Integrative single-cell and bulk transcriptomes analyses reveals heterogeneity of serine-glycine-one-carbon metabolism with distinct prognoses and therapeutic vulnerabilities in HNSCC.

Metabolic heterogeneity plays a central role in sustaining uncontrolled cancer cell proliferation and shaping the tumor microenvironment (TME), which significantly compromises the clinical outcomes and responses to therapy in head and neck squamous cell carcinoma (HNSCC) patients. This highlights the urgent need to delineate the intrinsic heterogeneity and biological roles of metabolic vulnerabilities to advance precision oncology. The metabolic heterogeneity of malignant cells was identified using single-cell RNA sequencing (scRNA-seq) profiles and validated through bulk transcriptomes. Serine-glycine-one-carbon (SGOC) metabolism was screened out to be responsible for the aggressive malignant properties and poor prognosis in HNSCC patients. A 4-SGOC gene prognostic signature, constructed by LASSO-COX regression analysis, demonstrated good predictive performance for overall survival and therapeutic responses. Patients in the low-risk group exhibited greater infiltration of exhausted CD8+ T cells, and demonstrated better clinical outcomes after receiving immunotherapy and chemotherapy. Conversely, high-risk patients exhibited characteristics of cold tumors, with enhanced IMPDH1-mediated purine biosynthesis, resulting in poor responses to current therapies. IMPDH1 emerged as a potential therapeutic metabolic target. Treatment with IMPDH inhibitors effectively suppressed HNSCC cell proliferation and metastasis and induced apoptosis in vitro and in vivo by triggering GTP-exhaustion nucleolar stress. Our findings underscore the metabolic vulnerabilities of HNSCC in facilitating accurate patient stratification and individualized precise metabolic-targeted treatment.

Assessing the risk of E. coli contamination from manure application in Chinese farmland by integrating machine learning and Phydrus.

This study aims to present a comprehensive study on the risks associated with the residual presence and transport of Escherichia coli (E. coli) in soil following the application of livestock manure in Chinese farmlands by integrating machine learning algorithms with mechanism-based models (Phydrus). We initially review 28 published papers to gather data on E. coli's die-off and attachment characteristics in soil. Machine learning models, including deep learning and gradient boosting machine, are employed to predict key parameters such as the die-off rate of E. coli and first-order attachment coefficient in soil. Then, Phydrus was used to simulate E. coli transport and survival in 23692 subregions in China. The model considered regional differences in E. coli residual risk and transport, influenced by soil properties, soil depths, precipitation, seasonal variations, and regional disparities. The findings indicate higher residual risks in regions such as the Northeast China, Eastern Qinghai-Tibet Plateau, and pronounced transport risks in the fringe of the Sichuan Basin fringe, the Loess Plateau, the North China Plain, the Northeast Plain, the Shigatse Basin, and the Shangri-La region. The study also demonstrates a significant reduction in both residual and transport risks one month after manure application, highlighting the importance of timing manure application and implementing region-specific standards. This research contributes to the broader understanding of pathogen behavior in agricultural soils and offers practical guidelines for managing the risks associated with manure use. This study's comprehensive method offers a potentially valuable tool for evaluating microbial contaminants in agricultural soils across the globe.

Annual atrazine residue estimation in Chinese agricultural soils by integrated modeling of machine learning and mechanism-based models.

This study presents a comprehensive approach to estimating annual atrazine residues in China's agricultural soils, integrating machine learning algorithms and mechanism-based models. First, machine learning was used to predict essential parameters influencing atrazine's adsorption, degradation, and dispersivity of solute transport. The results demonstrated that soil organic matter was the most important input variable for predicting adsorption and degradation; clay content was the primary variable for predicting dispersivity. The SHapley Additive exPlanations (SHAP) contribution of various soil properties on target variables were also analyzed to reveal whether each input variable has a positive, negative, or complex effect. Subsequently, these parameters inform the construction of a detailed model across 23,692 subregions of China, with a 20 km × 20 km resolution. The model considered regional variations and soil layer heterogeneity, including rainfall, soil depth-specific properties, and parameters for adsorption, degradation, and dispersivity. Utilizing the convection-dispersion equations and the Phydrus, the model simulated atrazine's transport and degradation patterns across diverse soil environments after applying 250 mL of atrazine (40%) per Chinese mu. The outcomes provided a spatially explicit distribution of atrazine residues, specifying that the arid areas have the highest residual risk, followed by the Northeast, Southwest, and Southeast. Atrazine levels may exceed national drinking water standards at 50 cm depth in Inner Mongolia, the Qinghai-Tibet Plateau, and the Jungar Basin. This study's integrative approach may also offer valuable insights and tools for evaluating residues of various pesticides and herbicides in agricultural soils.

Radiation-induced exosomes promote oral squamous cell carcinoma progression via enhancing SLC1A5-glutamine metabolism.

BACKGROUND: Radiotherapy (RT) can drive cancer cells to enter a state of cellular senescence in which cells can secrete senescence-associated secretory phenotype (SASP) and produce small extracellular vesicles (sEVs) to interact with cells in the tumor microenvironment (TME). Tumor-derived sEVs that are taken up by recipient cells contribute to cancer cell metabolic plasticity, resistance to anticancer therapy, and adaptation to the TME. However, how radiation-induced sEVs support oral squamous cell carcinoma (OSCC) progression remains unclear. METHODS: Beta-galactosidase staining and SASP mRNA expression analysis were used to evaluate the senescence-associated activity of OSCC cells after irradiation. Nanoparticle tracking analysis was performed to identify radiation-induced sEVs. Liquid chromatography-tandem mass spectrometry (LC-MS) was used to explore changes in the levels of proteins in radiation-induced sEVs. Cell Counting Kit-8 and colony formation assays were performed to investigate the function of radiation-induced SASP and sEVs in vitro. A xenograft tumor model was established to investigate the functions of radiation-induced sEVs and V-9302 in vivo as well as the underlying mechanisms. Bioinformatics analysis was performed to determine the relationship between glutamine metabolism and OSCC recurrence. RESULTS: We determined that the radiation-induced SASP triggered OSCC cell proliferation. Additionally, radiation-induced sEVs exacerbated OSCC cell malignancy. LC-MS/MS and bioinformatics analyses revealed that SLC1A5, which is a cellular receptor that participates in glutamine uptake, was significantly enriched in radiation-induced sEVs. In vitro and in vivo, inhibiting SLC1A5 could block the oncogenic effects of radiation-induced sEVs in OSCC. CONCLUSION: Radiation-induced sEVs might promote the proliferation of unirradiated cancer cells by enhancing glutamine metabolism; this might be a novel molecular mechanism underlying radiation resistance in OSCC patients.

Prediction of atrazine degradation in soil based on XGBoost model.

We established the optimal model by using the automatic machine learning method to predict the degradation efficiency of herbicide atrazine in soil, which could be used to assess the residual risk of atrazine in soil. We collected 494 pairs of data from 49 published articles, and selected seven factors as input features, including soil pH, organic matter content, saturated hydraulic conductivity, soil moisture, initial concentration of atrazine, incubation time, and inoculation dose. Using the first-order reaction rate constant of atrazine in soil as the output feature, we established six models to predict the degradation efficiency of atrazine in soil, and conducted comprehensive analysis of model performance through linear regression and related evaluation indicators. The results showed that the XGBoost model had the best performance in predicting the first-order reaction rate constant (k). Based on the prediction model, the feature importance ranking of each factor was in an order of soil moisture > incubation time > pH > organic matter > initial concentration of atrazine > saturated hydraulic conductivity > inoculation dose. We used SHAP to explain the potential relationship between each feature and the degradation ability of atrazine in soil, as well as the relative contribution of each feature. Results of SHAP showed that time had a negative contribution and saturated hydraulic conductivity had a positive contribution. High values of soil moisture, initial concentration of atrazine, pH, inoculation dose and organic matter content were generally distributed on both sides of SHAP=0, indicating their complex contributions to the degradation of atrazine in soil. The XGBoost model method combined with the SHAP method had high accuracy in predicting the performance and interpretability of the k model. By using machine learning method to fully explore the value of historical experimental data and predict the degradation efficiency of atrazine using environmental parameters, it is of great significance to set the threshold for atrazine application, reduce the residual and diffusion risks of atrazine in soil, and ensure the safety of soil environment.

Consolidation chemotherapy after definitive concurrent chemoradiotherapy in patients with inoperable esophageal squamous cell carcinoma: a multicenter non-inferiority phase III randomized clinical trial.

BACKGROUND: Definitive concurrent chemoradiotherapy (dCCRT) is the gold standard for the treatment of locally advanced esophageal squamous cell carcinoma (ESCC). However, the potential benefits of consolidation chemotherapy after dCCRT in patients with esophageal cancer remain debatable. Prospective randomized controlled trials comparing the outcomes of dCCRT with or without consolidation chemotherapy in patients with ESCC are lacking. In this study, we aim to generate evidence regarding consolidation chemotherapy efficacy in patients with locally advanced, inoperable ESCC. METHODS: This is a multicenter, prospective, open-label, phase-III randomized controlled trial comparing non-inferiority of dCCRT alone to consolidation chemotherapy following dCCRT. In total, 600 patients will be enrolled and randomly assigned in a 1:1 ratio to receive either consolidation chemotherapy after dCCRT (Arm A) or dCCRT alone (Arm B). Overall survival will be the primary endpoint, whereas progression-free survival, locoregional progression-free survival, distant metastasis-free survival, and treatment-related toxicity will be the secondary endpoints. DISCUSSION: This study aid in further understanding the effects of consolidation chemotherapy after dCCRT in patients with locally advanced, inoperable ESCC. TRIAL REGISTRATION: ChiCTR1800017646.

Predicting bacterial transport through saturated porous media using an automated machine learning model.

Escherichia coli, as an indicator of fecal contamination, can move from manure-amended soil to groundwater under rainfall or irrigation events. Predicting its vertical transport in the subsurface is essential for the development of engineering solutions to reduce the risk of microbiological contamination. In this study, we collected 377 datasets from 61 published papers addressing E. coli transport through saturated porous media and trained six types of machine learning algorithms to predict bacterial transport. Eight variables, including bacterial concentration, porous medium type, median grain size, ionic strength, pore water velocity, column length, saturated hydraulic conductivity, and organic matter content were used as input variables while the first-order attachment coefficient and spatial removal rate were set as target variables. The eight input variables have low correlations with the target variables, namely, they cannot predict target variables independently. However, using the predictive models, input variables can effectively predict the target variables. For scenarios with higher bacterial retention, such as smaller median grain size, the predictive models showed better performance. Among six types of machine learning algorithms, Gradient Boosting Machine and Extreme Gradient Boosting outperformed other algorithms. In most predictive models, pore water velocity, ionic strength, median grain size, and column length showed higher importance than other input variables. This study provided a valuable tool to evaluate the transport risk of E.coli in the subsurface under saturated water flow conditions. It also proved the feasibility of data-driven methods that could be used for predicting other contaminants' transport in the environment.

TIPE3 represses head and neck squamous cell carcinoma progression via triggering PGAM5 mediated mitochondria dysfunction.

Mitochondria are essential organelles in balancing oxidative stress and cell death during cancer cell proliferation. Rapid tumor growth induces tremendous stress on mitochondria. The mammalian tumor necrosis factor-α-induced protein 8-likes (TIPEs) family plays critical roles in balancing cancer cell death and survival. Yet, the roles of TIPEs in HNSCC tumorigenesis and mitochondria stress maintenance is unclear. Based on an integrative analysis of public HNSCC datasets, we identified that the downregulation of TIPE3 via its promoter hypermethylation modification is the major event of TIPEs alterations during HNSCC tumorigenesis. Low expression levels of TIPE3 were correlated with high malignancy and poor clinical outcomes of HNSCC patients. Restoring TIPE3 represses HNSCC proliferation, migration, and invasion in vitro and in vivo, while silencing TIPE3 acted on an opposite way. Mechanistically, TIPE3 band to the PGAM5 and electron transport chain (ETC) complex. Restoring TIPE3 promoted PGAM5 recruiting BAX and dephosphorylating p-DRP1(Ser637), which triggered mitochondrial outer membrane permeabilization and fragmentation. Ultimately, TIPE3 induced ETC damage and oxygen consumption rate decrease, ROS accumulation, mitochondrial membrane potential depolarization, and cell apoptosis. Collectively, our work reveals that TIPE3 plays critical role in maintaining mitochondrial stress and cancer cell progression in HNSCC, which might be a potential therapeutic target for HNSCC patients.

Epigallocatechin-3-Gallate Decreases Hypoxia-Inducible Factor-1 in Pancreatic Cancer Cells.

Hypoxia-inducible factor-1 (HIF-1) is an [Formula: see text]/[Formula: see text] heterodimeric transcription factor. In normal mammalian cells, HIF-1[Formula: see text] is hydroxylated and degraded upon biosynthesis. However, HIF-1[Formula: see text] is frequently expressed in cancer and adds to cancer malignancy. In this study, we investigated whether green tea-derived epigallocatechin-3-gallate (EGCG) decreased HIF-1[Formula: see text] in pancreatic cancer cells. After MiaPaCa-2 and PANC-1 pancreatic cancer cells were exposed to EGCG in vitro, we performed a Western blot to determine native and hydroxylated HIF-1[Formula: see text], which was in turn used to assess HIF-1[Formula: see text] production. In order to assess HIF-1[Formula: see text] stability, we determined the HIF-1[Formula: see text] after MiaPaCa-2 and PANC-1 cells were switched from hypoxia to normoxia. We found that EGCG decreased both production and stability of HIF-1[Formula: see text]. Further, the EGCG-induced decrease in HIF-1[Formula: see text] reduced intracellular glucose transporter-1 and glycolytic enzymes and attenuated glycolysis, ATP production, and cell growth. Because EGCG is known to inhibit cancer-induced insulin receptor (IR) and insulin-like growth factor-1 receptor (IGF1R), we created three MiaPaCa-2 sublines whose IR, IGF1R, and HIF-1[Formula: see text] were decreased using RNA interference. From wild-type MiaPaCa-2 cells and these sublines, we found evidence that suggested that the EGCG-induced inhibition of HIF-1[Formula: see text] was both dependent on and independent of IR and IGF1R. In vivo, we transplanted wild-type MiaPaCa-2 cells in athymic mice and treated the mice with EGCG or vehicle. When the resulting tumors were analyzed, we found that EGCG decreased tumor-induced HIF-1[Formula: see text] and tumor growth. In conclusion, EGCG decreased HIF-1[Formula: see text] in pancreatic cancer cells and sabotaged the cells. The anticancer effects of EGCG were both dependent on and independent of IR and IGF1R.

BASP1 is a prognostic biomarker associated with immunotherapeutic response in head and neck squamous cell carcinoma.

Backgrounds: Immunotherapy is effective in a subset of head and neck squamous cell carcinoma (HNSCC). However, the unfavorable response rate and inadequate biomarkers for stratifying patients have primarily limited its clinical application. Considering transcriptional factors (TFs) play essential roles in regulating immune activity during HNSCC progression, we comprehensively analyzed the expression alterations of TFs and their prognostic values. Methods: Gene expression datasets and clinical information of HNSCC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repository. Then, Brain abundant membrane attached signal protein 1 (BASP1) was screened out of differentially expressed TFs by univariate and multivariate survival analysis. Tumor immune dysfunction and exclusion (TIDE) was applied to analyze the response to immunotherapy of BASP1high/low patients. Meanwhile, GO, KEGG and GSEA analyses were used to enrich the pathways between the BASP1high and BASP1low groups. Single-sample gene set enrichment analysis (ssGSEA), CIBERSORT, EPIC and quanTiseq algorithms were applied to explore immune infiltrations. Also, immune cycle analysis was conducted by ssGSEA. Additionally, lipid peroxidation, glutathione and reactive oxygen species were performed to detect the ferroptosis alternations. Results: BASP1 was upregulated and associated with poor survival in HNSCC patients. BASP1high patients exhibited better response rates to anti-PD-1 immunotherapy and higher expressions of immune checkpoint inhibitors. GO, KEGG and GSEA analyses indicated that the expression of BASP1 was related to several immune-related pathways and immunogenic ferroptosis signature. The infiltration of activated CD8+ T cells was authenticated to be decreased in BASP1high patients. Furthermore, BASP1 was identified to be positively correlated with T cell dysfunction and immune escape. Moreover, silencing BASP1 triggered ferroptosis in HNSCC cells, representing as increased LDH, lipid peroxidation and ROS levels, and reduced glutathione synthesis. Conclusions: We demonstrated that BASP1 suppressed immunogenic ferroptosis to induce immunosuppressive tumor microenvironment. BASP1 plays a critical role in immune response, and might be a promising classifier for selecting HNSCC patients who benefit from current immunotherapy.