SHOX2 is a Potent Independent Biomarker to Predict Survival of WHO Grade II–III Diffuse Gliomas

Zhang, Yu An; Zhou, Yunyun; Luo, Xin; Song, Kai; Ma, Xiaotu; Sathe, Adwait Amod; Girard, Luc; Xiao, Guanghua; Gazdar, Adi F.

doi:10.1016/j.ebiom.2016.10.040

Cited by 44 publications

(36 citation statements)

References 35 publications

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“…These differences may be attributed to two major factors. First, researchers have analyzed glioma datasets from various perspectives; for instance, some studies have discussed some aspects only in patients with high-grade gliomas [ 14 , 20 , 23 – 27 , 29 ], or LGG [ 21 , 28 ], or at specific checkpoints such as the mitotic spindle checkpoint [ 19 ] or ion channel [ 27 ]. Second, studies have analyzed different types of datasets obtained from various high-throughput platforms such as microarray or next-generation data.…”

Section: Discussionmentioning

confidence: 99%

Identification of potential biomarkers related to glioma survival by gene expression profile analysis

et al. 2019

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Background Recent studies have proposed several gene signatures as biomarkers for different grades of gliomas from various perspectives. However, most of these genes can only be used appropriately for patients with specific grades of gliomas. Methods In this study, we aimed to identify survival-relevant genes shared between glioblastoma multiforme (GBM) and lower-grade glioma (LGG), which could be used as potential biomarkers to classify patients into different risk groups. Cox proportional hazard regression model (Cox model) was used to extract relative genes, and effectiveness of genes was estimated against random forest regression. Finally, risk models were constructed with logistic regression. Results We identified 104 key genes that were shared between GBM and LGG, which could be significantly correlated with patients’ survival based on next-generation sequencing data obtained from The Cancer Genome Atlas for gene expression analysis. The effectiveness of these genes in the survival prediction of GBM and LGG was evaluated, and the average receiver operating characteristic curve (ROC) area under the curve values ranged from 0.7 to 0.8. Gene set enrichment analysis revealed that these genes were involved in eight significant pathways and 23 molecular functions. Moreover, the expressions of ten ( CTSZ, EFEMP2 , ITGA5 , KDELR2 , MDK , MICALL2, MAP 2 K3 , PLAUR , SERPINE1 , and SOCS3 ) of these genes were significantly higher in GBM than in LGG, and comparing their expression levels to those of the proposed control genes ( TBP , IPO8 , and SDHA ) could have the potential capability to classify patients into high- and low- risk groups, which differ significantly in the overall survival. Signatures of candidate genes were validated, by multiple microarray datasets from Gene Expression Omnibus, to increase the robustness of using these potential prognostic factors. In both the GBM and LGG cohort study, most of the patients in the high-risk group had the IDH1 wild-type gene, and those in the low-risk group had IDH1 mutations. Moreover, most of the high-risk patients with LGG possessed a 1p/19q-noncodeletion. Conclusion In this study, we identified survival relevant genes which were shared between GBM and LGG, and those enabled to classify patients into high- and low-risk groups based on expression level analysis. Both the risk groups could be correlated with the well-known genetic variants, thus suggesting their potential prognostic value in clinical application. Electronic supplementary material The online version of this article (10.1186/s1...

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Section: Discussionmentioning

confidence: 99%

Identification of potential biomarkers related to glioma survival by gene expression profile analysis

et al. 2019

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“…Gliomas are graded as I to IV according to the histology and clinical criteria. Grade II and III glioma are designated as low-grade glioma (LGG) ( 1 – 4 ). Although LGG accounts for a minority of gliomas, it is the major cause of mortality for young adults ( 14 ).…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Glioma is the most frequent primary brain tumor with four grades from grade I to IV. Grade IV glioma is also known as glioblastoma, while grade II and III glioma refer to low-grade glioma (LGG) designated by World Health Organization (WHO) ( 1 – 4 ). LGG includes three histological types: astrocytoma, oligodendroglioma, and oligoastrocytoma ( 4 – 6 ), while oligoastrocytoma is no longer considered as a separate entity since the current WHO classification has included molecular markers (including IDH1 mutation and 1p/19q codeletion) to identify astrocytoma and oligodendroglioma, not oligoastrocytoma ( 3 , 7 ).…”

Section: Introductionmentioning

confidence: 99%

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OSlgg: An Online Prognostic Biomarker Analysis Tool for Low-Grade Glioma

Wang

Zhang

et al. 2020

Front. Oncol.

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Glioma is the most frequent primary brain tumor that causes high mortality and morbidity with poor prognosis. There are four grades of gliomas, I to IV, among which grade II and III are low-grade glioma (LGG). Although less aggressive, LGG almost universally progresses to high-grade glioma and eventual causes death if lacking of intervention. Current LGG treatment mainly depends on surgical resection followed by radiotherapy and chemotherapy, but the survival rates of LGG patients are low. Therefore, it is necessary to use prognostic biomarkers to classify patients into subgroups with different risks and guide clinical managements. Using gene expression profiling and long-term follow-up data, we established an Online consensus Survival analysis tool for LGG named OSlgg. OSlgg is comprised of 720 LGG cases from two independent cohorts. To evaluate the prognostic potency of genes, OSlgg employs the Kaplan-Meier plot with hazard ratio and p value to assess the prognostic significance of genes of interest. The reliability of OSlgg was verified by analyzing 86 previously published prognostic biomarkers of LGG. Using OSlgg, we discovered two novel potential prognostic biomarkers (CD302 and FABP5) of LGG, and patients with the elevated expression of either CD302 or FABP5 present the unfavorable survival outcome. These two genes may be novel risk predictors for LGG patients after further validation. OSlgg is public and free to the users at http:// bioinfo.henu.edu.cn/LGG/LGGList.jsp.

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“…In short, our previous study was limited in analyzing promoter methylation levels of proteincoding genes. Recent studies, however, show that DNA methylation in the body areas of genes might be involved in differential promoter usage and also in transcription elongation and alternative splicing [14][15][16]. Gene body methylation has long been ignored but is more prevalent in the genome than promoter hypermethylation.…”

Section: Introductionmentioning

confidence: 99%

Epigenome-Wide Tobacco-Related Methylation Signature Identification and Their Multilevel Regulatory Network Inference for Lung Adenocarcinoma

Dong

et al. 2020

BioMed Research International

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Tobacco exposure is one of the major risks for the initiation and progress of lung cancer. The exact corresponding mechanisms, however, are mainly unknown. Recently, a growing body of evidence has been collected supporting the involvement of DNA methylation in the regulation of gene expression in cancer cells. The identification of tobacco-related signature methylation probes and the analysis of their regulatory networks at different molecular levels may be of a great help for understanding tobacco-related tumorigenesis. Three independent lung adenocarcinoma (LUAD) datasets were used to train and validate the tobacco exposure pattern classification model. A deep selecting method was proposed and used to identify methylation signature probes from hundreds of thousands of the whole epigenome probes. Then, BIMC (biweight midcorrelation coefficient) algorithm, SRC (Spearman’s rank correlation) analysis, and shortest path tracing method were explored to identify associated genes at gene regulation level and protein-protein interaction level, respectively. Afterwards, the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis and GO (Gene Ontology) enrichment analysis were used to analyze their molecular functions and associated pathways. 105 probes were identified as tobacco-related DNA methylation signatures. They belong to 95 genes which are involved in hsa04512, hsa04151, and other important pathways. At gene regulation level, 33 genes are uncovered to be highly related to signature probes by both BIMC and SRC methods. Among them, FARSB and other eight genes were uncovered as Hub genes in the gene regulatory network. Meanwhile, the PPI network about these 33 genes showed that MAGOH, FYN, and other five genes were the most connected core genes among them. These analysis results may provide clues for a clear biological interpretation in the molecular mechanism of tumorigenesis. Moreover, the identified signature probes may serve as potential drug targets for the precision medicine of LUAD.

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SHOX2 is a Potent Independent Biomarker to Predict Survival of WHO Grade II–III Diffuse Gliomas

Cited by 44 publications

References 35 publications

Identification of potential biomarkers related to glioma survival by gene expression profile analysis

Identification of potential biomarkers related to glioma survival by gene expression profile analysis

OSlgg: An Online Prognostic Biomarker Analysis Tool for Low-Grade Glioma

Epigenome-Wide Tobacco-Related Methylation Signature Identification and Their Multilevel Regulatory Network Inference for Lung Adenocarcinoma

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