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.

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.

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.

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.

Leveraging single-cell sequencing analysis and bulk-RNA sequencing analysis to forecast necroptosis in cutaneous melanoma prognosis.

Cutaneous melanoma, a malignancy of melanocytes, presents a significant challenge due to its aggressive nature and rising global incidence. Despite advancements in treatment, the variability in patient responses underscores the need for further research into novel therapeutic targets, including the role of programmed cell death pathways such as necroptosis. The melanoma datasets used for analysis, GSE215120, GSE19234, GSE22153 and GSE65904, were downloaded from the GEO database. The melanoma data from TCGA were downloaded from the UCSC website. Using single-cell sequencing, we assess the heterogeneity of necroptosis in cutaneous melanoma, identifying distinct cell clusters and necroptosis-related gene expression patterns. A combination of 101 machine learning algorithms was employed to construct a necroptosis-related signature (NRS) based on key genes associated with necroptosis. The prognostic value of NRS was evaluated in four cohorts (one TCGA and three GEO cohorts), and the tumour microenvironment (TME) was analysed to understand the relationship between necroptosis, tumour mutation burden (TMB) and immune infiltration. Finally, we focused on the role of key target TSPAN10 in the prognosis, pathogenesis, immunotherapy relevance and drug sensitivity of cutaneous melanoma. Our study revealed significant heterogeneity in necroptosis among melanoma cells, with a higher prevalence in epithelial cells, myeloid cells and fibroblasts. The NRS, developed through rigorous machine learning techniques, demonstrated robust prognostic capabilities, distinguishing high-risk patients with poorer outcomes in all cohorts. Analysis of the TME showed that high NRS scores correlated with lower TMB and reduced immune cell infiltration, indicating a potential mechanism through which necroptosis influences melanoma progression. Finally, TSPAN10 has been identified as a key target for cutaneous melanoma and is highly associated with poor prognosis. The findings highlight the complex role of necroptosis in cutaneous melanoma and introduce the NRS as a novel prognostic tool with potential to guide therapeutic decisions.

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.

Exploring the prognostic value of BRMS1 + microglia based on single-cell anoikis regulator patterns in the immunologic microenvironment of GBM.

BACKGROUND: Anoikis is a specialized form of programmed cell death induced by the loss of cell adhesion to the extracellular matrix (ECM). Acquisition of anoikis resistance is a significant marker for cancer cell invasion, metastasis, therapy resistance, and recurrence. Although current research has identified multiple factors that regulate anoikis resistance, the pathological mechanisms of anoikis-mediated tumor microenvironment (TME) in glioblastoma (GBM) remain largely unexplored. METHODS: Utilizing single-cell RNA sequencing (scRNA-seq) data and employing non-negative matrix factorization (NMF), we identified and characterized TME cell clusters with distinct anoikis-associated gene signatures. Prognostic and therapeutic response analyses were conducted using TCGA and CGGA datasets to assess the clinical significance of different TME cell clusters. The spatial relationship between BRMS1 + microglia and tumor cells was inferred from spatial transcriptome RNA sequencing (stRNA-seq) data. To simulate the tumor immune microenvironment, co-culture experiments were performed with microglia (HMC3) and GBM cells (U118/U251), and microglia were transfected with a BRMS1 overexpression lentivirus. Western blot or ELISA were used to detect BRMS1, M2 macrophage-specific markers, PI3K/AKT signaling proteins, and apoptosis-related proteins. The proliferation and apoptosis capabilities of tumor cells were evaluated using CCK-8, colony formation, and apoptosis assays, while the invasive and migratory abilities of tumor cells were assessed using Transwell assays. RESULTS: NMF-based analysis successfully identified CD8 + T cell and microglia cell clusters with distinct gene signature characteristics. Trajectory analysis, cell communication, and gene regulatory network analyses collectively indicated that anoikis-mediated TME cell clusters can influence tumor cell development through various mechanisms. Notably, BRMS1 + AP-Mic exhibited an M2 macrophage phenotype and had significant cell communication with malignant cells. Moreover, high expression of BRMS1 + AP-Mic in TCGA and CGGA datasets was associated with poorer survival outcomes, indicating its detrimental impact on immunotherapy. Upregulation of BRMS1 in microglia may lead to M2 macrophage polarization, activate the PI3K/AKT signaling pathway through SPP1/CD44-mediated cell interactions, inhibit tumor cell apoptosis, and promote tumor proliferation and invasion. CONCLUSION: This pioneering study used NMF-based analysis to reveal the important predictive value of anoikis-regulated TME in GBM for prognosis and immunotherapeutic response. BRMS1 + microglial cells provide a new perspective for a deeper understanding of the immunosuppressive microenvironment of GBM and could serve as a potential therapeutic target in the future.

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.

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.

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.

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.

Geometric evaluation and quantifying dosimetric impact of diverse deformable image registration algorithms on abdomen images with biomechanically modeled deformations.

PURPOSE: Deformable image registration (DIR) has been increasingly used in radiation therapy (RT). The accuracy of DIR algorithms and how it impacts on the RT plan dosimetrically were examined in our study for abdominal sites using biomechanically modeled deformations. METHODS: Five pancreatic cancer patients were enrolled in this study. Following the guidelines of AAPM TG-132, a patient-specific quality assurance (QA) workflow was developed to evaluate DIR for the abdomen using the TG-132 recommended virtual simulation software ImSimQA (Shrewsbury, UK). First, the planning CT was deformed to simulate respiratory motion using the embedded biomechanical model in ImSimQA. Additionally, 5 mm translational motion was added to the stomach, duodenum, and small bowel. The original planning CT and the deformed CT were then imported into Eclipse and MIM to perform DIR. The output displacement vector fields (DVFs) were compared with the ground truth from ImSimQA. Furthermore, the original treatment plan was recalculated on the ground-truth deformed CT and the deformed CT (with Eclipse and MIM DVF). The dose errors were calculated on a voxel-to-voxel basis. RESULTS: Data analysis comparing DVF from Eclipse versus MIM show the average mean DVF magnitude errors of 2.8 ± 1.0  versus 1.1 ± 0.7 mm for stomach and duodenum, 5.2 ± 4.0  versus 2.5 ± 1.0 mm for small bowel, and 4.8 ± 4.1  versus 2.7 ± 1.1 mm for the gross tumor volume (GTV), respectively, across all patients. The mean dose error on stomach+duodenum and small bowel were 2.3 ± 0.6% for Eclipse, and 1.0 ± 0.3% for MIM. As the DIR magnitude error increases, the dose error range increase, for both Eclipse and MIM. CONCLUSION: In our study, an initial assessment was conducted to evaluate the accuracy of DIR and its dosimetric impact on radiotherapy. A patient-specific DIR QA workflow was developed for pancreatic cancer patients. This workflow exhibits promising potential for future implementation as a clinical workflow.

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.

Stereoselective Synthesis of Oxetanes Catalyzed by an Engineered Halohydrin Dehalogenase.

Although biocatalysis has garnered widespread attention in both industrial and academic realms, the enzymatic synthesis of chiral oxetanes remains an underdeveloped field. Halohydrin dehalogenases (HHDHs) are industrially relevant enzymes that have been engineered to accomplish the reversible transformation of epoxides. In our work, a biocatalytic platform was constructed for the stereoselective kinetic resolution of chiral oxetanes and formation of 1,3-disubstituted alcohols. HheC from Agrobacterium radiobacter AD1 was engineered to identify key variants capable of catalyzing the dehalogenation of γ-haloalcohols (via HheC M1-M3) and ring opening of oxetanes (via HheC M4-M5) to access both (R)- and (S)-configured products with high stereoselectivity and remarkable catalytic activity, yielding up to 49% with enantioselectivities exceeding 99% ee and E>200. The current strategy is broadly applicable as demonstrated by expansion of substrate scope to include up to 18 examples for dehalogenations and 16 examples for ring opening. Additionally, the functionalized products are versatile building blocks for pharmaceutical applications. To shed light on the molecular recognition mechanisms for the relevant variants, molecular dynamic (MD) simulations were performed. The current strategy expands the scope of HHDH-catalyzed chiral oxetane ring constructions, offering efficient access to both enantiomers of chiral oxetanes and 1,3-disubstituted alcohols.

Biocatalytic Desymmetrization for the Atroposelective Synthesis of Axially Chiral Biaryls Using an Engineered Imine Reductase.

The enzymatic atroposelective synthesis of biaryl compounds is relatively rare, despite considerable attention received by biocatalysis in academic and industrial sectors. Imine reductases (IREDs) are an important class of enzymes that have been applied in the asymmetric synthesis of chiral amine building blocks. In this work, two IREDs (IR140 and IR189) were identified to catalyze the efficient desymmetrization of biaryls utilizing various amine donors. Further protein engineering enabled the identification of variants (IR189 M8-M9 and IR189 M13-M14) that are able to catalyze the formation of both (R) and (S) atropisomers in excellent yields and atroposelectivities for up to 24 examples (up to 99% ee and yield). The absolute configuration and rotational barriers were confirmed, and the reactions were readily enlarged to allow isolation of the atropisomeric products in 99% ee and 82% isolated yields. The optically pure biaryl amines were further derivatized into various synthetically useful atropisomers. To shed light on the molecular recognition mechanisms, molecular dynamics (MD) simulations were performed, offering plausible explanations for the improved atroposelectivities and enzymatic activities. The current strategy expands the scope of IRED-catalyzed synthesis of axially chiral biaryl amines, contributing significantly to the field of atroposelective biocatalysis.