Dataset Information

A bioinformatic study of IGFBPs in glioma regarding their diagnostic, prognostic, and therapeutic prediction value.

ABSTRACT:

Background

It is essential to develop better biomarkers for diagnosis, prediction, and treating glioma patients to ensure successful clinical outcomes. The clinical application of Insulin-Like Growth Factor Binding Proteins (IGFBPs) for glioma is yet to be investigated.

Methods

Cohorts were obtained from TCGA, GTEx, CGGA, HPA, Oncomine, CancerSEA, TISCH, etc. The expressions, methylation, and survival association of IGFBPs were analyzed. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to construct a prognostic model. The correlation of IGFBPs and immune cells or immune molecules was analyzed. The effect of IGFBPs on immune therapy and chemotherapy was analyzed. The top 500 correlated genes of IGFBPs were enriched in GO terms. Correlations between IGFBPs and functional states in glioma single-cells and correlations between IGFBPs and chemokines in samples were analyzed.

Results

IGFBPs were overexpressed in glioma. Malignant cells were the major cell types that expressed IGFBPs. Higher-grade glioma had a higher expression of IGFBPs, which were associated with worse survival. A survival model of IGFBPs for glioma patients was trained and validated. A nomogram was generated. Generally, IGFBPs mRNA expression was negatively correlated with B cells and T cells. IGFBPs were associated with multiple immune molecules and drug sensitivity.

Conclusion

IGFBPs were powerful diagnostic, prognostic, and therapeutic prediction biomarkers for glioma.

SUBMITTER: Liu H

PROVIDER: S-EPMC10086936 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

A bioinformatic study of IGFBPs in glioma regarding their diagnostic, prognostic, and therapeutic prediction value.

Liu Hengrui H Tang Tao T

American journal of translational research 20230315 3

<h4>Background</h4>It is essential to develop better biomarkers for diagnosis, prediction, and treating glioma patients to ensure successful clinical outcomes. The clinical application of Insulin-Like Growth Factor Binding Proteins (IGFBPs) for glioma is yet to be investigated.<h4>Methods</h4>Cohorts were obtained from TCGA, GTEx, CGGA, HPA, Oncomine, CancerSEA, TISCH, etc. The expressions, methylation, and survival association of IGFBPs were analyzed. The Least Absolute Shrinkage and Selection ...[more]

PMID: 37056850

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Project description:Background As a common primary intracranial tumor, the diagnosis and therapy of low-grade glioma (LGG) remains a pivotal barrier. Cuproptosis, a new way induces cell death, has attracted worldwide attention. However, the relationship between cuproptosis and LGG remains unknown. Our study is all about finding out if there are any genes related to coproptosis that can be used to predict the outcome of LGG. Methods RNA data and clinical information were selected from Cancer Genome Atlas (TCGA) datasets and the Genotype-Tissue Expression (GTEx), 5 lncRNAs (GAS5.AS1, MYLK.AS1, AC142472.1, AC011346.1, AL359643.3) were identified by Cox univariate and multivariate regression, as well as LASSO Cox regression. In the training and test sets, a dual validation of the predictive signature comprised of these 5 lncRNAs was undertaken. The findings demonstrate that the risk model is able to predict the survival regression of LGG patients and has a good performance in either the KM curve approach or the ROC curve. GO, GSEA and KEGG were carried out to explore the possible molecular processes that affecting the prognosis of LGG. The characteristics of immune microenvironment were investigated by using CIBERSORT, ESTIMATE and ssGSEA. Results We identified five lncRNAs related with cuproptosis that were closely associated with the prognosis of LGG and used these five lncRNAs to develop a risk model. Using this risk model, LGG patients were then divided into high-risk and low-risk groups. The two patient groups had significantly distinct survival characteristics. Analyses of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed that the differential genes of the two patient groups were primarily concentrated in neural active ligand-receptor interaction and cytokine-cytokine receptor interaction. The ssGSEA score determined the information related to immune infiltration, and the two groups were differentially expressed in immune subpopulations such as T cells and B cells as well. Conclusion Our study discovered 5 cuproptosis-related lncRNAs which contribute to predicting patients’ survival of LGG and provide ideas for the exploration of new targets for LGG in the future.

Dataset Information

A bioinformatic study of IGFBPs in glioma regarding their diagnostic, prognostic, and therapeutic prediction value.

Background

Methods

Results

Conclusion

Publications

A bioinformatic study of IGFBPs in glioma regarding their diagnostic, prognostic, and therapeutic prediction value.

Similar Datasets

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