SYT16 is a prognostic biomarker and correlated with immune infiltrates in glioma: A study based on TCGA data.


Journal

International immunopharmacology
ISSN: 1878-1705
Titre abrégé: Int Immunopharmacol
Pays: Netherlands
ID NLM: 100965259

Informations de publication

Date de publication:
Jul 2020
Historique:
received: 15 11 2019
revised: 16 03 2020
accepted: 05 04 2020
pubmed: 15 4 2020
medline: 26 2 2021
entrez: 15 4 2020
Statut: ppublish

Résumé

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. 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. 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). SYT16 is a Prognostic Biomarker and Correlated with Immune Infiltrates in LGG.

Sections du résumé

BACKGROUND 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 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 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 CONCLUSIONS
SYT16 is a Prognostic Biomarker and Correlated with Immune Infiltrates in LGG.

Identifiants

pubmed: 32289666
pii: S1567-5769(19)32622-0
doi: 10.1016/j.intimp.2020.106490
pii:
doi:

Substances chimiques

Biomarkers, Tumor 0
Neoplasm Proteins 0
Synaptotagmins 134193-27-4

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

106490

Informations de copyright

Copyright © 2020 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest None.

Auteurs

Jianfeng Chen (J)

Department of Spine, Wuxi Hospital Affiliated to Nanjing University of Chinese Medicine, Wuxi 214000, PR China. Electronic address: chengjfhcy@126.com.

Ziheng Wang (Z)

Department of Clinical Biobank, Affiliated Hospital of Nantong University, Nantong, Jiangsu 226001, PR China; Nantong University Xinling College, Nantong, Jiangsu 226001, PR China.

Wei Wang (W)

Department of Clinical Biobank, Affiliated Hospital of Nantong University, Nantong, Jiangsu 226001, PR China; Nantong University Xinling College, Nantong, Jiangsu 226001, PR China.

Shiqi Ren (S)

Department of Clinical Biobank, Affiliated Hospital of Nantong University, Nantong, Jiangsu 226001, PR China; Nantong University Xinling College, Nantong, Jiangsu 226001, PR China.

Jinbiao Xue (J)

Department of Orthopaedics, Qidong Hospital of Chinese Medicine, Nantong, Jiangsu 226200, PR China.

Lin Zhong (L)

Nanjing University of Chinese Medicine, Nanjing, Jiangsu 2210023, PR China.

Tao Jiang (T)

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.

Hualin Wei (H)

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.

Chenlin Zhang (C)

Department of Orthopaedics, Qidong Hospital of Chinese Medicine, Nantong, Jiangsu 226200, PR China. Electronic address: 20161650@njucm.edu.cn.

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Classifications MeSH