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Abstract Pseudouridine widely affects the stability and function of RNA. However, our knowledge of pseudouridine properties in tumors is incomplete. We systematically analyzed pseudouridine synthases (PUSs) expression, genomic aberrations, and prognostic features in 10907 samples from 33 tumors. We found that the pseudouridine-associated pathway was abnormal in tumors and affected patient prognosis. Dysregulation of the PUSs expression pattern may arise from copy number variation (CNV) mutations and aberrant DNA methylation. Functional enrichment analyses determined that the PUSs expression was closely associated with the MYC, E2F, and MTORC1 signaling pathways. In addition, PUSs are involved in the remodeling of the tumor microenvironment (TME) in solid tumors, such as kidney and lung cancers. Particularly in lung cancer, increased expression of PUSs is accompanied by increased immune checkpoint expression and Treg infiltration. The best signature model based on more than 112 machine learning combinations had good prognostic ability in ACC, DLBC, GBM, KICH, MESO, THYM, TGCT, and PRAD tumors, and is expected to guide immunotherapy for 19 tumor types. The model was also effective in identifying patients with tumors amenable to etoposide, camptothecin, cisplatin, or bexarotene treatment. In conclusion, our work highlights the dysregulated features of PUSs and their role in the TME and patient prognosis, providing an initial molecular basis for future exploration of pseudouridine. Studies targeting pseudouridine are expected to lead to the development of potential diagnostic strategies and the evaluation and improvement of antitumor therapies.

Abstract Aims Neutrophil extracellular traps (NETs) have been implicated in thrombotic diseases. There is no definitive explanation for how NETs form during acute ischemic strokes (AIS). The purpose of our study was to investigate the potential mechanism and role of NETs formation in the AIS process. Methods As well as 45 healthy subjects, 45 patients with AIS had ELISA tests performed to detect NET markers. Expression of high-mobility group box 1 (HMGB1) on platelet microvesicles (PMVs) was analyzed by flow cytometry in healthy subjects and AIS patients’ blood samples. We established middle cerebral artery occlusion (MCAO) mice model to elucidate the interaction between PMPs and NETs. Results A significant elevation in NET markers was found in patient plasma in AIS patients, and neutrophils generated more NETs from patients’ neutrophils. HMGB1 expression was upregulated on PMVs from AIS patients and induced NET formation. NETs enhanced Procoagulant activity (PCA) through tissue factor and via platelet activation. Targeting lactadherin in genetical and in pharmacology could regulate the formation of NETs in MCAO model. Conclusions NETs mediated by PMVs derived HMGB1 exacerbate thrombosis and brain injury in AIS. Video Abstract

Intra-tumoral necrosis (ITN) is reported to be an independent prognostic factor in glioma. However, knowledge of ITN is mainly limited to pseudopalisadwe, while its other aspects were neglected. Therefore, a deeper understanding of ITN could be valuable for understanding its exact role in glioma. The only reliable ITN model was time-dependently achieved with the GL261 syngeneic mouse model. The ITN-associated expression pattern was enriched from RNA sequencing. TCGA glioma samples were clustered into a high-expression group (HEG) and a low-expression group (LEG) based on their pattern and their association with prognosis, clinical status, immune status, and therapeutic responsiveness were compared. Mouse glioma with ITN demonstrated invasive histology. Cytokine signaling was significantly enriched in necrotic mouse glioma compared with non-necrotic glioma tissues. Nine pro-inflammatory (IL6, PPBP, IL1A, TNFSF11, CXCL11, CXCL9, CXCL10, CXCL3, and CCL8) and two anti-inflammatory cytokine (IL1RN and IL10) genes were found to be related to ITN-associated cytokine patterns. Comparative analysis showed that HEG had a significantly shorter survival time, five differentially distributed clinical statuses, more infiltrated immune cells, greater expression of immune checkpoints, and better therapeutic responsiveness than LEG. In conclusion, the ITN-associated cytokine pattern is characteristically expressed in glioma with ITN and might indicate necrosis missed in histology diagnosis. Its expression pattern could predict the prognosis, tumor grade, immune status, and therapeutic responsiveness of glioma patients.

BackgroundPhotodynamic therapy (PDT) promotes significant tumor regression and extends the lifetime of patients. The actual operation of PDT often relies on the subjective judgment of experienced neurosurgeons. Patients can benefit more from precisely targeting PDT’s key operating zones.MethodsWe used magnetic resonance imaging scans and created 3D digital models of patient anatomy. Multiple images are aligned and merged in STL format. Neurosurgeons use HoloLens to import reconstructions and assist in PDT execution. Also, immunohistochemistry was used to explore the association of hyperperfusion sites in PDT of glioma with patient survival.ResultsWe constructed satisfactory 3D visualization of glioma models and accurately localized the hyperperfused areas of the tumor. Tumor tissue taken in these areas was rich in CD31, VEGFA and EGFR that were associated with poor prognosis in glioma patients. We report the first study using MR technology combined with PDT in the treatment of glioma. Based on this model, neurosurgeons can focus PDT on the hyperperfused area of the glioma. A direct benefit was expected for the patients in this treatment.ConclusionUsing the Mixed Reality technique combines multimodal imaging signatures to adjuvant glioma PDT can better exploit the vascular sealing effect of PDT on glioma.

Necroptosis is a programmed form of necrotic cell death that serves as a host gatekeeper for defense against invasion by certain pathogens. Previous studies have uncovered the essential role of necroptosis in tumor progression and implied the potential for novel therapies targeting necroptosis. However, no comprehensive analysis of multi-omics data has been conducted to better understand the relationship between necroptosis and tumor. We developed the necroptosis index (NI) to uncover the effect of necroptosis in most cancers. NI not only correlated with clinical characteristics of multiple tumors, but also could influence drug sensitivity in glioma. Based on necroptosis-related differentially expressed genes, the consensus clustering was used to classify glioma patients into two NI subgroups. Then, we revealed NI subgroup I were more sensitive to immunotherapy, particularly anti-PD1 therapy. This new NI-based classification may have prospective predictive factors for prognosis and guide physicians in prioritizing immunotherapy for potential responders.

Rapid non-destructive testing technologies are effectively used to analyze and evaluate the linoleic acid content while processing fresh meat products. In current study, hyperspectral imaging (HSI) technology was combined with deep learning optimization algorithm to model and analyze the linoleic acid content in 252 mixed red meat samples. A comparative study was conducted by experimenting mixed sample data preprocessing methods and feature wavelength extraction methods depending on the distribution of linoleic acid content. Initially, convolutional neural network Bi-directional long short-term memory (CNN-Bi-LSTM) model was constructed to reduce the loss of the fully connected layer extracted feature information and optimize the prediction effect. In addition, the prediction process of overfitting phenomenon in the CNN-Bi-LSTM model was also targeted. The Bayesian-CNN-Bi-LSTM (Bayes-CNN-Bi-LSTM) model was proposed to improve the linoleic acid prediction in red meat through iterative optimization of Gaussian process acceleration function. Results showed that best preprocessing effect was achieved by using the detrending algorithm, while 11 feature wavelengths extracted by variable combination population analysis (VCPA) method effectively contained characteristic group information of linoleic acid. The Bi-directional LSTM (Bi-LSTM) model combined with the feature extraction data set of VCPA method predicted 0.860 Rp2 value of linoleic acid content in red meat. The CNN-Bi-LSTM model achieved an Rp2 of 0.889, and the optimized Bayes-CNN-Bi-LSTM model was constructed to achieve the best prediction with an Rp2 of 0.909. This study provided a reference for the rapid synchronous detection of mixed sample indicators, and a theoretical basis for the development of hyperspectral on-line detection equipment.

Extra-neural spread of glioblastoma (GBM) is extremely rare. We report a case of postoperative intracranial GBM spreading to the subcutaneous tissue via the channel of craniotomy defect in a 73-year-old woman. Radiological images and histopathology indicate that the tumor microenvironment of the subcutaneous tumor is clearly different from the intracranial tumor. We also model the invasion of GBM cells through the dura-skull defect in mouse. The retrospective analysis of GBM with scalp metastases suggests that craniectomy is a direct cause of subcutaneous metastasis in patients with GBM. Imaging examinations of other sites for systemic screening is also recommended to look for metastases outside the brain when GBM invades the scalp or metastasizes to it.

BackgroundCancer-associated fibroblasts (CAFs) are vital components of prominent cellular components in lower-grade gliomas (LGGs) that contribute to LGGs’ progression, treatment resistance, and immunosuppression. Epigenetic modification and immunity have significant implications for tumorigenesis and development.MethodsWe combined aberrant methylation and CAFs abundances to build a prognostic model and the impact on the biological properties of LGGs. Grouping based on the median CAFs abundances score of samples in the TCGA-LGGs dataset, differentially expressed genes and aberrantly methylated genes were combined for subsequent analysis.ResultsWe identified five differentially methylated and expressed genes (LAT32, SWAP70, GSAP, EMP3, and SLC2A10) and established a prognostic gene signature validated in the CGGA-LGGs dataset. Immunohistochemistry (IHC) and in vitro tests were performed to verify these expressions. The high-risk group increased in tumor-promoting immune cells and tumor mutational burden. Notably, risk stratification had different ICB sensitivities in LGGs, and there were also significant sensitivity differences for temozolomide and the other three novel chemotherapeutic agents.ConclusionOur study reveals characteristics of CAFs in LGGs, refines the direct link between epigenetics and tumor stroma, and might provide clinical implications for guiding tailored anti-CAFs therapy in combination with immunotherapy for LGGs patients.

Background: The CCN family of stromal proteins is involved in the regulation of many important biological functions. However, the role of dysregulated CCN proteins in lower-grade glioma (LGG) remain less understand.Methods: The clinical significance of the CCN proteins was explored based on RNA-seq profiles from multiple cohorts. A CCNScore was constructed using LASSO regression analysis. The PanCanAtlas data and MEXPRESS database were employed to elucidate molecular underpinnings.Results: The expression of CCN4 was associated with poor prognosis in LGG. The CCNScore (CCN1 = 0.06, CCN4 = 0.86) showed implication in prognosis prediction, subtype assessment and therapy selection. The gene mutation pattern of the high-CCNScore group was similar with glioblastoma, including EGFR, PTEN, and NF1 mutation frequently. Besides, the high-CCNScore group was comprised of samples mainly classic-like and mesenchymal-like, had lower methylation levels, higher stemness, higher inflammation, higher levels of extracellular matrix remodel and dysfunction of metabolic pathways. On the other hand, the low-CCNScore group consisted mainly of IDH-mutation LGG, and was characterized by TP53, CIC, and ATRX gene mutations, hyper-methylation status, lower stemness, lower proliferation, immune quietness and low extracellular matrix stiffness.Conclusion: In summary, these results outlined the role of CCN family in LGG and provided a potential and promising therapeutic target.

The pathological implications of tumor-associated macrophages in the glioma microenvironment have been highlighted, while there lacks a gene signature to characterize the functional status and clinical implications of these cells. Comprehensive bioinformatics approaches were employed to develop an M2 macrophage-associated gene signature at bulk-tumor and single-cell levels and explore immunological and metabolic features. Consequently, the PI3K pathway and fatty acid metabolism were correlated with the M2 fraction. Further distilling the pathway members resulted in a leukotriene synthesis-related gene signature (Macro index), including PIK3R5, PIK3R6, ALOX5, ALOX5AP, and ALOX15B, that was primarily expressed by monocytes/macrophages. Increased Macro index predicted IL13-induced macrophages, and was associated with T-cell dysfunction at both transcriptional and epigenetic levels and predicted an unfavorable outcome. Besides, the Macro index was proportional with PAI1 at the protein level, with high levels of the latter suggesting a decreased progression-free interval of glioblastoma. Notably, the monocytes/macrophages in the glioma environment contribute to the expression of immune checkpoints and the Macro index predicts glioma responsiveness to anti-PD1 treatment. Together, our study proposed a leukotriene synthesis-related M2 macrophage gene signature, which may provide insights into the role of these cells in the glioma microenvironment and facilitate individually tailored therapeutic strategies for the disease.

BackgroundThe IGFBP family of insulin-like growth factor binding proteins has important biological functions in the organism. However, the role of the IGFBP family in low-grade glioma (LGG) has not been fully explored.MethodsWe validated the clinical value of the IGFBP family using RNA-seq and clinical data of LGG in the TCGA and constructed an IGFBPScore using LASSO-regression analysis for prognosis prediction, subtype determination, and treatment sensitivity determination. Subsequently, we explored the role of the IGFBP family in the development of LGG using PanCanAtlas data.ResultsOur results suggest that most IGFBP family members were aberrantly expressed and were strongly associated with poor prognosis in LGG. By constructing an IGFBPScore representing the IGFBP family, we found that tumor samples with a high IGFBPScore had a glioblastoma-like mutation pattern characterized by IDH1wt, EGFRmut, PTENmut, and NF1mut with hypo-methylation and glioma stem cell (GSC) diversity. In contrast, the low IGFBPScore group was characterized by IDH1mut accompanied by TP53mut, CICmut, and ATRXmut, and had hyper-methylation status as well as the GSC restriction. Additionally, the high-IGFBPScore group had a high inflammation phenotype with increased immune antigenicity and increased infiltration of immune molecules and cells, as well as a high extracellular matrix phenotype and enhanced multiple metabolic pathways compared with the immune-quiet phenotype of the low-IGFBPScore group, which was strongly associated with poor prognosis.ConclusionOur study provides a summary analysis and a theoretical basis for the biological role and clinical value of the IGFBP family in LGG, providing an important therapeutic target for LGG.

Cuproptosis is a new type of cell death that is associated with mitochondrial respiration of the tricarboxylic acid cycle. Previous studies showed that long non-coding RNAs (lncRNAs) regulated low-grade glioma (LGG) progression. However, the potential applications of cuproptosis-related lncRNAs (CRLs) in LGG were not explored. A comprehensive analysis was performed in The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) cohorts. We first screened two distinct cuproptosis subtypes based on prognostic CRLs using consensus clustering. To facilitate individualized survival prediction in LGG, we constructed a prognostic signature (including CRNDE, HAR1A, and FAM181A-AS1) in the TCGA dataset. The prognostic signature exhibited excellent predictive ability and reliability, which was validated in the CGGA_325 and CGGA_693 datasets. Notably, patients in the high-risk group had increased immune cell infiltration and expression of immune checkpoints, which indicated that they may benefit more from immune checkpoint blockade (ICB) therapy. Finally, the prognostic signature screened the population with sensitivity to chemotherapy and ICB therapy. In summary, this study initially explored the mechanism of CRLs in LGG and provides some insights into chemotherapy and ICB therapy of LGG.

Epithelial-mesenchymal transition (EMT) confers high invasive and migratory capacity to cancer cells, which limits the effectiveness of tumor therapy. Long non-coding RNAs (lncRNAs) can regulate the dynamic process of EMT at different levels through various complex regulatory networks. We aimed to comprehensively analyze and screen EMT-related lncRNAs to characterize lower-grade glioma (LGG) tumor biology and provide new ideas for current therapeutic approaches. We retrieved 1065 LGG samples from the Cancer Genome Atlas and Chinese Glioma Genome Atlas by machine learning algorithms, identified three hub lncRNAs including CRNDE, LINC00665, and NEAT1, and established an EMT-related lncRNA signature (EMTrLS). This novel signature had strong prognostic value and potential clinical significance. EMTrLS described LGG genomic alterations and clinical features including gene mutations, tumor mutational burden, World Health Organization (WHO) grade, IDH status, and 1p/19q status. Notably, stratified analysis revealed activation of malignancy-related and metabolic pathways in the EMTrLS-high cohort. Moreover, the population with increased EMTrLS scores had increased cells with immune killing function. However, this antitumor immune function may be suppressed by increased Tregs and macrophages. Meanwhile, the relatively high expression of immune checkpoints explained the immunosuppressive state of patients with high EMTrLS scores. Importantly, we validated this result by quantifying the course of antitumor immunity. In particular, EMTrLS stratification enabled assessment of the responsiveness of LGG to chemotherapeutic drug efficacy and PD1 blockade. In conclusion, our findings complement the foundation of molecular studies of LGG, provide valuable insight into our understanding of EMT-related lncRNAs, and offer new strategies for LGG therapy.

Glioma is one of the most human malignant diseases and the leading cause of cancer-related deaths worldwide. Nevertheless, the present stratification systems do not accurately predict the prognosis and treatment benefit of glioma patients. Currently, no comprehensive analyses of multi-omics data have been performed to better understand the complex link between pyroptosis and immune. In this study, we constructed four pyroptosis immune subgroups by pyroptosis regulators and obtained nine pyroptosis immune signatures by analyzing the differentially expressed genes between the four pyroptosis immune subgroups. Nine novel pyroptosis immune signatures were provided for assessing the complex heterogeneity of glioma by the analyses of multi-omics data. The pyroptosis immune prognostic model (PIPM) was constructed by pyroptosis immune signatures, and the PIPM risk score was established for glioma cohorts with a total of 1716 samples. Then, analyses of the tumor microenvironment revealed an unanticipated correlation of the PIPM risk score with stemness, immune checkpoint expression, infiltrating the immune system, and therapy response in glioma. The low PIPM risk score patients had a better response to immunotherapy and showed sensitivity to radio-chemotherapy. The results of the pan-cancer analyses revealed the significant correlation between the PIPM risk score and clinical outcome, immune infiltration, and stemness. Taken together, we conclude that pyroptosis immune signatures may be a helpful tool for overall survival prediction and treatment guidance for glioma and other tumors patients.

The epithelial-mesenchymal transition (EMT) is an important process that drives progression, metastasis, and oncology treatment resistance in cancers. Also, the adjacent non-tumor tissue may affect the biological properties of cancers and have potential prognostic implications. Our study aimed to identify EMT-related genes in LGG samples, explore their impact on the biological properties of lower grade gliomas (LGG) through the multi-omics analysis, and reveal the potential mechanism by which adjacent non-tumor tissue participated in the malignant progression of LGG. Based on the 121 differentially expressed EMT-related genes between normal samples from the GTEx database and LGG samples in the TCGA cohort, we identified two subtypes and constructed EMTsig. Because of the genetic, epigenetic, and transcriptomic heterogeneity, malignant features including clinical traits, molecular traits, metabolism, anti-tumor immunity, and stemness features were different between samples with C1 and C2. In addition, EMTsig could also quantify the EMT levels, variation in prognosis, and oncology treatment sensitivity of LGG patients. Therefore, EMTsig could assist us in developing objective diagnostic tools and in optimizing therapeutic strategies for LGG patients. Notably, with the GSVA, we found that adjacent non-tumor tissue might participate in the progression, metastasis, and formation of the tumor microenvironment in LGG. Therefore, the potential prognostic implications of adjacent non-tumor tissue should be considered when performing clinical interventions for LGG patients. Overall, our study investigated and validated the effects of EMT-related genes on the biological properties from multiple perspectives, and provided new insights into the function of adjacent non-tumor tissue in the malignant progression of LGG.

Background: Although ferroptosis has been validated to play a crucial role in some types of tumors, the influence of ferroptosis-related genes (FRGs) on the immune microenvironment in low-grade glioma (LGG) remains unclear. In this research, we screen the FRGs to assess the prognosis value and immune microenvironment in LGG, to provide reliable diagnosis and treatment evidence for the clinic.Methods: A total of 1,239 patients of LGG samples were selected for subsequent analyses from The Cancer Genome Atlas, Chinese Glioma Genome Atlas, and the Repository of Molecular Brain Neoplasia Data datasets. Univariate Cox regression analysis was used to screen for prognostic FRGs. Consensus clustering was utilized to determine ferroptosis subtypes of LGG patients. Next, the prognostic model was constructed based on differentially expressed FRGs and validation in the validating datasets. The immune microenvironment, biological pathway, and hypoxia score were explored by single-sample gene set enrichment analysis. The potential response of chemotherapy and immune checkpoint blockade therapy was also estimated. In addition, the correlation between the risk score and autophagy-related genes was examined by the Pearson correlation coefficient.Results: A total of three ferroptosis subtypes were identified by consensus clustering for prognostic FRGs which exhibited different outcomes, clinicopathological characteristics, and immune microenvironment. Afterward, a prognostic model that performed great predictive ability based on nine prognostic FRGs has been constructed and validated. Moreover, the prognostic model had the potential to screen the sensitivity to chemotherapy and immunotherapy in LGG patients. Finally, we also found that the prognostic model has a great connection to autophagy and hypoxia.Conclusion: We developed a ferroptosis-related prognostic model which strongly linked to diagnosis, treatment, prognosis, and recurrence of LGG. This study also reveals the connection between ferroptosis and tumor immune microenvironment.

5-Methylcytosine (m5C) methylation is an important RNA modification pattern that can participate in oncogenesis and progression of cancers by affecting RNA stability, expression of oncogenes, and the activity of cancer signaling pathways. Alterations in the expression pattern of long non-coding RNAs (lncRNAs) are potentially correlated with abnormalities in the m5C regulation features of cancers. Our aim was to reveal the mechanisms by which lncRNAs regulated the m5C process, to explore the impact of aberrant regulation of m5C on the biological properties of lower-grade gliomas (LGG), and to optimize current therapeutic. By searching 1017 LGG samples from the Cancer Genome Atlas and Chinese Glioma Genome Atlas, we first clarified the potential impact of m5C regulators on LGG prognosis in this study and used univariate Cox analysis and least absolute shrinkage and selection operator regression to explore clinically meaningful lncRNAs. Consequently, we identified four lncRNAs, including LINC00265, CIRBP-AS1, GDNF-AS1, and ZBTB20-AS4, and established a novel m5C-related lncRNAs signature (m5CrLS) that was effective in predicting prognosis. Notably, mutation rate, WHO class II, IDH mutation, 1p/19q co-deletion and MGMT promoter methylation were increased in the low m5CrLS score group. Patients with increased m5CrLS scores mostly showed activation of tumor malignancy-related pathways, increased immune infiltrating cells, and decreased anti-tumor immune function. Besides, the relatively high expression of immune checkpoints also revealed the immunosuppressed state of patients with high m5CrLS scores. In particular, m5CrLS stratification was sensitive to assess the efficacy of LGG to temozolomide and the responsiveness of immune checkpoint blockade. In conclusion, our results revealed the molecular basis of LGG, provided valuable clues for our understanding of m5C-related lncRNAs, and filled a gap between epigenetics and tumor microenvironment.

Ferroptosis, a recently discovered regulated programmed cell death, is associated with tumorigenesis and progression in glioblastoma. Based on widely recognized ferroptosis-related genes (FRGs), the regulation of ferroptosis patterns and corresponding characteristics of immune infiltration of 516 GBM samples with GSE13041, TCGA-GBM, and CGGA-325 were comprehensively analyzed. Here, we revealed the expression, mutations, and CNV of FRGs in GBM. We identified three distinct regulation patterns of ferroptosis and found the hub genes of immunity and stemness among DEGs in three patterns. A prognostic model was constructed based on five FRGs and verified at the mRNA and protein level. The risk score can not only predict the prognosis but also the degree of immune infiltration and ICB responsiveness by functional annotation. The overall assessment of FRGs in GBM patients will guide the direction of improved research and develop new prognostic prediction tools.

Effective treatment of glioblastoma (GBM) remains an open challenge. Given the critical role of the immune microenvironment in the progression of cancers, we aimed to develop an immune-related gene (IRG) signature for predicting prognosis and improving the current treatment paradigm of GBM. Multi-omics data were collected, and various bioinformatics methods, as well as machine learning algorithms, were employed to construct and validate the IRG-based signature and to explore the characteristics of the immune microenvironment of GBM. A five-gene signature (ARPC1B, FCGR2B, NCF2, PLAUR, and S100A11) was identified based on the expression of IRGs, and an effective prognostic risk model was developed. The IRG-based risk model had superior time-dependent prognostic performance compared to well-studied molecular pathology markers. Besides, we found prominent inflamed features in the microenvironment of the high-risk group, including neutrophil infiltration, immune checkpoint expression, and activation of the adaptive immune response, which may be associated with increased hypoxia, epidermal growth factor receptor (EGFR) wild type, and necrosis. Notably, the IRG-based risk model had the potential to predict the effectiveness of radiotherapy. Together, our study offers insights into the immune microenvironment of GBM and provides useful information for clinical management of this desperate disease.

The progression of most human cancers mainly involves the gradual accumulation of the loss of differentiated phenotypes and the sequential acquisition of progenitor and stem cell-like features. Glioblastoma multiforme (GBM) stem cells (GSCs), characterized by self-renewal and therapeutic resistance, play vital roles in GBM. However, a comprehensive understanding of GBM stemness remains elusive. Two stemness indices, mRNAsi and EREG-mRNAsi, were employed to comprehensively analyze GBM stemness. We observed that mRNAsi was significantly related to multi-omics parameters (such as mutant status, sample type, transcriptomics, and molecular subtype). Moreover, potential mechanisms and candidate compounds targeting the GBM stemness signature were illuminated. By combining weighted gene co-expression network analysis with differential analysis, we obtained 18 stemness-related genes, 10 of which were significantly related to survival. Moreover, we obtained a prediction model from both two independent cancer databases that was not only an independent clinical outcome predictor but could also accurately predict the clinical parameters of GBM. Survival analysis and experimental data confirmed that the five hub genes (CHI3L2, FSTL3, RPA3, RRM2, and YTHDF2) could be used as markers for poor prognosis of GBM. Mechanistically, the effect of inhibiting the proliferation of GSCs was attributed to the reduction of the ratio of CD133 and the suppression of the invasiveness of GSCs. The results based on an in vivo xenograft model are consistent with the finding that knockdown of the hub gene inhibits the growth of GSCs in vitro. Our approach could be applied to facilitate the development of objective diagnostic and targeted treatment tools to quantify cancer stemness in clinical tumors, and perhaps lead considerable benefits that could predict tumor prognosis, identify new stemness-related targets and targeted therapies, or improve targeted therapy sensitivity. The five genes identified in this study are expected to be the targets of GBM stem cell therapy.

Numerical simulation and analysis are effective means to explore the basic laws of the world, in which a synthetic model provides a model basis for research. A random medium model can effectively characterize the small-scale heterogeneity in artificial or natural complex media and has been widely used in reservoir, deposit, material, engineering, and other fields. Many efficient and accurate modeling methods have been proposed for stationary random medium models. However, the modeling efficiency of a random medium with nonstationary statistical characteristics is very low. In this paper, an efficient nonstationary random medium modeling method based on Fast Fourier transform moving average (FFT-MA) method and convolutional neural network is proposed. FFT-MA is used to quickly simulate a low-resolution random medium model, which satisfies the change of large-scale statistical characteristics. Then we transform the low-resolution model into a high-resolution model by a convolutional neural network. The convolutional neural network is trained using a set of stationary random medium models. The modeling experiments show that this method can accurately construct a nonstationary random medium model meeting the nonstationary statistical characteristics and improves the modeling efficiency.

Although the functions of metabolic enzymes and nuclear receptors in controlling physiological homeostasis have been established, their crosstalk in modulating metabolic disease has not been explored. Genetic ablation of the xenobiotic-metabolizing cytochrome P450 enzyme CYP2E1 in mice markedly induced adipose browning and increased energy expenditure to improve obesity. CYP2E1 deficiency activated the expression of hepatic peroxisome proliferator-activated receptor alpha (PPARα) target genes, including fibroblast growth factor (FGF) 21, that upon release from the liver, enhanced adipose browning and energy expenditure to decrease obesity. Nineteen metabolites were increased in Cyp2e1-null mice as revealed by global untargeted metabolomics, among which four compounds, lysophosphatidylcholine and three polyunsaturated fatty acids were found to be directly metabolized by CYP2E1 and to serve as PPARα agonists, thus explaining how CYP2E1 deficiency causes hepatic PPARα activation through increasing cellular levels of endogenous PPARα agonists. Translationally, a CYP2E1 inhibitor was found to activate the PPARα–FGF21–beige adipose axis and decrease obesity in wild-type mice, but not in liver-specific Ppara-null mice. The present results establish a metabolic crosstalk between PPARα and CYP2E1 that supports the potential for a novel anti-obesity strategy of activating adipose tissue browning by targeting the CYP2E1 to modulate endogenous metabolites beyond its canonical role in xenobiotic-metabolism.

Abstract Background Dried fruits of Psoralea corylifolia L. (Psoraleae Fructus) is one of the most popular traditional Chinese medicine with treatment for nephritis, spermatorrhea, pollakiuria, asthma, and various inflammatory diseases. Bakuchiol is main meroterpenoid with bioactive diversity from Psoraleae Fructus. This study was designed to seek structural diverse bakuchiol derivants with anti-inflammatory activities from this plant. Methods Various column chromatography methods were used for isolation experiment. Structures and configurations of these compounds were determined by spectroscopic methods and single-crystal X-ray diffraction. Their inhibition on nitric oxide (NO) production in lipopolysaccharide (LPS)-stimulated RAW264.7 macrophages were evaluated by the Griess reaction. Results Twelve unpresented bakuchiol dimmers, bisbakuchiols M–U (1–9) and bisbakuchiol ethers A–C (10–12), along with five known compounds (13–17), were isolated from the fruits of Psoralea corylifolia L. Compounds 1–3, 10–12, 16 and 17 exhibited inhibitory activities against LPS-induced NO production in RAW264.7 macrophages, and the inhibition of compound 1 (half maximal inhibitory concentration (IC50) value = 11.47 ± 1.57 μM) was equal to that of L-N(6)-(1-iminoethyl)-lysine (IC50 = 10.29 ± 1.10 μM) as a positive control. Conclusions Some compounds exhibited inhibitory activities against NO production, and the study of structure–activity relationship suggested that uncyclized compounds with oxygen substitution at C-12/12′ showed strong inhibitory activities, and carbonyl units contributed to enhanced activities.

Dendranthema zawadskii Herbich is one of 17 species of Dendranthema in China, and it is often used as an ornamental plant. The chloroplast genome size of Dendranthema zawadskii Herbich is 150,995 bp, including a large single-copy region (82,771 bp), a small single-copy region (18,308 bp), and a pair of inverted repeats regions (24,958 bp). Total 112 genes were annotated, including 79 protein-coding genes, four ribosomal RNA genes, and 29 transfer RNA genes. The phylogenetic position of Dendranthema zawadskii Herbich is close to Dendranthema indicum.

Abstract Background Fukeqianjin formula (FKQJF) is a Chinese medicine prescription, which has been widely used individually or in combination with other western medicine for the treatment of various gynecological inflammatory diseases, including chronic cervicitis, chronic pelvic inflammatory disease and endometritis, so on and so force. Methods The ultra-fast liquid chromatography coupled with triple quadrupole tandem mass spectrometry (UFLC–MS/MS), a quick and efficient method was established and applied to quantify the major constituents of Fukeqianjin formula in rat plasma, and its pharmacokinetics of oral absorption was studied. Nineteen components in Fukeqianjin formula were detected and identified as the major compounds absorbed into the blood according to their chromatographic behavior, molecular weight, ion fragments and other information of these compounds. Furthermore, the plasma drug concentration–time curves were established and the related kinetic parameters were analyzed. Results The results showed that all the 19 compounds could be rapidly absorbed by the gastrointestinal tract, the plasma drug concentration of most compounds could reach a peak at around 1–2 h, and the double-peaks on behalf of the enterohepatic circulation were found in most drug concentration–time curves. The method used in this experiment was validated comprehensively including specificity, linearity, precision, accuracy, stability, matrix effect, and recovery. Conclusions These results showed that the developed method was suitable for pharmacokinetic analysis of the main components of Fukeqianjin formula in rat plasma, and may provide useful information for the subsequent distribution studies in vivo.

Abstract Background Maize (Zea mays L.) is one of the main agricultural crops with the largest yield and acreage in the world. However, maize germplasm is very sensitive to low temperatures, mainly during germination, and low temperatures significantly affect plant growth and crop yield. Therefore, the identification of genes capable of increasing tolerance to low temperature has become necessary. Results In this study, fourteen phenotypic traits related to seed germination were used to assess the genetic diversity of maize through genome-wide association study (GWAS). A total of 30 single-nucleotide polymorphisms (SNPs) linked to low-temperature tolerance were detected (−log10(P) > 4), fourteen candidate genes were found to be directly related to the SNPs, further additional 68 genes were identified when the screen was extended to include a linkage disequilibrium (LD) decay distance of r 2 ≥ 0.2 from the SNPs. RNA-sequencing (RNA-seq) analysis was then used to confirm the linkage between the candidate gene and low-temperature tolerance. A total of ten differentially expressed genes (DEGs) (|log2 fold change (FC)| ≥ 0.585, P

Seismic impedance inversion is an important method to identify the spatial characteristics of underground rock physical properties. Seismic inversion results and uncertainty evaluation are the important scientific basis for risk decision-making in oil and gas development. Under the assumption that the impedance and the error of the observed seismic data meet the Gaussian distribution or log–Gaussian distribution, the Bayesian linear seismic inversion can analytically obtain the posterior probability distribution of impedance. However, errors from observation, calculation, model and other factors can lead to an inaccurate and incomplete uncertainty evaluation. In this paper, the noise variance is used to represent the noise level of seismic data and the uncertainties from seismic wavelet extraction and noise level estimation are considered in inversion. Assuming that the probability distribution of the noise variance meets the inverse gamma distribution and the seismic wavelet meets the Gaussian distribution, we could obtain the conditional distribution for one variable given another analytically using well-log data and seismic data. In order to integrate the uncertainty from noise level estimation and wavelet extraction into the seismic impedance inversion, the Gibbs sampler algorithm was applied to draw a set of realizations of noise variance and wavelet. For each realization, the corresponding posterior probability model of impedance was achieved by Bayesian linear inversion and the final posterior probability of the impedance model was obtained by integrating all the single posterior probabilities for each pair of wavelet and noise variance. Synthetic and real data experiments showed that the uncertainties of seismic wavelet extraction and noise level estimation have an important influence on inversion results and their uncertainties. The proposed method could effectively integrate the uncertainty of wavelet and noise estimation to obtain a more accurate and comprehensive uncertainty evaluation. Under the assumption that the model meets the linear relationship and the parameters meet some specified distribution, the proposed method has high calculation efficiency. However, it also loses some accuracy when the assumptions are not completely consistent with the actual situation.

Terahertz (THz) nondestructive testing (NDT) technology has been increasingly applied to the internal defect detection of composite materials. However, the THz image is affected by background noise and power limitation, leading to poor THz image quality. The recognition rate based on traditional machine vision algorithms is not high. The above methods are usually unable to determine surface defects in a timely and accurate manner. In this paper, we propose a method to detect the internal defects of composite materials by using terahertz images based on a faster region-convolutional neural networks (faster R-CNNs) algorithm. Terahertz images showing internal defects in composite materials are first acquired by a terahertz time-domain spectroscopy system. Then the terahertz images are filtered, the blurred images are removed, and the remaining images are enhanced with data and annotated with image defects to create a dataset consistent with the internal defects of the material. On the basis of the above work, an improved faster R-CNN algorithm is proposed in this paper. The network can detect various defects in THz images by changing the backbone network, optimising the training parameters, and improving the prior box algorithm to improve the detection accuracy and efficiency of the network. By taking the commonly used composite sandwich structure as a representative, a sample with typical defects is designed, and the image data are obtained through the test. Comparing the proposed method with other existing network methods, the former proves to have the advantages of a short training time and high detection accuracy. The results show that the mean average precision (mAP) without data enhancement reached 95.50%, and the mAP with data enhancement reached 98.35% and exceeded the error rate of human eye detection (5%). Compared with the original faster R-CNN algorithm of 84.39% and 85.12%, the improvement is 11.11% and 10.23%, respectively, which demonstrates superb feature extraction capability and reduces the occurrence of network errors and omissions.

Compound Kushen Injection (CKI) is a bis-herbal formulation extracted from Kushen (Radix Sophorae Flavescentis) and Baituling (Rhizoma Heterosmilacis Yunnanensis). Clinically, it is used as the adjuvant treatment of cancer. However, with the increased application, the cases of immediate hypersensitivity reactions (IHRs) also gradually rise. In this study, we investigated the underlying mechanism(s) and active constituent(s) for CKI-induced IHRs in experimental models. The obtained results showed that CKI did not elevate serum total IgE (tIgE) and mouse mast cell protease 1 (MMCP1) after consecutive immunization for 5 weeks, but could induce Evans blue extravasation (local) and cause obvious hypothermia (systemic) after a single injection. Further study showed that alkaloids in Kushen, especially matrine, were responsible for CKI-induced IHRs. Mechanism study showed that various platelet-activating factor (PAF) receptor antagonists could significantly counter CKI-induced IHRs locally or systemically. In cell system, CKI was able to promote PAF production in a non-cell-selective manner. In cell lysate, the effect of CKI on PAF production became stronger and could be abolished by blocking de novo pathway. In conclusion, our study identifies, for the first time, that CKI is a PAF inducer. It causes non-immunologic IHRs, rather than IgE-dependent IHRs, by promoting PAF production through de novo pathway. Alkaloids in Kushen, especially matrine, are the prime culprits for IHRs. Our findings may provide a potential approach for preventing and treating CKI-induced IHRs.

Dracaena fragrans is a shrub native to tropical Africa classified in the family Asparagaceae. It is a common horticultural plant grown as a hedge as well as an ornamental house plant for its green leaves, attractive shape and fragrant flowers. The species has been used for many medicinal purposes including inducing labor, treating malnutrition and fighting illness. Here, we investigated the complete plastid genome of D. fragrans using Illumina 150 bp next-generation sequencing technologies. De novo assembly and annotation showed that the chloroplast genome is a typical quadripartite structure with 37.5% GC content. A pair of inverted repeats (IRs, 26,507 bp each) were separated by one large single-copy region (LSC, 83,703 bp) and one small single-copy region (SSC, 18,466 bp). The chloroplast genome is 155,183 bp in length and contained 131 genes of which 12 are intron-containing genes, comprising 85 protein-coding, 38 tRNA, and 8 rRNA genes. Phylogenetic analysis of D. fragrans and 11 plastomes obtained from GenBank shows that it is closely related to D. elliptica.

Ceramic adhesive structures have been increasingly used in aerospace applications. However, the peaks of the signal on the upper and lower surface of the adhesive layer are difficult to measure directly due to the thin thickness of the adhesive layer and the effect of the attenuation dispersion of the ceramic layer. Thus, the existing non-destructive testing techniques have been ineffective in detecting adhesive quality. In this paper, the thickness of the adhesive layer is measured using terahertz time-domain spectroscopy. A sparse deconvolution method is proposed for the terahertz time-domain spectral signal of ceramic adhesive structures with different adhesive layer thicknesses. The results show that the methods proposed in this paper can realize the separation of reflection signals for glue layers with a thickness of 0.20 mm. By comparing with a wavelet denoising method and a modified covariance method (AR/MCM), the effectiveness of the sparse deconvolution method in estimating the thickness of the glue layer is demonstrated. This work will provide the theoretical and experimental basis for using terahertz time-domain spectroscopy to detect the homogeneity of ceramic adhesive structures.

BackgroundGrowing evidence demonstrates that the initiation and progression of colorectal carcinoma (CRC) is related to the presence of cancer stem cells (CSCs). However, the mechanism through which the stem cell features of CRC cells are maintained is poorly understood. In this study, we identified the oncogenic histone cluster 2 H2B family member F (HIST2H2BF) and aimed to investigate the function of upregulated HIST2H2BF expression in maintaining the stem cell features of CRC cells, which accelerate the progression of CRC.MethodsHIST2H2BF expression was quantified using real-time polymerase chain reaction, immunohistochemistry, and western blotting. The correlation between CpG island methylation status and HIST2H2BF re-expression was assessed through bisulfite sequencing polymerase chain reaction, methylation-specific polymerase chain reaction, and 5-Aza-dC treatment. Functional assays were performed on CRC cells and mice to investigate the HIST2H2BF-induced stem cell-like and cancer properties of CRC. Using the Notch pathway inhibitor FLI-06, the regulatory effect of HIST2H2BF on downstream Notch signaling was confirmed.ResultsHIST2H2BF was highly expressed in CRC tissues and cell lines. The reactivation of HIST2H2BF in CRC stems at least in part from the hypomethylated CpG islands. CRC patients with high HIST2H2BF expression have poor survival outcomes. Functional studies have shown that HIST2H2BF promotes CSC phenotype, malignancy, and liver metastasis through the activation of Notch signaling in CRC. Blockage of the Notch pathway reduced the stem cell-like and cancer properties.ConclusionOur study suggests that HIST2H2BF upregulation enhances the CSC phenotype, malignancy, and liver metastasis through the activation of Notch signaling in CRC. These results identified a new perspective on the mechanism by which the stem cell features of CRC cells are maintained and highlighted the potential novel therapeutic targets for CRC.

High-Frequency Pressure Fluctuation (HFPF) is an extensively observed hydraulic phenomenon in pumped-storage power stations and water conveyance projects. The investigation of the propagation characteristics of the pressure perturbation is of great significance for the safe operation of hydraulic facilities. In this study, a one-dimensional (1D)–three-dimensional (3D) coupling model is established based on the combination of the Method of Characteristics (MOC) and Computational Fluid Dynamics (CFD) and implanted in the open source software OpenFOAM. The established model in this study implants the dynamic mesh module into the original OpenFOAM solver sonicLiquidFoam and presents the complete solution procedure of the CFD model with the dynamic mesh considered. The vibration of the pipe walls modeled by the mesh motion is employed to numerically generate the HFPF in the hydraulic system, which could not be implemented in the traditional MOC model. The independence of the pressure perturbation in the pipeline system is validated by the time-domain pressure variation. The graphical method is applied to describe the multiple reflection and superposition characteristics of the traveling wave in a simplified hydraulic system. Based on this, the mechanism of the superimposed characteristic of the traveling and standing pressure waves in the hydraulic system are analyzed, and the theoretical superimposed time-domain processes and the variations of the pressure oscillation magnitude are analyzed and presented. The 1D–3D coupling method and the theoretical analysis method could be referenced by other complex hydraulic systems.

BackgroundThe role of ferroptosis in tumorigenesis has been confirmed in previous studies. However, the comprehensive analysis of ferroptosis-related gene (FRG) to study the role of FRG in soft tissue sarcoma (STS) is lacking.MethodsRNA sequencing profile of TCGA-SARC cohort and GTEx were used to select differentially expressed FRGs (DEFRGs). Univariate, LASSO, and multivariate Cox analyses were selected to determine overall survival (OS)- and disease-free survival (PFS)-related FRGs. Two prognostic signatures were established and validated in two independent sets from Gene Expression Omnibus (GEO). Finally, the expression of key FRGs were validated with RT-qPCR.ResultsIn total, 198 FRGs (90.4%) were abnormally expressed in STS. Twelve DEFRGs were incorporated in the final signatures and showed favorable discrimination in both training and validation cohorts. Patients in the different risk groups not only showed different prognosis, but also showed different infiltration of immune cells. Two nomograms combining signature and clinical variables were established and the C-indexes were 0.852 and 0.752 for the OS and DFS nomograms, respectively. Finally, the expression of NOX5, HELLS, and RPL8 were validated with RT-qPCR.ConclusionThis comprehensive analysis of the FRG landscape in STS revealed novel FRGs related to carcinogenesis and prognosis. These findings have implications for prognosis and therapeutic responses, which revealed potential prognostic biomarkers and promote precision medicine.

A method based on high performance liquid chromatography with evaporative light scattering detection (HPLC-ELSD) was developed for the quantitative analysis of three active compounds and chemical fingerprint analyses of saccharides in Morindae officinalis radix. Ten batches of Morindae officinalis radix were collected from different plantations in the Guangdong region of China and used to establish the fingerprint. The samples were separated with a COSMOIL Sugar-D column (4.6 mm × 250 mm, 5 μm) by using gradient elution with water (A) and acetonitrile (B). In addition, Trapped-Ion-Mobility (tims) Time-Of-Flight (tims TOF) was used to identify saccharides of Morindae officinalis radix. Fingerprint chromatogram presented 26 common characteristic peaks in the roots of Morinda officinalis How, and the similarities were more than 0.926. In quantitative analysis, the three compounds showed good regression (r = 0.9995–0.9998) within the test ranges, and the recoveries of the method were in the range of 96.7–101.7%. The contents of sucrose, kestose and nystose in all samples were determined as 1.21–7.92%, 1.02–3.37%, and 2.38–6.55%, respectively. The developed HPLC fingerprint method is reliable and was validated for the quality control and identification of Morindae officinalis radix and can be successfully used to assess the quality of Morindae officinalis radix.

Granular sludge is the key to the efficient operation of the UASB reactor, which directly affects the treatment efficiency of the reactor. The ordinary flocculent sludge is lighter, and it is easy to be flushed out of the reactor under the action of the influent water flushing in the UASB reactor and the gas scouring generated by the anaerobic reaction of the reactor sludge layer, resulting in reducing in the sludge concentration in the reactor, thus affecting the removal effect. Granular sludge has good settle ability and is not easily lost in the UASB reactor. In this paper, the performance of seed sludge inoculated by two different UASB process is studied to propose the best seed sludge inoculated under the condition of no anaerobic reactor sludge inoculation, which provides references for anaerobic biological treatment of high concentration wastewater.