Correlation between Heteroatom Coordination and Hydrogen Evolution for Single-site Pt on Carbon-based Nanocages.

The electrocatalytic performance of single-site catalysts (SSCs) is closely correlated with the electronic structure of metal atoms. Herein we construct a series of Pt SSCs on heteroatom-doped hierarchical carbon nanocages, which exhibit increasing hydrogen evolution reaction (HER) activities along S-doped, P-doped, undoped and N-doped supports. Theoretical simulation indicates a multi-H-atom adsorption process on Pt SSCs due to the low coordination, and a reasonable descriptor is figured out to evaluate the HER activities. Relative to C-coordinated Pt, N-coordinated Pt has higher reactivity due to the electron transfer of N-to-Pt, which enriches the density of states of Pt 5d orbital near the Fermi level and facilitates the capturing of protons, just the opposite to the situations for P- and S-coordinated ones. The stable N-coordinated Pt originates from the kinetic stability throughout the multi-H-atom adsorption process. This finding provides a significant guidance for rational design of advanced Pt SSCs on carbon-based supports.

Identification and validation of a novel prognostic signature based on mitochondria and oxidative stress related genes for glioblastoma.

BACKGROUND: Mitochondria represent a major source of reactive oxygen species (ROS) in cells, and the direct increase in ROS content is the primary cause of oxidative stress, which plays an important role in tumor proliferation, invasion, angiogenesis, and treatment. However, the relationship between mitochondrial oxidative stress-related genes and glioblastoma (GBM) remains unclear. This study aimed to investigate the value of mitochondria and oxidative stress-related genes in the prognosis and therapeutic targets of GBM. METHODS: We retrieved mitochondria and oxidative stress-related genes from several public databases. The LASSO regression and Cox analyses were utilized to build a risk model and the ROC curve was used to assess its performance. Then, we analyzed the correlation between the model and immunity and mutation. Furthermore, CCK8 and EdU assays were utilized to verify the proliferative capacity of GBM cells and flow cytometry was used to analyze apoptosis rates. Finally, the JC-1 assay and ATP levels were utilized to detect mitochondrial function, and the intracellular ROS levels were determined using MitoSOX and BODIPY 581/591 C11. RESULTS: 5 mitochondrial oxidative stress-related genes (CTSL, TXNRD2, NUDT1, STOX1, CYP2E1) were screened by differential expression analysis and Cox analysis and incorporated in a risk model which yielded a strong prediction accuracy (AUC value = 0.967). Furthermore, this model was strongly related to immune cell infiltration and mutation status and could identify potential targeted therapeutic drugs for GBM. Finally, we selected NUDT1 for further validation in vitro. The results showed that NUDT1 was elevated in GBM, and knockdown of NUDT1 inhibited the proliferation and induced apoptosis of GBM cells, while knockdown of NUDT1 damaged mitochondrial homeostasis and induced oxidative stress in GBM cells. CONCLUSION: Our study was the first to propose a prognostic model of mitochondria and oxidative stress-related genes, which provided potential therapeutic strategies for GBM patients.

Identification of MDK as a Hypoxia- and Epithelial-Mesenchymal Transition-Related Gene Biomarker of Glioblastoma Based on a Novel Risk Model and In Vitro Experiments.

BACKGROUND: Tumor cells are commonly exposed to a hypoxic environment, which can easily induce the epithelial-mesenchymal transition (EMT) of tumor cells, further affecting tumor proliferation, invasion, metastasis, and drug resistance. However, the predictive role of hypoxia and EMT-related genes in glioblastoma (GBM) has not been investigated. METHODS: Intersection genes were identified by weighted correlation network analysis (WGCNA) and differential expression analyses, and a risk model was further constructed by LASSO and Cox analyses. Clinical, immune infiltration, tumor mutation, drug treatment, and enrichment profiles were analyzed based on the risk model. The expression level of the MDK gene was tested using RT-PCR, immunohistochemistry, and immunofluorescence. CCK8 and EdU were employed to determine the GBM cells' capacity for proliferation while the migration and invasion ability were detected by a wound healing assay and transwell assay, respectively. RESULTS: Based on the GBM data of the TCGA and GTEx databases, 58 intersection genes were identified, and a risk model was constructed. The model was verified in the CGGA cohort, and its accuracy was confirmed by the ROC curve (AUC = 0.807). After combining clinical subgroups, univariate and multivariate Cox regression analyses showed that risk score and age were independent risk factors for GBM patients. Furthermore, our subsequent analysis of immune infiltration, tumor mutation, and drug treatment showed that risk score and high- and low-risk groups were associated with multiple immune cells, mutated genes, and drugs. Enrichment analysis indicated that the differences between high- and low-risk groups were manifested in tumor-related pathways, including the PI3K-AKT and JAK-STAT pathways. Finally, in vivo experiments proved that the hypoxia environment promoted the expression of MDK, and MDK knockdown reduced the proliferation, migration, and EMT of GBM cells induced by hypoxia. CONCLUSIONS: Our novel prognostic correlation model provided more potential treatment strategies for GBM patients.

The role of m6A modification in the risk prediction and Notch1 pathway of Alzheimer's disease.

N6-methyladenosine (m6A) methylation and abnormal immune responses are implicated in neurodegenerative diseases, yet their relationship in Alzheimer's disease (AD) remains unclear. We obtained AD datasets from GEO databases and used AD mouse and cell models, observing abnormal expression of m6A genes in the AD group, alongside disruptions in the immune microenvironment. Key m6A genes (YTHDF2, LRPPRC, and FTO) selected by machine learning were associated with the Notch pathway, with FTO and Notch1 displaying the strongest correlation. Specifically, FTO expression decreased and m6A methylation of Notch1 increased in AD mouse and cell models. We further silenced FTO expression in HT22 cells, resulting in upregulation of the Notch1 signaling pathway. Additionally, increased Notch1 expression in dendritic cells heightened inflammatory cytokine secretion in vitro. These results suggest that reduced FTO expression may contribute to the pathogenesis of AD by activating the Notch1 pathway to interfere with the immune response.

The role of lactate metabolism-related LncRNAs in the prognosis, mutation, and tumor microenvironment of papillary thyroid cancer.

Background: Lactate, a byproduct of glucose metabolism, is primarily utilized for gluconeogenesis and numerous cellular and organismal life processes. Interestingly, many studies have demonstrated a correlation between lactate metabolism and tumor development. However, the relationship between long non-coding RNAs (lncRNAs) and lactate metabolism in papillary thyroid cancer (PTC) remains to be explored. Methods: Lactate metabolism-related lncRNAs (LRLs) were obtained by differential expression and correlation analyses, and the risk model was further constructed by least absolute shrinkage and selection operator analysis (Lasso) and Cox analysis. Clinical, immune, tumor mutation, and enrichment analyses were performed based on the risk model. The expression level of six LRLs was tested using RT-PCR. Results: This study found several lncRNAs linked to lactate metabolism in both The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) datasets. Using Cox regression analysis, 303 lactate LRLs were found to be substantially associated with prognosis. Lasso was done on the TCGA cohort. Six LRLs were identified as independent predictive indicators for the development of a PTC prognostic risk model. The cohort was separated into two groups based on the median risk score (0.39717 -0.39771). Subsequently, Kaplan-Meier survival analysis and multivariate Cox regression analysis revealed that the high-risk group had a lower survival probability and that the risk score was an independent predictive factor of prognosis. In addition, a nomogram that can easily predict the 1-, 3-, and 5-year survival rates of PTC patients was established. Furthermore, the association between PTC prognostic factors and tumor microenvironment (TME), immune escape, as well as tumor somatic mutation status was investigated in high- and low-risk groups. Lastly, gene expression analysis was used to confirm the differential expression levels of the six LRLs. Conclusion: In conclusion, we have constructed a prognostic model that can predict the prognosis, mutation status, and TME of PTC patients. The model may have great clinical significance in the comprehensive evaluation of PTC patients.

Hierarchical Dual Single-Atom Catalysts with Coupled CoN4 and NiN4 Moieties for Industrial-Level CO2 Electroreduction to Syngas.

Renewable-driven electrochemical CO2 reduction reaction (CO2RR) to syngas is an encouraging alternative strategy to traditional fossil fuel-based syngas production, and the development of industrial-level electrocatalysts is vital. Herein, based on theoretical optimization of metal species, hierarchical CoxNi1-x-N-C dual single-atom catalyst (DSAC) with individual NiN4 (CO preferential) and CoN4 (H2 preferential) moieties was constructed by a two-step pyrolysis route. The Co0.5Ni0.5-N-C exhibits a stable CO Faradaic efficiency of 50 ± 5% and an industrial-level current density of 101-365 mA cm-2 in an ultrawide potential window of -0.5 to -1.1 V. The CO/H2 ratio of syngas can be conveniently tuned by regulating the Co/Ni ratio. The coupled effect of NiN4 and CoN4 moieties under a local high-pH microenvironment is responsible for the regulation of the CO/H2 selectivity and yield for the CoxNi1-x-N-C catalyst, which is not present in the mixed Co-N-C and Ni-N-C catalyst. This study provides a promising DSAC strategy for achieving industrial-level syngas production via CO2RR.

Interfacing MnO and FeCo alloy inside N-doped carbon hierarchical porous nanospheres derived from metal-organic framework to boost high-performance oxygen reduction for Zn-air batteries.

Efficient and strong non-precious metal catalysts are urgently needed for the oxygen reduction reaction (ORR). Here, a facile hydrothermal-pyrolysis process was implemented to engineer CoFe-MnO heterointerfaces encapsulated in N-doped carbon (CFM-NC) nanospheres with a metal-organic framework (MOF) as the precursor. Due to heterointerfaces and hierarchical porosity, CFM-NC-800 exhibited superior ORR activity (half-wave potential of 0.86 V) and durability (30 000 s). Importantly, CFM-NC-800-based Zn-air batteries (ZABs) were capable of extending the outstanding performance, with a high power density of 260 mW cm-2 and a specific capacity of 812 mA h g-1. Furthermore, the CFM-NC-800-RuO2-based ZABs showed remarkable stability (480 cycles), outperforming Pt/C-RuO2 (360 cycles). This work highlights the effects of CoFe-MnO heterointerfaces and hierarchical porosity in ORR electrocatalysis, thereby providing a new avenue for energy conversion and storage.

Identification and validation of a 17-gene signature to improve the survival prediction of gliomas.

Gliomas are one of the most frequent types of nervous system tumours and have significant morbidity and mortality rates. As a result, it is critical to fully comprehend the molecular mechanism of glioma to predict prognosis and target gene therapy. The goal of this research was to discover the hub genes of glioma and investigate their prognostic and diagnostic usefulness. In this study, we collected mRNA expression profiles and clinical information from glioma patients in the TCGA, GTEx, GSE68848, and GSE4920 databases. WGCNA and differential expression analysis identified 170 DEGs in the collected datasets. GO and KEGG pathway analyses revealed that DEGs were mainly enriched in gliogenesis and extracellular matrix. LASSO was performed to construct prognostic signatures in the TCGA cohort, and 17 genes were used to build risk models and were validated in the CGGA database. The ROC curve confirmed the accuracy of the prognostic signature. Univariate and multivariate Cox regression analyses showed that all independent risk factors for glioma except gender. Next, we performed ssGSEA to demonstrate a high correlation between risk score and immunity. Subsequently, 7 hub genes were identified by the PPI network and found to have great drug targeting potential. Finally, RPL39, as one of the hub genes, was found to be closely related to the prognosis of glioma patients. Knockdown of RPL39 in vitro significantly inhibited the proliferation and migration of glioma cells, whereas overexpression of RPL39 had the opposite effect. And we found that knockdown of RPL39 inhibited the polarization and infiltration of M2 phenotype macrophages. In conclusion, our new prognosis-related model provides more potential therapeutic strategies for glioma patients.

Effect of the m6ARNA gene on the prognosis of thyroid cancer, immune infiltration, and promising immunotherapy.

Background: Accumulating evidence suggests that N6-methyladenosine (m6A) RNA methylation plays an important role in tumor proliferation and growth. However, its effect on the clinical prognosis, immune infiltration, and immunotherapy response of thyroid cancer patients has not been investigated in detail. Methods: Clinical data and RNA expression profiles of thyroid cancer were extracted from the Cancer Genome Atlas-thyroid carcinoma (TCGA-THCA) and preprocessed for consensus clustering. The risk model was constructed based on differentially expressed genes (DEGs) using Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression analyses. The associations between risk score and clinical traits, immune infiltration, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), immune infiltration, and immunotherapy were assessed. Immunohistochemistry was used to substantiate the clinical traits of our samples. Results: Gene expression analysis showed that 17 genes, except YHTDF2, had significant differences (vs healthy control, P<0.001). Consensus clustering yielded 2 clusters according to their clinical features and estimated a poorer prognosis for Cluster 1 (P=0.03). The heatmap between the 2 clusters showed differences in T (P<0.01), N (P<0.001) and stage (P<0.01). Based on univariate Cox and LASSO regression, a risk model consisting of three high-risk genes (KIAA1429, RBM15, FTO) was established, and the expression difference between normal and tumor tissues of three genes was confirmed by immunohistochemical results of our clinical tissues. KEGG and GSEA analyses showed that the risk DEGs were related mainly to proteolysis, immune response, and cancer pathways. The levels of immune infiltration in the high- and low-risk groups were different mainly in iDCs (P<0.05), NK cells (P<0.05), and type-INF-II (P<0.001). Immunotherapy analysis yielded 30 drugs associated with the expression of each gene and 20 drugs associated with the risk score. Conclusions: Our risk model can act as an independent marker for thyroid cancer and provides promising immunotherapy targets for its treatment.