A retrospective analysis of GSE84010: Cell adhesion molecules might contribute to bevacizumab resistance in glioblastoma

Fu, Minjie; Hussain, Arshad; Dong, Youting; Yang, Fei

doi:10.1016/j.jocn.2021.01.019

Cited by 3 publications

(4 citation statements)

References 23 publications

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“…Cell adhesion molecules play a vital role in the invasion and migration of malignant tumor cells during metastasis [ 22 ]. In most solid tumors such as breast cancer and glioblastoma, increased expression of cell adhesion molecules contributes to the development of drug resistance [ 23 , 24 ]. Evidence suggests that in MM, treatment with bortezomib can reverse cell adhesion molecule-dependent drug resistance and that this effect may be specific for bortezomib because it has not been observed in other drugs such as vincristine or dexamethasone [ 25 ].…”

Section: Discussionmentioning

confidence: 99%

“…However, the mechanism by which the downregulation of cell adhesion molecules contributes to carfilzomib resistance remains unelucidated. Notably, we observed that cell adhesion molecules may play discrepant roles in myeloma cells when compared with other solid tumors because the upregulation of cell adhesion molecules is often associated with drug resistance in solid tumors [ 23 , 24 ].…”

Section: Discussionmentioning

confidence: 99%

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Identification of <i>TNFRSF1A</i> as a novel regulator of carfilzomib resistance in multiple myeloma

ZHAO,

YANG,

ZHANG

et al. 2024

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Multiple myeloma (MM) is a hematological tumor with high mortality and recurrence rate. Carfilzomib is a new-generation proteasome inhibitor that is used as the first-line therapy for MM. However, the development of drug resistance is a pervasive obstacle to treating MM. Therefore, elucidating the drug resistance mechanisms is conducive to the formulation of novel therapeutic therapies. To elucidate the mechanisms of carfilzomib resistance, we retrieved the GSE78069 microarray dataset containing carfilzomib-resistant LP-1 MM cells and parental MM cells. Differential gene expression analyses revealed major alterations in the major histocompatibility complex (MHC) and cell adhesion molecules. The upregulation of the tumor necrosis factor (TNF) receptor superfamily member 1A ( TNFRSF1A ) gene was accompanied by the downregulation of MHC genes and cell adhesion molecules. Furthermore, to investigate the roles of these genes, we established a carfilzomib-resistant cell model and observed that carfilzomib resistance induced TNFRSF1A overexpression and TNFRSF1A silencing reversed carfilzomib resistance and reactivated the expression of cell adhesion molecules. Furthermore, TNFRSF1A silencing suppressed the tumorigenesis of MM cells in immunocompetent mice, indicating that TNFRSF1A may lead to carfilzomib resistance by dampening antitumor immunity. Furthermore, our results indicated that TNFRSF1A overexpression conferred carfilzomib resistance in MM cells and suppressed the expression of MHC genes and cell adhesion molecules. The suppression of MHC genes and cell adhesion molecules may impair the interaction between immune cells and cancer cells to impair antitumor immunity. Future studies are warranted to further investigate the signaling pathway underlying the regulatory role of TNFRSF1A in MM cells.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Identification of <i>TNFRSF1A</i> as a novel regulator of carfilzomib resistance in multiple myeloma

ZHAO,

YANG,

ZHANG

et al. 2024

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“…Many genomic and epigenetic biomarkers for gliomas have been found in situ or blood [ 5 – 7 ]. Specific biomarkers also favor their expression in particular subtypes.…”

Section: Introductionmentioning

confidence: 99%

Gene clusters based on OLIG2 and CD276 could distinguish molecular profiling in glioblastoma

Zhang

Liu

et al. 2021

J Transl Med

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Background The molecular profiling of glioblastoma (GBM) based on transcriptomic analysis could provide precise treatment and prognosis. However, current subtyping (classic, mesenchymal, neural, proneural) is time-consuming and cost-intensive hindering its clinical application. A simple and efficient method for classification was imperative. Methods In this study, to simplify GBM subtyping more efficiently, we applied a random forest algorithm to conduct 26 genes as a cluster featured with hub genes, OLIG2 and CD276. Functional enrichment analysis and Protein–protein interaction were performed using the genes in this gene cluster. The classification efficiency of the gene cluster was validated by WGCNA and LASSO algorithms, and tested in GSE84010 and Gravandeel’s GBM datasets. Results The gene cluster (n = 26) could distinguish mesenchymal and proneural excellently (AUC = 0.92), which could be validated by multiple algorithms (WGCNA, LASSO) and datasets (GSE84010 and Gravandeel’s GBM dataset). The gene cluster could be functionally enriched in DNA elements and T cell associated pathways. Additionally, five genes in the signature could predict the prognosis well (p = 0.0051 for training cohort, p = 0.065 for test cohort). Conclusions Our study proved the accuracy and efficiency of random forest classifier for GBM subtyping, which could provide a convenient and efficient method for subtyping Proneural and Mesenchymal GBM.

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“…Many biomarkers associated with genomics, epigenetics and metabolism have been found in situ or in blood [5][6][7]. Speci c biomarkers favor their expression in particular tumor types.…”

Section: Introductionmentioning

confidence: 99%

Gene Clusters Based on OLIG2 and CD276 Could Distinguish Molecular Profiling in Glioblastoma

Zhang

Liu

et al. 2021

Preprint

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BackgroundThe molecular classification of glioblastoma (GBM) based on transcriptomic analysis could provide precise treatment and prognosis. However, current subtyping (Classic, Mesenchymal, Neural, Proneural) is a time-consuming and cost-intensive process, which hinders its clinical application. A simple and efficient method for classification was imperative.MethodsRandom forest algorithm was applied to conduct a gene cluster featured with hub genes, OLIG2 and CD276. Functional enrichment analysis and Protein-protein interaction were performed using the genes in this gene cluster. The classification efficiency of the gene cluster was validated by WGCNA and LASSO algorithms, and tested in GSE84010 and Gravandeel’s GBM datasets. ResultsThe gene cluster (n = 26) could distinguish mesenchymal and proneural excellently (AUC = 0.92), which could be validated by multiple algorithms (WGCNA, LASSO) and datasets (GSE84010 and Gravandeel’s GBM dataset). The gene cluster could be functionally enriched in DNA elements and T cell associated pathways. Additionally, five genes in the signature could predict the prognosis well (p = 0.0051 for training cohort, p = 0.065 for test cohort). ConclusionsThis study proved the accuracy and efficiency of random forest classifier for GBM subtyping and provided a convenient and efficient method for subtyping Proneural and Mesenchymal GBM.

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A retrospective analysis of GSE84010: Cell adhesion molecules might contribute to bevacizumab resistance in glioblastoma

Cited by 3 publications

References 23 publications

Identification of <i>TNFRSF1A</i> as a novel regulator of carfilzomib resistance in multiple myeloma

Identification of <i>TNFRSF1A</i> as a novel regulator of carfilzomib resistance in multiple myeloma

Gene clusters based on OLIG2 and CD276 could distinguish molecular profiling in glioblastoma

Gene Clusters Based on OLIG2 and CD276 Could Distinguish Molecular Profiling in Glioblastoma

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