A three-gene signature for prognosis in patients with MGMT promoter-methylated glioblastoma

Transcriptional Profiling of a Patient-Matched Cohort of Glioblastoma (IDH-Wildtype) for Therapeutic Target and Repurposing Drug Identification

Glioblastoma (GBM) is the most prevalent and aggressive adult brain tumor. Despite multi-modal therapies, GBM recurs, and patients have poor survival (~14 months). Resistance to therapy may originate from a subpopulation of tumor cells identified as glioma-stem cells (GSC), and new treatments are urgently needed to target these. The biology underpinning GBM recurrence was investigated using whole transcriptome profiling of patient-matched initial and recurrent GBM (recGBM). Differential expression analysis identified 147 significant probes. In total, 24 genes were validated using expression data from four public cohorts and the literature. Functional analyses revealed that transcriptional changes to recGBM were dominated by angiogenesis and immune-related processes. The role of MHC class II proteins in antigen presentation and the differentiation, proliferation, and infiltration of immune cells was enriched. These results suggest recGBM would benefit from immunotherapies. The altered gene signature was further analyzed in a connectivity mapping analysis with QUADrATiC software to identify FDA-approved repurposing drugs. Top-ranking target compounds that may be effective against GSC and GBM recurrence were rosiglitazone, nizatidine, pantoprazole, and tolmetin. Our translational bioinformatics pipeline provides an approach to identify target compounds for repurposing that may add clinical benefit in addition to standard therapies against resistant cancers such as GBM.

Potent predictive CpG signature for temozolomide response in non-glioma-CpG island methylator phenotype glioblastomas with methylated MGMT promoter

Aim: We aimed to identify potent CpG signatures predicting temozolomide (TMZ) response in glioblastomas (GBMs) that do not have the glioma-CpG island methylator phenotype (G-CIMP) but have a methylated promoter of MGMT (me MGMT). Materials & methods: Different datasets of non-G-CIMP me MGMT GBMs with molecular and clinical data were analyzed. Results: A panel of 77 TMZ efficacy-related CpGs and a seven-CpG risk signature were identified and validated for distinguishing differential outcomes to radiotherapy plus TMZ versus radiotherapy alone in non-G-CIMP me MGMT GBMs. An integrated classification scheme was also proposed for refining a MGMT-based TMZ-guiding approach in all G-CIMP-GBMs. Conclusion: The CpG signatures may serve as promising predictive biomarker candidates for guiding optimal TMZ usage in non-G-CIMP me MGMT GBMs.

The Expression Profile, Clinical Application and Potential Tumor Suppressing Mechanism of hsa_circ_0001675 in Head and Neck Carcinoma

Purpose

This study sought to identify circular RNAs (circRNA) that participate in the regulation of head and neck cancer (HNC), analyze their clinical application, and predict their molecular mechanism during HNC.

Materials and Methods

High-throughput sequencing was used to analyze circRNA expression in 18 matched HNC and adjacent normal tissues. Target circRNAs with significantly differential expression were obtained. In 103 HNC and adjacent normal tissues, real-time fluorescent quantitative PCR (qRT-PCR) was used to verify the differential expression of target circRNAs. This data was combined with clinicopathological information to analyze the diagnostic value of target circRNA. Bioinformatics was used to find target circRNAs that acted as competitive endogenous RNA (ceRNA) and construct a circRNA-miRNA-mRNA regulatory network. mRNA expression was verified by immunohistochemistry (IHC).

Results

A total of 714 differentially expressed circRNAs were detected in HNC, and the low expression of hsa_circ_0001675 was particularly significant (fold change [FC] = -4.85, P = 6.305E-05). hsa_circ_0001675 had significantly lower expression in HNC than in normal tissue (P < 0.01). Low hsa_circ_0001675 expression was positively associated with tumor invasion and clinical staging (P < 0.05), and its area under the ROC curve (AUC) was 0.7776. Low hsa_circ_0001675 expression also correlated with the overall survival (OS) rate and the progression-free survival (PFS) rate of HNC patients (P < 0.001). Bioinformatics was used to construct a ceRNA network of hsa_circ_0001675 with six differentially expressed miRNAs (hsa-miR-330-5p, hsa-miR-498, hsa-miR-532-3p, hsa-miR-577, hsa-miR-1248, and hsa-miR-1305) and 411 differentially expressed mRNAs and found that the neuroactive ligand-receptor interaction, and the cAMP and calcium signaling pathways were particularly enriched. Further bioinformatics and IHC analysis showed that miR577/TESC is the likely downstream signaling pathway for hsa_circ_0001675.

Conclusion

This study showed that hsa_circ_0001675 is downregulated in HNC and could be an effective biomarker for HNC diagnosis. In addition, hsa_circ_0001675 may have a potential ceRNA mechanism and suppress HNC disease progression through the hsa_circ_0001675-miRNA-mRNA axis.

Reboot: a straightforward approach to identify genes and splicing isoforms associated with cancer patient prognosis

Nowadays, the massive amount of data generated by modern sequencing technologies provides an unprecedented opportunity to find genes associated with cancer patient prognosis, connecting basic and translational research. However, treating high dimensionality of gene expression data and integrating it with clinical variables are major challenges to perform these analyses. Here, we present Reboot, an integrative approach to find and validate genes and transcripts (splicing isoforms) associated with cancer patient prognosis from high dimensional expression datasets. Reboot innovates by using a multivariate strategy with penalized Cox regression (LASSO method) combined with a bootstrap approach, in addition to statistical tests and plots to support the findings. Applying Reboot on data from 154 glioblastoma patients, we identified a three-gene signature (IKBIP, OSMR, PODNL1) whose increased derived risk score was significantly associated with worse patients' prognosis. Similarly, Reboot was able to find a seven-splicing isoforms signature related to worse overall survival in 177 pancreatic adenocarcinoma patients with elevated risk scores after uni- and multivariate analyses. In summary, Reboot is an efficient, intuitive and straightforward way of finding genes or splicing isoforms signatures relevant to patient prognosis, which can democratize this kind of analysis and shed light on still under-investigated cancer-related genes and splicing isoforms.

Hub biomarkers for the diagnosis and treatment of glioblastoma based on microarray technology

BACKGROUND

Glioblastoma (GBM) is the most common clinical intracranial malignancy worldwide, and the most common supratentorial tumor in adults. GBM mainly causes damage to the brain tissue, which can be fatal. This research explored potential gene targets for the diagnosis and treatment of GBM using bioinformatic technology.

METHODS

Public data from patients with GBM and controls were downloaded from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) were identified by Gene Expression Profiling Interactive Analysis (GEPIA) and Gene Expression Omnibus 2R (GEO2R). Construction of the protein-protein interaction network and the identification of a significant module were performed. Subsequently, hub genes were identified, and their expression was examined and compared by real-time quantitative (RT-q)PCR between patients with GBM and controls.

RESULTS

GSE122498 (GPL570 platform), GSE104291 (GPL570 platform), GSE78703_DMSO (GPL15207 platform), and GSE78703_LXR (GPL15207 platform) datasets were obtained from the GEO. A total of 130 DEGs and 10 hub genes were identified by GEPIA and GEO2R between patients with GBM and controls. Of these, strong connections were identified in correlation analysis between CCNB1, CDC6, KIF23, and KIF20A. RT-qPCR showed that all 4 of these genes were expressed at significantly higher levels in patients with GBM compared with controls.

CONCLUSIONS

The hub genes CCNB1, CDC6, KIF23, and KIF20A are potential biomarkers for the diagnosis and treatment of GBM.

Prognostic value of test(s) for O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation for predicting overall survival in people with glioblastoma treated with temozolomide

BACKGROUND

Glioblastoma is an aggressive form of brain cancer. Approximately five in 100 people with glioblastoma survive for five years past diagnosis. Glioblastomas that have a particular modification to their DNA (called methylation) in a particular region (the O6-methylguanine-DNA methyltransferase (MGMT) promoter) respond better to treatment with chemotherapy using a drug called temozolomide.

OBJECTIVES

To determine which method for assessing MGMT methylation status best predicts overall survival in people diagnosed with glioblastoma who are treated with temozolomide.

SEARCH METHODS

We searched MEDLINE, Embase, BIOSIS, Web of Science Conference Proceedings Citation Index to December 2018, and examined reference lists. For economic evaluation studies, we additionally searched NHS Economic Evaluation Database (EED) up to December 2014.

SELECTION CRITERIA

Eligible studies were longitudinal (cohort) studies of adults with diagnosed glioblastoma treated with temozolomide with/without radiotherapy/surgery. Studies had to have related MGMT status in tumour tissue (assessed by one or more method) with overall survival and presented results as hazard ratios or with sufficient information (e.g. Kaplan-Meier curves) for us to estimate hazard ratios. We focused mainly on studies comparing two or more methods, and listed brief details of articles that examined a single method of measuring MGMT promoter methylation. We also sought economic evaluations conducted alongside trials, modelling studies and cost analysis.

DATA COLLECTION AND ANALYSIS

Two review authors independently undertook all steps of the identification and data extraction process for multiple-method studies. We assessed risk of bias and applicability using our own modified and extended version of the QUality In Prognosis Studies (QUIPS) tool. We compared different techniques, exact promoter regions (5'-cytosine-phosphate-guanine-3' (CpG) sites) and thresholds for interpretation within studies by examining hazard ratios. We performed meta-analyses for comparisons of the three most commonly examined methods (immunohistochemistry (IHC), methylation-specific polymerase chain reaction (MSP) and pyrosequencing (PSQ)), with ratios of hazard ratios (RHR), using an imputed value of the correlation between results based on the same individuals.

MAIN RESULTS

We included 32 independent cohorts involving 3474 people that compared two or more methods. We found evidence that MSP (CpG sites 76 to 80 and 84 to 87) is more prognostic than IHC for MGMT protein at varying thresholds (RHR 1.31, 95% confidence interval (CI) 1.01 to 1.71). We also found evidence that PSQ is more prognostic than IHC for MGMT protein at various thresholds (RHR 1.36, 95% CI 1.01 to 1.84). The data suggest that PSQ (mainly at CpG sites 74 to 78, using various thresholds) is slightly more prognostic than MSP at sites 76 to 80 and 84 to 87 (RHR 1.14, 95% CI 0.87 to 1.48). Many variants of PSQ have been compared, although we did not see any strong and consistent messages from the results. Targeting multiple CpG sites is likely to be more prognostic than targeting just one. In addition, we identified and summarised 190 articles describing a single method for measuring MGMT promoter methylation status.

AUTHORS' CONCLUSIONS

PSQ and MSP appear more prognostic for overall survival than IHC. Strong evidence is not available to draw conclusions with confidence about the best CpG sites or thresholds for quantitative methods. MSP has been studied mainly for CpG sites 76 to 80 and 84 to 87 and PSQ at CpG sites ranging from 72 to 95. A threshold of 9% for CpG sites 74 to 78 performed better than higher thresholds of 28% or 29% in two of three good-quality studies making such comparisons.

A Novel Six-mRNA Signature Predicts Survival of Patients With Glioblastoma Multiforme

Glioblastoma multiforme (GBM) is a devastating brain tumor and displays divergent clinical outcomes due to its high degree of heterogeneity. Reliable prognostic biomarkers are urgently needed for improving risk stratification and survival prediction. In this study, we analyzed genome-wide mRNA profiles in GBM patients derived from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify mRNA-based signatures for GBM prognosis with survival analysis. Univariate Cox regression model was used to evaluate the relationship between the expression of mRNA and the prognosis of patients with GBM. We established a risk score model that consisted of six mRNA (AACS, STEAP1, STEAP2, G6PC3, FKBP9, and LOXL1) by the LASSO regression method. The six-mRNA signature could divide patients into a high-risk and a low-risk group with significantly different survival rates in training and test sets. Multivariate Cox regression analysis confirmed that it was an independent prognostic factor in GBM patients, and it has a superior predictive power as compared with age, IDH mutation status, MGMT, and G-CIMP methylation status. By combining this signature and clinical risk factors, a nomogram can be established to predict 1-, 2-, and 3-year OS in GBM patients with relatively high accuracy.

Identification of immune-related lncRNA signature for predicting immune checkpoint blockade and prognosis in hepatocellular carcinoma

BACKGROUND

An increasing body of evidence has supported that long non-coding RNAs (lncRNAs) can play as essential roles of various physiological process and pathological diseases. We aimed to construct a robust immune-associated lncRNA signature associated with the prognosis for HCC survival prediction.

METHODS

7 immune-associated lncRNAs presenting significant correlation with survival were screened through stepwise univariate Cox regression and LASSO algorithm, and multivariate Cox regression. Kaplan-Meier analysis, proportional hazards model, and ROC analyses further conducted. Gene set enrichment analysis (GSEA) was applied for functional annotation. We conducted quantitative real-time polymerase chain reaction to determine NRAV expression and preliminarily explored the latent role of NRAV in prognosis of HCC patients.

RESULTS

Finally, 7 immune-related lncRNA signature composed of AC007405.3, AC023157.3, NRAV, CASC19, MSC-AS1, GASAL1, and LINC00942 were validated. This lncRNAs signature can serve as an independent predictive biomolecular factor. This signature was further confirmed in the validation group and the entire cohort. We demonstrated that NRAV was significantly upregulated in HCC cell lines and it may serve as a key regulator in HCC. Our signature was associated to apoptosis and immunologic characteristics. This signature mediated immune cell infiltration (i.e., Dendritic, etc.,) and immune checkpoint blockade (ICB) immunotherapy-related molecules (i.e., CD274, etc.,).

CONCLUSION

This immune-related lncRNA signature possesses promising prognostic value in HCC and may have the potentiality to predict clinical outcome of ICB immunotherapy.

Identification of a Transcriptomic Prognostic Signature by Machine Learning Using a Combination of Small Cohorts of Prostate Cancer

Determining which treatment to provide to men with prostate cancer (PCa) is a major challenge for clinicians. Currently, the clinical risk-stratification for PCa is based on clinico-pathological variables such as Gleason grade, stage and prostate specific antigen (PSA) levels. But transcriptomic data have the potential to enable the development of more precise approaches to predict evolution of the disease. However, high quality RNA sequencing (RNA-seq) datasets along with clinical data with long follow-up allowing discovery of biochemical recurrence (BCR) biomarkers are small and rare. In this study, we propose a machine learning approach that is robust to batch effect and enables the discovery of highly predictive signatures despite using small datasets. Gene expression data were extracted from three RNA-Seq datasets cumulating a total of 171 PCa patients. Data were re-analyzed using a unique pipeline to ensure uniformity. Using a machine learning approach, a total of 14 classifiers were tested with various parameters to identify the best model and gene signature to predict BCR. Using a random forest model, we have identified a signature composed of only three genes (JUN, HES4, PPDPF) predicting BCR with better accuracy [74.2%, balanced error rate (BER) = 27%] than the clinico-pathological variables (69.2%, BER = 32%) currently in use to predict PCa evolution. This score is in the range of the studies that predicted BCR in single-cohort with a higher number of patients. We showed that it is possible to merge and analyze different small and heterogeneous datasets altogether to obtain a better signature than if they were analyzed individually, thus reducing the need for very large cohorts. This study demonstrates the feasibility to regroup different small datasets in one larger to identify a predictive genomic signature that would benefit PCa patients.

Congress of neurological surgeons systematic review and evidence-based guidelines update on the role of emerging developments in the management of newly diagnosed glioblastoma

TARGET POPULATION

These recommendations apply to adult patients with newly diagnosed or suspected glioblastoma.

IMAGING

Question What imaging modalities are in development that may be able to provide improvements in diagnosis, and therapeutic guidance for individuals with newly diagnosed glioblastoma?

RECOMMENDATION

Level III: It is suggested that techniques utilizing magnetic resonance imaging for diffusion weighted imaging, and to measure cerebral blood and magnetic spectroscopic resonance imaging of N-acetyl aspartate, choline and the choline to N-acetyl aspartate index to assist in diagnosis and treatment planning in patients with newly diagnosed or suspected glioblastoma.

SURGERY

Question What new surgical techniques can be used to provide improved tumor definition and resectability to yield better tumor control and prognosis for individuals with newly diagnosed glioblastoma?

RECOMMENDATIONS

Level II: The use of 5-aminolevulinic acid is recommended to improve extent of tumor resection in patients with newly diagnosed glioblastoma. Level II: The use of 5-aminolevulinic acid is recommended to improve median survival and 2 year survival in newly diagnosed glioblastoma patients with clinical characteristics suggesting poor prognosis. Level III: It is suggested that, when available, patients be enrolled in properly designed clinical trials assessing the value of diffusion tensor imaging in improving the safety of patients with newly diagnosed glioblastoma undergoing surgery.

NEUROPATHOLOGY

Question What new pathology techniques and measurement of biomarkers in tumor tissue can be used to provide improved diagnostic ability, and determination of therapeutic responsiveness and prognosis for patients with newly diagnosed glioblastomas?

RECOMMENDATIONS

Level II: Assessment of tumor MGMT promoter methylation status is recommended as a significant predictor of a longer progression free survival and overall survival in patients with newly diagnosed with glioblastoma. Level II: Measurement of tumor expression of neuron-glia-2, neurofilament protein, glutamine synthetase and phosphorylated STAT3 is recommended as a predictor of overall survival in patients with newly diagnosed with glioblastoma. Level III: Assessment of tumor IDH1 mutation status is suggested as a predictor of longer progression free survival and overall survival in patients with newly diagnosed with glioblastoma. Level III: Evaluation of tumor expression of Phosphorylated Mitogen-Activated Protein Kinase protein, EGFR protein, and Insulin-like Growth Factor-Binding Protein-3 is suggested as a predictor of overall survival in patients with newly diagnosed with glioblastoma.

RADIATION

Question What radiation therapy techniques are in development that may be used to provide improved tumor control and prognosis for individuals with newly diagnosed glioblastomas?

RECOMMENDATIONS

Level III: It is suggested that patients with newly diagnosed glioblastoma undergo pretreatment radio-labeled amino acid tracer positron emission tomography to assess areas at risk for tumor recurrence to assist in radiation treatment planning. Level III: It is suggested that, when available, patients be with newly diagnosed glioblastomas be enrolled in properly designed clinical trials of radiation dose escalation, altered fractionation, or new radiation delivery techniques.

CHEMOTHERAPY

Question What emerging chemotherapeutic agents or techniques are available to provide better tumor control and prognosis for patients with newly diagnosed glioblastomas?

RECOMMENDATION

Level III: As no emerging chemotherapeutic agents or techniques were identified in this review that improved tumor control and prognosis it is suggested that, when available, patients with newly diagnosed glioblastomas be enrolled in properly designed clinical trials of chemotherapy.

MOLECULAR AND TARGETED THERAPY

Question What new targeted therapy agents are available to provide better tumor control and prognosis for individuals with newly diagnosed glioblastomas?

RECOMMENDATION

Level III: As no new molecular and targeted therapies have clearly provided better tumor control and prognosis it is suggested that, when available, patients with newly diagnosed glioblastomas be enrolled in properly designed clinical trials of molecular and targeted therapies IMMUNOTHERAPY: Question What emerging immunotherapeutic agents or techniques are available to provide better tumor control and prognosis for patients with newly diagnosed glioblastomas?

RECOMMENDATION

Level III: As no immunotherapeutic agents have clearly provided better tumor control and prognosis it is suggested that, when available, patients with newly diagnosed glioblastomas be enrolled in properly designed clinical trials of immunologically-based therapies.

NOVEL THERAPIES

Question What novel therapies or techniques are in development to provide better tumor control and prognosis for individuals with newly diagnosed glioblastomas?

RECOMMENDATIONS

Level II: The use of tumor-treating fields is recommended for patients with newly diagnosed glioblastoma who have undergone surgical debulking and completed concurrent chemoradiation without progression of disease at the time of tumor-treating field therapy initiation. Level II: It is suggested that, when available, enrollment in properly designed studies of vector containing herpes simplex thymidine kinase gene and prodrug therapies be considered in patients with newly diagnosed glioblastoma.

Overexpression of FES might inhibit cell proliferation, migration, and invasion of osteosarcoma cells

Background

This study aimed to screen osteosarcoma (OS) prognosis relevant genes for methylation dysregulation, and the functional mechanisms of FES overexpression in OS cells were investigated.

Methods

The OS prognosis relevant genes with differentially methylated positions (DMPs) identified from the GSE36001 and GSE36002 datasets, and the UCSC database, were used as a training set to construct a risk model, while the GSE21257 dataset was used as validation set. The expression levels of several key genes in OS cells after 5-Aza-2′-deoxycytidine treatment were detected by qPCR. The effects of FES overexpression on cell proliferation, cell cycle, migration, and invasion of MNNG/HOS were analyzed by CCK8, flow cytometry, and Transwell assays.

Results

A total of 31 candidate genes, corresponding to 36 DMPs, were identified as OS prognosis relevant genes; from these, the top 10 genes were used to construct a risk model. Following validation of the risk model, FES, LYL1, MAP4K1, RIPK3, SLC15A3, and STAT3 showed expression changes between the OS and control samples. qPCR results showed that the expression of FES was significantly downregulated in three OS cell lines and increased after 5-Aza-DC treatment. The proliferation, cell cycle progression, migration, and invasion of MNNG/HOS cells were significantly inhibited after transfection with FES overexpression plasmid, and the protein expression of FYN and β catenin were decreased in MNNG/HOS cells by FES overexpression.

Conclusions

The decrease in FES by hypermethylation was associated with OS prognosis, and might contribute to the proliferation, migration, and invasion of OS cells. FES, and its upstream FYN and β catenin, might coordinately exert a tumor suppressor effect in OS cells.

Expression and prognostic value of mRNAs in lower grade glioma with MGMT promoter methylated

Due to the varied overall survival (OS), limited studies focus on the factors that affect the prognosis for lower grade glioma patients (LGGs) with MGMT promoter methylated. A total of 579 samples (TCGA LGGs 456; CGGA LGGs 123) were included to identify potential genes for LGGs with MGMT promoter methylated. All bioinformatics analyses were conducted using SPSS software and GraphPad Prism 6. Based on COX regression analysis, we established a four-gene signature (ALDOC, APOBEC3C, ANXA1 and ARPP21) and divided LGGs into two groups based on median risk score. The OS of LGGs in high risk group was shorter than low risk group (P < 0.0001). Furthermore, the OS in high risk group were shorter than low risk group in Grade II and III, respectively (P = 0.0003; P = 0.0104). It showed that the signature was an independent prognosis factor on multivariate Cox regression analysis (P = 0.033). Patients in high group tended to displayed high grade (GIII), IDH1 wild type and mesenchymal subtype preference. Four-gene signature was discovered for LGGs with MGMT promoter methylated. Our findings suggested that the four genes could serve as prognostic biomarkers.

An eight-mRNA signature outperforms the lncRNA-based signature in predicting prognosis of patients with glioblastoma

Background

The prognosis of the glioblastoma (GBM) is dismal. This study aims to select an optimal RNA signature for prognostic prediction of GBM patients.

Methods

For the training set, the long non-coding RNA (lncRNA) and mRNA expression profiles of 151 patients were downloaded from the TCGA. Differentially expressed mRNAs (DEGs) and lncRNAs (DE-lncRNAs) were identified between good prognosis and bad prognosis patients. Optimal prognostic mRNAs and lncRNAs were selected respectively, by using univariate Cox proportional-hazards (PH) regression model and LASSO Cox-PH model. Subsequently, four prognostic scoring models were built based on expression levels or expression status of the selected prognostic lncRNAs or mRNAs, separately. Each prognostic model was applied to the training set and an independent validation set. Function analysis was used to uncover the biological roles of these prognostic DEGs between different risk groups classified by the mRNA-based signature.

Results

We obtained 261 DEGs and 33 DE-lncRNAs between good prognosis and bad prognosis patients. A panel of eight mRNAs and a combination of ten lncRNAs were determined as predictive RNAs by LASSO Cox-PH model. Among the four prognostic scoring models using the eight-mRNA signature or the ten-lncRNA signature, the one based on the expression levels of the eight mRNAs showed the greatest predictive power. The DEGs between different risk groups using the eight prognostic mRNAs were functionally involved in calcium signaling pathway, neuroactive ligand-receptor interaction pathway, and Wnt signaling pathway.

Conclusion

The eight-mRNA signature has greater prognostic value than the ten-lncRNA-based signature for GBM patients based on bioinformatics analysis.

The relationship between repeat resection and overall survival in patients with glioblastoma: a time-dependent analysis

OBJECTIVEPrevious studies assessed the relationship between repeat resection and overall survival (OS) in patients with glioblastoma, but ignoring the timing of repeat resection may have led to biased conclusions. Statistical methods that take time into account are well established and applied consistently in other medical fields. The goal of this study was to illustrate the change in the effect of repeat resection on OS in patients with glioblastoma once timing of resection is incorporated.METHODSThe authors conducted a retrospective study of patients initially diagnosed with glioblastoma between January 2005 and December 2014 who were treated at Memorial Sloan Kettering Cancer Center. Patients underwent at least 1 craniotomy with both pre- and postoperative MRI data available. The effect of repeat resection on OS was assessed with time-dependent extended Cox regression controlling for extent of resection, initial Karnofsky Performance Scale score, sex, age, multifocal status, eloquent status, and postoperative treatment.RESULTSEighty-nine (55%) of 163 patients underwent repeat resection with a median time between resections of 7.7 months (range 0.5-50.8 months). Median OS was 18.8 months (95% confidence interval [CI] 16.3-20.5 months) from initial resection. When timing of repeat resection was ignored, repeat resection was associated with a lower risk of death (hazard ratio [HR] 0.62, 95% CI 0.43-0.90, p = 0.01); however, when timing was taken into account, repeat resection was associated with a higher risk of death (HR 2.19, 95% CI 1.47-3.28, p < 0.001).CONCLUSIONSIn this study, accounting for timing of repeat resection reversed its protective effect on OS, suggesting repeat resection may not benefit OS in all patients. These findings establish a foundation for future work by accounting for timing of repeat resection using time-dependent methods in the evaluation of repeat resection on OS. Additional recommendations include improved data capture that includes mutational data, development of algorithms for determining eligibility for repeat resection, more rigorous statistical analyses, and the assessment of additional benefits of repeat resection, such as reduction of symptom burden and enhanced quality of life.

Screening and authentication of molecular markers in malignant glioblastoma based on gene expression profiles

Glioblastoma (GBM) is a malignant tumor of the central nervous system with high mortality rates. Gene expression profiling may determine the chemosensitivity of GBMs. However, the molecular mechanisms underlying GBM remain to be determined. To screen the novel key genes in its occurrence and development, two glioma databases, GSE122498 and GSE104291, were analyzed in the present study. Bioinformatics analyses were performed using the Database for Annotation, Visualization and Integrated Discovery, the Search Tool for the Retrieval of Interacting Genes, Cytoscape, cBioPortal, and Gene Expression Profiling Interactive Analysis softwares. Patients with recurrent GBM showed worse overall survival rate. Overall, 341 differentially expressed genes (DEGs) were authenticated based on two microarray datasets, which were primarily enriched in ‘cell division’, ‘mitotic nuclear division’, ‘DNA replication’, ‘nucleoplasm’, ‘cytosol, nucleus’, ‘protein binding’, ‘ATP binding’, ‘protein C-terminus binding’, ‘the cell cycle’, ‘DNA replication’, ‘oocyte meiosis’ and ‘valine’. The protein-protein interaction network was composed of 1,799 edges and 237 nodes. Its significant module had 10 hub genes, and CDK1, BUB1B, NDC80, NCAPG, BUB1, CCNB1, TOP2A, DLGAP5, ASPM and MELK were significantly associated with carcinogenesis and the development of GBM. The present study indicated that the DEGs and hub genes, identified based on bioinformatics analyses, had significant diagnostic value for patients with GBM.

Combined Therapy Sensitivity Index Based on a 13-Gene Signature Predicts Prognosis for IDH Wild-type and MGMT Promoter Unmethylated Glioblastoma Patients

Glioblastoma (GBM) is one of the lethal tumors with poor prognosis. However, prognostic prediction approaches need to be further explored. Therefore, we developed an evaluation system that could be used for prognostic prediction of GBM patients. Published mRNA expression datasets from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Chinese Glioma Genome Atlas (CGGA) were analyzed. Quantitative Realtime-PCR of signature genes and molecular aberrations of 178 Xiangya GBM patients were used for confirmation. Gene set enrichment analysis (GSEA) was performed for functional annotation. As a result, we established a 13-gene signature which named Combined Therapy Sensitivity Index (CTSI). Based on a cutoff point, we divided patients into high-risk group and low-risk group. Based on Kaplan-Meier analysis and multivariate Cox regression analysis, we found that patients in the high-risk group had a shorter overall survival time than patients in the low-risk group (p<0.001 in TCGA and CGGA datasets, p=0.047 in GSE4271 dataset, p=0.008 in Xiangya GBM cohort, HR: 1.65-3.42). By comparing the status of IDH mutation, TERT promoter mutation (TERTp-mut) and MGMT promoter methylation, CTSI was predictable in IDH wild-type (IDH-wt)/MGMT promoter unmethylated (MGMTp-unmeth) patients (p=0.037 in IDH-wt/TERTp-mut/MGMTp-unmeth subgroup, HR: 1.98; p=0.032 in IDH-wt/TERTp-wt/MGMTp-unmeth subgroup, HR: 2.09). Based on GESA, the Gene Ontology (GO) gene sets were enriched differently between CTSI high-risk and low-risk groups. Our results showed CTSI risk score can predict the prognosis of IDH-wt/MGMTp-unmeth GBM patients. Based on CTSI, combined with the status of IDH mutation, TERT promoter mutation and MGMT promoter methylation, a stepwise prognosis evaluation system which can provide precise prognosis prediction for GBM patients was established.

Integrative analysis of DNA methylation and gene expression to identify key epigenetic genes in glioblastoma

Glioblastoma (GBM) ranks the most common and aggressive primary brain malignant tumor worldwide. However, the survival rates of patients remain very poor. Therefore, molecular oncology of GBM are urgently needed. In this study, we performed an integrative analysis of DNA methylation and gene expression to identify key epigenetic genes in GBM. The methylation and gene expression of GBM patients in The Cancer Genome Atlas (TCGA) database were downloaded. After data preprocessing, we identified 4,881 differentially expressed genes (DEGs) between tumor and normal samples, including 1,111 upregulated and 3,770 downregulated genes. Then, we randomly separated all samples into training set (n = 69) and testing set (n = 69). We next obtained 11,269 survival-methylation sites by univariate and multivariate Cox regression analyses. In the correlation analysis, we defined 198 low promoter methylation with high gene expression as epigenetically induced (EI) genes and 111 high promoter methylation with low gene expression as epigenetically suppressed (ES) genes. Key markers including C1orf61 and FAM50B were selected with a Pearson correlation coefficient greater than 0.75. Further, we chose the 20 CpG methylation sites of above two genes in unsupervised clustering analysis using the Euclidean distance. We found that the prognosis of the hypomethylated group was significantly better than that in the hypermethylated group (log-rank test p-value = 0.011). Based on the validation in the TCGA testing set and GEO dataset, we validated the prognostic value of our signature (p-value = 0.02 in TCGA and 0.012 in GEO). In conclusion, our findings provided predictive and prognostic value as methylation-based biomarkers for the diagnosis and treatment of GBM.

Discovery and validation of methylated-differentially expressed genes in Helicobacter pylori-induced gastric cancer

DNA methylation has an important role in Helicobacter pylori (H. pylori)-induced gastric cancer (GC) processes and development. The aim of this study was to search genome-scale epigenetic modifications for studying pathogenesis of H. pylori-induced GC, and to find factors and powerful signature related to survival and prognosis. In this study, we conducted a comprehensive analysis of DNA methylation and gene expression profiles in the Gene Expression Omnibus (GEO), to identified differentially expressed genes (DEGs) and differentially methylated genes (DMGs). Functional enrichment analysis of the screened genes was performed, and a protein-protein interaction network was constructed. The TCGA DNA methylation databases and 55 H. pylori-infected GC cases of GEO RNA sequencing (GSE62254) were utilized for prognostic value validation of hub genes. Finally, a prognosis-related risk signature was identified by a series of bioinformatics analysis for H. pylori-induced GC patients. Totally, 161 DMGs were identified. Pathway analysis showed that all MDEGs mainly associated with Ras signaling pathway, renal cell carcinoma, mitogen-activated protein kinase signaling pathway. Five hub genes including CACNB2, GNB4, GRIN2A, MEF2C, and PREX1 were screened as independent prognostic factors in H. pylori-induced GC patients. Two-gene (CACNB2 and MEF2C) risk signature was constructed for predicting the overall survival of H. pylori-induced GC patients. Our study indicated possible MDEGs and pathways in H. pylori-induced GC by bioinformatics analysis, which may provide novel insights for unraveling pathogenesis of H. pylori-induced GC. Hub genes might serve as aberrantly methylation-based biomarkers for clinical diagnostic and prognostic evaluation of H. pylori-induced GC.

Molecular differences in IDH wildtype glioblastoma according to MGMT promoter methylation

Background

O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation status is a predictive biomarker in glioblastoma. We investigated whether this marker furthermore defines a molecularly distinct tumor subtype with clinically different outcome.

Methods

We analyzed copy number variation (CNV) and methylation profiles of 1095 primary and 92 progressive isocitrate dehydrogenase wildtype glioblastomas, including paired samples from 49 patients. DNA mutation data from 182 glioblastoma samples of The Cancer Genome Atlas (TCGA) and RNA expression from 107 TCGA and 55 Chinese Glioma Genome Atlas samples were analyzed.

Results

Among untreated glioblastomas, MGMT promoter methylated (mMGMT) and unmethylated (uMGMT) tumors did not show different CNV or specific gene mutations, but a higher mutation count in mMGMT tumors. We identified 3 methylation clusters. Cluster 1 showed the highest average methylation and was enriched for mMGMT tumors. Seventeen genes including gastrulation brain homeobox 2 (GBX2) were found to be hypermethylated and downregulated on the mRNA level in mMGMT tumors. In progressive glioblastomas, platelet derived growth factor receptor alpha (PDGFRA) and GLI2 amplifications were enriched in mMGMT tumors. Methylated MGMT tumors gain PDGFRA amplification of PDGFRA, whereas uMGMT tumors with amplified PDGFRA frequently lose this amplification upon progression. Glioblastoma patients surviving <6 months and with mMGMT harbored less frequent epidermal growth factor receptor (EGFR) amplifications, more frequent TP53 mutations, and a higher tumor necrosis factor-nuclear factor-kappaB (TNF-NFκB) pathway activation compared with patients surviving >12 months.

Conclusions

MGMT promoter methylation status does not define a molecularly distinct glioblastoma subpopulation among untreated tumors. Progressive mMGMT glioblastomas and mMGMT tumors of patients with short survival tend to have more unfavorable molecular profiles.

Analysis of long non-coding RNAs in glioblastoma for prognosis prediction using weighted gene co-expression network analysis, Cox regression, and L1-LASSO penalization

Purpose

This study focused on identification of long non-coding RNAs (lncRNAs) for prognosis prediction of glioblastoma (GBM) through weighted gene co-expression network analysis (WGCNA) and L1-penalized least absolute shrinkage and selection operator (LASSO) Cox proportional hazards (PH) model.

Materials and methods

WGCNA was performed based on RNA expression profiles of GBM from Chinese Glioma Genome Atlas (CGGA), National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO), and the European Bioinformatics Institute ArrayExpress for the identification of GBM-related modules. Subsequently, prognostic lncRNAs were determined using LASSO Cox PH model, followed by constructing a risk scoring model based on these lncRNAs. The risk score was used to divide patients into high- and low-risk groups. Difference in survival between groups was analyzed using Kaplan-Meier survival analysis. IncRNA-mRNA networks were built for the prognostic lncRNAs, followed by pathway enrichment analysis for these networks.

Results

This study identified eight preserved GBM-related modules, including 188 lncRNAs. Consequently, C20orf166-AS1, LINC00645, LBX2-AS1, LINC00565, LINC00641, and PRRT3-AS1 were identified by LASSO Cox PH model. A risk scoring model based on the lncRNAs was constructed that could divide patients into different risk groups with significantly different survival rates. Prognostic value of this six-lncRNA signature was validated in two independent sets. C20orf166-AS1 was associated with antigen processing and presentation and cell adhesion molecule pathways, involving nine common genes. LBX2-AS1, LINC00641, PRRT3-AS1, and LINC00565 were related to focal adhesion, extracellular matrix receptor interaction, and mitogen-activated protein kinase signaling pathways, which shared 12 common genes.

Conclusion

This prognostic six-lncRNA signature may improve prognosis prediction of GBM. This study reveals many pathways and genes involved in the mechanisms behind these lncRNAs.

A comprehensive analysis of prognosis prediction models based on pathway‑level, gene‑level and clinical information for glioblastoma

The present study aimed to develop a pathway-based prognosis prediction model for glioblastoma (GBM). Univariate and multivariate Cox regression analysis were used to identify prognosis-related genes and clinical factors using mRNA-seq data of GBM samples from The Cancer Genome Atlas (TCGA) database. The expression matrix of prognosis-related genes was transformed into pathway deregulation score (PDS) based on the Gene Set Enrichment Analysis (GSEA) repository using Pathifier software. With PDS scores as input, L1-penalized estimation-based Cox-proportional hazards (PH) model was used to identify prognostic pathways. Consequently, a prognosis prediction model based on these prognostic pathways was constructed for classifying patients in the TCGA set or each of the three validation sets into two risk groups. The survival difference between these risk groups was then analyzed using Kaplan-Meier survival analysis and log-rank test. In addition, a gene-based prognostic model was constructed using the Cox-PH model. The model of prognostic pathway combined with clinical factors was also evaluated. In total, 148 genes were discovered to be associated with prognosis. The Cox-PH model identified 13 prognostic pathways. Subsequently, a prognostic model based on the 13 pathways was constructed, and was demonstrated to successfully differentiate overall survival in the TCGA set and in three independent sets. However, the gene-based prognosis model was validated in only two of the three independent sets. Furthermore, the pathway+clinic factor-based model exhibited better predictive results compared with the pathway-based model. In conclusion, the present study suggests a promising prognosis prediction model of 13 pathways for GBM, which may be superior to the gene-level information-based prognostic model.

Identification of a DNA Repair-Related Multigene Signature as a Novel Prognostic Predictor of Glioblastoma

BACKGROUND

Glioblastoma (GBM) is an extremely challenging malignancy to treat. Although temozolomide (TMZ) is a standard treatment regimen, many patients with GBM develop chemoresistance. The aim of this study was to identify a DNA repair-related gene signature to better stratify patients treated with TMZ.

METHODS

We selected 89 cases of primary GBM (pGBM) from the Chinese Glioma Genome Atlas RNA-seq dataset as the training cohort, whereas The Cancer Genome Atlas RNA-seq and Gene Set Enrichment (GSE) 16011 mRNA array sets were used as validation cohorts. Regression analysis and linear risk score assessment were performed to build a DNA repair-related signature. We used Kaplan-Meier analysis to evaluate the predictive value of the signature for overall survival (OS) in the different groups. Multivariate Cox regression analysis was used to determine whether the 5-gene signature could independently predict OS.

RESULTS

Using our 5-gene signature panel of APEX1, APRT, PARP2, PMS2L2, and POLR2L, we divided patients with pGBM into high- and low-risk groups. Patients with a low-risk score were predicted to have favorable survival and greater benefit from TMZ therapy compared with patients from the high-risk group (P < 0.05). Moreover, receiver operating characteristic curves showed that the multigene signature was the most sensitive and specific model for survival prediction (P < 0.05).

CONCLUSIONS

Among patients with pGBM, classification based on a risk score determined using a 5-gene panel indicated different OS and reaction to TMZ. The findings in this study demonstrate that this unique 5-gene signature could be a novel model to predict OS and provide accurate therapy for patients with pGBM.

ABCG2 downregulation in glioma stem cells enhances the therapeutic efficacy of demethoxycurcumin

We analyzed the role of ABCG2, a drug transporter, in determining the sensitivity of glioma stem cells (GSCs) to demethoxycurcumin (DMC). We first demonstrated that ABCG2 is more highly expressed in GSCs than primary astrocytes. Modulation of ABCG2 levels in GSCs by transfection of ABCG2 shRNA or a lentiviral vector encoding ABCG2 revealed an inverse relation between ABCG2 levels and DMC-induced GSC growth inhibition. Suppressing ABCG2 increased DMC-induced apoptosis and G0/G1 cell cycle arrest in GSCs. It also increased levels reactive oxygen species (ROS) in GSCs treated with DMC, resulting in increased cytochrome C and caspase-3 activity. When GSCs transfected with ABCG2 shRNA or overexpressing ABCG2 were xenografted and the tumor-bearing, immunodeficient mice were treated with DMC, ABCG2 expression suppressed the tumor proliferation rate (T/C %). These findings demonstrate that ABCG2 expression is critical for DMC resistance in GSCs and is a potential therapeutic target for GBM.

Long noncoding RNA LINC00152 is a potential prognostic biomarker in patients with high-grade glioma

AIMS

To investigate the role of LINC00152 in high-grade glioma (HGG).

METHODS

We collected data from the Chinese Glioma Genome Atlas (CGGA) microarray, CGGA RNA sequencing, and GSE16011 datasets to evaluate the expression and prognostic relationship of LINC00152 in patients with HGGs. A knockdown assay was performed to determine the function of LINC00152 in glioma development and progression in vitro and in vivo.

RESULTS

The expression of LINC00152 was increased with glioma grade, especially in the mesenchymal TCGA subtype. LINC00152 was independently associated with poor prognosis, and the overall survival (OS) of the high expression group was shorter than the low expression group (median OS 14.77 vs 9.65 months; P = 0.0216) in the CGGA microarray dataset. The results were validated in the other 2 datasets. Based on the expression of LINC00152, 4288 (2519 positively; 1769 negatively) probes were extracted to perform a biological process analysis using the Database for Annotation, Visualization, and Integrated Discovery. Positively regulated genes were enriched in immune response, apoptotic process, cell adhesion, and regulation of cell proliferation. The clinical and molecular features of HGG patients indicated that patients in the LINC00152 high expression group tended to display the mesenchymal type, older (≥46 years), isocitrate dehydrogenase1 wild-type, O(6)-methylguanine DNA methyltransferase unmethylated, nonchemotherapy, and low karnofsky performance status. Functionally, knockdown of LINC00152 inhibited cell proliferation, migration, and invasion and increased the sensitivity of chemotherapy in vitro.

CONCLUSION

Our results indicate that knockdown of LINC00152 could inhibit tumor growth in vivo. LINC00152 could serve as a potential prognostic biomarker in patients with HGG.

Effects of low-intensity ultrasound combined with low-dose carboplatin in an orthotopic hamster model of tongue cancer: A preclinical study

Low-intensity ultrasound (LIUS) combined with chemotherapy is an innovative modality for cancer treatment, but its effect on orthotopic carcinoma remains unknown. Our previous study revealed that LIUS enhanced the growth inhibitory effects of several chemotherapeutic drugs in nude mice with transplanted tumors. In the present study, we used 7,12-dimethylbenz(alpha)anthracene to induce orthotopic tongue carcinogenesis in hamsters. We used the first-line chemotherapy drug for tongue cancer, carboplatin (CBP) in combination with LIUS to investigate the synergistic effect. The results revealed that LIUS combined with low-dose CBP enhanced the inhibitory effects of CBP on tumor growth, prolonged survival, and did not increase the incidence of side-effects. It also enhanced the inherent DNA damage caused by CBP, suppressed the expression of the DNA repair proteins O⁶-methylguanine DNA methyltransferase (MGMT) and Chk1, and increased the expression of DNA damage-inducible protein GADD45α. Furthermore, compared with CBP alone, LIUS combined with CBP reduced the expression of cyclin D1 and cyclin B1, induced the expression of caspase-3, cleaved caspase-3, caspase-8, Bax, and Bak, and inhibited the expression of Bcl-2. Examination of clinical samples revealed that MGMT, Chk1, and Gadd45α were higher in OTSCC than in adjacent normal tissue. Hence, our results indicated that LIUS enhanced the ability of low-dose CBP to damage DNA in an orthotopic hamster model of tongue cancer, induced apoptosis, inhibited tumor growth and progression, while it did not increase the toxic side-effects of the drug, suggesting additional clinical benefits for patients treated with the combination of CBP with LIUS.

An immune-related lncRNA signature for patients with anaplastic gliomas

We investigated immune-related long non-coding RNAs (lncRNAs) that may be exploited as potential therapeutic targets in anaplastic gliomas. We obtained 572 lncRNAs and 317 immune genes from the Chinese Glioma Genome Atlas microarray and constructed immune-related lncRNAs co-expression networks to identify immune-related lncRNAs. Two additional datasets (GSE16011, REMBRANDT) were used for validation. Gene set enrichment analysis and principal component analysis were used for functional annotation. Immune-lncRNAs co-expression networks were constructed. Nine immune-related lncRNAs (SNHG8, PGM5-AS1, ST20-AS1, LINC00937, AGAP2-AS1, MIR155HG, TUG1, MAPKAPK5-AS1, and HCG18) signature was identified in patients with anaplastic gliomas. Patients in the low-risk group showed longer overall survival (OS) and progression-free survival than those in the high-risk group (P < 0.0001; P < 0.0001). Additionally, patients in the high-risk group displayed no-deletion of chromosomal arms 1p and/or 19q, isocitrate dehydrogenase wild-type, classical and mesenchymal TCGA subtype, G3 CGGA subtype, and lower Karnofsky performance score (KPS). Moreover, the signature was an independent factor and was significantly associated with the OS (P = 0.000, hazard ratio (HR) = 1.434). These findings were further validated in two additional datasets (GSE16011, REMBRANDT). Low-risk and high-risk groups displayed different immune status based on principal components analysis. Our results showed that the nine immune-related lncRNAs signature has prognostic value for anaplastic gliomas.

A 16-gene signature predicting prognosis of patients with oral tongue squamous cell carcinoma

Background

Oral tongue squamous cell carcinoma (OTSCC) is the most common subtype of oral cancer. A predictive gene signature is necessary for prognosis of OTSCC.

Methods

Five microarray data sets of OTSCC from the Gene Expression Omnibus (GEO) and one data set from The Cancer Genome Atlas (TCGA) were obtained. Differentially expressed genes (DEGs) of GEO data sets were identified by integrated analysis. The DEGs associated with prognosis were screened in the TCGA data set by univariate survival analysis to obtain a gene signature. A risk score was calculated as the summation of weighted expression levels with coefficients by Cox analysis. The signature was used to distinguish carcinoma, estimated by receiver operator characteristic curves and the area under the curve (AUC). All were validated in the GEO and TCGA data sets.

Results

Integrated analysis of GEO data sets revealed 300 DEGs. A 16-gene signature and a risk score were developed after survival analysis. The risk score was effective to stratify patients into high-risk and low-risk groups in the TCGA data set (P < 0.001). The 16-gene signature was valid to distinguish the carcinoma from normal samples (AUC 0.872, P < 0.001).

Discussion

We identified a useful 16-gene signature for prognosis of OTSCC patients, which could be applied to clinical practice. Further studies were needed to prove the findings.

Relationship between RAS Association Domain Family Protein 1A Promoter Methylation and the Clinicopathological Characteristics in Patients with Ovarian Cancer: A Systematic Meta-Analysis

BACKGROUND

To investigate the relationship between RAS association domain family protein 1A (RASSF1A) promoter methylation and the clinical features, and the survival of ovarian cancer patients.

METHODS

A comprehensive literature search was conducted in the PubMed, Embase, EBSCO, and Cochrane Library databases. The overall ORs with their 95% CIs were calculated in this meta-analysis.

RESULTS

Finally 17 relevant publications with 1,108 ovarian cancer samples were available for the current meta-analysis. RASSF1A promoter methylation had a significantly higher level in ovarian cancer than in low malignant potential (LMP) tumors. No significant relationship was observed between RASSF1A promoter methylation and the clinicopathological characteristics in ovarian cancer. Two studies reported that RASSF1A promoter methylation was not correlated with the survival of patients with ovarian cancer.

CONCLUSIONS

Our findings suggest that the use of RASSF1A promoter methylation could distinguish ovarian cancer and LMP tumors. -RASSF1A promoter methylation may not be correlated with the clinical features and the survival of ovarian cancer patients. More studies with large sample sizes are essential in the future.

Influence of neutrophil-lymphocyte ratio in prognosis of glioblastoma multiforme

Neutrophil-lymphocyte ratio (NLR) is a hematological marker of systemic inflammation and several studies demonstrate an association between a higher NLR and a worse prognosis in many malignancies. However, literature analyzing its prognostic value in glioblastoma multiforme (GBM) is still scarce. We intended to analyze the correlation of NLR with overall survival and progression-free survival in patients with GBM performing a retrospective review of the patients with diagnosis of GBM submitted to a resection surgery in the department of neurosurgery of a tertiary care hospital, between January/2005 and January/2013. 140 patients were included. Mean age at surgery was 62.9 ± 10.0 years and mean age at death was 64.4 ± 9.8 years. Mean overall survival was 19.4 ± 14.3 months and mean progression-free survival was 9.4 ± 8.7 months. There was no correlation of NLR, platelets-lymphocyte ratio (PLR) or absolute counts of neutrophils, lymphocytes and platelets with overall survival in multivariate analysis. However, a preoperative NLR ≤ 5 correlated with a shorter progression-free survival [HR 1.56 (SD 95% 1.04-2.34); p = 0.032]. We performed a subgroup analysis of patients who completed Stupp protocol. In this subgroup of 117 patients, a preoperative NLR > 7 correlated with a shorter overall survival [HR 1.65 (SD 95% 1.07-2.53); p = 0.023]. The results from our total cohort didn't confirm the correlation between a higher NRL and worse survival in GBM. However, in the subgroup analysis of patients who completed Stupp protocol, a higher NLR was an independent prognostic factor to a shorter overall survival, similar to existent literature data about GBM.

DNA methylation profiles in cancer diagnosis and therapeutics

Cancer initiation and proliferation is regulated by both epigenetic and genetic events with epigenetic modifications being increasingly identified as important targets for cancer research. DNA methylation catalyzed by DNA methyltransferases (DNMTs) is one of the essential epigenetic mechanisms that control cell proliferation, apoptosis, differentiation, cell cycle, and transformation in eukaryotes. Recent progress in epigenetics revealed a deeper understanding of the mechanisms of tumorigenesis and provided biomarkers for early detection, diagnosis, and prognosis in cancer patients. Although DNA methylation biomarker possesses potential contributing to precision medicine, there are still limitations to be overcome before it reaches clinical setting. Hence, the current status of DNA methylation biomarkers was reviewed and the future use in clinic was also predicted.

LncRNA profile study reveals four-lncRNA signature associated with the prognosis of patients with anaplastic gliomas

Anaplastic glioma is Grade III and the median overall survival is about 37.6 months. However, there are still other factors that affect the prognosis for anaplastic glioma patients due to variable overall survival. So we screened four-lncRNA signature (AGAP2-AS1, TPT1-AS1, LINC01198 and MIR155HG) from the lncRNA expression profile from the GSE16011, CGGA and REMBRANDT datasets. The patients in low risk group had longer overall survival than high risk group (median OS 2208.25 vs. 591.30 days; P < 0.0001). Moreover, patients in the low risk group showed similar overall survival to Grade II patients (P = 0.1669), while the high risk group showed significant different to Grade IV (P = 0.0005) with similar trend. So based on the four-lncRNA, the anaplastic gliomas could be divided into grade II-like and grade IV-like groups. On the multivariate analysis, it showed the signature was an independent prognostic factor (P = 0.000). The expression of four lncRNAs in different grades showed that AGAP2-AS1, LINC01198 and MIR155HG were increased with tumor grade, while TPT1-AS1 was decreased. Knockdown of AGAP2-AS1 can inhibit the cell proliferation, migration and invasion, while increase the apoptosis cell rates in vitro. In conclusion, our results showed that the four-lncRNA signature has prognostic value for anaplastic glioma. Moreover, clinicians should conduct corresponding therapies to achieve best treatment with less side effects for two groups patients.