DataSheet4_Weighted Gene Co-Expression Network Analysis (WGCNA) Reveals the Functions of Syndecan-1 to Regulate Immune Infiltration by Influenced T Ce.ZIP (22.37 kB)
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DataSheet4_Weighted Gene Co-Expression Network Analysis (WGCNA) Reveals the Functions of Syndecan-1 to Regulate Immune Infiltration by Influenced T Cells in Glioma.ZIP

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posted on 20.05.2022, 17:06 authored by Jiacheng Zhong, Shuang Shi, Wen Peng, Bei Liu, Biao Yang, Wenyong Niu, Biao Zhang, Chuan Qin, Dong Zhong, Hongjuan Cui, Zhengbao Zhang, Xiaochuan Sun

Our previous studies shown that syndecan-1 (SDC1) may be a novel class of biomarkers for the diagnosis and treatment of glioma, but its specific roles and the in-depth molecular mechanism remain elusive. Here, we used Estimation of STromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithms and single-sample Gene Set Enrichment Analysis (ssGSEA) algorithms to evaluate the immune score of tumor samples and quantify the relative infiltration of immune cells in the tumor microenvironment (TME), respectively, in different data sets obtained from the Chinese Glioma Genome Atlas and The Cancer Gene Atlas. Next, we calculate the correlation of the immune score and immune cells with SDC1, respectively. To identify the specific process regulated by SDC1, the differentially expressed genes (DEGs) analysis between the high and low expression of SDC1 of glioma samples were used to discover the hub genes through Weighted Gene Coexpression Network Analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed cardinal biological processes and pathways involved in genes and tumor grade correlation and survival analysis verified its significance in glioma. The results show that SDC1 is associated with the immune infiltration of glioma in the TME, especially activated CD4+T cells and CD8+T cells. The three data sets filter 8,887 DEGs, the genes in the blue modules were selected as hub genes in WGCNA. GO and KEGG analysis found eight genes in the blue modules involved in antigen processing and presentation in T cells in glioma. Kaplan–Meier estimator and log-rank test statistic determined that the introduced genes are associated with poor prognosis in glioma. Protein–protein network interaction analysis showed that SDC1 may regulate antigen processing and presentation through CTSL or CD4 in glioma. Finally, this study provided insights and clues for the next research direction of SDC1 and identified the key pathways and genes that might participate in the immune escape of glioma. These results might provide a new insight on the study of immune infiltration of glioma in the future.

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