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

Opposite expression of NCOA4 in glioblastoma tissues and cell lines

Recent research has found that ferroptosis is the most prevalent type of programmed cell death in glioma tissues and is associated with malignant progression, poor prognosis, and exacerbated immune suppression in glioblastoma (GBM). In recent years, nuclear receptor coactivator 4 (NCOA4) has been identified as a key protein in ferroptosis, but its expression in GBM tissues remains unclear. We observed an intriguing phenomenon where the expression pattern of NCOA4 was opposite in GBM tissues compared to three GBM cell lines (U87-MG, U251, and LN229), with NCOA4 expression being elevated in brain tissue but decreased in the GBM cells. This observation was further confirmed through bioinformatics analysis and experiments. Based on this finding, we hypothesize that immune cells in GBM tissues may exhibit more pronounced signs of iron depletion compared to tumor cells, which could contribute to the therapeutic resistance of GBM. The increase in NCOA4 observed in tumor tissues does not necessarily reflect increased ferroptosis in tumor cells but might indicate increased ferroptosis in non-tumor cells. This point should be considered when evaluating the efficacy of inducing ferroptosis via NCOA4 in GBM research. This observation could potentially impact the proposed strategy of inducing iron depletion as a treatment for GBM. We recognize the importance of this finding for guiding future GBM research and believe it warrants further investigation. This phenomenon may also be present in other types of tumors.

Lipidomics-driven drug discovery and delivery strategies in glioblastoma

With few viable treatment options, glioblastoma (GBM) is still one of the most aggressive and deadly types of brain cancer. Recent developments in lipidomics have demonstrated the potential of lipid metabolism as a therapeutic target in GBM. The thorough examination of lipids in biological systems, or lipidomics, is essential to comprehending the changed lipid profiles found in GBM, which are linked to the tumor's ability to grow, survive, and resist treatment. The use of lipidomics in drug delivery and discovery is examined in this study, focusing on how it may be used to find new biomarkers, create multi-target directed ligands, and improve drug delivery systems. We also cover the use of FDA-approved medications, clinical trials that use lipid-targeted medicines, and the integration of lipidomics with other omics technologies. This study emphasizes lipidomics as a possible tool in developing more effective treatment methods for GBM by exploring various lipid-centric techniques.

Comprehensive machine learning-based integration develops a novel prognostic model for glioblastoma

In this study, we developed a new prognostic model for glioblastoma (GBM) based on an integrated machine learning algorithm. We used univariate Cox regression analysis to identify prognostic genes by combining six GBM cohorts. Based on the prognostic genes, 10 machine learning algorithms were integrated into 117 algorithm combinations, and the artificial intelligence prognostic signature (AIPS) with the greatest average C-index was chosen. The AIPS was compared with 10 previously published models by univariate Cox analysis and the C-index. We compared the differences in prognosis, tumor immune microenvironment (TIME), and immunotherapy sensitivity between the high and low AIPS score groups. The AIPS based on the random survival forest algorithm with the highest average C-index (0.868) was selected. Compared with the previous 10 prognostic models, our AIPS has the highest C-index. The AIPS was closely linked to the clinical features of GBM. We discovered that patients in the low score group had improved prognoses, a more active TIME, and were more sensitive to immunotherapy. Finally, we verified the expression of several key genes by western blotting and immunohistochemistry. We identified an ideal prognostic signature for GBM, which might provide new insights into stratified treatment approaches for GBM patients.

MT1G induces lipid droplet accumulation through modulation of H3K14 trimethylation accelerating clear cell renal cell carcinoma progression

BACKGROUND

Lipid droplet formation is a prominent histological feature in clear cell renal cell carcinoma (ccRCC), but the significance and mechanisms underlying lipid droplet accumulation remain unclear.

METHODS

Expression and clinical significance of MT1G in ccRCC were analyzed by using TCGA data, GEO data and scRNASeq data. MT1G overexpression or knockdown ccRCC cell lines were constructed and in situ ccRCC model, lung metastasis assay, metabolomics and lipid droplets staining were performed to explore the role of MT1G on lipid droplet accumulation in ccRCC.

RESULTS

Initially, we observed low MT1G expression in ccRCC tissues, whereas high MT1G expression correlated with advanced disease stage and poorer prognosis. Elevated MT1G expression promoted ccRCC growth and metastasis both in vitro and in vivo. Mechanistically, MT1G significantly suppressed acylcarnitine levels and downstream tricarboxylic acid (TCA) cycle activity, resulting in increased fatty acid and lipid accumulation without affecting cholesterol metabolism. Notably, MT1G inhibited H3K14 trimethylation (H3K14me3) modification. Under these conditions, MT1G-mediated H3K14me3 was recruited to the CPT1B promoter through direct interaction with specific promoter regions, leading to reduced CPT1B transcription and translation.

CONCLUSIONS

Our study unveils a novel mechanism of lipid droplet accumulation in ccRCC, where MT1G inhibits CPT1B expression through modulation of H3K14 trimethylation, consequently enhancing lipid droplet accumulation and promoting ccRCC progression. Graphical abstract figure Schematic diagram illustrating MT1G/H3K14me3/CPT1B-mediated lipid droplet accumulation promoted ccRCC progression via FAO inhibition.

Glutathione‑degrading enzymes in the complex landscape of tumors (Review)

Glutathione (GSH)‑degrading enzymes are essential for starting the first stages of GSH degradation. These enzymes include extracellular γ‑glutamyl transpeptidase (GGT) and intracellular GSH‑specific γ‑glutamylcyclotransferase 1 (ChaC1) and 2. These enzymes are essential for cellular activities, such as immune response, differentiation, proliferation, homeostasis regulation and programmed cell death. Tumor tissue frequently exhibits abnormal expression of GSH‑degrading enzymes, which has a key impact on the development and spread of malignancies. The present review summarizes gene and protein structure, catalytic activity and regulation of GSH‑degrading enzymes, their vital roles in tumor development (including regulation of oxidative and endoplasmic reticulum stress, control of programmed cell death, promotion of inflammation and tumorigenesis and modulation of drug resistance in tumor cells) and potential role as diagnostic biomarkers and therapeutic targets.

Emerging Lipid Targets in Glioblastoma

GBM accounts for most of the fatal brain cancer cases, making it one of the deadliest tumor types. GBM is characterized by severe progression and poor prognosis with a short survival upon conventional chemo- and radiotherapy. In order to improve therapeutic efficiency, considerable efforts have been made to target various features of GBM. One of the targetable features of GBM is the rewired lipid metabolism that contributes to the tumor's aggressive growth and penetration into the surrounding brain tissue. Lipid reprogramming allows GBM to acquire survival, proliferation, and invasion benefits as well as supportive modulation of the tumor microenvironment. Several attempts have been made to find novel therapeutic approaches by exploiting the lipid metabolic reprogramming in GBM. In recent studies, various components of de novo lipogenesis, fatty acid oxidation, lipid uptake, and prostaglandin synthesis have been considered promising targets in GBM. Emerging data also suggest a significant role hence therapeutic potential of the endocannabinoid metabolic pathway in GBM. Here we review the lipid-related GBM characteristics in detail and highlight specific targets with their potential therapeutic use in novel antitumor approaches.

Single cell sequencing revealed the mechanism of CRYAB in glioma and its diagnostic and prognostic value

Background

We explored the characteristics of single-cell differentiation data in glioblastoma and established prognostic markers based on CRYAB to predict the prognosis of glioblastoma patients. Aberrant expression of CRYAB is associated with invasive behavior in various tumors, including glioblastoma. However, the specific role and mechanisms of CRYAB in glioblastoma are still unclear.

Methods

We assessed RNA-seq and microarray data from TCGA and GEO databases, combined with scRNA-seq data on glioma patients from GEO. Utilizing the Seurat R package, we identified distinct survival-related gene clusters in the scRNA-seq data. Prognostic pivotal genes were discovered through single-factor Cox analysis, and a prognostic model was established using LASSO and stepwise regression algorithms. Moreover, we investigated the predictive potential of these genes in the immune microenvironment and their applicability in immunotherapy. Finally, in vitro experiments confirmed the functional significance of the high-risk gene CRYAB.

Results

By analyzing the ScRNA-seq data, we identified 28 cell clusters representing seven cell types. After dimensionality reduction and clustering analysis, we obtained four subpopulations within the oligodendrocyte lineage based on their differentiation trajectory. Using CRYAB as a marker gene for the terminal-stage subpopulation, we found that its expression was associated with poor prognosis. In vitro experiments demonstrated that knocking out CRYAB in U87 and LN229 cells reduced cell viability, proliferation, and invasiveness.

Conclusion

The risk model based on CRYAB holds promise in accurately predicting glioblastoma. A comprehensive study of the specific mechanisms of CRYAB in glioblastoma would contribute to understanding its response to immunotherapy. Targeting the CRYAB gene may be beneficial for glioblastoma patients.