Interaction gene set between osteoclasts and regulatory CD4+ T cells can accurately predict the prognosis of patients with osteosarcoma
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Xinli Zhan | Qingjun Wei | Xiaoting Luo | Yun Liu | Shijie Liao | Shangyu Liu | Wenyu Feng | Xiang-de Li | Kai Luo | Haijun Tang | Chaoyi Zhong | Feicui Li | Jianmin Liang
[1] Xue Liang,et al. Single‐cell RNA sequencing technologies and applications: A brief overview , 2022, Clinical and translational medicine.
[2] Yong Yang,et al. Osteosarcoma and Metastasis , 2021, Frontiers in Oncology.
[3] P. Meltzer,et al. New Horizons in the Treatment of Osteosarcoma. , 2021, The New England journal of medicine.
[4] Xue Zhang,et al. Follicular Helper CD4+ T Cells, Follicular Regulatory CD4+ T Cells, and Inducible Costimulator and Their Roles in Multiple Sclerosis and Experimental Autoimmune Encephalomyelitis , 2021, Mediators of inflammation.
[5] A. S. Thind,et al. Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology , 2021, Briefings Bioinform..
[6] Xinli Zhan,et al. Single-Cell Transcriptomics Reveals the Complexity of the Tumor Microenvironment of Treatment-Naive Osteosarcoma , 2021, Frontiers in Oncology.
[7] K. Stamatopoulos,et al. RPS15 mutations rewire RNA translation in chronic lymphocytic leukemia. , 2021, Blood advances.
[8] R. Gorlick,et al. Advancing therapy for osteosarcoma , 2021, Nature Reviews Clinical Oncology.
[9] A. Granito,et al. Hepatocellular carcinoma in viral and autoimmune liver diseases: Role of CD4+ CD25+ Foxp3+ regulatory T cells in the immune microenvironment , 2021, World journal of gastroenterology.
[10] Xue-jian Wu,et al. Hsa_circ_0032463 acts as the tumor promoter in osteosarcoma by regulating the miR-330-3p/PNN axis , 2021, International journal of molecular medicine.
[11] Lu Xie,et al. Immunotherapy for osteosarcoma: Fundamental mechanism, rationale, and recent breakthroughs. , 2020, Cancer letters.
[12] Zhenhua Zhou,et al. Single-cell RNA landscape of intratumoral heterogeneity and immunosuppressive microenvironment in advanced osteosarcoma , 2020, Nature Communications.
[13] Nadezhda T. Doncheva,et al. The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets , 2020, Nucleic Acids Res..
[14] Raphael Gottardo,et al. Integrated analysis of multimodal single-cell data , 2020, Cell.
[15] Chaofei Yang,et al. Bone Microenvironment and Osteosarcoma Metastasis , 2020, International journal of molecular sciences.
[16] Xinli Zhan,et al. CCT6A, a novel prognostic biomarker for Ewing sarcoma , 2020, Medicine.
[17] P. Ohashi,et al. The Roles of CD8+ T Cell Subsets in Antitumor Immunity. , 2020, Trends in cell biology.
[18] Xiaodong Yang,et al. Ligand-receptor interaction atlas within and between tumor cells and T cells in lung adenocarcinoma , 2020, International journal of biological sciences.
[19] Jung-Il Lee,et al. Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma , 2020, Nature Communications.
[20] Jia Wei,et al. Inducers, Attractors and Modulators of CD4+ Treg Cells in Non-Small-Cell Lung Cancer , 2020, Frontiers in Immunology.
[21] F. Verrecchia,et al. The Osteosarcoma Microenvironment: A Complex but Targetable Ecosystem , 2020, Cells.
[22] Mirjana Efremova,et al. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes , 2020, Nature Protocols.
[23] Yanlin Huang,et al. Overexpressing CCT6A Contributes To Cancer Cell Growth By Affecting The G1-To-S Phase Transition And Predicts A Negative Prognosis In Hepatocellular Carcinoma , 2019, OncoTargets and therapy.
[24] C. Gray,et al. Human leukocyte antigen (HLA) diversity and clinical applications in South Africa. , 2019, South African medical journal = Suid-Afrikaanse tydskrif vir geneeskunde.
[25] L. Shevde,et al. The Tumor Microenvironment Innately Modulates Cancer Progression. , 2019, Cancer research.
[26] Itay Tirosh,et al. Single-Cell RNA Sequencing in Cancer: Lessons Learned and Emerging Challenges. , 2019, Molecular cell.
[27] Anna Teti,et al. Immune Function and Diversity of Osteoclasts in Normal and Pathological Conditions , 2019, Front. Immunol..
[28] D. Chabot-Richards,et al. HLA testing in the molecular diagnostic laboratory , 2018, Virchows Archiv.
[29] Fan Zhang,et al. Fast, sensitive, and accurate integration of single cell data with Harmony , 2018, bioRxiv.
[30] Qiong Zhang,et al. GSCALite: a web server for gene set cancer analysis , 2018, Bioinform..
[31] Ambrose J. Carr,et al. Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment , 2018, Cell.
[32] Guanqun Huang,et al. Targeting DTL induces cell cycle arrest and senescence and suppresses cell growth and colony formation through TPX2 inhibition in human hepatocellular carcinoma cells , 2018, OncoTargets and therapy.
[33] Jun Yu,et al. DEAD-box helicase 27 promotes colorectal cancer growth and metastasis and predicts poor survival in CRC patients , 2018, Oncogene.
[34] M. Heymann,et al. The contribution of immune infiltrates and the local microenvironment in the pathogenesis of osteosarcoma. , 2017, Cellular immunology.
[35] H. Guan,et al. CCT6A suppresses SMAD2 and promotes prometastatic TGF-&bgr; signaling , 2017, The Journal of clinical investigation.
[36] S. Bielack,et al. Novel insights and therapeutic interventions for pediatric osteosarcoma. , 2017, Future oncology.
[37] P. Laurent-Puig,et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression , 2016, Genome Biology.
[38] Dorian Obino,et al. Inflammatory Osteoclasts Prime TNFα‐Producing CD4+ T Cells and Express CX3CR1 , 2016, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.
[39] J. Mesirov,et al. The Molecular Signatures Database Hallmark Gene Set Collection , 2015 .
[40] Martin A. Nowak,et al. Mutations driving CLL and their evolution in progression and relapse , 2015, Nature.
[41] Matthew E. Ritchie,et al. limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.
[42] Chung-Yen Lin,et al. cytoHubba: identifying hub objects and sub-networks from complex interactome , 2014, BMC Systems Biology.
[43] Paul Geeleher,et al. pRRophetic: An R Package for Prediction of Clinical Chemotherapeutic Response from Tumor Gene Expression Levels , 2014, PloS one.
[44] Justin Guinney,et al. GSVA: gene set variation analysis for microarray and RNA-Seq data , 2013, BMC Bioinformatics.
[45] Helga Thorvaldsdóttir,et al. Molecular signatures database (MSigDB) 3.0 , 2011, Bioinform..
[46] C. Manferdini,et al. T cell suppression by osteoclasts in vitro , 2011, Journal of cellular physiology.
[47] Xavier Robin,et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves , 2011, BMC Bioinformatics.
[48] Anne-Marie Cleton-Jansen,et al. Tumor-Infiltrating Macrophages Are Associated with Metastasis Suppression in High-Grade Osteosarcoma: A Rationale for Treatment with Macrophage Activating Agents , 2011, Clinical Cancer Research.
[49] Sungyoul Hong,et al. Cross talk between the bone and immune systems: osteoclasts function as antigen-presenting cells and activate CD4+ and CD8+ T cells. , 2009, Blood.
[50] R. Aurora,et al. Cross-Presentation by Osteoclasts Induces FoxP3 in CD8+ T Cells , 2009, The Journal of Immunology.
[51] Michael C. Ostrowski,et al. NFATc1 in mice represses osteoprotegerin during osteoclastogenesis and dissociates systemic osteopenia from inflammation in cherubism. , 2008, The Journal of clinical investigation.
[52] P. Shannon,et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.
[53] R. Tibshirani. The lasso method for variable selection in the Cox model. , 1997, Statistics in medicine.
[54] J. Mesirov,et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. , 2015, Cell systems.