Ferroptosis is an iron- and lipid reactive oxygen species (ROS)-dependent form of programmed cell death that is distinct from other forms of regulatory cell death at the morphological, biological, and genetic levels. Emerging evidence suggests critical roles for ferroptosis in cell metabolism, the redox status, and various diseases, such as cancers, nervous system diseases, and ischemia-reperfusion injury, with ferroptosis-related proteins. Ferroptosis is inhibited in diverse cancer types and functions as a dynamic tumor suppressor in cancer development, indicating that the regulation of ferroptosis can be utilized as an interventional target for tumor treatment. Small molecules and nanomaterials that reprogram cancer cells to undergo ferroptosis are considered effective drugs for cancer therapy. Here, we systematically summarize the molecular basis of ferroptosis, the suppressive effect of ferroptosis on tumors, the effect of ferroptosis on cellular metabolism and the tumor microenvironment (TME), and ferroptosis-inducing agents for tumor therapeutics. An understanding of the latest progress in ferroptosis could provide references for proposing new potential targets for the treatment of cancers.

Dendritic cells (DCs) are a type of an antigen-presenting cell which undertake a job on capturing antigens coming from pathogens or tumors and presenting to T cells for immune response. The metabolism of DCs controls its development, polarization, and maturation processes and provides energy support for its functions. However, the immune activity of DCs in tumor microenvironment (TME) is inhibited generally. Abnormal metabolism of tumor cells causes metabolic changes in TME, such as hyperglycolysis, lactate and lipid accumulation, acidification, tryptophan deprivation, which limit the function of DCs and lead to the occurrence of tumor immune escape. Combined metabolic regulation with immunotherapy can strengthen the ability of antigen-presentation and T cell activation of DCs, improve the existing anti-tumor therapy, and overcome the defects of DC-related therapies in the current stage, which has great potential in oncology therapy. Therefore, we reviewed the glucose, lipid, and amino acid metabolism of DCs, as well as the metabolic changes after being affected by TME. Together with the potential metabolic targets of DCs, possible anti-tumor therapeutic pathways were summarized.

Background: Although the diagnosis and treatment of glioblastoma (GBM) is significantly improved with recent progresses, there is still a large heterogeneity in therapeutic effects and overall survival. The aim of this study is to analyze gene expressions of transcription factors (TFs) in GBM so as to discover new tumor markers. Methods: Differentially expressed TFs are identified by data mining using public databases. The GBM transcriptome profile is downloaded from The Cancer Genome Atlas (TCGA). The nonnegative matrix factorization (NMF) method is used to cluster the differentially expressed genes to discover hub genes and signal pathways. The TFs affecting the prognosis of GBM are screened by univariate and multivariate COX regression analysis, and the receiver operating characteristic (ROC) curve is determined. The GBM hazard model and nomogram map are constructed by integrating the clinical data. Finally, the TFs involving potential signaling pathways in GBM are screened by Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Results: There are 68 differentially expressed TFs in GBM, of which 43 genes are upregulated and 25 genes are downregulated. NMF clustering analysis suggested that GBM patients are divided into three groups: Clusters A, B, and C. LHX2, MEOX2, SNAI2, and ZNF22 are identified from the above differential genes by univariate/multivariate regression analysis. The risk score of those four genes are calculated based on the beta coefficient of each gene, and we found that the predictive ability of the risk score gradually increased with the prolonged predicted termination time by time-dependent ROC curve analysis. The nomogram results have showed that the integration of risk score, age, gender, chemotherapy, radiotherapy, and 1p/19q can further improve predictive ability towards the survival of GBM. The pathways in cancer, phosphoinositide 3-kinases (PI3K)–Akt signaling, Hippo signaling, and proteoglycans, are highly enriched in high-risk groups by GSEA. These genes are mainly involved in cell migration, cell adhesion, epithelial–mesenchymal transition (EMT), cell cycle, and other signaling pathways by GO and KEGG analysis. Conclusion: The four-factor combined scoring model of LHX2, MEOX2, SNAI2, and ZNF22 can precisely predict the prognosis of patients with GBM.

Aging has a significant role in the proliferation and development of cancers. This study explored the expression profiles, prognostic value, and potential roles of aging-related genes in gliomas. We designed risk score and cluster models based on aging-related genes and glioma cases using LASSO Cox regression analysis, consensus clustering analysis and univariate cox regression analyses. High risk score was related to malignant clinical features and poor prognosis based on 10 datasets, 2953 cases altogether. Genetic alterations analysis revealed that high risk scores were associated with genomic aberrations of aging-related oncogenes. GSVA analysis exhibited the potential function of the aging-related genes. More immune cell infiltration was found in high-risk group cases, and glioma patients in high-risk group may be more responsive to immunotherapy. Knock-down of CTSC, an aging-related gene, can inhibit cell cycle progression, colony formation, cell proliferation and increase cell senescence in glioma cell lines in vitro. Indeed, high expression of CTSC was associated with poor prognosis in glioma cases. In conclusion, this study revealed that aging-related genes have prognostic potential for glioma patients and further identified potential mechanisms for aging-related genes in tumorigenesis and progression in gliomas.

Background: Glioma is the most common malignant tumor of the central nervous system and is associated with a poor prognosis. This study aimed to explore the function of growth factor receptor-bound protein 10(GRB 10) in glioma. Methods: The expression of GRB10 in glioma was determined based on the glioma transcriptome profile downloaded from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Gene Expression Omnibus (GEO) databases. RT-qPCR was performed to detect the expression of GRB10 in tissue samples obtained from 68 glioma patients. The patients were followed up via telephone or in-person outpatient visits to determine survival. Kaplan-Meier survival analyses were used to evaluate the effect of GRB10 on the prognosis of glioma patients. Further, we constructed GRB10 knockdown cell lines were constructed to investigate the effect of GRB10 on glioma. The cell growth, colony formation, cell cycle assay, EdU assay, and tumor formation in xenograft were performed. Results: The expression level of GRB10 was positively correlated to the histological grades of gliomas. In addition, Kaplan-Meier survival curves revealed that glioma patients with lower expression of GRB10 had more prolonged survival. The knockdown of GRB10 was shown to inhibit cell proliferation, colony formation, and tumor formation in the xenograft models. Cell cycle assay revealed that the knockdown of GRB10 can inhibit the cells entering the G2/M phase from the S phase. The analysis of GSEA suggests that the expression of GRB10 was positively correlated with the hypoxia and EMT signaling pathway. Conclusions: Our data revealed that GRB10 regulated tumorigenesis in glioma and played a vital role in promoting the glioma progression, which indicated that GRB10 could be used as a potential prognostic marker.

Objective: Moyamoya disease (MMD) is a unique cerebrovascular occlusive disease characterized by progressive steno-occlusion within the terminal segment of the internal carotid artery. However, good collaterals from an external carotid artery are essential to compensate for the ischemia in moyamoya disease. This study aimed to investigate the transforming growth factor-beta 1 (TGFβ1) in plasma as a potential biomarker for predicting collateral formation in ischemic MMD.Methods: The transcriptome profile downloaded from Gene Expression Omnibus (GEO) was used to analyze the differential expression of genes between the ischemic MMD and the control groups. We prospectively recruited 23 consecutive patients with ischemic MMD that was diagnosed via digital subtraction angiography (DSA). Nine patients with intracranial aneurysms and four healthy people served as controls. The collaterals from the external carotid artery were examined using DSA. We evaluated whether the collateral formation was associated with TGFβ1 in patients with ischemic MMD. Western blot, RT-qPCR, ELISA, and tube formation assay were used to explore the relationship between TGFβ1 and angiogenesis, as well as the potential mechanisms.Results: The mRNA levels of TGFβ1 were upregulated in the patients with ischemic MMD. The plasma TGFβ1 levels were higher in the patients with ischemic MMD than in the aneurysm and healthy patients (p < 0.05). The collateral formation group has higher levels of serum TGFβ1 than the non-collateral formation group (p < 0.05). The levels of vascular endothelial growth factor (VEGF) are positively correlated with TGFβ1 levels in the plasma (R² = 0.6115; p < 0.0001). TGFβ1 regulates VEGF expression via the activation of the TGFβ pathway within HUVEC cells, as well as TGFβ1 stimulating HUVEC cells to secrete VEGF into the cell culture media. An in vitro assay revealed that TGFβ1 promotes angiogenesis within the endothelial cells.Conclusion: Our findings suggest that TGFβ1 plays a vital role in promoting collateral formation by upregulating VEGF expression in ischemic MMD.