Molecular subtyping of glioblastoma based on immune-related genes for prognosis

Histone acetylation risk model predicts prognosis and guides therapy selection in glioblastoma: implications for chemotherapy and anti-CTLA-4 immunotherapy

BACKGROUND

Glioblastoma is characterized by high aggressiveness, frequent recurrence, and poor prognosis. Histone acetylation-associated genes have been implicated in its occurrence and development, yet their predictive ability in glioblastoma prognosis remains unclear.

RESULTS

This study constructs a histone acetylation risk model using Cox and LASSO regression analyses to evaluate glioblastoma prognosis. We assessed the model's prognostic ability with univariate and multivariate Cox regression analyses. Additionally, immune infiltration was evaluated using ESTIMATE and TIMER algorithms, and the SubMAP algorithm was utilized to predict responses to CTLA4 inhibitor. Multiple drug databases were applied to assess drug sensitivity in high- and low-risk groups. Our results indicate that the histone acetylation risk model is independent and reliable in predicting prognosis.

CONCLUSIONS

Low-risk patients showed higher immune activity and longer overall survival, suggesting anti-CTLA4 immunotherapy suitability, while high-risk patients might benefit more from chemotherapy. This model could guide personalized therapy selection for glioblastoma patients.

Deciphering glycosylation-driven prognostic insights and therapeutic prospects in glioblastoma through a comprehensive regulatory model

The oncogenesis and development of glioblastoma multiforme have been linked to glycosylation modifications, which are common post-translational protein modifications. Abnormal glycosyltransferase development leads to irregular glycosylation patterns, which hold clinical significance for GB prognosis. By utilizing both single-cell and bulk data, we developed a scoring system to assess glycosylation levels in GB. Moreover, a glycosylation-based signature was created to predict GB outcomes and therapy responsiveness. The study led to the development of an glyco-model incorporating nine key genes. This risk assessment tool effectively stratified GB patients into two distinct groups. Extensive validation through ROC analysis, RMST, and Kaplan-Meier (KM) survival analysis emphasized the model's robust predictive capabilities. Additionally, a nomogram was constructed to predict survival rates at specific time intervals. The research revealed substantial disparities in immune cell infiltration between low-risk and high-risk groups, characterized by differences in immune cell abundance and elevated immune scores. Notably, the glyco-model predicted diverse responses to immune checkpoint inhibitors and drug therapies, with high-risk groups exhibiting a preference for immune checkpoint inhibitors and demonstrated superior responses to drug treatments. Furthermore, the study identified two potential drug targets and utilized Connectivity Map analysis to pinpoint promising therapeutic agents. Clofarabine and YM155 were identified as potent candidates for the treatment of high-risk GB. Our well-crafted glyco-model effectively discriminates patients by calculating the risk score, accurately predicting GB outcomes, and significantly enhancing prognostic assessment while identifying novel immunotherapeutic and chemotherapeutic strategies for GB treatment.

Machine learning unveils immune-related signature in multicenter glioma studies

In glioma molecular subtyping, existing biomarkers are limited, prompting the development of new ones. We present a multicenter study-derived consensus immune-related and prognostic gene signature (CIPS) using an optimal risk score model and 101 algorithms. CIPS, an independent risk factor, showed stable and powerful predictive performance for overall and progression-free survival, surpassing traditional clinical variables. The risk score correlated significantly with the immune microenvironment, indicating potential sensitivity to immunotherapy. High-risk groups exhibited distinct chemotherapy drug sensitivity. Seven signature genes, including IGFBP2 and TNFRSF12A, were validated by qRT-PCR, with higher expression in tumors and prognostic relevance. TNFRSF12A, upregulated in GBM, demonstrated inhibitory effects on glioma cell proliferation, migration, and invasion. CIPS emerges as a robust tool for enhancing individual glioma patient outcomes, while IGFBP2 and TNFRSF12A pose as promising tumor markers and therapeutic targets.

Cuproptosis in glioblastoma: unveiling a novel prognostic model and therapeutic potential

Glioblastoma, a notably aggressive brain tumor, is characterized by a brief survival period and resistance to conventional therapeutic approaches. With the recent identification of "Cuproptosis," a copper-dependent apoptosis mechanism, this study aimed to explore its role in glioblastoma prognosis and potential therapeutic implications. A comprehensive methodology was employed, starting with the identification and analysis of 65 cuproptosis-related genes. These genes were subjected to differential expression analyses between glioblastoma tissues and normal counterparts. A novel metric, the "CP-score," was devised to quantify the cuproptosis response in glioblastoma patients. Building on this, a prognostic model, the CP-model, was developed using Cox regression techniques, designed to operate on both bulk and single-cell data. The differential expression analysis revealed 31 genes with distinct expression patterns in glioblastoma. The CP-score was markedly elevated in glioblastoma patients, suggesting an intensified cuproptosis response. The CP-model adeptly stratified patients into distinct risk categories, unveiling intricate associations between glioblastoma prognosis, immune response pathways, and the tumor's immunological environment. Further analyses indicated that high-risk patients, as per the CP-model, exhibited heightened expression of certain immune checkpoints, suggesting potential therapeutic targets. Additionally, the model hinted at the possibility of personalized therapeutic strategies, with certain drugs showing increased efficacy in high-risk patients. The CP-model offers a promising tool for glioblastoma prognosis and therapeutic strategy development, emphasizing the potential of Cuproptosis in cancer treatment.

Small Molecule Tyrosine Kinase Inhibitors (TKIs) for Glioblastoma Treatment

In the last decade, many small molecules, usually characterized by heterocyclic scaffolds, have been designed and synthesized as tyrosine kinase inhibitors (TKIs). Among them, several compounds have been tested at preclinical and clinical levels to treat glioblastoma multiforme (GBM). GBM is the most common and aggressive type of cancer originating in the brain and has an unfavorable prognosis, with a median survival of 15-16 months and a 5-year survival rate of 5%. Despite recent advances in treating GBM, it represents an incurable disease associated with treatment resistance and high recurrence rates. For these reasons, there is an urgent need for the development of new pharmacological agents to fight this malignancy. In this review, we reported the compounds published in the last five years, which showed promising activity in GBM preclinical models acting as TKIs. We grouped the compounds based on the targeted kinase: first, we reported receptor TKIs and then, cytoplasmic and peculiar kinase inhibitors. For each small molecule, we included the chemical structure, and we schematized the interaction with the target for some representative compounds with the aim of elucidating the mechanism of action. Finally, we cited the most relevant clinical trials.

Somatic POLE Mutation and Ultra-Hypermutated Genotype in a De Novo High-Grade, Isocitrate Dehydrogenase Wild-Type Glioma: Treatment Implications

Pembrolizumab leads to a durable response in ultra-hypermutated, high-grade, glioma.

Immune Escape in Glioblastoma: Mechanisms of Action and Implications for Immune Checkpoint Inhibitors and CAR T-Cell Therapy

Glioblastoma, the most common primary brain cancer in adults, is characterized by a poor prognosis and resistance to standard treatments. The advent of immunotherapy has revolutionized the treatment of several cancers in recent years but has failed to demonstrate benefit in patients with glioblastoma. Understanding the mechanisms by which glioblastoma exerts tumor-mediated immune suppression in both the tumor microenvironment and the systemic immune landscape is a critical step towards developing effective immunotherapeutic strategies. In this review, we discuss the current understanding of immune escape mechanisms in glioblastoma that compromise the efficacy of immunotherapies, with an emphasis on immune checkpoint inhibitors and chimeric antigen receptor T-cell therapy. In parallel, we review data from preclinical studies that have identified additional therapeutic targets that may enhance overall treatment efficacy in glioblastoma when administered alongside existing immunotherapies.

Drug resistance in glioblastoma: from chemo- to immunotherapy

As the most common and aggressive type of primary brain tumor in adults, glioblastoma is estimated to end over 10,000 lives each year in the United States alone. Stand treatment for glioblastoma, including surgery followed by radiotherapy and chemotherapy (i.e., Temozolomide), has been largely unchanged since early 2000. Cancer immunotherapy has significantly shifted the paradigm of cancer management in the past decade with various degrees of success in treating many hematopoietic cancers and some solid tumors, such as melanoma and non-small cell lung cancer (NSCLC). However, little progress has been made in the field of neuro-oncology, especially in the application of immunotherapy to glioblastoma treatment. In this review, we attempted to summarize the common drug resistance mechanisms in glioblastoma from Temozolomide to immunotherapy. Our intent is not to repeat the well-known difficulty in the area of neuro-oncology, such as the blood-brain barrier, but to provide some fresh insights into the molecular mechanisms responsible for resistance by summarizing some of the most recent literature. Through this review, we also hope to share some new ideas for improving the immunotherapy outcome of glioblastoma treatment.

Comprehensively analysis of immunophenotyping signature in triple-negative breast cancer patients based on machine learning

Immunotherapy is a promising strategy for triple-negative breast cancer (TNBC) patients, however, the overall survival (OS) of 5-years is still not satisfactory. Hence, developing more valuable prognostic signature is urgently needed for clinical practice. This study established and verified an effective risk model based on machine learning methods through a series of publicly available datasets. Furthermore, the correlation between risk signature and chemotherapy drug sensitivity were also performed. The findings showed that comprehensive immune typing is highly effective and accurate in assessing prognosis of TNBC patients. Analysis showed that IL18R1, BTN3A1, CD160, CD226, IL12B, GNLY and PDCD1LG2 are key genes that may affect immune typing of TNBC patients. The risk signature plays a robust ability in prognosis prediction compared with other clinicopathological features in TNBC patients. In addition, the effect of our constructed risk model on immunotherapy response was superior to TIDE results. Finally, high-risk groups were more sensitive to MR-1220, GSK2110183 and temsirolimus, indicating that risk characteristics could predict drug sensitivity in TNBC patients to a certain extent. This study proposes an immunophenotype-based risk assessment model that provides a more accurate prognostic assessment tool for patients with TNBC and also predicts new potential compounds by performing machine learning algorithms.

Glioblastoma treatment slowly moves toward change: novel druggable targets and translational horizons in 2022

INTRODUCTION

Glioblastoma (GBM) is the most common primary brain tumor in adults. GBM treatment options have been the same for the past 30 years and have only modestly extended survival, despite aggressive multimodal treatments. The progressively better knowledge of GBM biology and a comprehensive analysis of its genomic profile have elucidated GBM heterogeneity, contributing to a more effective molecular classification and to the development of innovative targeted therapeutic approaches.

AREAS COVERED

This article reports all the noteworthy innovations for immunotherapy and targeted therapy, providing insights into the current advances in trial designs, including combination therapies with immuno-oncology agents and target combinations.

EXPERT OPINION

GBM molecular heterogeneity and brain anatomical characteristics critically restrain drug effectiveness. Nevertheless, stimulating insights for future research and drug development come from innovative treatment strategies for GBM, such as multi-specific 'off-the-shelf' CAR-T therapy, oncolytic viral therapy and autologous dendritic cell vaccination. Disappointing results from targeted therapies-clinical trials are mainly due to complex interferences between signaling pathways and biological processes leading to drug resistance: hence, it is imperative in the future to develop combinatorial approaches and multimodal therapies.

Models of ultrasonic radiomics and clinical characters for lymph node metastasis assessment in thyroid cancer: a retrospective study

Background

Preoperative prediction of cervical lymph node metastasis in papillary thyroid carcinoma provided a basis for tumor staging and treatment decision. This study aimed to investigate the utility of machine learning and develop different models to preoperatively predict cervical lymph node metastasis based on ultrasonic radiomic features and clinical characteristics in papillary thyroid carcinoma nodules.

Methods

Data from 400 papillary thyroid carcinoma nodules were included and divided into training and validation group. With the help of machine learning, clinical characteristics and ultrasonic radiomic features were extracted and selected using randomforest and least absolute shrinkage and selection operator regression before classified by five classifiers. Finally, 10 models were built and their area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, positive predictive value and negative predictive value were measured.

Results

Among the 10 models, RF-RF model revealed the highest area under curve (0.812) and accuracy (0.7542) in validation group. The top 10 variables of it included age, seven textural features, one shape feature and one first-order feature, in which eight were high-dimensional features.

Conclusions

RF-RF model showed the best predictive performance for cervical lymph node metastasis. And the importance features selected by it highlighted the unique role of higher-dimensional statistical methods for radiomics analysis.

Radiated glioblastoma cell-derived exosomal circ_0012381 induce M2 polarization of microglia to promote the growth of glioblastoma by CCL2/CCR2 axis

Background

Radiotherapy is the primary therapeutic option for glioblastoma. Some studies proved that radiotherapy increased the release of exosomes from cells. The mechanism by which these exosomes modify the phenotype of microglia in the tumor microenvironment to further determine the fate of irradiated glioblastoma cells remains to be elucidated.

Methods

We erected the co-culture system of glioblastoma cells and microglia. After radiation, we analyzing the immunophenotype of microglia and the proliferation of radiated glioblastoma cells. By whole transcriptome sequencing, we analyzed of circRNAs in exosomes from glioblastoma cells and microglia. We used some methods, which included RT-PCR, dual-luciferase reporter, et al., to identify how circ_0012381 from radiated glioblastoma cell-derived exosomes regulated the immunophenotype of microglia to further affect the proliferation of radiated glioblastoma cells.

Results

Radiated glioblastoma cell-derived exosomes markedly induced M2 microglia polarization. These M2-polarized microglia promoted the proliferation of irradiated glioblastoma cells. Circ_0012381 expression was increased in the irradiated glioblastoma cells, and circ_0012381 entered the microglia via exosomes. Circ_0012381 induced M2 microglia polarization by sponging with miR-340-5p to increase ARG1 expression. M2-polarized microglia suppressed phagocytosis and promoted the growth of the irradiated glioblastoma cells by CCL2/CCR2 axis. Compared with the effects of radiotherapy alone, the inhibition of exosomes significantly inhibited the growth of irradiated glioblastoma cells in a zebrafish model.

Conclusions

Our data suggested that the inhibition of exosome secretion might represent a potential therapeutic strategy to increase the efficacy of radiotherapy in patients with glioblastoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03607-0.

Development of a prognostic signature based on immune-related genes and the correlation with immune microenvironment in breast cancer

Breast cancer (BC) is an inflammatory tumor caused by a variety of pathological factors, and is still the most common malignant tumor in women. Immune-related genes (IRGs) play a prominent role in the oncogenesis and progression of BC, and are of tumor-specific expression patterns that would benefit the prognosis evaluation. However, there were no systematic studies concerning the possibilities of IRGs in BC prognosis. In this study, the Cancer Genome Atlas (TCGA) database was used to integrate the expression profiles of IRG with the overall survival (OS) rate of 1039 breast cancer patients. The Cox regression analysis was used to predict the survival-related IRGs in BC. Then, we successfully screened a total of 6 IRGs, including PSME2, ULBP2, IGHE, SCG2, SDC1, and SSTR1, and accordingly constructed a prognosis prediction model of BC. Based on the IRG-related model, the BC patients were divided into high- and low-risk groups, and the association between the prognostic model and tumor immune microenvironment (TME) was further explored. The prognostic model reflected the infiltration of various immune cells. Moreover, the low-risk group was found to be with higher immunophenoscore and distinct mutation signatures compared with the high-risk group. The histological validation showed that SDC1, as well as M2 macrophage biomarker CD206, were both of higher abundance in BC samples of high-risk patients, compared with those of low-risk patients. Our results identify the clinically significant IRGs and demonstrate the importance of the IRG-based immune prognostic model in BC monitoring, prognosis prediction, and therapy.

A Novel Extracellular Matrix Gene-Based Prognostic Model to Predict Overall Survive in Patients With Glioblastoma

Background: Glioblastoma (GBM), one of the most prevalent brain tumor types, is correlated with an extremely poor prognosis. The extracellular matrix (ECM) genes could activate many crucial pathways that facilitate tumor development. This study aims to provide online models to predict GBM survival by ECM genes.

Methods: The associations of ECM genes with the prognosis of GBM were analyzed, and the significant prognosis-related genes were used to develop the ECM index in the CGGA dataset. Furthermore, the ECM index was then validated on three datasets, namely, GSE16011, TCGA-GBM, and GSE83300. The prognosis difference, differentially expressed genes, and potential drugs were obtained. Multiple machine learning methods were selected to construct the model to predict the survival status of GBM patients at 6, 12, 18, 24, 30, and 36 months after diagnosis.

Results: Five ECM gene signatures (AEBP1, F3, FLNC, IGFBP2, and LDHA) were recognized to be associated with the prognosis. GBM patients were divided into high– and low–ECM index groups with significantly different overall survival rates in four datasets. High–ECM index patients exhibited a worse prognosis than low–ECM index patients. Four small molecules (podophyllotoxin, lasalocid, MG-262, and nystatin) that might reduce GBM development were predicted by the Cmap dataset. In the independent dataset (GSE83300), the maximum values of prediction accuracy at 6, 12, 18, 24, 30, and 36 months were 0.878, 0.769, 0.748, 0.720, 0.705, and 0.868, respectively. These machine learning models were provided on a publicly accessible, open-source website (https://ospg.shinyapps.io/OSPG/).

Conclusion: In summary, our findings indicated that ECM genes were prognostic indicators for patient survival. This study provided an online server for the prediction of survival curves of GBM patients.

Identification of Immune-Related Risk Characteristics and Prognostic Value of Immunophenotyping in TNBC

Background: Triple-negative breast cancer (TNBC) is not sensitive to targeted therapy with HER-2 monoclonal antibody and endocrine therapy due to lack of ER, PR, and HER-2 receptors. TNBC is a breast cancer subtype with the worst prognosis and the highest mortality rate compared with other subtypes. Materials and Methods: Breast cancer-related data were retrieved from The Cancer Genome Atlas (TCGA) database, and 116 cases of triple-negative breast cancer were identified from the data. GSE31519 dataset was retrieved from Gene Expression Omnibus (GEO) database, comprising a total of 68 cases with TNBC. Survival analysis was performed based on immune score, infiltration score and mutation score to explore differences in prognosis of different immune types. Analysis of differentially expressed genes was conducted and GSEA analysis based on these genes was conducted to explore the potential mechanism. Results: The findings showed that comprehensive immune typing is highly effective and accurate in assessing prognosis of TNBC patients. Analysis showed that MMP9, CXCL9, CXCL10, CXCL11 and CD7 are key genes that may affect immune typing of TNBC patients and play an important role in prediction of prognosis in TNBC patients. Conclusion: The current study presents an evaluation system based on immunophenotyping, which provides a more accurate prognostic evaluation tool for TNBC patients. Differentially expressed genes can be targeted to improve treatment of TNBC.

Glioma: molecular signature and crossroads with tumor microenvironment

In patients with glioblastoma, the average survival time with current treatments is short, mainly due to recurrences and resistance to therapy. This insufficient treatment success is, in large parts, due to the tremendous molecular heterogeneity of gliomas, which affects the overall prognosis and response to therapies and plays a vital role in gliomas’ grading. In addition, the tumor microenvironment is a major player for glioma development and resistance to therapy. Active communication between glioma cells and local or neighboring healthy cells and the immune environment promotes the cancerogenic processes and contributes to establishing glioma stem cells, which drives therapy resistance. Besides genetic alterations in the primary tumor, tumor-released factors, cytokines, proteins, extracellular vesicles, and environmental influences like hypoxia provide tumor cells the ability to evade host tumor surveillance machinery and promote disease progression. Moreover, there is increasing evidence that these players affect the molecular biological properties of gliomas and enable inter-cell communication that supports pro-cancerogenic cell properties. Identifying and characterizing these complex mechanisms are inevitably necessary to adapt therapeutic strategies and to develop novel measures. Here we provide an update about these junctions where constant traffic of biomolecules adds complexity in the management of glioblastoma.