Molecular subtyping reveals immune alterations in IDH wild‐type lower‐grade diffuse glioma

Mechanism of action for Troxerutin targeting the sialylation-related gene EGLN3 for the treatment of LUAD

Studies have demonstrated that sialylation changes play a vital part in lung adenocarcinoma (LUAD), yet the specific mechanism is uncertain. Hence, in the present research, we screened sialylation-related biomarkers in LUAD using the bioinformatic strategy, predicted the drugs and performed relevant experiments to explore their role in regulating LUAD. The TCGA-LUAD, GSE31210, and GSE13213 datasets were combined to form LUAD ensemble. The sialylation-related genes (SRGs) linked with LUAD prognosis were determined by univariate Cox regression analysis, and their expressions and mutations in LUAD were analyzed in GSCA database. Then, depending on the consistent clustering of prognostic SRGs, LUAD patients were divided into sialylation-related subgroups, followed by the investigation of survival, immunity, and clinical characteristics in the subgroups. LASSO regression analysis was further employed to identify prognostic gene signatures and to build a sialylation-related model to predict the prognosis of LUAD patients. The gene signature were validated using RT-qPCR and used for predicting target medicines using molecular docking to further investigate the potential therapies for LUAD patients. A total of 26 SRGs in LUAD ensemble were associated with prognosis, and LUAD samples were classified into two sialylation-related subgroups based on these SRGs. Intergroup comparisons revealed that patients in Cluster A had greater survival rates, as well as higher immune infiltration. The risk prognostic model built based on 6 prognostic gene signature was able to effectively predict the survival of LUAD patients. Finally, the experimental findings indicated that Troxerutin exhibits a strong binding energy to the sialylation-related gene EGLN3, which could greatly reduce the growth of LUAD by inhibiting the expression of EGLN3, thus limiting the capacity of LUAD cells in the proliferation, migration, and invasion. Troxerutin could target and lower the expression of sialylation-related gene EGLN3, reducing LUAD cells' ability to proliferate, migrate, and invade, making it an essential reference for LUAD prevention and treatment.

Identification of inflammation-related genes signature to establish a prognostic model in MGMT unmethylated glioblastoma patients

BACKGROUND

Patients with unmethylated O6-methylguanine-DNA methyltransferase promoter (uMGMT) glioblastoma (GBM) have a poor prognosis. Inflammatory response can affect the prognosis, for it may have a significant impact on the tumor microenvironment (TME). This study aims to identify a prognostic signature of inflammation-related genes, which can predict the prognosis of uMGMT GBM patients.

METHODS

We examined the gene expression, somatic mutations, and overall survival of 159 GBM patients with uMGMT using the TCGA and CGGA databases. We identified molecular subtypes of uMGMT GBM patients based on the expression of inflammation-related genes. Furthermore, we determined principal component analysis (PCA), gene ontology (GO) analysis, pathway analysis and immune infiltration analysis between high and low-inflammation subtypes. We also examined the spatial and longitudinal heterogeneity of these two subtypes. The LASSO-Cox analyses were used to develop an inflammation-related prognostic model.

RESULTS

Our findings indicate that patients with uMGMT GBM can be divided into high-inflammation and low-inflammation subtypes. Patients with high levels of inflammation are more likely to develop an immunosuppressive microenvironment, which stimulates the production of immunosuppressive cytokines, immune checkpoints, and immunosuppressive cells. Nine inflammation-related genes (EREG, BDKRB1, DCBLD2, CD14, AHR, CLEC5A, LTA, SLC4A4, and LY6E) were found to have excellent predictive potential for patient survival in the prognostic model.

CONCLUSIONS

In conclusion, we created a new prognostic model including 9 inflammation-related genes. This model has produced meaningful results in evaluating patient prognosis, which may help with future therapeutic strategies for patients with uMGMT GBM.

DSN1 may predict poor prognosis of lower-grade glioma patients and be a potential target for immunotherapy

DSN1 has been previously found to be positively correlated with various cancers. However, the effect of DSN1 or its methylation on the prognosis, molecular characteristics, and immune cell infiltration of low-grade glioma (LGG) has not yet been studied. We obtained 1046 LGG samples from the The Cancer Genome Atlas, The Chinese Glioma Genome Atlas (CGGA) microarray, and CGGA RNA-Seq databases. Bioinformatic methods (gene set enrichment analysis (GSEA), chi-square test, multivariate), and laboratory validation were used to investigate DSN1 in LGG. The expression levels of DSN1 mRNA and protein in LGG were substantially higher than those in normal brain tissue, and their expression was negatively regulated by methylation. The survival time of patients with low expression of DSN1 and cg12601032 hypermethylation was considerably prolonged. DSN1 was a risk factor, and of good diagnostic and prognostic value for LGG. Importantly, the expression of DSN1 is related to many types of tumor-infiltrating immune cells and has a positive correlation with PDL1. DSN1 promoted the activation of multiple cancer-related pathways, such as the cell cycle. Additionally, knockdown of DSN1 substantially inhibited the proliferation and invasion of LGG cells. To the best of our knowledge, this study is the first comprehensive analysis of the mechanism of DSN1 leading to poor prognosis of LGG, which provides a new perspective for revealing the pathogenesis of LGG. DSN1 or its methylation has diagnostic value for the prognosis of glioma, and may become a new biological target of anti-tumor immunotherapy.

The cuproptosis-related signature predicts the prognosis and immune microenvironments of primary diffuse gliomas: a comprehensive analysis

BACKGROUND

Evidence has revealed a connection between cuproptosis and the inhibition of tumor angiogenesis. While the efficacy of a model based on cuproptosis-related genes (CRGs) in predicting the prognosis of peripheral organ tumors has been demonstrated, the impact of CRGs on the prognosis and the immunological landscape of gliomas remains unexplored.

METHODS

We screened CRGs to construct a novel scoring tool and developed a prognostic model for gliomas within the various cohorts. Afterward, a comprehensive exploration of the relationship between the CRG risk signature and the immunological landscape of gliomas was undertaken from multiple perspectives.

RESULTS

Five genes (NLRP3, ATP7B, SLC31A1, FDX1, and GCSH) were identified to build a CRG scoring system. The nomogram, based on CRG risk and other signatures, demonstrated a superior predictive performance (AUC of 0.89, 0.92, and 0.93 at 1, 2, and 3 years, respectively) in the training cohort. Furthermore, the CRG score was closely associated with various aspects of the immune landscape in gliomas, including immune cell infiltration, tumor mutations, tumor immune dysfunction and exclusion, immune checkpoints, cytotoxic T lymphocyte and immune exhaustion-related markers, as well as cancer signaling pathway biomarkers and cytokines.

CONCLUSION

The CRG risk signature may serve as a robust biomarker for predicting the prognosis and the potential viability of immunotherapy responses. Moreover, the key candidate CRGs might be promising targets to explore the underlying biological background and novel therapeutic interventions in gliomas.

A robust primary liver cancer subtype related to prognosis and drug response based on a multiple combined classifying strategy

The recurrence or resistance to treatment of primary liver cancer (PLL) is significantly related to the heterogeneity present within the tumor. In this study, we integrated prognosis risk score, mRNAsi index, and immune characteristics clustering to classify patients. The four subtypes obtained from the combined classification are associated with PLC's prognosis and drug response. In these subtypes, we observed mRNAsiH_ICCA subtype, the intersection between high mRNAsi and immune characteristics clustering A, had the worst prognosis. Specifically, immune characteristics clustering B (ICC_B) had high drug sensitivity in most drugs regardless of the value of mRNAsi. On the other hand, patients with low mRNAsi responded better to ten drugs including KU-55933 and NU7441, while patients with high mRNAsi might benefit from drugs like Leflunomide. By matching the specific characteristics of each combined subtype with the drug-induced cell line expression profile, we identified a group of potential therapeutic drugs that might regulate the expression of disease signature genes. We developed a feasible multiple combined typing strategy, hoping to guide therapeutic selection and promote the development of precision medicine.

WD repeat domain 76 predicts poor prognosis in lower grade glioma and provides an original target for immunotherapy

BACKGROUND

The WD40 repeat (WDR) domain provides scaffolds for numerous protein-protein interactions in multiple biological processes. WDR domain 76 (WDR76) has complex functionality owing to its diversified interactions; however, its mechanism in LGG has not yet been reported.

METHODS

Transcriptomic data from public databases were multifariously analyzed to explore the role of WDR76 in LGG pathology and tumor immunity. Laboratory experiments were conducted to confirm these results.

RESULTS

The results first confirmed that high expression of WDR76 in LGG was not only positively associated with clinical and molecular features of malignant LGG, but also served as an independent prognostic factor that predicted shorter survival in patients with LGG. Furthermore, high expression of WDR76 resulted in the upregulation of oncogenes, such as PRC1 and NUSAP1, and the activation of oncogenic mechanisms, such as the cell cycle and Notch signaling pathway. Finally, WDR76 was shown to be involved in LGG tumor immunity by promoting the infiltration of immune cells, such as M2 macrophages, and the expression of immune checkpoints, such as PDCD1 (encoding PD-1).

CONCLUSIONS

This study shows for the first time the diagnostic and prognostic value of WDR76 in LGG and provides a novel personalized biomarker for future targeted therapy and immunotherapy. Thus, WDR76 may significantly improve the prognosis of patients with LGG.

MrGPS: an m6A-related gene pair signature to predict the prognosis and immunological impact of glioma patients

N6-methyladenosine (m6A) RNA methylation is the predominant epigenetic modification for mRNAs that regulates various cancer-related pathways. However, the prognostic significance of m6A modification regulators remains unclear in glioma. By integrating the TCGA lower-grade glioma (LGG) and glioblastoma multiforme (GBM) gene expression data, we demonstrated that both the m6A regulators and m6A-target genes were associated with glioma prognosis and activated various cancer-related pathways. Then, we paired m6A regulators and their target genes as m6A-related gene pairs (MGPs) using the iPAGE algorithm, among which 122 MGPs were significantly reversed in expression between LGG and GBM. Subsequently, we employed LASSO Cox regression analysis to construct an MGP signature (MrGPS) to evaluate glioma prognosis. MrGPS was independently validated in CGGA and GEO glioma cohorts with high accuracy in predicting overall survival. The average area under the receiver operating characteristic curve (AUC) at 1-, 3- and 5-year intervals were 0.752, 0.853 and 0.831, respectively. Combining clinical factors of age and radiotherapy, the AUC of MrGPS was much improved to around 0.90. Furthermore, CIBERSORT and TIDE algorithms revealed that MrGPS is indicative for the immune infiltration level and the response to immune checkpoint inhibitor therapy in glioma patients. In conclusion, our study demonstrated that m6A methylation is a prognostic factor for glioma and the developed prognostic model MrGPS holds potential as a valuable tool for enhancing patient management and facilitating accurate prognosis assessment in cases of glioma.

CircTTLL13 Promotes TMZ Resistance in Glioma via Modulating OLR1-Mediated Activation of the Wnt/β-Catenin Pathway

Glioma, originating from neuroglial progenitor cells, is a type of intrinsic brain tumor with poor prognosis. temozolomide (TMZ) is the first-line chemotherapeutic agent for glioma. Exploring the mechanisms of circTTLL13 underlying TMZ resistance in glioma is of great significance to improve glioma treatment. Bioinformatics was adopted to identify target genes. The circular structure of circTTLL13 and its high expression in glioma cells were disclosed by quantitative real time-PCR (qRT-PCR) and PCR-agarose gel electrophoresis. Functional experiments proved that oxidized LDL receptor 1 (OLR1) promotes TMZ resistance of glioma cells. CircTTLL13 enhances TMZ resistance of glioma cells via modulating OLR1. Luciferase reporter, RNA-binding protein immunoprecipitation (RIP), RNA pulldown, mRNA stability, N6-methyladenosine (m6A) dot blot and RNA total m6A quantification assays were implemented, indicating that circTTLL13 stabilizes OLR1 mRNA via recruiting YTH N6-methyladenosine RNA binding protein 1 (YTHDF1) and promotes m6A methylation of OLR1 pre-mRNA through recruiting methyltransferase-like 3 (METTL3). TOP/FOP-flash reporter assay and western blot verified that circTTLL13 activates Wnt/β-catenin signaling pathway by regulating OLR1. CircTTLL13 promotes TMZ resistance in glioma through regulating OLR1-mediated Wnt/β-catenin pathway activation. This study offers an insight into the efficacy improvement of TMZ for glioma treatment.

Tyrosine metabolic reprogramming coordinated with the tricarboxylic acid cycle to drive glioma immune evasion by regulating PD-L1 expression

Due to the existence of the blood-brain barrier in glioma, traditional drug therapy has a poor therapeutic outcome. Emerging immunotherapy has been shown to have satisfactory therapeutic effects in solid tumors, and it is clinically instructive to explore the possibility of immunotherapy in glioma. We performed a retrospective analysis of RNA-seq data and clinical information in 1027 glioma patients, utilizing machine learning to explore the relationship between tyrosine metabolizing enzymes and clinical characteristics. In addition, we also assessed the role of tyrosine metabolizing enzymes in the immune microenvironment including immune infiltration and immune evasion. Highly expressed tyrosine metabolizing enzymes 4-hydroxyphenylpyruvate dioxygenase, homogentisate 1,2-dioxygenase, and fumarylacetoacetate hydrolase not only promote the malignant phenotype of glioma but are also closely related to poor prognosis. The expression of tyrosine metabolizing enzymes could distinguish the malignancy degree of glioma. More importantly, tyrosine metabolizing enzymes regulate the adaptive immune process in glioma. Mechanistically, multiple metabolic enzymes remodel fumarate metabolism, promote α-ketoglutarate production, induce programmed death-ligand 1 expression, and help glioma evade immune surveillance. Our data suggest that the metabolic subclass driven by tyrosine metabolism provides promising targets for the immunotherapy of glioma.

Prognostic biomarker CCR6 and its correlation with immune infiltration in cutaneous melanoma

Background

Cutaneous melanoma (CM) is an aggressive type of skin cancer. Even after standard treatment, the recurrence and malignant progression of CM were almost inevitable. The overall survival (OS) of patients with CM varied widely, making it critical for prognostic prediction. Based on the correlation between CCR6 and melanoma incidence, we aimed to investigate the prognostic role of CCR6 and its relationship with immune infiltration in CM.

Methods

We obtained RNA sequencing data from The Cancer Genome Atlas (TCGA) to analyze the CM expression. Functional enrichment analyses, immune infiltration analyses, immune checkpoint analyses, and clinicopathology analyses were performed. Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors. A nomogram model had been developed. Kaplan-Meier survival analysis and log-rank test were used to estimate the relationship between OS and CCR6 expression.

Results

CCR6 was significantly upregulated in CM. Functional enrichment analyses revealed that CCR6 was correlated with immune response. Most immune cells and immune checkpoints were positively correlated with CCR6 expression. Kaplan-Meier analyses showed that high CCR6 expression was associated with a good outcome in CM and its subtypes. Cox regression showed that CCR6 was an independent prognostic factor in patients with CM (HR = 0.550, 95% CI = 0.332-0.912, p<0.05).

Conclusions

CCR6 is considered to be a new prognostic biomarker for patients with CM, and our study provides a potential therapeutic target for CM treatment.

Natural and synthetic compounds for glioma treatment based on ROS-mediated strategy

Glioma is the most frequent and most malignant tumor of the central nervous system (CNS)，accounting for about 50% of all CNS tumor and approximately 80% of the malignant primary tumors in the CNS. Patients with glioma benefit from surgical resection, chemo- and radio-therapy. However these therapeutical strategies do not significantly improve the prognosis, nor increase survival rates owing to restricted drug contribution in the CNS and to the malignant characteristics of glioma. Reactive oxygen species (ROS) are important oxygen-containing molecules that regulate tumorigenesis and tumor progression. When ROS accumulates to cytotoxic levels, this can lead to anti-tumor effects. Multiple chemicals used as therapeutic strategies are based on this mechanism. They regulate intracellular ROS levels directly or indirectly, resulting in the inability of glioma cells to adapt to the damage induced by these substances. In the current review, we summarize the natural products, synthetic compounds and interdisciplinary techniques used for the treatment of glioma. Their possible molecular mechanisms are also presented. Some of them are also used as sensitizers: they modulate ROS levels to improve the outcomes of chemo- and radio-therapy. In addition, we summarize some new targets upstream or downstream of ROS to provide ideas for developing new anti-glioma therapies.

Co-amplified with PDGFRA, IGFBP7 is a prognostic biomarker correlated with the immune infiltrations of glioma

BACKGROUND

A subgroup of glioma carry genetic 4q12 amplification including platelet derived growth factor receptor α (PDGFRA) and insulin like growth factor binding protein 7 (IGFBP7). However, the prognosis of PDGFRA and IGFBP7 in glioma is unclear.

METHODS

The prognosis of PDGFRA and IGFBP7 was determined using cox regression and Kaplan-Meier survival analysis. Pathways associated with IGFBP7 were analyzed through gene set enrichment analysis (GSEA). Immune profiling of glioma was determined using "ESTIMATE" and "TIMER" database.

RESULTS

PDGFRA amplification or expression was not correlated with the outcomes of glioblastoma (GBM). IGFBP7 but not PDGFRA was over-expressed in GBM. IGFBP7 over-expression was correlated with the unfavorable outcomes of GBM. In lower grade glioma (LGG), PDGFRA over-expression was not correlated with the unfavorable prognosis of LGG, while, IGFBP7 was a prognostic biomarker of LGG. LGG patients with IGFBP7 lower expressions had prolonged clinical overall survival. Combination of IDH mutation, LGG grade and IGFBP7 achieved even better prognostic effects in LGG. Moreover, IGFBP7 was over-expressed in glioma patients with wild type IDH or with high grades. IGFBP7 over-expression was correlated with the unfavorable outcomes of glioma. Furthermore, IGFBP7 was hypo-methylated in GBM or LGG patients without IDH mutations. IGFBP7 hyper-methylation was correlated with the lower overall survival of GBM or LGG. LGG patients with wild type IDH and with IGFBP7 hypo-methylation demonstrated even worse prognosis. IGFBP7 was associated with multiple immune-related signaling pathways in GBM or LGG. The stromal score, immune score and the infiltrations of immune cells were also correlated with IGFBP7 and the prognosis of LGG.

CONCLUSIONS

IGFBP7 but not PDGFRA served an ideal prognostic marker and therapeutic target of glioma.

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.

Metabolism-associated molecular classification of uterine corpus endometrial carcinoma

Uterine corpus endometrial carcinoma (UCEC) is one of the most common gynecologic malignancies. Currently, for UCEC cancer, molecular classification based on metabolic gene characteristics is rarely established. Here, we describe the molecular subtype features of UCEC by classifying metabolism-related gene profiles. Therefore, integrative analysis was performed on UCEC patients from the TCGA public database. Consensus clustering of RNA expression data on 2,752 previously reported metabolic genes identified two metabolic subtypes, namely, C1 and C2 subtypes. Two metabolic subtypes for prognostic characteristics, immune infiltration, genetic alteration, and responses to immunotherapy existed with distinct differences. Then, differentially expressed genes (DEGs) among the two metabolic subtypes were also clustered into two subclusters, and the aforementioned features were similar to the metabolic subtypes, supporting that the metabolism-relevant molecular classification is reliable. The results showed that the C1 subtype has high metabolic activity, high immunogenicity, high gene mutation, and a good prognosis. The C2 subtype has some features with low metabolic activity, low immunogenicity, high copy number variation (CNV) alteration, and poor prognosis. Finally, a model was identified, with three gene metabolism-related signatures, which can predict the prognosis. These findings of this study demonstrate a new classification in UCEC based on the metabolic pattern, thereby providing valuable information for understanding UCEC's molecular characteristics.

Immunological profiles of human oligodendrogliomas define two distinct molecular subtypes

BACKGROUND

Human oligodendroglioma presents as a heterogeneous disease, primarily characterized by the isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion. Therapy development for this tumor is hindered by incomplete knowledge of somatic driving alterations and suboptimal disease classification. We herein aim to identify intrinsic molecular subtypes through integrated analysis of transcriptome, genome and methylome.

METHODS

137 oligodendroglioma patients from the Cancer Genome Atlas (TCGA) dataset were collected for unsupervised clustering analysis of immune gene expression profiles and comparative analysis of genome and methylome. Two independent datasets containing 218 patients were used for validation.

FINDINGS

We identified and independently validated two reproducible subtypes associated with distinct molecular characteristics and clinical outcomes. The proliferative subtype, named Oligo1, was characterized by more tumors of CNS WHO grade 3, as well as worse prognosis compared to the Oligo2 subtype. Besides the clinicopathologic features, Oligo1 exhibited enrichment of cell proliferation, regulation of cell cycle and Wnt signaling pathways, and significantly altered genes, such as EGFR, NOTCH1 and MET. In contrast, Oligo2, with favorable outcome, presented increased activation of immune response and metabolic process. Higher T cell/APC co-inhibition and inhibitory checkpoint levels were observed in Oligo2 tumors. Finally, multivariable analysis revealed our classification was an independent prognostic factor in oligodendrogliomas, and the robustness of these molecular subgroups was verified in the validation cohorts.

INTERPRETATION

This study provides further insights into patient stratification as well as presents opportunities for therapeutic development in human oligodendrogliomas.

FUNDING

The funders are listed in the Acknowledgement.

A SERPINE1-Based Immune Gene Signature Predicts Prognosis and Immunotherapy Response in Gastric Cancer

Immune checkpoint inhibitors (ICIs) therapy has been successfully utilized in the treatment of multiple tumors, but only a fraction of patients with gastric cancer (GC) could greatly benefit from it. A recent study has shown that the tumor microenvironment (TME) can greatly affect the effect of immunotherapy in GC. In this study, we established a novel immune risk signature (IRS) for prognosis and predicting response to ICIs in GC based on the TCGA-STAD dataset. Characterization of the TME was explored and further validated to reveal the underlying survival mechanisms and the potential therapeutic targets of GC. The GC patients were stratified into high- and low-risk groups based on the IRS. Patients in the high-risk group, associated with poorer outcomes, were characterized by significantly higher immune function. Further analysis showed higher T cell immune dysfunction and probability of potential immune escape. In vivo, we detected the expressions of SERPINE1 by the quantitative real-time polymerase chain reaction (qPCR)in tumor tissues and adjacent normal tissues. In vitro, knockdown of SERPINE1 significantly attenuated malignant biological behaviors of tumor cells in GC. Our signature can effectively predict the prognosis and response to immunotherapy in patients with GC.

Integrated analysis of inflammatory response subtype-related signature to predict clinical outcomes, immune status and drug targets in lower-grade glioma

Background: The inflammatory response in the tumor immune microenvironment has implications for the progression and prognosis in glioma. However, few inflammatory response-related biomarkers for lower-grade glioma (LGG) prognosis and immune infiltration have been identified. We aimed to construct and identify the prognostic value of an inflammatory response-related signature, immune infiltration, and drug targets for LGG.

Methods: The transcriptomic and clinical data of LGG samples and 200 inflammatory response genes were obtained from public databases. The LGG samples were separated into two inflammatory response-related subtypes based on differentially expressed inflammatory response genes between LGG and normal brain tissue. Next, inflammatory response-related genes (IRRGs) were determined through a difference analysis between the aforementioned two subtypes. An inflammatory response-related prognostic model was constructed using IRRGs by using univariate Cox regression and Lasso regression analyses and validated in an external database (CGGA database). ssGSEA and ESTIMATE algorithms were conducted to evaluate immune infiltration. Additionally, we performed integrated analyses to investigate the correlation between the prognostic signature and N 6-methyladenosine mRNA status, stemness index, and drug sensitivity. We finally selected MSR1 from the prognostic signature for further experimental validation.

Results: A total of nine IRRGs were identified to construct the prognostic signature for LGG. LGG patients in the high-risk group presented significantly reduced overall survival than those in the low-risk group. An ROC analysis confirmed the predictive power of the prognostic model. Multivariate analyses identified the risk score as an independent predictor for the overall survival. ssGSEA revealed that the immune status was definitely disparate between two risk subgroups, and immune checkpoints such as PD-1, PD-L1, and CTLA4 were significantly expressed higher in the high-risk group. The risk score was strongly correlated with tumor stemness and m6A. The expression levels of the genes in the signature were significantly associated with the sensitivity of tumor cells to anti-tumor drugs. Finally, the knockdown of MSR1 suppressed LGG cell migration, invasion, epithelial–mesenchymal transition, and proliferation.

Conclusion: The study constructed a novel signature composed of nine IRRGs to predict the prognosis, potential drug targets, and impact immune infiltration status in LGG, which hold promise for screening prognostic biomarkers and guiding immunotherapy for LGG.

Radiogenomics to characterize the immune-related prognostic signature associated with biological functions in glioblastoma

OBJECTIVES

The tumor microenvironment and immune cell infiltration (ICI) associated with glioblastoma (GBM) play a vital role in cancer development, progression, and prognosis. This study aimed to establish an ICI-related prognostic biomarker and explore the associations between ICI signatures and radiomic features in patients with GBM.

METHODS

The gene expression and survival data of patients with GBM were obtained from three databases. Based on the ICI pattern, an individualized ICI score for each GBM patient was developed in the discovery set (n = 400) and independently verified in the validation set (n = 374). A total of 5915 radiomic features were extracted from the intratumoral and peritumoral regions. Recursive feature elimination and support vector machine methods were performed to select the key features and generate a model predictive of low- or high- ICI scores. The prognostic value of the identified radio genomic model was examined in an independent dataset (n = 149) using imaging and survival data.

RESULTS

We found that higher ICI scores often indicated worse patient prognosis (multivariable hazard ratio: 0.48 and 0.63 in discovery and validation set, respectively) and higher expression levels of immune checkpoint-related genes. A model that combined 11 radiomic features could well distinguish tumors with different ICI scores (AUC = 0.96, accuracy = 94%). This model was proven to be helpful for noninvasive prognostic stratification in an independent validation cohort.

CONCLUSIONS

ICI scores may serve as an effective prognostic biomarker to characterize potential biological processes in patients with GBM. This ICI signature can be evaluated noninvasively through radiogenomic analysis.

KEY POINTS

• Immune cell infiltration (ICI) scores can serve as an effective prognostic biomarker in patients with glioblastoma. • The ICI signature can be evaluated noninvasively through radiomic features derived from the intratumoral and peritumoral regions. • The prognostic value of the radiogenomic model can be verified by independent survival and MRI data.

Tumor-Associated Microenvironment of Adult Gliomas: A Review

The glioma-associated tumor microenvironment involves a multitude of different cells ranging from immune cells to endothelial, glial, and neuronal cells surrounding the primary tumor. The interactions between these cells and glioblastoma (GBM) have been deeply investigated while very little data are available on patients with lower-grade gliomas. In these tumors, it has been demonstrated that the composition of the microenvironment differs according to the isocitrate dehydrogenase status (mutated/wild type), the presence/absence of codeletion, and the expression of specific alterations including H3K27 and/or other gene mutations. In addition, mechanisms by which the tumor microenvironment sustains the growth and proliferation of glioma cells are still partially unknown. Nonetheless, a better knowledge of the tumor-associated microenvironment can be a key issue in the optic of novel therapeutic drug development.

Prognostic Features of the Tumor Immune Microenvironment in Glioma and Their Clinical Applications: Analysis of Multiple Cohorts

Glioma is the most common malignant tumor of the central nervous system. Tumor purity is a source of important prognostic factor for glioma patients, showing the key roles of the microenvironment in glioma prognosis. In this study, we systematically screened functional characterization related to the tumor immune microenvironment and constructed a risk model named Glioma MicroEnvironment Functional Signature (GMEFS) based on eight cohorts. The prognostic value of the GMEFS model was also verified in another two glioma cohorts, glioblastoma (GBM) and low-grade glioma (LGG) cohorts, from The Cancer Genome Atlas (TCGA). Nomograms were established in the training and testing cohorts to validate the clinical use of this model. Furthermore, the relationships between the risk score, intrinsic molecular subtypes, tumor purity, and tumor-infiltrating immune cell abundance were also evaluated. Meanwhile, the performance of the GMEFS model in glioma formation and glioma recurrence was systematically analyzed based on 16 glioma cohorts from the Gene Expression Omnibus (GEO) database. Based on multiple-cohort integrated analysis, risk subpathway signatures were identified, and a drug–subpathway association network was further constructed to explore candidate therapy target regions. Three subpathways derived from Focal adhesion (path: 04510) were identified and contained known targets including platelet derived growth factor receptor alpha (PDGFRA), epidermal growth factor receptor (EGFR), and erb-b2 receptor tyrosine kinase 2 (ERBB2). In conclusion, the novel functional signatures identified in this study could serve as a robust prognostic biomarker, and this study provided a framework to identify candidate therapeutic target regions, which further guide glioma patients’ clinical decision.

New biomarker: the gene HLA-DRA associated with low-grade glioma prognosis

BACKGROUND

Low-grade gliomas (LGG) are WHO grade II tumors presenting as the most common primary malignant brain tumors in adults. Currently, LGG treatment involves either or a combination of surgery, radiation therapy, and chemotherapy. Despite the knowledge of constitutive genetic risk factors contributing to gliomas, the role of single genes as diagnostic and prognostic biomarkers is limited. The aim of the current study is to discover the predictive and prognostic genetic markers for LGG.

METHODS

Transcriptome data and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. We first performed the tumor microenvironment (TME) survival analysis using the Kaplan-Meier method. An analysis was undertaken to screen for differentially expressed genes. The function of these genes was studied by Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Following which a protein-protein interaction network (PPI) was constructed and visualized. Univariate and multivariate COX analyses were performed to obtain the probable prognostic genes. The key genes were selected by an intersection of core and prognostic genes. A clinical correlation analysis of single-gene expression was undertaken. GSEA enrichment analysis was performed to identify the function of key genes. Finally, a single gene-related correlation analysis was performed to identify the core immune cells involved in the development of LGG.

RESULTS

A total of 529 transcriptome data and 515 clinical samples were obtained from the TCGA. Immune cells and stromal cells were found to be significantly increased in the LGG microenvironment. The top five core genes intersected with the top 38 prognostically relevant genes and two key genes were identified. Our analysis revealed that a high expression of HLA-DRA was associated with a poor prognosis of LGG. Correlation analysis of immune cells showed that HLA-DRA expression level was related to immune infiltration, positively related to macrophage M1 phenotype, and negatively related to activation of NK cells.

CONCLUSIONS

HLA-DRA may be an independent prognostic indicator and an important biomarker for diagnosing and predicting survival in LGG patients. It may also be associated with the immune infiltration phenotype in LGG.

Prognostic biomarker SGSM1 and its correlation with immune infiltration in gliomas

Objective

Glioma was the most common type of intracranial malignant tumor. Even after standard treatment, the recurrence and malignant progression of lower-grade gliomas (LGGs) were almost inevitable. The overall survival (OS) of patients with LGG varied widely, making it critical for prognostic prediction. Small G Protein Signaling Modulator 1 (SGSM1) has hardly been studied in gliomas. Therefore, we aimed to investigate the prognostic role of SGSM1 and its relationship with immune infiltration in LGGs.

Methods

We obtained RNA sequencing data from The Cancer Genome Atlas (TCGA) to analyze SGSM1 expression. Functional enrichment analyses, immune infiltration analyses, immune checkpoint analyses, and clinicopathology analyses were performed. Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors. And nomogram model has been developed. Kaplan–Meier survival analysis and log-rank test were used to estimate the relationship between OS and SGSM1 expression. The survival analyses and Cox regression were validated in datasets from the Chinese Glioma Genome Atlas (CGGA).

Results

SGSM1 was significantly down-regulated in LGGs. Functional enrichment analyses revealed SGSM1 was correlated with immune response. Most immune cells and immune checkpoints were negatively correlated with SGSM1 expression. The Kaplan–Meier analyses showed that low SGSM1 expression was associated with a poor outcome in LGG and its subtypes. The Cox regression showed SGSM1 was an independent prognostic factor in patients with LGG (HR = 0.494, 95%CI = 0.311–0.784, P = 0.003).

Conclusion

SGSM1 was considered to be a new prognostic biomarker for patients with LGG. And our study provided a potential therapeutic target for LGG treatment.

CENPF/CDK1 signaling pathway enhances the progression of adrenocortical carcinoma by regulating the G2/M-phase cell cycle

Background

Adrenocortical carcinoma (ACC) is an aggressive and rare malignant tumor and is prone to local invasion and metastasis. And, overexpressed Centromere Protein F (CENPF) is closely related to the oncogenesis of various neoplasms, including ACC. However, the prognosis and exact biological function of CENPF in ACC remains largely unclear.

Methods

In the present essay, the expression patterns and prognostic value of CENPF in ACC were investigated in clinical specimens and public cancer databases, including GEO and TCGA. The potential signaling mechanism of CENPF in ACC was studied based on gene-set enrichment analysis (GSEA). Furthermore, a small RNA interference experiment was conducted to probe the underlying biological function of CENPF in the human ACC cell line, SW13 cells. Lastly, two available therapeutic strategies, including immunotherapy and chemotherapy, have been further explored.

Results

The expression of CENPF in human ACC samples, GEO, and TCGA databases depicted that CENPF was overtly hyper-expressed in ACC patients and positively correlated with tumor stage. The aberrant expression of CENPF was significantly correlated with unfavorable overall survival (OS) in ACC patients. Then, the GSEA analysis declared that CENPF was mainly involved in the G2/M-phase mediated cell cycle and p53 signaling pathway. Further, the in vitro experiment demonstrated that the interaction between CENPF and CDK1 augmented the G2/M-phase transition of mitosis, cell proliferation and might induce p53 mediated anti-tumor effect in human ACC cell line, SW13 cells. Lastly, immune infiltration analysis highlighted that ACC patients with high CENPF expression harbored significantly different immune cell populations, and high TMB/MSI score. The gene-drug interaction network stated that CENPF inhibitors, such as Cisplatin, Sunitinib, and Etoposide, might serve as potential drugs for the therapy of ACC.

Conclusion

The result points out that CENPF is significantly overexpressed in ACC patients. The overexpressed CENPF predicts a poor prognosis of ACC and might augment the progress of ACC. Thus, CENPF and related genes might serve as a novel prognostic biomarker or latent therapeutic target for ACC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03277-y.

Phospholipase Cγ1 (PLCG1) overexpression is associated with tumor growth and poor survival in IDH wild-type lower-grade gliomas in adult patients

Gliomas are the most common and recalcitrant intracranial tumors, approximately a quarter of which are classified as lower-grade gliomas (WHO II-III). Although the prognosis of lower-grade gliomas (LGGs) is significantly better than that of higher-grade gliomas, as a highly heterogeneous tumor type, the prognosis of LGGs varies greatly based on the molecular diagnosis. IDH wild-type used to be regarded as a dismal prognostic biomarker in LGGs; however, several studies revealed that IDH wild-type LGGs might not always be equivalent to glioblastoma (WHO IV). Hence, we hypothesize that underlying biological events in LGGs can result in different prognosis. In our study, transcriptome profiling was performed in 24 samples of LGG, and the results showed that the expression of phospholipase Cγ1 (PLCG1) was significantly correlated with IDH1/2 status and patients' clinical outcome. Furthermore, the cancer genome atlas (TCGA) and the Chinese glioma genome atlas (CGGA) databases verified that elevated PLCG1 expression was associated with tumor progression and poor survival in LGG patients. Moreover, PLCG1-targeted siRNA dramatically affected the growth, migration and invasiveness of IDH wild-type LGG cell lines. In in vitro and in vivo experiments, the PLC-targeted drug significantly suppressed the tumor growth of IDH wild-type LGG cell lines in vitro and tumors in mouse models. Taken together, our results demonstrated that higher PLCG1 expression was associated with tumor growth and worse prognosis in IDH wild-type LGGs and PLCG1 could serve as a potential therapeutic target for IDH wild-type LGG patients.

Multi-Omics Analysis Based on Genomic Instability for Prognostic Prediction in Lower-Grade Glioma

Background: Lower-grade gliomas (LGGs) are a heterogeneous set of gliomas. One of the primary sources of glioma heterogeneity is genomic instability, a novel characteristic of cancer. It has been reported that long noncoding RNAs (lncRNAs) play an essential role in regulating genomic stability. However, the potential relationship between genomic instability and lncRNA in LGGs and its prognostic value is unclear.

Methods: In this study, the LGG samples from The Cancer Genome Atlas (TCGA) were divided into two clusters by integrating the lncRNA expression profile and somatic mutation data using hierarchical clustering. Then, with the differentially expressed lncRNAs between these two clusters, we identified genomic instability-related lncRNAs (GInLncRNAs) in the LGG samples and analyzed their potential function and pathway by co-expression network. Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were conducted to establish a GInLncRNA prognostic signature (GInLncSig), which was assessed by internal and external verification, correlation analysis with somatic mutation, independent prognostic analysis, clinical stratification analysis, and model comparisons. We also established a nomogram to predict the prognosis more accurately. Finally, we performed multi-omics-based analyses to explore the relationship between risk scores and multi-omics data, including immune characteristics, N⁶-methyladenosine (m⁶A), stemness index, drug sensitivity, and gene set enrichment analysis (GSEA).

Results: We identified 52 GInLncRNAs and screened five from them to construct the GInLncSig model (CRNDE, AC025171.5, AL390755.1, AL049749.1, and TGFB2-AS1), which could independently and accurately predict the outcome of patients with LGG. The GInLncSig model was significantly associated with somatic mutation and outperformed other published signatures. GSEA revealed that metabolic pathways, immune pathways, and cancer pathways were enriched in the high-risk group. Multi-omics-based analyses revealed that T-cell functions, m⁶A statuses, and stemness characteristics were significantly disparate between two risk subgroups, and immune checkpoints such as PD-L1, PDCD1LG2, and HAVCR2 were significantly highly expressed in the high-risk group. The expression of GInLncSig prognostic genes dramatically correlated with the sensitivity of tumor cells to chemotherapy drugs.

Conclusion: A novel signature composed of five GInLncRNAs can be utilized to predict prognosis and impact the immune status, m⁶A status, and stemness characteristics in LGG. Furthermore, these lncRNAs may be potential and alternative therapeutic targets.

Correlation between ITGB2 expression and clinical characterization of glioma and the prognostic significance of its methylation in low-grade glioma(LGG)

INTRODUCTION

Glioma is the most common primary tumor in the brain.Integrin beta 2(ITGB2) is a member of the leukocyte integrin family (leukocyte integrin), participating in lymphocyte recycling and homing, cell adhesion, and cell surface-mediated signal transduction. However, few studies on ITGB2 in gliomas have been reported yet.This study first discussed the relationship between ITGB2 expression and clinical characterization of glioma and the prognostic significance of its methylation in low-grade glioma.

METHODS

We collected Clinical data and transcription of glioma patients from TCGA, CGGA, and Rembrant datasets to analyze the differential expression of ITGB2 mRNA in glioma tissues and normal tissues. The box polts to evaluated the expression patterns of ITGB2 in different molecular subtypes. Receiver operating characteristic curve (ROC) were used to evaluate and verify the reliability of the model. Kaplan-Meier survival curves to evaluated the relationship between the level of ITGB2 mRNA expression and overall survival (OS). Using cox regression analysis to verify the ability of ITGB2 as an independent predictor of OS in glioma patients. We use TIMER to analyze and visualize the association between immune infiltration levels and a range of variables. The methylation of GBMLGG patients were obtained from the TCGA database through the biological portal.

RESULTS

ITGB2 can be a potential marker for mesenchymal molecular subtype gliomas. COX regression analysis shows that ITGB2 is an independent predictive marker of OS in malignant glioma patients. Biological processes show that ITGB2 has involved glioma immune-related activities, especially closely related to B cells, CD4+Tcells, macrophages, neutrophils, and dendritic cells. ITGB2 is negatively regulated by ITGB2 methylation, resulting in low expression in LGG tissues. Low expression of ITGB2 and high methylation indicate good OS in patients with LGG. The ITGB2 methylation risk score (ITMRS) obtained from the ITGB2 methylation CpG site can better predict the OS of LGG patients. We used univariate and multivariate cox regression analysis of methylationsites, used the R language predict function to obtain the risk score of these ITGB2 methylation sites(ITMRS).

DISCUSSION

ITGB2 can be used as a potential marker of mesenchymal molecular subtypes of gliomas and as an independent predictive marker of OS in patients with malignant gliomas. The ITMRS we established can be used as an independent prognostic factor for LGG and provide a new idea for the diagnosis and treatment of LGG.

CCNB2 is a novel prognostic factor and a potential therapeutic target in Low-grade glioma (LGG)

Background: Cyclin B2 (CCNB2) is an important component of the cyclin pathway and plays a key role in the occurrence and development of cancer. However, the correlation between prognosis of low-grade glioma (LGG), CCNB2, and tumor infiltrating lymphocytes is not clear. Methods: The expression of CCNB2 in LGG was queried in Gene Expression Profiling Interactive Analysis 2 (GEPIA2) and TIMER databases. The relationships between CCNB2 and the clinicopathological features of LGG were analyzed using the Chinese Glioma Genome Atlas (CGGA) database. The relationship between CCNB2 expression and overall survival (OS) was evaluated by GEPIA2. The correlation between CCNB2 and LGG immune infiltration was analyzed by the TIMER database. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was performed to detect CCNB2 expression. Results: The expression of CCNB2 differed across different tumor tissues, but was higher in LGG than in normal tissues. LGG patients with high expression of CCNB2 have poorer prognosis. The expression of CCNB2 was correlated with age, WHO grade, IDH mutational status, 1p/19q codeletion status, and other clinicopathological features. The expression of CCNB2 in LGG was positively correlated with the infiltration level of B cells, dendritic cells, and macrophages. qRT-PCR results revealed that the expression of CCNB2 in LGG tissues was higher than normal tissues and higher expression of CCNB2 was associated with worse prognosis. Conclusion: CCNB2 may be used as a potential biomarker to determine the prognosis of LGG and is also related to immune infiltration.

Identification of immune subtypes of Ph-neg B-ALL with ferroptosis related genes and the potential implementation of Sorafenib

BACKGROUND

The clinical outcome of Philadelphia chromosome-negative B cell acute lymphoblastic leukemia (Ph-neg B-ALL) varies considerably from one person to another after clinical treatment due to lack of targeted therapies and leukemia's heterogeneity. Ferroptosis is a recently discovered programmed cell death strongly correlated with cancers. Nevertheless, few related studies have reported its significance in acute lymphoblastic leukemia.

METHODS

Herein, we collected clinical data of 80 Ph-neg B-ALL patients diagnosed in our center and performed RNA-seq with their initial bone marrow fluid samples. Throughout unsupervised machine learning K-means clustering with 24 ferroptosis related genes (FRGs), the clustered patients were parted into three variant risk groups and were performed with bioinformatics analysis.

RESULTS

As a result, we discovered significant heterogeneity of both immune microenvironment and genomic variance. Furthermore, the immune check point inhibitors response and potential implementation of Sorafenib in Ph-neg B-ALL was also analyzed in our cohort. Lastly, one prognostic model based on 8 FRGs was developed to evaluate the risk of Ph-neg B-ALL patients.

CONCLUSION

Jointly, our study proved the crucial role of ferroptosis in Ph-neg B-ALL and Sorafenib is likely to improve the survival of high-risk Ph-neg B-ALL patients.

EMS1/DLL4-Notch Signaling Axis Augments Cell Cycle-Mediated Tumorigenesis and Progress in Human Adrenocortical Carcinoma

Adrenocortical carcinoma (ACC) is a rare malignant neoplasm that is prone to local invasion and metastasis. Meanwhile, overexpressed endothelial cell-specific molecule 1 (ESM1) is closely related to tumorigenesis of multitudinous tumors. However, the prognosis value and biological function of ESM1 in ACC remains undefined. In the current essay, the assessment in human ACC samples and multiple public cancer databases suggested that ESM1 was significantly overexpressed in ACC patients. The abnormal expression of ESM1 was evidently correlated with dismal overall survival (OS) in ACC patients. Then, the gene-set enrichment analysis (GSEA) was applied to unravel that ESM1 was mostly involved in cell cycle and Notch4 signaling pathway. Furthermore, in vitro experiment, RNA interference of ESM1 was carried out to state that ESM1 augments CDK1 and p21-mediated G2/M-phase transition of mitosis, cell proliferation via DLL4-Notch signaling pathway in human ACC cell line, SW13 cells. Additionally, two possible or available therapeutic strategies, including immunotherapy and chemotherapy, have been further explored. Immune infiltration analysis highlighted that no significant difference was found in ACC patients between EMS1^high and EMS1^low group for immune checkpoint-related genes. In addition, the overexpression of ESM1 might trigger the accumulation of tumor mutation burden (TMB) during the cell cycle of DNA replication in ACC. The gene-drug interaction network then indicated that ESM1 inhibitors, such as cisplatin, might serve as potential drugs for the therapy of ACC. Collectively, the results asserted that ESM1 and related regulators might act as underlying prognostic biomarkers or novel therapeutic targets for ACC.

Prognostic characterization of immune molecular subtypes in non-small cell lung cancer to immunotherapy

Background

Non-small cell lung cancer (NSCLC) was usually associated with poor prognosis and invalid therapeutical response to immunotherapy due to biological heterogeneity. It is urgent to screen reliable biomarkers, especially immunotherapy-associated biomarkers, that can predict outcomes of these patients.

Methods

Gene expression profiles of 1026 NSCLC patients were collected from The Cancer Genome Atlas (TCGA) datasets with their corresponding clinical and somatic mutation data. Based on immune infiltration scores, molecular clustering classification was performed to identify immune subtypes in NSCLC. After the functional enrichment analysis of subtypes, hub genes were further screened using univariate Cox, Lasso, and multivariate Cox regression analysis, and the risk score was defined to construct the prognostic model. Other microarray data and corresponding clinical information of 603 NSCLC patients from the GEO datasets were applied to conduct random forest models for the prognosis of NSCLC with 100 runs of cross-validation. Finally, external datasets with immunotherapy and chemotherapy were further applied to explore the significance of risk-scores in clinical immunotherapy response for NSCLC patients.

Results

Compared with Subtype-B, the Subtype-A, associated with better outcomes, was characterized by significantly higher stromal and immune scores, T lymphocytes infiltration scores and up-regulation of immunotherapy markers. In addition, we found and validated an eleven -gene signatures for better application of distinguishing high- and low-risk NSCLC patients and predict patients’ prognosis and therapeutical response to immunotherapy. Furthermore, combined with other clinical characteristics based on multivariate Cox regression analysis, we successfully constructed and validated a nomogram to effectively predict the survival rate of NSCLC patients. External immunotherapy and chemotherapy cohorts validated the patients with higher risk-scores exhibited significant therapeutic response and clinical benefits.

Conclusion

These results demonstrated the immunological and prognostic heterogeneity within NSCLC and provided a new clinical application in predicting the prognosis and benefits of immunotherapy for the disease.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-021-01765-3.

Prognostic and Predictive Biomarkers in Gliomas

Gliomas are the most common central nervous system tumors. New technologies, including genetic research and advanced statistical methods, revolutionize the therapeutic approach to the patient and reveal new points of treatment options. Moreover, the 2021 World Health Organization Classification of Tumors of the Central Nervous System has fundamentally changed the classification of gliomas and incorporated many molecular biomarkers. Given the rapid progress in neuro-oncology, here we compile the latest research on prognostic and predictive biomarkers in gliomas. In adult patients, IDH mutations are positive prognostic markers and have the greatest prognostic significance. However, CDKN2A deletion, in IDH-mutant astrocytomas, is a marker of the highest malignancy grade. Moreover, the presence of TERT promoter mutations, EGFR alterations, or a combination of chromosome 7 gain and 10 loss upgrade IDH-wildtype astrocytoma to glioblastoma. In pediatric patients, H3F3A alterations are the most important markers which predict the worse outcome. MGMT promoter methylation has the greatest clinical significance in predicting responses to temozolomide (TMZ). Conversely, mismatch repair defects cause hypermutation phenotype predicting poor response to TMZ. Finally, we discussed liquid biopsies, which are promising diagnostic, prognostic, and predictive techniques, but further work is needed to implement these novel technologies in clinical practice.

Metabolic expression profiling stratifies diffuse lower-grade glioma into three distinct tumour subtypes

BACKGROUND

Lower-grade gliomas (LGGs) show highly metabolic heterogeneity and adaptability. To develop effective therapeutic strategies targeting metabolic processes, it is necessary to identify metabolic differences and define metabolic subtypes. Here, we aimed to develop a classification system based on metabolic gene expression profile in LGGs.

METHODS

The metabolic gene profile of 402 diffuse LGGs from the Cancer Genome Atlas (TCGA) was used for consensus clustering to determine robust clusters of patients, and the reproducibility of the classification system was evaluated in three Chinese Glioma Genome Atlas (CGGA) cohorts. Then, the metadata set for clinical characteristics, immune infiltration, metabolic signatures and somatic alterations was integrated to characterise the features of each subtype.

RESULTS

We successfully identified and validated three highly distinct metabolic subtypes in LGGs. M2 subtype with upregulated carbohydrate, nucleotide and vitamin metabolism correlated with worse prognosis, whereas M1 subtype with upregulated lipid metabolism and immune infiltration showed better outcome. M3 subtype was associated with low metabolic activities and displayed good prognosis. Three metabolic subtypes correlated with diverse somatic alterations. Finally, we developed and validated a metabolic signature with better performance of prognosis prediction.

CONCLUSIONS

Our study provides a new classification based on metabolic gene profile and highlights the metabolic heterogeneity within LGGs.

Expression, methylation and prognostic feature of EMILIN2 in Low-Grade-Glioma

Low-grade gliomas (LGGs) are slow-growing brain cancer in central nervous system neoplasms. EMILIN2 is an extracellular matrix (ECM) protein which could influence the progress of some tumour which is unclear in LGG. In our study, the methylation, expression, prognosis and immune value of EMILIN2 in LGG were analysed through bioinformatics analysis. We analysed the LGG data from The Cancer Genome Atlas (TCGA) and discovered that the EMILIN2 expression, negatively correlated to the EMILIN2 methylation, could predict a poor prognosis and was associated with different clinical parameters. Moreover, univariate and multivariate Cox regression were performed in CGGA, which showed that the EMILIN2 could be an independent prognostic biomarker in LGG. Moreover, EMILIN2 expression showed a correlation with gene makers in some immune cells, which identified the significance of EMILIN2 in immune infiltration. Finally, we used RT-PCR to verify the EMILIN2 expression level in different grades which showed there were significantly different (P < 0.05). Similarly, high expression of EMILIN2 could predict a poor prognosis (P = 0.0078). In conclusion, EMILIN2 could act as an independent prognostic biomarker which might be associated with the malignancy and development of gliomas and play a crucial role in glioma in immune infiltration.

RUNX1 and REXO2 are associated with the heterogeneity and prognosis of IDH wild type lower grade glioma

Based on isocitrate dehydrogenase (IDH) alterations, lower grade glioma (LGG) is divided into IDH mutant and wild type subgroups. However, the further classification of IDH wild type LGG was unclear. Here, IDH wild type LGG patients in The Cancer Genome Atlas and Chinese Glioma Genome Atlas were divided into two sub-clusters using non-negative matrix factorization. IDH wild type LGG patients in sub-cluster2 had prolonged overall survival and low frequency of CDKN2A alterations and low immune infiltrations. Differentially expressed genes in sub-cluster1 were positively correlated with RUNX1 transcription factor. Moreover, IDH wild type LGG patients with higher stromal score or immune score were positively correlated with RUNX1 transcription factor. RUNX1 and its target gene REXO2 were up-regulated in sub-cluster1 and associated with the worse prognosis of IDH wild type LGG. RUNX1 and REXO2 were associated with the higher immune infiltrations. Furthermore, RUNX1 and REXO2 were correlated with the worse prognosis of LGG or glioma. IDH wild type LGG in sub-cluster2 was hyper-methylated. REXO2 hyper-methylation was associated with the favorable prognosis of LGG or glioma. At last, we showed that, age, tumor grade and REXO2 expression were independent prognostic factors in IDH wild type LGG.

In Vitro Glioblastoma Models: A Journey into the Third Dimension

Simple Summary

In this review, the thorny issue of glioblastoma models is addressed, with a focus on 3D in vitro models. In the first part of the manuscript, glioblastoma features and classification are recapitulated, in order to highlight the major critical aspects that should be taken into account when choosing a glioblastoma 3D model. In the second part of the review, the 3D models described in the literature are critically discussed, considering the advantages, disadvantages, and feasibility for each experimental model, in the light of the potential issues that researchers want to address.

Abstract

Glioblastoma multiforme (GBM) is the most lethal primary brain tumor in adults, with an average survival time of about one year from initial diagnosis. In the attempt to overcome the complexity and drawbacks associated with in vivo GBM models, together with the need of developing systems dedicated to screen new potential drugs, considerable efforts have been devoted to the implementation of reliable and affordable in vitro GBM models. Recent findings on GBM molecular features, revealing a high heterogeneity between GBM cells and also between other non-tumor cells belonging to the tumoral niche, have stressed the limitations of the classical 2D cell culture systems. Recently, several novel and innovative 3D cell cultures models for GBM have been proposed and implemented. In this review, we first describe the different populations and their functional role of GBM and niche non-tumor cells that could be used in 3D models. An overview of the current available 3D in vitro systems for modeling GBM, together with their major weaknesses and strengths, is presented. Lastly, we discuss the impact of groundbreaking technologies, such as bioprinting and multi-omics single cell analysis, on the future implementation of 3D in vitro GBM models.

Pan-cancer application of a lung-adenocarcinoma-derived gene-expression-based prognostic predictor

Gene-expression profiling can be used to classify human tumors into molecular subtypes or risk groups, representing potential future clinical tools for treatment prediction and prognostication. However, it is less well-known how prognostic gene signatures derived in one malignancy perform in a pan-cancer context. In this study, a gene-rule-based single sample predictor (SSP) called classifier for lung adenocarcinoma molecular subtypes (CLAMS) associated with proliferation was tested in almost 15 000 samples from 32 cancer types to classify samples into better or worse prognosis. Of the 14 malignancies that presented both CLAMS classes in sufficient numbers, survival outcomes were significantly different for breast, brain, kidney and liver cancer. Patients with samples classified as better prognosis by CLAMS were generally of lower tumor grade and disease stage, and had improved prognosis according to other type-specific classifications (e.g. PAM50 for breast cancer). In all, 99.1% of non-lung cancer cases classified as better outcome by CLAMS were comprised within the range of proliferation scores of lung adenocarcinoma cases with a predicted better prognosis by CLAMS. This finding demonstrates the potential of tuning SSPs to identify specific levels of for instance tumor proliferation or other transcriptional programs through predictor training. Together, pan-cancer studies such as this may take us one step closer to understanding how gene-expression-based SSPs act, which gene-expression programs might be important in different malignancies, and how to derive tools useful for prognostication that are efficient across organs.

Against the Resilience of High-Grade Gliomas: The Immunotherapeutic Approach (Part I)

The resilience of high-grade gliomas (HGGs) against conventional chemotherapies is due to their heterogeneous genetic landscape, adaptive phenotypic changes, and immune escape mechanisms. Innovative immunotherapies have been developed to counteract the immunosuppressive capability of gliomas. Nevertheless, further research is needed to assess the efficacy of the immuno-based approach. The aim of this study is to review the newest immunotherapeutic approaches for glioma, focusing on the drug types, mechanisms of action, clinical pieces of evidence, and future challenges. A PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis)-based literature search was performed on PubMed/Medline and ClinicalTrials.gov databases using the keywords “active/adoptive immunotherapy,” “monoclonal antibodies,” “vaccine,” and “engineered T cell.”, combined with “malignant brain tumor”, “high-grade glioma.” Only articles written in English published in the last 10 years were selected, filtered based on best relevance. Active immunotherapies include systemic temozolomide, monoclonal antibodies, and vaccines. In several preclinical and clinical trials, adoptive immunotherapies, including T, natural killer, and natural killer T engineered cells, have been shown to be potential treatment options for relapsing gliomas. Systemic temozolomide is considered the backbone for newly diagnosed HGGs. Bevacizumab and rindopepimut are promising second-line treatments. Adoptive immunotherapies have been proven for relapsing tumors, but further evidence is needed.

Centromere Protein F (CENPF) Serves as a Potential Prognostic Biomarker and Target for Human Hepatocellular Carcinoma

Overexpression of Centromere Protein F (CENPF) is associated with tumorigenesis of many human malignant tumors. But the molecular mechanism and prognostic value of CENPF in patients with hepatocellular carcinoma (HCC) are still unclear. In this essay, expression of CENPF in HCC tumors were evaluated in a series of databases, including GEO, TCGA, Oncomine, GEPIA, The Human Protein Atlas and Kaplan-Meier plotter. It was apparent that mRNA and protein expression levels of CENPF were significantly increased in patients with HCC and were manifestly associated with the tumor stage of HCC. Aberrant expressions of CENPF were significantly linked with worse overall survival (OS) and progression-free survival (PFS) in HCC patients. Then, immunohistochemistry of CENPF in human HCC samples was carried out to suggest that CENPF protein was over-expressed in HCC tissues, compared with paired adjacent non-cancerous samples. And small interfering RNAs of CENPF in the human HepG2 cells were further performed to reveal that down-regulation of CENPF significantly inhibited cell proliferation, cell migration, and cell invasion, but slightly promoted cell apoptosis in human HepG2 cells. Moreover, the gene-set enrichment analysis (GSEA) was conducted to probe the biology process and molecular signaling pathway of CENPF in HCC. The GSEA analysis pointed out that CENPF was principally enriched in cell cycle and closely related to E2F1 and CDK1 in the regulation of cell cycle, especially during G2/M transition of mitosis in HCC. Additionally, immune infiltration analysis by CIBERSORTx revealed that mutilpe immune cells, including T_reg, etc., were significantly different in HCC samples with CENPF^high, compared with CENPF^low. These results collectively demonstrated that CENPF might serve as a potential prognostic biomarker and novel therapeutic target for HCC. However, further research is needed to validate our findings and promote the clinical application of CENPF in HCC.

Metabolism-Associated Molecular Classification of Colorectal Cancer

The high heterogeneity of colorectal cancer (CRC) is the main clinical challenge for individualized therapies. Molecular classification will contribute to drug discovery and personalized management optimizing. Here, we aimed to characterize the molecular features of CRC by a classification system based on metabolic gene expression profiles. 435 CRC samples from the Genomic Data Commons data portal were chosen as training set while 566 sample in GSE39582 were selected as testing set. Then, a non-negative matrix factorization clustering was performed, and three subclasses of CRC (C1, C2, and C3) were identified in both training set and testing set. Results showed that subclass C1 displayed high metabolic activity and good prognosis. Subclass C2 was associated with low metabolic activities and displayed high immune signatures as well as high expression of immune checkpoint genes. C2 had the worst prognosis among the three subtypes. Subclass C3 displayed intermediate metabolic activity, high gene mutation numbers and good prognosis. Finally, a 27-gene metabolism-related signature was identified for prognosis prediction. Our works deepened the understanding of metabolic hallmarks of CRC, and provided valuable information for “multi-molecular” based personalized therapies.

Characterization and prognostic significance of alternative splicing events in lower-grade diffuse gliomas

Alternative splicing (AS) is assumed to play important roles in the progression and prognosis of cancer. Currently, the comprehensive analysis and clinical relevance of AS in lower-grade diffuse gliomas have not been systematically addressed. Here, we gathered alternative splicing data of lower-grade diffuse gliomas from SpliceSeq. Based on the Percent Spliced In (PSI) values of 515 lower-grade diffuse glioma patients from the Cancer Genome Atlas (TCGA), we performed subtype-differential AS analysis and consensus clustering to determine robust clusters of patients. A total of 48 050 AS events in 10 787 genes in lower-grade diffuse gliomas were profiled. Subtype-differential splicing analysis and functional annotation revealed that spliced genes were significantly enriched in numerous cancer-related biological phenotypes and signalling pathways. Consensus clustering using AS events identified three robust clusters of patients with distinguished pathological and prognostic features. Moreover, each cluster was also associated with distinct genomic alterations. Finally, we developed and validated an AS-related signature with Cox proportional hazards model. The signature, significantly associated with clinical and molecular features, could serve as an independent prognostic factor for lower-grade diffuse gliomas. Thus, our results indicated that AS events could discriminate molecular subtypes and have prognostic impact in lower-grade diffuse gliomas.

Prognostic significance of ARL9 and its methylation in low-grade glioma

This study aimed to determine the value of ARL9 expression or methylation as a biomarker for LGG survival. We investigated the expression, methylation, prognosis and immune significance of ARL9 through bioinformatics analysis. ARL9 is negatively regulated by ARL9 methylation, leading to its low expression in LGG tissues. Both low ARL9 expression and hypermethylation predicted favorable OS and PFS in LGG patients, according to the TCGA database. Cox regression demonstrated that low ARL9 expression and ARL9 hypermethylation were independent biomarkers for OS. Moreover, three other glioma databases were utilized to verify the prognostic role of ARL9 in LGG, and the similar results were reached. A meta-analysis revealed that low ARL9 expression was closely relevant to better OS. Finally, ARL9 expression exhibited a close correlation with some immune cells, especially CD8+ T cells. ARL9 could constitute a promising prognostic biomarker, and probably plays an important role in immune cell infiltration in LGG.

•

This is the first study to report the clinical and prognostic significance of ARL9, a methylation-driven gene，in LGG.

•

Meta-analysis could be used for bioinformatics analysis to assess the overall effect of the gene from different datasets.

•

ARL9 probably plays a role in the infiltration of immune cells, and acts as a promising prognostic marker in LGG patients.