Dataset Information

Protein disulfide isomerases are promising targets for predicting the survival and tumor progression in glioma patients.

ABSTRACT: The present study focused on the expression patterns, prognostic values and potential mechanism of the PDI family in gliomas. Most PDI family members' mRNA expressions were observed significantly different between gliomas classified by clinical features. Construction of the PDI signature, cluster and risk score models of glioma was done using GSVA, consensus clustering analysis, and LASSO Cox regression analysis respectively. High values of PDI signature/ risk score and cluster 1 in gliomas were associated with malignant clinicopathological characteristics and poor prognosis. Analysis of the distinctive genomic alterations in gliomas revealed that many cases having high PDI signature and risk score were associated with genomic aberrations of driver oncogenes. GSVA analysis showed that PDI family was involved in many signaling pathways in ERAD, apoptosis, and MHC class I among many more. Prognostic nomogram revealed that the risk score was a good prognosis indicator for gliomas. The qRT-PCR and immunohistochemistry confirmed that P4HB, PDIA4 and PDIA5 were overexpressed in gliomas. In summary, this research highlighted the clinical importance of PDI family in tumorigenesis and progression in gliomas.

SUBMITTER: Peng Z

PROVIDER: S-EPMC7041756 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Protein disulfide isomerases are promising targets for predicting the survival and tumor progression in glioma patients.

Peng Zhigang Z Chen Yu Y Cao Hui H Zou Hecun H Wan Xin X Zeng Wenjing W Liu Yanling Y Hu Jiaqing J Zhang Nan N Xia Zhiwei Z Liu Zhixiong Z Cheng Quan Q

Aging 20200205 3

The present study focused on the expression patterns, prognostic values and potential mechanism of the PDI family in gliomas. Most PDI family members' mRNA expressions were observed significantly different between gliomas classified by clinical features. Construction of the PDI signature, cluster and risk score models of glioma was done using GSVA, consensus clustering analysis, and LASSO Cox regression analysis respectively. High values of PDI signature/ risk score and cluster 1 in gliomas were ...[more]

PMID: 32023222

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Project description:Aim: This study aimed to translate a known drug-resistance mechanism of long-term CSF1R inhibition into multicellular biomarkers that can serve as potential therapeutic targets as well as predictive markers for the survival of glioma patients. Methods: Using existing data from a published mouse study of drug resistance in immunotherapy for glioma, we identified multicellular differentially expressed genes (DEGs) between drug-sensitive and drug-resistant mice and translated the DEGs in mouse genome to human homolog. We constructed correlation gene networks for drug resistance in mice and glioma patients and selected candidate genes via concordance analysis of human with mouse gene networks. Markers of drug resistance and an associated predictive signature for patient survival were developed using regularized Cox models with data of glioma patients from The Cancer Genome Atlas (TCGA) database. Predictive performance of the identified predictive signature was evaluated using an independent human dataset from the Chinese Glioma Genome Atlas (CGGA) database. Results: Fourteen genes (CCL22, ADCY2, PDK1, ZFP36, CP, CD2, PLAUR, ACAP1, COL5A1, FAM83D, PBK, FANCA, ANXA7, and TACC3) were identified as genetic biomarkers that were all associated with pathways in glioma progression and drug resistance. Five of the 14 genes (CCL22, ADCY2, PDK1, CD2, and COL5A1) were used to construct a signature that is predictive of patient survival in the proneural subtype GBM patients with an AUC under the time-dependent receiver operating characteristic (ROC) of 2-year survival equal to 0.89. This signature also shows promising predictive accuracy for the survival of LGG patients but not for non-proneural type GBMs. Conclusion: Our translational approach can utilize gene correlation networks from multiple types of cells in the tumor microenvironment of animals. The identified biomarkers of drug resistance have good power to predict patient survival in some major subtypes of gliomas (the proneural subtype of GBM and LGG). The expression levels of the biomarkers of drug resistance may be modified for the development of personalized immunotherapies to prolong survival for a large portion of glioma patients.

Dataset Information

Protein disulfide isomerases are promising targets for predicting the survival and tumor progression in glioma patients.

Publications

Protein disulfide isomerases are promising targets for predicting the survival and tumor progression in glioma patients.

Similar Datasets

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