Novel Immune-Related Gene Signature for Risk Stratification and Prognosis of Survival in Lower-Grade Glioma

Zhang, Mingwei; Wang, Xuezhen; Chen, Xiaoping; Zhang, Qiuyu; Hong, Jinsheng

doi:10.3389/fgene.2020.00363

Cited by 147 publications

(118 citation statements)

References 61 publications

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“…Zhang et al identified six immune-related genes (CANX, HSPA1B, KLRC2, PSM C6, RFXAP, and TAP1) as risk signatures. Importantly, Kaplan-Meier and ROC curves, as well as risk plotting, verified their performance in TCGA and CGGA datasets 28 . Zhang et al observed that a high immune score was associated with low methylation and copy number variation levels, a high expression of immunosuppressive markers (CD27, PDL1 and CTLA4), and a shorter recurrence-free survival 29 .…”

Section: Discussionmentioning

confidence: 68%

Molecular subtyping of glioblastoma based on immune-related genes for prognosis

Chen

Fan

Zhao

et al. 2020

Sci Rep

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Glioblastoma (GBM) is associated with an increasing mortality and morbidity and is considered as an aggressive brain tumor. Recently, extensive studies have been carried out to examine the molecular biology of GBM, and the progression of GBM has been suggested to be correlated with the tumor immunophenotype in a variety of studies. Samples in the current study were extracted from the ImmPort and TCGA databases to identify immune-related genes affecting GBM prognosis. A total of 92 immune-related genes displaying a significant correlation with prognosis were mined, and a shrinkage estimate was conducted on them. Among them, the 14 most representative genes showed a marked correlation with patient prognosis, and LASSO and stepwise regression analysis was carried out to further identify the genes for the construction of a predictive GBM prognosis model. Then, samples in training and test cohorts were incorporated into the model and divided to evaluate the efficiency, stability, and accuracy of the model to predict and classify the prognosis of patients and to identify the relevant immune features according to the median value of RiskScore (namely, Risk-H and Risk-L). In addition, the constructed model was able to instruct clinicians in diagnosis and prognosis prediction for various immunophenotypes.

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Section: Discussionmentioning

confidence: 68%

Molecular subtyping of glioblastoma based on immune-related genes for prognosis

Chen

Fan

Zhao

et al. 2020

Sci Rep

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“…For example, Deng et al [34] found up to 397 IRGs were signi cantly associated with LGG survival. Zhang et al [21] established a risk model based on 6 IRGs (CANX, HSPA1B, KLRC2, PSMC6, RFXAP, and TAP1). However, one common disadvantage of all the above assays lies in standardizing gene expression pro les, which is di cult to avoid.…”

Section: Discussionmentioning

confidence: 99%

“…Immune-checkpoint inhibitors and immunotherapy play a part in improving gliomas prognosis [15,16]. As to prognosis biomarkers, researchers have explored the possibility of stratifying patients with malignant tumors (including gliomas) based on gene expression pro les and have constructed a multigene-expression model that can be applied to stratify high-risk subgroup [17][18][19][20][21][22]. One common drawback of the above studies is that gene expression pro les require suitable normalization.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of an immune-related gene pairs signature in lower-grade glioma

Zhang

Shuai

Zhang

et al. 2020

Preprint

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Background Lower-grade gliomas (LGG) are the prevalent primary intracerebral malignancy tumor. Increasing evidence indicated an association between immune signature and LGG prognosis. Thus, we aim to develop an immune-related gene pairs (IRGPs) signature that can predict prognosis for LGG. Method: Gene expression levels and clinical information of LGG patients (LGGs) were collected from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. The two databases were divided into training cohort (n = 515) and an independent validation cohort (n = 604). IGRPs significantly associated with prognosis were selected by Cox regression. Gene set enrichment analysis and filtration were performed on IGRPs. Results Within 1991 immune genes, an 8 IRGPs signature including 15 unique genes was constructed, which had a significant association with survival. In the validation dataset, the IRGPs signature significantly stratified LGGs into low- and high-risk groups (P < 0.001), and it remained an independent prognostic factor in univariate and multivariate analyses (P < 0.001). Additionally, 26 functional pathways were filtrated through the intersection of Gene set enrichment analysis (GSEA) and gene ontology (GO) enrichment analysis. Conclusion The IGRPs signature demonstrated good prognostic value in lower-grade glioma, which may provide new insights into individual treatment for glioma patients. And the IGRPs might take effect through these filtrated 26 functional pathways.

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“…Additionally, gene expression pro les and clinical information of 214 patients with SKCM from the Gene Expression Omnibus (GEO) GSE65904 corhort (https://www.ncbi.nlm.nih.gov/geo/) were downloaded as a validation cohort [34]. All gene expression les were log2-transformed, and pro les from different platforms were normalized using the R package "sva" to remove batch effects reported by previous studies [35,36]. Furthermore, 1399 protein domains of OS were downloaded from the GeneCards database (https://www.genecards.org) with relevance score ≥ 7, and subsequently applied for further exploration.…”

Section: Data Acquisitionmentioning

confidence: 99%

Novel Oxidative Stress-related Prognostic Biomarkers for Melanoma Associated With Tumor Metastasis

Zhao

2020

Preprint

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Background Skin cutaneous melanoma (SKCM) is a prevalent skin cancer whose metastatic form is dangerous due to its high morbidity and mortality. Previous studies have systematically established the vital role of oxidative stress (OS) in melanoma progression. This study aimed to identify prognostic OS genes closely associated with SKCM and illustrate their potential mechanisms. Methods Transcriptome data and corresponding clinical traits of patients with SKCM were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A weighted gene co-expression network analysis was conducted to identify relationships between clinical features and OS genes in specific modules. Subsequently, Cox regression analysis was performed on candidate OS genes; four hub prognosis-associated OS genes (AKAP9, VPS13C, ACSL4, and HMOX2) were identified to construct a prognostic model. Results After a series of bioinformatics analysis, our prognostic model was identified significantly associated with the overall survival of patients with SKCM and metastatic ability of the cancer. Furthermore, our risk model demonstrated improved diagnostic accuracy in TCGA and GEO cohorts. In addition, we established two nomograms based on either risk score or hub genes, which displayed favorable discriminating ability for SKCM. Conclusions Together, our results provide novel insight into the potential applications of OS-associated genes in SKCM.

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Novel Immune-Related Gene Signature for Risk Stratification and Prognosis of Survival in Lower-Grade Glioma

Cited by 147 publications

References 61 publications

Molecular subtyping of glioblastoma based on immune-related genes for prognosis

Molecular subtyping of glioblastoma based on immune-related genes for prognosis

Development and validation of an immune-related gene pairs signature in lower-grade glioma

Novel Oxidative Stress-related Prognostic Biomarkers for Melanoma Associated With Tumor Metastasis

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