CGCG clinical practice guidelines for the management of adult diffuse gliomas

Approaches to selective and potent inhibition of glioblastoma by vanadyl complexes: Inducing mitotic catastrophe and methuosis

Drug resistance has been a major problem for cancer chemotherapy, especially for glioblastoma multiforme that is aggressive, heterogeneous and recurrent with <3% of a five-year survival and limited methods of clinical treatment. To overcome the problem, great efforts have recently been put in searching for agents inducing death of tumor cells via various non-apoptotic pathways. In the present work, we report for the first time that vanadyl complexes, i.e. bis(acetylacetonato)oxidovanadium (IV) (VO(acac)2), can cause mitotic catastrophe and methuotic death featured by catastrophic macropinocytic vacuole accumulation particularly in glioblastoma cells (GCs). Hence, VO(acac)2 strongly suppressed growth of GCs with both in vitro (IC50 = 4-6 μM) and in vivo models, and is much more potent than the current standard-of-care drug Temozolomide. The selective index is as high as ∼10 or more on GCs over normal neural cells. Importantly, GCs respond well to vanadium treatment regardless whether they are carrying IDH1 wild type gene that causes drug resistance. VO(acac)2 may induce methuosis via the Rac-Mitogen-activated protein kinase kinase 4 (MKK4)-c-Jun N-terminal kinase (JNK) signaling pathway. Furthermore, VO(acac)2-induced methuosis is not through a immunogenicity mechanism, making vanadyl complexes safe for interventional therapy. Overall, our results may encourage development of novel vanadium complexes promising for treatment of neural malignant tumor cells.

Identification of TNFAIP6 as a reliable prognostic indicator of low-grade glioma

Glioma is the most common primary malignant tumor in the brain, characterizing by high disability rate and high recurrence rate. Although low-grade glioma (LGG) has a relative benign biological behavior, the prognosis of LGG patients still varies greatly. Glioma stem cells (GSCs) are considered as the chief offenders of glioma cell proliferation, invasion and resistance to therapies. Our study screened a series of glioma stem cell-related genes (GSCRG) based on mDNAsi and WCGNA, and finally established a reliable single-gene prognostic model through 101 combinations of 10 machine learning methods. Our result suggested that the expression level of TNFAIP6 is negatively correlated with the prognosis of LGG patients, which may be the result of pro-cancer signaling pathways activation and immunosuppression. In general, this study revealed that TNFAIP6 is a robust and valuable prognostic factor in LGG, and may be a new target for LGG treatment.

Tumor treating fields for newly diagnosed high-grade glioma based on the criteria of 2021 WHO CNS5: A retrospective analysis of Chinese patients in a single center

BACKGROUND AND OBJECTIVE

High-grade glioma (HGG) is known to be characterized by a high degree of malignancy and a worse prognosis. The classical treatment is safe resection supplemented by radiotherapy and chemotherapy. Tumor treating fields (TTFields), an emerging physiotherapeutic modality that targets malignant solid tumors using medium-frequency, low-intensity, alternating electric fields to interfere with cell division, have been used for the treatment of new diagnosis of glioblastoma, however, their administration in HGG requires further clinical evidence. The efficacy and safety of TTFields in Chinese patients with HGG were retrospectively evaluated by us in a single center.

METHODS

We enrolled and analyzed 52 patients with newly diagnosed HGG undergoing surgery and standard chemoradiotherapy regimens from December 2019 to June 2022, and followed them until June 2023. Based on whether they used TTFields, they were divided into a TTFields group and a non-TTFields group. Progression-free survival (PFS) and overall survival (OS) were compared between the two groups.

RESULTS

There were 26 cases in the TTFields group and 26 cases in the non-TTFields group. In the TTFields group, the median PFS was 14.2 months (95% CI: 9.50-18.90), the median OS was 19.7 months (95% CI: 14.95-24.25) , the median interval from surgery to the start of treatment with TTFields was 2.47 months (95% CI: 1.47-4.13), and the median duration of treatment with TTFields was 10.6 months (95% CI: 9.57-11.63). 15 (57.69%) patients experienced an adverse event and no serious adverse event was reported. In the non-TTFields group, the median PFS was 9.57 months (95% CI: 6.23-12.91) and the median OS was 16.07 months (95% CI: 12.90-19.24). There was a statistically significant difference in PFS (p = 0.005) and OS (p = 0.007) between the two groups.

CONCLUSIONS

In this retrospective analysis, TTFields were observed to improve newly diagnosed HGG patients' median PFS and OS. Compliance was much higher than reported in clinical trials and safety remained good.

Mitochondrial RNA modification-based signature to predict prognosis of lower grade glioma: a multi-omics exploration and verification study

Mitochondrial RNA modification (MRM) plays a crucial role in regulating the expression of key mitochondrial genes and promoting tumor metastasis. Despite its significance, comprehensive studies on MRM in lower grade gliomas (LGGs) remain unknown. Single-cell RNA-seq data (GSE89567) was used to evaluate the distribution functional status, and correlation of MRM-related genes in different cell types of LGG microenvironment. We developed an MRM scoring system by selecting potential MRM-related genes using LASSO regression analysis and the Random Survival Forest algorithm, based on multiple bulk RNA-seq datasets from TCGA, CGGA, GSE16011, and E-MTAB-3892. Analysis was performed on prognostic and immunological features, signaling pathways, metabolism, somatic mutations and copy number variations (CNVs), treatment responses, and forecasting of potential small-molecule agents. A total of 35 MRM-related genes were selected from the literature. Differential expression analysis of 1120 normal brain tissues and 529 LGGs revealed that 22 and 10 genes were upregulated and downregulated, respectively. Most genes were associated with prognosis of LGG. METLL8, METLL2A, TRMT112, and METTL2B were extensively expressed in all cell types and different cell cycle of each cell type. Almost all cell types had clusters related to mitochondrial RNA processing, ribosome biogenesis, or oxidative phosphorylation. Cell-cell communication and Pearson correlation analyses indicated that MRM may promoting the development of microenvironment beneficial to malignant progression via modulating NCMA signaling pathway and ICP expression. A total of 11 and 9 MRM-related genes were observed by LASSO and the RSF algorithm, respectively, and finally 6 MRM-related genes were used to establish MRM scoring system (TRMT2B, TRMT11, METTL6, METTL8, TRMT6, and TRUB2). The six MRM-related genes were then validated by qPCR in glioma and normal tissues. MRM score can predict the malignant clinical characteristics, abundance of immune infiltration, gene variation, clinical outcome, the enrichment of signaling pathways and metabolism. In vitro experiments demonstrated that silencing METTL8 significantly curbs glioma cell proliferation and enhances apoptosis. Patients with a high MRM score showed a better response to immunotherapies and small-molecule agents such as arachidonyl trifluoromethyl ketone, MS.275, AH.6809, tacrolimus, and TTNPB. These novel insights into the biological impacts of MRM within the glioma microenvironment underscore its potential as a target for developing precise therapies, including immunotherapeutic approaches.

Gelsolin knockdown confers radiosensitivity to glioblastoma cells

OBJECTIVE

Radiotherapy (RT) is a cornerstone of the glioblastoma (GBM) treatment. However, the resistance of tumour cells to radiation results in early recurrence. The mechanisms underlying GBM radioresistance remain unclear. Screening for differentially expressed genes (DEGs) related to radiation might be a potential solution to this problem.

METHOD

RT-associated DEGs were screened based on the RNA sequencing of 15 paired primary and recurrent GBMs. The mRNA and protein expression of candidate genes were validated in RNA sequencing of The Chinese Genome Atlas (CGGA) dataset and 18 cases of GBM samples. The relationship between the candidate gene and radiation was confirmed in irradiated GBM cells. The association of candidate gene with clinical characteristics and survival was investigated in the CGGA and TCGA dataset. Biological function and pathway analysis were explored by gene ontology analysis. The association of the candidate gene with radiosensitivity was verified using cell counting Kit-8, comet, and colony formation assays in vitro and subcutaneous tumour xenograft experiments in vivo.

RESULTS

Gelsolin (GSN) was selected for further study. GSN expression was significant elevated in recurrent GBM and up-regulated in irradiated GBM cell lines. High expression of GSN was enriched in malignant phenotype of glioma. Moreover, high expression of GSN was associated with poor prognosis. Further investigation demonstrated that GSN-knockdown (GSN-KD) combined with RT significantly inhibited cell proliferation and enhanced radiosensitivity in vivo and in vitro. Mechanistically, GSN-KD could lead to more serious DNA damage and promotes apoptosis after RT.

CONCLUSION

Radiation induced up-regulated of GSN. GSN-KD could enhance the radiosensitivity of GBM.

Promoter hypomethylated PDZK1 acts as a tumorigenic gene in glioma by interacting with AKT1

Glioma is the most frequently diagnosed primary brain tumor and typically has a poor prognosis because of malignant proliferation and invasion. It is urgent to elucidate the mechanisms driving glioma tumorigenesis and develop novel treatments to address this deadly disease. Here, we first revealed that PDZK1 is expressed at high levels in gliomas. Promoter hypomethylation may cause high expression of PDZK1 in glioma. Knockdown of PDZK1 inhibits glioma cell proliferation and invasion in vitro. Mechanistically, further investigations revealed that the loss of PDZK1 expression by siRNA inhibited the activation of the AKT/mTOR signaling pathway, leading to cell cycle arrest and apoptosis. Clinically, high expression of PDZK1 predicts a poorer prognosis for glioma patients than low expression of PDZK1. Overall, our study revealed that PDZK1 acts as a novel oncogene in glioma by binding to AKT1 and maintaining the activation of the AKT/mTOR signaling pathway. Thus, PDZK1 may be a potential therapeutic target for glioma.

Pathway-based stratification of gliomas uncovers four subtypes with different TME characteristics and prognosis

Increasing evidences have found that the interactions between hypoxia, immune response and metabolism status in tumour microenvironment (TME) have clinical importance of predicting clinical outcomes and therapeutic efficacy. This study aimed to develop a reliable molecular stratification based on these key components of TME. The TCGA data set (training cohort) and two independent cohorts from CGGA database (validation cohort) were enrolled in this study. First, the enrichment score of 277 TME-related signalling pathways was calculated by gene set variation analysis (GSVA). Then, consensus clustering identified four stable and reproducible subtypes (AFM, CSS, HIS and GLU) based on TME-related signalling pathways, which were characterized by differences in hypoxia and immune responses, metabolism status, somatic alterations and clinical outcomes. Among the four subtypes, HIS subtype had features of immunosuppression, oxygen deprivation and active energy metabolism, resulting in a worst prognosis. Thus, for better clinical application of this acquired stratification, we constructed a risk signature by using the LASSO regression model to identify patients in HIS subtype accurately. We found that the risk signature could accurately screen out the patients in HIS subtype and had important reference value for individualized treatment of glioma patients. In brief, the definition of the TME-related subtypes was a valuable tool for risk stratification in gliomas. It might serve as a reliable prognostic classifier and provide rational design of individualized treatment, and follow-up scheduling for patients with gliomas.

Brain Organoids: A Game-Changer for Drug Testing

Neurological disorders are the second cause of death and the leading cause of disability worldwide. Unfortunately, no cure exists for these disorders, but the actual therapies are only able to ameliorate people's quality of life. Thus, there is an urgent need to test potential therapeutic approaches. Brain organoids are a possible valuable tool in the study of the brain, due to their ability to reproduce different brain regions and maturation stages; they can be used also as a tool for disease modelling and target identification of neurological disorders. Recently, brain organoids have been used in drug-screening processes, even if there are several limitations to overcome. This review focuses on the description of brain organoid development and drug-screening processes, discussing the advantages, challenges, and limitations of the use of organoids in modeling neurological diseases. We also highlighted the potential of testing novel therapeutic approaches. Finally, we examine the challenges and future directions to improve the drug-screening process.

Integrated profiling identifies ferredoxin 1 as an immune-related biomarker of malignant phenotype in glioma

Background

Glioma, a highly resistant and recurrent type of central nervous system tumor, poses a significant challenge in terms of effective drug treatments and its associated mortality rates. Despite the discovery of Ferredoxin 1 (FDX1) as a crucial participant in cuproptosis, an innovative mechanism of cellular demise, its precise implications for glioma prognosis and tumor immune infiltration remain inadequately elucidated.

Methods

To analyze pan-cancer data, we employed multiple public databases. Gene expression evaluation was performed using tissue microarray (TMA) and single-cell sequencing data. Furthermore, four different approaches were employed to assess the prognostic importance of FDX1 in glioma. We conducted the analysis of differential expression genes (DEGs) and Gene Set Enrichment Analysis (GSEA) to identify immune-related predictive signaling pathways. Somatic mutations were assessed using Tumor Mutation Burden (TMB) and waterfall plots. Immune cell infiltration was evaluated with five different algorithms. Furthermore, we performed in vitro investigations to evaluate the biological roles of FDX1 in glioma.

Results

Glioma samples exhibited upregulation of FDX1, which in turn predicted poor prognosis and was positively associated with unfavorable clinicopathological characteristics. Notably, the top four enriched signaling pathways were immune-related, and the discovery revealed a connection between the expression of FDX1 and the frequency of mutations or the TMB. The FDX1_high group exhibited heightened infiltration of immune cells, and there existed a direct association between the expression of FDX1 and the regulation of immune checkpoint. In vitro experiments demonstrated that FDX1 knockdown reduced proliferation, migration, invasion and transition from G2 to M phase in glioma cells.

Conclusion

In glioma, FDX1 demonstrated a positive association with the advancement of malignancy and changes in the infiltration of immune cells.

Apigenin and Temozolomide Synergistically Inhibit Glioma Growth Through the PI3K/AKT Pathway

Background: Glioma is a devastating disease with the worst prognosis among human malignant tumors. Although temozolomide (TMZ) improves the overall survival of glioma patients, there are still many glioma patients who are resistant to TMZ. In this study, we focused on the effect of apigenin (API) and TMZ on glioma cells in vitro and in vivo, and we studied the underlying molecular mechanisms. Materials and Methods: To investigate the effect of API on glioblastoma cell proliferation, cell viability was assessed after glioma cells were incubated with various concentrations of API with or without TMZ using MTT assays. Then, we explored the synergistic effect of API and TMZ on glioma cell cycle, apoptosis, and migration. To investigate the molecular mechanism behind the synergism of API and TMZ, we examined the related genes of the major signaling pathways involved in glioma pathogenesis by Western blotting. Results: In this study, we found that API significantly suppressed the proliferation of glioma cells in a dose- and time-dependent manner. Combining API and TMZ significantly induced glioma cells arrest at the G2 phase and inhibited glioma cells proliferation compared with API or TMZ alone. In addition, API promoted the ability of TMZ to induce glioma cells apoptosis and inhibit glioma cells invasion. Furthermore, compared with treatment with individual agents, the combination of API and TMZ significantly inhibited the growth of subcutaneous tumors in mice. These results implied that API could synergistically suppress the growth of glioma cells when combined with TMZ. Combining API and TMZ significantly inhibited the protein expression of p-AKT, cyclin D1, Bcl-2, Matrix Metallopeptidase 2, and Matrix Metallopeptidase 9. Conclusion: API and TMZ synergistically inhibited glioma growth through the PI3K/AKT pathway.

The levels of immunosuppressive checkpoint protein PD-L1 and tumor-infiltrating lymphocytes were integrated to reveal the glioma tumor microenvironment

In spite of significant strides in the realm of cancer biology and therapeutic interventions, the clinical prognosis for patients afflicted with glioblastoma (GBM) remains distressingly dismal. The tumor immune microenvironment (TIME), a crucial player in the progression, treatment response, and prognostic trajectory of glioma, warrants thorough exploration. Within this intricate microcosm, the immunosuppressive checkpoint protein PD-L1 and tumor-infiltrating lymphocytes (TILs) emerge as pivotal constituents, underscoring their potential role in deciphering glioma biology and informing treatment strategies. However, prognostic models based on the association between PD-L1 expression and TIL infiltration in the tumor immune microenvironment have not been established. The aim of this study was to explore TIME genes associated with PD-L1 expression and TIL invasion and to construct a risk score for predicting the overall survival (OS) of GBM patients based on these genes. The samples were separately classified according to the PD-L1 expression level and TIL score and TIME-related genes were identified using differential expression and weighted gene co-expression network analysis. The DEGs were subjected to least absolute contraction and selection operator (LASSO) -Cox regression to construct TIME associated risk score (TIMErisk). A TIMErisk was developed based on STEAP3 and CXCL13 genes. The STLEAP3 was demonstrated to be involved in glioma progression. The results showed that the patients in the high TIMErisk group had poor OS compared with subjects in the low TIMErisk group. The biological phenotypes associated with TIMErisk were analyzed in terms of functional enrichment, tumor immune profile, and tumor mutation profile. The results on tumor immune dysfunction and exclusion dysfunction (TIDE) score and immune surface score (IPS) showed that GBM patients with different TIME risks had different responses to immunotherapy. Tumor purity analysis indicated that PD-L1 and TIL scores were positively correlated with TIMErisk score and negatively correlated with tumor purity. These results show that the TIMErisk-based prognostic model had high predictive value for the prognosis and immune characteristics of GBM patients. Immunohistochemical staining images of patients in the high and low TIMErisk groups were analyzed, showing that the degree of immune cell infiltration was higher in the high TIMErisk group relative to the low TIMErisk group. The present study provides a basis for understanding glioma tumor microenvironment and a foundation for conducting comprehensive immunogenomic analysis.

Cuproptosis facilitates immune activation but promotes immune escape, and a machine learning-based cuproptosis-related signature is identified for predicting prognosis and immunotherapy response of gliomas

AIMS

Cell death, except for cuproptosis, in gliomas has been extensively studied, providing novel targets for immunotherapy by reshaping the tumor immune microenvironment through multiple mechanisms. This study aimed to explore the effect of cuproptosis on the immune microenvironment and its predictive power in prognosis and immunotherapy response.

METHODS

Eight glioma cohorts were included in this study. We employed the unsupervised clustering algorithm to identify novel cuproptosis clusters and described their immune microenvironmental characteristics, mutation landscape, and altered signaling pathways. We verified the correlation among FDX1, SLC31A1, and macrophage infiltration in 56 glioma tissues. Next, based on multicenter cohorts and 10 machine learning algorithms, we constructed an artificial intelligence-driven cuproptosis-related signature named CuproScore.

RESULTS

Our findings suggested that glioma patients with high levels of cuproptosis had a worse prognosis owing to immunosuppression caused by unique immune escape mechanisms. Meanwhile, we experimentally validated the positive association between cuproptosis and macrophages and its tumor-promoting mechanism in vitro. Furthermore, our CuproScore exhibited powerful and robust prognostic predictive ability. It was also capable of predicting response to immunotherapy and chemotherapy drug sensitivity.

CONCLUSIONS

Cuproptosis facilitates immune activation but promotes immune escape. The CuproScore could predict prognosis and immunotherapy response in gliomas.

Tumor microenvironment remodeling plus immunotherapy could be used in mesenchymal-like tumor with high tumor residual and drug resistant rate

Epithelial-mesenchymal transition (EMT) is a common process during tumor progression and is always related to residual tumor, drug resistance and immune suppression. However, considering the heterogeneity in EMT process, there is still a need to establish robust EMT classification system with reasonable molecular, biological and clinical implications to investigate whether these unfavorable survival factors are common or unique in different individuals. In our work, we classify tumors with four EMT status, that is, EMTlow, EMTmid, EMThigh-NOS (Not Otherwise Specified), and EMThigh-AKT (AKT pathway overactivation) subtypes. We find that EMThigh-NOS subtype is driven by intrinsic somatic alterations. While, EMThigh-AKT subtype is maintained by extrinsic cellular interplay between tumor cells and macrophages in an AKT-dependent manner. EMThigh-AKT subtype is both unresectable and drug resistant while EMThigh-NOS subtype can be treated with cell cycle related drugs. Importantly, AKT activation in EMThigh-AKT not only enhances EMT process, but also contributes to the immunosuppressive microenvironment. By remodeling tumor immune-microenvironment by AKT inhibition, EMThigh-AKT can be treated by immune checkpoint blockade therapies. Meanwhile, we develop TumorMT website ( http://tumormt.neuroscience.org.cn/ ) to apply this EMT classification and provide reasonable therapeutic guidance.

Leveraging a gene signature associated with disulfidptosis identified by machine learning to forecast clinical outcomes, immunological heterogeneities, and potential therapeutic targets within lower-grade glioma

Background

Disulfidptosis, a newly defined type of programmed cell death, has emerged as a significant regulatory process in the development and advancement of malignant tumors, such as lower-grade glioma (LGG). Nevertheless, the precise biological mechanisms behind disulfidptosis in LGG are yet to be revealed, considering the limited research conducted in this field.

Methods

We obtained LGG data from the TCGA and CGGA databases and performed comprehensive weighted co-expression network analysis, single-sample gene set enrichment analysis, and transcriptome differential expression analyses. We discovered nine genes associated with disulfidptosis by employing machine learning methods like Cox regression, LASSO regression, and SVM-RFE. These were later used to build a predictive model for patients with LGG. To confirm the expression level, functional role, and impact on disulfidptosis of ABI3, the pivotal gene of the model, validation experiments were carried out in vitro.

Results

The developed prognostic model successfully categorized LGG patients into two distinct risk groups: high and low. There was a noticeable difference in the time the groups survived, which was statistically significant. The model's predictive accuracy was substantiated through two independent external validation cohorts. Additional evaluations of the immune microenvironment and the potential for immunotherapy indicated that this risk classification could function as a practical roadmap for LGG treatment using immune-based therapies. Cellular experiments demonstrated that suppressing the crucial ABI3 gene in the predictive model significantly reduced the migratory and invasive abilities of both SHG44 and U251 cell lines while also triggering cytoskeletal retraction and increased cell pseudopodia.

Conclusion

The research suggests that the prognostic pattern relying on genes linked to disulfidptosis can provide valuable insights into the clinical outcomes, tumor characteristics, and immune alterations in patients with LGG. This could pave the way for early interventions and suggests that ABI3 might be a potential therapeutic target for disulfidptosis.

Expression of TMEM59L associated with radiosensitive in glioblastoma

Radiotherapy is one of the cornerstone of the glioblastoma treatment paradigm. However, the resistance of tumor cells to radiation results in poor survival. The mechanism of radioresistance has not been fully elucidated. This study aimed to screen the differential expressed genes related with radiosensitivity. The differentially expressed genes were screened based on RNA sequencing in 15 pairs of primary and recurrent glioblastoma that have undergone radiotherapy. Candidate genes were validated in 226 primary and 134 recurrent glioblastoma (GBM) obtained from the Chinese Glioma Genome Atlas (CGGA) database. RNA and protein expression were verified by Quantitative Real-time PCR (qPCR) and western blot in irradiated GBM cell lines. The candidate gene was investigated to explore the relationship between mRNA levels and clinical characteristics in the CGGA and The Cancer Genome Atlas dataset. Kaplan-Meier survival analysis and Cox regression analysis were used for survival analysis. Gene ontology and KEGG pathway analysis were used for bioinformatics analysis. Four genes (TMEM59L, Gelsolin, ZBTB7A and ATX) were screened. TMEM59L expression was significantly elevated in recurrent glioblastoma and lower in normal brain tissue. We selected TMEM59L as the target gene for further study. The increasing of TMEM59L expression induced by radiation was confirmed by mRNA and western blot in irradiated GBM cell. Further investigation revealed that high expression of TMEM59L was enriched in IDH mutant and MGMT methylated gliomas and associated with a better prognosis. Gene ontology and KEGG pathway analysis revealed that TMEM59L was closely related to the DNA damage repair and oxidative stress respond process. We speculated that the high expression of TMEM59L might enhance radiotherapy sensitivity by increasing ROS-induced DNA damage and inhibiting DNA damage repair process.

The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma

Background

Gliomas, the most prevalent primary malignant tumors of the central nervous system in adults, exhibit slow growth in lower-grade gliomas (LGG). However, the majority of LGG cases progress to high-grade gliomas, posing challenges for prognostication. The tumor microenvironment (TME), characterized by telomere-related genes and immune cell infiltration, strongly influences glioma growth and therapeutic response. Therefore, our objective was to develop a Telomere-TME (TM-TME) classifier that integrates telomere-related genes and immune cell landscape to assess prognosis and therapeutic response in glioma.

Methods

This study encompassed LGG patients from the TCGA and CCGA databases. TM score and TME score were derived from the expression signatures of telomere-related genes and the presence of immune cells in LGG, respectively. The TM-TME classifier was established by combining TM and TME scores to effectively predict prognosis. Subsequently, we conducted Kaplan-Meier survival estimation, univariate Cox regression analysis, and receiver operating characteristic curves to validate the prognostic prediction capacity of the TM-TME classifier across multiple cohorts. Gene Ontology (GO) analysis, biological processes, and proteomaps were performed to annotate the functional aspects of each subgroup and visualize the cellular signaling pathways.

Results

The TM_low+TME_high subgroup exhibited superior prognosis and therapeutic response compared to other subgroups (P<0.001). This finding could be attributed to distinct tumor somatic mutations and cancer cellular signaling pathways. GO analysis indicated that the TM_low+TME_high subgroup is associated with the neuronal system and modulation of chemical synaptic transmission. Conversely, the TM_high+TME_low subgroup showed a strong association with cell cycle and DNA metabolic processes. Furthermore, the classifier significantly differentiated overall survival in the TCGA LGG cohort and served as an independent prognostic factor for LGG patients in both the TCGA cohort (P<0.001) and the CGGA cohort (P<0.001).

Conclusion

Overall, our findings underscore the significance of the TM-TME classifier in predicting prognosis and immune therapeutic response in glioma, shedding light on the complex immune landscape within each subgroup. Additionally, our results suggest the potential of integrating risk stratification with precision therapy for LGG.

Temozolomide protects against the progression of glioblastoma via SOX4 downregulation by inhibiting the LINC00470-mediated transcription factor EGR2

OBJECTIVE

Temozolomide is extensively applied in chemotherapy for glioblastoma with unclear exact action mechanisms. This article seeks to address the potential molecular mechanisms in temozolomide therapy for glioblastoma involving LINC00470.

METHODS

Bioinformatics analysis was conducted to predict the potential mechanism of LINC00470 in glioblastoma, which was validated by dual-luciferase reporter, RIP, ChIP, and RNA pull-down assays. LINC00470 expression and the predicted downstream transcription factor early growth response 2 (EGR2) were detected in the collected brain tissues from glioblastoma patients. Following temozolomide treatment and/or gain- and loss-of-function approaches in glioblastoma cells, cell viability, invasion, migration, cycle distribution, angiogenesis, autophagy, and apoptosis were measured. In addition, the expression of mesenchymal surface marker proteins was assessed by western blot. Tumor xenograft in nude mice was conducted for in vivo validation.

RESULTS

Mechanistic analysis and bioinformatics analysis revealed that LINC00470 transcriptionally activated SRY-related high-mobility-group box 4 (SOX4) through the transcription factor EGR2. LINC00470 and EGR2 were highly expressed in brain tissues of glioblastoma patients. LINC00470 and EGR2 mRNA expression gradually decreased with increasing concentrations of temozolomide in glioblastoma cells, and SOX4 expression was reduced in cells by temozolomide and LINC00470 knockdown. Temozolomide treatment induced cell cycle arrest, diminished cell viability, migration, invasion, and angiogenesis, and increased apoptosis and autophagy in glioblastoma, which was counteracted by overexpressing LINC00470 or SOX4 but was further promoted by LINC00470 knockdown. Temozolomide restrained glioblastoma growth and angiogenesis in vivo, while LINC00470 or SOX4 overexpression nullified but LINC00470 knockdown further facilitated these trends.

CONCLUSION

Conclusively, temozolomide repressed glioblastoma progression by repressing the LINC00470/EGR2/SOX4 axis.

Comprehensive analysis of the clinical and biological significances of cholesterol metabolism in lower-grade gliomas

BACKGROUND

As a component of membrane lipids and the precursor of oxysterols and steroid hormones, reprogrammed cholesterol metabolism contributes to the initiation and progression of multiple cancers. Thus, we aim to further investigate the significances of cholesterol metabolism in lower-grade gliomas (LGGs).

METHODS

The present study included 413 LGG samples from TCGA RNA-seq dataset (training cohort) and 172 LGG samples from CGGA RNA-seq dataset (validation cohort). The cholesterol metabolism-related signature was identified by the LASSO regression model. Bioinformatics analyses were performed to explore the functional roles of this signature in LGGs. Kaplan-Meier and Cox regression analyses were enrolled to estimate prognostic value of the risk signature.

RESULTS

Our findings suggested that cholesterol metabolism was tightly associated clinicopathologic features and genomic alterations of LGGs. Bioinformatics analyses revealed that cholesterol metabolism played a key role in immunosuppression of LGGs, mainly by promoting macrophages polarization and T cell exhaustion. Kaplan-Meier curve and Cox regression analysis showed that cholesterol metabolism was an independent prognostic indicator for LGG patients. To improve the clinical application value of the risk signature, we also constructed a nomogram model to predict the 1-, 3- and 5-year survival of LGG patients.

CONCLUSION

The cholesterol metabolism was powerful prognostic indicator and could serve as a promising target to enhance personalized treatment of LGGs.

Development and validation of a predictive model in diagnosis and prognosis of primary glioblastoma patients based on Homeobox A family

BACKGROUND

Homeobox A (HOXA) family is involved in the development of malignancies as either tumor suppressors or oncogenes. However, their roles in glioblastoma (GBM) and clinical significance have not been fully elucidated.

METHODS

HOXA mutation and expressions in pan-cancers were investigated using GSCA and Oncomine, which in GBM were validated by cBioPortal, Chinese Glioma Genome Atlas (CGGA), and The Cancer Genome Atlas (TCGA) datasets. Kaplan-Meier analyses were conducted to determine prognostic values of HOXAs at genetic and mRNA levels. Diagnostic roles of HOXAs in tumor classification were explored by GlioVis and R software. Independent prognostic HOXAs were identified using Cox survival analyses, the least absolute shrinkage and selection operator (LASSO) regression, quantitative real-time PCR, and immunohistochemical staining. A HOXAs-based nomogram survival prediction model was developed and evaluated using Kaplan-Meier analysis, time-dependent Area Under Curve, calibration plots, and Decision Curve Analysis in training and validation cohorts.

RESULTS

HOXAs were highly mutated and overexpressed in pan-cancers, especially in CGGA and TCGA GBM datasets. Genetic alteration and mRNA expression of HOXAs were both found to be prognostic. Specific HOXAs could distinguish IDH mutation (HOXA1-7, HOXA9, HOXA13) and molecular GBM subtypes (HOXA1-2, HOXA9-11, HOXA13). HOXA1/2/3/10 were confirmed to be independent prognostic members, with high expressions validated in clinical GBM tissues. The HOXAs-based nomogram model exhibited good prediction performance and net benefits for patients in training and validation cohorts.

CONCLUSION

HOXA family has diagnostic values, and the HOXAs-based nomogram model is effective in survival prediction, providing a novel approach to support the treatment of GBM patients.

Characterization of tumor-associated reactive astrocytes in gliomas by single-cell and bulk tumor sequencing

Objective

Astrocytes constitute approximately 30% of cells in gliomas and play important roles in synapse construction and survival. Recently, JAK/STAT pathway activation associated with a new type of astrocyte was reported. However, the implications of these tumor-associated reactive astrocytes (TARAs) in glioma are not known.

Methods

We comprehensively assessed TARAs in gliomas, both in single cells and at the bulk tumor level, by analyzing five independent datasets. First, we analyzed two single-cell RNA sequencing datasets of 35,563 cells from 23 patients to estimate the infiltration level of TARAs in gliomas. Second, we collected clinical information and genomic and transcriptomic data of 1,379 diffuse astrocytoma and glioblastoma samples from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas datasets to evaluate the genomic, transcriptomic and clinical characteristics of TARA infiltration. Third, we downloaded expression profiles of recurrent glioblastoma samples from patients receiving PD-1 inhibitors to analyze the predictive value of TARAs for immune checkpoint inhibition.

Results

Single-cell RNA sequencing data showed TARAs were abundant in the glioma micro-environment (15.7% in the CGGA dataset and 9.1% in the Gene Expression Omnibus GSE141383 dataset, respectively). Bulk tumor sequencing data showed that the extent of TARA infiltration was highly associated with major clinical and molecular features of astrocytic gliomas. Patients with more TARA infiltration were more likely to have MUC16, FLG, and PICK3A mutations, chromosome 9p21.3, 10q23.3, and 13q14.2 deletions and 7p11.2 amplification. Gene Ontology analysis revealed that the high level of astrocyte infiltration was characterized by immune and oncogenic pathways, such as the inflammatory response, positive regulation of the JAK-STAT cascade, positive regulation of NIK/NF-kappa B signaling and the tumor necrosis factor biosynthetic process. Patients with greater TARA infiltration showed inferior prognosis. Meanwhile, the extent of reactive astrocyte infiltration exhibited a predictive value for recurrent glioblastoma patients undergoing anti-PD-1 immune therapy.

Conclusion

TARA infiltration might promote glioma tumor progression and can be used as a diagnostic, predictive and prognostic marker in gliomas. Prevention of TARA infiltration might be a new therapeutic strategy for glioma.

VSIG4 Silencing Inhibits Glioblastoma Growth by Regulating the JAK2/STAT3 Pathway

Background

Glioblastoma (GBM) is the most common malignant primary brain tumour in adults. VSIG4 has been identified to be associated with GBM. We aimed to determine the downstream regulatory mechanisms of VSIG4 in GBM.

Methods

Differential expression of VSIG4 was analysed using GEPIA. The expression of VSIG4 was assessed by RT-qPCR and its downstream genes were screened by transcriptome sequencing. The expression of pyroptosis-related proteins and the JAK2/STAT3 pathway was measured by Western blotting. GBM cell viability, migration, and invasion were detected using CCK-8, scratch, and Transwell assays. The levels of pyroptosis-related factors were measured using ELISA. The effect of VSIG4 on GBM tumour growth in vivo was explored by constructing a xenograft tumour model.

Results

VSIG4 expression was upregulated in GBM. Functionally, silencing of VSIG4 inhibited proliferation, invasion, and migration of U251 and LN229 cells, and promoted pyroptosis. Mechanically, transcriptome sequencing revealed that the JAK2/STAT3 pathway might be a downstream regulator of VSIG4. Further studies proved that silencing of VSIG4 enhanced the expression of p-JAK2 and p-STAT3, and the JAK2/STAT3 pathway inhibitor relieved the suppression of VSIG4 silencing on GBM cell viability, invasion, and migration. Furthermore, in vivo experiments further validated that knockdown of VSIG4 inhibited the growth of GBM tumors.

Conclusion

In GBM, silencing VSIG4 promoted pyroptosis and inhibited tumor progression by regulating the JAK2/STAT3 signaling pathway.

Identification of heterogeneous subtypes and a prognostic model for gliomas based on mitochondrial dysfunction and oxidative stress-related genes

Objective

Mitochondrial dysfunction and oxidative stress are known to involved in tumor occurrence and progression. This study aimed to explore the molecular subtypes of lower-grade gliomas (LGGs) based on oxidative stress-related and mitochondrial-related genes (OMRGs) and construct a prognostic model for predicting prognosis and therapeutic response in LGG patients.

Methods

A total of 223 OMRGs were identified by the overlap of oxidative stress-related genes (ORGs) and mitochondrial-related genes (MRGs). Using consensus clustering analysis, we identified molecular subtypes of LGG samples from TCGA database and confirmed the differentially expressed genes (DEGs) between clusters. We constructed a risk score model using LASSO regression and analyzed the immune-related profiles and drug sensitivity of different risk groups. The prognostic role of the risk score was confirmed using Cox regression and Kaplan-Meier curves, and a nomogram model was constructed to predict OS rates. We validated the prognostic role of OMRG-related risk score in three external datasets. Quantitative real-time PCR (qRT-PCR) and immunohistochemistry (IHC) staining confirmed the expression of selected genes. Furthermore, wound healing and transwell assays were performed to confirm the gene function in glioma.

Results

We identified two OMRG-related clusters and cluster 1 was significantly associated with poor outcomes (P<0.001). The mutant frequencies of IDH were significantly lower in cluster 1 (P<0.05). We found that the OMRG-related risk scores were significantly correlated to the levels of immune infiltration and immune checkpoint expression. High-risk samples were more sensitive to most chemotherapeutic agents. We identified the prognostic role of OMRG-related risk score in LGG patients (HR=2.665, 95%CI=1.626-4.369, P<0.001) and observed that patients with high-risk scores were significantly associated with poor prognosis (P<0.001). We validated our findings in three external datasets. The results of qRT-PCR and IHC staining verified the expression levels of the selected genes. The functional experiments showed a significant decrease in the migration of glioma after knockdown of SCNN1B.

Conclusion

We identified two molecular subtypes and constructed a prognostic model, which provided a novel insight into the potential biological function and prognostic significance of mitochondrial dysfunction and oxidative stress in LGG. Our study might help in the development of more precise treatments for gliomas.

Decapping enzyme 2 is a novel immune-related biomarker that predicts poor prognosis in glioma

This study analyzed sequencing and clinical data from the Cancer Genome Atlas (TCGA) and gene expression synthesis, and used Chinese glioma Genome Atlas (CGGA) data for external validation. The expression of DCP2 in normal brain and tumor tissue was compared. We analyzed the clinical and molecular characteristics and prognostic value of DCP2 in glioma. In addition, DCP2 expression levels were evaluated in 30 glioma tissue samples and upregulated in glioma samples compared to normal brain tissue (p < 0.001). Multivariate data analysis from TCGA showed that increased DCP2 expression was an independent risk factor for overall survival and prognosis of glioma patients. As indicated by the analysis of the TCGA data set. The expression level of DCP2 is closely related to tumor immunity, including tumor immune cell infiltration, immune score, and co-expression of multiple immune-related genes. In addition, DCP2 was positively correlated with IL-6 and IL-7. Glioma cell proliferation and invasion were evaluated using cell viability, colony formation, wound healing, and transwell assays.Apoptosis and cell cycle were detected by flow cytometry. DCP2 promoted the proliferation, invasion and migration of glioma cells T98G and U251, inhibited apoptosis and blocked the S phase of the cell cycle. As a result of the altered expression of DCP2, a new prognostic biomarker may be identified that can improve patient survival.These findings suggest DCP2 as a potential biomarker for the prognosis of glioma and a candidate immunotherapy target.

Deciphering the molecular mechanism of the THBS1 gene in the TNF signaling axis in glioma stem cells

Glioma stem cells (GSCs) are thought to be responsible for the initiation and progression of glioblastoma (GBM). GBM presents highly invasive growth with a very high recurrence rate, so it has become a clinical problem to be solved urgently. RNAseq demonstrates that thrombospondin 1 (THBS1) acts not only in the angiogenic core of glioma but also with a high degree of invasiveness and infiltration. Nevertheless, defects in the signaling pathway research lead to a poor prognosis in glioma patients. To investigate the relevant molecular mechanism and signal pathway of glioma stem cell behavior mediated by THBS1, U251 astroglioma cells and GSCs were taken as model cells for in vitro experiments. The biological effects of THBS1 on glioma proliferation, migration, and adhesion were evaluated using Cell Counting Kit-8(CCK8) assays, EdU incorporation assays, migration assays, Transwell assays, Western blotting, and RNAseq. We found that the knockout of the THBS1 gene by CRISPR/Cas9 promoted proliferation and migration in U251 cells and GSCs, as well as influencing cell cycle progression by regulating the TNF/MAPK/NF-κB and TGF-β/Smad signaling pathways. Moreover, U251 cells and GSCs showed different responses to THBS1 knockout, suggesting specific and potential targets for GSCs in signaling pathways mediated by THBS1.

Identification of a copper metabolism-related gene signature for predicting prognosis and immune response in glioma

BACKGROUND

Gliomas are highly refractory intracranial cancers characterized by genetic and transcriptional heterogeneity. However, therapeutic options are limited. In the last years, copper-induced cell death is becoming a prospective treatment strategy for gliomas and other solid tumors, but copper metabolism-related genes associated with cancer development remain unclear.

METHODS

We first collected gene expression data from The Cancer Genome Atlas (TCGA) to identify significantly differentially expressed copper metabolism-related genes in gliomas. Using these genes, we performed COX regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression to construct the prognostic model. The prognostic value of the model was further validated by CGGA testing set. Subsequently, functional analyses were carried out, including gene set enrichment analysis (GSEA), immune infiltration analysis, and mutation analysis. Finally, the expression levels of these genes were verified by immunohistochemical analysis.

RESULTS

The prognostic model consisted of 7 genes: CDK1, LOXL2, LOXL3, NFE2L2, SLC31A1, SUMF1 and FDX1. According to this prognosis model, glioma patients could be split into the high-risk group or low-risk group, and the low-risk group showed significantly better prognostic survival (p < 0.001). Moreover, the high-risk group had higher levels of immune cell infiltration, immune checkpoint genes expression, and higher tumor mutational burden (TMB), which indicates that they might benefit more from immunotherapy. Finally, we confirmed the expression level of FDX1, SUMF1, and SLC31A1 protein as significantly different in glioblastoma, lower-grade glioma, and non-tumor brain tissues by immunohistochemical analysis, and the high expression of FDX1 and SLC31A1 protein was related to poor survival in glioma patients.

CONCLUSIONS

Our study could contribute to the prognosis prediction and decision-making in patients with gliomas.

Development and validation of a pyroptosis-related genes signature for risk stratification in gliomas

Background: Glioma is a highly heterogeneous disease, causing the prognostic prediction a challenge. Pyroptosis, a programmed cell death mediated by gasdermin (GSDM), is characterized by cell swelling and the release of inflammatory factors. Pyroptosis occurs in several types of tumor cells, including gliomas. However, the value of pyroptosis-related genes (PRGs) in the prognosis of glioma remains to be further clarified. Methods: In this study, mRNA expression profiles and clinical data of glioma patients were acquired from TCGA and CGGA databases, and one hundred and eighteen PRGs were obtained from the Molecular Signatures Database and GeneCards. Then, consensus clustering analysis was performed to cluster glioma patients. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to establish a polygenic signature. Functional verification of the pyroptosis-related gene GSDMD was achieved by gene knockdown and western blotting. Moreover, the immune infiltration status between two different risk groups were analyzed through the "gsva" R package. Results: Our results demonstrated that the majority of PRGs (82.2%) were differentially expressed between lower-grade gliomas (LGG) and glioblastoma (GBM) in the TCGA cohort. In univariate Cox regression analysis, eighty-three PRGs were shown to be associated with overall survival (OS). A five-gene signature was constructed to divide patients into two risk groups. Compared with patients in the low-risk group, patients in the high-risk group had obviously shorter OS (p < 0.001). Also, we found that the high-risk group showed a higher infiltrating score of immune cells and immune-related functions. Risk score was an independent predictor of OS (HR > 1, p < 0.001). Furthermore, knockdown of GSDMD decreased the expression of IL-1β and cleaved caspase-1. Conclusion: Our study constructed a new PRGs signature, which can be used to predict the prognosis of glioma patients. Targeting pyroptosis might serve as a potential therapeutic strategy for glioma.

Functional characterization of TSPAN7 as a novel indicator for immunotherapy in glioma

Glioma is the most common primary malignant tumor of the central nervous system in clinical practice. Most adult diffuse gliomas have poor efficacy after standard treatment, especially glioblastoma. With the in-depth understanding of brain immune microenvironment, immunotherapy as a new treatment has attracted much attention. In this study, through analyzing a large number of glioma cohorts, we reported that TSPAN7, a member of the tetraspanin family, decreased in high-grade gliomas and low expression was associated with poor prognosis in glioma patients. Meanwhile, the expression pattern of TSPAN7 was verified in glioma clinical samples and glioma cell lines by qPCR, Western Blotting and immunofluorescence. In addition, functional enrichment analysis showed that cell proliferation, EMT, angiogenesis, DNA repair and MAPK signaling pathways were activated in the TSPAN7 lower expression subgroup. Lentiviral plasmids were used to overexpress TSPAN7 in U87 and LN229 glioma cell lines to explore the anti-tumor role of TSPAN7 in glioma. Moreover, by analyzing the relationship between TSPAN7 expression and immune cell infiltration in multiple datasets, we found that TSPAN7 was significantly negatively correlated with the immune infiltration of tumor-related macrophages, especially M2-type macrophages. Further analysis of immune checkpoints showed that, the expression level of TSPAN7 was negatively correlated with the expression of PD-1, PD-L1 and CTLA-4. Using an independent anti-PD-1 immunotherapy cohorts of GBM, we demonstrated that TSPAN7 expression may had a synergistic effect with PD-L1 on the response to immunotherapy. Based on the above findings, we speculate that TSPAN7 can serve as a biomarker for prognosis and a potential immunotherapy target in glioma patients.

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

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

The value of convolutional neural networks-based deep learning model in differential diagnosis of space-occupying brain diseases

Objectives

It is still a challenge to differentiate space-occupying brain lesions such as tumefactive demyelinating lesions (TDLs), tumefactive primary angiitis of the central nervous system (TPACNS), primary central nervous system lymphoma (PCNSL), and brain gliomas. Convolutional neural networks (CNNs) have been used to analyze complex medical data and have proven transformative for image-based applications. It can quickly acquire diseases' radiographic features and correct doctors' diagnostic bias to improve diagnostic efficiency and accuracy. The study aimed to assess the value of CNN-based deep learning model in the differential diagnosis of space-occupying brain diseases on MRI.

Methods

We retrospectively analyzed clinical and MRI data from 480 patients with TDLs (n = 116), TPACNS (n = 64), PCNSL (n = 150), and brain gliomas (n = 150). The patients were randomly assigned to training (n = 240), testing (n = 73), calibration (n = 96), and validation (n = 71) groups. And a CNN-implemented deep learning model guided by clinical experts was developed to identify the contrast-enhanced T1-weighted sequence lesions of these four diseases. We utilized accuracy, sensitivity, specificity, and area under the curve (AUC) to evaluate the performance of the CNN model. The model's performance was then compared to the neuroradiologists' diagnosis.

Results

The CNN model had a total accuracy of 87% which was higher than senior neuroradiologists (74%), and the AUC of TDLs, PCNSL, TPACNS and gliomas were 0.92, 0.92, 0.89 and 0.88, respectively.

Conclusion

The CNN model can accurately identify specific radiographic features of TDLs, TPACNS, PCNSL, and gliomas. It has the potential to be an effective auxiliary diagnostic tool in the clinic, assisting inexperienced clinicians in reducing diagnostic bias and improving diagnostic efficiency.

Identification of thioredoxin domain containing family members' expression pattern and prognostic value in diffuse gliomas via in silico analysis

BACKGROUND

Gliomas are the most prevalent primary tumors of the central nervous system. Their aggressive nature and the obstacles arising during therapy highlights the importance of finding new prognostic markers and therapy targets for gliomas. TXNDC genes are members of the thioredoxin superfamily and were shown to play a role in redox homeostasis, protein folding, electron transfer and also acting as cellular adapters. The well known contribution of these processes in cancer progression prompted us to investigate if TXNDC family members may also play a role in carcinogenesis, in particular diffuse gliomas.

METHODS

The present study used in silico analysis tools GEPIA, UCSC Xena, Gliovis, cBioPortal, and Ivy GAP to evaluate the expression pattern, prognostic value and clinical significance of TXNDC family members in diffuse gliomas.

RESULTS

Our analysis showed that TXNDC family members' expression pattern differ between tumors and healthy tissues and among tumors with different grades. The detailed analysis of TXNDC5 in glioma pathogenesis revealed that TXNDC5 expression is associated with more aggressive clinical and molecular features and poor therapy success both in LGG and GBM samples. Kaplan-Meier survival curves represented a worse prognosis for patients with leveated TXNDC5 levels in LGG and all grade glioma patients. The levels of TXNDC5 was shown to be possibly regulated by hypoxia-ER stress axis and a potential mechanism for TXNDC5-driven glioma progression was found to be extracellular matrix (ECM) production which is known to promote tumor aggressiveness.

CONCLUSIONS

Our results uncovered the previously unknown role of TXNDC family members in glioma pathogenesis and showed that TXNDC5 levels could serve as a predictor of clinical outcome and therapy success and may very well be used for targeted therapy.

Arachidonate lipoxygenases 5 is a novel prognostic biomarker and correlates with high tumor immune infiltration in low-grade glioma

Objective: To investigate the prognostic value of arachidonate lipoxygenases 5 (ALOX5) expression and methylation, and explore the immune functions of arachidonate lipoxygenases 5 expression in low-grade glioma (LGG). Materials and Methods: Using efficient bioinformatics approaches, the differential expression of arachidonate lipoxygenases 5 and the association of its expression with clinicopathological characteristics were evaluated. Then, we analyzed the prognostic significance of arachidonate lipoxygenases 5 expression and its methylation level followed by immune cell infiltration analysis. The functional enrichment analysis was conducted to determine the possible regulatory pathways of arachidonate lipoxygenases 5 in low-grade glioma. Finally, the drug sensitivity analysis was performed to explore the correlation between arachidonate lipoxygenases 5 expression and chemotherapeutic drugs. Results: arachidonate lipoxygenases 5 mRNA expression was increased in low-grade glioma and its expression had a notable relation with age and subtype (p < 0.05). The elevated mRNA level of arachidonate lipoxygenases 5 could independently predict the disease-specific survival (DSS), overall survival (OS), and progression-free interval (PFI) (p < 0.05). Besides, arachidonate lipoxygenases 5 expression was negatively correlated with its methylation level and the arachidonate lipoxygenases 5 hypomethylation led to a worse prognosis (p < 0.05). The arachidonate lipoxygenases 5 expression also showed a positive connection with immune cells, while low-grade glioma patients with higher immune cell infiltration had poor survival probability (p < 0.05). Further, arachidonate lipoxygenases 5 might be involved in immune- and inflammation-related pathways. Importantly, arachidonate lipoxygenases 5 expression was negatively related to drug sensitivity. Conclusion: arachidonate lipoxygenases 5 might be a promising biomarker, and it probably occupies a vital role in immune cell infiltration in low-grade glioma.

RUNX1/CD44 axis regulates the proliferation, migration, and immunotherapy of gliomas: A single-cell sequencing analysis

Background

Glioma is one of the most common, primary, and lethal adult brain tumors because of its extreme aggressiveness and poor prognosis. Several recent studies relevant to the immune function of CD44, a transmembrane glycoprotein as a significant hyaluronic acid receptor, have achieved great success, revealing the critical role of CD44 in immune infiltration in gliomas. The overexpression of CD44 has been verified to correlate with cancer aggressiveness and migration, while the clinical and immune features of CD44 expression have not yet been thoroughly characterized in gliomas.

Methods

Molecular and clinical data of glioma collected from publicly available genomic databases were analyzed.

Results

CD44 was up-expressed in malignant gliomas, notably in the 1p/19q non-codeletion cases, isocitrate dehydrogenase (IDH) wild-type, and mesenchymal subtypes in GBM samples. CD44 expression level strongly correlates with stromal and immune cells, mainly infiltrating the glioma microenvironment by single-cell sequencing analysis. Meanwhile, CD44 can be a promising biomarker in predicting immunotherapy responses and mediating the expression of PD-L1. Finally, RUNX1/CD44 axis could promote the proliferation and migration of gliomas.

Conclusions

Therefore, CD44 was responsible for glioma growth and progression. It could potentially lead to a novel target for glioma immunotherapy or a prognostic biomarker.

Ferroptosis-related NFE2L2 and NOX4 Genes are Potential Risk Prognostic Biomarkers and Correlated with Immunogenic Features in Glioma

Ferroptosis is a newfound mode of regulated cell death that may have potential to associate with prognostic or diagnostic factors in glioma. In this research, 5 genes related to glioma were screened through the FerrDb database, and we analyzed the combination between genes and glioma of survival and prognosis via TCGA, GEPIA, TIMER, and other databases. Survival curve and prognostic analysis showed that the overexpression of NFE2L2 and NOX4, respectively, has a remarkable link with a worse prognosis in glioma. Then, the association between the expression of the two genes and tumor-infiltrating immune cells level was explored based on the GSCA, and the immunity of NFE2L2 and NOX4 based on the TISIDB database was also investigated. In glioma, especially GBM, there is a strong association between gene expression and immune infiltration, even in macrophages, nTreg, and Th2 cells, which play immunosuppressive functions in TME. In conclusion, these results indicate that NFE2L2 and NOX4 could be risk prognosis biomarkers in glioma, and they bound up with immune infiltration and tumor immunity in tumorigenesis.

Molecular pathology and clinical implications of diffuse glioma

ABSTRACT

The prognosis for diffusely infiltrating gliomas at World Health Organization (WHO) grade 2-4 remains dismal due to their heterogeneity. The rapid development of genome-wide molecular-profiling-associated studies has greatly promoted the accuracy of glioma classification. Thus, the latest version of the WHO classification of the central nervous system tumors published in 2021 has incorporated more molecular biomarkers together with histological features for the diagnosis of gliomas. Advanced usage of molecular pathology in clinical diagnostic practice provides also new opportunities for the therapy of patients with glioma, including surgery, radiotherapy and chemotherapy, targeted therapy, immunotherapy, and more precision clinical trials. Herein, we highlight the updates in the classification of gliomas according to the latest WHO guidelines and summarize the clinically relevant molecular markers by focusing on their applications in clinical practice. We also review the advances in molecular features of gliomas, which can facilitate the development of glioma therapies, thereby discussing the challenges and future directions of molecular pathology toward precision medicine for patients with glioma.

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.

Profiling and Bioinformatics Analyses of Differential Circular RNA Expression in Glioblastoma Multiforme Cells Under Hypoxia

The hypoxia microenvironment is highly associated with GBM's malignant phenotypes. CircRNAs were reported involved in GBM's biological characteristics and regulated by HIF-1α. However, the differential expression profile and role of circRNAs in GBM cells under hypoxia are still unclear. The expression profiles of circRNAs in LN229 and T98G under hypoxia were explored via circRNA sequencing analysis. Those circRNAs significantly dysregulated both in LN229 and T98G and could be found in circBase were selected and validated by qRT-PCR, RNase R digestion reaction, and Sanger sequencing. Normal cell line and fresh GBM tissues were also used for qRT-PCR validation. The roles of differentially expressed circRNAs were evaluated by bioinformatics analyses. There were 672 dysregulated circRNAs in LN229 and 698 dysregulated circRNAs in T98G. GO analysis indicated that the alteration of circRNA expression related to GBM cell's biogenesis and metabolism. KEGG analysis demonstrated that TGF-β signaling pathway, HIF-1 signaling pathway, and metabolism-related signaling pathway were closely associated with differentially expressed circRNAs under hypoxia. These results were confirmed by GSEA analysis. The 6 selected and dysregulated circRNAs both in LN229 and T98G including hsa_circ_0000745, hsa_circ_0020093, hsa_circ_0020094, hsa_circ_0000943, hsa_circ_0004874, and hsa_circ_0002359 were validated by qRT-PCR. Inhibition of hsa_circ_0000745 inhibited GBM cell's proliferation, migration, and invasion. HIF-1α centered circRNA-miRNA-mRNA networks analysis showed that the 6 validated circRNAs could cross-talk with 11 related miRNAs. The circRNA expressions are dysregulated in GBM cell under hypoxia. The 6 validated circRNAs could participate in GBM's development and progression when hypoxia occurs. They might be the candidates for prognostic markers and adjuvant therapeutics of GBM in the future.

Machine learning-based identification of SOX10 as an immune regulator of macrophage in gliomas

Gliomas, originating from the glial cells, are the most lethal type of primary tumors in the central nervous system. Standard treatments like surgery have not significantly improved the prognosis of glioblastoma patients. Recently, immune therapy has become a novel and effective option. As a conserved group of transcriptional regulators, the Sry-type HMG box (SOX) family has been proved to have a correlation with numerous diseases. Based on the large-scale machine learning, we found that the SOX family, with significant immune characteristics and genomic profiles, can be divided into two distinct clusters in gliomas, among which SOX10 was identified as an excellent immune regulator of macrophage in gliomas. The high expression of SOX10 is related to a shorter OS in LGG, HGG, and pan-cancer groups but benefited from the immunotherapy. It turned out in single-cell sequencing that SOX10 is high in neurons, M1 macrophages, and neural stem cells. Also, macrophages are found to be elevated in the SOX10 high-expression group. SOX10 has a positive correlation with macrophage cytokine production and negative regulation of macrophages' chemotaxis and migration. In conclusion, our study demonstrates the outstanding cluster ability of the SOX family, indicating that SOX10 is an immune regulator of macrophage in gliomas, which can be an effective target for glioma immunotherapy.

Siglec10-An immunosuppressor and negative predictor of survival prognosis in gliomas

Glioma is a type of tumor occurring in the central nervous system. In recent decades, specific gene mutations and molecular aberrations have been used to conduct the glioma classification and clinical decisions. Siglec10 is a member of the sialic acid-binding immunoglobulin superfamily. In this study, we investigated the expression and functions of siglec10 in gliomas. We analyzed the siglec10 expression in glioma patients with immunohistochemical (IHC) staining and evaluated the survival prognosis. The high siglec10 expression had a shorter survival prognosis than the low siglec10 expression in patients, especially in malignant gliomas. Bioinformatic datasets, including TCGA and CGGA, validated the IHC results and discovered the expression of siglec10 was higher in the malignant subtype than a benign subtype of gliomas. So, siglec10 is associated with the poor prognosis of gliomas. Furthermore, the related mechanisms of siglec10 in gliomas were investigated by functional enrichment analysis, including GSEA, GO, and KEGG analysis. Siglec10 was correlated with inflammatory mediators, inflammatory cells, and inflammatory pathways in gliomas. Siglec10 might take part in the immune response in the tumor microenvironment to induce glioma's progression and metastasis. This study showed siglec10 was a biomarker in glioma, and it might be the potential target of glioma immunotherapy in the future.

Effect of glioma-derived immunoglobulin on biological function of glioma cells

INTRODUCTION

Glioma is the most common and most invasive primary central nervous system tumour, and it is urgent to develop new specific therapeutic targets. Studies have confirmed that epithelial-derived tumour cells promote tumour cell proliferation and metastasis by secreting a large number of immunoglobulins (Igs), but the role of tumour-derived Igs in glioma has never been reported.

METHODS

The Gene Expression Profiling Interactive Analysis and Chinese Glioma Genome Atlas databases were used to analyse the Ig transcription and its correlation with the prognosis of patients with glioma. Immunohistochemistry and immunofluorescence were used to detect the protein expression of IgG and IgM in the glioma tissues of patients and glioma cell lines. When IgG was knocked down by small interfering RNA or knocked out by CRISPR-Cas9, the function of proliferation and migration of glioma cells were analysed by CCK-8, clone formation, wound healing, and transwell assays. Changes in proteins and their phosphorylation in signalling pathways were detected by western blotting. The nude mouse subcutaneous tumour-bearing model was established to analyse the effect of IgG in vivo.

RESULTS

The transcriptional level of IgG was pretty high in glioma tissues and was positively correlated with high WHO grade, recurrence, and poor prognosis. The expression of IgG and IgM was found in tumour tissues and human glioma cell lines U87 and U251, and the main expression form was secreted. Decreased IgG inhibited the proliferation and migration of glioma cells. Knockout or knockdown of IgG downregulated the phosphorylation of the key molecules in the MAPK and PI3K/Akt pathway through the HGF/SF-Met or FAK/Src pathway. In vivo tumourigenesis mouse model confirmed that reduced IgG expression inhibited glioma growth.

CONCLUSION

Ig was expressed in glioma tissues and cell lines, and a high expression level predicted a poor prognosis of patients. Glioma-derived IgG promoted glioma cell proliferation and migration through the HGF/SF-Met or FAK/Src pathway.

Differences of macrophages in the tumor microenvironment as an underlying key factor in glioma patients

Background

Macrophages, the major immune cells in glioma microenvironment, are closely related to tumor prognosis. Further studies are needed to investigate macrophages, which will be helpful to fully understand the role of it and early achieve clinical translation.

Methods

A total of 1334 glioma cases were enrolled in this study from 3 databases. In our works, the single cell cohorts from GSE89567, GSE84465, and the Chinese Glioma Genome Atlas (CGGA) datasets were used to analyze the key genes of macrophage. The bulk sequencing data from the Cancer Genome Atlas (TCGA) and CGGA datasets were respectively divided into the training set and validation set to test prognostic value of the key genes from single cell analysis.

Results

Quantitative and functional differences significantly emerge in macrophage clusters between LGG and GBM. Firstly, we used the Seurat R package to identify 281 genes differentially expressed genes in macrophage clusters between LGG and GBM. Furthermore, based on these genes, we developed a predictive risk model to predict prognosis and reflect the immune microenvironment in glioma. The risk score calculation formula was yielded as follows: Risk score = (0.11 × EXPMACC1) + (−0.31 × EXPOTUD1) + (−0.09 × EXPTCHH) + (0.26 × EXPADPRH) + (-0.40× EXPABCG2) + (0.21 × EXPPLBD1) + (0.12 × EXPANG) + (0.29 × EXPQPCT). The risk score was independently related to prognosis. Further, significant differences existed in immunological characteristics between the low- and high-risk score groups. What is more, mutation analysis found different genomic patterns associated with the risk score.

Conclusion

This study further confirms that the proportion of macrophage infiltration is not only significantly different, but the function of them is also different. The signature, identified from the differentially expressed macrophage-related genes impacts poor prognosis and short overall survival and may act as therapeutic targets in the future.

Identification of tumor-associated antigens and immune subtypes of lower-grade glioma and glioblastoma for mRNA vaccine development

Background

mRNA became a promising therapeutic approach in many diseases. This study aimed to identify the tumor antigens specifically expressed in tumor cells for lower-grade glioma (LGG) and glioblastoma (GBM) patients.

Methods

In this work, the mRNA microarray expression profile and clinical data were obtained from 301 samples in the Chinese Glioma Genome Atlas (CGGA) database, the mRNA sequencing data and clinical data of 701 samples were downloaded from The Cancer Genome Atlas (TCGA) database. Genetic alterations profiles were extracted from CGGA and cBioPortal datasets. R language and GraphPad Prism software were applied for the statistical analysis and graph work.

Results

PTBP1 and SLC39A1, which were overexpressed and indicated poor prognosis in LGG patients, were selected as tumor-specific antigens for LGG patients. Meanwhile, MMP9 and SLC16A3, the negative prognostic factors overexpressed in GBM, were identified as tumor-specific antigens for GBM patients. Besides, three immune subtypes (LGG1-LGG3) and eight WGCNA modules were identified in LGG patients. Meanwhile, two immune subtypes (GBM1–GBM2) and 10 WGCNA modules were selected in GBM. The immune characteristics and potential functions between different subtypes were diversity. LGG2 and GBM1 immune subtype were associated with longer overall survival than other subtypes.

Conclusion

In this study, PTBP1 and SLC39A1 are promising antigens for mRNA vaccines development in LGG, and MMP9 and SLC16A3 were potential antigens in GBM. Our analyses indicated that mRNA vaccine immunotherapy was more suitable for LGG2 and GBM1 subtypes. This study was helpful for the development of glioma immunotherapies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41016-022-00301-4.

Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes

Objective

Increasing studies have indicated that senescence was associated with tumorigenesis and progression. Lower-grade glioma (LGG) presented a less invasive nature, however, its treatment efficacy and prognosis prediction remained challenging due to the intrinsic heterogeneity. Therefore, we established a senescence-related signature and investigated its prognostic role in LGGs.

Methods

The gene expression data and clinicopathologic features were from The Cancer Genome Atlas (TCGA) database. The experimentally validated senescence genes (SnGs) from the CellAge database were obtained. Then LASSO regression has been performed to build a prognostic model. Cox regression and Kaplan-Meier survival curves were performed to investigate the prognostic value of the SnG-risk score. A nomogram model has been constructed for outcome prediction. Immunological analyses were further performed. Data from the Chinese Glioma Genome Atlas (CGGA), Repository of Molecular Brain Neoplasia Data (REMBRANDT), and GSE16011 were used for validation.

Results

The 6-SnG signature has been established. The results showed SnG-risk score could be considered as an independent predictor for LGG patients (HR=2.763, 95%CI=1.660-4.599, P<0.001). The high SnG-risk score indicated a worse outcome in LGG (P<0.001). Immune analysis showed a positive correlation between the SnG-risk score and immune infiltration level, and the expression of immune checkpoints. The CGGA datasets confirmed the prognostic role of the SnG-risk score. And Kaplan-Meier analyses in the additional datasets (CGGA, REMBRANDT, and GSE16011) validated the prognostic role of the SnG-signature (P<0.001 for all).

Conclusion

The SnG-related prognostic model could predict the survival of LGG accurately. This study proposed a novel indicator for predicting the prognosis of LGG and provided potential therapeutic targets.

Prognosis and immune response of a cuproptosis-related lncRNA signature in low grade glioma

Cuproptosis is a newly discovered new mechanism of programmed cell death, and its unique pathway to regulate cell death is thought to have a unique role in understanding cancer progression and guiding cancer therapy. However, this regulation has not been studied in low grade glioma (LGG) at present. In this study, data on low grade glioma patients were downloaded from the TCGA database. We screened the genes related to cuproptosis from the published papers and confirmed the lncRNAs related to them. We applied univariate/multivariate, and LASSO regression algorithms, finally identified 11 lncRNAs for constructing prognosis prediction models, and constructed a risk scoring model. The reliability and validity test of the model indicated that the model could well distinguish the prognosis and survival of LGG patients. Furthermore, the analyses of immunotherapy, immune microenvironment, as well as functional enrichment were also performed. Finally, we verified the expression of these six prognostic key lncRNAs using real-time polymerase chain reaction (RT-PCR). In conclusion, this study is the first analysis based on cuproptosis-related lncRNAs in LGG and aims to open up new directions for LGG therapy.

Patient-derived tumor organoids as a platform of precision treatment for malignant brain tumors

Malignant brain tumors consist of malignancies originated primarily within the brain and the metastatic lesions disseminated from other organs. In spite of intensive studies, malignant brain tumors remain to be a medical challenge. Patient-derived organoid (PDO) can recapitulate the biological features of the primary tumor it was derived from and has emerged as a promising drug-screening model for precision therapy. Here we show a proof-of-concept based on early clinical study entailing the organoids derived from the surgically resected tumors of 26 patients with advanced malignant brain tumors enrolled during December 2020 to October 2021. The tumors included nine glioma patients, one malignant meningioma, one primary lymphoma patient, and 15 brain metastases. The primary tumor sites of the metastases included five from the lungs, three from the breasts, two from the ovaries, two from the colon, one from the testis, one of melanoma origin, and one of chondrosarcoma. Out of the 26 tissues, 13 (50%) organoids were successfully generated with a culture time of about 2 weeks. Among these patients, three were further pursued to have the organoids derived from their tumor tissues tested for the sensitivity to different therapeutic drugs in parallel to their clinical care. Our results showed that the therapeutic effects observed by the organoid models were consistent to the responses of these patients to their treatments. Our study suggests that PDO can recapitulate patient responses in the clinic with high potential of implementation in personalized medicine of malignant brain tumors.

COMMD4 is a novel prognostic biomarker and relates to potential drug resistance mechanism in glioma

Background: Glioma as the most frequently discovered tumor affecting the brain shows significant morbidity and fatality rates with unfavorable prognosis. There is an urgent need to find novel therapeutic targets to overcome the low chemotherapeutic efficacy of glioma. This research examined whether the copper-metabolism-domain protein, COMMD4, had predictive and therapeutic significance in glioma.

Methods: Using the freely accessible CGGA (The Chinese Glioma Atlas) and TCGA (The Cancer Genome Atlas) databases, we examined the function of COMMD4 in GBM and LGG. CIBERSORT and TIMER were utilized to assess the associations between COMMD4 and immune cells. The Gene Set Enrichment Analysis (GSEA) was employed to examine the functional data. Furthermore, the link between COMMD4 expression and predicted treatment response was evaluated via CellMiner Cross-Database. Meanwhile, qRT-PCR was conducted to examine COMMD4 expression in human glioma. Finally, Migration and invasion of glioma cells (U-87, U-251) were assessed using transwell assays. R was used to analyze the statistical data.

Results: According to our findings, COMMD4 expression level was higher in patients having grade-dependent glioma who also showed an unfavorable prognosis. Furthermore, qRT-PCR confirmed the high expression of COMMD4 in glioma tissues and cells. Additionally, using integrated correlation analysis, we acquired significant prognostic findings between isocitrate dehydrogenase 1(IDH1) and COMMD4. Meanwhile, a link between COMMD4 and many tumor-infiltrating immune cells was observed. GSEA and drug response analysis revealed the potential mechanism of COMMD4 in drug resistance of glioma.

Conclusion: The current findings validated COMMD4 as a novel biological marker, which might offer insights into the possible drug resistance mechanisms and the impact of the immune microenvironment on glioma. COMMD4 might be used to predict glioma prognosis.

Hepcidin is upregulated and is a potential therapeutic target associated with immunity in glioma

Background

Glioma is the most common primary malignant brain tumor with high mortality and poor prognosis. Hepcidin is a fascinating iron metabolism regulator. However, the prognostic value of hepcidin HAMP in gliomas and its correlation with immune cell infiltration remain unclear. Here, we comprehensively elucidate the prognostic value and potential role of hepcidin in gliomas.

Methods

Hepcidin gene expression and clinical characteristics in glioma were analyzed using the CGGA, TCGA, Rembrandt and Gravendeel glioma databases. A survival analysis was conducted using Kaplan-Meier and Cox regression analyses. A gene set enrichment analysis (GSEA) was conducted to select the pathways significantly enriched for hepcidin associations. The correlations between hepcidin and immune cell infiltration and immunotherapy were analyzed using network platforms such as CIBERSORT and TIMER.

Results

In glioma tissues, the expression of hepcidin was significantly increased. High hepcidin expression is related to grade, age, PRS type, IDH mutation, chemotherapy status and 1p19q codeletion status, which significantly indicates the poor prognosis of glioma patients. Hepcidin can be used as an independent prognostic factor for glioma through the multivariate COX regression analysis. The results of Gene Ontology (GO), Kyoto Encyclopedia of Gene and Genome (KEGG) and gene set enrichment analysis (GSEA) indicated that hepcidin was involved in the immune response. In addition, hepcidin expression was positively correlated with the degree of immune cell infiltration, the expression of various immune cell markers and the efficacy of immunotherapy.

Conclusion

Our results indicate that hepcidin can be used as a candidate biomarker to judge the prognosis and immune cell invasion of gliomas.

TRAF3IP3 promotes glioma progression through the ERK signaling pathway

TRAF3IP3 was reportedly associated with poor prognosis in patients with melanoma; however, its role in glioma is unknown. We aimed to demonstrate the relationship between TRAF3IP3 and glioma and to investigate the potential role of TRAF3IP3 in glioma. Datasets were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We used the Wilcoxon rank-sum test to compared TRAF3IP3 expression in normal and glioma tissues. Kaplan–Meier analysis was performed to evaluate the correlation between TRAF3IP3 and patient survival rate. Gene set enrichment analysis (GSEA) was used to annotate the biological function of TRAF3IP3 in glioma. We also examined the effects of TRAF3IP3 on glioma progression, including characteristics such as cell proliferation, migration, and invasion, using cell proliferation, wound healing, and Transwell assays, respectively, paired with in vitro glioma cell lines and in vivo mouse xenograft models to determine the molecular mechanisms underlying these effects. High TRAF3IP3 expression in glioma tissues was associated with patients with neoplasm cancer tissue source site, and poorer overall survival (OS) (p = 0.03), which was validated using TCGA. GSEA revealed the enrichment of neuroactive ligand–receptor interactions, the olfactory pathway, proteasome pathway, cytokine–cytokine receptor interactions, and calcium signaling pathway in the TRAF3IP3 high-expression phenotype. TRAF3IP3 knockdown markedly suppressed the proliferation, migration, and invasion abilities of U251 glioma cells, whereas TRAF3IP3 overexpression notably promoted the progression of U118 cell tumors. Mechanistic studies revealed that TRAF3IP3 upregulated p-ERK expression in glioma cells. Notably, the ERK signaling pathway inhibitor U0126 drastically attenuated the effects of TRAF3IP3 on p-ERK and markedly blocked its tumor-promoting activity. TRAF3IP3 overexpression also promoted in vivo tumor growth in a nude mouse xenograft model. Collectively, TRAF3IP3 stimulates glioma cell proliferation, migration, and invasion, at least partly by activating the ERK signaling pathway. We hypothesize that TRAF3IP3 may participate in glioma development via the ERK signaling pathway and that elevated TRAF3IP3 expression may serve as a potential biomarker for glioma prognosis.

Novel macrophage-related gene prognostic index for glioblastoma associated with M2 macrophages and T cell dysfunction

This study aims to construct a Macrophage-Related Gene Prognostic Index (MRGPI) for glioblastoma (GBM) and explore the underlying molecular, metabolic, and immunological features. Based on the GBM dataset from The Cancer Genome Atlas (n = 156), 13 macrophage-related hub genes were identified by weighted gene co-expression network (WGCNA) analysis. 5 prognostic genes screened by Kaplan-Meire (K-M) analysis and Cox regression model were used to construct the MRGPI, including GPR84, NCF2, HK3, LILRB2, and CCL18. Multivariate Cox regression analysis found that the MRGPI was an independent risk factor (HR = 2.81, CI95: 1.13-6.98, p = 0.026), leading to an unfavorable outcome for the MRGPI-high group, which was further validated by 4 validation GBM cohorts (n = 728). Thereafter, the molecular, metabolic, and immune features and the clinical implications of the MRGPI-based groups were comprehensively characterized. Gene set enrichment analysis (GSEA) found that immune-related pathways, including inflammatory and adaptive immune response, and activated eicosanoid metabolic pathways were enriched in the MRGPI-high group. Besides, genes constituting the MRGPI was primarily expressed by monocytes and macrophages at single-cell scope and was associated with the alternative activation of macrophages. Moreover, correlation analysis and receiver operating characteristic (ROC) curves revealed the relevance between the MRGPI with the expression of immune checkpoints and T cell dysfunction. Thus, the responsiveness of samples in the MRGPI-high group to immune checkpoint inhibitors (ICI) was detected by algorithms, including Tumor Immune Dysfunction and Exclusion (TIDE) and Submap. In contrast, the MRGPI-low group had favorable outcome, was less immune active and insensitive to ICI. Together, we have developed a promising biomarker to classify the prognosis, metabolic and immune features for GBM, and provide references for facilitating the personalized application of ICI in GBM.

Machine learning identification of cuproptosis and necroptosis-associated molecular subtypes to aid in prognosis assessment and immunotherapy response prediction in low-grade glioma

Both cuproptosis and necroptosis are typical cell death processes that serve essential regulatory roles in the onset and progression of malignancies, including low-grade glioma (LGG). Nonetheless, there remains a paucity of research on cuproptosis and necroptosis-related gene (CNRG) prognostic signature in patients with LGG. We acquired patient data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) and captured CNRGs from the well-recognized literature. Firstly, we comprehensively summarized the pan-cancer landscape of CNRGs from the perspective of expression traits, prognostic values, mutation profiles, and pathway regulation. Then, we devised a technique for predicting the clinical efficacy of immunotherapy for LGG patients. Non-negative matrix factorization (NMF) defined by CNRGs with prognostic values was performed to generate molecular subtypes (i.e., C1 and C2). C1 subtype is characterized by poor prognosis in terms of disease-specific survival (DSS), progression-free survival (PFS), and overall survival (OS), more patients with G3 and tumour recurrence, high abundance of immunocyte infiltration, high expression of immune checkpoints, and poor response to immunotherapy. LASSO-SVM-random Forest analysis was performed to aid in developing a novel and robust CNRG-based prognostic signature. LGG patients in the TCGA and GEO databases were categorized into the training and test cohorts, respectively. A five-gene signature, including SQSTM1, ZBP1, PLK1, CFLAR, and FADD, for predicting OS of LGG patients was constructed and its predictive reliability was confirmed in both training and test cohorts. In both the training and the test datasets (cohorts), higher risk scores were linked to a lower OS rate. The time-dependent ROC curve proved that the risk score had outstanding prediction efficiency for LGG patients in the training and test cohorts. Univariate and multivariate Cox regression analyses showed the CNRG-based prognostic signature independently functioned as a risk factor for OS in LGG patients. Furthermore, we developed a highly reliable nomogram to facilitate the clinical practice of the CNRG-based prognostic signature (AUC > 0.9). Collectively, our results gave a promising understanding of cuproptosis and necroptosis in LGG, as well as a tailored prediction tool for prognosis and immunotherapeutic responses in patients.