Identification of Gender-Specific Molecular Differences in Glioblastoma (GBM) and Low-Grade Glioma (LGG) by the Analysis of Large Transcriptomic and Epigenomic Datasets

A comparative study of diffusion kurtosis imaging and diffusion tensor imaging in detecting corticospinal tract impairment in diffuse glioma patients

PURPOSE

This study aimed to investigate the diagnostic performance of diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) in identifying aberrations in the corticospinal tract (CST), whilst elucidating the relationship between abnormalities of CST and patients' motor function.

METHODS

Altogether 21 patients with WHO grade II or grade IV glioma were enrolled and divided into Group 1 and Group 2, according to the presence or absence of preoperative paralysis. DKI and DTI metrics were generated and projected onto the CST. Histograms of the CST along x, y, and z axes were developed based on DKI and DTI metrics, and compared subsequently to determine regions of aberrations on the fibers. The receiver operating characteristic curve was performed to investigate the diagnostic efficacy of DKI and DTI metrics.

RESULTS

In Group 1, a significantly lower fractional anisotropy, radial kurtosis and mean kurtosis, and a higher mean diffusivity were found in the ipsilateral CST as compared to the contralateral CST. Significantly higher relative axial diffusivity, relative radial diffusivity, and relative mean diffusivity (rMD) were found in Group 1, as compared to Group 2. The relative volume of ipsilateral CST abnormalities higher than the maximum value of mean kurtosis combined with rMD exhibited the best diagnostic performance in distinguishing dysfunction of CST with an AUC of 0.93.

CONCLUSION

DKI is sensitive in detecting subtle changes of CST distal from the tumor. The combination of DKI and DTI is feasible for evaluating the impairment of the CST.

Sex-specific molecular differences in glioblastoma: assessing the clinical significance of genetic variants

Introduction

Glioblastoma multiforme (GBM) is one of the most aggressive types of brain cancer, and despite rigorous research, patient prognosis remains poor. The characterization of sex-specific differences in incidence and overall survival (OS) of these patients has led to an investigation of the molecular mechanisms that may underlie this dimorphism.

Methods

We reviewed the published literature describing the gender specific differences in GBM Biology reported in the last ten years and summarized the available information that may point towards a patient-tailored GBM therapy.

Results

Radiomics analyses have revealed that imaging parameters predict OS and treatment response of GBM patients in a sex-specific manner. Moreover, gender-based analysis of the transcriptome GBM tumors has found differential expression of various genes, potentially impacting the OS survival of patients in a sex-dependent manner. In addition to gene expression differences, the timing (subclonal or clonal) of the acquisition of common GBM-driver mutations, metabolism requirements, and immune landscape of these tumors has also been shown to be sex-specific, leading to a differential therapeutic response by sex. In male patients, transformed astrocytes are more sensitive to glutaminase 1 (GLS1) inhibition due to increased requirements for glutamine uptake. In female patients, GBM is more sensitive to anti-IL1β due to an increased population of circulating granulocytic myeloid-derived suppressor cells (gMDSC).

Conclusion

Moving forward, continued elucidation of GBM sexual dimorphism will be critical in improving the OS of GBM patients by ensuring that treatment plans are structured to exploit these sex-specific, molecular vulnerabilities in GBM tumors.

Graph-based multi-modality integration for prediction of cancer subtype and severity

Personalised cancer screening before therapy paves the way toward improving diagnostic accuracy and treatment outcomes. Most approaches are limited to a single data type and do not consider interactions between features, leaving aside the complementary insights that multimodality and systems biology can provide. In this project, we demonstrate the use of graph theory for data integration via individual networks where nodes and edges are individual-specific. We showcase the consequences of early, intermediate, and late graph-based fusion of RNA-Seq data and histopathology whole-slide images for predicting cancer subtypes and severity. The methodology developed is as follows: (1) we create individual networks; (2) we compute the similarity between individuals from these graphs; (3) we train our model on the similarity matrices; (4) we evaluate the performance using the macro F1 score. Pros and cons of elements of the pipeline are evaluated on publicly available real-life datasets. We find that graph-based methods can increase performance over methods that do not study interactions. Additionally, merging multiple data sources often improves classification compared to models based on single data, especially through intermediate fusion. The proposed workflow can easily be adapted to other disease contexts to accelerate and enhance personalized healthcare.

Disclosing transcriptomics network-based signatures of glioma heterogeneity using sparse methods

Gliomas are primary malignant brain tumors with poor survival and high resistance to available treatments. Improving the molecular understanding of glioma and disclosing novel biomarkers of tumor development and progression could help to find novel targeted therapies for this type of cancer. Public databases such as The Cancer Genome Atlas (TCGA) provide an invaluable source of molecular information on cancer tissues. Machine learning tools show promise in dealing with the high dimension of omics data and extracting relevant information from it. In this work, network inference and clustering methods, namely Joint Graphical lasso and Robust Sparse K-means Clustering, were applied to RNA-sequencing data from TCGA glioma patients to identify shared and distinct gene networks among different types of glioma (glioblastoma, astrocytoma, and oligodendroglioma) and disclose new patient groups and the relevant genes behind groups' separation. The results obtained suggest that astrocytoma and oligodendroglioma have more similarities compared with glioblastoma, highlighting the molecular differences between glioblastoma and the others glioma subtypes. After a comprehensive literature search on the relevant genes pointed our from our analysis, we identified potential candidates for biomarkers of glioma. Further molecular validation of these genes is encouraged to understand their potential role in diagnosis and in the design of novel therapies.

Sexually dimorphic effect of H-ferritin genetic manipulation on survival and tumor microenvironment in a mouse model of glioblastoma

PURPOSE

Iron plays a crucial role in various biological mechanisms and has been found to promote tumor growth. Recent research has shown that the H-ferritin (FTH1) protein, traditionally recognized as an essential iron storage protein, can transport iron to GBM cancer stem cells, reducing their invasion activity. Moreover, the binding of extracellular FTH1 to human GBM tissues, and brain iron delivery in general, has been found to have a sex bias. These observations raise questions, addressed in this study, about whether H-ferritin levels extrinsic to the tumor can affect tumor cell pathways and if this impact is sex-specific.

METHODS

To interrogate the role of systemic H-ferritin in GBM we introduce a mouse model in which H-ferritin levels are genetically manipulated. Mice that were genetically manipulated to be heterozygous for H-ferritin (Fth1+/-) gene expression were orthotopically implanted with a mouse GBM cell line (GL261). Littermate Fth1 +/+ mice were used as controls. The animals were evaluated for survival and the tumors were subjected to RNA sequencing protocols. We analyzed the resulting data utilizing the murine Microenvironment Cell Population (mMCP) method for in silico immune deconvolution. mMCP analysis estimates the abundance of tissue infiltrating immune and stromal populations based on cell-specific gene expression signatures.

RESULTS

There was a clear sex bias in survival. Female Fth1+/- mice had significantly poorer survival than control females (Fth1+/+). The Fth1 genetic status did not affect survival in males. The mMCP analysis revealed a significant reduction in T cells and CD8 + T cell infiltration in the tumors of females with Fth1+/- background as compared to the Fth1+/+. Mast and fibroblast cell infiltration was increased in females and males with Fth1+/- background, respectively, compared to Fth1+/+ mice.

CONCLUSION

Genetic manipulation of Fth1 which leads to reduced systemic levels of FTH1 protein had a sexually dimorphic impact on survival. Fth1 heterozygosity significantly worsened survival in females but did not affect survival in male GBMs. Furthermore, the genetic manipulation of Fth1 significantly affected tumor infiltration of T-cells, CD8 + T cells, fibroblasts, and mast cells in a sexually dimorphic manner. These results demonstrate a role for FTH1 and presumably iron status in establishing the tumor cellular landscape that ultimately impacts survival and further reveals a sex bias that may inform the population studies showing a sex effect on the prevalence of brain tumors.

Impact of age and gender on glioblastoma onset, progression, and management

Glioblastoma (GBM) is the most common primary malignant brain tumor in adults, while its frequency in pediatric patients is 10-15%. For this reason, age is considered one of the major risk factors for the development of GBM, as it correlates with cellular aging phenomena involving glial cells and favoring the process of tumor transformation. Gender differences have been also identified, as the incidence of GBM is higher in males than in females, coupled with a worse outcome. In this review, we analyze age- and gender- dependent differences in GBM onset, mutational landscape, clinical manifestations, and survival, according to the literature of the last 20 years, focusing on the major risk factors involved in tumor development and on the mutations and gene alterations most frequently found in adults vs young patients and in males vs females. We then highlight the impact of age and gender on clinical manifestations and tumor localization and their involvement in the time of diagnosis and in determining the tumor prognostic value.

Peptide ARHGEF9 Inhibits Glioma Progression via PI3K/AKT/mTOR Pathway

Background

The most prevalent malignant tumor in a human brain nervous system is called glioma. Peptide is a compound formed by the peptide bond of α-amino acids, and the development of polypeptide drugs has been widely used in many fields. We plan to investigate the underlying peptides with clinical value in glioma.

Method

Based on public databases, we targeted the common genes between glioma differentially expressed genes (DEGs) and peptide genes related to glioma prognosis. Then, these common genes were analyzed by LASSO-Cox analysis, prognostic risk model, and nomogram to identify key prognostic peptide genes and the target gene in this study. Next, the mechanism of target gene in glioma was explored by bioinformatics analysis and functional experiments.

Results

We obtained a total of 26 overlapping genes for the following study. After that, 6 independent prognostic factors (REPIN1, PSD3, RDX, CDK4, FANCI, and ARHGEF9) were obtained and applied to construct the prognostic nomogram, and ARHGEF9 was the target gene in the study. Next, peptide ARHGEF9 was found to inhibit glioma cell development. Through Spearman's correlation analysis, ARHGEF9 had a close relation with PI3K/AKT/mTOR pathway. In functional experiments, peptide ARHGEF9 could suppress the protein expressions of p-PIK3K, p-AKT and p-mTOR, while IGF-1 could reverse this effect.

Conclusion

This study identifies 6 new prognostic biomarkers for glioma patients. Among them, peptide ARHGEF9 gene is an inhibitory gene functioning by targeting PI3K/AKT/mTOR pathway.

The Prognostic Impact of Gender, Therapeutic Strategies, Molecular Background, and Tumor-Infiltrating Lymphocytes in Glioblastoma: A Still Unsolved Jigsaw

Despite the adoption of novel therapeutical approaches, the outcomes for glioblastoma (GBM) patients remain poor. In the present study, we investigated the prognostic impact of several clinico-pathological and molecular features as well as the role of the cellular immune response in a series of 59 GBM. CD4+ and CD8+ tumor-infiltrating lymphocytes (TILs) were digitally assessed on tissue microarray cores and their prognostic role was investigated. Moreover, the impact of other clinico-pathological features was evaluated. The number of CD4+ and CD8+ is higher in GBM tissue compared to normal brain tissue (p < 0.0001 and p = 0.0005 respectively). A positive correlation between CD4+ and CD8+ in GBM is present (rs = 0.417-p = 0.001). CD4+ TILs are inversely related to overall survival (OS) (HR = 1.79, 95% CI 1.1-3.1, p = 0.035). The presence of low CD4+ TILs combined with low CD8+ TILs is an independent predictor of longer OS (HR 0.38, 95% CI 0.18-0.79, p = 0.014). Female sex is independently related to longer OS (HR 0.42, 95% CI 0.22-0.77, p = 0.006). Adjuvant treatment, methylguanine methyltransferase (MGMT) promoter methylation, and age remain important prognostic factors but are influenced by other features. Adaptive cell-mediated immunity can affect the outcomes of GBM patients. Further studies are needed to elucidate the commitment of the CD4+ cells and the effects of different TILs subpopulations in GBM.

A survival nomogram model constructed with common clinical characteristics to assist clinical decisions for diffuse low-grade gliomas: A population analysis based on SEER database

Background

The prognosis of diffuse low-grade gliomas (DLGGs, WHO grade 2) is highly variable, making it difficult to evaluate individual patient outcomes. In this study, we used common clinical characteristics to construct a predictive model with multiple indicators.

Methods

We identified 2459 patients diagnosed with astrocytoma and oligodendroglioma from 2000 to 2018 in the SEER database. After removing invalid information, we randomly divided the cleaned patient data into training and validation groups. We performed univariate and multivariate Cox regression analyses and constructed a nomogram. Receiver operating characteristic (ROC) curve, c-index, calibration curve, and subgroup analyses were used to assess the accuracy of the nomogram by internal and external validation.

Results

After univariate and multivariate Cox regression analyses, we identified seven independent prognostic factors, namely, age (P<0.001), sex (P<0.05), histological type (P<0.001), surgery (P<0.01), radiotherapy (P<0.001), chemotherapy (P<0.05) and tumor size (P<0.001). The ROC curve, c-index, calibration curve, and subgroup analyses of the training group and the validation group showed that the model had good predictive value. The nomogram for DLGGs predicted patients' 3-, 5- and 10-year survival rates based on these seven variables.

Conclusions

The nomogram constructed with common clinical characteristics has good prognostic value for patients with DLGGs and can help physicians make clinical decisions.

Multi-view spectral clustering with latent representation learning for applications on multi-omics cancer subtyping

Driven by multi-omics data, some multi-view clustering algorithms have been successfully applied to cancer subtypes prediction, aiming to identify subtypes with biometric differences in the same cancer, thereby improving the clinical prognosis of patients and designing personalized treatment plan. Due to the fact that the number of patients in omics data is much smaller than the number of genes, multi-view spectral clustering based on similarity learning has been widely developed. However, these algorithms still suffer some problems, such as over-reliance on the quality of pre-defined similarity matrices for clustering results, inability to reasonably handle noise and redundant information in high-dimensional omics data, ignoring complementary information between omics data, etc. This paper proposes multi-view spectral clustering with latent representation learning (MSCLRL) method to alleviate the above problems. First, MSCLRL generates a corresponding low-dimensional latent representation for each omics data, which can effectively retain the unique information of each omics and improve the robustness and accuracy of the similarity matrix. Second, the obtained latent representations are assigned appropriate weights by MSCLRL, and global similarity learning is performed to generate an integrated similarity matrix. Third, the integrated similarity matrix is used to feed back and update the low-dimensional representation of each omics. Finally, the final integrated similarity matrix is used for clustering. In 10 benchmark multi-omics datasets and 2 separate cancer case studies, the experiments confirmed that the proposed method obtained statistically and biologically meaningful cancer subtypes.

Cost Matrix of Molecular Pathology in Glioma-Towards AI-Driven Rational Molecular Testing and Precision Care for the Future

Gliomas are the most common and aggressive primary brain tumors. Gliomas carry a poor prognosis because of the tumor's resistance to radiation and chemotherapy leading to nearly universal recurrence. Recent advances in large-scale genomic research have allowed for the development of more targeted therapies to treat glioma. While precision medicine can target specific molecular features in glioma, targeted therapies are often not feasible due to the lack of actionable markers and the high cost of molecular testing. This review summarizes the clinically relevant molecular features in glioma and the current cost of care for glioma patients, focusing on the molecular markers and meaningful clinical features that are linked to clinical outcomes and have a realistic possibility of being measured, which is a promising direction for precision medicine using artificial intelligence approaches.

Prognostic Factors of Low-Grade Gliomas in Adults

Adult low-grade gliomas are a rare and aggressive pathology of the central nervous system. Some of their characteristics contribute to the patient's life expectancy and to their management. This study aimed to characterize and identify the main prognostic factors of low-grade gliomas. The six-year retrospective study statistically analyzed the demographic, imaging, and morphogenetic characteristics of the patient group through appropriate parameters. Immunohistochemical tests were performed: IDH1, Ki-67, p53, and Nestin, as well as FISH tests on the CDKN2A gene and 1p/19q codeletion. The pathology was prevalent in females, with patients having an average age of 56.31 years. The average tumor volume was 41.61 cm³, producing a midline shift with an average of 7.5 mm. Its displacement had a negative impact on survival. The presence of a residual tumor resulted in decreased survival and is an independent risk factor for mortality. Positivity for p53 identified a low survival rate. CDKN2A mutations were an independent risk factor for mortality. We identified that a negative prognosis is influenced by the association of epilepsy with headache, tumor volume, and immunoreactivity to IDH1 and p53. Independent factors associated with mortality were midline shift, presence of tumor residue, and CDKN2A gene deletions and amplifications.

Identification of Gender- and Subtype-Specific Gene Expression Associated with Patient Survival in Low-Grade and Anaplastic Glioma in Connection with Steroid Signaling

Simple Summary

Gliomas are primary brain tumors that are initially slow growing but progress to be more aggressive and, ultimately, fatal within a few years. They are more common in men than in women, suggesting a protective role for female hormones. By analyzing patient data collected in the public TGCA-LGG database, we have demonstrated a link between the expression level of key steroid biosynthesis enzymes or hormone receptors with patient survival, in ways that are dependent on gender and molecular subtype. We also determined the genes which expression associated with these actors of steroid signaling and the functions they perform, to decipher the mechanisms underlying gender-dependent differences. Together, these results establish, for the first time, the involvement of hormones in low-grade and anaplastic gliomas and provide clues for refining their classification and, thus, facilitating more personalized management of patients.

Abstract

Low-grade gliomas are rare primary brain tumors, which fatally evolve to anaplastic gliomas. The current treatment combines surgery, chemotherapy, and radiotherapy. If gender differences in the natural history of the disease were widely described, their underlying mechanisms remain to be determined for the identification of reliable markers of disease progression. We mined the transcriptomic and clinical data from the TCGA-LGG and CGGA databases to identify male-over-female differentially expressed genes and selected those associated with patient survival using univariate analysis, depending on molecular characteristics (IDH wild-type/mutated; 1p/19q codeleted/not) and grade. Then, the link between the expression levels (low or high) of the steroid biosynthesis enzyme or receptors of interest and survival was studied using the log-rank test. Finally, a functional analysis of gender-specific correlated genes was performed. HOX-related genes appeared to be differentially expressed between males and females in both grades, suggesting that a glioma could originate in perturbation of developmental signals. Moreover, aromatase, androgen, and estrogen receptor expressions were associated with patient survival and were mainly related to angiogenesis or immune response. Therefore, consideration of the tight control of steroid hormone production and signaling seems crucial for the understanding of glioma pathogenesis and emergence of future targeted therapies.