{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Huang R"],"funding":["National Natural Science Foundation of China"],"pagination":["10803-10815"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7521258"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["24(18)"],"pubmed_abstract":["Some studies suggested the prognosis value of immune gene in lower grade glioma (LGG). Recurrence in LGG is a tough clinical problem for many LGG patients. Therefore, prognosis biomarker is required. Multivariate prognosis Cox model was constructed and then calculated the risk score. And differential expressed transcription factors (TFs) and differential expressed immune genes (DEIGs) were co-analysed. Besides, significant immune cells/pathways were identified by single sample gene set enrichment analysis (ssGSEA). Moreover, gene set variation analysis (GSVA) and univariate Cox regression were applied to filter prognostic signalling pathways. Additionally, significant DEIG and immune cells/pathways, and significant DEIG and pathways were co-analysed. Further, differential enriched pathways"],"journal":["Journal of cellular and molecular medicine"],"pubmed_title":["Collagen Type III Alpha 1 chain regulated by GATA-Binding Protein 6 affects Type II IFN response and propanoate metabolism in the recurrence of lower grade glioma."],"pmcid":["PMC7521258"],"funding_grant_id":["81702659","81501203","81772856"],"pubmed_authors":["Yin H","Meng T","Li Z","Lin R","Yan P","Zhang J","Huang Z","Song D","Wu S","Zhu X","Huang R","Hu P"],"additional_accession":[]},"is_claimable":false,"name":"Collagen Type III Alpha 1 chain regulated by GATA-Binding Protein 6 affects Type II IFN response and propanoate metabolism in the recurrence of lower grade glioma.","description":"Some studies suggested the prognosis value of immune gene in lower grade glioma (LGG). Recurrence in LGG is a tough clinical problem for many LGG patients. Therefore, prognosis biomarker is required. Multivariate prognosis Cox model was constructed and then calculated the risk score. And differential expressed transcription factors (TFs) and differential expressed immune genes (DEIGs) were co-analysed. Besides, significant immune cells/pathways were identified by single sample gene set enrichment analysis (ssGSEA). Moreover, gene set variation analysis (GSVA) and univariate Cox regression were applied to filter prognostic signalling pathways. Additionally, significant DEIG and immune cells/pathways, and significant DEIG and pathways were co-analysed. Further, differential enriched pathways","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 Sep","modification":"2025-04-26T21:24:56.271Z","creation":"2020-10-08T07:28:31Z"},"accession":"S-EPMC7521258","cross_references":{"pubmed":["32757451"],"doi":["10.1111/jcmm.15705"]}}