Prognostic Value of an Autophagy-Related Five-Gene Signature for Lower-Grade Glioma Patients

IL-6 significantly correlated with the prognosis in low-grade glioma and the mediating effect of immune microenvironment

To screen immune-related prognostic biomarkers in low-grade glioma (LGG), and reveal the potential regulatory mechanism. The differential expressed genes (DEGs) between alive and dead patients were initially identified, then the key common genes between DEGs and immune-related genes were obtained. Regarding the key DEGs associated with the overall survival (OS), their clinical value was assessed by Kaplan-Meier, RCS, logistic regression, ROC, and decision curve analysis methods. We also assessed the role of immune infiltration on the association between key DEGs and OS. All the analyses were based on the TGCA-LGG data. Finally, we conducted the molecular docking analysis to explore the targeting binding of key DEGs with the therapeutic agents in LGG. Among 146 DEGs, only interleukin-6 (IL-6) was finally screened as an immune-related biomarker. High expression of IL-6 significantly correlated with poor OS time (all P < .05), showing a linear relationship. The combination of IL-6 with IDH1 mutation had the most favorable prediction performance on survival status and they achieved a good clinical net benefit. Next, we found a significant relationship between IL-6 and immune microenvironment score, and the immune microenvironment played a mediating effect on the association of IL-6 with survival (all P < .05). Detailly, IL-6 was positively related to M1 macrophage infiltration abundance and its biomarkers (all P < .05). Finally, we obtained 4 therapeutic agents in LGG targeting IL-6, and their targeting binding relationships were all verified. IL6, as an immune-related biomarker, was associated with the prognosis in LGG, and it can be a therapeutic target in LGG.

PTK6 inhibits autophagy to promote uveal melanoma tumorigenesis by binding to SOCS3 and regulating mTOR phosphorylation

Autophagy dysfunction is one of the common causes of tumor formation and plays an important role in uveal melanoma (UM). However, little is known about the regulatory mechanisms of autophagy in UM. Here, we show that PTK6 can promote the proliferation, migration, and invasion of UM cells by inhibiting autophagy. SOCS3 can inhibit the proliferation, migration, and invasion of UM cells. Overexpression of SOCS3 can partially rescue the PTK6-induced promotion of UM cell proliferation, migration, and invasion. Mechanistically, PTK6 can bind to SOCS3, and SOCS3 can downregulate the expression of PTK6. Furthermore, PTK6 can upregulate the phosphorylation of mTOR to inhibit autophagy. Taken together, our findings demonstrate the functions of PTK6 and SOCS3 in UM cells and targeting the SOCS3-PTK6 signaling axis might be a novel and promising therapeutic strategy for patients with UM.

Constructing a signature based on the SIRT family to help the prognosis and treatment sensitivity in glioma patients

Enzymes of the silent information regulator (SIRT) family exert crucial roles in basic cellular physiological processes including apoptosis, metabolism, ageing, and cell cycle progression. They critically contribute to promoting or inhibiting cancers such as glioma. In the present study, a new gene signature of this family was identified for use in risk assessment and stratification of glioma patients. To this end, the transcriptome and relevant clinical records of patients diagnosed with glioma were obtained from the Cancer Genomic Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). LASSO regression and multivariate Cox analyses were used to establish the signature. Using Kaplan-Meier analyses, overall survival (OS) was assessed and compared between a training and an external test datasets which showed lower OS in patients with high risk of glioma compared to those with low risk. Further, ROC curve analyses indicated that the SIRT-based signature had the desired accuracy and universality for evaluating the prognosis of glioma patients. Using univariate and multivariate Cox regression analyses, the SIRT-based signature was confirmed as an independent prognostic factor applicable to subjects in the TCGA and CGGA databases. We also developed an OS nomogram including gender, age, risk score, pathological grade, and IDH status for clinical decision-making purposes. ssGSEA analysis showed a higher score for various immune subgroups (e.g., CD8⁺ T cells, DC, and TIL) in samples from high-risk patients, compared to those of low-risk ones. qPCR and western blotting confirmed the dysregulated expression of SIRTs in gliomas. Taken together, we developed a new signature on the basis of five SIRT family genes, which can help accurately predict OS of glioma patients. In addition, the findings of the present study suggest that this characteristic is associated with differences in immune status and infiltration levels of various immune cells in the tumor microenvironment.

Development of a Hallmark Pathway-Related Gene Signature Associated with Immune Response for Lower Grade Gliomas

Although some biomarkers have been used to predict prognosis of lower-grade gliomas (LGGs), a pathway-related signature associated with immune response has not been developed. A key signaling pathway was determined according to the lowest adjusted p value among 50 hallmark pathways. The least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox analyses were performed to construct a pathway-related gene signature. Somatic mutation, drug sensitivity and prediction of immunotherapy analyses were conducted to reveal the value of this signature in targeted therapies. In this study, an allograft rejection (AR) pathway was considered as a crucial signaling pathway, and we constructed an AR-related five-gene signature, which can independently predict the prognosis of LGGs. High-AR LGG patients had higher tumor mutation burden (TMB), Immunophenscore (IPS), IMmuno-PREdictive Score (IMPRES), T cell-inflamed gene expression profile (GEP) score and MHC I association immunoscore (MIAS) than low-AR patients. Most importantly, our signature can be validated in four immunotherapy cohorts. Furthermore, IC50 values of the six classic chemotherapeutic drugs were significantly elevated in the low-AR group compared with the high-AR group. This signature might be regarded as an underlying biomarker in predicting prognosis for LGGs, possibly providing more therapeutic strategies for future clinical research.

A Metabolic Plasticity-Based Signature for Molecular Classification and Prognosis of Lower-Grade Glioma

Background: Glioma is one of the major health problems worldwide. Biomarkers for predicting the prognosis of Glioma are still needed. Methods: The transcriptome data and clinic information on Glioma were obtained from the CGGA, TCGA, GDC, and GEO databases. The immune infiltration status in the clusters was compared. The genes with differential expression were identified, and a prognostic model was developed. Several assays were used to detect RPH3A’s role in Glioma cells, including CCK-8, colony formation, wound healing, and transwell migration assay. Results: Lower Grade Glioma (LGG) was divided into two clusters. The immune infiltration difference was observed between the two clusters. We screened for genes that differed between the two groups. WGCNA was used to construct a co-expressed network using the DEGs, and four co-expressed modules were identified, which are blue, green, grey, and yellow modules. High-risk patients have a lower overall survival rate than low-risk patients. In addition, the risk score is associated with histological subtypes. Finally, the role of RPH3A was detected. The overexpression of RPH3A in LGG cells can significantly inhibit cell proliferation and migration and regulate EMT-regulated proteins. Conclusion: Our study developed a metabolic-related model for the prognosis of Glioma cells. RPH3A is a potential therapeutic target for Glioma.

P-Rex1 Signaling Hub in Lower Grade Glioma Patients, Found by In Silico Data Mining, Correlates With Reduced Survival and Augmented Immune Tumor Microenvironment

Systematic analysis of tumor transcriptomes, combined with deep genome sequencing and detailed clinical assessment of hundreds of patients, constitutes a powerful strategy aimed to identify potential biomarkers and therapeutic targets to guide personalized treatments. Oncogenic signaling cascades are integrated by multidomain effector proteins such as P-Rex1, a guanine nucleotide exchange factor for the Rac GTPase (RacGEF), known to promote metastatic dissemination of cancer cells. We hypothesized that patients with high P-Rex1 expression and reduced survival might be characterized by a particular set of signaling proteins co-expressed with this effector of cell migration as a central component of a putative signaling hub indicative of poor prognosis. High P-Rex1 expression correlated with reduced survival of TCGA Lower Grade Glioma (LGG) patients. Thus, guided by PREX1 expression, we searched for signaling partners of this RacGEF by applying a systematic unbiased in silico data mining strategy. We identified 30 putative signaling partners that also correlated with reduced patient survival. These included GPCRs such as CXCR3, GPR82, FZD6, as well as MAP3K1, MAP2K3, NEK8, DYRK3 and RPS6KA3 kinases, and PTPN2 and PTPN22 phosphatases, among other transcripts of signaling proteins and phospho-substrates. This PREX1 signaling hub signature correlated with increased risk of shorter survival of LGG patients from independent datasets and coincided with immune and endothelial transcriptomic signatures, indicating that myeloid infiltration and tumor angiogenesis might contribute to worsen brain tumor pathology. In conclusion, P-Rex1 and its putative signaling partners in LGG are indicative of a signaling landscape of the tumor microenvironment that correlates with poor prognosis and might guide the characterization of signaling targets leading the eventual development of immunotherapeutic strategies.

A Novel Defined Pyroptosis-Related Gene Signature for Predicting Prognosis and Treatment of Glioma

Pyroptosis, a form of programmed cell death, that plays a significant role in the occurrence and progression of tumors, has been frequently investigated recently. However, the prognostic significance and therapeutic value of pyroptosis in glioma remain undetermined. In this research, we revealed the relationship of pyroptosis-related genes to glioma by analyzing whole transcriptome data from The Cancer Genome Atlas (TCGA) dataset serving as the training set and the Chinese Glioma Genome Atlas (CGGA) dataset serving as the validation set. We identified two subgroups of glioma patients with disparate prognostic and clinical features by performing consensus clustering analysis on nineteen pyroptosis-related genes that were differentially expressed between glioma and normal brain tissues. We further derived a risk signature, using eleven pyroptosis-related genes, that was demonstrated to be an independent prognostic factor for glioma. Furthermore, we used Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to implement functional analysis of our gene set, and the results were closely related to immune and inflammatory responses in accordance with the characteristics of pyroptosis. Moreover, Gene Set Enrichment Analysis (GSEA) results showed that that the high-risk group exhibited enriched characteristics of malignant tumors in accordance with its poor prognosis. Next, we analyzed different immune cell infiltration between the two risk groups using ssGSEA. Finally, CASP1 was identified as a core gene, so we subsequently selected an inhibitor targeting CASP1 and simulated molecular docking. In addition, the inhibitory effect of belnacasan on glioma was verified at the cellular level. In conclusion, pyroptosis-related genes are of great significance for performing prognostic stratification and developing treatment strategies for glioma.

The Hallmarks of Glioblastoma: Heterogeneity, Intercellular Crosstalk and Molecular Signature of Invasiveness and Progression

In 2021 the World Health Organization published the fifth and latest version of the Central Nervous System tumors classification, which incorporates and summarizes a long list of updates from the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy work. Among the adult-type diffuse gliomas, glioblastoma represents most primary brain tumors in the neuro-oncology practice of adults. Despite massive efforts in the field of neuro-oncology diagnostics to ensure a proper taxonomy, the identification of glioblastoma-tumor subtypes is not accompanied by personalized therapies, and no improvements in terms of overall survival have been achieved so far, confirming the existence of open and unresolved issues. The aim of this review is to illustrate and elucidate the state of art regarding the foremost biological and molecular mechanisms that guide the beginning and the progression of this cancer, showing the salient features of tumor hallmarks in glioblastoma. Pathophysiology processes are discussed on molecular and cellular levels, highlighting the critical overlaps that are involved into the creation of a complex tumor microenvironment. The description of glioblastoma hallmarks shows how tumoral processes can be linked together, finding their involvement within distinct areas that are engaged for cancer-malignancy establishment and maintenance. The evidence presented provides the promising view that glioblastoma represents interconnected hallmarks that may led to a better understanding of tumor pathophysiology, therefore driving the development of new therapeutic strategies and approaches.

Immunotherapeutic Value of MAP1LC3C and Its Candidate FDA-Approved Drugs Identified by Pan-Cancer Analysis, Virtual Screening and Sensitivity Analysis

Background: The autophagy pathway within the tumour microenvironment can be regulated to inhibit or promote tumour development. In the fight against tumour growth, immunotherapy induces an anti-tumour immune response, whereas autophagy modulates this immune response. A key protein in the autophagy pathway, microtubule-associated protein 1 light chain 3 (MAP1LC3), has recently become a hotspot for tumour research. As a relatively novel member, the function of MAP1LC3C in tumours still need to be investigated. Therefore, the goal of this study was to look into the possible link between MAP1LC3C and immunotherapy for 33 kinds of human malignancies by using pan-cancer analysis. Methods: High-throughput sequencing data from The Cancer Genome Atlas, Genotype-Tissue Expression Project and Cancer Cell Line Encyclopedia databases, combined with clinical data, were used to analyze the expression of MAP1LC3C in 33 types of cancer, as well as patient prognosis and neoplasm staging. Activity scores were calculated using ssGSEA to assess the MAP1LC3C activity in pan-cancer. Associations between MAP1LC3C and the tumour microenvironment, including immune cell infiltration and immunomodulators, were analyzed. Moreover, tumour tissue ImmuneScores and StromalScores were analyzed using the ESTIMATE algorithm. Additionally, associations between MAP1LC3C and tumour mutational burden/microsatellite instability, were investigated. Finally, based on the expression and structure of MAP1LC3C, the United States Food and Drug Administration (FDA)-approved drugs, were screened by virtual screening, molecular docking and NCI-60 drug sensitivity analysis. Results: Our study found that MAP1LC3C was differentially expressed in tumour and normal tissues in 23 of 33 human cancer types, among which MAP1LC3C had prognostic effects in 12 cancer types, and MAP1LC3C expression was significantly correlated with tumour stage in four cancer types. In addition, MAP1LC3C activity in 14 cancer types was consistent with changes in transcription levels. Moreover, MAP1LC3C strongly correlated with immune infiltration, immune modulators and immune markers. Finally, a number of FDA-approved drugs were identified via virtual screening and drug sensitivity analysis. Conclusion: Our study investigated the prognostic and immunotherapeutic value of MAP1LC3C in 33 types of cancer, and several FDA-approved drugs were identified to be highly related to MAP1LC3C and can be potential cancer therapeutic candidates.

Development and Validation of a Novel Prognostic Model for Lower-Grade Glioma Based on Enhancer RNA-Regulated Prognostic Genes

Enhancer RNAs (eRNAs) are present specifically in tumors, where they affect the expression of eRNA-regulated genes (ERGs). Owing to this characteristic, ERGs were hypothesized to improve prognosis of overall survival in heterogeneous low-grade and intermediate-grade gliomas. This study aimed to construct and validate an ERG prognostic tool to facilitate clinical management, and offer more effective diagnostic and therapeutic biomarkers for glioma. Survival-related eRNAs were identified, and their ERGs were selected based on eRNA and target gene information. The ERG prognostic model was constructed and validated using internal and external validation cohorts. Finally, biological differences related to the ERG signature were analysed to explore the potential mechanisms influencing survival outcomes. Thirteen ERGs were identified and used to build an ERG risk signature, which included five super-enhancer RNA (seRNA)-regulated genes and five LGG-specific eRNA-regulated genes. The prognostic nomogram established based on combining the ERG score, age, and sex was evaluated by calibration curves, clinical utility, Harrell’s concordance index (0.86; 95% CI: 0.83-0.90), and time-dependent receiver operator characteristic curves. We also explored potential immune-related mechanisms that might cause variation in survival. The established prognostic model displayed high validity and robustness. Several immune-related genes regulated by seRNAs or specific eRNAs were identified, indicating that these transcripts or their genes were potential targets for improving immunotherapeutic/therapeutic outcomes. The functions of an important specific eRNA-regulated gene (USP28) were validated in robust vitro experiments. In addition, the ERG risk signature was significantly associated with the immune microenvironment and other immune-related features.

Identification of a Nomogram with an Autophagy-Related Risk Signature for Survival Prediction in Patients with Glioma

Background

Glioma is a common type of tumor in the central nervous system characterized by high morbidity and mortality. Autophagy plays vital roles in the development and progression of glioma, and is involved in both normal physiological and various pathophysiological progresses.

Patients and Methods

A total of 531 autophagy-related genes (ARGs) were obtained and 1738 glioma patients were collected from three public databases. We performed least absolute shrinkage and selection operator regression to identify the optimal prognosis-related genes and constructed an autophagy-related risk signature. The performance of the signature was validated by receiver operating characteristic analysis, survival analysis, clinic correlation analysis, and Cox regression. A nomogram model was established by using multivariate Cox regression analysis. Schoenfeld’s global and individual test were used to estimate time-varying covariance for the assumption of the Cox proportional hazard regression analysis. The R programming language was used as the main data analysis and visualizing tool.

Results

An overall survival-related risk signature consisting of 15 ARGs was constructed and significantly stratified glioma patients into high- and low-risk groups (P < 0.0001). The area under the ROC curve of 1-, 3-, 5-year survival was 0.890, 0.923, and 0.889, respectively. Univariate and multivariate Cox analyses indicated that the risk signature was a satisfactory independent prognostic factor. Moreover, a nomogram model integrating risk signature with clinical information for predicting survival rates of patients with glioma was constructed (C-index=0.861±0.024).

Conclusion

This study constructed a novel and reliable ARG-related risk signature, which was verified as a satisfactory prognostic marker. The nomogram model could provide a reference for individually predicting the prognosis for each patient with glioma and promoting the selection of optimal treatment.

Identification of autophagic target RAB13 with small-molecule inhibitor in low-grade glioma via integrated multi-omics approaches coupled with virtual screening of traditional Chinese medicine databases

Objectives

Autophagy, a highly conserved lysosomal degradation process in eukaryotic cells, has been widely reported closely related to the progression of many types of human cancers, including LGG; however, the intricate relationship between autophagy and LGG remains to be clarified.

Materials and methods

Multi‐omics methods were used to integrate omics data to determine potential autophagy regulators in LGG. The expression of ZFP36L2 and RAB13 in SW1088 cells was experimentally manipulated using cDNAs and small interfering RNAs (siRNA). RT‐qPCR detects RNAi gene knockout and cDNA overexpression efficiency. The expression levels of proteins in SW1088 cells were evaluated using Western blot analysis and immunofluorescence analysis. Homology modelling and molecular docking were used to identify compounds from Multi‐Traditional Chinese Medicine (TCM) Databases. The apoptosis ratios were determined by flow cytometry analysis of Annexin‐V/PI double staining. We detect the number of autophagosomes by GFP‐MRFP‐LC3 plasmid transfection to verify the process of autophagy flow.

Results

We integrated various omics data from LGG, including EXP, MET and CNA data, with the SNF method and the LASSO algorithm, and identified ZFP36L2 and RAB13 as positive regulators of autophagy, which are closely related to the core autophagy regulators. Both transcription level and protein expression level of the four autophagy regulators, including ULK1, FIP200, ATG16L1 and ATG2B, and LC3 puncta were increased by ZFP36L2 and RAB13 overexpression. In addition, RAB13 participates in autophagy through ATG2B, FIP200, ULK1, ATG16L1 and Beclin‐1. Finally, we screened multi‐TCM databases and identified gallic acid as a novel potential RAB13 inhibitor, which was confirmed to negatively regulate autophagy as well as to induce cell death in SW1088 cells.

Conclusion

Our study identified the key autophagic regulators ZFP36L2 and Rab13 in LGG progression, and demonstrated that gallic acid is a small molecular inhibitor of RAB13, which negatively regulates autophagy and provides a possible small molecular medicine for the subsequent treatment of LGG.