Quantitative proteomics reveals the novel co-expression signatures in early brain development for prognosis of glioblastoma multiforme

Glutamine Metabolism Heterogeneity in Glioblastoma Unveils an Innovative Combination Therapy Strategy

Treatment of glioblastoma multiforme (GBM) remains challenging. Unraveling the orchestration of glutamine metabolism may provide a novel viewpoint on GBM therapy. The study presented a full and comprehensive comprehending of the glutamine metabolism atlas and heterogeneity in GBM for facilitating the development of a more effective therapeutic choice. Transcriptome data from large GBM cohorts were integrated in this study. A glutamine metabolism-based classification was established through consensus clustering approach, and a classifier by LASSO analysis was defined for differentiating the classification. Prognosis, signaling pathway activity, tumor microenvironment, and responses to immune checkpoint blockade (ICB) and small molecular drugs were characterized in each cluster. A combinational therapy of glutaminase inhibitor CB839 with dihydroartemisinin (DHA) was proposed, and the influence on glutamine metabolism, apoptosis, reactive oxygen species (ROS), and migration was measured in U251 and U373 cells. We discovered that GBM presented heterogeneous glutamine metabolism-based clusters, with unique survival outcomes, activity of signaling pathways, tumor microenvironment, and responses to ICB and small molecular compounds. In addition, the classifier could accurately differentiate the two clusters. Strikingly, the combinational therapy of CB839 with DHA synergistically attenuated glutamine metabolism, triggered apoptosis and ROS accumulation, and impaired migrative capacity in GBM cells, demonstrating the excellent preclinical efficacy. Altogether, our findings unveil the glutamine metabolism heterogeneity in GBM and propose an innovative combination therapy of CB839 with DHA for this malignant disease.

RARRES2 is Downregulated in Isocitrate Dehydrogenase 1 Mutant Glioma Patients and Served as an Unfavorable Prognostic Factor of Glioma

BACKGROUND

Mutations in isocitrate dehydrogenase 1 (IDH1) induce extensive transcriptional alterations to promote glioma development. However, IDH1 mutation contributes the better clinical outcomes of glioma. Further understanding the transcriptional and DNA methylation changes mediated by IDH1 mutation will provide new therapeutic targets for glioma.

METHODS

Public glioma cohorts were collected and processed using R software. The transcriptional changes mediated by IDH1 mutation were determined and presented using heatmap. The differentially expressed genes in IDH1 mutant glioma were overlapped using TBtools. The prognostic effects of IDH1 regulated genes were determined by Kaplan-Meier survival analysis.

RESULTS

Retinoic acid receptor responder 2 (RARRES2) was upregulated in IDH1 wild type lower-grade glioma (LGG) patients, and higher expression levels of RARRES2 were associated with worse clinical outcomes of LGG. Moreover, IDH1 wild type LGG patients with higher expression levels of RARRES2 had even worse overall survival. Compared with LGG, RARRES2 was upregulated in grade IV glioma (glioblastoma multiforme, GBM). Also, RARRES2 represented an unfavorable prognostic factor of glioma. In GBM, RARRES2 was also associated with IDH1 mutation. In both LGG and GBM, IDH1 mutation induced extensive DNA hypermethylation, and more than half genes that were downregulated in IDH1 mutant glioma were contributed by DNA hypermethylation. RARRES2 was also hypermethylated in IDH1 mutant LGG or GBM patients. Furthermore, RARRES2 hypomethylation was an unfavorable prognostic factor in patients with LGG.

CONCLUSIONS

RARRES2 was downregulated by IDH1 mutation and served as an unfavorable prognostic factor in glioma.

Genomic analysis of immunogenic cell death-related subtypes for predicting prognosis and immunotherapy outcomes in glioblastoma multiforme

Immunogenic cell death (ICD), a unique form of cancer cell death, has therapeutic potential in anti-tumour immunotherapy. The aim of this study is to explore the predictive potential of ICD in the prognosis and immunotherapy outcomes of glioblastoma multiforme (GBM). RNA sequencing data and clinical information were downloaded from three databases. Unsupervised consistency clustering analysis was used to identify ICD-related clusters and gene clusters. Additionally, the ICD scores were determined using principal component analysis and the Boruta algorithm via dimensionality reduction techniques. Subsequently, three ICD-related clusters and three gene clusters with different prognoses were identified, with differences in specific tumour immune infiltration-related lymphocytes in these clusters. Moreover, the ICD score was well differentiated among patients with GBM, and the ICD score was considered an independent prognostic factor for patients with GBM. Furthermore, two datasets were used for the external validation of ICD scores as predictors of prognosis and immunotherapy outcomes. The validation analysis suggested that patients with high ICD scores had a worse prognosis. Additionally, a higher proportion of patients with high ICD scores were non-responsive to immunotherapy. Thus, the ICD score has the potential as a biomarker to predict the prognosis and immunotherapy outcomes of patients with GBM.

Construction of a telomere-related gene signature to predict prognosis and immune landscape for glioma

Background

Glioma is one of the commonest malignant tumors of the brain. However, glioma present with a poor clinical prognosis. Therefore, specific detection markers and therapeutic targets need to be explored as a way to promote the survival rate of BC patients. Therefore, we need to search for quality immune checkpoints to support the efficacy of immunotherapy for glioma.

Methods

We first recognized differentially expressed telomere-related genes (TRGs) and accordingly developed a risk model by univariate and multivariate Cox analysis. The accuracy of the model is then verified. We evaluated the variations in immune function and looked at the expression levels of immune checkpoint genes. Finally, to assess the anti-tumor medications often used in the clinical treatment of glioma, we computed the half inhibitory concentration of pharmaceuticals.

Results

We finally identified nine TRGs and built a risk model. Through the validation of the model, we found good agreement between the predicted and observed values. Then, we found 633 differentially expressed genes between various risk groups to identify the various molecular pathways between different groups. The enrichment of CD4+ T cells, CD8+ T cells, fibroblasts, endothelial cells, macrophages M0, M1, and M2, mast cells, myeloid dendritic cells, and neutrophils was favorably correlated with the risk score, but the enrichment of B cells and NK cells was negatively correlated with the risk score. The expression of several immune checkpoint-related genes differed significantly across the risk groups. Finally, in order to create individualized treatment plans for diverse individuals, we searched for numerous chemotherapeutic medications for patients in various groups.

Conclusion

The findings of this research provide evidence that TRGs may predict a patient's prognosis for glioma, assist in identifying efficient targets for glioma immunotherapy, and provide a foundation for an efficient, customized approach to treating glioma patients.

Integrating machine learning and bioinformatics analysis to m6A regulator-mediated methylation modification models for predicting glioblastoma patients' prognosis and immunotherapy response

BACKGROUND

Epigenetic regulations of immune responses are essential for cancer development and growth. As a critical step, comprehensive and rigorous explorations of m6A methylation are important to determine its prognostic significance, tumor microenvironment (TME) infiltration characteristics and underlying relationship with glioblastoma (GBM).

METHODS

To evaluate m6A modification patterns in GBM, we conducted unsupervised clustering to determine the expression levels of GBM-related m6A regulatory factors and performed differential analysis to obtain m6A-related genes. Consistent clustering was used to generate m6A regulators cluster A and B. Machine learning algorithms were implemented for identifying TME features and predicting the response of GBM patients receiving immunotherapy.

RESULTS

It is found that the m6A regulatory factor significantly regulates the mutation of GBM and TME. Based on Europe, America, and China data, we established m6Ascore through the m6A model. The model accurately predicted the results of 1206 GBM patients from the discovery cohort. Additionally, a high m6A score was associated with poor prognoses. Significant TME features were found among the different m6A score groups, which demonstrated positive correlations with biological functions (i.e., EMT2) and immune checkpoints.

CONCLUSIONS

m6A modification was important to characterize the tumorigenesis and TME infiltration in GBM. The m6Ascore provided GBM patients with valuable and accurate prognosis and prediction of clinical response to various treatment modalities, which could be useful to guide patient treatments.

Prognostic Prediction Model for Glioblastoma: A Ferroptosis-Related Gene Prediction Model and Independent External Validation

Glioblastoma (GBM) is the most common primary malignant intracranial tumor with a poor prognosis. Ferroptosis is a newly discovered, iron-dependent, regulated cell death, and recent studies suggest its close correlation to GBM. The transcriptome and clinical data were obtained for patients diagnosed with GBM from TCGA, GEO, and CGGA. Ferroptosis-related genes were identified, and a risk score model was constructed using Lasso regression analyses. Survival was evaluated by univariate or multivariate Cox regressions and Kaplan-Meier analyses, and further analyses were performed between the high- and low-risk groups. There were 45 ferroptosis-related different expressed genes between GBM and normal brain tissues. The prognostic risk score model was based on four favorable genes, CRYAB, ZEB1, ATP5MC3, and NCOA4, and four unfavorable genes, ALOX5, CHAC1, STEAP3, and MT1G. A significant difference in OS between high- and low-risk groups was observed in both the training cohort (p < 0.001) and the validation cohorts (p = 0.029 and 0.037). Enrichment analysis of pathways and immune cells and functioning was conducted between the two risk groups. A novel prognostic model for GBM patients was developed based on eight ferroptosis-related genes, suggesting a potential prediction effect of the risk score model in GBM.

Identification of ITPR1 as a Hub Gene of Group 3 Medulloblastoma and Coregulated Genes with Potential Prognostic Values

The Group 3 Medulloblastoma (Grp3-MB) is an aggressive molecular subtype with a high incidence of metastasis and deaths. In this study, were used an RNA sequencing data (RNA-Seq) from a Brazilian cohort of MBs to identify hub genes associated with the metastatic risk. Data validation were performed by using multiple large datasets from MBs (GSE85217, GSE37418, and EGAS00001001953). DESeq2 package in R software was used to identify the differentially expressed genes (DEGs) in our RNA-Seq data. The DEGs data were accessed to construct the modules/graphs of co-expression and to identify hub genes through Cytoscape platform. The coregulated genes were enriched by the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and the protein-protein interaction (PPI) network was visualized by Cytoscape. The Kaplan-Meier plotter and ROC curves were used to validate the diagnostic and prognostic values of specific biomarkers identified through this model. We identified that inositol 1,4,5-trisphosphate receptor type 1 (ITPR1) as a downregulated hub gene, with a high diagnostic accuracy to Grp3-MBs and associated with tumor metastasis. In addition, we identified genes significantly correlated with ITPR1 that were associated with metastasis in Grp3-MB (ATP1A2, MTTL7A, and RGL1) and worst overall survival in MBs (ANTXR1 and RGL1). Our findings suggest that the ITPR1 hub gene is potentially involved in the metastatic process for Grp3-MB. Our data also provide evidence of targets that may serve as prognostic predictors and/or regulators for the metastatic process that maybe explored for further research of individualized therapy to Grp3-MBs.

Low expression of CDHR1 is an independent unfavorable prognostic factor in glioma

Background: Analysis of the differentially expressed genes between lower grade glioma (LGG) and glioblastoma (GBM) will identify genes involved in a more aggressive phenotype of glioma. Methods: Differentially expressed genes between GBM and LGG were identified using published datasets. Kaplan-Meier estimator was used to determine the overall survival of different groups of glioma patients. The biological functions of CDHR1 in glioma were tested using CCK-8 and trans-well assays. Results: CCDC109B, CD58, CLIC1, EFEMP2, EMP3, LAMC1, LGALS1, PDLIM1 and TNFRSF1A were over-expressed, while, CDHR1 was down-regulated in GBM in The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), GSE4412 and GSE43378 datasets. Compared with normal brain tissues, CDHR1 was down-regulated in glioma tissues. And low expression of CDHR1 was an unfavorable prognostic factor in glioma. Moreover, CDHR1 was lowly expressed in mesenchymal GBM subtype and lower expression of CDHR1 was associated with the worse clinical prognosis of GBM. Furthermore, CDHR1 was down-regulated in astrocytoma LGG subtype and low expression of CDHR1 was a bad prognosis of LGG. CDHR1 expression levels were also associated with IDH mutation. IDH mutant LGG or GBM patients were with higher CDHR1 expression. High expression of CDHR1 was a favorable prognosis in IDH mutant or IDH wild type LGG patients. CHDR1 expression was associated with MGMT methylation and CDHR1 was down-regulated in chemotherapy un-responsive LGG patients. CDHR1 was an independent prognostic factor and negatively associated with EMP3 expression. Glioma patients with low CDHR1 and high EMP3 expression had worse clinical outcomes. At last, we showed that over-expression of CDHR1 could inhibit glioma cell growth and invasion. Conclusion: Low expression of CDHR1 was an independent unfavorable prognostic factor in glioma.

Prognostic Prediction Model for Glioblastoma: A Metabolic Gene Signature and Independent External Validation

Background: Glioblastoma (GBM) is the most common primary malignant intracranial tumor and closely related to metabolic alteration. However, few accepted prognostic models are currently available, especially models based on metabolic genes.

Methods: The transcriptome data were obtained for all of the patients diagnosed with GBM from the Gene Expression Omnibus (GEO) (training cohort, n=369) and The Cancer Genome Atlas (TCGA) (validation cohort, n=152) with the following variables: age at diagnosis, sex, follow-up and overall survival (OS). Metabolic genes according to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were contracted, and a Lasso regression model was constructed. Survival was assessed by univariate or multivariate Cox proportional hazards regression and Kaplan-Meier analysis, and an independent external validation was also conducted to examine the model.

Results: There were 341 metabolic genes showed significant differences between normal brain and GBM tissues in both the training and validation cohorts, among which 56 genes were dramatically correlated to the OS of patients. Lasso regression revealed that the metabolic prognostic model was composed of 18 genes, including COX10, COMT, and GPX2 with protective effects, as well as OCRL and RRM2 with unfavorable effects. Patients classified as high-risk by the risk score from this model had markedly shorter OS than low-risk patients (P<0.0001), and this significant result was also observed in independent external validation (P<0.001).

Conclusions: The prognosis of GBM was dramatically related to metabolic pathways, and our metabolic prognostic model had high accuracy and application value in predicting the OS of GBM patients.

Prognostic Power of a Chaperonin Containing TCP-1 Subunit Genes Panel for Hepatocellular Carcinoma

Chaperonin containing TCP-1 (T-complex protein 1) (CCT) is a large molecular weight complex that contains nine subunits (TCP1, CCT2, CCT3, CCT4, CCT5, CCT6A, CCT6B, CCT7, CCT8). This study aimed to reveal key genes which encode CCT subunits for prognosis and establish prognostic gene signatures based on CCT subunit genes. The data was downloaded from The Cancer Genome Atlas, International Cancer Genome Consortium and Gene Expression Omnibus. CCT subunit gene expression levels between tumor and normal tissues were compared. Corresponding Kaplan-Meier analysis displayed a distinct separation in the overall survival of CCT subunit genes. Correlation analysis, protein-protein interaction network, Gene Ontology analysis, immune cells infiltration analysis, and transcription factor network were performed. A nomogram was constructed for the prediction of prognosis. Based on multivariate Cox regression analysis and shrinkage and selection method for linear regression model, a three-gene signature comprising CCT4, CCT6A, and CCT6B was constructed in the training set and significantly associated with prognosis as an independent prognostic factor. The prognostic value of the signature was then validated in the validation and testing set. Nomogram including the signature showed some clinical benefit for overall survival prediction. In all, we built a novel three-gene signature and nomogram from CCT subunit genes to predict the prognosis of hepatocellular carcinoma, which may support the medical decision for HCC therapy.

Investigating Glioblastoma Response to Hypoxia

Glioblastoma (GB) is the most common and deadly type of primary malignant brain tumor with an average patient survival of only 15–17 months. GBs typically have hypoxic regions associated with aggressiveness and chemoresistance. Using patient derived GB cells, we characterized how GB responds to hypoxia. We noted a hypoxia-dependent glycolytic switch characterized by the up-regulation of HK2, PFKFB3, PFKFB4, LDHA, PDK1, SLC2A1/GLUT-1, CA9/CAIX, and SLC16A3/MCT-4. Moreover, many proangiogenic genes and proteins, including VEGFA, VEGFC, VEGFD, PGF/PlGF, ADM, ANGPTL4, and SERPINE1/PAI-1 were up-regulated during hypoxia. We detected the hypoxic induction of invasion proteins, including the plasminogen receptor, S100A10, and the urokinase plasminogen activator receptor, uPAR. Furthermore, we observed a hypoxia-dependent up-regulation of the autophagy genes, BNIP-3 and DDIT4 and of the multi-functional protein, NDRG1 associated with GB chemoresistance; and down-regulation of EGR1 and TFRC (Graphical abstract). Analysis of GB patient cohorts’ revealed differential expression of these genes in patient samples (except SLC16A3) compared to non-neoplastic brain tissue. High expression of SLC2A1, LDHA, PDK1, PFKFB4, HK2, VEGFA, SERPINE1, TFRC, and ADM was associated with significantly lower overall survival. Together these data provide important information regarding GB response to hypoxia which could support the development of more effective treatments for GB patients.

A Comprehensive Proteomic SWATH-MS Workflow for Profiling Blood Extracellular Vesicles: A New Avenue for Glioma Tumour Surveillance

Improving outcomes for diffuse glioma patients requires methods that can accurately and sensitively monitor tumour activity and treatment response. Extracellular vesicles (EV) are membranous nanoparticles that can traverse the blood-brain-barrier, carrying oncogenic molecules into the circulation. Measuring clinically relevant glioma biomarkers cargoed in circulating EVs could revolutionise how glioma patients are managed. Despite their suitability for biomarker discovery, the co-isolation of highly abundant complex blood proteins has hindered comprehensive proteomic studies of circulating-EVs. Plasma-EVs isolated from pre-operative glioma grade II-IV patients (n = 41) and controls (n = 11) were sequenced by Sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) and data extraction was performed by aligning against a custom 8662-protein library. Overall, 4054 proteins were measured in plasma-EVs. Differentially expressed proteins and putative circulating-EV markers were identified (adj. p-value < 0.05), including those reported in previous in-vitro and ex-vivo glioma-EV studies. Principal component analysis showed that plasma-EV protein profiles clustered according to glioma histological-subtype and grade, and plasma-EVs resampled from patients with recurrent tumour progression grouped with more aggressive glioma samples. The extensive plasma-EV proteome profiles achieved here highlight the potential for SWATH-MS to define circulating-EV biomarkers for objective blood-based measurements of glioma activity that could serve as ideal surrogate endpoints to assess tumour progression and allow more dynamic, patient-centred treatment protocols.

Long Noncoding RNA MIR17HG Promotes Colorectal Cancer Progression via miR-17-5p

Immune dysregulation plays a vital role in colorectal cancer initiation and progression. Long noncoding RNAs (lncRNA) exhibit multiple functions including regulation of gene expression. Here, we identified an immune-related lncRNA, MIR17HG, whose expression was gradually upregulated in adjacent, adenoma, and colorectal cancer tissue. MIR17HG promoted tumorigenesis and metastasis in colorectal cancer cells both in vitro and in vivo. Mechanistically, MIR17HG increased the expression of NF-κB/RELA by competitively sponging the microRNA miR-375. In addition, RELA transcriptionally activated MIR17HG in a positive feedback loop by directly binding to its promoter region. Moreover, miR-17-5p, one of the transcribed miRNAs from MIR17HG, reduced the expression of the tumor suppressor B-cell linker (BLNK), resulting in increased migration and invasion of colorectal cancer cells. MIR17HG also upregulated PD-L1, indicating its potential role in immunotherapy. Overall, these findings demonstrate that MIR17HG plays an oncogenic role in colorectal cancer and may serve as a promising therapeutic target. SIGNIFICANCE: These findings provide mechanistic insight into the role of the lncRNA MIR17HG and its miRNA members in regulating colorectal cancer carcinogenesis and progression.

Relevance of a TCGA-derived Glioblastoma Subtype Gene-Classifier among Patient Populations

Glioblastoma multiforme (GBM), a deadly cancer, is the most lethal and common malignant brain tumor, and the leading cause of death in adult brain tumors. While genomic data continues to rocket, clinical application and translation to patient care are lagging behind. Big data now deposited in the TCGA network offers a window to generate novel clinical hypotheses. We hypothesized that a TCGA-derived gene-classifier can be applied across different gene profiling platforms and population groups. This gene-classifier validated three robust GBM-subtypes across six different platforms, among Caucasian, Korean and Chinese populations: Three Caucasian-predominant TCGA-cohorts (Affymetrix U133A = 548, Agilent Custom-Array = 588, RNA-seq = 168), and three Asian-cohorts (Affymetrix Human Gene 1.0ST-Array = 61, Illumina = 52, Agilent 4 × 44 K = 60). To understand subtype-relevance in patient therapy, we investigated retrospective TCGA patient clinical sets. Subtype-specific patient survival outcome was similarly poor and reflected the net result of a mixture of treatment regimens with/without surgical resection. As a proof-of-concept, in subtype-specific patient-derived orthotopic xenograft (PDOX) mice, Classical-subtype demonstrated no survival difference comparing radiation-therapy versus temozolomide monotherapies. Though preliminary, a PDOX model of Proneural/Neural-subtype demonstrated significantly improved survival with temozolomide compared to radiation-therapy. A larger scale study using this gene-classifier may be useful in clinical outcome prediction and patient selection for trials based on subtyping.

mGAP: the macaque genotype and phenotype resource, a framework for accessing and interpreting macaque variant data, and identifying new models of human disease

Background

Non-human primates (NHPs), particularly macaques, serve as critical and highly relevant pre-clinical models of human disease. The similarity in human and macaque natural disease susceptibility, along with parallel genetic risk alleles, underscores the value of macaques in the development of effective treatment strategies. Nonetheless, there are limited genomic resources available to support the exploration and discovery of macaque models of inherited disease. Notably, there are few public databases tailored to searching NHP sequence variants, and no other database making use of centralized variant calling, or providing genotype-level data and predicted pathogenic effects for each variant.

Results

The macaque Genotype And Phenotype (mGAP) resource is the first public website providing searchable, annotated macaque variant data. The mGAP resource includes a catalog of high confidence variants, derived from whole genome sequence (WGS). The current mGAP release at time of publication (1.7) contains 17,087,212 variants based on the sequence analysis of 293 rhesus macaques. A custom pipeline was developed to enable annotation of the macaque variants, leveraging human data sources that include regulatory elements (ENCODE, RegulomeDB), known disease- or phenotype-associated variants (GRASP), predicted impact (SIFT, PolyPhen2), and sequence conservation (Phylop, PhastCons). Currently mGAP includes 2767 variants that are identical to alleles listed in the human ClinVar database, of which 276 variants, spanning 258 genes, are identified as pathogenic. An additional 12,472 variants are predicted as high impact (SnpEff) and 13,129 are predicted as damaging (PolyPhen2). In total, these variants are predicted to be associated with more than 2000 human disease or phenotype entries reported in OMIM (Online Mendelian Inheritance in Man). Importantly, mGAP also provides genotype-level data for all subjects, allowing identification of specific individuals harboring alleles of interest.

Conclusions

The mGAP resource provides variant and genotype data from hundreds of rhesus macaques, processed in a consistent manner across all subjects (https://mgap.ohsu.edu). Together with the extensive variant annotations, mGAP presents unprecedented opportunity to investigate potential genetic associations with currently characterized disease models, and to uncover new macaque models based on parallels with human risk alleles.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5559-7) contains supplementary material, which is available to authorized users.

Villi development core-related gene expression associated with lung squamous cancer prognosis

Similarities between embryonic development and tumorigenesis are reflected in biological behavior and gene expression. Although the gene signature during development and the clinical phenotype of different cancers show certain correlation pattern, the correlation between early embryo development and cancer remains largely unexplored. To compare the gene expression profile between development and cancer, our study analyzed the gene expression of chorionic villi samples at different gestational ages (6, 7, 8, 9, 10, 40 weeks) obtained from gene expression omnibus (GEO) datasets using correlation test. Then the villi development-related genes that gradually showed a positive correlation (upregulated) (n = 394) or negative correlation (downregulated) (n = 325) with time were used to construct protein-protein interaction (PPI) networks. Three subnetworks among the gradually upregulated genes and 3 subnetworks among the downregulated genes were identified using the molecular complex detection (MCODE) plugin in Cytoscape software. The most significant GO terms for villi-correlated genes were immune, inflammatory response and cell division. These gene clusters were also dysregulated in lung squamous cell carcinoma (SCC). Moreover the prognostic value of the gene clusters was then analyzed with TCGA lung SCC data, which showed 4 clusters that were associated with prognosis. Our results demonstrate the gene expression similarity between development and lung SCC and identified development-associated gene clusters that could contain prognostic information for lung SCC patients.

Comparison of different functional prediction scores using a gene-based permutation model for identifying cancer driver genes

Background

Identifying cancer driver genes (CDG) is a crucial step in cancer genomic toward the advancement of precision medicine. However, driver gene discovery is a very challenging task because we are not only dealing with huge amount of data; but we are also faced with the complexity of the disease including the heterogeneity of background somatic mutation rate in each cancer patient. It is generally accepted that CDG harbor variants conferring growth advantage in the malignant cell and they are positively selected, which are critical to cancer development; whereas, non-driver genes harbor random mutations with no functional consequence on cancer. Based on this fact, function prediction based approaches for identifying CDG have been proposed to interrogate the distribution of functional predictions among mutations in cancer genomes (eLS 1–16, 2016). Assuming most of the observed mutations are passenger mutations and given the quantitative predictions for the functional impact of the mutations, genes enriched of functional or deleterious mutations are more likely to be drivers. The promises of these methods have been continually refined and can therefore be applied to increase accuracy in detecting new candidate CDGs. However, current function prediction based approaches only focus on coding mutations and lack a systematic way to pick the best mutation deleteriousness prediction algorithms for usage.

Results

In this study, we propose a new function prediction based approach to discover CDGs through a gene-based permutation approach. Our method not only covers both coding and non-coding regions of the genes; but it also accounts for the heterogeneous mutational context in cohort of cancer patients. The permutation model was implemented independently using seven popular deleteriousness prediction scores covering splicing regions (SPIDEX), coding regions (MetaLR, and VEST3) and pan-genome (CADD, DANN, Fathmm-MKL coding and Fathmm-MKL noncoding). We applied this new approach to somatic single nucleotide variants (SNVs) from whole-genome sequences of 119 breast and 24 lung cancer patients and compared the seven deleteriousness prediction scores for their performance in this study.

Conclusion

The new function prediction based approach not only predicted known cancer genes listed in the Cancer Gene Census (CGC), but also new candidate CDGs that are worth further investigation. The results showed the advantage of utilizing pan-genome deleteriousness prediction scores in function prediction based methods. Although VEST3 score, a deleteriousness prediction score for missense mutations, has the best performance in breast cancer, it was topped by CADD and Fathmm-MKL coding, two pan-genome deleteriousness prediction scores, in lung cancer.

Electronic supplementary material

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Current Challenges and Opportunities in Treating Glioblastoma

Glioblastoma multiforme (GBM), the most common and aggressive primary brain tumor, has a high mortality rate despite extensive efforts to develop new treatments. GBM exhibits both intra- and intertumor heterogeneity, lending to resistance and eventual tumor recurrence. Large-scale genomic and proteomic analysis of GBM tumors has uncovered potential drug targets. Effective and “druggable” targets must be validated to embark on a robust medicinal chemistry campaign culminating in the discovery of clinical candidates. Here, we review recent developments in GBM drug discovery and delivery. To identify GBM drug targets, we performed extensive bioinformatics analysis using data from The Cancer Genome Atlas project. We discovered 20 genes, BOC, CLEC4GP1, ELOVL6, EREG, ESR2, FDCSP, FURIN, FUT8-AS1, GZMB, IRX3, LITAF, NDEL1, NKX3-1, PODNL1, PTPRN, QSOX1, SEMA4F, TH, VEGFC, and C20orf166AS1 that are overexpressed in a subpopulation of GBM patients and correlate with poor survival outcomes. Importantly, nine of these genes exhibit higher expression in GBM versus low-grade glioma and may be involved in disease progression. In this review, we discuss these proteins in the context of GBM disease progression. We also conducted computational multi-parameter optimization to assess the blood-brain barrier (BBB) permeability of small molecules in clinical trials for GBM treatment. Drug delivery in the context of GBM is particularly challenging because the BBB hinders small molecule transport. Therefore, we discuss novel drug delivery methods, including nanoparticles and prodrugs. Given the aggressive nature of GBM and the complexity of targeting the central nervous system, effective treatment options are a major unmet medical need. Identification and validation of biomarkers and drug targets associated with GBM disease progression present an exciting opportunity to improve treatment of this devastating disease.

Identification of glioblastoma gene prognosis modules based on weighted gene co-expression network analysis

Background

Glioblastoma multiforme, the most prevalent and aggressive brain tumour, has a poor prognosis. The molecular mechanisms underlying gliomagenesis remain poorly understood. Therefore, molecular research, including various markers, is necessary to understand the occurrence and development of glioma.

Method

Weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network in TCGA glioblastoma samples. Gene ontology (GO) and pathway-enrichment analysis were used to identify significance of gene modules. Cox proportional hazards regression model was used to predict outcome of glioblastoma patients.

Results

We performed weighted gene co-expression network analysis (WGCNA) and identified a gene module (yellow module) related to the survival time of TCGA glioblastoma samples. Then, 228 hub genes were calculated based on gene significance (GS) and module significance (MS). Four genes (OSMR + SOX21 + MED10 + PTPRN) were selected to construct a Cox proportional hazards regression model with high accuracy (AUC = 0.905). The prognostic value of the Cox proportional hazards regression model was also confirmed in GSE16011 dataset (GBM: n = 156).

Conclusion

We developed a promising mRNA signature for estimating overall survival in glioblastoma patients.

Electronic supplementary material

The online version of this article (10.1186/s12920-018-0407-1) contains supplementary material, which is available to authorized users.

Transferrin receptor 1 in cancer: a new sight for cancer therapy

Iron as an important element plays crucial roles in various physiological and pathological processes. Iron metabolism behaves in systemic and cellular two levels that usually are in balance conditions. The disorders of the iron metabolism balances relate with many kinds of diseases including Alzheimer's disease, osteoporosis and various cancers. In systemic iron metabolism that is regulated by hepcidin-ferroportin axis, plasma iron is bound with transferrin (TF) which has two high-affinity binding sites for ferric iron. The generic cellular iron metabolism consists of iron intake, utilization and efflux. During the iron intake process in generic cells, transferrin receptors (TFRs) act as the most important receptor mediated controls. TFR1 and TFR2 are two subtypes of TFRs those bind with iron-transferrin complex to facilitate iron into cells. TFR1 is ubiquitously expressed on the surfaces of generic cells, whereas TFR2 is specially expressed in liver cells. TFR1 has attracted more attention than TFR2 by having diverse functions in both invertebrates and vertebrates. Recently reports showed that TFR1 involved in many kinds of diseases including anemia, neurodegenerative diseases and cancers. Most importantly, TFR1 has been verified to be abnormally expressed in various cancers. Some experimental and clinical drugs and antibodies targeting TFR1 have showed strong anti-tumor effects, herein TFR1 probably become a potential molecular target for diagnosis and treatment for cancer therapy. This paper reviewed the research progresses of the roles of TFR1 in the tumorigenesis and cancer progression, the regulations of TFR1, and the therapeutic effects of targeting TFR1 on many kinds of cancers.

A composite network of conserved and tissue specific gene interactions reveals possible genetic interactions in glioma

Differential co-expression network analyses have recently become an important step in the investigation of cellular differentiation and dysfunctional gene-regulation in cell and tissue disease-states. The resulting networks have been analyzed to identify and understand pathways associated with disorders, or to infer molecular interactions. However, existing methods for differential co-expression network analysis are unable to distinguish between various forms of differential co-expression. To close this gap, here we define the three different kinds (conserved, specific, and differentiated) of differential co-expression and present a systematic framework, CSD, for differential co-expression network analysis that incorporates these interactions on an equal footing. In addition, our method includes a subsampling strategy to estimate the variance of co-expressions. Our framework is applicable to a wide variety of cases, such as the study of differential co-expression networks between healthy and disease states, before and after treatments, or between species. Applying the CSD approach to a published gene-expression data set of cerebral cortex and basal ganglia samples from healthy individuals, we find that the resulting CSD network is enriched in genes associated with cognitive function, signaling pathways involving compounds with well-known roles in the central nervous system, as well as certain neurological diseases. From the CSD analysis, we identify a set of prominent hubs of differential co-expression, whose neighborhood contains a substantial number of genes associated with glioblastoma. The resulting gene-sets identified by our CSD analysis also contain many genes that so far have not been recognized as having a role in glioblastoma, but are good candidates for further studies. CSD may thus aid in hypothesis-generation for functional disease-associations.

Villi-specific Gene Expression Reveals Novel Prognostic Biomarkers in Multiple Human Cancers

Despite many striking connections, the biological similarities between embryonic development and tumorigenesis have not been well explored. Development of the placental villi is a crucial process involving many cellular activities, including immunity, proliferation, and cell adhesion. In this study, we designed a strategy to identify the gene expression pattern of villi development and to explore the corresponding features in tumors. We discovered villi-specific genes that are highly expressed in the villus as opposed to the mature placenta and then measured the expression levels of these genes in tumors. We found large changes in the expression of villi-specific genes in multiple types of cancer. These villi-specific genes showed distinct expression patterns and were primarily involved in three biological processes: immune-related (5), proliferation-related (6), and focal adhesion-related (8); these genes were extracted from the corresponding enriched Gene Ontology (GO) terms. We observed that these genes were also dysregulated at the transcriptional level across several tumor types. Moreover, the expression of these three gene groups was associated with poor prognosis in a subset of tumors. Based on villi-specific gene expression, this correlation study indicated the existence of common gene expression patterns between embryonic development and tumorigenesis. Therefore, a systematic analysis of villi-specific gene aberrations in various tumors could serve as an indicator for identifying novel prognostic biomarkers.

Identification of two novel biomarkers of rectal carcinoma progression and prognosis via co-expression network analysis

mRNA expression profiles provide important insights on a diversity of biological processes involved in rectal carcinoma (RC). Our aim was to comprehensively map complex interactions between the mRNA expression patterns and the clinical traits of RC. We employed the integrated analysis of five microarray datasets and The Cancer Genome Atlas rectal adenocarcinoma database to identify 2118 consensual differentially expressed genes (DEGs) in RC and adjacent normal tissue samples, and then applied weighted gene co-expression network analysis to parse DEGs and eight clinical traits in 66 eligible RC samples. A total of 16 co-expressed gene modules were identified. The green-yellow and salmon modules were most appropriate to the pathological stage (R = 0.36) and the overall survival (HR =13.534, P = 0.014), respectively. A diagnostic model of the five pathological stage hub genes (SCG3, SYP, CDK5R2, AP3B2, and RUNDC3A) provided a powerful classification accuracy between localized RC and non-localized RC. We also found increased Secretogranin III (SCG3) expression with higher pathological stage and poorer prognosis in the test and validation set. The increased Homer scaffolding protein 2 (HOMER2) expression with the favorable survival prediction efficiency significantly correlated with the markedly reduced overall survival of RC patients and the higher pathological stage during the test and validation set. Our findings indicate that the SCG3 and HOMER2 mRNA levels should be further evaluated as predictors of pathological stage and survival in patients with RC.

From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data

The reductionist approach of dissecting biological systems into their constituents has been successful in the first stage of the molecular biology to elucidate the chemical basis of several biological processes. This knowledge helped biologists to understand the complexity of the biological systems evidencing that most biological functions do not arise from individual molecules; thus, realizing that the emergent properties of the biological systems cannot be explained or be predicted by investigating individual molecules without taking into consideration their relations. Thanks to the improvement of the current -omics technologies and the increasing understanding of the molecular relationships, even more studies are evaluating the biological systems through approaches based on graph theory. Genomic and proteomic data are often combined with protein-protein interaction (PPI) networks whose structure is routinely analyzed by algorithms and tools to characterize hubs/bottlenecks and topological, functional, and disease modules. On the other hand, co-expression networks represent a complementary procedure that give the opportunity to evaluate at system level including organisms that lack information on PPIs. Based on these premises, we introduce the reader to the PPI and to the co-expression networks, including aspects of reconstruction and analysis. In particular, the new idea to evaluate large-scale proteomic data by means of co-expression networks will be discussed presenting some examples of application. Their use to infer biological knowledge will be shown, and a special attention will be devoted to the topological and module analysis.

WGCNA Application to Proteomic and Metabolomic Data Analysis

Progresses in mass spectrometric instrumentation and bioinformatics identification algorithms made over the past decades allow quantitative measurements of relative or absolute protein/metabolite amounts in cells in a high-throughput manner, which has significantly expedited the exploration into functions and dynamics of complex biological systems. However, interpretation of high-throughput data is often restricted by the limited availability of suitable computational methods and enough statistical power. While many computational methodologies have been developed in the past decades to address the issue, it becomes clear that network-focused rather than individual gene/protein-focused strategies would be more appropriate to obtain a complete picture of cellular responses. Recently, an R analytical package named as weighted gene coexpression network analysis (WGCNA) was developed and applied to high-throughput microarray or RNA-seq datasets since it provides a systems-level insights, high sensitivity to low abundance, or small fold changes genes without any information loss. The approach was also recently applied to proteomic and metabolomic data analysis. However, due to the fact that low coverage of the current proteomic and metabolomic analytical technologies, causing the format of datasets are often incomplete, the method needs to be modified so that it can be properly utilized for meaningful biologically interpretation. In this chapter, we provide a detailed introduction of the modified protocol and its tutorials for applying the WGCNA approach in analyzing proteomic and metabolomic datasets.