Prostate cancer (PRAD) is a common and fatal malignancy. It is difficult to manage clinically due to drug resistance and poor prognosis, thus creating an urgent need for novel therapeutic targets and prognostic biomarkers. Although G protein-coupled receptors (GPCRs) have been most attractive for drug development, there have been lack of an exhaustive assessment on GPCRs in PRAD like their molecular features, prognostic and therapeutic values. To close this gap, we herein systematically investigate multi-omics profiling for GPCRs in the primary PRAD by analyzing somatic mutations, somatic copy-number alterations (SCNAs), DNA methylation and mRNA expression. GPCRs exhibit low expression levels and mutation frequencies while SCNAs are more prevalent. 46 and 255 disease-related GPCRs are identified by the mRNA expression and DNA methylation analysis, respectively, complementing information lack in the genome analysis. In addition, the genomic alterations do not exhibit an observable correlation with the GPCR expression, reflecting the complex regulatory processes from DNA to RNA. Conversely, a tight association is observed between the DNA methylation and mRNA expression. The virtual screening and molecular dynamics simulation further identify four potential drugs in repositioning to PRAD. The combination of 3 clinical characteristics and 26 GPCR molecular features revealed by the transcriptome and genome exhibit good performance in predicting progression-free survival in patients with the primary PRAD, providing candidates as new biomarkers. These observations from the multi-omics analysis on GPCRs provide new insights into the underlying mechanism of primary PRAD and potential of GPCRs in developing therapeutic strategies on PRAD. Prostate cancer (PRAD) is the second commonly diagnosed cancer and leading cause of mortality in men worldwide. 248,530 new cases were reported in 2021, resulting in approximately 3.4 thousand deaths . With thG protein-coupled receptors (GPCRs), with a seven-transmembrane domain structure, an extracellular amino terminus and an intracellular carboxyl terminus, are the largest and most diverse protein family of cell surface receptors in many species, accounting for ∼4% of the encoded human genome . As key Although the correlation of GPCRs with oncology has been confirmed by growing studies, previous works mainly focused on a few members of GPCRs. Consequently, some receptors were well studied due to their well-known importance while others were ignored. In addition, Insel P.A. et al. indicated that many GPCRs are not well understood and findings derived from specific GPCRs may not be applicable to all, or even most GPCRs . UnfortuAs accepted, disease-related genes could be identified by comparing the expression level of genes in normal and tumor tissues . Thus, iOR51E1, OR51E2 and GPR160, which are highly expressed (>10 TPM) and significantly upregulated in our analysis, were reported to have antitumor potential for PRAD in previous studies. Specifically, OR51E1 and OR51E2 were reported to suppress proliferation and promote cell death in LNCaP cells  were identified. 24 and 22 genes are significantly over- and under-expressed in primary prostate tumors, respectively vide . The abeaP cells , thus bee arrest . Moreoved tumors . In addiLosartan . ADRB3 ilmeterol , and PTHparatide . Combinip = 0.0005) and Olfactory Receptor , while the other subfamilies do not exhibit significant enrichment  with respect to the other G proteins, followed by Gq/11 (57 GPCRs)  . This obnd Gq/11 . Also, tt cancer . Our obsADGRB3 occurs in only 3.62% (10/276) of the samples. These observations indicate that the low mutation frequency of GPCRs likely has contributed to the limited use of GPCR-targeted drugs as cancer therapeutics  in tumor samples  gene have been implicated in various tumors including PRAD, which are associated with altered protein function, downstream pathways, migration, and angiogenesis, thus contributing to tumorigenic progression  of the DEGpcrs don’t harbor any mutation. Only  samples . The aboOR51D1 is significantly over-expressed in samples with mutations of ADGRE2, ADGRL3, GABBR2, OR4P4, OR5W2, and OR6K2. However, the correlation is not observed in its own mutant subgroups, as evidenced by Although the direct correlation between the mutation status of GPCRs and their mRNA expression is not significant, are their expressions associated with the mutation status of other genes? It is found that the mRNA expression levels of 39 DEGpcrs exhibit significant differences between the mutated and not mutated groups of the other genes. As shown in ADRA1A and amplified FZD6 occur in 52.20 and 26.80% of the tumor samples analyzed, respectively.SCNA is another molecular feature on the genomic level, which may cause the genome copy number of the affected cells to deviate from the normal diploid state such that affecting the stability of the genome and promoting the development of tumor cells . Thus, wq value <0.25) were identified. ADCYAP1R1, CRHR2, GPR160, and GHRHR, and the deletions of GALR1, MC4R, and HTR2A are possibly attributed to their corresponding recurrent arm-level SCNAs.It was proposed that amplified and deleted GPCRs may have potential as biomarkers . Thus, wGPR160 is over-expressed in primary prostate tumor samples, while the other two (CRHR2 and ADGYAP1R1) are significantly under-expressed. Except for ADGRD, the expressions of the 9 deleted GPCRs don’t show statistically significant differences between the tumor and normal samples. These observations indicate that the expression of GPCRs could not be inferred from their copy number variant status. Except for GPR160 and ADGRD1, SCNA alone does not generally predict the direction and extent of expressional dysregulation in prostate tumors compared to normal tissues  were observed in 504 regions of 252 GPCR genes, in which 243 differentially methylated positions (DMPs) are hypermethylated and 261 DMPs are hypomethylated in the primary prostate tumor samples, as reflected by GPR26, GPRC5C, GRM1 and OPRM1) in all the 3 regions. The observations imply that the DNA methylation is region-specific in PRAD and certain genomic regions may be more susceptible to changes than others, for example, the promoter region that involves in the most frequent methylation changes. In fact, the promoter region has been a focus of attention in DNA methylation studies and its methylation level is considered to be closely related with cancer development , which locate around 200 kb upstream of GPR26. In contrast, the expression of GPR26 presents positive correlation with the methylation of cg25912428 (Pearson r = 0.2359) locating in the gene body. The observation indicates that the overexpression of GPR26 in primary prostate tumors is probably due to its hypermethylation in promoter and hypomethylation in gene body. In fact, the positive and negative correlations have been widely reported, i.e., hypermethylation of CpG sites in promoters typically leads to transcriptional silencing, whereas hypomethylation of CpG sites in a gene body frequently results in an increase in gene expression  and under-expressed. CRHR2, belonging to class B1 GPCRs, is best known as regulators of the stress response in the central nervous system. Although there have been seldomly reports about CRHR2 in cancer, its role in tumor formation and angiogenesis is becoming increasingly studied. For example, low or absent CRHR2 expression was found in exocrine ductal pancreatic carcinomas, PRAD and non-small cell lung cancer, in line with our findings. It was reported that expression loss of CRHR2 may contribute to prostate tumorigenesis, progression and neoangiogenesis . In addiHowever, it is noted that we screened the four high-affinity drugs for the potential targets (GPR160 and CRHR2) and evaluated the anticancer potential only by means of the computational way. As known, the docking and structure-based virtual screening aim to predict the binding mode of a ligand and its affinity to the target protein, but cannot distinguish its efficacy like agonists or antagonists or inverse agonists , which np < 0.05). However, none of the GPCR methylation and somatic mutations reaches the statistical significance, which is different from the observations in some previous survival analyses on other genes and cancers. For example, EGFR and TP53 mutations are accepted as prognostic factors in advanced non-small cell lung cancer . In order to compare the sensitivity and specificity of the risk score on the prognosis of patients with primary PRAD, time-dependent receiver operating characteristics (ROC) analysis was also performed, and the areas under the ROC curves (AUC) in the training set at 1, 3, and 5 years are 0.974, 0.966, and 0.933, respectively  GPCRs exhibit low expression levels and mutation frequencies, which should contribute to less focus on GPCRs in oncology. However, the mRNA expression and DNA methylation analysis identify 46 and 255 disease-related GPCRs, respectively, complementing information lack in the genome analysis.2) The associations between distinct omics layers are found to be different. Most receptors don’t exhibit a significant correlation between the genome and transcriptome while a tight association is observed between the transcriptome and epigenome of GPCRs, confirming the complex regulatory mechanism from DNA to RNA.3) Four drugs  targeting GPR 160 and CRHR2, which show significant alterations at different levels, are identified as potential candidates to reposition for prostate cancer by the virtual screening and molecular dynamic simulations.4) The combination of 3 clinical characteristics and 26 GPCR molecular features identified by the transcriptome and genome exhibit good performance in predicting progression-free survival in patients with the primary PRAD, thus providing new potential biomarkers for the clinical decision.In the work, we offer the first comprehensive landscape of multi-omics features of the GPCR family in the primary PRAD using an unbiased (-ome) approach. Several important observations are obtained:In a whole, these observations on the GPCR family at the genomic, epigenomic, and transcriptomic levels provide new insights for understanding the mechanism of the primary PRAD, theoretically revealing the therapeutic and prognostic potential of GPCRs in PRAD. In addition, our result further confirms that the analysis of just one omics level generally provides a biased and incomplete snapshot in the complex disease progression, most probably missing some key cancer drivers. Thus, the integrated analysis considering multi-omics is beneficial to the development of new therapeutic strategies and prognostic markers. However, it is noted that our data from the public databases (TCGA and GTEX) were generated from whole blood or specific tissue samples, thus cannot capture the complex heterogeneity of single cells or the regulatory relationships between them. Recently, single-cell technologies are advancing. It is likely to generate omic data on single cells from different tissue types of interest in the future such that can accelerate new and more refined analyses.www.guidetopharmacology.org)   and prevhttps://portal.gdc.cancer.gov/). The RNA expression data generated by TCGA (https://genome.ucsc.edu) (https://gdac.broadinstitute.org/), which is listed in To comprehensively characterize the GPCR family in patients with primary PRAD, we collected genomic, transcriptomic and epigenomic data from publicly available databases, including The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx). Specifically, we downloaded both the mutation annotation format (MAF) files and SCNA data from GDC Data Portal  . In addi2 fold-change| > 2 and adjusted p-value < 0.05 to select DEGpcrs in the primary prostate tumor samples compared with non-tumor ones   .p < 0.05 were considered to be significantly mutated.Maftools , which ihttps://cloud.genepattern.org/gp/pages/index.jsf. The parameters were set as follows: “-refgene: Human_Hg38.UCSC.add_miR.160920. refgene.mat, -ta (-td): 0.25, focal length cutoff: 0.70, -genegistic: yes, -conf: 0.9, -qvt: 0.25, -broad: yes, -armpeel: yes”.SCNAs of primary PRAD patients were analyzed by the GISTIC2 algorithm , which iChAMP package (RRID:SCR_012891)  . We detehttps://gpcrdb.org/structure/refined/6PB1), and amino acids located at the vicinity of 4 Å from the ligand are considered as main binding residues. Thereafter, crystallographic ligands, stimulatory G protein and water molecules were excluded from the crystal structures. Polar hydrogens were then added to each protein by using Autodock tools  and a test set , in which the samples were proportionally allocated from each PFI type without replacement. Then, the significant features with timeROC” .U test was performed to test the significant difference. p < 0.05 was considered statistically significant.For the correlation analyses between the gene expression and other molecular profiles, the following methods were used: 1) for continuous variables, including relative linear copy number values and DNA methylation levels, Pearson correlation was performed; 2) for categorical variables, the samples were divided into two groups based on a specific attribute , and then the non-parametric Mann-Whitney


A tumor occurs because of abnormal cell multiplication caused by many variables like a significant disturbance in the regulation of cell growth and the instability of chromosome mitosis. Budding uninhibited by benzimidazoles 1 (BUB1), BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B), and budding uninhibited by benzimidazoles 3 (BUB3) are key regulators of mitosis, and their abnormal expression is highly correlated with breast cancer (BrCa), sarcoma, hepatic carcinoma, and other malignant tumors. However, the occurrence of BUBs  and the development of BrCa have not been systematically explained. Findings. We found that the high expression level of BUBs in BrCa tissues proposed a poor prognosis. The multivariate Cox regression analysis suggested that BUB1B and BUB3 might be independent prognostic factors of BrCa. In addition, the Metascape functional enrichment analysis showed that BUBs may be involved in the composition of the spindle, chromosome, and other structures and play a role in mitosis, sister chromatid separation, and other processes. Pathway enrichment suggests that BUBs may affect the cell cycle and lead to abnormal proliferation. Meanwhile, we also found that BUB3 can negatively regulate B lymphocytes, and BUB1 and BUB1B inhibit immune responses by promoting the secretion level of checkpoint molecules of the immune system, leading to immune escape of tumor cells.  Find out the target gene by looking up literature on PubMed and CNKI. Using the R software, TCGA, GEO, Kaplan-Meier Plotter, TIMER, and other databases, we studied the level of transcription, genetic changes, and physiological functions of BUBs in BrCa patients and their relationship with the origin, development, prognosis, immunity, and drug resistance of BrCa patients.  We speculate that BUB1, BUB1B, and BUB3 may be therapeutic targets for BrCa patients and also provide new therapeutic strategies for BrCa treatment. According to the latest global cancer data for 2020, released by the International Agency for Research on Cancer, BrCa has overtaken lung cancer as cancer with the highest number of new cases. What is more worrying is that the incidence of BrCa in China has been increasing year by year in recent years. BrCa has become the primary reason for cancer-associated mortality for females in third-world nations. As with other malignancies, aneuploidy is a common feature of BrCa and affects its behavior. Aneuploidy is related to the inappropriate activity of the spindle assembly checkpoint (SAC). SAC is a monitoring mechanism. When the integrity of the genome is impaired, a sister chromatid separation error during mitosis will lead to aneuploidy, which leads to the occurrence of cancer , 2.When it comes to spindle mitosis, the BUB gene family has important functions to perform. BUB1 is necessary for SAC signaling and correct alignment of chromosomes . As a paThe Genome Project has accumulated a large amount of biological information, and the task of bioinformatics is to mine and utilize this information. Therefore, bioinformatics will play an extremely important role in the “postgenome” era, which will help to understand the information of the human genome. Many cancers, including BrCa, have been deeply explored in terms of genome and transcriptome , 7. Throhttps://portal.gdc.cancer.gov/), and a total of 1109 tumor samples and 113 normal samples were obtained. Meanwhile, the clinical information of BrCa patients was downloaded from TCGA database. We found dataset GSE155478 in the GEO database (https://www.ncbi.nlm.nih.gov/geo/) and divided it into two groups according to drug sensitivity: the drug resistance group (3 cases) and the sensitive group (3 cases).The level 3 HTSeq-FPKM format RNAseq data of BrCa was downloaded from TCGA database  website to detect the differential expression levels of BUBs in different molecular types in BrCa.We examine the differential expression of BUBs in BrCa tissues and normal samples in TCGA database by using the R package ggplot2. Then we still use the ggplot2 R package, select the level 3 HTSeq-FPKM format RNAseq data and clinical data in TCGA BrCa project, transform the FPKM format RNAseq data into log2, and examine the relationship between the expression of BUBs and pathological stages. UALCAN is a sophisticated and dynamic web resource , gastric cancer , ovarian cancer , and breast cancer . The datasets were compiled by data from GEO, EGA, and TCGA. The major goal of the tool is to create and validate survival biomarkers using meta-analytic techniques. First, we select Start KM Plotter for BrCa, then click Use Multiple Genes in the blank column of Gene Symbol to input BUB1, BUB1B, and BUB3 genes, and then select median to divide patients. In the Probe set options, User Selected Probe Set was selected, and OS and RFS were selected successively in Survival to obtain the overall survival (OS) as well as relapse-free survival (RFS) data of BUB transcription in BrCa patients, respectively. To do additional research on the possible prognostic significance of BUBs in BrCa patients, uni- and multivariate Cox regression analyses were performed first, and the forest plot R tool was used to create forest plots to represent the hazard ratio, p value, and 95 percent confidence interval for each variable. Because of the multivariate Cox proportional hazard analysis, it was necessary to apply the R software package rms to create a nomogram that could forecast the overall recurrence rate in one, three, and five years. The nomogram provides a graphical representation of these factors, and the prognostic risk of a single patient can be calculated by the points related to each risk factor.Using the Kaplan-Meier Plotter, researchers can determine whether 54000 genes  have an impact on survival in 21 cancer types, notably lung cancer (http://gepia.cancer-pku.cn/) was used to analyze the correlation between BUB1, BUB1B, and BUB3. BUBs and closely linked genes were investigated by using STRING (http://string-db.org/), which was used to construct a protein interaction network for BUBs and closely associated genes. Subsequently, the Cytoscape software (http://www.cytoscape.org/) was used for visual analysis of the protein-protein interaction (PPI) network. It is more important for the overall stability of the network and if in case the nodes have a higher degree of connection than others. CytoHubba is a Cytoscape plugin that may be used to determine the degree of each protein node in a network of proteins. The top ten genes found in our study were termed “hub genes.” In addition, we used the Metascape (http://metascape.org) website for the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Genetic Ontology (GO) analyses. The latter is composed of three components: biological process (BP), molecular function (MF), and cellular component (CC), and it can be used to predict the functions of genes that are closely associated to BUBs, while the former can describe the gene pathways related to BUBs. Items having a minimum count of 5, a p value less than 0.01, and an enrichment factor of enrichment more than 2.0 were collected for further analysis. We use the GeneMANIA (http://genemania.org/) database to construct the BUB interactive network.Using the stat R package, the top 20 genes most associated with BUBs' expression in BrCa were obtained from TCGA database. The duplicate genes were removed, and 45 genes were obtained for further enrichment analysis. The GEPIA online software (https://cistrome.shinyapps.io/timer/) database, first for the systematic analysis of different cancer types of immune invasion sites, and then choose the Survival module that analyzes the 6 kinds of common immune infiltrating cells in their relationship with prognosis. Then we use the TISIDB (http://cis.hku.hk/TISIDB/index.php) database to further analyze the relations between the BUBs and B lymphocytes; TISIDB is a website of tumor-immune system relationship, and it integrates a variety of data types. To verify the above conclusion again, we picked five typical B lymphocyte gene markers from the Systems website of R&D (https://www.rndsystems.com/cn/resources/cell-markers/immune-cells) and used them in our experiments. The correlation between them and BUBs was analyzed by using the Correlation module of TIMER. Then, we selected seven major immune checkpoint molecules. In TISIDB, the expression level of BUBs was at the x-axis, and the abundance of immune checkpoint molecules was at the y-axis. It was decided to utilize scatter plots to demonstrate the relationship between the quantity of each immunological checkpoint molecule and the transcription of BUBs.Using the GSVA package in the R software and GSEA calculation method, we calculated the correlation between BUB1, BUB1B, BUB3, and 24 kinds of common immune cells. Then we use the TIMER  takes the intersection to obtain differential genes coexpressed with BUBs in BrCa resistant cells.Using the 45 coexpressed genes, most relevant to BUB expression in BrCa obtained above, and the differentially expressed genes in the GSE155478 dataset related to BrCa resistance found from the GEO database , and that of BUB1B was 1.64 (p = 2.3E − 07), while the expression of BUB3 did not affect the OS rate (p = 0.49 > 0.05). The RFS of BrCa patients was significantly and negatively correlated with the expression of BUBs. The risk ratio of BUB1 was 1.74 (p < 1E − 16), BUB1B was 1.77 (p < 1E − 16), and BUB3 was 1.16 (p = 0.0041)  .p = 0.03634) and BUB1B  suggested that BrCa patients had poor progression-free survival (PFS). The worse T stage  and pathological stage  indicate the worse PFS of BrCa patients (p = 0.00861), BUB1B , and pathological stages   = 1.16125, 95%confidence interval (CI): 1.00955, 1.33575, patients . Therefopatients : BUB3 (H0.00014) . The res0.00014) .TCGA database was used to find out the coexpression genes with BUBs, the correlation coefficients were arranged in descending order, the first 20 genes were selected, and 45 coexpression genes were obtained by removing duplicate genes. We separately studied the correlation between BUBs and their coexpressed genes Figures . AdditioTo further clarify the functional role of the BUB gene family, we used the Metascape online tool to conduct an analysis of the function and enrichment of pathways of BUBs and their closely related genes. The function of BUBs was evaluated from three aspects: BP, MF, and CC . We founρ| ≥ 0.2, BUB1 and BUB1B were negatively correlated with mast cells, NK cells, cd56 bright NK cells, eosinophils, plasmacytoid dendritic cells, type 17 T helper cells, and immature dendritic cells, and BUB1 was positively correlated with type 2 T helper cells, activated dendritic cells, regulatory T cells, type 1 T helper cells, and cd56dim natural killer cells; BUB1B was positively correlated with type 2 T helper cells, activated dendritic cells, and regulatory T cells; BUB3 was negatively correlated with dendritic cells, neutrophils, B lymphocytes, macrophages, cytotoxic cells, type 1 T helper cells, and plasmacytoid dendritic cells and positively correlated with type 2 T helper cells. We also used the TIMER to evaluate the impact of six common immune infiltrating cells on the prognosis of BrCa patients (p = 0.046), and the impact of other immune infiltrating cells on the prognosis is not statistically significant. Therefore, we reverified the link between the degree of expression of BUBs and B lymphocyte infiltration on the TISIDB website (Tumor infiltration lymphocyte (TIL) is closely related to the prognosis of BrCa and subsequent immunotherapy , 9. In tpatients . The hig website . The neg website . BUB1 anHowever, in the TIME, not only TILs but also immune checkpoint molecules play a role. The immune checkpoint molecule is a regulatory molecule that plays an inhibitory role in the immune system. It is very important to maintain self-tolerance, prevent autoimmune response, and minimize tissue damage by controlling the time and intensity of the immune response. Immune checkpoint molecules expressed on immune cells will obstruct immune cell function, preventing the body from producing an effective antitumor-immune response and promoting tumor-immune escape . To explt-test analyzes the difference in BUB gene expression between BrCa MCF-7/ADR and MCF-7 (Figures BrCa is a major public health problem worldwide and is one of the leading causes of death in women. At present, many BrCa treatment drugs have been applied in clinical practice. But long-term use of these drugs can develop resistance, reducing their effectiveness against BrCa and leading to poor survival. In cell development, control of the cell cycle is crucial and plays an important role in the process of tumor drug resistance . Therefo Figures . BUBs an Figures , but no  Figures . To avoiCdc20 and chromosome alignment, kinase activity in SAC activity is only a bit important [Cdc20. It is essential for SAC signaling and correct chromosome alignment [SAC is a monitoring system that assists in the appropriate separation of chromosomes during mitosis. Because of less time during mitosis, the chromosomes do not join and separate in the normal manner, resulting in daughter cells with an abnormal number of chromosomes, a state called aneuploidy. Aneuploidy can result in failure to survive, developmental abnormalities, or the initiation and development of diseases such as cancer, depending on its sternness –16. Somelignment . BUB1B, lignment . Like BUlignment , 23.There are reports of BUB gene imbalance in many cancers. For example, BUB1 can enhance the proliferation of hepatocellular carcinoma cells by activating the phosphorylation of SMAD2 , and BUBThrough the above analysis, BUBs as biomarkers of BrCa may be potential treatment targets.


Tripterygium wilfordii, a traditional Chinese medicine, which has shown excellent therapeutic potential in the fields of immune inflammation and cancer treatment. In this study, 1,106 Web-of-Science-indexed manuscripts and 1,160 Chinese-National-Knowledge-Infrastructure-indexed manuscripts regarding triptolide published between 2011 and 2021 were analyzed, mapping the co-occurrence networks of keywords and clusters using CiteSpace software. The research frontier and development trend were determined by keyword frequency and cluster analysis, which can be used to predict the future research development of triptolide. Non–small cell lung cancer (NSCLC) is most common in lung cancer patients, accounting for about 80% of all lung cancer patients. New evidence suggests that triptolide effectively inhibits the development and metastasis of NSCLC by the induction of apoptosis, reversion of EMT, and regulation of gene expression. Specifically, it acts on NF-κB, MAPKs, P53, Wnt/β-catenin, and microRNAs (miRNAs), signaling pathways and molecular mechanisms. Consequently, this article reviews the research progress of the anti-NSCLC effect of triptolide. In addition, attenuated studies on triptolide and the potential of tumor immunotherapy are also discussed.In recent decades, natural products derived from plants and their derivatives have attracted great interest in the field of disease treatment. Triptolide is a tricyclic diterpene extracted from  Tripterygium wilfordii Hook F in 1972  were the most common cause of cancer deaths in men (approximately 1.3 million) and the leading cause of cancer deaths in women  . In receer drugs  and overer drugs . This stThe English literature was retrieved from the core collection database of Web of Science with the subject word “Triptolide.” The publication year was from January 1, 2011 to December 21, 2021, and 1,106 valid works of the literature were finally included after excluding irrelevant literature.The Chinese literature was retrieved from the Chinese National Knowledge Infrastructure (CNKI) database with the subject word “Triptolide.” The publication year was from January 1, 2011, to December 21, 2021, and 1,160 valid works of the literature were finally included after excluding irrelevant literature.N = 50), pruning map with Pathfinder, pruning networks, pruning the merged network pruning algorithm, keyword co-occurrence analysis, and cluster analysis were carried out for triptolide.CiteSpace 5.8. R3 was used for the analysis and time slice. From 2011 to 2021, each year is a time slice. Term source selection, node keyword threshold  means that the clustering structure is significant, and the average clustering contour value S = 0.9046 means that the clustering result is reliable. Cluster #0, cluster #1, cluster #4, and cluster #9 indicated the significant effect of triptolide in the field of cancer treatment, and clusters #10 and #12 focused on the toxicity of triptolide. Clusters #3 and #6 focused on the study of triptolide in the field of immunity and inflammation.According to the co-occurrence of keywords, the LLR algorithm was used to extract keywords, and a cluster analysis was carried out. After visualization of the English literature, there are 308 nodes and 1,097 links, forming 15 cluster labels, as shown in Q = 0.8972 (Q > 0.3) means that the clustering structure is significant, and the average clustering contour value S = 0.9577 means that the clustering result is reliable. Clusters #9, #10, and #12 indicated the significant effect of triptolide in the field of cancer treatment, and clusters #6 and #8 focused on the toxicity of triptolide. Clusters #1 and #2 focused on the study of triptolide in the field of immunity and inflammation.After visualization of the Chinese literature, there are 1,121 nodes and 1,816 links, forming 15 cluster labels, as shown in Keywords contained in the cluster are expanded along the time axis in the TimeLine chart to show the development of the cluster over time, as shown in A total of 1,106 studies from the WOS were analyzed using CiteSpace software. Studies published between 2011 and 2021 were selected for analysis over a one-year time slice, and the most cited or recurring items from each slice were selected. Tripterygium wilfordii. The significant effects of triptolide, its main active ingredient, on anti-inflammatory and autoimmune diseases were emphasized, and the biological activities of terpenoids in addition to triptolide were also mentioned  only articles written in English were included; and 2) these studies should be research papers used to evaluate the efficacy of triptolide in the treatment of NSCLC. Titles and abstracts of all publications retrieved were reviewed to select potentially eligible studies. From a total of 167 results, 53 duplicate studies were removed, and a total of 41 articles were included after Chinese literature, reviews, and other irrelevant literature were removed.Nuclear factor kappaB (NF-κB) refers to a family of transcription factors including NF-κB1 (p50/p105), NF-κB2 (p52/p100), RelA (p65), c-Rel, and RelB , involvein vivo and in vitro. MicroRNAs (miRNAs) refer to small non-coding RNAs that regulate gene expression after transcription . They arMitogen-activated protein kinases (MAPKs) are key regulatory factors involved in cell growth, movement, survival, and apoptosis in physiological and pathological processes . There aThe Wnt/β-catenin signaling pathway is an evolutionarily conserved pathway that plays a key role in embryonic development and is involved in the regulation of adult stem cell homeostasis and tissue regeneration. The activation of the Wnt/β-catenin pathway is closely associated with many cancers and diseases including non–small cell lung cancer. In the absence of Wnt, β-catenin is bound and regulated by a degradation complex composed of the scaffold protein (AXIN), adenomatous polyposis coli (APC), casein kinase 1α(CK1α), and glycogen synthase kinase 3β (GSK 3β) . This coWnt inhibitory factor-1 (WIF1) is a typical Wnt antagonist  and can Metastasis is the leading cause of death in patients with NSCLC, epithelial-mesenchymal transition (EMT) is considered to be a key mechanism in cancer progression and metastasis . StudiesTumor suppressor p53 is encoded by TP53  and is iAkt is a serine/threonine kinase and a carcinogenic protein that regulates cell survival, proliferation, growth, apoptosis, and glycogen metabolism. It is involved in the PI3K/Akt signaling pathway . Akt is STAT3 refers to the signal transduction and transcriptional activator 3. Abnormal activation of STAT3 triggers tumor progression through oncogene expression, leading to a malignant tumor . STAT3 ain vivo.The Keap1-Nrf2 signaling pathway plays a key role in the oxidative stress response of lung cancer. Keap1 refers to the Kelch-like ECH-related protein, and Nrf2 is the E2-related factor 2 encoded by NFE2L2 . The conA common feature in the different signaling pathways mentioned previously is that a large number of transcription factors and cell cycle regulators are downregulated. Triptolide has been reported to have global transcriptional inhibitory activity . Wang etin vitro and in vivo to clarify its toxicological mechanism and dose-dependent characteristics and widen its application scope. Presently, studies on the application of triptolide are mainly divided into two categories. One kind of study is to develop targeted delivery systems for triptolide or modify its structure, including the use of nano-carriers and the design of antibody conjugation drugs (Clinical studies have shown that triptolide exposure can cause significant organ damage in humans, including hepatotoxicity, nephrotoxicity, cardiotoxicity, and reproductive toxicity . Accordion drugs , to reduEGFR mutations are typical carcinogenic drivers in NSCLC and have been observed in about 50% of Asian patients . Epidermin vitro. Cisplatin-based combination chemotherapy is the standard first-line therapy for patients with unresectable advanced NSCLC , and plaFor a long time, the high incidence and mortality of lung cancer has been the main reason causing the death of cancer patients. Drug resistance and metastasis have brought great challenges to the effective treatment of NSCLC, and there is an important need for effective and safe new drugs and treatment strategies. In this study, CiteSpace was used to conduct a visual analysis of triptolide research based on 1,106 English literature and 1,160 Chinese literature data on triptolide research retrieved from the WOS core collection and CNKI from 2011 to 2021. Through the exploration of keyword clustering, the first is the significant therapeutic effect of triptolide in the fields of immune inflammation and cancer treatment. It is worth noting that the toxin study of triptolide should not be ignored, which is also the significant feature that distinguishes its clinical application from other traditional Chinese herbal active ingredients. Through a visual knowledge spectrum, this study helps to enrich the existing knowledge system of triptolide research worldwide. It provides valuable guidance for researchers and related personnel.Tripterygium wilfordii was initially used to treat rheumatoid arthritis and other immune inflammation. Accordingly, the effect of triptolide on immune inflammation was not disappointing, but its clinical application was limited by its serious organ and reproductive toxicity. Interestingly, the remarkable anticancer activity found in triptolide in recent years has given it a new lease of life. This article reviews the current research progress on the anti-NSCLC effects of triptolide. The anticancer activity of triptolide involves multiple signaling pathways, including the NF-κB pathway, microRNAs, MAPKs, Wnt/β-catenin pathway, etc. These studies have demonstrated the clinical therapeutic potential of triptolide, with special emphasis on apoptosis, EMT, and gene expression regulation. In particular, the signaling pathway represented by NF-κB is highly correlated with immunity and inflammation. In addition, studies have shown that Wnt/β-catenin can affect the immune invasion of tumor tissues, which is expected to become a new tumor immunotherapy target (As a traditional Chinese medicine with a long history of application, y target . There hy target . Howeverin vivo should also be paid attention to, and even the concentration monitoring of triptolide should be done to guide the individualization and safety.In addition, because of the toxicity and poor water solubility of triptolide, some studies on drug combination were summarized. Beyond that, structural modifications and nano-drug delivery systems are also common strategies. Minnelide is synthesized from triptolide, which has been used in clinical trials . Studies


The findings reveal that the average air quality of Istanbul was approximately 17% improved during the long-term lockdowns. Therefore, the restriction-related changes in emission distributions continued in the long-term period of 476 days. However, it is unknown how long this effect will continue, which will be the subject of future studies. Moreover, it was observed that the emission probability density functions changed considerably during the lockdowns compared to the years before. Accordingly, notable decreases were detected in air quality limit exceedances in terms of both excessive pollutant concentrations and frequency of occurrence, respectively, for PM10 (−13% and −13%), PM2.5 (−16% and −30%), and NO2 (−3% and −8%), but not for O3 (+200% and +540%) and SO2 (−10% and +2.5%).The World Health Organization (WHO) have set sustainability development goals to reduce diseases, deaths, and the environmental impact of cities due to air pollution. In Istanbul, although average pollutant concentrations have been on a downward trend in recent years, extreme values and their annual exceedance numbers are high based on the air quality standards of WHO and the EU. Due to COVID-19 lockdowns, statistically significant reductions in emissions were observed for short periods. However, how long the effect of the lockdowns will last is unknown. For this reason, this study aims to investigate the impact of long-term lockdowns on Istanbul’s air quality. The restriction period is approximated to the same periods of the previous years to eliminate seasonal effects. A series of paired t-tests ( In contrast, excessive PM2.5, PM10, NO2, and SO2 concentrations are associated with respiratory and heart diseases. PMs are either emitted from a source or formed due to a complex reaction of air pollutants. Particles smaller than PM10 can harm the lungs and bloodstream. Sensitive groups and people with heart and lung disease are risk groups. PMs can also adversely affect the environment by damaging sensitive forests, acidifying lakes and streams, and affecting ecosystem diversity, as they can spread over long distances [Air pollution is a risk factor for human health in Europe . Exceedi2 is the indicator pollutant of the reactive gas group called NOx, including nitrous and nitric acid. It generally occurs with fuel combustion, and source emissions are fuel-powered vehicles, power plants, and field equipment. When inhaled in high concentrations, it can damage the human respiratory system, and prolonged exposure may cause the development of asthma. Therefore, children, the elderly, and people with asthma are at greater risk. NO2 exposure can also harm sensitive ecosystems, and these unfavorable environmental effects might form acid rain, nutrient pollution in coastal water, and reduced visibility due to haze in the atmosphere [NOmosphere . Ozone occurs at two different atmospheric levels, ground level and stratosphere. Ground-level ozone has an unfavorable impact on human health and the environment as the main component of smoke. On the other hand, stratospheric ozone is not defined as an air pollutant due to its protection from the sun’s harmful ultraviolet rays . Therefo2 exposes the burning of sulfur compounds built-in coal and fuel oil naturally. Its primary sources worldwide are industrial processes, domestic fuels, and thermal power plants used for heating purposes. Very few amounts are due to diesel-fired vehicles. High concentrations of SO2 may cause respiratory failure and reduce lung function. It can also cause the corrosion of stone buildings and other materials, damage plants, and be the source of acid rain. The atmospheric concentrations of SO2 are very high in cities where coal is used daily for domestic heating. The outdoor concentrations of SO2 are usually high in city centers and industrial areas [SOal areas .Anthropogenic effects are one of the main reasons for air pollution. In addition to meteorological conditions, air pollution can reach threshold levels that harm human health in the city due to activities related to high populations . For instance, Istanbul, the metropolitan city with the highest population in Turkey, will reach 16.3 million in 2023. Istanbul’s population increase rate has been approximately 227,000 per year since 2000. In 2020, the population decreased by approximately 56,000 for the first time since 2000 and remained at approximately 15.8 million [10, SO2, and NO2 in Istanbul [10, PM2.5, and NO2 episodes in the megacity [Moreover, Istanbul is an essential air transportation hub, and while the number of transit passengers in Istanbul was around 100 million in 2009, this number decreased to 40 million by 2020. Thence, the mortality rates of cardiovascular and respiratory diseases are also associated with high exposure to PMIstanbul . Similarmegacity .In addition to population density, the factors that cause air pollution are complex , and met2.5, PM10, NO2 (including NO and NOx), SO2, and O3 concentration levels were investigated for three 476-day phases, from 12 March 2016, until 1 July 2021, when quarantines were completed, at 36 air quality stations in Istanbul. The main findings indicate that the reduction related to the lockdown continued during the long-term period of 476 days. The distributions show different behaviors than those for the regular periods for kurtosis and the length of tails.Moreover, the reduction rates of pollutants vary from country to country because each country applies its own lockdown strategies. Thus, there is a gap in the literature in that we do not know the long-term effects of lockdowns and how long they would last for a city of interest, especially on the urban scale. Thus, the main goal of this study is to determine the long-term effects of lockdowns in Istanbul. For this reason, various factors were evaluated, such as station types on an urban scale, wind direction and speed, and time on different scales. Another aim of this research is to evaluate the long-term distributions of the pollutants to detect a shift in limit exceedance frequencies concerning the air quality standards of WHO and the EU. Therefore, PM10, PM2.5, O3, NO2 (including NO and NOx), and SO2. In addition, wind patterns were included in determining relations among pollutants, wind direction (WD), and speed (WS). If the missing data, shown with yellow grids in Air pollution concentration and wind data from the Istanbul Metropolitan Municipality  were anaThe meteorologically best available station was chosen to describe the details of the study, the statistical analyses were applied to all stations, and the results were discussed in general.Following the WHO’s announcement of COVID-19, a global pandemic, on 11 March 2020, reasonable measures were taken in Turkey regarding the issue. Starting from 13 March 2020, schools were closed, and arrangements were made to allow people to work remotely. Due to the pandemic, the first lockdown began on 11 April 2020. This continued until June 2020 and was not applied during the summer. Afterward, between December 2020 and 1 July 2021, lockdowns, which were only valid on weekends and nights from 21:00 until 05:00, started to be implemented.Since the lockdowns were applied at different time intervals, the analyses were carried out to cover the long-term variations and compare the differences between the time series before and during the restriction periods. In this way, the expected long-term improvement in air quality, depending on the effect of atmospheric conditions during the lockdowns, was demonstrated.Annual wind roses of the Sile station are presented in p-value < 0.05) were applied for hourly data to determine whether there was a significant dissimilarity between the preceding lockdown period (22 November 2018–11 March 2020) (preceding-term hereafter) and the during-lockdown period (12 March 2020–30 June 2021) (Phase III hereafter), which is 476 days in total. Thus, the null hypothesis is that the mean discrepancy between before-lockdown and during-lockdown periods is zero for all pollutants, observation stations, and periods.The OpenAir  and stanThe number of exceedances was estimated according to the threshold values used in calculating the air quality guidelines to analyze the effect of the improvement due to lockdowns on extreme values. EU  and WHO Phase I: 12 March 2016–30 June 2017 (Before Lockdowns I)Phase II: 12 March 2018–30 June 2019 (Before Lockdowns II)Phase III: 12 March 2020–30 June 2021 (During Lockdowns)In order to follow the same seasonal cycle, for each pollutant, the air quality standards exceeding numbers were compared for the following 476-day periods (long-term hereafter):In addition, hourly, diurnal, and weekly analyses were carried out for each phase, completed in the 2-year cycle . For example, extended Phase I started from 30 June 2015 to 30 June 2017, in which the period split into two sub-periods called before and after, on the day lockdowns began, 12 March. Therefore, for biannual-term Phase I, the sub-period “before” is between 30 June 2015 and 11 March 2016 (256 days), whereas the sub-period “after” is 12 March 2016 and 30 June 2017, i.e., Phase I itself.p < 0.05) differences for Istanbul’s 35 air quality stations regarding PM2.5 and PM10. For both pollutants, differences were not statistically significant for only one station, Sile, the background station located on the upwind side of the urban pollutant source. During the lockdowns, Istanbul’s air quality improved by 18% for PM10 and 22% for PM2.5, an average for the metropolis. Different rates of decrease were observed at each station depending on the station types and their distances to the pollutant source concerning the prevailing wind direction . Probability density functions (PDF) of PM10 and PM2.5, respectively  decreased by 44% [10 and PM2.5 were observed in the traffic and quarry-type stations, and these results follow most similar studies [Similar studies for Istanbul ,43 show d by 44%  on avera studies ,23.x were most evident at the Esenler station , the exceedance numbers reveal an increase (2.5%) in the long-term analyses. Similar findings were presented in Hangzhou, China [2 limit exceedance numbers were increased in Istanbul is related to the increased rates of industrial activities to compensate for the economic loss in production.Although the lockdown effect points to a decrease in average SOu, China . The reax, and VOC concentration play a significant role.Most previous studies stated that ozone increases due to lockdowns ,39,58. Fx emissions, show a different distribution in Phase III than in the other phases. However, the bivariate normal distribution seen in Phases I and II tends to leave its place in the standard distribution in Phase III. Although the ozone distributions of each station are different, the most prominent feature in Phase III is the modification of the tail length depending on the station. For example, the tail lengths of PDFs were shortened at the Maslak and Kadikoy stations , PM2.5 (−30%), and NO2 (−8%) showed a considerable decrease.The long-term effect of restrictions due to COVID-19 was investigated on an urban scale for Istanbul. Results indicate that the most dramatic improvements in air quality occurred in areas where human activity is most intense. That is, each district’s response to lockdown is different. In neighborhoods with already good air quality, the recovery rate due to lockdown is weak compared to neighborhoods with poor air quality. Thus, it has become clear that air quality improvement studies across Istanbul should be carried out at the scale of districts. Comparing preceding-term and long-term analyses showed that the emission decrease rates increase when we do not compare the same calendar cycles. This is most likely because other effects, such as meteorological, photochemical, or advective, cancel each other out. Therefore, we should trust same-period, long-term analyses rather than comparisons of short-term periods or periods with different cycles in Istanbul. Air quality standards are determined by the average and threshold exceedance occurrences, which can only be evaluated using long-term time series. It was found that lockdowns positively impact excessive concentration occurrences depending on the applied lockdown strategies. The distribution of the limit exceedance values varies according to the station, and it was determined that OConsequently, this study leaves three critical questions for future studies that need to be solved, setting innovative strategies to sustain Istanbul’s clean air despite increasing population density: (1) How long will these long-term effects of lockdowns last for Istanbul? (2) Which lockdown strategy helped to produce the most prolonged improvements in air quality? and (3) How can we quantify the percentage of each effect separately including atmospheric stability?Air pollution in Istanbul can be controlled by afforestation and promoting the use of electric vehicles. Moreover, lockdown-like strategies should also be considered on days when air quality thresholds are likely to be exceeded due to meteorological conditions.


The association between changes in estimated glomerular filtration rate (eGFR) over time and the risk of stroke remains inconclusive. We aimed to evaluate the relation of eGFR change during the China Stroke Primary Prevention Trial (CSPPT) with the risk of first stroke during the subsequent post-trial follow-up.A total of 11,742 hypertensive participants with two eGFR measurements  and without a history of stroke from the CSPPT were included in this analysis.Over a median post-trial follow-up of 4.4 years, 729 first strokes were identified, of which 635 were ischemic, 88 were hemorrhagic, and 6 were uncertain types of strokes. Compared with those with 1 to <2% per year increase in eGFR (with the lowest stroke risk), those with an increase in eGFR of ≥4% per year had significantly increased risks of first stroke  and first ischemic stroke . Similarly, those with a decline in eGFR of ≥5% per year also had significantly increased first stroke  and first ischemic stroke  risk. However, there was no significant association between eGFR change and first hemorrhagic stroke. A similar result was found when the change in eGFR was quantified as an absolute annual change.In Chinese hypertensive patients, both the decline and increase of eGFR levels were independently associated with the risks of first stroke or first ischemic stroke. Indeed, there has been a growing interest in the association between change in eGFR and risk of end-stage renal disease (ESRD),7 cardiovascular disease (CVD),8 and mortality.9 However, only a few of them have reported the association between the changes in eGFR over time with the risk of stroke, and they reported inconsistent findings.8,10–15 Moreover, although a Japanese longitudinal 10-year follow-up study16 reported that the rate of eGFR decline was significantly higher among participants with hypertension compared to the general population, few related studies have been conducted in hypertensive patients. More importantly, the possible effect modifiers on the association between eGFR change and first stroke have not been comprehensively evaluated in previous studies.Chronic kidney disease (CKD) is a worldwide public health problem, with increasing prevalence, poor outcomes, and high treatment costs.Using data from the China Stroke Primary Prevention Trial (CSPPT) and its post-trial follow-up, we aimed to investigate the association between change in eGFR over time and the risk of first stroke in hypertensive adults. We explored change in eGFR using two indices: the annual percentage change and the absolute annual change.17–20 and the renal sub-study of the CSPPT21 have been reported previously. Briefly, the CSPPT was a randomized, double-blind, controlled trial conducted from May 19, 2008, to August 24, 2013, in 32 communities in China. Eligible participants were men and women aged 45 to 75 years who had hypertension, defined as seated, resting systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg at both the screening and recruitment visit, or who were on antihypertensive medication. The major exclusion criteria included a history of physician-diagnosed stroke, heart failure, myocardial infarction, coronary revascularization, or congenital heart disease. The renal sub-study of the CSPPT  enrolled eligible CSPPT participants from 20 communities in Jiangsu Province, excluding those with an eGFR <30 mL/min/1.73 m2 or missing eGFR at baseline.The study design and major results of the CSPPT22 was conducted from August 24, 2013, to December 31, 2017, in Lianyungang of Jiangsu Province.The post-trial follow-up of the CSPPTThe first collection of overnight fasting venous blood samples and spot urine samples of each study participant was carried out at the baseline of CSPPT in 2008, while the second collection of samples was at the exit visit of CSPPT in 2013. The median measurement interval was 4.4  years. The outcome ascertainment was performed during the post-trial follow-up .n = 345), and without endpoint information (n = 829), a total of 11,742 hypertensive subjects were included in the final analysis  was approved by the Ethics Committee of the Institute of Biomedicine, Anhui Medical University, Hefei, China (FWA assurance number: FWA00001263). The current study was approved by the Ethics Committee of the First People’s Hospital of Lianyungang, Lianyungang, China, and the Ethics Committee of Nanfang Hospital, Guangzhou, China. Informed consent was waived for the retrospective nature and the lack of participant interaction. The data that support the findings of this study will be available from the corresponding authors upon request, after the request is submitted and formally reviewed and approved by the Ethics Committee of the Institute of Biomedicine, Anhui Medical University; the Ethics Committee of the First People’s Hospital of Lianyungang; and the Ethics Committee of Nanfang Hospital.Serum creatinine, total homocysteine (tHcy), uric acid, fasting lipids, and fasting glucose were measured using automatic clinical analyzers  at the core laboratory of the National Clinical Research Center for Kidney Disease, Guangzhou, China. Serum folate and vitamin B12 were measured by a commercial laboratory using a chemiluminescent immunoassay . Specifically, serum creatinine was measured using an enzymatic assay that has been calibrated to be isotope dilution mass spectrometry traceable. The coefficient of variation for the assay was 1.4%. Proteinuria was determined using a dipstick test .2 and/or proteinuria at baseline were classified as having CKD. Hyperfiltration was defined as an eGFR greater than 95th percentile, and a normal glomerular filtration rate was defined as an eGFR between the 25th and 75th percentiles after stratification for age decade and sex.23,24Diabetes was defined as a fasting glucose ≥7.0 mmol/L or, use of glucose-lowering drugs or, self-reported history of diabetes. Participants with an eGFR less than 60 mL/min/1.73 m25 Change in eGFR over time was estimated using the two eGFR measurements for each patient at the baseline and exit visit of CSPPT, with a median measurement interval of 4.4  years.eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation.^[1/years elapsed between visits] − 1)*100. The annual percentage change in eGFR was categorized as ≤−5%, −5% to <−4%, −4% to <−3%, −3% to <−2%, −2% to <−1%, −1% to <1%, 1% to <2%, 2% to <3%, 3% to <4%, 4% to <5%, and ≥5% per year.The primary method we used to describe the change in eGFR was the annual percentage change, which was calculated as /(years elapsed between visits). The absolute annual change in eGFR was categorized as ≤−5, −5 to <−4, −4 to <−3, −3 to <−2, −2 to <−1, −1 to <1, 1 to <2, 2 to <3, 3 to <4, 4 to <5, and ≥5 mL/min per 1.73 mThe primary outcome of this study was the first stroke. Secondary outcomes include first ischemic stroke and first hemorrhagic stroke. According to the International Classification of Diseases, 10th Revision (ICD-10), the study outcome  included first ischemic stroke (I63), first hemorrhagic stroke (I60–I61) and no type stroke (I64).26,27 local authorities from medical institutions are required to report all new cases of stroke to the local Center for Disease Control and Prevention. They are required to submit a report card, which includes information on demographics, diagnostic basis, and date of stroke on the 28th of each month. Trained officials and the local Center for Disease Control and Prevention carry out the quality control, including finding and deleting repeated cases, error checking, and determining any missed cases. Besides, 5% of all uploaded cases are randomly chosen for further confirmation by phone or door-to-door interviews.Information on stroke incidence was obtained via the Center for Disease Control and Prevention of Ganyu and Donghai counties and checked against the national health insurance system with electronic linkage to all hospitalizations or ascertained through active follow-up. As shown in the government documentation,Baseline characteristics (at the first measurement) are presented as means and standard deviations (SD) for continuous variables and proportions for categorical variables by the annual percentage change in eGFR . The differences in population characteristics were compared using ANOVA tests or chi-square tests, accordingly.Cox proportional hazards models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for the risk of first stroke and its subtypes associated with each group of change in eGFR, with and without adjustment for age, sex, treatment group, body mass index (BMI), systolic blood pressure (SBP), fasting glucose, total cholesterol (TC), triglycerides, high-density lipoprotein cholesterol (HDL-C), tHcy, smoking and drinking statuses, proteinuria, eGFR at baseline (the first measurement), as well as time-averaged SBP during treatment.2), current smoking and alcohol drinking status (yes vs no), SBP (<160 vs ≥160 mm Hg), tHcy (<15 vs ≥15 µmol/L), TC (<5.2 vs ≥5.2 mmol/L), diabetes (yes vs no), CKD (yes vs no), treatment group  at baseline (the first measurement), and time-averaged SBP during the treatment period (<140 vs ≥140 mm Hg).In addition, possible modifications of the association between annual percentage change in eGFR  and first stroke was also assessed for the following variables: sex, age (<60 vs ≥60 years), BMI  was used for all statistical analyses.A two-tailed The flow chat of participants was presented in The average age of the participants was 59.4  years old and 4,424 (37.7%) were men. The distribution of annual percentage change appeared normal and centered near the origin. The mean annual percentage change in eGFR was −1.34% , with a median of −1.02% .Table Over a median post-trial follow-up of 4.4 years, 729 first-time strokes were identified, of which 635 were ischemic stroke, 88 were hemorrhagic stroke, and 6 were uncertain types of strokes.Overall, we observed a U-shaped relation between percentage change in eGFR per year and first stroke Figure A. ParticConsistently, a significantly higher risk of first ischemic stroke was observed in participants with an increase in eGFR of ≥4% per year  and with a decline in eGFR of ≥5% per year , compared with those with 1% to <2% per year increase in eGFR   and first stroke in various subgroups. None of the variables, including sex, age, BMI, treatment group, smoking and alcohol intake status, SBP, TC, tHcy, CKD and diabetes at baseline (the first measurement), as well as time-averaged SBP during the CSPPT treatment period, significantly modified the association between annual percentage change in eGFR and first stroke  Figure .P for interaction = 0.878; females: P for interaction = 0.669)  .In this community-based hypertensive cohort, we found that change in eGFR over time was significantly associated with an increased risk of first stroke or first ischemic stroke. Compared with participants with 1% to <2% per year increase in eGFR, both declining and increasing eGFR were associated with a higher risk of first stroke or first ischemic stroke, independent of baseline hyperfiltration status, CKD status, and other covariates. Moreover, the results were consistent among participants with different baseline characteristics.8 and Van Pottelbergh et al11 found that, compared to participants with stable eGFR, those with the greatest annual eGFR decline (≤−5 mL/min/1.73 m2 per year) had increased risk of stroke. Turin et al13 and Nagai et al15 also suggested that the decline in eGFR was a significant and independent risk factor for the incidence of stroke among the general population. Consistently, Guo et al14 reported an absolute decrease in eGFR ≥15 mL/min/1.73 m2 or a relative reduction (a decline of ≥25%) in eGFR independently predicted the risk for ischemic stroke at 6 months in patients with atrial fibrillation. Nevertheless, findings from the Cardiovascular Health Study (CHS),10 a community-based cohort of ambulatory elderly individuals, suggested that a rapid decline in eGFR (>3 mL/min per 1.73 m2 per year) was not significantly associated with stroke . Furthermore, a secondary analysis of the Antihypertensive and Lipid Lowering Treatment to Prevent Heart Attack Trial (ALLHAT)12 found that the annual decline in eGFR was not associated with stroke risk among hypertensive participants. It should be noted that most of this prior studies8,10–13 did not report any data on urinary protein, an important determinant of renal disease and cardiovascular outcomes28 and closely associated with the change in kidney function. Moreover, none of the studies thoroughly examined possible effect modifiers on the eGFR change-stroke association.Actually, previous studies have linked the change in eGFR with stroke and reported inconsistent results. In community-based cohorts, Turin et al8,11,13–15 the risk of first stroke significantly increased with the decline in eGFR. This finding is biologically plausible based on the available literature, although the exact mechanisms remain to be delineated. Declining eGFR may contribute to increased stroke risk by aggravating endothelial dysfunction, inflammation,29 and oxidative stress,30 as well as through activation of the renin-angiotensin system induced by renal impairment.31 Furthermore, decreased kidney function may also be a biological integrating marker of adverse vascular conditions that cannot be fully captured by traditional risk factors.32Our current study provided some new insights into this field. Overall, the change in eGFR over time was significantly associated with first stroke. First, consistent with previous studies,9 The mechanism underlying this association is still uncertain. Some researchers have mentioned that this phenomenon might be attributable to lower serum creatinine generation as a result of reduced muscle mass associated with chronic debilitating conditions.33 Nevertheless, our study showed that BMI levels did not significantly modify the association of eGFR with stroke risk.34 Furthermore, a rising eGFR could be seen with hyperfiltration in remnant nephrons, which could be associated with subsequent kidney disease progression and cardiovascular risk.23 However, the adjustments for hyperfiltration did not materially change our findings; therefore, hyperfiltration may possibly only partly affect the association. Overall, more studies are needed to verify our findings and further investigate the underlying mechanisms involved in this association.Second, our study expands the results of previously published studies by demonstrating that increasing eGFR over time was also associated with first stroke risk. Indeed, some reports have demonstrated that an increase in eGFR is associated with excess mortality in the general population.35 and proteinuria. However, our study also had some limitations. First, although a number of potential confounders had been adjusted in the analysis, it is possible that the results could be affected by unmeasured or unidentified factors. Second, our present study was conducted in Chinese hypertensive participants, so the generalizability of the results to the general Chinese population or other ethnicities should be further verified. Third, our study may be underpowered for evaluating the relation of eGFR change with the risk of hemorrhagic stroke. Fourth, only two eGFR measurements were available for each subject. As such, we could not evaluate the process of GFR change. More frequent measurements of GFR would have provided more information. Finally, we did not have accurate measurements for muscle mass. Therefore, our findings should be further confirmed in future studies.In our study, the serum creatinine levels were measured in a single central laboratory and were, therefore, highly standardized. We also included a comprehensive adjustment and stratified analysis for a series of important confounders, especially those that may be associated with the change in kidney function, including BMIIn this sample of Chinese hypertensive patients, we found that both declining and increasing eGFR over time were significantly associated with first stroke risk, independent of baseline hyperfiltration status, CKD status, and other covariates. If further confirmed, monitoring the trajectory of change in eGFR over time would be beneficial for clinical decision-making to identify patients at greater risk of stroke.


Resveratrol, an activator for longevity regulatory genes-sirtuin family (SIRTs) and Sirtuin 2 (SIRT2) is an important factor of SIRTs which demonstrated biological function in cancers, but the underlying mechanism is unrevealed.We investigated the mRNA and protein levels of SIRT2 in a variety of cancers and the potential role for clinical prognosis, as well as analysed the association between the gene and immune infiltration in various cancers. And an analysis of two types of lung cancer was conducted to construct a systematic prognostic landscape. Finally, putative binding site of the triacetylresveratrol bound to SIRT2 was built from homology modeling.We concluded that higher mRNA and protein levels of SIRT2 affected prognosis in various types of cancers, especially in LUAD cohorts. In addition, SIRT2 is linked with a better overall survival (OS) in LUAD patients. Further research suggested a possible explanation for this phenotype might be that SIRT2 mRNA levels are positively correlated with infiltrating status of multiple immunocytes in LU-AD but not LUSC, i.e. SIRT2 expression may contribute to the recruitment of CD8+T cell, CD4+ T cell, T cell CD4+ memory resting, Tregs, T cell NK and positively correlated to the expression of PD-1, also excluding neutrophil, T cell CD8+ naïve and B cell plasma cells in LUAD. We found that triacetyl-resveratrol demonstrated the most potent agonist efficiency to SIRT2 and the EC 50 as low as 142.79 nM. As a result, SIRT2 appears to be a promising novel biomarker for prognosis prediction in patients with LUAD and triacetylresveratrol might be a potential immunomodulator of LUAD to anti-PD-1 based immunotherapy combination therapies. Lung cancer is one of the most common malignancies worldwide . It is tOver the past 20 years, NSCLC was once considered to be a non-immunogenic disease. However, a growing number of research on tumor immune interactions has argued against this model in lung cancer and a number of other types of cancer.Based on the fact that tumorigenesis and tumor development are closely linked to immune-related interaction mechanisms, immunotherapy shows a broad prospect of clinical application in cancer treatment, and scientists are attempting to harness the body’s own immune system to fight and defeat malignant tumors . More reIn recent years, the discovery of immune checkpoints and the development of their inhibitors have revolutionized the treatment of NSCLC, including programmed cell death protein-1 (PD-1) and programmed cell death protein ligand-1 (PD-L1). Meanwhile, therapies targeting both of these immune checkpoints have shown promising anti-tumor effects in several other cancers .An increasing number of studies have investigated the role of tumor-infiltrating lymphocytes (TILs) in regulating chemotherapy response and clinical prognosis in a variety of cancers, for example, tumor-infiltrating neutrophils (TINs) and tumor-associated macrophages (TAMs) have been reported to be associated with prognosis . TherefoLongevity regulatory genes, sirtuin family, have been identified in many eukaryotes . SirtuinResveratrol, a phytoalexin produced by vine, has been shown potent activity in the sirtuin family. Resveratrol increases DNA stability and extends lifespan by 70% by enhancing Sir2 to mimic calorie restriction in yeast , 17. TheThis project was permitted and under the supervision of the Ethics Committee of Shanghai Jiao Tong University School of Medicine.P-value of 10-6, a fold change of 2, and a gene ranking in the top 5%.Different databases were utilized to investigate the SIRT2 mRNA expression level in different cancer types, including TIMER 2.0, UALCAN, and GEPIA2 –22, whicOncolnc, GEPIA2, and PrognoScan databases were applied to study the association between SIRT2 expression and survival rates in different types of cancer , 24, whiThe association of SIRT2 expression and clinicopathological parameters, including cancer stages, patient race, gender, age, and smoking habit were analyzed by UALCAN and MEXPRESS  platformThe MEXPRESS platform was accessed to identify the methylation level in promoter region SIRT2. Utilizing UALCAN, we assessed the level of methylation and expression, as well as the survival of a specific target gene across several clinicopathological features, including stages and age. T-tests were performed to compare statistical significance.GeneMANIA is identified single genes related to a set of input genes  to constThe LinkedOmics database contains data from the TCGA covering 32 types of cancer and more than 10,000 patients . LinkFinVarious types of malignancy were examined for SIRT2 expression, as well as its association with immune-infiltrating cells, such as T cells , B cells, macrophages, neutrophils, and DCs. Additionally, correlation modules were used to examine the associations between SIRT2 expression levels and markers of TIICs. The TIIC marker genes include markers for T cells , B cells, TAMs, monocytes, macrophages , neutrophils, natural killer (NK) cells, dendritic cells (DCs), T-helper cells , follicular helper T cells (Tfh), T regulatory cells (Tregs) and exhausted T cells. And our previous publication referenced the gene marker sets –31. GeneTo clarify the SIRT2 binding mode further, we performed a docking simulation. The molecular docking was carried out with AutoDock 4.2.6 software  to deterp or Cox p values. In addition, Gene expression correlation coefficients were evaluated using Spearman rank correlations, with P < 0.05 considered statistically significant.A log-rank test is used to determine the impact of Kaplan–Meier plots, GEPIA, and PrognoScan on HR and via GEPIA2 , we found that SIRT2 was highly expressed in CHOL, ESCA, KICH, KICP, LIHC and conversely low expressed in BRCA, KIRP, LUAD, LUSC, STAD, UCEC (P = 0.001496), lung cancer significantly , moreover SIRT2 also affected relapse free survival of lung cancer   Table 1.SIRT2 is a member of the sirtuin family and there are seven of them, SIRT1 to SIRT7 in mammals . SIRT1 iIn this work, TCGA data online and independent datasets were used to determine SIRT2 expression levels and construct systematic prognostic landscapes for different types of lung cancer. Various types of cancer have also been examined for variation in SIRT2 expression levels between cancer and normal tissues. By comparing normal tissues using the TIMER2.0 database, we found high levels of SIRT2 expression in tumor tissues of CHOL, ESCA, KICH, KIRC and LIHC, while the converse is true in the case of KIRP, LUAD, LUSC, STAD and UCEC. However, compared with paired adjacent non-tumor tissues, the redetermination of the TCGA data revealed SIRT2 was highly expressed in CHOL, HNSC, KICH, KIRC, and LIHC, but significantly lower in BRCA, KIRC, LUAD, UCEC, and slightly lower in LUSC tissue.Moreover, LGG is the only type of cancer in which the SIRT2 expression level is higher than the paired normal tissue, while SIRT2 is lowly expressed in OV, TGCT, and UCEC based on the GEPIA database. The expression levels of SIRT2 in multiple cancers may differ from database to database, which may depend on the underlying biological mechanisms and the data collection approaches, and also the comparison criterion might be a reason.Nevertheless, in the remaining databases , we found an association between SIRT2 expression and the prognosis of HNSC, LUAD and OV. Among these three types of cancer, our results show that SIRT2 expression levels are positively correlated with prognosis in the HNSC and LUAD, and negatively in the OV cohort. Furthermore, by analysing the patient cohorts in the PrognoScan database and Kaplan-Meier Plotter, we found that high SIRT2 expression level was associated with better prognosis (OS or DFS) in brain, colorectal, and lung cancer, while the prognosis in the bladder, breast, and ovarian cancers was the opposite. Two PrognoScan datasets showed that higher SIRT2 expression levels were predictive of a better outcome in LUAD. Moreover, high levels of SIRT2 expression were associated with a better early prognosis in LUAD, with a better HR [0.41 (0.23-0.74)] when SIRT2 was highly expressed in LUAD but not in LUSC. This collectively suggests that SIRT2 is a potential prognostic biomarker in LUAD.Another important finding was the correlation between the level of SIRT2 expression and the level of cancer-associated immune infiltration, particularly in LUAD. Here, our results show the infiltration level of T cells (CD4+ T cells and CD8+ T cells), neutrophils, macrophages, and DCs in LUAD is strongly positively correlated with SIRT2 expression. Additionally, the correlation pattern of infiltrating level and SIRT2 expression differs between LUAD and LUSC. According to the association between SIRT2 expression and tumor immune infiltration, we speculate that SIRT2 may be involved in regulating tumor immunology. One possible reason for this particular phenomenon is that the gene expression product of SIRT2 is involved in the coordination of the function of several immune marker gene sets. On this basis we propose the thesis that aberrant expression of SIRT2 is an important contributor to the development of a variety of malignancies and has the prognostic potential for specific types of cancer.Firstly, M1 macrophages show a negative correlation with SIRT2 expression in LUSC rather than in LUAD, respectively Table 2.Secondly, It appears that SIRT2 is capable of activating different types of T cells  as well as natural killer cells, inactivating Tregs. CD8A, an important glycoprotein on the surface of T cells involved in intercellular interactions in the immune response, is highly correlated with SIRT2 expression in LUAD which are types of cancers with better prognosis. Furthermore, CD8A did not show a clear pattern of correlation in LUSC. This pattern of correlation with SIRT2 gene expression is also seen in general T cell markers  and in some other immune cell populations , such as LEF1 which has been shown to predict variations in response to treatment in AML.Thirdly, in tumor and normal tissues of LUAD and LUSC, the levels of SIRT2 expression showed different patterns of correlation with the regulation of various immune cells . For example, Th1 has both pro- and anti-cancer effects in different human cancers, it is highly correlated with SIRT2 expression levels in LUAD while in LUSC there are no significant correlations.Thus, these explanations specifically explain why high SIRT2 expression represents a better prognosis in LUAD but is not significantly correlated to LUSC.The AcO group of triacetylresveratrol binds to SIRT2 in a cavity through hydrogen bonding with Ile169 and Leu103 of SIRT2, van der Waals contact with Thr101, and forms a salt bridge to Arg97 (via SIRT2. These results provide insight into the possible role of SIRT2 in tumor immunology and its application as a prognostic biomarker and as a novel therapeutic target for LUAD. So we believe that SIRT2 agonists could be immunomodulators of LUAD to immunotherapy combination therapies, and triacetylresveratrol would be a novel, potent activity agonist for SIRT2 than resveratrol.In this study, we proposed a possible hypothesis about why SIRT2 expression levels correlate with immune infiltration levels as well as prognosis in LUAD and LUSC, that is the recruiting and activating TILs The original contributions presented in the study are included in the article/JH contributed to the idea, conception and study design. JH and NQ collected and analyzed the datasets. ZS and JL conducted the analysis of the data, ZL and XZ verified the result of the study. JH, MM, and NQ wrote the manuscript and generated the figures. SD and HW revised and proofread the article. All authors contributed to the article and approved the submitted version.


Mycoplasma bovis and Pasteurella increased. This indicated that the composition of the microbial community had changed in diseased bovines compared with healthy ones. The data also strongly suggested that the reduced relative abundance of probiotics, including Pasteurellales and Lactobacillales, in diseased samples contributes to the susceptibility to bovine respiratory disease. Furthermore, serum metabolomic analysis showed altered concentrations of metabolites in BRD and that a significant decrease in lactic acid and sarcosine may impair the ability of bovines to generate energy and an immune response to pathogenic bacteria. Based on the correlation analysis between microbial diversity and the metabolome, lactic acid (2TMS) was positively correlated with Gammaproteobacteria and Bacilli and negatively correlated with Mollicutes. In summary, microbial communities and serum metabolites in BRD were characterized by integrative analysis. This study provides a reference for monitoring biomarkers of BRD, which will be critical for the prevention and treatment of BRD in the future.Bovine respiratory disease (BRD) continues to pose a serious threat to the cattle industry, resulting in substantial economic losses. As a multifactorial disease, pathogen infection and respiratory microbial imbalance are important causative factors in the occurrence and development of BRD. Integrative analyses of 16S rRNA sequencing and metabolomics allow comprehensive identification of the changes in microbiota and metabolism associated with BRD, making it possible to determine which pathogens are responsible for the disease and to develop new therapeutic strategies. In our study, 16S rRNA sequencing and metagenomic analysis were used to describe and compare the composition and diversity of nasal microbes in healthy cattle and cattle with BRD from different farms in Yinchuan, Ningxia, China. We found a significant difference in nasal microbial diversity between diseased and healthy bovines; notably, the relative abundance of  Bovine herpesvirus type 1, Bovine parainfluenza-3, Bovine coronavirus, and Bovine viral diarrhea virus [Mannheimia haemolytica, Pasteurella multocida, Histophilus, and Mycoplasma [Bovine respiratory disease (BRD) is a serious health problem in cattle worldwide . Bovinesea virus ,8, whilecoplasma ,10. Incrcoplasma ,12. It hcoplasma . Thus, tcoplasma ,14.Although colonization of the nasopharynx by pathogens and changes in metabolites in the serum of the organism are the precursors to BRD , little Following amplification and sequencing of 16S rRNA from the deep nasal swabs, the raw data were cleaned by checking by FASTQC software and filtered to remove adaptor or low-quality sequences. The data volume of clean reads was distributed between 35,615 and 78,264, and the effective sequences obtained for analysis were allocated between 15,577–45,590 . Valid tProteobacteria and Firmicutes. The third most prominent phylum in the D group was Tenericutes, while that in the H group was Bacteroidetes  different from those in the H group. In the four taxons, i.e., phylum, class, order, and family, the RAs of both the D group and the H group were ranked third. The genus Mycoplasma was the top microflora that allowed us to distinguish the D group from the H group, although Pasteurella was also abundant in the D group.To further demonstrate the microbiota structure of each sample, we created a heatmap of RAs at the phylum level F. In sump < 0.05) (p <0.01)  A. At the < 0.05) . Then, w < 0.05)  and prin < 0.05) , as well < 0.05)  on the Op <0.01) . CollectMycoplasmataceae was the key type of bacteria in group D, while Firmicutes was prominent in group H. A Metastats analysis . According to analysis  and t-teanalysis  reconfirp < 0.05). In contrast, compared with samples from the H group, the metabolisms of urea (68) and hexadecanoic acid (182) showed higher levels in samples from the D group, although their VIP score was <1.To explore the metabolic changes in diseased cattle, a metabolomic analysis was performed on serum from diseased and healthy cattle. We identified a total of 41 metabolites. Gammaproteobacteria and Bacilli while showing a negative correlation with Mollicutes. Interestingly, we found that non-dominant bacterial species also had strong associations with other non-dominant metabolites. For example, Fibrobacteria, Betaproteobacteria, and Ellin6529 showed a strong correlation with glycerol. Furthermore, to determine the relationship between metabolites and microbial gene functions, we used the same heatmap to display an association between the 41 metabolites screened from metabolomics and microbial gene functions , clustering those bacteria with a similar metabolomic profile in heatmap plots. As shown in unctions B. The anMollicutes, Actinobacteria, and Flavobacteria, which were of low prevalence in healthy cattle, were abundantly enriched in diseased subjects. Recent studies in southern Alberta, northwest Piedmont, and Egypt all demonstrated that Mycoplasma, a member of Mollicutes, was one of the most enriched genera in the microbiota of the upper  and lower  respiratory tracts of bovines with respiratory disease [Mycoplasma as one of the genera with high relative abundance among bovines with respiratory disease in Ningxia, China. Metagenomic sequencing further confirmed that Mycoplasma bovis is one of the main pathogenic bacteria that can cause respiratory disease in cattle. This finding also suggests there are no regional differences in the effects of Mycoplasma bovis on bovine respiratory disease. It can be seen that members of the genus Mycoplasma, which are believed to play a negative role in the respiratory tract microbiota, participate in the development and progression of respiratory disease.Evidence is accumulating for a potential role of the nasal microbiota in the pathogenesis of cattle with respiratory disease ,17. Usin disease ,13,18,19Gammaproteobacteria and Bacilli were the top two dominant flora in both groups, the enrichment with Bacilli was significantly lower in the diseased group compared with the healthy group. Also, members of the Clostridia, which was the third most abundant in healthy cattle, decreased in the diseased group. The RAs and changes of the most abundant taxa at subordinated levels were further compared and we get a similar trend. Lactobacillales belong to the Bacilli Class [Pasteurellales, and Lactobacillales, which were decreased in the diseased group. It is well known that some members of the Pasteurellales and Lactobacillales have positive significance in resisting pathogenic infection [Lactobacillales such as lactic acid bacteria (LAB) have potential as topical probiotics in the respiratory tract [M. haemolytica to bovine turbinate cells, they were selected as the best candidates for intranasal bacterial therapeutics to mitigate M. haemolytica infection in cattle [Streptococcus genus could act as “natural” antibiotics to treat or prevent bacterial infections [Pasteurellales and Lactobacillales may also contribute to the susceptibility of bovines to respiratory disease. This further hints at a correlation between the pathogenesis of respiratory disease and microbiome homeostasis.Differently, our study also observed a higher alpha- and beta-diversity in nasal microbiota of cattle with respiratory disease, indicating a more heterogeneous community structure among diseased cattle than among healthy cattle. At the class level, although li Class . Our stunfection ,22. Severy tract ,24,25. Sn cattle . Notablyn cattle . The bacfections . EvidentIn order to determine whether changes in bacterial communities had an impact on metabolic output, the metabolic changes in serum samples were evaluated by GC-MSD-based metabolomics. In the present study, we found that bovines with respiratory disease had lower serum concentrations of lactic acid, phosphoric acid, and sarcosine than healthy cattle. Lactate, as a fulcrum of metabolism, has the potential to reshape the energy metabolism of an organism ,30. LactGammaproteobacteria and Bacilli, but negatively correlated with Mollicutes. In our study, bovines with respiratory disease had a lower abundance of Lactobacillales and decreased lactic acid levels, but an increased abundance of Mycoplasma bovis, the main pathogen. This demonstrates the potential probiotic properties of LAB strains and confirms the hazards of Mycoplasma bovis to cattle. In addition, from our analysis of the association of microorganisms and metabolites, we found that non-dominant microorganisms also had strong associations with non-dominant metabolites. BRD is a multifactorial disease and a mixed infection by multiple pathogenic microorganisms can lead to metabolic disorders of organisms [An integrative analysis of the nasal microbiome and serum metabolome was performed to gain insights into the links between nasal flora dysbiosis and respiratory disease and we found that lactic acid (2TMS) levels in serum were positively correlated with rganisms . Due to From February until May 2019, bovine nose swab samples and serum samples were collected from five large breeding farms in Ningxia, China . A health check of the herd was performed prior to collecting samples. A total of 72 deep nasal swabs from 18 bovines with respiratory disease were collected using sterile disposable swab collectors . At the same time, a total of 44 deep nasal swabs from 11 healthy bovines were collected. Furthermore, serum was collected from 15 diseased bovines and 10 healthy bovines from the same farms that were injected with anesthetics. All specimens were sealed and placed on ice, and their information was registered in detail. They were transported to the Key Laboratory of the Ministry of Education for Protection and Utilization of Special Biological Resources in western China within 2 h where the plasma was separated by centrifugation and stored at −80 °C until further use. The study protocol was reviewed and approved by the Animal Welfare & Ethics Committee of Ningxia University  (No. NXU-2019-008).Total DNA from deep nasal swab samples was extracted using the QIAamp DNA Mini Kit  following the manufacturer’s instructions. Next, the bacterial 16S rRNA V3-V4 region was selected for PCR amplification. Then, the PCR products were purified using the QIAquick Gel Extraction Kit , and a NanoDrop or Qubit were used to quantify the library. According to the data volume of 50,000 reads per sample, an appropriate library volume was added, and HiSeq2500/MiSeq was selected for PE250 sequencing.Total DNA extracted from nasal swabs was quantified using a NanoDrop  and confirmed by 0.8% agarose gel electrophoresis. The DNA was broken into DNA fragments with 350 bp approximately. A library based on the samples was constructed following end repair, sequencing adaptor addition, and purification. Each library was sequenced using Illumina HiSeq 2000 equipment and the key species that distinguish diseased from healthy bovines were identified by metagenomic sequencing.p < 0.05). Finally, Linear discriminant analysis (LDA) was used to assess the effect size of each feature (logLDA score = 3.6 as cutoff).Based on the OTU clustering and annotation of the samples, bacterial alpha-diversity was determined by the R package “QIIME2”, and presented by observed Shannon, Simpson, PD_whole_tree, Goods_coverage, Chao 1, and Observed_species indices. Sequence RAs were calculated for every sample and each taxon, and constructed the matrix on bacterial phylum, class, order, family, and genus level, respectively. PCoA and PCA were used to display the samples’ microflora beta-diversity. Unweighted-Unifrac distances and weighted-Unifrac distances were calculated using the QIMME package. Based on the normalized relative abundance matrix, using the non-parametric factorial Kruskal-Wallis (KW) sum-rank test to estimate the features with significantly different RAs  of each serum sample was accurately transferred to a 1.5 mL Eppendorf tube. Next, 1 mL extracting solution (isopropanol: acetonitrile: water = 3:2:2) and 5 μL myristic acid solution (3 mg/mL) were added and the mixture was vortexed for 1 min, followed by centrifugation  at 4 °C for 15 min to obtain the supernatant, which was then dried under nitrogen. Subsequently, following these derivatization steps, 20 μL of methoxyamine hydrochloride/pyridine (40 mg/mL) were added and incubated at 30 °C for 90 min. Next, 90 μL of N-Methyl-N-(trimethylsilyl) trifluoroacetamide containing 1% Trimethylchlorosilane were added and incubated at 37 °C for 30 min. The derivatized samples were centrifuged  at 4 °C for 5 min and the supernatant was used for LC-MS analysis. An equal volume of the prepared samples was mixed and divided into 7 quality control samples to monitor the precision and stability of the instrument. For data processing, GC-MSD metabolic profiling was performed using Agilent 7890A and Agilent 5975C MSDs. The chromatographic column used was Agilent DB5-MS (30 m × 250 mm × 0.25 μm), the inlet temperature was 250 °C, and the MSD interface temperature was 230 °C. The flow rate was set to 1.1 mL/min, and the injection volume was 1 μL. Data acquisition was done in full-scan mode (m/z ranged from 50 to 500).p < 0.05 were considered differential metabolites.The raw GC-MSD data were converted to a NetCDF format using Agilent Chrom Station software . Under the R software platform, the eRah procedure was used for peak identification, retention time alignment, automatic integration, and other preprocessing. Then, we compared the spectral data with the Golm Metabolome database (GMD) to select the results with higher retention similarity and retention index and obtained qualitative results including retention time, retention index, metabolite name, and CAS number. Furthermore, PCA and PLS-DA were used to observe the overall metabolic differences between the D and H groups, and cross-validation (CV) was used to evaluate the quality of the PLS-DA model. The VIP of PLS-DA was employed to measure the importance and contribution of metabolites. Meanwhile, a two-tailed Student’s t-test of normalized peak areas was also used to assess the statistical significance. Metabolites with VIP values > 1.0 and p < 0.05. Finally, significantly correlated metabolites and nasal microbial classes were obtained and displayed with heatmaps, where blue indicated positive correlation and red indicated negative correlation.A Pearson correlation analysis was performed between altered metabolites screened from serum metabolomics and perturbed nasal microbial genera screened from 16S rRNA gene sequencing. The screen criteria included a correlation coefficient |r| > 0.60, Mycoplasma bovis, Pasteurellales and Lactobacillales, provide new insights into the pathogenesis of BRD. The metabolites identified in serum might play various roles in supporting the needs of bovines for energy purposes and potential unidentified immune responses. Those indexes may serve as potential biomarkers for monitoring BRD prior to the appearance of clinical signs. Regulating the composition and stability of bacterial microbiota in the nasopharynx could prevent the onset of bovine respiratory disease and play a major role in the prevention and control of BRD.In conclusion, the current study found that the altered levels of several metabolites, especially lactic acid, phosphoric acid, and sarcosine, were associated with microbial changes, indicating an abnormality in metabolism and microbial levels in bovines with respiratory disease. The microbes identified from the nasal swabs, such as