A Potential Prognostic Gene Signature for Predicting Survival for Glioblastoma Patients

The effects of the combination therapy of chemotherapy drugs on the fluctuations of genes involved in the TLR signaling pathway in glioblastoma multiforme therapy

One of the most lethal and aggressive types of malignancies with a high mortality rate and poor response to treatment is glioblastoma multiforme (GBM). This means that modernizing the medications used in chemotherapy, in addition to medicines licensed for use in other illnesses and chosen using a rationale process, can be beneficial in treating this illness. Meaningly, drug combination therapy with chemical or herbal originations or implanting a drug wafer in tumors to control angiogenesis is of great importance. Importantly, the primary therapeutic hurdles in GBM are the development of angiogenesis and the blood-brain barrier (BBB), which keeps medications from getting to the tumor. This malignancy can be controlled if the drug's passage through the BBB and the VEGF (vascular endothelial growth factor), which promotes angiogenesis, are inhibited. In this way, the effect of combination therapy on the genes of different main signaling pathways like TLRs may be indicated as an impressive therapeutic strategy for treating GBM. This article aims to discuss the effects of chemotherapeutic drugs on the expression of various genes and associated translational factors involved in the TLR signaling pathway.

Integrative genomic analysis facilitates precision strategies for glioblastoma treatment

Glioblastoma (GBM) is the most common form of malignant primary brain tumor with a dismal prognosis. Currently, the standard treatments for GBM rarely achieve satisfactory results, which means that current treatments are not individualized and precise enough. In this study, a multiomics-based GBM classification was established and three subclasses (GPA, GPB, and GPC) were identified, which have different molecular features both in bulk samples and at single-cell resolution. A robust GBM poor prognostic signature (GPS) score model was then developed using machine learning method, manifesting an excellent ability to predict the survival of GBM. NVP−BEZ235, GDC−0980, dasatinib and XL765 were ultimately identified to have subclass-specific efficacy targeting patients with a high risk of poor prognosis. Furthermore, the GBM classification and GPS score model could be considered as potential biomarkers for immunotherapy response. In summary, an integrative genomic analysis was conducted to advance individual-based therapies in GBM.

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A multiomics-based classification of GBM was established

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Single-cell transcriptomic profiling of GBM subclasses was revealed using Scissor

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A robust prognostic risk model was developed for GBM by machine learning method

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Prediction of potential agents based on molecular and prognostic risk stratification

Brain radiotoxicity-related 15CAcBRT gene expression signature predicts survival prognosis of glioblastoma patients

BACKGROUND

Glioblastoma is the most common and devastating primary brain cancer. Radiotherapy is standard of care; however, it is associated with brain radiation toxicity (BRT). This study used a multi-omics approach to determine whether BRT-related genes (RGs) harbor survival prognostic value and whether their encoded proteins represent novel therapeutic targets for glioblastoma.

METHODS

RGs were identified through analysis of single-nucleotide variants associated with BRT (R-SNVs). Functional relationships between RGs were established using Protein-Protein Interaction networks. The influence of RGs and their functional groups on glioblastoma prognosis was evaluated using clinical samples from the Glioblastoma Bio-Discovery Portal database and validated using the Chinese Glioma Genome Atlas dataset. The identification of clusters of radiotoxic and putative pathogenic variants in proteins encoded by RGs was achieved by computational 3D structural analysis.

RESULTS

We identified the BRT-related 15CAcBRT molecular signature with prognostic value in glioblastoma, by analysis of the COMT and APOE protein functional groups. Its external validation confirmed clinical relevance independent of age, MGMT promoter methylation status, and IDH mutation status. Interestingly, the genes IL6, APOE, and MAOB documented significant gene expression levels alteration, useful for drug repositioning. Biological networks associated with 15CAcBRT signature involved pathways relevant to cancer and neurodegenerative diseases. Analysis of 3D clusters of radiotoxic and putative pathogenic variants in proteins coded by RGs unveiled potential novel therapeutic targets in neuro-oncology.

CONCLUSIONS

15CAcBRT is a BRT-related molecular signature with prognostic significance for glioblastoma patients and represents a hub for drug repositioning and development of novel therapies.

Non-negative matrix factorization and differential expression analyses identify hub genes linked to progression and prognosis of glioblastoma multiforme

One of the most prevailing primary brain tumors in adult human male is glioblastoma multiforme (GBM), which is categorized by rapid cellular growth. Even though the combination therapy comprises surgery, chemotherapy, and adjuvant therapies, the survival rate, on average, is 14.6 months. Glioma stem cells (GSCs) have key roles in tumorigenesis, progression, and defiance against chemotherapy and radiotherapy. In our study, firstly, the gene expression dataset GSE124145 was retrieved; the non-negative matrix factorization (NMF) method was applied on GBM dataset, and differentially expressed genes analysis (DEGs) was performed. After which, overlapping genes between metagenes and DEGs were detected to examine the Gene Ontology (GO) categories in the biological process (BP) in the stemness of GBM. The common hub genes were used to construct protein-protein interaction (PPI) network and further GO, while Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway was utilized to pinpoint the real hub genes. The analysis of hub genes particular for the same GO categories demonstrated that specific hub genes triggered distinct features of the same biological processes. After utilizing GSE124145 and The Cancer Genome Atlas (TCGA) dataset for survival analysis, we screened five real hub genes: GUCA1A, RFC2, GNG11, MMP19, and NRG1, which are strongly associated with the progression and prognosis of GBM. The DEGs analysis revealed that all real hub genes were overexpressed in GBM and TCGA datasets, which further validates our results. The constructed study of PPI, GO, and KEGG pathway on common hub genes was performed. Finally, the KEGG pathways performed on the top 15 candidate hub genes (including six real hub genes) of the PPI network in the GBM gene expression dataset study found mitogen-activated protein kinase (Mapk) signaling pathway to be the most significant pathway. The rest of the hub genes reviewed throughout the analysis might be favorable targets for diagnosing and treating GBM and lower-grade gliomas.

Paradoxical Association Between Relative Cerebral Blood Volume Dynamics Following Chemoradiation and Increased Progression-Free Survival in Newly Diagnosed IDH Wild-Type MGMT Promoter Methylated Glioblastoma With Measurable Disease

BACKGROUND AND PURPOSE

While relative cerebral blood volume (rCBV) may be diagnostic and prognostic for survival in glioblastoma (GBM), changes in rCBV during chemoradiation in the subset of newly diagnosed GBM with subtotal resection and the impact of MGMT promoter methylation status on survival have not been explored. This study aimed to investigate the association between rCBV response, MGMT methylation status, and progression-free (PFS) and overall survival (OS) in newly diagnosed GBM with measurable enhancing lesions.

METHODS

1,153 newly diagnosed IDH wild-type GBM patients were screened and 53 patients (4.6%) had measurable post-surgical tumor (>1mL). rCBV was measured before and after patients underwent chemoradiation. Patients with a decrease in rCBV >10% were considered rCBV Responders, while patients with an increase or a decrease in rCBV <10% were considered rCBV Non-Responders. The association between change in enhancing tumor volume, change in rCBV, MGMT promotor methylation status, and PFS or OS were explored.

RESULTS

A decrease in tumor volume following chemoradiation trended towards longer OS (p=0.12; median OS=26.8 vs. 16.3 months). Paradoxically, rCBV Non-Responders had a significantly improved PFS compared to Responders (p=0.047; median PFS=9.6 vs. 7.2 months). MGMT methylated rCBV Non-Responders exhibited a significantly longer PFS compared to MGMT unmethylated rCBV Non-Responders (p<0.001; median PFS=0.5 vs. 7.1 months), and MGMT methylated rCBV Non-Responders trended towards longer PFS compared to methylated rCBV Responders (p=0.089; median PFS=20.5 vs. 13.8 months).

CONCLUSIONS

This preliminary report demonstrates that in newly diagnosed IDH wild-type GBM with measurable enhancing disease after surgery (5% of patients), an enigmatic non-response in rCBV was associated with longer PFS, particularly in MGMT methylated patients.

Establishment of an Immune-Related Gene Signature for Risk Stratification for Patients with Glioma

Glioma is a frequently seen primary malignant intracranial tumor, characterized by poor prognosis. The study is aimed at constructing a prognostic model for risk stratification in patients suffering from glioma. Weighted gene coexpression network analysis (WGCNA), integrated transcriptome analysis, and combining immune-related genes (IRGs) were used to identify core differentially expressed IRGs (DE IRGs). Subsequently, univariate and multivariate Cox regression analyses were utilized to establish an immune-related risk score (IRRS) model for risk stratification for glioma patients. Furthermore, a nomogram was developed for predicting glioma patients' overall survival (OS). The turquoise module (cor = 0.67; P < 0.001) and its genes (n = 1092) were significantly pertinent to glioma progression. Ultimately, multivariate Cox regression analysis constructed an IRRS model based on VEGFA, SOCS3, SPP1, and TGFB2 core DE IRGs, with a C-index of 0.811 (95% CI: 0.786-0.836). Then, Kaplan-Meier (KM) survival curves revealed that patients presenting high risk had a dismal outcome (P < 0.0001). Also, this IRRS model was found to be an independent prognostic indicator of gliomas' survival prediction, with HR of 1.89 (95% CI: 1.252-2.85) and 2.17 (95% CI: 1.493-3.14) in the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets, respectively. We established the IRRS prognostic model, capable of effectively stratifying glioma population, convenient for decision-making in clinical practice.

Promoting Prognostic Model Application: A Review Based on Gliomas

Malignant neoplasms are characterized by poor therapeutic efficacy, high recurrence rate, and extensive metastasis, leading to short survival. Previous methods for grouping prognostic risks are based on anatomic, clinical, and pathological features that exhibit lower distinguishing capability compared with genetic signatures. The update of sequencing techniques and machine learning promotes the genetic panels-based prognostic model development, especially the RNA-panel models. Gliomas harbor the most malignant features and the poorest survival among all tumors. Currently, numerous glioma prognostic models have been reported. We systematically reviewed all 138 machine-learning-based genetic models and proposed novel criteria in assessing their quality. Besides, the biological and clinical significance of some highly overlapped glioma markers in these models were discussed. This study screened out markers with strong prognostic potential and 27 models presenting high quality. Conclusively, we comprehensively reviewed 138 prognostic models combined with glioma genetic panels and presented novel criteria for the development and assessment of clinically important prognostic models. This will guide the genetic models in cancers from laboratory-based research studies to clinical applications and improve glioma patient prognostic management.

Promoting Prognostic Model Application: A Review Based on Gliomas

Malignant neoplasms are characterized by poor therapeutic efficacy, high recurrence rate, and extensive metastasis, leading to short survival. Previous methods for grouping prognostic risks are based on anatomic, clinical, and pathological features that exhibit lower distinguishing capability compared with genetic signatures. The update of sequencing techniques and machine learning promotes the genetic panels-based prognostic model development, especially the RNA-panel models. Gliomas harbor the most malignant features and the poorest survival among all tumors. Currently, numerous glioma prognostic models have been reported. We systematically reviewed all 138 machine-learning-based genetic models and proposed novel criteria in assessing their quality. Besides, the biological and clinical significance of some highly overlapped glioma markers in these models were discussed. This study screened out markers with strong prognostic potential and 27 models presenting high quality. Conclusively, we comprehensively reviewed 138 prognostic models combined with glioma genetic panels and presented novel criteria for the development and assessment of clinically important prognostic models. This will guide the genetic models in cancers from laboratory-based research studies to clinical applications and improve glioma patient prognostic management.

The CPNE Family and Their Role in Cancers

Lung cancer is the leading cause of cancer-related deaths worldwide. Despite significant advances in cancer research and treatment, the overall prognosis of lung cancer patients remains poor. Therefore, the identification for novel therapeutic targets is critical for the diagnosis and treatment of lung cancer. CPNEs (copines) are a family of membrane-bound proteins that are highly conserved, soluble, ubiquitous, calcium dependent in a variety of eukaryotes. Emerging evidences have also indicated CPNE family members are involved in cancer development and progression as well. However, the expression patterns and clinical roles in cancer have not yet been well understood. In this review, we summarize recent advances concerning CPNE family members and provide insights into new potential mechanism involved in cancer development.

High expression of CPNE5 and CPNE9 predicts positive prognosis in multiple myeloma

BACKGROUND

CPNEs are significant biomarkers which can affect the progression and prognosis of various tumor diseases. However, the prognosis role of CPNEs in multiple myeloma (MM) is still unclear.

OBJECTIVES

To investigate the prognosis role of CPNEs in MM.

METHODS

Seven hundred and thirty-five samples from two independent data sets were involved to analyze the clinical and molecular characteristics, and prognosis role of the expression of CPNE1-9 in MM.

RESULTS

MM patients with higher expressions of CPNE5 and CPNE9 had longer event-free survival (EFS) and overall survival (OS) compared with CPNE5low and CPNE9low expression groups (EFS: P= 0.0054, 0.0065; OS: P= 0.015, 0.016, respectively). Multivariate regression analysis showed that CPNE5 was an independent favorable predictor for EFS and OS (EFS: P= 0.005; OS: P= 0.006), and CPNE9 was an independent positive indicator for EFS (P= 0.002). Moreover, the survival probability and the cumulative event of EFS and OS in CPNE5highCPNE9high group were significantly longer than other groups.

CONCLUSIONS

High expressions of CPNE5 and CPNE9 might be used as positive indicators for MM, and their combination was a better predictor for the survival of MM patients.

Characterization of the Survival Influential Genes in Carcinogenesis

The genes influencing cancer patient mortality have been studied by survival analysis for many years. However, most studies utilized them only to support their findings associated with patient prognosis: their roles in carcinogenesis have not yet been revealed. Herein, we applied an in silico approach, integrating the Cox regression model with effect size estimated by the Monte Carlo algorithm, to screen survival-influential genes in more than 6000 tumor samples across 16 cancer types. We observed that the survival-influential genes had cancer-dependent properties. Moreover, the functional modules formed by the harmful genes were consistently associated with cell cycle in 12 out of the 16 cancer types and pan-cancer, showing that dysregulation of the cell cycle could harm patient prognosis in cancer. The functional modules formed by the protective genes are more diverse in cancers; the most prevalent functions are relevant for immune response, implying that patients with different cancer types might develop different mechanisms against carcinogenesis. We also identified a harmful set of 10 genes, with potential as prognostic biomarkers in pan-cancer. Briefly, our results demonstrated that the survival-influential genes could reveal underlying mechanisms in carcinogenesis and might provide clues for developing therapeutic targets for cancers.

Identification of Novel Transcriptome Signature as a Potential Prognostic Biomarker for Anti-Angiogenic Therapy in Glioblastoma Multiforme

Glioblastoma multiforme (GBM) is the most common and devastating type of primary brain tumor, with a median survival time of only 15 months. Having a clinically applicable genetic biomarker would lead to a paradigm shift in precise diagnosis, personalized therapeutic decisions, and prognostic prediction for GBM. Radiogenomic profiling connecting radiological imaging features with molecular alterations will offer a noninvasive method for genomic studies of GBM. To this end, we analyzed over 3800 glioma and GBM cases across four independent datasets. The Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases were employed for RNA-Seq analysis, whereas the Ivy Glioblastoma Atlas Project (Ivy-GAP) and The Cancer Imaging Archive (TCIA) provided clinicopathological data. The Clinical Proteomic Tumor Analysis Consortium Glioblastoma Multiforme (CPTAC-GBM) was used for proteomic analysis. We identified a simple three-gene transcriptome signature—SOCS3, VEGFA, and TEK—that can connect GBM’s overall prognosis with genes’ expression and simultaneously correlate radiographical features of perfusion imaging with SOCS3 expression levels. More importantly, the rampant development of neovascularization in GBM offers a promising target for therapeutic intervention. However, treatment with bevacizumab failed to improve overall survival. We identified SOCS3 expression levels as a potential selection marker for patients who may benefit from early initiation of angiogenesis inhibitors.

A SEMA3 Signaling Pathway-Based Multi-Biomarker for Prediction of Glioma Patient Survival

Glioma is a lethal central nervous system tumor with poor patient survival prognosis. Because of the molecular heterogeneity, it is a challenge to precisely determine the type of the tumor and to choose the most effective treatment. Therefore, novel biomarkers are essential to improve the diagnosis and prognosis of glioma tumors. Class 3 semaphorin proteins (SEMA3) play an important role in tumor biology. SEMA3 transduce their signals by using neuropilin and plexin receptors, which functionally interact with the vascular endothelial growth factor-mediated signaling pathways. Therefore, the aim of this study was to explore the potential of SEMA3 signaling molecules for prognosis of glioma patient survival. The quantitative real-time PCR method was used to evaluate mRNA expression of SEMA3(A-G), neuropilins (NRP1 and NRP2), plexins (PLXNA2 and PLXND1), cadherins (CDH1 and CDH2), integrins (ITGB1, ITGB3, ITGA5, and ITGAV), VEGFA and KDR genes in 59 II-IV grade glioma tissues. Seven genes significantly associated with patient overall survival were used for multi-biomarker construction, which showed 64%, 75%, and 68% of accuracy of predicting the survival of 1-, 2-, and 3-year glioma patients, respectively. The results suggest that the seven-gene signature could serve as a novel multi-biomarker for more accurate prognosis of a glioma patient’s outcome.

Identification of a cullin5-RING E3 ligase transcriptome signature in glioblastoma multiforme

Glioblastoma multiforme (GBM) is the deadliest type of brain tumor. The median survival time for patients with GBM is only 15 months, even following maximal surgical resection and chemotherapy and radiation therapy. A genetic biomarker could enable a paradigm shift in precise diagnosis, personalized therapeutics and prognosis. In this study, we employed the Chinese Glioma Genome Atlas, The Cancer Genome Atlas, and the Ivy Glioblastoma Atlas Project databases for RNA sequencing (RNA-seq) analysis and clinicopathological studies. We demonstrated that elevated expression of the RNF7, TCEB1, SOCS1 and SOCS3 genes, which encode components of cullin5-RING E3 ligase (CRL5), predict unfavorable GBM prognoses. In GBM and glioma cases carrying IDH1 mutations, SOCS1 and SOCS3 methylation was increased and their expression was downregulated. This study has thus identified a simple transcriptome signature for GBM prognosis.

Genomic profiling of thymoma using a targeted high-throughput approach

Introduction

Thymomas and thymic carcinoma (TC) are the most common neoplasms localised in the thymus. These diseases are poorly understood, but progress made in next-generation sequencing (NGS) technology has provided novel data on their molecular pathology.

Material and methods

Genomic DNA was isolated from formalin-fixed paraffin-embedded tumour tissue. We investigated somatic variants in 35 thymoma patients using amplicon-based TruSeq Amplicon Cancer Panel (TSACP) that covers 48 cancer related genes. We also analysed three samples from healthy individuals by TSACP platform and 32 healthy controls using exome sequencing.

Results

The total number of detected variants was 4447, out of which 2906 were in the coding region (median per patient 83, range: 2-300) and 1541 were in the non-coding area (median per patient 44, range: 0-172). We identified four genes, APC, ATM, ERBB4, and SMAD4, having more than 100 protein-changing variants. Additionally, more than 70% of the analysed cases harboured protein-changing variants in SMAD4, APC, ATM, PTEN, KDR, and TP53. Moreover, this study revealed 168 recurrent variants, out of which 15 were shown to be pathogenic. Comparison to controls revealed that the variants we reported in this study were somatic thymoma-specific variants. Additionally, we found that the presence of variants in SMAD4 gene predicted shorter overall survival in thymoma patients.

Conclusions

The most frequently mutated genes in thymoma samples analysed in this study belong to the EGFR, ATM, and TP53 signalling pathways, regulating cell cycle check points, gene expression, and apoptosis. The results of our study complement the knowledge of thymoma molecular pathogenesis.

The effects of melatonin on signaling pathways and molecules involved in glioma

Glioblastoma is one of the most common brain tumors with high invasion and malignancy. Despite extensive research in this area and the use of new and advanced therapies, the survival rate in this disease is very low. In addition, resistance to treatment has also been observed in this disease. One of the reasons for rapid progression and failure in treatment for this disease is the presence of a class of cells with high proliferation and high differentiation, a class called glioblastoma stem-like cells shown as being the source of glioblastoma tumors. It has been reported that several oncogenes are expressed in this disease. One important issue in recognizing the pathogenesis of this disease, and which could improve the treatment process, is the identification of involved oncogenes as well as molecules that affect the reduction of the expression of these oncogenes. Melatonin regulates the biological rhythm and inhibits the proliferation of malignant glioma cells due to antioxidant and anti-apoptotic effects. Melatonin has been considered in biological processes and in signaling pathways involved in the development of glioma. The aim of this review is to investigate the effects of melatonin on signaling pathways and molecules involved in the progression of glioma.