Jianfeng Chen1, Ziheng Wang2, Wei Wang2, Shiqi Ren2, Jinbiao Xue3, Lin Zhong4, Tao Jiang5, Hualin Wei5, Chenlin Zhang6. 1. Department of Spine, Wuxi Hospital Affiliated to Nanjing University of Chinese Medicine, Wuxi 214000, PR China. Electronic address: chengjfhcy@126.com. 2. Department of Clinical Biobank, Affiliated Hospital of Nantong University, Nantong, Jiangsu 226001, PR China; Nantong University Xinling College, Nantong, Jiangsu 226001, PR China. 3. Department of Orthopaedics, Qidong Hospital of Chinese Medicine, Nantong, Jiangsu 226200, PR China. 4. Nanjing University of Chinese Medicine, Nanjing, Jiangsu 2210023, PR China. 5. Department of Spine, Wuxi Hospital Affiliated to Nanjing University of Chinese Medicine, Wuxi 214000, PR China; Nanjing University of Chinese Medicine, Nanjing, Jiangsu 2210023, PR China. 6. Department of Orthopaedics, Qidong Hospital of Chinese Medicine, Nantong, Jiangsu 226200, PR China. Electronic address: 20161650@njucm.edu.cn.
Abstract
BACKGROUND: Glioma is the most lethal primary brain tumor. Lower-grade glioma (LGG) is the crucial pathological type of Glioma. Immune-infiltration of the tumor microenvironment positively associated with overall survival in LGG. SYT16 is a gene has not been reported in cancer. We assess the role of SYT16 in LGG, via the publicly available TCGA database. METHODS: Gene Expression Profiling Interactive Analysis (GEPIA) was used to analyze the expression of SYT16 in LGG. We evaluated the influence of SYT16 on survival of LGG patients by survival module. Then, datasets of LGG were downloaded from TCGA. The correlations between the clinical information and SYT16 expression were analyzed using logistic regression. Univariable survival and Multivariate Cox analysis was used to compare several clinical characteristics with survival. we also explore the correlation between SYT16 and cancer immune infiltrates using CIBERSORT and correlation module of GEPIA. Gene set enrichment analysis (GSEA) was performed using the TCGA dataset. In addition, we use TIMER to explore the collection of SYT16 Expression and Immune Infiltration Level in LGG and to explore cumulative survival in LGG. RESULTS: The univariate analysis using logistic regression, indicated that increased SYT16 expression significantly correlated with the tumor grade. Moreover, multivariate analysis revealed that the up-regulated SYT16 expression is an independent prognostic factor for good prognosis. Specifically, SYT16 expression level has significant negative correlations with infiltrating levels of B cell, CD4+ T cells, Macrophages, Neutrophils and DCs in LGG. In addition, GSEA identified ingle organism behavior, gated channel activity, cognition, transporter complex and ligand gated channel activity in Gene Ontology (GO) were differentially enriched in the high SYT16 expression phenotype pathway. Neuroactive ligand receptor interaction, calcium signaling pathway, long term potentiation, type II diabetes mellitus and long term depression were identified as differentially enriched pathway in Kyoto Encyclopedia of Genes and Genomes (KEGG). CONCLUSION: SYT16 is a Prognostic Biomarker and Correlated with Immune Infiltrates in LGG.
BACKGROUND:Glioma is the most lethal primary brain tumor. Lower-grade glioma (LGG) is the crucial pathological type of Glioma. Immune-infiltration of the tumor microenvironment positively associated with overall survival in LGG. SYT16 is a gene has not been reported in cancer. We assess the role of SYT16 in LGG, via the publicly available TCGA database. METHODS: Gene Expression Profiling Interactive Analysis (GEPIA) was used to analyze the expression of SYT16 in LGG. We evaluated the influence of SYT16 on survival of LGG patients by survival module. Then, datasets of LGG were downloaded from TCGA. The correlations between the clinical information and SYT16 expression were analyzed using logistic regression. Univariable survival and Multivariate Cox analysis was used to compare several clinical characteristics with survival. we also explore the correlation between SYT16 and cancer immune infiltrates using CIBERSORT and correlation module of GEPIA. Gene set enrichment analysis (GSEA) was performed using the TCGA dataset. In addition, we use TIMER to explore the collection of SYT16 Expression and Immune Infiltration Level in LGG and to explore cumulative survival in LGG. RESULTS: The univariate analysis using logistic regression, indicated that increased SYT16 expression significantly correlated with the tumor grade. Moreover, multivariate analysis revealed that the up-regulated SYT16 expression is an independent prognostic factor for good prognosis. Specifically, SYT16 expression level has significant negative correlations with infiltrating levels of B cell, CD4+ T cells, Macrophages, Neutrophils and DCs in LGG. In addition, GSEA identified ingle organism behavior, gated channel activity, cognition, transporter complex and ligand gated channel activity in Gene Ontology (GO) were differentially enriched in the high SYT16 expression phenotype pathway. Neuroactive ligand receptor interaction, calcium signaling pathway, long term potentiation, type II diabetes mellitus and long term depression were identified as differentially enriched pathway in Kyoto Encyclopedia of Genes and Genomes (KEGG). CONCLUSION:SYT16 is a Prognostic Biomarker and Correlated with Immune Infiltrates in LGG.