A Novel 16-Genes Signature Scoring System as Prognostic Model to Evaluate Survival Risk in Patients with Glioblastoma

Knockdown of PGBD5 inhibits the malignant progression of glioma through upregulation of the PPAR pathway

Glioma is the most common type of primary intracranial malignant tumor, and because of its high invasiveness and recurrence, its prognosis remains poor. The present study investigated the biological function of piggyBac transportable element derived 5 (PGBD5) in glioma. Glioma and para-cancerous tissues were obtained from five patients. Reverse transcription-quantitative PCR and western blotting were used to detect the expression levels of PGBD5. Transwell assay and flow cytometry were used to evaluate cell migration, invasion, apoptosis and cell cycle distribution. In addition, a nude mouse tumor transplantation model was established to study the downstream pathways of PGBD5 and the molecular mechanism was analyzed using transcriptome sequencing. The mRNA and protein expression levels of PGBD5 were increased in glioma tissues and cells. Notably, knockdown of PGBD5 in vitro could inhibit the migration and invasion of glioma cells. In addition, the knockdown of PGBD5 expression promoted apoptosis and caused cell cycle arrest in the G2/M phase, thus inhibiting cell proliferation. Furthermore, in vivo experiments revealed that knockdown of PGBD5 expression could inhibit Ki67 expression and slow tumor growth. Changes in PGBD5 expression were also shown to be closely related to the peroxisome proliferator-activated receptor (PPAR) signaling pathway. In conclusion, interference with PGBD5 could inhibit the malignant progression of glioma through the PPAR pathway, suggesting that PGBD5 may be a potential molecular target of glioma.

The construction of a novel prognostic prediction model for glioma based on GWAS-identified prognostic-related risk loci

Backgrounds

Glioma is a highly malignant brain tumor with a grim prognosis. Genetic factors play a role in glioma development. While some susceptibility loci associated with glioma have been identified, the risk loci associated with prognosis have received less attention. This study aims to identify risk loci associated with glioma prognosis and establish a prognostic prediction model for glioma patients in the Chinese Han population.

Methods

A genome-wide association study (GWAS) was conducted to identify risk loci in 484 adult patients with glioma. Cox regression analysis was performed to assess the association between GWAS-risk loci and overall survival as well as progression-free survival in glioma. The prognostic model was constructed using LASSO Cox regression analysis and multivariate Cox regression analysis. The nomogram model was constructed based on the single nucleotide polymorphism (SNP) classifier and clinical indicators, enabling the prediction of survival rates at 1-year, 2-year, and 3-year intervals. Additionally, the receiver operator characteristic (ROC) curve was employed to evaluate the prediction value of the nomogram. Finally, functional enrichment and tumor-infiltrating immune analyses were conducted to examine the biological functions of the associated genes.

Results

Our study found suggestive evidence that a total of 57 SNPs were correlated with glioma prognosis (p < 5 × 10-5). Subsequently, we identified 25 SNPs with the most significant impact on glioma prognosis and developed a prognostic model based on these SNPs. The 25 SNP-based classifier and clinical factors (including age, gender, surgery, and chemotherapy) were identified as independent prognostic risk factors. Subsequently, we constructed a prognostic nomogram based on independent prognostic factors to predict individualized survival. ROC analyses further showed that the prediction accuracy of the nomogram (AUC = 0.956) comprising the 25 SNP-based classifier and clinical factors was significantly superior to that of each individual variable.

Conclusion

We identified a SNP classifier and clinical indicators that can predict the prognosis of glioma patients and established a prognostic prediction model in the Chinese Han population. This study offers valuable insights for clinical practice, enabling improved evaluation of patients' prognosis and informing treatment options.

Advancing precision prognostication in neuro-oncology: Machine learning models for data-driven personalized survival predictions in IDH-wildtype glioblastoma

Background

Glioblastoma (GBM) remains associated with a dismal prognoses despite standard therapies. While population-level survival statistics are established, generating individualized prognosis remains challenging. We aim to develop machine learning (ML) models that generate personalized survival predictions for GBM patients to enhance prognostication.

Methods

Adult patients with histologically confirmed IDH-wildtype GBM from the National Cancer Database (NCDB) were analyzed. ML models were developed with TabPFN, TabNet, XGBoost, LightGBM, and Random Forest algorithms to predict mortality at 6, 12, 18, and 24 months postdiagnosis. SHapley Additive exPlanations (SHAP) were employed to enhance the interpretability of the models. Models were primarily evaluated using the area under the receiver operating characteristic (AUROC) values, and the top-performing models indicated by the highest AUROCs for each outcome were deployed in a web application that was created for individualized predictions.

Results

A total of 7537 patients were retrieved from the NCDB. Performance evaluation revealed the top-performing models for each outcome were built using the TabPFN algorithm. The TabPFN models yielded mean AUROCs of 0.836, 0.78, 0.732, and 0.724 in predicting 6, 12, 18, and 24 month mortality, respectively.

Conclusions

This study establishes ML models tailored to individual patients to enhance GBM prognostication. Future work should focus on external validation and dynamic updating as new data emerge.

A molecular signature for the G6PC3/SLC37A2/SLC37A4 interactors in glioblastoma disease progression and in the acquisition of a brain cancer stem cell phenotype

BACKGROUND

Glycogen plays an important role in glucose homeostasis and contributes to key functions related to brain cancer cell survival in glioblastoma multiforme (GBM) disease progression. Such adaptive molecular mechanism is dependent on the glycogenolytic pathway and intracellular glucose-6-phosphate (G6P) sensing by brain cancer cells residing within those highly hypoxic tumors. The involvement of components of the glucose-6-phosphatase (G6Pase) system remains however elusive.

OBJECTIVE

We questioned the gene expression levels of components of the G6Pase system in GBM tissues and their functional impact in the control of the invasive and brain cancer stem cells (CSC) phenotypes.

METHODS

In silico analysis of transcript levels in GBM tumor tissues was done by GEPIA. Total RNA was extracted and gene expression of G6PC1-3 as well as of SLC37A1-4 members analyzed by qPCR in four human brain cancer cell lines and from clinically annotated brain tumor cDNA arrays. Transient siRNA-mediated gene silencing was used to assess the impact of TGF-β-induced epithelial-to-mesenchymal transition (EMT) and cell chemotaxis. Three-dimensional (3D) neurosphere cultures were generated to recapitulate the brain CSC phenotype.

RESULTS

Higher expression in G6PC3, SLC37A2, and SLC37A4 was found in GBM tumor tissues in comparison to low-grade glioma and healthy tissue. The expression of these genes was also found elevated in established human U87, U251, U118, and U138 GBM cell models compared to human HepG2 hepatoma cells. SLC37A4/G6PC3, but not SLC37A2, levels were induced in 3D CD133/SOX2-positive U87 neurospheres when compared to 2D monolayers. Silencing of SLC37A4/G6PC3 altered TGF-β-induced EMT biomarker SNAIL and cell chemotaxis.

CONCLUSION

Two members of the G6Pase system, G6PC3 and SLC37A4, associate with GBM disease progression and regulate the metabolic reprogramming of an invasive and CSC phenotype. Such molecular signature may support their role in cancer cell survival and chemoresistance and become future therapeutic targets.

Molecular signature to predict quality of life and survival with glioblastoma using Multiview omics model

Glioblastoma multiforme (GBM) patients show a variety of signs and symptoms that affect their quality of life (QOL) and self-dependence. Since most existing studies have examined prognostic factors based only on clinical factors, there is a need to consider the value of integrating multi-omics data including gene expression and proteomics with clinical data in identifying significant biomarkers for GBM prognosis. Our research aimed to isolate significant features that differentiate between short-term (≤ 6 months) and long-term (≥ 2 years) GBM survival, and between high Karnofsky performance scores (KPS ≥ 80) and low (KPS ≤ 60), using the iterative random forest (iRF) algorithm. Using the Cancer Genomic Atlas (TCGA) database, we identified 35 molecular features composed of 19 genes and 16 proteins. Our findings propose molecular signatures for predicting GBM prognosis and will improve clinical decisions, GBM management, and drug development.

AVEN: a novel oncogenic biomarker with prognostic significance and implications of AVEN-associated immunophenotypes in lung adenocarcinoma

Introduction: AVEN, an apoptosis and caspase activation inhibitor, has been associated with adverse clinical outcomes and poor prognosis in Acute myeloid leukemia (AML). Targeting AVEN in AML improves apoptosis sensitivity and chemotherapy efficacy, making it a promising therapeutic target. However, AVEN's role has not been studied in solid tumors. Therefore, our study investigated AVEN as a prognostic biomarker in a more comprehensive manner and developed an AVEN-derived prognostic model in Lung adenocarcinoma (LUAD). Method: Pan-cancer analysis was performed to examine AVEN expression in 33 cancer types obtained from the TCGA database. GEPIA analysis was used to determine the predictive value of AVEN in each cancer type with cancer-specific AVEN expression. Lung Adenocarcinomas (LUAD) patients were grouped into AVENhigh and AVENlow based on AVEN expression level. Differentially expressed genes (DEGs) and pathway enrichment analysis were performed to gain insight into the biological function of AVEN in LUAD. In addition, several deconvolution tools, including Timer, CIBERSORT, EPIC, xCell, Quanti-seq and MCP-counter were used to explore immune infiltration. AVEN-relevant prognostic genes were identified by Random Survival Forest analysis via univariate Cox regression. The AVEN-derived genomic model was established using a multivariate-Cox regression model and GEO datasets (GSE31210, GSE50081) were used to validate its prognostic effect. Results: AVEN expression was increased in several cancer types compared to normal tissue, but its impact on survival was only significant in LUAD in the TCGA cohort. High AVEN expression was significantly correlated with tumor progression and shorter life span in LUAD patients. Pathway analysis was performed with 838 genes associated with AVEN expression and several oncogenic pathways were altered such as the Cell cycle, VEGFA-VEGFR2 pathway, and epithelial-mesenchymal-transition pathway. Immune infiltration was also analyzed, and less infiltrated B cells was observed in AVENhigh patients. Furthermore, an AVEN-derived genomic model was established, demonstrating a reliable and improved prognostic value in TCGA and GEO databases. Conclusion: This study provided evidence that AVEN is accumulated in LUAD compared to adjacent tissue and is associated with poor survival, high tumor progression, and immune infiltration alteration. Moreover, the study introduced the AVEN-derived prognostic model as a promising prognosis tool for LUAD.

CHRM3 is a novel prognostic factor of poor prognosis and promotes glioblastoma progression via activation of oncogenic invasive growth factors

Glioblastoma (GBM) is the most aggressive cancer of the brain and has a high mortality rate due to the lack of effective treatment strategy. Clarification of molecular mechanisms of GBM's characteristic invasive growth is urgently needed to improve the poor prognosis. Single-nuclear sequencing of primary and recurrent GBM samples revealed that levels of M3 muscarinic acetylcholine receptor (CHRM3) were significantly higher in the recurrent samples than in the primary samples. Moreover, immunohistochemical staining of an array of GBM samples showed that high levels of CHRM3 correlated with poor prognosis, consistent with The Cancer Genome Atlas database. Knockdown of CHRM3 inhibited GBM cell growth and invasion. An assay of orthotopic GBM animal model in vivo indicated that inhibition of CHRM3 significantly suppressed GBM progression with prolonged survival time. Transcriptome analysis revealed that CHRM3 knockdown significantly reduced an array of classic factors involved in cancer invasive growth, including MMP1/MMP3/MMP10/MMP12 and CXCL1/CXCL5/CXCL8. Taken together, CHRM3 is a novel and vital factor of GBM progression via regulation of multiple oncogenic genes and may serve as a new biomarker for prognosis and therapy of GBM patients.

Identification of a Novel Eight-Gene Risk Model for Predicting Survival in Glioblastoma: A Comprehensive Bioinformatic Analysis

Glioblastoma (GBM) is one of the most progressive and prevalent cancers of the central nervous system. Identifying genetic markers is therefore crucial to predict prognosis and enhance treatment effectiveness in GBM. To this end, we obtained gene expression data of GBM from TCGA and GEO datasets and identified differentially expressed genes (DEGs), which were overlapped and used for survival analysis with univariate Cox regression. Next, the genes' biological significance and potential as immunotherapy candidates were examined using functional enrichment and immune infiltration analysis. Eight prognostic-related DEGs in GBM were identified, namely CRNDE, NRXN3, POPDC3, PTPRN, PTPRN2, SLC46A2, TIMP1, and TNFSF9. The derived risk model showed robustness in identifying patient subgroups with significantly poorer overall survival, as well as those with distinct GBM molecular subtypes and MGMT status. Furthermore, several correlations between the expression of the prognostic genes and immune infiltration cells were discovered. Overall, we propose a survival-derived risk score that can provide prognostic significance and guide therapeutic strategies for patients with GBM.

Development of Prognostic Indicator Based on AU-Rich Elements-Related Genes in Glioblastoma

BACKGROUND

AREs (AU-rich elements) are important cis-acting short sequences in the 3'UTR (3'-untranslated region) that affect messenger RNA stability and translation. However, there were no systematic researches about AREs-related genes to predict the survival of patients with GBM (glioblastoma).

METHODS

Differentially expressed genes were acquired from The Cancer Genome Atlas and Chinese Glioma Genome Atlas databases. Differentially expressed AREs-related genes were filtered by overlapping differentially expressed genes and AREs-related genes. The prognostic genes were selected to construct a risk model. Patients with GBM were categorized into 2 risk groups depending on the medium value of risk score. Gene Set Enrichment Analysis was performed to explore the potential biological pathways. We explored the correlation between the risk model and immune cells. The chemotherapy sensitivity was predicted in different risk groups.

RESULTS

A risk model was constructed by 10 differentially expressed AREs-related genes (GNS, ANKH, PTPRN2, NELL1, PLAUR, SLC9A2, SCARA3, MAPK1, HOXB2, and EN2), and it could accurately predict the prognosis of patients with GBM. Higher risk scores for patients with GBM had a lower survival probability. The predictive power of risk model was decent. The risk score and treatment type were regarded as independent prognostic indicators. The mainly Gene Set Enrichment Analysis enrichment pathways were primary immunodeficiency and chemokine signaling pathway. Six immune cells were significant different in the 2 risk groups. There were higher abundance of macrophages M2 and neutrophils and higher sensitivity of 11 chemotherapy drugs in the high-risk group.

CONCLUSIONS

The 10 biomarkers might be important prognostic markers and potential therapeutic targets for patients with GBM.

Defining a Correlative Transcriptional Signature Associated with Bulk Histone H3 Acetylation Levels in Adult Glioblastomas

Glioblastoma (GB) is the most prevalent primary brain cancer and the most aggressive form of glioma because of its poor prognosis and high recurrence. To confirm the importance of epigenetics in glioma, we explored The Cancer Gene Atlas (TCGA) database and we found that several histone/DNA modifications and chromatin remodeling factors were affected at transcriptional and genetic levels in GB compared to lower-grade gliomas. We associated these alterations in our own cohort of study with a significant reduction in the bulk levels of acetylated lysines 9 and 14 of histone H3 in high-grade compared to low-grade tumors. Within GB, we performed an RNA-seq analysis between samples exhibiting the lowest and highest levels of acetylated H3 in the cohort; these results are in general concordance with the transcriptional changes obtained after histone deacetylase (HDAC) inhibition of GB-derived cultures that affected relevant genes in glioma biology and treatment (e.g., A2ML1, CD83, SLC17A7, TNFSF18). Overall, we identified a transcriptional signature linked to histone acetylation that was potentially associated with good prognosis, i.e., high overall survival and low rate of somatic mutations in epigenetically related genes in GB. Our study identifies lysine acetylation as a key defective histone modification in adult high-grade glioma, and offers novel insights regarding the use of HDAC inhibitors in therapy.

A Genomic Instability-Associated Prognostic Signature for Glioblastoma Patients

BACKGROUND

Genomic instability and aberrant tumor mutation burden are widely accepted hallmarks of cancer. Glioblastoma (GBM) is a common brain tumor in adults, and survival of patients with GBM is poor. This study aimed to investigate the prognostic value of genomic instability-derived genes in GBM.

METHODS

GBM data were downloaded from The Cancer Genome Atlas and Chinese Glioma Genome Atlas databases. Differential expression analysis of all samples with different tumor mutation burden was performed. Univariate Cox and LASSO Cox regression analyses were integrated to determine the optimal genes for constructing a risk score model. Multivariate Cox regression analysis and survival analysis determined independent prognostic indicators. Immune cell infiltration was analyzed by CIBERSORT algorithm.

RESULTS

In GMB patients with high and low tumor mutation burden, we identified 154 differentially expressed genes, which were significantly enriched in 47 Gene Ontology terms and 6 Kyoto Encyclopedia of Genes and Genomes pathways. To establish a risk score, 9 genes were further screened, including SDC1, CXCL1, CXCL6, RGS4, PCDHGB2, CA9, ZAR1, CHRM3, and SLN. High-risk patients had worse prognosis in two databases. The performance of a nomogram including prognostic factors (risk score and age) was good. Moreover, mast cells resting was significantly differentially infiltrated between high- and low-risk GBM samples.

CONCLUSIONS

The risk score constructed by 9 genomic instability-derived genes could reliably predict prognosis of GBM patients. The nomogram based on age and risk score also had a good prognostic predictive value.

Anti-SEZ6L2 antibodies in paraneoplastic cerebellar syndrome: case report and review of the literature

Seizure Related 6 Homolog Like 2 (SEZ6L2) protein has been shown to have implications in neuronal and especially motor function development. In oncology, overexpression of SEZ6L2 serves as a negative prognostic marker in several tumor entities. Recently, few cases of anti-SEZ6L2 antibody mediated cerebellar syndromes were reported. In this article, we present a case of a 70-year-old woman with subacute onset of gait disturbance, dysarthria and limb ataxia. Serum anti-SEZ6L2 antibodies were markedly increased, and further diagnostic workup revealed left sided breast cancer. Neurological symptoms and SEZ6L2 titer significantly improved after curative tumor therapy. This is a very rare and educationally important report of anti-SEZ6L2 autoimmune cerebellar syndrome with a paraneoplastic etiology. Additionally, we performed a review of the current literature for SEZ6L2, focusing on comparing the published cases on autoimmune cerebellar syndrome.

Seven Fatty Acid Metabolism-Related Genes as Potential Biomarkers for Predicting the Prognosis and Immunotherapy Responses in Patients with Esophageal Cancer

Background: Esophageal cancer (ESCA) is a major cause of cancer-related mortality worldwide. Altered fatty acid metabolism is a hallmark of cancer. However, studies on the roles of fatty acid metabolism-related genes (FRGs) in ESCA remain limited. Method: We identified differentially expressed FRGs (DE-FRGs). Then, the DE-FRGs prognostic model was constructed and validated using a comprehensive analysis. Moreover, the correlation between the risk model and clinical characteristics was investigated. A nomogram for predicting survival was established and evaluated. Subsequently, the difference in tumor microenvironment (TME) was compared between two risk groups. The sensitivity of key DE-FRGs to chemotherapeutic interventions and their correlation with immune cells were investigated. Finally, DEGs between two risk groups were measured and the prognostic value of key DE-FRGs in ESCA was confirmed in other databases. Results: A prognostic model was constructed based on seven selected DEG-FRGs. TNM staging and CD8+ T cells were significantly correlated with high-risk groups. Low-risk groups exhibited more infiltrated M0 macrophages, an activation of type II interferon (IFN-γ) responses, and were found to be more suitable for immunotherapy. Seven key DE-FRGs with prognostic value were found to be considerably influenced by different chemotherapy drugs. Conclusion: A prognostic model based on seven DE-FRGs may efficiently predict patient prognosis and immunotherapy response, helping to develop individualized treatment strategies in ESCA.

Comprehensive Analyses of Ferroptosis-Related Alterations and Their Prognostic Significance in Glioblastoma

Background: This study was designed to explore the implications of ferroptosis-related alterations in glioblastoma patients.

Method: After obtaining the data sets CGGA325, CGGA623, TCGA-GBM, and GSE83300 online, extensive analysis and mutual verification were performed using R language-based analytic technology, followed by further immunohistochemistry staining verification utilizing clinical pathological tissues.

Results: The analysis revealed a substantial difference in the expression of ferroptosis-related genes between malignant and paracancerous samples, which was compatible with immunohistochemistry staining results from clinicopathological samples. Three distinct clustering studies were run sequentially on these data. All of the findings were consistent and had a high prediction value for glioblastoma. Then, the risk score predicting model containing 23 genes (CP, EMP1, AKR1C1, FMOD, MYBPH, IFI30, SRPX2, PDLIM1, MMP19, SPOCD1, FCGBP, NAMPT, SLC11A1, S100A10, TNC, CSMD3, ATP1A2, CUX2, GALNT9, TNFAIP6, C15orf48, WSCD2, and CBLN1) on the basis of “Ferroptosis.gene.cluster” was constructed. In the subsequent correlation analysis of clinical characteristics, tumor mutation burden, HRD, neoantigen burden and chromosomal instability, mRNAsi, TIDE, and GDSC, all the results indicated that the risk score model might have a better predictive efficiency.

Conclusion: In glioblastoma, there were a large number of abnormal ferroptosis-related alterations, which were significant for the prognosis of patients. The risk score-predicting model integrating 23 genes would have a higher predictive value.