Ferroptosis-related lncRNAs: Distinguishing heterogeneity of the tumour microenvironment and predicting immunotherapy response in bladder cancer.

Ferroptosis, a cell death pathway dependent on iron, has been shown in research to play a role in the development, advancement, and outlook of tumours through ferroptosis-related lncRNAs (FRLRs). However, the value of the FRLRs in bladder cancer (BLCA) has not been thoroughly investigated. This research project involved developing a predictive model using ten specific FRLRs (AC099850.4, AL731567.1, AL133415.1, AC021321.1, SPAG5-AS1, HMGA2-AS1, RBMS3-AS3, AC006160.1, AL583785.1, and AL662844.4) through univariate COX and LASSO regression techniques. The validation of this signature as a standalone predictor was confirmed in a group of 65 patients from the urology bladder tumour database at the First Affiliated Hospital of Wenzhou Medical University in Wenzhou, China. Patients were categorized based on their median risk score into either a low-risk group or a high-risk group. Enrichment analysis identified possible molecular mechanisms that could explain the variations in clinical outcomes observed in high-risk and low-risk groups. Moreover, we explored the correlation between FLPS and immunotherapy-related indicators. The ability of FLPS to forecast the effectiveness of immunotherapy was validated by the elevated levels of immune checkpoint genes (PD-L1, CTLA4, and PD-1) in the group at high risk. We also screened the crucial FRLR (HMGA2-AS1) through congruent expression and prognostic conditions and established a ceRNA network, indicating that HMGA2-AS1 may affect epithelial-mesenchymal transition by modulating the Wnt signalling pathway through the ceRNA mechanism. We identified the top five mRNAs (NFIB, NEGR1, JAZF1, JCAD, and ESM1) based on random forest algorithm and analysed the relationship between HMGA2-AS1, the top five mRNAs, and immunotherapy, and their interactions with drug sensitivities. Our results suggest that patients with BLCA have a greater sensitivity to four drugs (dasatinib, pazopanib, erismodegib and olaparib). Our study provides new insights into the TME, key signalling pathways, genome, and potential therapeutic targets of BLCA, with future guidance for immunotherapy and targeted precision drugs.

Design, Synthesis, and Bioactivity Evaluation of a TF-Based Cancer Vaccine Candidate Using Lipid A Mimetics As a Built-In Adjuvant.

This study describes the design and synthesis of five TF-based cancer vaccine candidates using a lipid A mimetic as the carrier and a built-in adjuvant. All synthesized conjugates elicited robust and consistent TF-specific immune responses in mice without external adjuvants. Immunological studies subsequently conducted in wild-type and TLR4 knockout C57BL/6 mice demonstrated that the activation of TLR4 was the main reason that the synthesized lipid A mimetics increased the TF-specific immune responses. All antisera induced by these conjugates can specifically recognize, bind to, and induce the lysis of TF-positive cancer cells. Moreover, representative conjugates 2 and 3 could effectively reduce the growth of tumors and prolong the survival time of mice in vivo, and the efficacies were better than glycoprotein TF-CRM197 with alum adjuvant. Lipid A mimetics could therefore be a promising platform for the development of new carbohydrate-based vaccine carriers with self-adjuvanting properties for the treatment of cancer.

Exosome-related lncRNA score: A value-based individual treatment strategy for predicting the response to immunotherapy in clear cell renal cell carcinoma.

BACKGROUND: Exosomes play a crucial role in intercellular communication in clear cell renal cell carcinoma (ccRCC), while the long non-coding RNAs (lncRNAs) are implicated in tumorigenesis and progression. AIMS: The purpose of this study is to construction a exosomes-related lncRNA score and a ceRNA network to predict the response to immunotherapy and potential targeted drug in ccRCC. METHODS: Data of ccRCC patients were obtained from the TCGA database. Pearson correlation analysis was used to identify eExosomes-related lncRNAs (ERLRs) from Top10 exosomes-related genes that have been screened. The entire cohort was randomly divided into a training cohort and a validation cohort in equal scale. LASSO regression and multivariate cox regression was used to construct the ERLRs-based score. Differences in clinicopathological characteristics, immune microenvironment, immune checkpoints, and drug susceptibility between the high- and low-risk groups were also investigated. Finally, the relevant ceRNA network was constructed by machine learning to analyze their potential targets in immunotherapy and drug use of ccRCC patients. RESULTS: A score consisting of 4ERLRs was identified, and patients with higher ERLRs-based score tended to have a worse prognosis than those with lower ERLRs-based score. ROC curves and multivariate Cox regression analysis demonstrated that the score could be considered as a risk factor for prognosis in both training and validation cohorts. Moreover, patients with high scores are predisposed to experience poor overall survival, a larger prevalence of advanced stage (III-IV), a greater tumor mutational burden, a higher infiltration of immunosuppressive cells, and a greater likelihood of responding favorably to immunotherapy. The importance of EMX2OS was determined by mechanical learning, and the ceRNA network was constructed, and EMX2OS may be a potential therapeutic target, possibly exerting its function through the EMX2OS/hsa-miR-31-5p/TLN2 axis. CONCLUSIONS: Based on machine learning, a novel ERLRs-based score was constructed for predicting the survival of ccRCC patients. The ERLRs-based score is a promising potential independent prognostic factor that is closely correlated with the immune microenvironment and clinicopathological characteristics. Meanwhile, we screened out key lncRNAEMX2OS and identified the EMX2OS/hsa-miR-31-5p/TLN2 axis, which may provide new clues for the targeted therapy of ccRCC.

SCPLPA: An miRNA-disease association prediction model based on spatial consistency projection and label propagation algorithm.

Identifying the association between miRNA and diseases is helpful for disease prevention, diagnosis and treatment. It is of great significance to use computational methods to predict potential human miRNA disease associations. Considering the shortcomings of existing computational methods, such as low prediction accuracy and weak generalization, we propose a new method called SCPLPA to predict miRNA-disease associations. First, a heterogeneous disease similarity network was constructed using the disease semantic similarity network and the disease Gaussian interaction spectrum kernel similarity network, while a heterogeneous miRNA similarity network was constructed using the miRNA functional similarity network and the miRNA Gaussian interaction spectrum kernel similarity network. Then, the estimated miRNA-disease association scores were evaluated by integrating the outcomes obtained by implementing label propagation algorithms in the heterogeneous disease similarity network and the heterogeneous miRNA similarity network. Finally, the spatial consistency projection algorithm of the network was used to extract miRNA disease association features to predict unverified associations between miRNA and diseases. SCPLPA was compared with four classical methods (MDHGI, NSEMDA, RFMDA and SNMFMDA), and the results of multiple evaluation metrics showed that SCPLPA exhibited the most outstanding predictive performance. Case studies have shown that SCPLPA can effectively identify miRNAs associated with colon neoplasms and kidney neoplasms. In summary, our proposed SCPLPA algorithm is easy to implement and can effectively predict miRNA disease associations, making it a reliable auxiliary tool for biomedical research.

Targeting Mitochondrial Complex I Deficiency in MPP+/MPTP-induced Parkinson's Disease Cell Culture and Mouse Models by Transducing Yeast NDI1 Gene.

BACKGROUND: MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine), original found in synthetic heroin, causes Parkinson's disease (PD) in human through its metabolite MPP+ by inhibiting complex I of mitochondrial respiratory chain in dopaminergic neurons. This study explored whether yeast internal NADH-quinone oxidoreductase (NDI1) has therapeutic effects in MPTP- induced PD models by functionally compensating for the impaired complex I. MPP+-treated SH-SY5Y cells and MPTP-treated mice were used as the PD cell culture and mouse models respectively. The recombinant NDI1 lentivirus was transduced into SH-SY5Y cells, or the recombinant NDI1 adeno-associated virus (rAAV5-NDI1) was injected into substantia nigra pars compacta (SNpc) of mice. RESULTS: The study in vitro showed NDI1 prevented MPP+-induced change in cell morphology and decreased cell viability, mitochondrial coupling efficiency, complex I-dependent oxygen consumption, and mitochondria-derived ATP. The study in vivo revealed that rAAV-NDI1 injection significantly improved the motor ability and exploration behavior of MPTP-induced PD mice. Accordingly, NDI1 notably improved dopaminergic neuron survival, reduced the inflammatory response, and significantly increased the dopamine content in striatum and complex I activity in substantia nigra. CONCLUSIONS: NDI1 compensates for the defective complex I in MPP+/MPTP-induced models, and vastly alleviates MPTP-induced toxic effect on dopaminergic neurons. Our study may provide a basis for gene therapy of sporadic PD with defective complex I caused by MPTP-like substance.

KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection.

BACKGROUND: Clinical studies have shown that miRNAs are closely related to human health. The study of potential associations between miRNAs and diseases will contribute to a profound understanding of the mechanism of disease development, as well as human disease prevention and treatment. MiRNA-disease associations predicted by computational methods are the best complement to biological experiments. RESULTS: In this research, a federated computational model KATZNCP was proposed on the basis of the KATZ algorithm and network consistency projection to infer the potential miRNA-disease associations. In KATZNCP, a heterogeneous network was initially constructed by integrating the known miRNA-disease association, integrated miRNA similarities, and integrated disease similarities; then, the KATZ algorithm was implemented in the heterogeneous network to obtain the estimated miRNA-disease prediction scores. Finally, the precise scores were obtained by the network consistency projection method as the final prediction results. KATZNCP achieved the reliable predictive performance in leave-one-out cross-validation (LOOCV) with an AUC value of 0.9325, which was better than the state-of-the-art comparable algorithms. Furthermore, case studies of lung neoplasms and esophageal neoplasms demonstrated the excellent predictive performance of KATZNCP. CONCLUSION: A new computational model KATZNCP was proposed for predicting potential miRNA-drug associations based on KATZ and network consistency projections, which can effectively predict the potential miRNA-disease interactions. Therefore, KATZNCP can be used to provide guidance for future experiments.

Promoting nickel oxidation state transitions in single-layer NiFeB hydroxide nanosheets for efficient oxygen evolution.

Promoting the formation of high-oxidation-state transition metal species in a hydroxide catalyst may improve its catalytic activity in the oxygen evolution reaction, which remains difficult to achieve with current synthetic strategies. Herein, we present a synthesis of single-layer NiFeB hydroxide nanosheets and demonstrate the efficacy of electron-deficient boron in promoting the formation of high-oxidation-state Ni for improved oxygen evolution activity. Raman spectroscopy, X-ray absorption spectroscopy, and electrochemical analyses show that incorporation of B into a NiFe hydroxide causes a cathodic shift of the Ni2+(OH)2 → Ni3+δOOH transition potential. Density functional theory calculations suggest an elevated oxidation state for Ni and decreased energy barriers for the reaction with the NiFeB hydroxide catalyst. Consequently, a current density of 100 mA cm-2 was achieved in 1 M KOH at an overpotential of 252 mV, placing it among the best Ni-based catalysts for this reaction. This work opens new opportunities in electronic engineering of metal hydroxides (or oxides) for efficient oxygen evolution in water-splitting applications.

Trace Key Mechanistic Features of the Arsenite Sequestration Reaction with Nanoscale Zerovalent Iron.

Nanoscale zerovalent iron (nZVI) is considered as a highly efficient material for sequestrating arsenite, but the origin of its high efficacy as well as the chemical transformations of arsenite during reaction is not well understood. Here, we report an in situ X-ray absorption spectroscopy (XAS) study to investigate the complex mechanism of nZVI reaction with arsenite under anaerobic conditions at the time scale from seconds to days. The time-resolved XAS analysis revealed a gradual oxidation of AsIII to AsV in the course of minutes to hours in both the solid and liquid phase for the high (above 0.5 g/L) nZVI dose system. When the reaction time increased up to 60 days, AsV became the dominant species. The quick-scanning extended X-ray absorption fine structure (QEAXFS) was introduced to discover the transient intermediate at the highly reactive stage, and a small red-shift in As K-edge absorption edge was observed. The QEAXFS combined with density functional theory (DFT) calculation suggested that the red-shift is likely due to the electron donation in a Fe-O-As complex and possible active sites of As sequestrations include Fe(OH)4 and 4-Fe cluster. This is the first time that the transient reaction intermediate was identified in the As-nZVI sequestration system at the fast-reacting early stage. This study also demonstrated usefulness of in situ monitoring techniques in environmental water research.

Atomic-layered Au clusters on α-MoC as catalysts for the low-temperature water-gas shift reaction.

The water-gas shift (WGS) reaction (where carbon monoxide plus water yields dihydrogen and carbon dioxide) is an essential process for hydrogen generation and carbon monoxide removal in various energy-related chemical operations. This equilibrium-limited reaction is favored at a low working temperature. Potential application in fuel cells also requires a WGS catalyst to be highly active, stable, and energy-efficient and to match the working temperature of on-site hydrogen generation and consumption units. We synthesized layered gold (Au) clusters on a molybdenum carbide (α-MoC) substrate to create an interfacial catalyst system for the ultralow-temperature WGS reaction. Water was activated over α-MoC at 303 kelvin, whereas carbon monoxide adsorbed on adjacent Au sites was apt to react with surface hydroxyl groups formed from water splitting, leading to a high WGS activity at low temperatures.

Enabling unassisted solar water splitting by iron oxide and silicon.

Photoelectrochemical (PEC) water splitting promises a solution to the problem of large-scale solar energy storage. However, its development has been impeded by the poor performance of photoanodes, particularly in their capability for photovoltage generation. Many examples employing photovoltaic modules to correct the deficiency for unassisted solar water splitting have been reported to-date. Here we show that, by using the prototypical photoanode material of haematite as a study tool, structural disorders on or near the surfaces are important causes of the low photovoltages. We develop a facile re-growth strategy to reduce surface disorders and as a consequence, a turn-on voltage of 0.45 V (versus reversible hydrogen electrode) is achieved. This result permits us to construct a photoelectrochemical device with a haematite photoanode and Si photocathode to split water at an overall efficiency of 0.91%, with NiFeOx and TiO2/Pt overlayers, respectively.