Genomic instability is a hallmark of cancer and may encourage the formation, heterogeneity, and spread of tumors. Several recent studies have shown that genes associated with genomic instability can aid in predicting the prognoses of patients with tumors. However, the gene screening methodology and the process of prognostic modeling are incomplete. We applied a quantitative integrated genomic instability framework to identify genes associated with genomic instability in The Cancer Genome Atlas glioma cohort. A comprehensive machine learning-based technique was used to create a genomic instability-related signature (GIRS). GIRS aided in predicting patient prognosis with greater accuracy and clinical benefits than classical clinical and molecular pathological features of gliomas. Compared with the 126 published models, GIRS had better predictive and generalization properties. In addition, GIRS reflects the degree of genomic instability and can be used as a new indicator of genomic instability. In addition, the GIRS can be used to recommend treatments for patients undergoing radiotherapy and chemotherapy to obtain the best therapeutic effects. We successfully identified genomic instability-related genes in gliomas and developed a GIRS risk model, a novel marker of genomic instability, to effectively predict the prognosis of patients with glioma.
Abstract Background Glioma stands out as the most malignant ailment affecting the central nervous system. Regulated cell death, orchestrated by a multitude of genes, serves as a pivotal determinant in shaping cellular destiny and significantly contributes to tumor advancement. However, there is a dearth of literature delving into the evolution of glioma disease through the prism of cell death patterns. Hence, our objective is to delve into the pertinent molecular mechanisms underlying glioma, with a specific focus on the potential role of regulated cell death. Results Different patterns of regulated cell death collectively contribute to the progression of glioma. Clusters characterized by relatively specific high expression of alkalosis and netotic cell death exhibit relatively malignant clinical features. Through differential gene screening, we constructed a prognostic signature consisting of genes such as TIMP1. This model demonstrates good prognostic predictive ability, with its scoring reflecting the progression of glioma. Finally, experimental validation of TIMP1 confirms its involvement in the progression of malignant cells. Conclusion These findings provide new insights into understanding the relationship between regulated cell death and glioma development and identify novel biomarkers may help to guiding precise treatments to glioma.
Glioma is the common histological subtype of malignancy in the central nervous system, with high morbidity and mortality. Glioma cancer stem cells (CSCs) play essential roles in tumor recurrence and treatment resistance. Thus, exploring the stem cell-related genes and subtypes in glioma is important. In this study, we collected the RNA-sequencing (RNA-seq) data and clinical information of glioma patients from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. With the differentially expressed genes (DEGs) and weighted gene correlation network analysis (WGCNA), we identified 86 mRNA expression-based stemness index (mRNAsi)-related genes in 583 samples from TCGA RNA-seq dataset. Furthermore, these samples from TCGA database could be divided into two significantly different subtypes with different prognoses based on the mRNAsi corresponding gene, which could also be validated in the CGGA database. The clinical characteristics and immune cell infiltrate distribution of the two stemness subtypes are different. Then, functional enrichment analyses were performed to identify the different gene ontology (GO) terms and pathways in the two different subtypes. Moreover, we constructed a stemness subtype-related risk score model and nomogram to predict the prognosis of glioma patients. Finally, we selected one gene (ETV2) from the risk score model for experimental validation. The results showed that ETV2 can contribute to the invasion, migration, and epithelial-mesenchymal transition (EMT) process of glioma. In conclusion, we identified two distinct molecular subtypes and potential therapeutic targets of glioma, which could provide new insights for the development of precision diagnosis and prognostic prediction for glioma patients.
Background Glioma, the most prevalent malignant intracranial tumor, poses a significant threat to patients due to its high morbidity and mortality rates, but its prognostic indicators remain inaccurate. Although TRAF-associated NF-kB activator (TANK) interacts and cross-regulates with cytokines and microenvironmental immune cells, it is unclear whether TANK plays a role in the immunologically heterogeneous gliomas. Methods TANK mRNA expression patterns in public databases were analyzed, and qPCR and IHC were performed in an in-house cohort to confirm the clinical significance of TANK. Then, we systematically evaluated the relationship between TANK expression and immune characteristics in the glioma microenvironment. Additionally, we evaluated the ability of TANK to predict treatment response in glioma. TANK-associated risk scores were developed by LASSO-Cox regression and machine learning, and their prognostic ability was tested. Results TANK was specifically overexpressed in glioma and enriched in the malignant phenotype, and its overexpression was related to poor prognosis. The presence of a tumor microenvironment that is immunosuppressive was evident by the negative correlations between TANK expression and immunomodulators, steps in the cancer immunity cycle, and immune checkpoints. Notably, treatment for cancer may be more effective when immunotherapy is combined with anti-TANK therapy. Prognosis could be accurately predicted by the TANK-related risk score. Conclusions High expression of TANK is associated with the malignant phenotype of glioma, as it shapes an immunosuppressive tumor microenvironment. Additionally, TANK can be used as a predictive biomarker for responses to various treatments and prognosis.
Despite advancements in breast cancer treatment, therapeutic resistance, and tumor recurrence continue to pose formidable challenges. Therefore, a deep knowledge of the intricate interplay between the tumor and the immune system is necessary. In the pursuit of combating breast cancer, the awakening of antitumor immunity has been proposed as a compelling avenue. Tumor stroma in breast cancers contains multiple stromal and immune cells that impact the resistance to therapy and also the expansion of malignant cells. Activating or repressing these stromal and immune cells, as well as their secretions can be proposed for exhausting resistance mechanisms and repressing tumor growth. NK cells and T lymphocytes are the prominent components of breast tumor immunity that can be triggered by adjuvants for eradicating malignant cells. However, stromal cells like endothelial and fibroblast cells, as well as some immune suppressive cells, consisting of premature myeloid cells, and some subsets of macrophages and CD4+ T lymphocytes, can dampen antitumor immunity in favor of breast tumor growth and therapy resistance. This review article aims to research the prospect of harnessing the power of drugs, adjuvants, and nanoparticles in awakening the immune reactions against breast malignant cells. By investigating the immunomodulatory properties of pharmacological agents and the synergistic effects of adjuvants, this review seeks to uncover the mechanisms through which antitumor immunity can be triggered. Moreover, the current review delineates the challenges and opportunities in the translational journey from bench to bedside.
Water-dispersible and anticorrosion nanocomposites have attracted extensive attention. In this study, tea polyphenol (TP)/graphene (GE) was fabricated with a one-step route. The preparation and modification of graphene nanosheets was carried out by graphene employing tea polyphenols as reduction and functionalization reagents. Our study adopted a nontoxic reductant without an extra functionalization reagent. This method is convenient, inexpensive, and environmentally friendly. The final functionalized graphene nanosheets had a single-layer structure. For evaluating performance, Raman spectroscopy was adapted for evaluating π–π interactions between TP and graphene. Elemental and structural composition was analyzed using X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). Sample morphology was characterized by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Results indicated that the TP could effectively augment the dispersive performance of graphene in the solution. The durable anticorrosion capacity of the epoxy matrix noticeably increased after adding the appropriate amount of tea polyphenols–graphene (TPG) (0.3 wt.%). Electrochemical impedance spectroscopy (EIS) studies showed that the impedance of artificial defects was enhanced. The anticorrosion property was attributed to the uniform dispersion of graphene by adding TP.
Abstract Accumulating evidence suggests that a wide variety of cell deaths are deeply involved in cancer immunity. However, their roles in glioma have not been explored. We employed a logistic regression model with the shrinkage regularization operator (LASSO) Cox combined with seven machine learning algorithms to analyse the patterns of cell death (including cuproptosis, ferroptosis, pyroptosis, apoptosis and necrosis) in The Cancer Genome Atlas (TCGA) cohort. The performance of the nomogram was assessed through the use of receiver operating characteristic (ROC) curves and calibration curves. Cell‐type identification was estimated by using the cell‐type identification by estimating relative subsets of known RNA transcripts (CIBERSORT) and single sample gene set enrichment analysis methods. Hub genes associated with the prognostic model were screened through machine learning techniques. The expression pattern and clinical significance of MYD88 were investigated via immunohistochemistry (IHC). The cell death score represents an independent prognostic factor for poor outcomes in glioma patients and has a distinctly superior accuracy to that of 10 published signatures. The nomogram performed well in predicting outcomes according to time‐dependent ROC and calibration plots. In addition, a high‐risk score was significantly related to high expression of immune checkpoint molecules and dense infiltration of protumor cells, these findings were associated with a cell death‐based prognostic model. Upregulated MYD88 expression was associated with malignant phenotypes and undesirable prognoses according to the IHC. Furthermore, high MYD88 expression was associated with poor clinical outcomes and was positively related to CD163, PD‐L1 and vimentin expression in the in‐horse cohort. The cell death score provides a precise stratification and immune status for glioma. MYD88 was found to be an outstanding representative that might play an important role in glioma.
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