonine kinase that regulates the cell cycle, is reported to be associated with carcinogenesis. However, the biological role and clinical significance of CHEK1 in HCC are still incompletely known. Methods In this research, CHEK1 messenger RNA (mRNA) levels in various liver hepatocellular carcinoma (LIHC) cohorts from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were evaluated. The Kaplan-Meier database was applied to identify the correlation between survival time and CHEK1 expression in patients with HCC. Gene set enrichment analysis (GSEA) was performed to explore the potential mechanism of CHEK1 in HCC, and NetworkAnalyst v. 3.0 (https://www.networkanalyst.ca/) was used to construct the regulatory networks of CHEK1 in HCC. Discriminant Regulon Expression Analysis (DoRothEA) was used to detect the activity of transcriptional factors (TFs) in gene-enriched cells (EC) with CHEK1 coexpression. In vitro experiments were conducted to investigate the effects of CHEK1 on the biological function of HCC cells. Results The CHEK1 mRNA level was overexpressed in HCC, and increased CHEK1 expression correlated with poor survival outcomes. The homo sapiens-microRNA-195 (hsa-miR-195) may have contributed to the upregulation of CHEK1 in HCC. GSEA and NetworkAnalyst v. 3.0 showed that CHEK1 played a crucial part in tumor proliferation of HCC and may be regulated by TF E2F1. DoRothEA showed increased transcriptional activity of E2F1 in gene-EC with CHEK1 coexpression. Moreover, experiments of cell function showed that the knockdown of CHEK1 weakened the aggressive behavior and proliferation of HCC cells. Overexpression of E2F1 increased the proliferation and invasion of HCC cells in vitro, while the silencing of CHEK1 dampened cell invasion induced by E2F1 overexpression. Conclusions These results identified the prognostic significance and expression characteristics of CHEK1 in HCC through bioinformatics analysis and experimental verification. This lays the foundation for further research on the diagnosis and treatment of HCC.
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Affiliation(s)
- Encheng Bai
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai, China;,Department of Gastroenterology and Hepatology, Xiamen Branch, Zhongshan Hospital, Fudan University, Xiamen, China
| | - Mingwei Dong
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai, China;,Department of Gastroenterology and Hepatology, Xiamen Branch, Zhongshan Hospital, Fudan University, Xiamen, China
| | - Xiahui Lin
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Dalong Sun
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai, China;,Department of Gastroenterology and Hepatology, Xiamen Branch, Zhongshan Hospital, Fudan University, Xiamen, China;,Shanghai Institute of Liver Disease, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Ling Dong
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai, China;,Shanghai Institute of Liver Disease, Zhongshan Hospital, Fudan University, Shanghai, China
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13
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Vera J, Lai X, Baur A, Erdmann M, Gupta S, Guttà C, Heinzerling L, Heppt MV, Kazmierczak PM, Kunz M, Lischer C, Pützer BM, Rehm M, Ostalecki C, Retzlaff J, Witt S, Wolkenhauer O, Berking C. Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence. Brief Bioinform 2022; 23:6761961. [PMID: 36252807 DOI: 10.1093/bib/bbac433] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 07/28/2022] [Accepted: 09/08/2022] [Indexed: 12/19/2022] Open
Abstract
We live in an unprecedented time in oncology. We have accumulated samples and cases in cohorts larger and more complex than ever before. New technologies are available for quantifying solid or liquid samples at the molecular level. At the same time, we are now equipped with the computational power necessary to handle this enormous amount of quantitative data. Computational models are widely used helping us to substantiate and interpret data. Under the label of systems and precision medicine, we are putting all these developments together to improve and personalize the therapy of cancer. In this review, we use melanoma as a paradigm to present the successful application of these technologies but also to discuss possible future developments in patient care linked to them. Melanoma is a paradigmatic case for disruptive improvements in therapies, with a considerable number of metastatic melanoma patients benefiting from novel therapies. Nevertheless, a large proportion of patients does not respond to therapy or suffers from adverse events. Melanoma is an ideal case study to deploy advanced technologies not only due to the medical need but also to some intrinsic features of melanoma as a disease and the skin as an organ. From the perspective of data acquisition, the skin is the ideal organ due to its accessibility and suitability for many kinds of advanced imaging techniques. We put special emphasis on the necessity of computational strategies to integrate multiple sources of quantitative data describing the tumour at different scales and levels.
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Affiliation(s)
- Julio Vera
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | - Xin Lai
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | - Andreas Baur
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | - Michael Erdmann
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | - Shailendra Gupta
- Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock 18051, Germany
| | - Cristiano Guttà
- Institute of Cell Biology and Immunology, University of Stuttgart, 70569 Stuttgart, Germany
| | - Lucie Heinzerling
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany.,Department of Dermatology, LMU University Hospital, Munich, Germany
| | - Markus V Heppt
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | | | - Manfred Kunz
- Department of Dermatology, Venereology and Allergology, University of Leipzig, 04103 Leipzig, Germany
| | - Christopher Lischer
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | - Brigitte M Pützer
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, 18057 Rostock, Germany
| | - Markus Rehm
- Institute of Cell Biology and Immunology, University of Stuttgart, 70569 Stuttgart, Germany.,Stuttgart Research Center Systems Biology, University of Stuttgart, 70569 Stuttgart, Germany
| | - Christian Ostalecki
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | - Jimmy Retzlaff
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | | | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock 18051, Germany
| | - Carola Berking
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
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14
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Advances in the Application of Nanomaterials to the Treatment of Melanoma. Pharmaceutics 2022; 14:pharmaceutics14102090. [PMID: 36297527 PMCID: PMC9610396 DOI: 10.3390/pharmaceutics14102090] [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: 08/03/2022] [Revised: 09/25/2022] [Accepted: 09/27/2022] [Indexed: 11/22/2022] Open
Abstract
Melanoma can be divided into cutaneous melanoma, uveal melanoma, mucosal melanoma, etc. It is a very aggressive tumor that is prone to metastasis. Patients with metastatic melanoma have a poor prognosis and shorter survival. Although current melanoma treatments have been dramatically improved, there are still many problems such as systemic toxicity and the off-target effects of drugs. The use of nanoparticles may overcome some inadequacies of current melanoma treatments. In this review, we summarize the limitations of current therapies for cutaneous melanoma, uveal melanoma, and mucosal melanoma, as well as the adjunct role of nanoparticles in different treatment modalities. We suggest that nanomaterials may have an effective intervention in melanoma treatment in the future.
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15
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Lu Z, Zheng Z, Xu Y, Wang C, Lin Y, Lin K, Fu L, Zhou H, Pi L, Che D, Gu X. The Associated of the Risk of IVIG Resistance in Kawasaki Disease with ZNF112 Gene and ZNF180 Gene in a Southern Chinese Population. J Inflamm Res 2022; 15:5053-5062. [PMID: 36081762 PMCID: PMC9448350 DOI: 10.2147/jir.s378080] [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: 06/10/2022] [Accepted: 08/05/2022] [Indexed: 11/23/2022] Open
Abstract
Background Kawasaki disease (KD) was one of the most common primary vasculitis. IVIG resistance was associated with an increased risk of coronary artery aneurysm. Accumulating evidences demonstrated that inflammatory gene polymorphisms might play important roles in IVIG resistance, and zinc finger proteins were closely related to immune inflammation regulation, but the effect of ZNF112/rs8113807 and ZNF180/rs2571051 on IVIG resistance in KD patients has not been reported. Methods A total of 996 KD patients were recruited, and the assay of TaqMan-real-time polymerase chain reaction was used for ZNF112/rs8113807 and ZNF180/rs2571051 genotyping. Odds ratio (OR) and 95% confidence interval (CI) were calculated for estimating the relationship between the polymorphisms of the both SNPs (ZNF112/rs8113807 and ZNF180/rs2571051) and the risk of IVIG resistance. Results Both of the ZNF112/rs8113807 CC/TC genotype and the ZNF180/rs2571051 TT/CT genotype increased the risk of IVIG resistance in KD (rs8113807: CC vs TT: adjusted OR = 1.83, 95% CI = 1.06–3.16, p = 0.0293; CC/TC vs TT adjusted: OR = 1.49, 95% CI = 1.10–2.02, p = 0.0094. rs2571051: TT vs CC adjusted: OR = 2.64, 95% CI = 1.62–4.29, p < 0.0001; TT/CT vs CC adjusted: OR = 2.14, 95% CI = 1.37–3.37, p = 0.0009; TT vs CC/CT adjusted: OR = 1.66, 95% CI = 1.22–2.27, p = 0.0014). Furthermore, the combinative analysis of risk genotypes in ZNF112/rs8113807 and ZNF180/rs2571051 showed that patients with two unfavorable genotypes were more likely to increase risk of IVIG resistance than those who carried with zero or one unfavorable genotypes (adjusted: OR = 1.68, 95% CI = 1.24–2.27, p = 0.0008). Conclusion Our findings enriched the genetic background of IVIG resistance risk in the KD development and suggested that the ZNF112/rs8113807 C-carrier and the ZNF180/rs2571051 T-carrier were associated with increased risk of IVIG resistance in KD patients in Chinese southern population.
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Affiliation(s)
- Zhaojin Lu
- Department of Clinical Biological Resource Bank, Guangzhou Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Zepeng Zheng
- Department of Clinical Biological Resource Bank, Guangzhou Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Yufen Xu
- Department of Clinical Biological Resource Bank, Guangzhou Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Chenlu Wang
- Department of Clinical Biological Resource Bank, Guangzhou Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Yueling Lin
- Department of Clinical Biological Resource Bank, Guangzhou Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Kun Lin
- Department of Clinical Biological Resource Bank, Guangzhou Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - LanYan Fu
- Department of Clinical Biological Resource Bank, Guangzhou Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Huazhong Zhou
- Department of Clinical Biological Resource Bank, Guangzhou Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Lei Pi
- Department of Clinical Biological Resource Bank, Guangzhou Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Di Che
- Department of Clinical Biological Resource Bank, Guangzhou Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, People’s Republic of China
- Correspondence: Di Che, Department of Clinical Biological Resource Bank, Guangzhou Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, 9 Jinsui Road, Guangzhou, 510623, Guangdong, People’s Republic of China, Tel/Fax +86-20-38076562, Email
| | - Xiaoqiong Gu
- Department of Clinical Biological Resource Bank, Guangzhou Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, People’s Republic of China
- Department of Clinical Laboratory, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, People’s Republic of China
- Xiaoqiong Gu, Department of Clinical Biological Resource Bank, Department of Clinical Laboratory, Guangzhou Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, 9 Jinsui Road, Guangzhou, 510623, Guangdong, People’s Republic of China, Tel/Fax +86-20-38076561, Email
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16
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Wang F, Bai J, Li F, Liu J, Wang Y, Li N, Wang Y, Xu J, Liu W, Xu L, Chen L. Investigation of the mechanism of the anti-cancer effects of Astragalus propinquus Schischkin and Pinellia pedatisecta Schott (A&P) on melanoma via network pharmacology and experimental verification. Front Pharmacol 2022; 13:895738. [PMID: 36034875 PMCID: PMC9411814 DOI: 10.3389/fphar.2022.895738] [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: 03/14/2022] [Accepted: 07/15/2022] [Indexed: 11/13/2022] Open
Abstract
Melanoma is a commonly malignant cutaneous tumor in China. Astragalus propinquus Schischkin and Pinellia pedatisecta Schott (A&P) have been clinically used as adjunctive drugs in the treatment of malignant melanoma. However, the effect and mechanism of A&P on melanoma have yet to be explored. The current investigation seeks to characterize the active components of A&P and their potential roles in treating malignant melanoma using network pharmacology and in vitro and in vivo experiments. We first used the traditional Chinese medicine systems pharmacology (TCMSP) database and high-performance liquid chromatography-mass spectrometry (HPLC-MS/MS) to identify a total of 13 effective compounds within A&P. 70 common genes were obtained by matching 487 potential genes of A&P with 464 melanoma-related genes, and then we built up protein-protein interaction (PPI) network of these 70 genes, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The results revealed that A&P might influence the pathobiology of melanoma through the PI3K/Akt pathway. Molecular docking also confirmed that higher content of ingredients in A&P, including hederagenin, quercetin, beta-sitosterol and stigmasterol, had a strong binding activity (affinity < −5 kcal/mol) with the core targets AKT1, MAPK3 and ESR1. Furthermore, we confirmed A&P could inhibit melanoma cells proliferation and induce cells apoptosis through suppressing the PI3K/Akt signaling pathway by in vitro and in vivo xenograft model experiments. These findings indicate that A&P may function as a useful therapy for melanoma through the PI3K/Akt pathway.
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Affiliation(s)
- Fang Wang
- Department of Pharmacy, Xi’an International Medical Center Hospital, Xi’an, Shaanxi Province, China
| | - Juan Bai
- Department of Pharmacy, Xi’an International Medical Center Hospital, Xi’an, Shaanxi Province, China
| | - Feng Li
- Department of Pharmacy, Xi’an International Medical Center Hospital, Xi’an, Shaanxi Province, China
| | - Jing Liu
- Department of Pharmacy, Xi’an International Medical Center Hospital, Xi’an, Shaanxi Province, China
| | - Yanli Wang
- Department of Pharmacy, Xi’an International Medical Center Hospital, Xi’an, Shaanxi Province, China
| | - Ning Li
- Department of Pharmacy, Xi’an International Medical Center Hospital, Xi’an, Shaanxi Province, China
| | - Yaqi Wang
- Department of Pharmacy, Xi’an International Medical Center Hospital, Xi’an, Shaanxi Province, China
| | - Jin Xu
- Department of Pharmacy, Xi’an International Medical Center Hospital, Xi’an, Shaanxi Province, China
| | - Wanbao Liu
- Department of Pharmacy, Xi’an International Medical Center Hospital, Xi’an, Shaanxi Province, China
| | - Liting Xu
- Department of Pharmacy, Xi’an International Medical Center Hospital, Xi’an, Shaanxi Province, China
| | - Lin Chen
- Department of Plastic Surgery, Xijing Hospital, Fourth Military Medical University, Xi’an, Shaanxi Province, China
- *Correspondence: Lin Chen,
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Wang M, Song WM, Ming C, Wang Q, Zhou X, Xu P, Krek A, Yoon Y, Ho L, Orr ME, Yuan GC, Zhang B. Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application. Mol Neurodegener 2022; 17:17. [PMID: 35236372 PMCID: PMC8889402 DOI: 10.1186/s13024-022-00517-z] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 01/18/2022] [Indexed: 12/13/2022] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.
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Affiliation(s)
- Minghui Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Won-min Song
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Chen Ming
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Qian Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Xianxiao Zhou
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Peng Xu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Azra Krek
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Yonejung Yoon
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Lap Ho
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Miranda E. Orr
- Department of Internal Medicine, Section of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
- Sticht Center for Healthy Aging and Alzheimer’s Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
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18
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Xu P, Wang M, Song WM, Wang Q, Yuan GC, Sudmant PH, Zare H, Tu Z, Orr ME, Zhang B. The landscape of human tissue and cell type specific expression and co-regulation of senescence genes. Mol Neurodegener 2022; 17:5. [PMID: 35000600 PMCID: PMC8744330 DOI: 10.1186/s13024-021-00507-7] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 12/07/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Cellular senescence is a complex stress response that impacts cellular function and organismal health. Multiple developmental and environmental factors, such as intrinsic cellular cues, radiation, oxidative stress, oncogenes, and protein accumulation, activate genes and pathways that can lead to senescence. Enormous efforts have been made to identify and characterize senescence genes (SnGs) in stress and disease systems. However, the prevalence of senescent cells in healthy human tissues and the global SnG expression signature in different cell types are poorly understood. METHODS This study performed an integrative gene network analysis of bulk and single-cell RNA-seq data in non-diseased human tissues to investigate SnG co-expression signatures and their cell-type specificity. RESULTS Through a comprehensive transcriptomic network analysis of 50 human tissues in the Genotype-Tissue Expression Project (GTEx) cohort, we identified SnG-enriched gene modules, characterized SnG co-expression patterns, and constructed aggregated SnG networks across primary tissues of the human body. Our network approaches identified 51 SnGs highly conserved across the human tissues, including CDKN1A (p21)-centered regulators that control cell cycle progression and the senescence-associated secretory phenotype (SASP). The SnG-enriched modules showed remarkable cell-type specificity, especially in fibroblasts, endothelial cells, and immune cells. Further analyses of single-cell RNA-seq and spatial transcriptomic data independently validated the cell-type specific SnG signatures predicted by the network analysis. CONCLUSIONS This study systematically revealed the co-regulated organizations and cell type specificity of SnGs in major human tissues, which can serve as a blueprint for future studies to map senescent cells and their cellular interactions in human tissues.
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Affiliation(s)
- Peng Xu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Minghui Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Won-min Song
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Qian Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
- Institute for Precision Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Peter H. Sudmant
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA 94720 USA
- Center for Computational Biology, University of California Berkeley, Berkeley, CA 94720 USA
| | - Habil Zare
- Department of Cell Systems & Anatomy, The University of Texas Health Science Center, San Antonio, TX 78229 USA
- Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX 78229 USA
| | - Zhidong Tu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Miranda E. Orr
- Section of Gerontology and Geriatric Medicine, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27157 USA
- Sticht Center for Healthy Aging and Alzheimer’s Prevention, Wake Forest School of Medicine, Winston-Salem, NC 27157 USA
- Salisbury VA Medical Center, Salisbury, NC 28144 USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
- Department of Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
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19
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Wei S, Shao X, Liu Y, Xiong B, Cui P, Liu Z, Li Q. Genome editing of PD-L1 mediated by nucleobase-modified polyamidoamine for cancer immunotherapy. J Mater Chem B 2022; 10:1291-1300. [DOI: 10.1039/d1tb02688g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Immune checkpoint blockade therapy against programmed death protein-1 and its ligand (PD-1/PD-L1) has been accepted as a promising approach to activate the immune system's anti-tumor response. Although small interfering RNA...
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20
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Brendlin AS, Peisen F, Almansour H, Afat S, Eigentler T, Amaral T, Faby S, Calvarons AF, Nikolaou K, Othman AE. A Machine learning model trained on dual-energy CT radiomics significantly improves immunotherapy response prediction for patients with stage IV melanoma. J Immunother Cancer 2021; 9:jitc-2021-003261. [PMID: 34795006 PMCID: PMC8603266 DOI: 10.1136/jitc-2021-003261] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/13/2021] [Indexed: 11/03/2022] Open
Abstract
BACKGROUND To assess the additive value of dual-energy CT (DECT) over single-energy CT (SECT) to radiomics-based response prediction in patients with metastatic melanoma preceding immunotherapy. MATERIAL AND METHODS A total of 140 consecutive patients with melanoma (58 female, 63±16 years) for whom baseline DECT tumor load assessment revealed stage IV and who were subsequently treated with immunotherapy were included. Best response was determined using the clinical reports (81 responders: 27 complete response, 45 partial response, 9 stable disease). Individual lesion response was classified manually analogous to RECIST 1.1 through 1291 follow-up examinations on a total of 776 lesions (6.7±7.2 per patient). The patients were sorted chronologically into a study and a validation cohort (each n=70). The baseline DECT was examined using specialized tumor segmentation prototype software, and radiomic features were analyzed for response predictors. Significant features were selected using univariate statistics with Bonferroni correction and multiple logistic regression. The area under the receiver operating characteristic curve of the best subset was computed (AUROC). For each combination (SECT/DECT and patient response/lesion response), an individual random forest classifier with 10-fold internal cross-validation was trained on the study cohort and tested on the validation cohort to confirm the predictive performance. RESULTS We performed manual RECIST 1.1 response analysis on a total of 6533 lesions. Multivariate statistics selected significant features for patient response in SECT (min. brightness, R²=0.112, padj. ≤0.001) and DECT (textural coarseness, R²=0.121, padj. ≤0.001), as well as lesion response in SECT (mean absolute voxel intensity deviation, R²=0.115, padj. ≤0.001) and DECT (iodine uptake metrics, R²≥0.12, padj. ≤0.001). Applying the machine learning models to the validation cohort confirmed the additive predictive power of DECT (patient response AUROC SECT=0.5, DECT=0.75; lesion response AUROC SECT=0.61, DECT=0.85; p<0.001). CONCLUSION The new method of DECT-specific radiomic analysis provides a significant additive value over SECT radiomics approaches for response prediction in patients with metastatic melanoma preceding immunotherapy, especially on a lesion-based level. As mixed tumor response is not uncommon in metastatic melanoma, this lends a powerful tool for clinical decision-making and may potentially be an essential step toward individualized medicine.
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Affiliation(s)
- Andreas Stefan Brendlin
- Department of Diagnostic and Interventional Radiology, Universitätsklinikum Tübingen, Tubingen, Germany
| | - Felix Peisen
- Department of Diagnostic and Interventional Radiology, Universitätsklinikum Tübingen, Tubingen, Germany
| | - Haidara Almansour
- Department of Diagnostic and Interventional Radiology, Universitätsklinikum Tübingen, Tubingen, Germany
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, Universitätsklinikum Tübingen, Tubingen, Germany
| | - Thomas Eigentler
- Center of Dermatooncology, Department of Dermatology, Eberhard Karls Universitat Tubingen, Tubingen, Germany.,Department of Dermatology, Venereology and Allergology, Charite Universitatsmedizin Berlin, Berlin, Germany
| | - Teresa Amaral
- Center of Dermatooncology, Department of Dermatology, Eberhard Karls Universitat Tubingen, Tubingen, Germany
| | - Sebastian Faby
- Computed Tomography, Siemens Healthcare GmbH, Erlangen, Germany
| | | | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, Universitätsklinikum Tübingen, Tubingen, Germany.,Image-guided and Functionally Instructed Tumor Therapies (iFIT), The Cluster of Excellence 2180, Tuebingen, Germany
| | - Ahmed E Othman
- Department of Diagnostic and Interventional Radiology, Universitätsklinikum Tübingen, Tubingen, Germany .,Institute of Neuroradiology, Johannes Gutenberg University Hospital Mainz, Mainz, Germany
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21
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Pan D, Jia D. Application of Single-Cell Multi-Omics in Dissecting Cancer Cell Plasticity and Tumor Heterogeneity. Front Mol Biosci 2021; 8:757024. [PMID: 34722635 PMCID: PMC8554142 DOI: 10.3389/fmolb.2021.757024] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 09/29/2021] [Indexed: 12/20/2022] Open
Abstract
Tumor heterogeneity, a hallmark of cancer, impairs the efficacy of cancer therapy and drives tumor progression. Exploring inter- and intra-tumoral heterogeneity not only provides insights into tumor development and progression, but also guides the design of personalized therapies. Previously, high-throughput sequencing techniques have been used to investigate the heterogeneity of tumor ecosystems. However, they could not provide a high-resolution landscape of cellular components in tumor ecosystem. Recently, advance in single-cell technologies has provided an unprecedented resolution to uncover the intra-tumoral heterogeneity by profiling the transcriptomes, genomes, proteomes and epigenomes of the cellular components and also their spatial distribution, which greatly accelerated the process of basic and translational cancer research. Importantly, it has been demonstrated that some cancer cells are able to transit between different states in order to adapt to the changing tumor microenvironment, which led to increased cellular plasticity and tumor heterogeneity. Understanding the molecular mechanisms driving cancer cell plasticity is critical for developing precision therapies. In this review, we summarize the recent progress in dissecting the cancer cell plasticity and tumor heterogeneity by use of single-cell multi-omics techniques.
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Affiliation(s)
- Deshen Pan
- Laboratory of Cancer Genomics and Biology, Department of Urology, and Institute of Translational Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Deshui Jia
- Laboratory of Cancer Genomics and Biology, Department of Urology, and Institute of Translational Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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22
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Qi X, Wang XQ, Jin L, Gao LX, Guo HF. Uncovering potential single nucleotide polymorphisms, copy number variations and related signaling pathways in primary Sjogren's syndrome. Bioengineered 2021; 12:9313-9331. [PMID: 34723755 PMCID: PMC8809958 DOI: 10.1080/21655979.2021.2000245] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Primary Sjogren’s syndrome (pSS) is a complex systemic autoimmune disease, which is difficult to accurately diagnose due to symptom diversity in patients, especially at earlier stages. We tried to find potential single nucleotide polymorphisms (SNPs), copy number variations (CNVs) and related signaling pathways. Genomic DNA was extracted from peripheral blood of 12 individuals (7 individuals from 3 pSS pedigrees and 5 sporadic cases) for whole-exome sequencing (WES) analysis. SNPs and CNVs were identified, followed by functional annotation of genes with SNPs and CNVs. Gene expression profile (involving 64 normal controls and 166 cases) was downloaded from the Gene Expression Omnibus database (GEO) dataset for differentially expression analysis. Sanger sequencing and in vitro validation was used to validate the identified SNPs and differentially expressed genes, respectively. A total of 5 SNPs were identified in both pedigrees and sporadic cases, such as FES, PPM1J, and TRAPPC9. A total of 3402 and 19 CNVs were identified in pedigrees and sporadic cases, respectively. Fifty-one differentially expressed genes were associated with immunity, such as BATF3, LAP3, BATF2, PARP9, and IL15RA. AMPK signaling pathway and cell adhesion molecules (CAMs) were the most significantly enriched signaling pathways of identified SNPs. Identified CNVs were associated with systemic lupus erythematosus, mineral absorption, and HTLV-I infection. IL2-STAT5 signaling, interferon-gamma response, and interferon-alpha response were significantly enriched immune related signaling pathways of identified differentially expressed genes. In conclusion, our study found some potential SNPs, CNVs, and related signaling pathways, which could be useful in understanding the pathological mechanism of pSS.
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Affiliation(s)
- Xuan Qi
- Department of Rheumatism and Immunology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Xi-Qin Wang
- Internal Medicine, Yuhua Yunfang Integrated Traditional Chinese and Western Medicine Clinic, Shijiazhuang, Hebei, China
| | - Lu Jin
- Department of Rheumatism and Immunology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Li-Xia Gao
- Department of Rheumatism and Immunology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Hui-Fang Guo
- Department of Rheumatism and Immunology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
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23
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KIF2C Is a Novel Prognostic Biomarker and Correlated with Immune Infiltration in Endometrial Cancer. Stem Cells Int 2021; 2021:1434856. [PMID: 34650608 PMCID: PMC8510809 DOI: 10.1155/2021/1434856] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 08/24/2021] [Accepted: 09/02/2021] [Indexed: 11/30/2022] Open
Abstract
Endometrial cancer (EC) is commonly diagnosed cancer in women, and the prognosis of advanced types of EC is extremely poor. Kinesin family member 2C (KIF2C) has been reported as an oncogene in cancers. However, its pathophysiological roles and the correlation with tumor-infiltrating lymphocytes in EC remain unclear. The mRNA and protein levels of KIF2C in EC tissues were detected by qRT-PCR, Western blot (WB), and IHC. CCK8, Transwell, and colony formation assay were applied to assess the effects of KIF2C on cell proliferation, migration, and invasion. Cell apoptosis and cell cycle were analyzed by flow cytometry. The antitumor effect was further validated in the nude mouse xenograft cancer model and humanized mouse model. KIF2C expression was higher in EC. Knockdown of KIF2C prolonged the G1 phases and inhibited EC cell proliferation, migration, and invasion in vitro. Bioinformatics analysis indicated that KIF2C is negatively correlated with the infiltration level of CD8+ T cells but positively with the poor prognosis of EC patients. The apoptosis of CD8+ T cell was inhibited after the knockdown of KIF2C and was further inhibited when it is combined with anti-PD1. Conversely, compared to the knockdown of KIF2C expression alone, the combination of anti-PD1 further promoted the apoptosis of Ishikawa and RL95-2 cells. Moreover, the knockdown of KIF2C inhibited the expression of Ki-67 and the growth of tumors in the nude mouse xenograft cancer model. Our study found that the antitumor efficacy was further evaluated by the combination of anti-PD1 and KIF2C knockdown in a humanized mouse model. This study indicated that KIF2C is a novel prognostic biomarker that determines cancer progression and also a target for the therapy of EC and correlated with tumor immune cells infiltration in EC.
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