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month download article/chapter single-cell rna-seq data gene-centric approach identifies cell pyroptosis-related genes single-cell transcriptomics lmo1 super-enhancer polymorphism multimodal single-cell data immune-infiltrated tumor microenvironment triple-negative breast cancer muscle-invasive bladder cancer e-mtab-8248 cohort verified single-cell characterization gene expression omnibus gene expression data related subjects interpreting omics data single-cell atlas full article pdf moe key laboratory privacy choices/manage cookies irwin ms transcriptomic profiling high-risk neuroblastoma patients rna-seq era universal enrichment tool optimized cox model rna-seq data mesenchymal signature genes oldridge da relative cell abundances t-cell receptors improving risk stratification important prognostic marker prognostic indicator independent neuroblastoma cell lines p53/p21 pathway multivariate survival analysis high-risk mycn holds exclusive rights wenjun mou risk group classification human leukemia cells capital medical university article cheng human ovarian cancer pediatric oncology surgery article journal scrna-seq data european economic area predictive accuracy

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         headline:Integration of clinical characteristics and molecular signatures of the tumor microenvironment to predict the prognosis of neuroblastoma
         description:This study aimed to analyze the clinical characteristics, cell types, and molecular characteristics of the tumor microenvironment to better predict the prognosis of neuroblastoma (NB). The gene expression data and corresponding clinical information of 498 NB patients were obtained from the Gene Expression Omnibus (GEO: GSE62564) and ArrayExpress (accession: E-MTAB-8248). The relative cell abundances were estimated using single-sample gene set enrichment analysis (ssGSEA) with the R gene set variation analysis (GSVA) package. We performed Cox regression analyses to identify marker genes indicating cell subsets and combined these with prognostically relevant clinical factors to develop a new prognostic model. Data from the E-MTAB-8248 cohort verified the predictive accuracy of the prognostic model. Single-cell RNA-seq data were analyzed by using the R Seurat package. Multivariate survival analysis for each gene, using clinical characteristics as cofactors, identified 34 prognostic genes that showed a significant correlation with both event-free survival (EFS) and overall survival (OS) (log-rank test, P value < 0.05). The pathway enrichment analysis revealed that these prognostic genes were highly enriched in the marker genes of NB cells with mesenchymal features and protein translation. Ultimately, USP39, RPL8, IL1RAPL1, MAST4, CSRP2, ATP5E, International Neuroblastoma Staging System (INSS) stage, age, and MYCN status were selected to build an optimized Cox model for NB risk stratification. These samples were divided into two groups using the median of the risk score as a cutoff. The prognosis of samples in the poor prognosis group (PP) was significantly worse than that of samples in the good prognosis group (GP) (log-rank test, P value < 0.0001, median EFS: 640.5 vs. 2247 days, median OS: 1279.5 vs. 2519 days). The risk model was also regarded as a prognostic indicator independent of MYCN status, age, and stage. Finally, through scRNA-seq data, we found that as an important prognostic marker, USP39 might participate in the regulation of RNA splicing in NB. Our study established a multivariate Cox model based on gene signatures and clinical characteristics to better predict the prognosis of NB and revealed that mesenchymal signature genes of NB cells, especially USP39, were more abundant in patients with a poor prognosis than in those with a good prognosis.
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            Tumor microenvironment (TME)
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            Cox model
            Prognosis
            Molecular Medicine
            Human Genetics
            Internal Medicine
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      headline:Integration of clinical characteristics and molecular signatures of the tumor microenvironment to predict the prognosis of neuroblastoma
      description:This study aimed to analyze the clinical characteristics, cell types, and molecular characteristics of the tumor microenvironment to better predict the prognosis of neuroblastoma (NB). The gene expression data and corresponding clinical information of 498 NB patients were obtained from the Gene Expression Omnibus (GEO: GSE62564) and ArrayExpress (accession: E-MTAB-8248). The relative cell abundances were estimated using single-sample gene set enrichment analysis (ssGSEA) with the R gene set variation analysis (GSVA) package. We performed Cox regression analyses to identify marker genes indicating cell subsets and combined these with prognostically relevant clinical factors to develop a new prognostic model. Data from the E-MTAB-8248 cohort verified the predictive accuracy of the prognostic model. Single-cell RNA-seq data were analyzed by using the R Seurat package. Multivariate survival analysis for each gene, using clinical characteristics as cofactors, identified 34 prognostic genes that showed a significant correlation with both event-free survival (EFS) and overall survival (OS) (log-rank test, P value < 0.05). The pathway enrichment analysis revealed that these prognostic genes were highly enriched in the marker genes of NB cells with mesenchymal features and protein translation. Ultimately, USP39, RPL8, IL1RAPL1, MAST4, CSRP2, ATP5E, International Neuroblastoma Staging System (INSS) stage, age, and MYCN status were selected to build an optimized Cox model for NB risk stratification. These samples were divided into two groups using the median of the risk score as a cutoff. The prognosis of samples in the poor prognosis group (PP) was significantly worse than that of samples in the good prognosis group (GP) (log-rank test, P value < 0.0001, median EFS: 640.5 vs. 2247 days, median OS: 1279.5 vs. 2519 days). The risk model was also regarded as a prognostic indicator independent of MYCN status, age, and stage. Finally, through scRNA-seq data, we found that as an important prognostic marker, USP39 might participate in the regulation of RNA splicing in NB. Our study established a multivariate Cox model based on gene signatures and clinical characteristics to better predict the prognosis of NB and revealed that mesenchymal signature genes of NB cells, especially USP39, were more abundant in patients with a poor prognosis than in those with a good prognosis.
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      keywords:
         Neuroblastoma
         Tumor microenvironment (TME)
         Mesenchymal features
         Cox model
         Prognosis
         Molecular Medicine
         Human Genetics
         Internal Medicine
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               name:Department of Surgical Oncology, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
               type:PostalAddress
            type:Organization
      name:Saishuo Chang
      affiliation:
            name:National Center for Children’s Health
            address:
               name:Department of Surgical Oncology, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
               type:PostalAddress
            type:Organization
      name:Yaqiong Jin
      affiliation:
            name:MOE Key Laboratory of Major Diseases in Children, Beijing Pediatric Research Institute, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health
            address:
               name:Beijing Key Laboratory for Pediatric Diseases of Otolaryngology, Head and Neck Surgery, MOE Key Laboratory of Major Diseases in Children, Beijing Pediatric Research Institute, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
               type:PostalAddress
            type:Organization
      name:Wenjun Mou
      affiliation:
            name:Beijing Pediatric Research Institute, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health
            address:
               name:Laboratory of Tumor Immunology, Beijing Pediatric Research Institute, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
               type:PostalAddress
            type:Organization
      name:Hong Qin
      affiliation:
            name:National Center for Children’s Health
            address:
               name:Department of Surgical Oncology, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
               type:PostalAddress
            type:Organization
      name:Wei Yang
      affiliation:
            name:National Center for Children’s Health
            address:
               name:Department of Surgical Oncology, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
               type:PostalAddress
            type:Organization
      name:Xianwei Zhang
      affiliation:
            name:Children’s Hospital Affiliated to Zhengzhou University, Henan Children’s Hospital, Zhengzhou Children’s Hospital
            address:
               name:Zhengzhou Key Laboratory of Precise Diagnosis and Treatment of Children’s Malignant Tumors, Department of Pediatric Oncology Surgery, Children’s Hospital Affiliated to Zhengzhou University, Henan Children’s Hospital, Zhengzhou Children’s Hospital, Zhengzhou, China
               type:PostalAddress
            type:Organization
      name:Wancun Zhang
      affiliation:
            name:Children’s Hospital Affiliated to Zhengzhou University, Henan Children’s Hospital, Zhengzhou Children’s Hospital
            address:
               name:Zhengzhou Key Laboratory of Precise Diagnosis and Treatment of Children’s Malignant Tumors, Department of Pediatric Oncology Surgery, Children’s Hospital Affiliated to Zhengzhou University, Henan Children’s Hospital, Zhengzhou Children’s Hospital, Zhengzhou, China
               type:PostalAddress
            type:Organization
      name:Huanmin Wang
      url:http://orcid.org/0000-0001-6769-9643
      affiliation:
            name:National Center for Children’s Health
            address:
               name:Department of Surgical Oncology, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
               type:PostalAddress
            type:Organization
      email:[email protected]
PostalAddress:
      name:Department of Surgical Oncology, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
      name:Shanghai Institute of Precision Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, China
      name:Department of Surgical Oncology, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
      name:Department of Surgical Oncology, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
      name:Department of Surgical Oncology, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
      name:Beijing Key Laboratory for Pediatric Diseases of Otolaryngology, Head and Neck Surgery, MOE Key Laboratory of Major Diseases in Children, Beijing Pediatric Research Institute, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
      name:Laboratory of Tumor Immunology, Beijing Pediatric Research Institute, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
      name:Department of Surgical Oncology, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
      name:Department of Surgical Oncology, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
      name:Zhengzhou Key Laboratory of Precise Diagnosis and Treatment of Children’s Malignant Tumors, Department of Pediatric Oncology Surgery, Children’s Hospital Affiliated to Zhengzhou University, Henan Children’s Hospital, Zhengzhou Children’s Hospital, Zhengzhou, China
      name:Zhengzhou Key Laboratory of Precise Diagnosis and Treatment of Children’s Malignant Tumors, Department of Pediatric Oncology Surgery, Children’s Hospital Affiliated to Zhengzhou University, Henan Children’s Hospital, Zhengzhou Children’s Hospital, Zhengzhou, China
      name:Department of Surgical Oncology, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
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