Overexpression of oncostatin M receptor regulates local immune response in glioblastoma

Guo, Qing; Guan, Gefei; Cao, Ji; Zou, Cunyi; Zhu, Chen; Cheng, Wen; Xu, Xiaowu; Lin, Zhiguo; Peng, Cheng; Wu, Anhua

doi:10.1002/jcp.28197

Cited by 22 publications

(22 citation statements)

References 35 publications

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“…Consistent with this result, bioinformatics analysis revealed that high OSMR expression indicated poor survival for glioblastoma patients [28]. In a more recent study, OSMR was found to contribute to local immune response and extracellular matrix progress in GBM and was an independent predictive factor for progressive malignancy and prognostic marker in the response prediction to radiotherapy and chemotherapy [29]. In this study, we found the antisense RNA of OSM was an independent risk factor for patient survival.…”

Section: Discussionsupporting

confidence: 85%

Prediction of the Outcome for Patients with Glioblastoma with lncRNA Expression Profiles

Liu

et al. 2019

BioMed Research International

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Background. Progress in gene sequencing has paved the way for precise outcome prediction of the heterogeneous disease of glioblastoma. The aim was to assess the potential of utilizing the lncRNA expression profile for predicting glioblastoma patient survival. Materials and Methods. Clinical and lncRNA expression data were downloaded from the public database of the cancer genome atlas. Differentially expressed lncRNAs between glioblastoma and normal brain tissue were screened by bioinformatics analysis. The samples were randomly separated into the training and testing sets. Univariate Cox regression, least absolute shrinkage, selection operator regression, and multivariate Cox regression were performed to develop the prediction model with the training set, which was presented as a forest plot. The performance of the model was validated by discrimination and calibration analysis in both the training and testing sets. Patient survival between model-predicted low- and high-risk subgroups was compared in both the training and testing sets. Results. One thousand two hundred and fifty-five differentially expressed lncRNAs between glioblastoma and normal brain tissues were screened. After univariate Cox regression and the least absolute shrinkage and selection operator regression, a 12 lncRNA constituted prediction model was developed by multivariate Cox regression. Of the 12 lncRNAs, 4 lncRNAs were independent risk factors for patient survival. The areas under the receiver operating characteristic curves of the model for predicting 0.5-, 1-, 1.5-, and 2-year patient survival was 0.788, 0.824, 0.874, and 0.886, respectively in the training set and 0.723, 0.84, 0.816, and 0.773 in the testing set. The calibration curves of the prediction model fitted well. Significant survival disparity was observed between the model dichotomized low- and high-risk subgroups in both the training and testing set. Conclusions. LncRNA expression signature can predict glioblastoma patient survival, promising lncRNA-based survival prediction.

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Section: Discussionsupporting

confidence: 85%

Prediction of the Outcome for Patients with Glioblastoma with lncRNA Expression Profiles

Liu

et al. 2019

BioMed Research International

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“…Several studies demonstrated immune cell infiltration in the brain of patients with malignant glioma, although it was previously considered as an immune-privileged organ [39,40]. However, tumor-derived factors suppressed immune responses of these cells [41]. Previous studies showed that tumor-residing glioma cells regulate the immune response through recruitment of immune-suppressive cells, which represents a challenge in the development of cell immunotherapies [42].…”

Section: Discussionmentioning

confidence: 99%

HSP70/IL-2 Treated NK Cells Effectively Cross the Blood Brain Barrier and Target Tumor Cells in a Rat Model of Induced Glioblastoma Multiforme (GBM)

Sharifzad

Mardpour

et al. 2020

IJMS

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Natural killer (NK) cell therapy is one of the most promising treatments for Glioblastoma Multiforme (GBM). However, this emerging technology is limited by the availability of sufficient numbers of fully functional cells. Here, we investigated the efficacy of NK cells that were expanded and treated by interleukin-2 (IL-2) and heat shock protein 70 (HSP70), both in vitro and in vivo. Proliferation and cytotoxicity assays were used to assess the functionality of NK cells in vitro, after which treated and naïve NK cells were administrated intracranially and systemically to compare the potential antitumor activities in our in vivo rat GBM models. In vitro assays provided strong evidence of NK cell efficacy against C6 tumor cells. In vivo tracking of NK cells showed efficient homing around and within the tumor site. Furthermore, significant amelioration of the tumor in rats treated with HSP70/Il-2-treated NK cells as compared to those subjected to nontreated NK cells, as confirmed by MRI, proved the efficacy of adoptive NK cell therapy. Moreover, results obtained with systemic injection confirmed migration of activated NK cells over the blood brain barrier and subsequent targeting of GBM tumor cells. Our data suggest that administration of HSP70/Il-2-treated NK cells may be a promising therapeutic approach to be considered in the treatment of GBM.

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“…The association between the polymorphism of OSMR gene and bladder cancer was determined, which would affect the recurrence rate, overall survival and tumor grade [17]. Xu et al have discovered that OSMR might be a prognostic biomarker in glioblastoma and overexpression of OSMR can regulate the immune response of glioblastoma [18,19].…”

Section: Discussionmentioning

confidence: 99%

Identification of novel candidate biomarkers and efficacious small molecule drugs for pancreatic adenocarcinoma based on comprehensive bioinformatics technology

Yang¹,

Wang²,

Li³

2020

Preprint

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Background: Pancreatic adenocarcinoma (PAAD) is considered as one of most serious solid tumors with high mortality as well as incidence. We performed this present study to screen out novel biomarkers and potential efficacious small drugs in PAAD using integrated bioinformatics analyses. Methods: Expression profiles derived from TCGA and GTEx datasets were integrated by following bioinformatics methods: DEGs analysis, WGCNA, KEGG and GO enrichment analyses. Kaplan-Meier method was conducted to assess the prognostic performance of genes, and the predictive value of identified genes was determined by the ROC analysis. CMap analysis was utilized to identify the related small molecule drugs in PAAD. Results: A total of 4777 DEGs containing 2536 up-regulated and 2241 down-regulated genes were identified. WGCNA identified six modules that were related with clinical characteristics, and functional enrichment analyses showed that all genes of blue module were enriched in EMT, PI3K/Akt signaling pathway and other important biological processes and pathways. Moreover, three genes ( ITGB1 , ITGB5 , and OSMR ) of blue module were determined as key genes with higher expression levels and significant prognostic value. ROC analysis revealed that these three genes had excellent diagnostic efficiency for PAAD. These three key genes were highly regulated in PAAD tissues compared with normal controls. A panel of small molecule drugs including thapsigargin, viomycin, adiphenine, and CP-690334-01 were screened out to treat PAAD. Conclusion: In conclusion, our findings identified three key genes implicated in PAAD and screened out several potential small drugs to treat it, which may shed novel insights into the development of PAAD and its treatment.

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Overexpression of oncostatin M receptor regulates local immune response in glioblastoma

Cited by 22 publications

References 35 publications

Prediction of the Outcome for Patients with Glioblastoma with lncRNA Expression Profiles

Prediction of the Outcome for Patients with Glioblastoma with lncRNA Expression Profiles

HSP70/IL-2 Treated NK Cells Effectively Cross the Blood Brain Barrier and Target Tumor Cells in a Rat Model of Induced Glioblastoma Multiforme (GBM)

Identification of novel candidate biomarkers and efficacious small molecule drugs for pancreatic adenocarcinoma based on comprehensive bioinformatics technology

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