Machine learning-based identification of a cell death-related signature associated with prognosis and immune infiltration in glioma.

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.

Association of TNFRSF19 with a TNF family-based prognostic model and subtypes in gliomas using machine learning.

Purpose: TNF family members (TFMs) play a crucial role in different types of cancers, with TNF Receptor Superfamily Member 19 (TNFRSF19) standing out as a particularly important member in this category. Further research is necessary to investigate the potential impact of TFMs on prognosis prediction and to elucidate the function and potential therapeutic targets linked to TNFRSF19 expression in gliomas. Methods: Three databases provided the data on gene expression and clinical information. Fourteen prognostic members were found through univariate Cox analysis and subsequently utilized to construct TFMs-based model in LASSO and multivariate Cox analyses. TFMs-based subtypes based on the expression profile were identified using an unsupervised clustering method. Machine learning algorithm identified key genes linked to prognostic model and subtype. A sequence of immune infiltrations was evaluated using the ssGSEA and ESTIMATE algorithms. Immunohistochemistry was used to examine the patterns of expression and the clinical significance of TNFRSF19. Results: Our development of a prognostic model and subtypes based on the TNF family was successful, resulting in accurate predictions of prognosis. The findings indicate that TNFRSF19 exhibited strong performance. Upregulation of TNFRSF19 was correlated with malignant phenotypes and poor prognosis, which was confirmed through immunohistochemistry. TNFRSF19 played a role in reshaping the immunosuppressive microenvironment in gliomas, and multiple drug-targeted TNFRSF19 molecules were identified. Conclusions: The TMF-based prognostic model and subtype can facilitate treatment decisions for glioma. TNFRSF19 is an outstanding representative of a predictor of prognosis and immunotherapy effect in gliomas.

Aptamers combined with immune checkpoints for cancer detection and targeted therapy: A review.

In recent years, remarkable strides have been made in the field of immunotherapy, which has emerged as a standard treatment for many cancers. As a kind of immunotherapy drug, monoclonal antibodies employed in immune checkpoint therapy have proven beneficial for patients with diverse cancer types. However, owing to the extensive heterogeneity of clinical responses and the complexity and variability of the immune system and tumor microenvironment (TME), accurately predicting its efficacy remains a challenge. Recent advances in aptamers provide a promising approach for monitoring alterations within the immune system and TME, thereby facilitating targeted immunotherapy, particularly focused on immune checkpoint blockade, with enhanced antitumor efficiency. Aptamers have been widely used in tumor cell detection, biosensors, drug discovery, and biomarker screening due to their high specificity and high affinity with their targets. This review aims to comprehensively examine the research status and progress of aptamers in cancer diagnosis and immunotherapy, with a specific emphasis on those related to immune checkpoints. Additionally, we will discuss the future research directions and potential therapeutic targets for aptamer-based immune checkpoint therapy, aiming to provide a theoretical basis for targeting immunotherapy molecules and blocking tumor immune escape.

Integrated profiling identifies ferredoxin 1 as an immune-related biomarker of malignant phenotype in glioma.

Background: Glioma, a highly resistant and recurrent type of central nervous system tumor, poses a significant challenge in terms of effective drug treatments and its associated mortality rates. Despite the discovery of Ferredoxin 1 (FDX1) as a crucial participant in cuproptosis, an innovative mechanism of cellular demise, its precise implications for glioma prognosis and tumor immune infiltration remain inadequately elucidated. Methods: To analyze pan-cancer data, we employed multiple public databases. Gene expression evaluation was performed using tissue microarray (TMA) and single-cell sequencing data. Furthermore, four different approaches were employed to assess the prognostic importance of FDX1 in glioma. We conducted the analysis of differential expression genes (DEGs) and Gene Set Enrichment Analysis (GSEA) to identify immune-related predictive signaling pathways. Somatic mutations were assessed using Tumor Mutation Burden (TMB) and waterfall plots. Immune cell infiltration was evaluated with five different algorithms. Furthermore, we performed in vitro investigations to evaluate the biological roles of FDX1 in glioma. Results: Glioma samples exhibited upregulation of FDX1, which in turn predicted poor prognosis and was positively associated with unfavorable clinicopathological characteristics. Notably, the top four enriched signaling pathways were immune-related, and the discovery revealed a connection between the expression of FDX1 and the frequency of mutations or the TMB. The FDX1_high group exhibited heightened infiltration of immune cells, and there existed a direct association between the expression of FDX1 and the regulation of immune checkpoint. In vitro experiments demonstrated that FDX1 knockdown reduced proliferation, migration, invasion and transition from G2 to M phase in glioma cells. Conclusion: In glioma, FDX1 demonstrated a positive association with the advancement of malignancy and changes in the infiltration of immune cells.

Identification of Anoikis-Related Genes in Spinal Cord Injury: Bioinformatics and Experimental Validation.

Spinal cord injury (SCI) is a serious disease without effective therapeutic strategies. To identify the potential treatments for SCI, it is extremely important to explore the underlying mechanism. Current studies demonstrate that anoikis might play an important role in SCI. In this study, we aimed to identify the key anoikis-related genes (ARGs) providing therapeutic targets for SCI. The mRNA expression matrix of GSE45006 was downloaded from the Gene Expression Omnibus (GEO) database, and the ARGs were downloaded from the Molecular Signatures Database (MSigDB database). Then, the potential differentially expressed ARGs were identified. Next, correlation analysis, gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) analysis were employed for the differentially expressed ARGs. Moreover, miRNA-gene networks were constructed by the hub ARGs. Finally, RNA expression of the top ten hub ARGs was validated in the SCI cell model and rat SCI model. A total of 27 common differentially expressed ARGs were identified at different time points (1, 3, 7, and 14 days) following SCI. The GO and KEGG enrichment analysis of these ARGs indicated several enriched terms related to proliferation, cell cycle, and apoptotic process. The PPI results revealed that most of the ARGs interacted with each other. Ten hub ARGs were further screened, and all the 10 genes were validated in the SCI cell model. In the rat model, only seven genes were validated eventually. We identified 27 differentially expressed ARGs of the SCI through bioinformatic analysis. Seven real hub ARGs (CCND1, FN1, IGF1, MYC, STAT3, TGFB1, and TP53) were identified eventually. These results may expand our understanding of SCI and contribute to the exploration of potential SCI targets.

Construction of functional neural network tissue combining CBD-NT3-modified linear-ordered collagen scaffold and TrkC-modified iPSC-derived neural stem cells for spinal cord injury repair.

Induced pluripotent stem cells (iPSCs) can be personalized and differentiated into neural stem cells (NSCs), thereby effectively providing a source of transplanted cells for spinal cord injury (SCI). To further improve the repair efficiency of SCI, we designed a functional neural network tissue based on TrkC-modified iPSC-derived NSCs and a CBD-NT3-modified linear-ordered collagen scaffold (LOCS). We confirmed that transplantation of this tissue regenerated neurons and synapses, improved the microenvironment of the injured area, enhanced remodeling of the extracellular matrix, and promoted functional recovery of the hind limbs in a rat SCI model with complete transection. RNA sequencing and metabolomic analyses also confirmed the repair effect of this tissue from multiple perspectives and revealed its potential mechanism for treating SCI. Together, we constructed a functional neural network tissue using human iPSCs-derived NSCs as seed cells based on the interaction of receptors and ligands for the first time. This tissue can effectively improve the therapeutic effect of SCI, thus confirming the feasibility of human iPSCs-derived NSCs and LOCS for SCI repair and providing a valuable direction for SCI research.