Deciphering lung adenocarcinoma evolution: Integrative single-cell genomics identifies the prognostic lung progression associated signature.

We employed single-cell analysis techniques, specifically the inferCNV method, to dissect the complex progression of lung adenocarcinoma (LUAD) from adenocarcinoma in situ (AIS) through minimally invasive adenocarcinoma (MIA) to invasive adenocarcinoma (IAC). This approach enabled the identification of Cluster 6, which was significantly associated with LUAD progression. Our comprehensive analysis included intercellular interaction, transcription factor regulatory networks, trajectory analysis, and gene set variation analysis (GSVA), leading to the development of the lung progression associated signature (LPAS). Interestingly, we discovered that the LPAS not only accurately predicts the prognosis of LUAD patients but also forecasts genomic alterations, distinguishes between 'cold' and 'hot' tumours, and identifies potential candidates suitable for immunotherapy. PSMB1, identified within Cluster 6, was experimentally shown to significantly enhance cancer cell invasion and migration, highlighting the clinical relevance of LPAS in predicting LUAD progression and providing a potential target for therapeutic intervention. Our findings suggest that LPAS offers a novel biomarker for LUAD patient stratification, with significant implications for improving prognostic accuracy and guiding treatment decisions.

Integrating machine learning and single-cell analysis to uncover lung adenocarcinoma progression and prognostic biomarkers.

The progression of lung adenocarcinoma (LUAD) from atypical adenomatous hyperplasia (AAH) to invasive adenocarcinoma (IAC) involves a complex evolution of tumour cell clusters, the mechanisms of which remain largely unknown. By integrating single-cell datasets and using inferCNV, we identified and analysed tumour cell clusters to explore their heterogeneity and changes in abundance throughout LUAD progression. We applied gene set variation analysis (GSVA), pseudotime analysis, scMetabolism, and Cytotrace scores to study biological functions, metabolic profiles and stemness traits. A predictive model for prognosis, based on key cluster marker genes, was developed using CoxBoost and plsRcox (CPM), and validated across multiple cohorts for its prognostic prediction capabilities, tumour microenvironment characterization, mutation landscape and immunotherapy response. We identified nine distinct tumour cell clusters, with Cluster 6 indicating an early developmental stage, high stemness and proliferative potential. The abundance of Clusters 0 and 6 increased from AAH to IAC, correlating with prognosis. The CPM model effectively distinguished prognosis in immunotherapy cohorts and predicted genomic alterations, chemotherapy drug sensitivity, and immunotherapy responsiveness. Key gene S100A16 in the CPM model was validated as an oncogene, enhancing LUAD cell proliferation, invasion and migration. The CPM model emerges as a novel biomarker for predicting prognosis and immunotherapy response in LUAD patients, with S100A16 identified as a potential therapeutic target.

Purine metabolism in lung adenocarcinoma: A single-cell analysis revealing prognostic and immunotherapeutic insights.

Lung adenocarcinoma (LUAD) is a prevalent subtype of lung cancer, yet the contribution of purine metabolism (PM) to its pathogenesis remains poorly elucidated. PM, a critical component of intracellular nucleotide synthesis and energy metabolism, is hypothesized to exert a significant influence on LUAD development. Herein, we employed single-cell analysis to investigate the role of PM within the tumour microenvironment (TME) of LUAD. PM scoring (PMS) across distinct cell types was determined using AUCell, UCell, singscore and AddModuleScore algorithms. Subsequently, we explored communication networks among cells within high- and low-PMS groups, establishing a robust PM-associated signature (PAS) utilizing a comprehensive dataset comprising LUAD samples from TCGA and five GEO datasets. Our findings revealed that the high-PMS group exhibited intensified cell interactions, while the PAS, constructed using PM-related genes, demonstrated precise prognostic predictive capability. Notably, analysis across the TCGA dataset and five GEO datasets indicated that low-PAS patients exhibited a superior prognosis. Furthermore, the low-PAS group displayed increased immune cell infiltration and elevated CD8A expression, coupled with reduced PD-L1 expression. Moreover, data from eight publicly available immunotherapy cohorts suggested enhanced immunotherapy outcomes in the low-PAS group. These results underscore a close association between PAS and tumour immunity, offering predictive insights into genomic alterations, chemotherapy drug sensitivity and immunotherapy responses in LUAD. The newly established PAS holds promise as a valuable tool for selecting LUAD populations likely to benefit from future clinical stratification efforts.

Prognostic and immunotherapeutic potential of regulatory T cell-associated signature in ovarian cancer.

Tumour-induced immunosuppressive microenvironments facilitate oncogenesis, with regulatory T cells (Tregs) serving as a crucial component. The significance of Treg-associated genes within the context of ovarian cancer (OC) remains elucidated insufficiently. Utilizing single-cell RNA sequencing (scRNA-Seq) for the identification of Treg-specific biomarkers, this investigation employed single-sample gene set enrichment analysis (ssGSEA) for the derivation of a Treg signature score. Weighted gene co-expression network analysis (WGCNA) facilitated the identification of Treg-correlated genes. Machine learning algorithms were employed to determine an optimal prognostic model, subsequently exploring disparities across risk strata in terms of survival outcomes, immunological infiltration, pathway activation and responsiveness to immunotherapy. Through WGCNA, a cohort of 365 Treg-associated genes was discerned, with 70 implicated in the prognostication of OC. A Tregs-associated signature (TAS), synthesized from random survival forest (RSF) and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms, exhibited robust predictive validity across both internal and external cohorts. Low TAS OC patients demonstrated superior survival outcomes, augmented by increased immunological cell infiltration, upregulated immune checkpoint expression, distinct pathway enrichment and differential response to immunotherapeutic interventions. The devised TAS proficiently prognosticates patient outcomes and delineates the immunological milieu within OC, offering a strategic instrument for the clinical stratification and selection of patients.

Targeting nucleotide metabolic pathways in colorectal cancer by integrating scRNA-seq, spatial transcriptome, and bulk RNA-seq data.

BACKGROUND: Colorectal cancer is a malignant tumor of the digestive system originating from abnormal cell proliferation in the colon or rectum, often leading to gastrointestinal symptoms and severe health issues. Nucleotide metabolism, which encompasses the synthesis of DNA and RNA, is a pivotal cellular biochemical process that significantly impacts both the progression and therapeutic strategies of colorectal cancer METHODS: For single-cell RNA sequencing (scRNA-seq), five functions were employed to calculate scores related to nucleotide metabolism. Cell developmental trajectory analysis and intercellular interaction analysis were utilized to explore the metabolic characteristics and communication patterns of different epithelial cells. These findings were further validated using spatial transcriptome RNA sequencing (stRNA-seq). A risk model was constructed using expression profile data from TCGA and GEO cohorts to optimize clinical decision-making. Key nucleotide metabolism-related genes (NMRGs) were functionally validated by further in vitro experiments. RESULTS: In both scRNA-seq and stRNA-seq, colorectal cancer (CRC) exhibited unique cellular heterogeneity, with myeloid cells and epithelial cells in tumor samples displaying higher nucleotide metabolism scores. Analysis of intercellular communication revealed enhanced signaling pathways and ligand-receptor interactions between epithelial cells with high nucleotide metabolism and fibroblasts. Spatial transcriptome sequencing confirmed elevated nucleotide metabolism states in the core region of tumor tissue. After identifying differentially expressed NMRGs in epithelial cells, a risk prognostic model based on four genes effectively predicted overall survival and immunotherapy outcomes in patients. High-risk group patients exhibited an immunosuppressive microenvironment and relatively poorer prognosis and responses to chemotherapy and immunotherapy. Finally, based on data analysis and a series of cellular functional experiments, ACOX1 and CPT2 were identified as novel therapeutic targets for CRC. CONCLUSION: In this study, a comprehensive analysis of NMRGs in CRC was conducted using a combination of single-cell sequencing, spatial transcriptome sequencing, and high-throughput data. The prognostic model constructed with NMRGs shows potential as a standalone prognostic marker for colorectal cancer patients and may significantly influence the development of personalized treatment approaches for CRC.

A Universal Electronic Structure Modulation Strategy: Is Strong Adsorption Always Correlated with High Catalysis?

Unveiling the inherent link between polysulfide adsorption and catalytic activity is key to achieving optimal performance in Lithium-sulfur (Li-S) batteries. Current research on the sulfur reaction process mainly relies on the strong adsorption of catalysts to confine lithium polysulfides (LiPSs) to the cathode side, effectively suppressing the shuttle effect of polysulfides. However, is strong adsorption always correlated with high catalysis? The inherent relationship between adsorption and catalytic activity remains unclear, limiting the in-depth exploration and rational design of catalysts. Herein, the correlation between "d-band center-adsorption strength-catalytic activity" in porous carbon nanofiber catalysts embedded with different transition metals (M-PCNF-3, M = Fe, Co, Ni, Cu) is systematically investigated, combining the d-band center theory and the Sabatier principle. Theoretical calculations and experimental analysis results indicate that Co-PCNF-3 electrocatalyst with appropriate d-band center positions exhibits moderate adsorption capability and the highest catalytic conversion activity for LiPSs, validating the Sabatier relationship in Li-S battery electrocatalysts. These findings provide indispensable guidelines for the rational design of more durable cathode catalysts for Li-S batteries.

The Sympathetic Nervous System Promotes Hepatic Lymphangiogenesis, which Is Protective Against Liver Fibrosis.

Liver is the largest lymph-producing organ. In cirrhotic patients, lymph production significantly increases concomitant with lymphangiogenesis. The aim of this study was to determine the mechanism of lymphangiogenesis in liver and its implication in liver fibrosis. Liver biopsies from portal hypertensive patients with portal-sinusoidal vascular disease (n = 22) and liver cirrhosis (n = 5) were evaluated for lymphangiogenesis and compared with controls (n = 9 and n = 6, respectively). For mechanistic studies, rats with partial portal vein ligation (PPVL) and bile duct ligation (BDL) were used. A gene profile data set (GSE77627), including 14 histologically normal liver, 18 idiopathic noncirrhotic portal hypertension, and 22 cirrhotic patients, was analyzed. Lymphangiogenesis was significantly increased in livers from patients with portal-sinusoidal vascular disease, cirrhotic patients, as well as PPVL and BDL rats. Importantly, Schwann cells of sympathetic nerves highly expressed vascular endothelial growth factor-C in PPVL rats. Vascular endothelial growth factor-C neutralizing antibody or sympathetic denervation significantly decreased lymphangiogenesis in livers of PPVL and BDL rats, which resulted in progression of liver fibrosis. Liver specimens from cirrhotic patients showed a positive correlation between sympathetic nerve/Schwann cell-positive areas and lymphatic vessel numbers, which was supported by gene set analysis from patients with noncirrhotic portal hypertension and cirrhotic patients. Sympathetic nerves promote hepatic lymphangiogenesis in noncirrhotic and cirrhotic livers. Increased hepatic lymphangiogenesis can be protective against liver fibrosis.

Deciphering cutaneous melanoma prognosis through LDL metabolism: Single-cell transcriptomics analysis via 101 machine learning algorithms.

Cutaneous melanoma poses a formidable challenge within the field of oncology, marked by its aggressive nature and capacity for metastasis. Despite extensive research uncovering numerous genetic and molecular contributors to cutaneous melanoma development, there remains a critical knowledge gap concerning the role of lipids, notably low-density lipoprotein (LDL), in this lethal skin cancer. This article endeavours to bridge this knowledge gap by delving into the intricate interplay between LDL metabolism and cutaneous melanoma, shedding light on how lipids influence tumour progression, immune responses and potential therapeutic avenues. Genes associated with LDL metabolism were extracted from the GSEA database. We acquired and analysed single-cell sequencing data (GSE215120) and bulk-RNA sequencing data, including the TCGA data set, GSE19234, GSE22153 and GSE65904. Our analysis unveiled the heterogeneity of LDL across various cell types at the single-cell sequencing level. Additionally, we constructed an LDL-related signature (LRS) using machine learning algorithms, incorporating differentially expressed genes and highly correlated genes. The LRS serves as a valuable tool for assessing the prognosis, immunity and mutation status of patients with cutaneous melanoma. Furthermore, we conducted experiments on A375 and WM-115 cells to validate the function of PPP2R1A, a pivotal gene within the LRS. Our comprehensive approach, combining advanced bioinformatics analyses with an extensive review of current literature, presents compelling evidence regarding the significance of LDL within the cutaneous melanoma microenvironment.

Unravelling the metabolic landscape of cutaneous melanoma: Insights from single-cell sequencing analysis and machine learning for prognostic assessment of lactate metabolism.

This manuscript presents a comprehensive investigation into the role of lactate metabolism-related genes as potential prognostic markers in skin cutaneous melanoma (SKCM). Bulk-transcriptome data from The Cancer Genome Atlas (TCGA) and GSE19234, GSE22153, and GSE65904 cohorts from GEO database were processed and harmonized to mitigate batch effects. Lactate metabolism scores were assigned to individual cells using the 'AUCell' package. Weighted Co-expression Network Analysis (WGCNA) was employed to identify gene modules correlated with lactate metabolism. Machine learning algorithms were applied to construct a prognostic model, and its performance was evaluated in multiple cohorts. Immune correlation, mutation analysis, and enrichment analysis were conducted to further characterize the prognostic model's biological implications. Finally, the function of key gene NDUFS7 was verified by cell experiments. Machine learning resulted in an optimal prognostic model, demonstrating significant prognostic value across various cohorts. In the different cohorts, the high-risk group showed a poor prognosis. Immune analysis indicated differences in immune cell infiltration and checkpoint gene expression between risk groups. Mutation analysis identified genes with high mutation loads in SKCM. Enrichment analysis unveiled enriched pathways and biological processes in high-risk SKCM patients. NDUFS7 was found to be a hub gene in the protein-protein interaction network. After the expression of NDUFS7 was reduced by siRNA knockdown, CCK-8, colony formation, transwell and wound healing tests showed that the activity, proliferation and migration of A375 and WM115 cell lines were significantly decreased. This study offers insights into the prognostic significance of lactate metabolism-related genes in SKCM.

Single-cell and bulk RNA-sequencing reveal SPP1 and CXCL12 as cell-to-cell communication markers to predict prognosis in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) generally presents as an immunosuppressive microenvironment. The characteristics of cell-to-cell communication in the LUAD microenvironment has been unclear. In this study, the LUAD bulk RNA-seq data and single-cell RNA-seq data were retrieved from public dataset. Differential expression genes (DEGs) between LUAD tumor and adjacent non-tumor tissues were calculated by limma algorithm, and then detected by PPI, KEGG, and GO analysis. Cell-cell interactions were explored using the single-cell RNA-seq data. Finally, the first 15 CytoHubba genes were used to establish related pathways and these pathways were used to characterize the immune-related ligands and their receptors in LUAD. Our analyses showed that monocytes or macrophages interact with tissue stem cells and NK cells via SPP1 signaling pathway and tissue stem cells interact with T and B cells via CXCL signaling pathway in different states. Hub genes of SPP1 participated in SPP1 signaling pathway, which was negatively correlated with CD4+ T cell and CD8+ T cell. The expression of SPP1 in LUAD tumor tissues was negatively correlated with the prognosis. While CXCL12 participated in CXCL signaling pathway, which was positively correlated with CD4+ T cell and CD8+ T cell. The role of CXCL12 in LUAD tumor tissues exhibits an opposite effect to that of SPP1. This study reveals that tumor-associated monocytes or macrophages may affect tumor progression. Moreover, the SPP1 and CXCL12 may be the critic genes of cell-to-cell communication in LUAD, and targeting these pathways may provide a new molecular mechanism for the treatment of LUAD.

PANoptosis-related signature in melanoma: Transcriptomic mapping and clinical prognostication.

Programmed cell death plays a pivotal role in maintaining tissue homeostasis, and recent advancements in cell biology have uncovered PANoptosis-a novel paradigm integrating pyroptosis, apoptosis, and necroptosis. This study investigates the implications of PANoptosis in melanoma, a formidable skin cancer known for its metastatic potential and resistance to conventional therapies. Leveraging bulk and single-cell transcriptome analyses, machine learning modeling, and immune correlation assessments, we unveil the molecular intricacies of PANoptosis in melanoma. Single-cell sequencing identifies diverse cell types involved in PANoptosis, while bulk transcriptome analysis reveals key gene sets correlated with PANoptosis. Machine learning algorithms construct a robust prognostic model, demonstrating consistent predictive power across diverse cohorts. Patients with different cohorts can be divided into high-risk and low-risk groups according to this PANoptosis score, with the high-risk group having a significantly worse prognosis. Immune correlation analyses unveil a link between PANoptosis and immunotherapy response, with potential therapeutic implications. Mutation analysis and enrichment studies provide insights into the mutational landscape associated with PANoptosis. Finally, we used cell experiments to verify the expression and function of key gene PARVA, showing that PARVA was highly expressed in melanoma cell lines, and after PARVA is knocked down, cell invasion, migration, and colony formation ability were significantly decreased. This study advances our understanding of PANoptosis in melanoma, offering a comprehensive framework for targeted therapeutic interventions and personalized medicine strategies in combating this aggressive malignancy.

Unraveling the role of low-density lipoprotein-related genes in lung adenocarcinoma: Insights into tumor microenvironment and clinical prognosis.

BACKGROUND: The hypothesized link between low-density lipoprotein (LDL) and oncogenesis has garnered significant interest, yet its explicit impact on lung adenocarcinoma (LUAD) remains to be elucidated. This investigation aims to demystify the function of LDL-related genes (LRGs) within LUAD, endeavoring to shed light on the complex interplay between LDL and carcinogenesis. METHODS: Leveraging single-cell transcriptomics, we examined the role of LRGs within the tumor microenvironment (TME). The expression patterns of LRGs across diverse cellular phenotypes were delineated using an array of computational methodologies, including AUCell, UCell, singscore, ssGSEA, and AddModuleScore. CellChat facilitated the exploration of distinct cellular interactions within LDL_low and LDL_high groups. The findmarker utility, coupled with Pearson correlation analysis, facilitated the identification of pivotal genes correlated with LDL indices. An integrative approach to transcriptomic data analysis was adopted, utilizing a machine learning framework to devise an LDL-associated signature (LAS). This enabled the delineation of genomic disparities, pathway enrichments, immune cell dynamics, and pharmacological sensitivities between LAS stratifications. RESULTS: Enhanced cellular crosstalk was observed in the LDL_high group, with the CoxBoost+Ridge algorithm achieving the apex c-index for LAS formulation. Benchmarking against 144 extant LUAD models underscored the superior prognostic acuity of LAS. Elevated LAS indices were synonymous with adverse outcomes, diminished immune surveillance, and an upsurge in pathways conducive to neoplastic proliferation. Notably, a pronounced susceptibility to paclitaxel and gemcitabine was discerned within the high-LAS cohort, delineating prospective therapeutic corridors. CONCLUSION: This study elucidates the significance of LRGs within the TME and introduces an LAS for prognostication in LUAD patients. Our findings accentuate putative therapeutic targets and elucidate the clinical ramifications of LAS deployment.

Clinical prognostication and immunotherapy response prediction in esophageal squamous cell carcinoma using the DNA damage repair-associated signature.

BACKGROUND: The relationship between DNA damage repair (DDR) and cancer is intricately intertwined; however, its specific role in esophageal squamous cell carcinoma (ESCC) remains enigmatic. METHODS: Employing single-cell analysis, we delineated the functionality of DDR-related genes within the tumor microenvironment (TME). A diverse array of scoring mechanisms, including AUCell, UCell, singscore, ssgsea, and AddModuleScore, were harnessed to scrutinize the activity of DDR-related genes across different cell types. Differential pathway alterations between high-and low-DDR activity cell clusters were compared. Furthermore, leveraging multiple RNA-seq datasets, we constructed a robust DDR-associated signature (DAS), and through integrative multiomics analysis, we explored differences in prognosis, pathways, mutational landscapes, and immunotherapy predictions among distinct DAS groups. RESULTS: Notably, high-DDR activity cell subpopulations exhibited markedly enhanced cellular communication. The DAS demonstrated uniformity across multiple datasets. The low-DAS group exhibited improved prognoses, accompanied by heightened immune infiltration and elevated immune checkpoint expression. SubMap analysis of multiple immunotherapy datasets suggested that low-DAS group may experience enhanced immunotherapy responses. The "oncopredict" R package analyzed and screened sensitive drugs for different DAS groups. CONCLUSION: Through the integration of single-cell and bulk RNA-seq data, we have developed a DAS associated with prognosis and immunotherapy response. This signature holds promise for the future stratification and personalized treatment of ESCC patients in clinical settings.

Prevention and Potential Treatment Strategies for Respiratory Syncytial Virus.

Respiratory syncytial virus (RSV) is a significant viral pathogen that causes respiratory infections in infants, the elderly, and immunocompromised individuals. RSV-related illnesses impose a substantial economic burden worldwide annually. The molecular structure, function, and in vivo interaction mechanisms of RSV have received more comprehensive attention in recent times, and significant progress has been made in developing inhibitors targeting various stages of the RSV replication cycle. These include fusion inhibitors, RSV polymerase inhibitors, and nucleoprotein inhibitors, as well as FDA-approved RSV prophylactic drugs palivizumab and nirsevimab. The research community is hopeful that these developments might provide easier access to knowledge and might spark new ideas for research programs.

Synthesis, antioxidant and anti-hypoxia activities of 6-hydroxygenistein and its methylated derivatives. / 6-ç¾ŸåŸºæŸ“æ–™æœ¨ç´ åŠå ¶ç”²åŸºåŒ–è¡ç”Ÿç‰©çš„åˆæˆå’ŒæŠ—æ°§åŒ–ä¸ŽæŠ—ç¼ºæ°§æ´»æ€§.

OBJECTIVES: Hypoxia is a common pathological phenomenon, usually caused by insufficient oxygen supply or inability to use oxygen effectively. Hydroxylated and methoxylated flavonoids have significant anti-hypoxia activity. This study aims to explore the synthesis, antioxidant and anti-hypoxia activities of 6-hydroxygenistein (6-OHG) and its methoxylated derivatives. METHODS: The 6-OHG and its methoxylated derivatives, including 4',6,7-trimethoxy-5-hydroxyisoflavone (compound 3), 4',5,6,7-tetramethoxyisoflavone (compound 4), 4',6-imethoxy-5,7-dihydroxyisoflavone (compound 6), and 4'-methoxy-5,6,7-trihydroxyisoflavone (compound 7), were synthesized by methylation, bromination, methoxylation, and demethylation using biochanin A as raw material. The structure of these products were characterized by 1hydrogen-nuclear magnetic resonance spectroscopy (1H-NMR) and mass spectrometry (MS). The purity of these compounds was detected by high pressure chromatography (HPLC). The antioxidant activity in vitro was investigated by 1,1-diphenyl-2-picrylhydrazyl radical (DPPH) free radical scavenging assay. PC12 cells were divided into a normal group, a hypoxia model group, rutin (1×10-9-1×10-5 mol/L) groups, and target compounds (1×10-9-1×10-5 mol/L) groups under normal and hypoxic conditions. Cell viability was detected by cell counting kit-8 (CCK-8) assay, the target compounds with excellent anti-hypoxia activity and the drug concentration at the maximum anti-hypoxia activity were screened. PC12 cells were treated with the optimal concentration of the target compound or rutin with excellent anti-hypoxia activity, and the cell morphology was observed under light microscope. The apoptotic rate was determined by flow cytometry, and the expressions of hypoxia inducible factor-1α (HIF-1α) and vascular endothelial growth factor (VEGF) were detected by Western blotting. RESULTS: The structure of 6-OHG and its 4 methylated derivatives were correct, and the purity was all more than 97%. When the concentration was 4 mmol/L, the DPPH free radical removal rates of chemical compounds 7 and 6-OHG were 81.16% and 86.94%, respectively, which were higher than those of rutin, the positive control. The removal rates of chemical compounds 3, 4, and 6 were all lower than 20%. Compared with the normal group, the cell viability of the hypoxia model group was significantly decreased (P<0.01). Compared with the hypoxia model group, compounds 3, 4, and 6 had no significant effect on cell viability under hypoxic conditions. At all experimental concentrations, the cell viability of the 6-OHG group was significantly higher than that of the hypoxia model group (all P<0.05). The cell viability of compound 7 group at 1×10-7 and 1×10-6 mol/L was significantly higher than that of the hypoxia model group (both P<0.05). The anti-hypoxia activity of 6-OHG and compound 7 was excellent, and the optimal drug concentration was 1×10-6 and 1×10-7 mol/L. After PC12 cells was treated with 6-OHG (1×10-6 mol/L) and compound 7 (1×10-7 mol/L), the cell damage was reduced, the apoptotic rate was significantly decreased (P<0.01), and the protein expression levels of HIF-1α and VEGF were significantly decreased in comparison with the hypoxia model group (both P<0.01). CONCLUSIONS: The optimized synthesis route can increase the yield of 6-OHG and obtain 4 derivatives by methylation and selective demethylation. 6-OHG and compound 7 have excellent antioxidant and anti-hypoxia activities, which are related to the structure of the A-ring ortho-triphenol hydroxyl group in the molecule.

Preliminary mechanism of inhibitor of SGLT2 in fatty liver cold ischemia injury.

With rapid development of liver transplantation technology, the demand for transplants have reached beyond the supply of organs, and thus development of effective strategies to reduce cold ischemia injury in fatty liver is important. Here, we explored the potential effect of SGLT-2 inhibitor in cold ischemia injury, fatty livers from 2 weeks methionine and choline deficient diet (MCD) rats were administered. After one week of intragastric administration of Sodium-dependent glucose transporters (SGLT-2) inhibitor empagliflozin (EMPA) or NaCI, liver were stored for 24 h. The results showed that EMPA could significantly reduce the cold ischemic injury in the mitochondria of fatty liver. To explore the mechanism, signal transducers and activators of transcription 3(STAT3) inhibitor AG490 group was used in a similar manner. We detected the changes in p-signal transducers and activators of transcription 3 (P-STAT3), alcohol-dehydrogenase 2 (ALDH2) and degree of apoptosis in three distinct groups. The results suggested that the protein expression of P-STAT3 and ALDH2 was higher in the EMPA group than in other two groups, whereas extent of apoptosis in the EMPA group was lower than other two groups. The data suggested that SGLT2 inhibitors could alleviate cold ischemia damage of mitochondria in fatty liver, which may be related to the inhibition of apoptosis and the activation of P-STAT3 and ALDH2.

Intrahepatic macrophage reprogramming associated with lipid metabolism in hepatitis B virus-related acute-on-chronic liver failure.

BACKGROUND: Acute-on-chronic liver failure (ACLF) is a severe syndrome with high short-term mortality, but the pathophysiology still remains largely unknown. Immune dysregulation and metabolic disorders contribute to the progression of ACLF, but the crosstalk between immunity and metabolism during ACLF is less understood. This study aims to depict the immune microenvironment in the liver during ACLF, and explore the role of lipid metabolic disorder on immunity. METHODS: Single-cell RNA-sequencing (scRNA-seq) was performed using the liver non-parenchymal cells (NPCs) and peripheral blood mononuclear cells (PBMCs) from healthy controls, cirrhosis patients and ACLF patients. A series of inflammation-related cytokines and chemokines were detected using liver and plasma samples. The lipid metabolomics targeted free fatty acids (FFAs) in the liver was also detected. RESULTS: The scRNA-seq analysis of liver NPCs showed a significant increase of monocytes/macrophages (Mono/Mac) infiltration in ACLF livers, whereas the resident Kupffer cells (KCs) were exhausted. A characterized TREM2+ Mono/Mac subpopulation was identified in ACLF, and showed immunosuppressive function. Combined with the scRNA-seq data from PBMCs, the pseudotime analysis revealed that the TREM2+ Mono/Mac were differentiated from the peripheral monocytes and correlated with lipid metabolism-related genes including APOE, APOC1, FABP5 and TREM2. The targeted lipid metabolomics proved the accumulation of unsaturated FFAs associated with α-linolenic acid (α-LA) and α-LA metabolism and beta oxidation of very long chain fatty acids in the ACLF livers, indicating that unsaturated FFAs might promote the differentiation of TREM2+ Mono/Mac during ACLF. CONCLUSIONS: The reprogramming of macrophages was found in the liver during ACLF. The immunosuppressive TREM2+ macrophages were enriched in the ACLF liver and contributed to the immunosuppressive hepatic microenvironment. The accumulation of unsaturated FFAs in the ACLF liver promoted the reprogramming of the macrophages. It might be a potential target to improve the immune deficiency of ACLF patients through regulating lipid metabolism.

Liquid-liquid phase separation throws novel insights into treatment strategies for skin cutaneous melanoma.

BACKGROUND: In recent years, there has been growing evidence indicating a relationship between liquid-liquid phase separation (LLPS) and cancer development. However, to date, the clinical significance of LLPS in skin cutaneous melanoma (SKCM, hereafter referred to as melanoma) remains to be elucidated. In the current study, the impact of LLPS-related genes on melanoma prognosis has been explored. METHODS: LLPS-related genes were retrieved from the DrLLPS database. The prognostic feature for LLPS in melanoma was developed in The Cancer Genome Atlas (TCGA) dataset and verified in the GSE65904 cohort. Based on risk scores, melanoma patients were categorized into high- and low-risk groups. Thereafter, the differences in clinicopathological correlation, functional enrichment, immune landscape, tumor mutational burden, and impact of immunotherapy between the two groups were investigated. Finally, the role of key gene TROAP in melanoma was validated by in vitro and in vivo experiments. RESULTS: The LLPS-related gene signature was developed based on MLKL, PARVA, PKP1, PSME1, RNF114, and TROAP. The risk score was a crucial independent prognostic factor for melanoma and patients with high-risk scores were related to a worse prognosis. Approximately, all immune-relevant characteristics, such as immune cell infiltration and immune scores, were extremely evident in patients with low-risk scores. The findings from the in vitro and in vivo experiments indicated that the viability, proliferation, and invasion ability of melanoma cells were drastically decreased after the knockdown of TROAP. CONCLUSION: Our gene signature can independently predict the survival of melanoma patients. It provides a basis for the exploration of the relationship between LLPS and melanoma and can offer a fresh perspective on the clinical diagnosis and treatment of the disease.

Establishing a prognostic model based on immune-related genes and identification of BIRC5 as a potential biomarker for lung adenocarcinoma patients.

BACKGROUND: Lung adenocarcinoma (LUAD) is an extraordinarily malignant tumor, with rapidly increasing morbidity and poor prognosis. Immunotherapy has emerged as a hopeful therapeutic modality for lung adenocarcinoma. Furthermore, a prognostic model (based on immune genes) can fulfill the purpose of early diagnosis and accurate prognostic prediction. METHODS: Immune-related mRNAs (IRmRNAs) were utilized to construct a prognostic model that sorted patients into high- and low-risk groups. Then, the prediction efficacy of our model was evaluated using a nomogram. The differences in overall survival (OS), the tumor mutation landscape, and the tumor microenvironment were further explored between different risk groups. In addition, the immune genes comprising the prognostic model were subjected to single-cell RNA sequencing to investigate the expression of these immune genes in different cells. Finally, the functions of BIRC5 were validated through in vitro experiments. RESULTS: Patients in different risk groups exhibited sharply significant variations in OS, pathway activity, immune cell infiltration, mutation patterns, and immune response. Single-cell RNA sequencing revealed that the expression level of BIRC5 was significantly high in T cells. Cell experiments further revealed that BIRC5 knockdown markedly reduced LUAD cell proliferation. CONCLUSION: This model can function as an instrumental variable in the prognostic, molecular, and therapeutic prediction of LUAD, shedding new light on the optimal clinical practice guidelines for LUAD patients.

Necroptosis-related LncRNAs in skin cutaneous melanoma: evaluating prognosis, predicting immunity, and guiding therapy.

BACKGROUND: An increasing amount of research has speculated that necroptosis could be a therapeutic strategy for treating cancer. However, understanding the prognostic value of the necroptosis-related long non-coding RNAs (NRLs) in skin cutaneous melanoma (SKCM, hereafter referred to as melanoma) remains poor and needs to be developed. Our research aims to construct a model based on NRLs for the prognosis of patients with melanoma. METHODS: We obtained the RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) database and retrieved 86 necroptosis-related genes from the GeneCards database. The lncRNAs associated with necroptosis were identified via the Pearson correlation coefficient, and the prognostic model of melanoma was constructed using LASSO regression. Next, we employed multiple approaches to verify the accuracy of the model. Melanoma patients were categorized into two groups (high-risk and low-risk) according to the results of LASSO regression. The relationships between the risk score and survival status, clinicopathological correlation, functional enrichment, immune infiltration, somatic mutation, and drug sensitivity were further investigated. Finally, the functions of AL162457.2 on melanoma proliferation, invasion, and migration were validated by in vitro experiments. RESULTS: The prognostic model consists of seven NRLs (EBLN3P, AC093010.2, LINC01871, IRF2-DT, AL162457.2, AC242842.1, HLA-DQB1-AS1) and shows high diagnostic efficiency. Overall survival in the high-risk group was significantly lower than in the low-risk group, and risk scores could be used to predict melanoma survival outcomes independently. Significant differences were evident between risk groups regarding the expression of immune checkpoint genes, immune infiltration, immunotherapeutic response and drug sensitivity analysis. A series of functional cell assays indicated that silencing AL162457.2 significantly inhibited cell proliferation, invasion, and migration in A375 cells. CONCLUSION: Our prognostic model can independently predict the survival of melanoma patients while providing a basis for the subsequent investigation of necroptosis in melanoma and a new perspective on the clinical diagnosis and treatment of melanoma.