Pan-cancer analysis for the prognostic and immunological role of CD47: interact with TNFRSF9 inducing CD8 + T cell exhaustion.

PURPOSE: The research endeavors to explore the implications of CD47 in cancer immunotherapy effectiveness. Specifically, there is a gap in comprehending the influence of CD47 on the tumor immune microenvironment, particularly in relation to CD8 + T cells. Our study aims to elucidate the prognostic and immunological relevance of CD47 to enhance insights into its prospective utilities in immunotherapeutic interventions. METHODS: Differential gene expression analysis, prognosis assessment, immunological infiltration evaluation, pathway enrichment analysis, and correlation investigation were performed utilizing a combination of R packages, computational algorithms, diverse datasets, and patient cohorts. Validation of the concept was achieved through the utilization of single-cell sequencing technology. RESULTS: CD47 demonstrated ubiquitous expression across various cancer types and was notably associated with unfavorable prognostic outcomes in pan-cancer assessments. Immunological investigations unveiled a robust correlation between CD47 expression and T-cell infiltration rather than T-cell exclusion across multiple cancer types. Specifically, the CD47-high group exhibited a poorer prognosis for the cytotoxic CD8 + T cell Top group compared to the CD47-low group, suggesting a potential impairment of CD8 + T cell functionality by CD47. The exploration of mechanism identified enrichment of CD47-associated differentially expressed genes in the CD8 + T cell exhausted pathway in multiple cancer contexts. Further analyses focusing on the CD8 TCR Downstream Pathway and gene correlation patterns underscored the significant involvement of TNFRSF9 in mediating these effects. CONCLUSION: A robust association exists between CD47 and the exhaustion of CD8 + T cells, potentially enabling immune evasion by cancer cells and thereby contributing to adverse prognostic outcomes. Consequently, genes such as CD47 and those linked to T-cell exhaustion, notably TNFRSF9, present as promising dual antigenic targets, providing critical insights into the field of immunotherapy.

Analysis of risk factors for severe acute kidney injury in patients with acute myocardial infarction: A retrospective study.

Background: Patients with acute myocardial infarction (AMI) complicated by acute kidney injury (AKI) tend to have a poor prognosis. However, the exact mechanism of the co-occurrence of the two diseases is unknown. Therefore, this study aims to determine the risk factors for severe AKI in patients with AMI. Methods: A total of 2022 patients were included in the Medical Information Mart for Intensive Care. Variables were identified via univariate logistic regression, and the variables were corrected via multivariate logistic regression. Restricted cubic splines were used to examine the risks associated with the variables. The Kaplan-Meier method was used to compare the risk of severe AKI among the patients. Results: Patients with severe AKI had a higher in-hospital mortality rate (28.6% vs. 9.0%, P < 0.001) and a longer duration of intensive care (6.5 days vs. 2.9 days, P < 0.001). In patients with AMI, the mean systolic blood pressure (SBP); international normalized ratio (INR); the levels of blood urea nitrogen (BUN), glucose, and calcium; and a history of liver disease were found to be the independent risk factors for developing severe AKI after their admission. Increased levels of BUN and blood glucose and a high INR increased the risk of severe AKI; however, increased levels of calcium decreased the risk; SBP presented a U-shaped curve relationship. Conclusions: Patients with severe AKI have a poor prognosis following an episode of AMI. Furthermore, in patients with AMI, SBP; INR; a history of liver disease; and the levels of BUN, glucose, and calcium are the independent risk factors for developing severe AKI after their admission.

Radiation-sensitive genetic prognostic model identifies individuals at risk for radiation resistance in head and neck squamous cell carcinoma.

BACKGROUND: The advantages of radiotherapy for head and neck squamous cell carcinoma (HNSCC) depend on the radiation sensitivity of the patient. Here, we established and verified radiological factor-related gene signature and built a prognostic risk model to predict whether radiotherapy would be beneficial. METHODS: Data from The Cancer Genome Atlas, Gene Expression Omnibus, and RadAtlas databases were subjected to LASSO regression, univariate COX regression, and multivariate COX regression analyses to integrate genomic and clinical information from patients with HNSCC. HNSCC radiation-related prognostic genes were identified, and patients classified into high- and low-risk groups, based on risk scores. Variations in radiation sensitivity according to immunological microenvironment, functional pathways, and immunotherapy response were investigated. Finally, the expression of HNSCC radiation-related genes was verified by qRT-PCR. RESULTS: We built a clinical risk prediction model comprising a 15-gene signature and used it to divide patients into two groups based on their susceptibility to radiation: radiation-sensitive and radiation-resistant. Overall survival was significantly greater in the radiation-sensitive than the radiation-resistant group. Further, our model was an independent predictor of radiotherapy response, outperforming other clinical parameters, and could be combined with tumor mutational burden, to identify the target population with good predictive value for prognosis at 1, 2, and 3 years. Additionally, the radiation-resistant group was more vulnerable to low levels of immune infiltration, which are significantly associated with DNA damage repair, hypoxia, and cell cycle regulation. Tumor Immune Dysfunction and Exclusion scores also suggested that the resistant group would respond less favorably to immunotherapy. CONCLUSIONS: Our prognostic model based on a radiation-related gene signature has potential for application as a tool for risk stratification of radiation therapy for patients with HNSCC, helping to identify candidates for radiation therapy and overcome radiation resistance.

A homologous and molecular dual-targeted biomimetic nanocarrier for EGFR-related non-small cell lung cancer therapy.

The abnormal activation of epidermal growth factor receptor (EGFR) drives the development of non-small cell lung cancer (NSCLC). The EGFR-targeting tyrosine kinase inhibitor osimertinib is frequently used to clinically treat NSCLC and exhibits marked efficacy in patients with NSCLC who have an EGFR mutation. However, free osimertinib administration exhibits an inadequate response in vivo, with only ∼3% patients demonstrating a complete clinical response. Consequently, we designed a biomimetic nanoparticle (CMNP@Osi) comprising a polymeric nanoparticle core and tumor cell-derived membrane-coated shell that combines membrane-mediated homologous and molecular targeting for targeted drug delivery, thereby supporting a dual-target strategy for enhancing osimertinib efficacy. After intravenous injection, CMNP@Osi accumulates at tumor sites and displays enhanced uptake into cancer cells based on homologous targeting. Osimertinib is subsequently released into the cytoplasm, where it suppresses the phosphorylation of upstream EGFR and the downstream AKT signaling pathway and inhibits the proliferation of NSCLC cells. Thus, this dual-targeting strategy using a biomimetic nanocarrier can enhance molecular-targeted drug delivery and improve clinical efficacy.

Combining autophagy and immune characterizations to predict prognosis and therapeutic response in lung adenocarcinoma.

Background: Autophagy, a key regulator of programmed cell death, is critical for maintaining the stability of the intracellular environment. Increasing evidence has revealed the clinical importance of interactions between autophagy and immune status in lung adenocarcinoma. The present study evaluated the potential of autophagy-immune-derived biomarkers to predict prognosis and therapeutic response in patients with lung adenocarcinoma. Methods: Patients from the GSE72094 dataset were randomized 7:3 to a training set and an internal validation set. Three independent cohorts, TCGA, GSE31210, and GSE37745, were used for external verification. Unsupervised hierarchical clustering based on autophagy- and immune-associated genes was used to identify autophagy- and immune-associated molecular patterns, respectively. Significantly prognostic autophagy-immune genes were identified by LASSO analysis and by univariate and multivariate Cox regression analyses. Differences in tumor immune microenvironments, functional pathways, and potential therapeutic responses were investigated to differentiate high-risk and low-risk groups. Results: High autophagy status and high immune status were associated with improved overall survival. Autophagy and immune subtypes were merged into a two-dimensional index to characterize the combined prognostic classifier, with 535 genes defined as autophagy-immune-related differentially expressed genes (DEGs). Four genes (C4BPA, CD300LG, CD96, and S100P) were identified to construct an autophagy-immune-related prognostic risk model. Survival and receiver operating characteristic (ROC) curve analyses showed that this model was significantly prognostic of survival. Patterns of autophagy and immune genes differed in low- and high-risk patients. Enrichment of most immune infiltrating cells was greater, and the expression of crucial immune checkpoint molecules was higher, in the low-risk group. TIDE and immunotherapy clinical cohort analysis predicted that the low-risk group had more potential responders to immunotherapy. GO, KEGG, and GSEA function analysis identified immune- and autophagy-related pathways. Autophagy inducers were observed in patients in the low-risk group, whereas the high-risk group was sensitive to autophagy inhibitors. The expression of the four genes was assessed in clinical specimens and cell lines. Conclusions: The autophagy-immune-based gene signature represents a promising tool for risk stratification in patients with lung adenocarcinoma, guiding individualized targeted therapy or immunotherapy.

Coal fly ash is a major carbon flux in the Chang Jiang (Yangtze River) basin.

Fly ash-the residuum of coal burning-contains a considerable amount of fossilized particulate organic carbon (FOCash) that remains after high-temperature combustion. Fly ash leaks into natural environments and participates in the contemporary carbon cycle, but its reactivity and flux remained poorly understood. We characterized FOCash in the Chang Jiang (Yangtze River) basin, China, and quantified the riverine FOCash fluxes. Using Raman spectral analysis, ramped pyrolysis oxidation, and chemical oxidation, we found that FOCash is highly recalcitrant and unreactive, whereas shale-derived FOC (FOCrock) was much more labile and easily oxidized. By combining mass balance calculations and other estimates of fly ash input to rivers, we estimated that the flux of FOCash carried by the Chang Jiang was 0.21 to 0.42 Mt C⋅y-1 in 2007 to 2008-an amount equivalent to 37 to 72% of the total riverine FOC export. We attributed such high flux to the combination of increasing coal combustion that enhances FOCash production and the massive construction of dams in the basin that reduces the flux of FOCrock eroded from upstream mountainous areas. Using global ash data, a first-order estimate suggests that FOCash makes up to 16% of the present-day global riverine FOC flux to the oceans. This reflects a substantial impact of anthropogenic activities on the fluxes and burial of fossil organic carbon that has been made less reactive than the rocks from which it was derived.