Dataset Information

GRB10 and E2F3 as Diagnostic Markers of Osteoarthritis and Their Correlation with Immune Infiltration.

ABSTRACT: This study aimed to find potential diagnostic markers for osteoarthritis (OA) and analyze the role of immune cells infiltration in this pathology. We used OA datasets from the Gene Expression Omnibus database. First, R software was used to identify differentially expressed genes (DEGs) and perform functional correlation analysis. Then least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination algorithms were used to screen and verify the diagnostic markers of OA. Finally, CIBERSORT was used to evaluate the infiltration of immune cells in OA tissues, and the correlation between diagnostic markers and infiltrating immune cells was analyzed. A total of 458 DEGs were screened in this study. GRB10 and E2F3 (AUC = 0.962) were identified as diagnostic markers of OA. Immune cell infiltration analysis found that resting mast cells, T regulatory cells, CD4 memory resting T cells, activated NK cells, and eosinophils may be involved in the OA process. In addition, GRB10 was correlated with NK resting cells, naive CD4 + T cells, and M1 macrophages, while E2F3 was correlated with resting mast cells. In conclusion, GRB10 and E2F3 can be used as diagnostic markers of osteoarthritis, and immune cell infiltration plays an important role in the occurrence and progression of OA.

SUBMITTER: Deng YJ

PROVIDER: S-EPMC7151213 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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GRB10 and E2F3 as Diagnostic Markers of Osteoarthritis and Their Correlation with Immune Infiltration.

Deng Ya-Jun YJ Ren En-Hui EH Yuan Wen-Hua WH Zhang Guang-Zhi GZ Wu Zuo-Long ZL Xie Qi-Qi QQ

Diagnostics (Basel, Switzerland) 20200322 3

This study aimed to find potential diagnostic markers for osteoarthritis (OA) and analyze the role of immune cells infiltration in this pathology. We used OA datasets from the Gene Expression Omnibus database. First, R software was used to identify differentially expressed genes (DEGs) and perform functional correlation analysis. Then least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination algorithms were used to screen ...[more]

PMID: 32235747

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Project description:BackgroundOsteoarthritis (OA), which is due to the progressive loss and degeneration of articular cartilage, is the leading cause of disability worldwide. Therefore, it is of great significance to explore OA biomarkers for the prevention, diagnosis, and treatment of OA.Methods and materialsThe GSE129147, GSE57218, GSE51588, GSE117999, and GSE98918 datasets with normal and OA samples were downloaded from the Gene Expression Omnibus (GEO) database. The GSE117999 and GSE98918 datasets were integrated, and immune infiltration was evaluated. The differentially expressed genes (DEGs) were analyzed using the limma package in R, and weighted gene co-expression network analysis (WGCNA) was used to explore the co-expression genes and co-expression modules. The co-expression module genes were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and hub genes were identified by the degree, MNC, closeness, and MCC algorithms. The hub genes were used to construct a diagnostic model based on support vector machines.ResultsThe Immune Score in the OA samples was significantly higher than in the normal samples, and a total of 2313 DEGs were identified. Through WGCNA, we found that the yellow module was significantly positively correlated with the OA samples and Immune Score and negatively correlated with the normal samples. The 142 DEGs of the yellow module were related to biological processes such as regulation of inflammatory response, positive regulation of inflammatory response, blood vessel morphogenesis, endothelial cell migration, and humoral immune response. The intersections of the genes obtained by the 4 algorithms resulted in 5 final hub genes, and the diagnostic model constructed with these 5 genes showed good performance in the training and validation cohorts.ConclusionsThe 5-gene diagnostic model can be used to diagnose OA and guide clinical decision-making.

Project description:BackgroundMKI67 plays a vital role in the tumour microenvironment (TME) and congenital immunity. The present work focuses on exploring the prognosis prediction performance of MKI67 and its associations with T cell activity and immune infiltration within numerous cancers, especially hepatocellular liver carcinoma (LIHC).MethodsOncomine, GEPIA2, and HPA were adopted to analyse MKI67 levels in different types of cancers. The prognostic prediction performance of MKI67 was evaluated through the TCGA portal, GEPIA2, LOGpc, and Kaplan-Meier Plotter databases. The associations of MKI67 with related gene marker sets and immune infiltration were inspected through TISIDB, GEPIA2, and TIMER. We chose MKI67 to analyse biological processes (BPs) and KEGG pathways related to the coexpressed genes. Furthermore, the gene-miRNA interaction network for MKI67 in liver cancer was also examined based on the miRWalk database.ResultsMKI67 expression decreased in many cancers related to the dismal prognostic outcome of LIHC. We found that MKI67 significantly affected the prognosis of LIHC in terms of histology and grade. Increased MKI67 levels were directly proportional to the increased immune infiltration degrees of numerous immune cells and functional T cells, such as exhausted T cells. In addition, several critical genes related to exhausted T cells, including TIM-3, TIGIT, PD-1, LAG3, and CXCL13, were strongly related to MKI67. Further analyses showed that MKI67 was associated with adaptive immunity, cell adhesion molecules (CAMs), and chemokine/immune response signal transduction pathways.ConclusionMKI67 acts as a prognostic prediction biomarker in several cancers, particularly LIHC. Upregulation of MKI67 elevates the degree of immune infiltration of many immune cell subtypes, including functional T cells, CD4+ T cells, and CD8+ T cells. Furthermore, MKI67 shows a close correlation with T cell exhaustion, which plays a vital role in promoting T cell exhaustion within LIHC. Detection of the MKI67 level contributes to prognosis prediction and MKI67 modulation within exhausted T cells, thus providing a new method to optimize the efficacy of anti-LIHC immunotherapy.

Dataset Information

GRB10 and E2F3 as Diagnostic Markers of Osteoarthritis and Their Correlation with Immune Infiltration.

Publications

GRB10 and E2F3 as Diagnostic Markers of Osteoarthritis and Their Correlation with Immune Infiltration.

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