Identification of prognostic gene signatures of glioblastoma: a study based on TCGA data analysis

Background

The Cancer Genome Atlas (TCGA) project is a large-scale effort with the goal of identifying novel molecular aberrations in glioblastoma (GBM).

Methods

Here, we describe an in-depth analysis of gene expression data and copy number aberration (CNA) data to classify GBMs into prognostic groups to determine correlates of subtypes that may be biologically significant.

Results

To identify predictive survival models, we searched TCGA in 173 patients and identified 42 probe sets (P = .0005) that could be used to divide the tumor samples into 3 groups and showed a significantly (P = .0006) improved overall survival. Kaplan-Meier plots showed that the median survival of group 3 was markedly longer (127 weeks) than that of groups 1 and 2 (47 and 52 weeks, respectively). We then validated the 42 probe sets to stratify the patients according to survival in other public GBM gene expression datasets (eg, {"type":"entrez-geo","attrs":{"text":"GSE4290","term_id":"4290"}}GSE4290 dataset). An overall analysis of the gene expression and copy number aberration using a multivariate Cox regression model showed that the 42 probe sets had a significant (P < .018) prognostic value independent of other variables.

Conclusions

By integrating multidimensional genomic data from TCGA, we identified a specific survival model in a new prognostic group of GBM and suggest that molecular stratification of patients with GBM into homogeneous subgroups may provide opportunities for the development of new treatment modalities.     

CXCR4 expression is associated with proneural-to-mesenchymal transition in glioblastoma

Glioblastoma (GBM) is the most common primary intracranial malignant tumor and consists of three molecular subtypes: proneural (PN), mesenchymal (MES) and classical (CL). Transition between PN to MES subtypes (PMT) is the glioma analog of the epithelial-mesenchymal transition (EMT) in carcinomas and is associated with resistance to therapy. CXCR4 signaling increases the expression of MES genes in glioma cell lines and promotes EMT in other cancers. RNA sequencing (RNAseq) data of PN GBMs in The Cancer Genome Atlas (TCGA) and secondary high-grade gliomas (HGGs) from an internal cohort were examined for correlation between CXCR4 expression and survival as well as expression of MES markers. Publicly available single-cell RNA sequencing (scRNAseq) data was analyzed for cell type specific CXCR4 expression. These results were validated in a genetic mouse model of PN GBM. Higher CXCR4 expression was associated with significantly reduced survival and increased expression of MES markers in TCGA and internal cohorts. CXCR4 was expressed in immune and tumor cells based on scRNAseq analysis. Higher CXCR4 expression within tumor cells on scRNAseq was associated with increased MES phenotype, suggesting a cell-autonomous effect. In a genetically engineered mouse model, tumors induced with CXCR4 exhibited a mesenchymal phenotype and shortened survival. These results suggest that CXCR4 signaling promotes PMT and shortens survival in GBM and highlights its inhibition as a potential therapeutic strategy.     

Identification of intrinsic subtype-specific prognostic microRNAs in primary glioblastoma

Background

Glioblastoma multiforme (GBM) is the most malignant type of glioma. Integrated classification based on mRNA expression microarrays and whole–genome methylation subdivides GBM into five subtypes: Classical, Mesenchymal, Neural, Proneural-CpG island methylator phenotype (G-CIMP) and Proneural-non G-CIMP. Biomarkers that can be used to predict prognosis in each subtype have not been systematically investigated.

Methods

In the present study, we used Cox regression and risk-score analysis to construct respective prognostic microRNA (miRNA) signatures in the five intrinsic subtypes of primary glioblastoma in The Cancer Genome Atlas (TCGA) dataset.

Results

Patients who had high-risk scores had poor overall survival compared with patients who had low-risk scores. The prognostic miRNA signature for the Mesenchymal subtype (four risky miRNAs: miR-373, miR-296, miR-191, miR-602; one protective miRNA: miR-223) was further validated in an independent cohort containing 41 samples.

Conclusion

We report novel diagnostic tools for deeper prognostic sub-stratification in GBM intrinsic subtypes based upon miRNA expression profiles and believe that such signature could lead to more individualized therapies to improve survival rates and provide a potential platform for future studies on gene treatment for GBM.     

Prognostic value of NUSAP1 in progression and expansion of glioblastoma multiforme

Nucleolar and spindle-associated protein (NUSAP1) is a microtubule and chromatin-binding protein that stabilizes microtubules to prevent depolymerization, maintains spindle integrity. NUSAP1 could cross-link spindles into aster-like structures, networks and fibers. It has also been found to play roles in progression of several cancers. However, the potential correlation between NUSAP1 and clinical outcome in patients with glioblastoma multiforme (GBM) remains largely unknown. In the current study, we demonstrated that NUSAP1 was significantly up-regulated in GBM tissues compared with adult non-tumor brain tissues both in a validated cohort and a TCGA cohort. In addition, Kaplan–Meier analysis indicated that patients with high NUSAP1 expression had significantly lower OS (P?=?0.0027). Additionally, in the TCGA cohort, NUSAP1 expression was relatively lower in GBM patients within the neural and mesenchymal subtypes compared to other subtypes, and associated with the status of several genetic aberrations such as PTEN deletion and wild type IDH1. The present study provides new insights and evidence that NUSAP1 over-expression was significantly correlated with progression and prognosis of GBM. Furthermore, knockdown of NUSAP1 revealed its regulation on G2/M progression and cell proliferation (both in vitro and in vivo). These data demonstrate that NUSAP1 could serve as a novel prognostic biomarker and a potential therapeutic target for GBM.     

Bioinformatic analysis of gene expression and methylation regulation in glioblastoma

Different gene expression and methylation profiles are identified in glioblastoma (GBM). To screen the differentially expressed genes affected by DNA methylation modification and further investigate their prognostic values for GBMs. We included The Cancer Genome Atlas (TCGA) RNA sequencing (676) and DNA methylation (Illumina Human Methylation 450K; 657) databases to detect the gene expression and methylation profiles. Chinese Glioma Genome Atlas (CGGA) RNA sequencing database and TCGA DNA methylation (Illumina Human Methylation 27K; 283) was included for validation. Gene expression and DNA methylation statues were identified using principal components analysis (PCA). A total of 3365 differentially expressed genes were identified. Among them, 2940 genes showed low methylation and high expression, while 425 genes showed high methylation and low expression in GBMs. An eight-gene (C9orf64, OSMR, MDK, MARVELD1, PTRF, MYD88, BIRC3, RPP25) signature was established to divide GBM patients into two groups based on the cut-off point (27.24). The high risk group had shorter overall survival (OS) than low risk group (median OS 15.77 vs. 10.61 months; P?=?0.0002). Moreover, the different clinical and molecular features were shown between two groups. These findings could be validated in additional datasets. The differentially expressed genes affected by DNA methylation modification were detected. Our results showed that the eight-gene signature has independently prognostic value for GBM patients.     

Role of ATG10 expression quantitative trait loci in non‐small cell lung cancer survival

The aim of this article was to evaluate whether genetic variants in autophagy‐related genes affect the overall survival (OS) of non‐small cell lung cancer (NSCLC) patients. We analyzed 14 single nucleotide polymorphisms (SNPs) in core autophagy‐related genes for OS in 1,001 NSCLC patients. Three promising SNPs in ATG10 were subsequently annotated by the expression quantitative trait loci (eQTL) and methylation quantitative trait loci (meQTL) analyses based on Genotype‐Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) datasets. We observed that the variants of rs10514231, rs1864182 and rs1864183 were associated with poor lung cancer survival (HR = 1.33, 95% CI = 1.07–1.65; HR = 1.43, 95% CI = 1.13–1.81; HR = 1.38, 95% CI = 1.14–1.68, respectively) and positively correlated with ATG10 expression (all p < 0.05) from GTEx and TCGA datasets. The elevated expression of ATG10 may predict shorter survival time in lung cancer patients in TCGA dataset (HR = 2.10, 95% CI = 1.33–3.29). Moreover, the variants of rs10514231 and rs1864182 were associated with the increased methylation levels of cg17942617 (meQTL), which in turn contributed to the elevated ATG10 expression and decreased survival time. Further functional assays revealed that ATG10 facilitated lung cancer cell proliferation and migration. Our findings suggest that eQTL/meQTL variations of ATG10 could influence lung cancer survival through regulating ATG10 expression.     

An integrated analysis of germline and somatic, genetic and epigenetic alterations at 9p21.3 in glioblastoma

BACKGROUND:

Glioblastoma multiforme (GBM) is the most prevalent and deadly brain tumor. A variety of germline and somatic, genetic and epigenetic alterations at 9p21.3, which encode CDKN2A/CDKN2B tumor suppressor genes, have been isolatedly reported to be associated with GBM risk and prognosis.

METHODS:

To obtain a comprehensive view of these events, we leveraged the wide‐spectrum GBM data available from The Cancer Genome Atlas project and performed an integrated analysis by systematically evaluating 9p21.3‐related germline single‐nucleotide polymorphisms, somatic copy number alterations (CNAs), DNA methylation, and microRNAs (miRNAs) with regard to CDKN2A/CDKN2B expression and patient prognosis in GBM.

RESULTS:

Our multivariate analysis indicated that expression of CDKN2A and CDKN2B was both strongly affected by CNAs (P = 1.00 × 10^?4 and 2.37 × 10^?14). The miRNAs hsa‐mir‐126, hsa‐mir‐517a, and hsa‐mir‐125b exhibited significant negative correlations with CDKN2A expression (P = 0.003, 0.041, and 0.050). Survival analysis showed that complete 9p21.3 loss and low CDKN2B expression were associated with worse prognosis for both tumor progression/recurrence‐free survival (P = .041 and .019) and patient overall survival (P = .043 and .021) after adjustment for age and treatment, and that higher methylation at cg17449661 predicted poorer overall survival (P = .048).

CONCLUSION:

Representing one of the first attempts to systematically integrate various levels of alterations associated with the often complex cancer genomes and phenotypes, our study provided a holistic view and a mechanistic explanation over the functional connections of multiple 9p21.3‐related events in GBM, as well as clinically useful biomarker information for predicting disease outcomes. Cancer 2012;. © 2011 American Cancer Society.     

Discovery in context: leveraging multidimensional glioblastoma datasets to identify targetable regulatory networks

Summary: The Cancer Genome Atlas (TCGA) promises to transform the treatment of patients with cancer by identifying new drug targets. However, extracting mechanistically insightful, therapeutically actionable information from complex multidimensional datasets remains a significant challenge. In this issue of Cancer Discovery, Genovese and colleagues apply a context-dependent modeling algorithm to the glioblastoma TCGA datasets and couple it with functional genetic screens and experimental validation to identify a novel, and potentially targetable, microRNA-mediated regulatory pathway. Cancer Discov; 2(8); 676-8. ?2012 AACR.     

IGFBP-3 基因mRNA表达水平在胶质母细胞瘤患者预后评价中的意义

目的 探讨IGFBP-3基因转录表达水平在胶质母细胞瘤（GBM）患者预后评价中的意义。方法 采用训练-验证分组方式，利用GBM在线样本库TCGA、REMBRANDT和GSE16011中病例的临床资料和芯片数据，通过Kaplan-Meier法和Cox回归分析，探讨IGFBP-3 mRNA表达与GBM患者总生存时间（OS）的关系。结果 在TCGA训练组中， IGFBP-3基因高表达患者的中位OS为14.3个月（95％CI：12.5～16.1个月），显著差于低表达的15.9个月（95％CI：13.7～18.1个月），差异具有统计学意义（P=0.002）； REMBRANDT病例组中，IGFBP-3高、低表达组患者的中位OS分别为13.2个月（95％CI：10.8～15.6个月）和16.8个月（95％CI：13.4～20.1个月），差异具有统计学意义（P=0.036）； GSE16011病例组的中位OS分别为7.4个月（95％CI：6.6～8.3个月）和13.1个月（95％CI：9.2～17.0个月），差异具有统计学意义（P＜0.001）。多因素Cox回归分析及亚型分层分析表明，IGFBP-3 mRNA表达分组的预后评价能力可能依赖于不同的GBM基因表达亚型。结论 IGFBP-3 mRNA表达水平与GBM患者的临床预后密切相关，且可能与不同的基因表达亚型有关。     

各基因亚型乳腺癌间基因组拷贝数变异的比较

目的 比较各基因亚型乳腺癌间全基因组拷贝数变异(CNV)差异,分析各亚型中特异的基因CNV.方法 在肿瘤基因组图谱(TCGA)的乳腺癌数据库中,利用AIMS软件进行基因分型(BasL型、Her2型、LumA型和LumB型),GISTIC2.0软件分析肿瘤组织中全基因组拷贝数变异情况.收集整理江门市中心医院(JMCH)3...     

The national landscape of human papillomavirus‐associated oropharynx squamous cell carcinoma

The head and neck squamous cell carcinoma (HNC) landscape is evolving with human papillomavirus (HPV) being a rising cause of oropharynx carcinoma (OPC). This study seeks to investigate a national database for HPV‐associated oropharynx carcinoma (HPV‐OPC). Using the National Cancer Data Base, we analyzed 22,693 patients with HPV‐OPC and known HPV status. Chi‐square tests and logistic regression models were utilized to examine differences between HPV positive and HPV negative OPC. 14,805 (65.2%) patients were HPV positive. Mean age at presentation was 58.4 years with HPV‐HNC patients being 2.8 years younger compared to the HPV‐negative cohort (58.4 vs. 61.2 years, p < 0.001). 67.6% of white patients were HPV‐positive compared to 42.3% of African American patients and 57.1% of Hispanics (p < 0.001). When combining race and socioeconomic status (SES), we found African American patients in high SES groups had HPV‐OPC prevalence that was significantly higher than African American patients in low SES groups (56.9% vs. 36.3%, p < 0.001). Geographic distribution of HPV‐OPC was also analyzed and found to be most prevalent in Western states and least prevalent in the Southern states (p < 0.001). The distribution of HPV‐OPC is variable across the country and among racial and socioeconomic groups. A broad understanding of these differences in HPV‐OPC should drive educational programs and improve clinical trials that benefit both prevention and current treatments.     

A gene expression‐based single sample predictor of lung adenocarcinoma molecular subtype and prognosis

Disease recurrence in surgically treated lung adenocarcinoma (AC) remains high. New approaches for risk stratification beyond tumor stage are needed. Gene expression‐based AC subtypes such as the Cancer Genome Atlas Network (TCGA) terminal‐respiratory unit (TRU), proximal‐inflammatory (PI) and proximal‐proliferative (PP) subtypes have been associated with prognosis, but show methodological limitations for robust clinical use. We aimed to derive a platform independent single sample predictor (SSP) for molecular subtype assignment and risk stratification that could function in a clinical setting. Two‐class (TRU/nonTRU=SSP2) and three‐class (TRU/PP/PI=SSP3) SSPs using the AIMS algorithm were trained in 1655 ACs (n = 9659 genes) from public repositories vs TCGA centroid subtypes. Validation and survival analysis were performed in 977 patients using overall survival (OS) and distant metastasis‐free survival (DMFS) as endpoints. In the validation cohort, SSP2 and SSP3 showed accuracies of 0.85 and 0.81, respectively. SSPs captured relevant biology previously associated with the TCGA subtypes and were associated with prognosis. In survival analysis, OS and DMFS for cases discordantly classified between TCGA and SSP2 favored the SSP2 classification. In resected Stage I patients, SSP2 identified TRU‐cases with better OS (hazard ratio [HR] = 0.30; 95% confidence interval [CI] = 0.18‐0.49) and DMFS (TRU HR = 0.52; 95% CI = 0.33‐0.83) independent of age, Stage IA/IB and gender. SSP2 was transformed into a NanoString nCounter assay and tested in 44 Stage I patients using RNA from formalin‐fixed tissue, providing prognostic stratification (relapse‐free interval, HR = 3.2; 95% CI = 1.2‐8.8). In conclusion, gene expression‐based SSPs can provide molecular subtype and independent prognostic information in early‐stage lung ACs. SSPs may overcome critical limitations in the applicability of gene signatures in lung cancer.