Together with application of next-generation sequencing technologies and increased accumulation of genomic variation data in different organism species, an opportunity for effectively identification of superior alleles of functional genes to facilitate marker-assisted selection is emerging, and the clarification of haplotypes of functional genes is becoming an essential target in recent study works.In this paper, we describe an R package ‘geneHapR’ developed for haplotypes identification, statistics and visualization analysis of candidate genes. This package could integrate genotype data, genomic annotating information and phenotypic variation data to clarify genotype variations, evolutionary-ship, and morphological effects among haplotypes through variants visualization, network construction and phenotypic comparison. ‘geneHapR’ also provides functions for Linkage Disequilibrium block analysis and visualizing of haplotypes geo-distribution.The R package ‘geneHapR’ provided an easy-to-use tool for haplotype identification, statistic and visualization for candidate gene and will provide useful clues for gene functional dissection and molecular-assistant pyramiding of beneficial alleles of functional locus in future breeding programs.The online version contains supplementary material available at 10.1186/s12859-023-05318-9. Haplotype is a linear combination of variants in specific genomic region. Identification of superior haplotypes of functional genes are essential for developing markers for effective breeding work  and dissHaplotypes of target genes could be identified from genomic variations using many programs, including pegas , DnaSP  and CandMarker-assisted breeding in crop species relies on pyramiding of superior haplotypes related to target traits . SuperioMoreover, screening of superior haplotypes from huge amount bio-data generated by next-generation sequencing platform were also severely restricted in current programs such as: pegas  and DnaSHere, we introduced geneHapR, a toolkit developed in R language, for haplotypic statistics of functional genes including haplotype identification, morphological effects analysis and results visualization for researchers.The geneHapR is developed in R language and three essential steps were required for haplotype analysis, including data importing, haplotype identification and haplotype visualization Fig. . In addiThe geneHapR requires three types of imported data: genotype, annotation, and accession information. The first type is genotype data that could be retrieved from published variants database, or obtained by Sanger sequencing or extracted from variants calling results of next generation sequencing or micro-array. Sequences were usually stored in FASTA format file and variants were usually stored in VCF, P.link or HapMap format files. Variants in published database could also be retrieved as above format.http://genome.ucsc.edu/FAQ/FAQformat.html#format1). The columns contents of BED6 format are: (1) chromosome name, (2) chromosome start, (3) chromosome end, (4) name, (5) score and (6) strand. In custom BED6 format, contents of the fourth column were custom as name and type, which were separated by a space. For example, “LOC_Os07g15770.1 CDS” indicates the CDS of LOC_Os07g15770.1. And the BED4 format contains the first 4 columns of custom BED6 format and DNA strands was set as positive by default.The second type of imported data is annotation files. Annotation files of sequenced species could be retrieved as GFF/GFF3 format from published database, such as: Phytozome . For speThe third type of imported data is accession/individual information, including phenotypic data, individual group/category and geographic coordinates including longitude and latitude information. An example of accession information was showed in Table All required data could be imported by functions listed in Table Usually, genomic variants of interested genes could be retrieved as VCF, P.link HapMap or table format from target database. However, for species with no variants database, users can easily extract interested genomic regions from the original variants file with functions like “filter*”. For example, the “filter_VCF” function provides a convenient way to extract variants from VCF document. There were three modes for variants extraction, (1) by position, require a chromosome name and boundary of target region; (2) by region type, require annotation and specified a type for extraction; (3) by both of position and region type.However, there is a bottleneck for extracting variants from huge documents using personal computer, due to R needs to import the entire dataset. So, we complimented functions looks like “filterLarge*” for variants extraction from huge documents, such as: “filterLargeVCF” for huge VCF file, and “filterLargeP.link” for huge P.link document.Haplotype was identified by functions like “*2hap” listed in Table There are two main steps for haplotype identification. Firstly, determine genotype of each accession and deal with accessions with missing genotypes; secondly, designate haplotypic names to all genotypes.In gene haplotype results, most users prefer nucleotide coordinates start from start codon. However, the coordinate of variants in next-generation sequencing database were usually based on chromosome. Thus, we complimented “hapSetATGas0” and “gffSetATGas0” functions for conveniently conversion of coordinate in gene haplotype and annotation files, respectively. Additionally, rare haplotypes could be eliminated by “filter_hap” function.Furthermore, haplotype results could be converted into standard format for other software by functions like hap2*, for example, function “hap2DNAbin” and “hap2hmp” can convert haplotype result into DNAbin object or HapMap format.hapResult and hapSummary class in R, both included a matrix storing the haplotype genotypes and other related information. For clear demonstration, the two matrices in hapResult and hapSummary were divided into six parts as shown in Fig. Haplotype results can be saved and re-imported for re-analysis using “write_hap” and “import_hap” function, respectively. For haplotype results, we defined hapResult and hapSummary is that each row of part IV–VI represents single individual in hapResult; but represents a haplotype in hapSummary and the frequent column only exists in hapSummary.The part I was fixed as “CHROM”, “POS”, “INFO” and “ALLELE” indicates the contents type of each line in Part II, which contains four lines storing variants information including chromosome, position, information of each variant and alleles, respectively. There is several summary information of current object in part III, like the total number of haplotypes, individuals and variants. The part IV, V and VI includes haplotypes related information, like haplotype names in part IV, genotypes in part V, accession names and frequents in part VI. The difference between In visualization functions, we introduced algorithms for network construction, LD-block calculation. And we personalized visualizing functions from pegas for haplotype network, from maps for geographic distribution, and from genetics for LD-block analysis. With personalized functions users can easily visualize their haplotypes results and statistics without complex format conversion.Firstly, haplotype variants could be visualized as a table-liked figure by “plotHapTable” function. The complementation of this function is based on the ggplot2 package. This function takes the haplotype result as input and generates an R object of ggplot class. Hence, theme of the visualization could be personalized with ggplot2 package. For visualizing the relative variant positions we presented “displayVarOnGeneModel” function which takes the annotation and haplotype result as inputs. With this function, users can easily display the variants and coordinates upon the gene schematic diagram.For illustrating relationships between haplotypes, “get_hapNet” function was developed for haplotype network construction and “plot_hapNet” function was developed for further visualization. In the haplotype network, each circle represents a haplotype and the size indicates individual number, and dots/short lines along the links represents variants between the haplotypes.Many traits are influenced by geographical location, due to differences of day length, rainfall, and altitude conditions. The “hapDistribution” function was developed for demonstrating distribution of major haplotypes across global/region map. The “symbol.size” was designed as circle size controller, and “show.label” controls exhibition of the accession number in each location (circle). Although there is no limitation of haplotype display in this function, for a better readability, we suggest no more than 4 major haplotypes included in this analysis.p values were marked upon corresponded comparison. The rare haplotype with group member less than 5 won’t be analyzed by default.To identify superior haplotype, “hapVsPheno” and “hapVsPhenos” function were developed for identification of phenotypic differences between haplotypes. The outliers (bigger than Q3 + 3*(Q3 – Q1) or less than Q1 – 3*(Q3 − Q1)) were removed before the calculation of significance by default. And the significances and After identification and confirmation of main effect genomic variants contributing to target traits, DNA markers need to be developed for molecular assisted selection. The genomic variant itself is an ideal choice for marker development. Moreover, linked genomic variations could also be used for marker development. The linkage disequilibrium analysis could be conducted by “plot_LDheatmap” function and will be helpful for screening of closely linked variants.OsGHD7 [OsGHD7 could be found in additional file For demonstrating the usage of geneHapR, the genotype and phenotype data and annotation of a grain size regulating gene OsGHD7 , 21 wereOsGHD7  and RiceOsGHD7  and RiceOsGHD7 , respectOsGHD7 was identified using “table2hap” function. In this step, accessions with heterozygotes or missing genotypes were eliminated. Finally, in line with previous studies [Firstly, genotype  of target genes for further research. We then adjusted the coordinates of start codon to zero in haplotype results and annotation files by “hapSetATGas0” and “gffSetATGas0” function, respectively. The genotype of each haplotype was visualized using “plotHapTable” function Fig. A. Differram Fig. B with “dThe evolutionary relationship between haplotypes was visualized as a haplotype network Fig. C. The vaOsGHD7 were illustrated in Fig. Global distributions of three main haplotypes in hapResult directly without format conversion through “plot_LDheatmap” function. In OsGHD7, the 3' end variants were closely linked with each other compared with that of 5' end variant  and 24 GB memory. In all comparisons, geneHapR shows a higher efficiency in haplotype identification process Table . In geneGHD7 with CandiHap. Accessions containing “DEL” were eliminated manually for compatibility. Finally, CandiHap identified seven haplotypes, harboring 202, 178, 73, 8, 6, 6 and 6 individuals, respectively, which is consistent with geneHapR. Furthermore, we also performed a comparison between geneHapR and DnaSP using genotype data of SiTOC1 (Additional file Setaria italica [To clarify the accuracy of geneHapR, we recalculated the haplotypes of  italica . The hap italica  except thttps://CRAN.R-project.org/package=geneHapR.The geneHapR package provides an easy-to-use toolkit for haplotype identification, statistics, phenotype association and result visualization analysis towards specific functional genes. The package has been submitted to CRAN and is available at: Additional file 1: The R scripts used for  identification and visualization of OsGHD7 haplotypes.Additional file 2: The genotype of OsGHD7.Additional file 3: The annotation of OsGHD7.Additional file 4: The phenotype of OsGHD7.Additional file 5: The informaion of individuals used for OsGHD7 haplotype identification.Additional file 6: The genotype data of SiTOC1 in fasta format.


CBD mitigates Aβ-induced cognitive deficits by modulating microglial activity, promoting neurotrophic factor release, and regulating inflammatory genes. The administration of CBD demonstrated a protective effect against Aβ toxicity both in vitro and in vivo, along with an amelioration of cognitive impairment in mice. These findings support the potential inclusion of CBD in future nutritional guidelines for Alzheimer’s disease prevention.Alzheimer’s disease (AD) is a leading neurodegenerative condition causing cognitive and memory decline. With small-molecule drugs targeting Aβ proving ineffective, alternative targets are urgently needed. Neuroinflammation, which is central to AD’s pathology, results in synaptic and neuronal damage, highlighting the importance of addressing inflammation and conserving neuronal integrity. Cannabidiol (CBD), derived from cannabis, is noted for its neuroprotective and anti-inflammatory properties, having shown efficacy in neuropathic pain management for epilepsy. To investigate the therapeutic efficacy of CBD in AD and to elucidate its underlying mechanisms, we aimed to contribute valuable insights for incorporating AD prevention recommendations into future CBD nutritional guidelines. Aβ Alzheimer’s disease (AD) is the foremost neurodegenerative condition leading to senile dementia. Afflicted individuals primarily face memory degradation and diminished learning capacities . The ecoAD is a multifaceted, diverse, and progressive pathology, hallmarked by extracellular plaques rich in β-amyloid (Aβ), intracellular neurofibrillary tangles containing tau, neuronal degeneration, and neuroinflammation ,5,6. YetRecent insights from neuroinflammation studies have suggested a coordinated role of microglia, astrocytes, and neurons in accelerating neurodegeneration. Specifically, Aβ is known to activate the nuclear factor-κB (NF-κB) pathway in astrocytes, which subsequently elevates the release of complementary C3. C3 then engages with C3a receptors on neurons and microglia, triggering neuronal impairment and microglial activation . ConversCompounds derived from the Cannabis sativa plant have historically been employed for various therapeutic purposes . Of moreThe detection of heightened inflammatory markers in AD patients, coupled with the linkage between AD susceptibility genes and innate immune functionalities, has accentuated the pivotal role of inflammation in the etiology of AD ,20,21. A2 at 37 °C.The SH-SY5Y cell line  was cultured in Dulbecco’s modified Eagle medium , which was added to 10% FBS, 100 IU/mL penicillin, and 100 µg/mL streptomycin in a humidified incubator with 5% CO1–42 for an additional 24 h. In the CBD treatment group, cells were pretreated with CBD at concentrations of 0.25, 0.5, 2.5, and 5 μM for a duration of 12 h before being exposed to 10 μM Aβ1–42 for another period of 24 h. Finally, cell viabilities were assessed using the MTT method.The SH-SY5Y cells were seeded into 96-well plates and incubated overnight until they reached approximately 70% confluence. Cells in the vehicle group were cultured in DMEM medium containing vehicle for 36 h, while those in the Aβ treatment group were cultured with DMEM medium for 12 h and then treated with 10 μM AβAll male C57/BL6 mice  were purchased from SPF (Beijing) Biotechnology Company . The animal culture and experiments were conducted in accordance with the ethical guidelines of the Ministry of Science and Technology of the People’s Republic of China and approved by the Center for Pharmaceutical Laboratory Animals of China Pharmaceutical University.1–42 peptide  was solubilized using sterile PBS for a final Aβ1–42 solution concentration of 2 mg/mL. Afterward, the Aβ1–42 solution was incubated at 37 °C for 24 h to obtain aggregated Aβ1–42. All mice were randomly divided into three groups (n = 6 per group): a sham+Veh (vehicle) group, an Aβ  + Veh group, and an Aβ+CBD  group. The sham+Veh group was injected with 5 μL PBS, and the other mice were treated with 5 μL aggregated Aβ1–42, which was inserted into the lateral ventricle using a brain stereo-positioning instrument (Harvard). CBD powder  was dissolved in a mixture of reagents , and the mice were given CBD by gavage continuously for 13 days after surgery. Mice in the AD + Veh and sham + Veh group were gavaged with normal saline.Recombinant human AβOn the 8th day after operation, a Morris water maze was utilized for cognitive function measurement. The MWM system mainly consists of a black circular pool, an aqueous solution dyed white by titanium dioxide powder, a circular concealed platform 1 cm underwater, and a video analysis system. The experimental maze was divided into four quadrants. We systematically altered the platform’s position within the four quadrants on a daily basis, designating the quadrant where the platform was situated as the third. The experiment lasted for a total of 5 days, consisting of 4 consecutive days of hidden platform training followed by a probe trial on the 5th day. During the hidden platform training, mice were given a swimming time of 90 s to locate the target platform, and this was repeated four times. Each mouse entered the pool from a consistent starting position opposite the third quadrant. The video analysis system automatically recorded the time at which the mouse successfully climbed onto the platform. If a mouse could not reach the platform within 90 s, it was artificially led to the target and allowed to remain on the platform for 10 s to find its location. On day 5, a probe trial was conducted in which various parameters, such as latency, path length, swimming velocity, target quadrant residence time, and traveled trajectory, were recorded and analyzed using video tracking technology.The mice were sacrificed under deep anesthesia and perfused transcardially with frozen PBS for 2 min (PH = 7.4). Brain tissues were stored at −20 °C.−ΔΔCt method, and the target gene value was normalized to GAPDH.Total RNA was obtained from the cerebral cortex using Trizol Reagent  and reverse transcribed to cDNA with HiScript III RT SuperMix (Vazyme). The primer sequence is listed in Mice were intraperitoneally perfused with frozen PBS (pH = 7.4) for 1 min, followed by perfusion with 4% paraformaldehyde (PFA) for 1 min to fix the tissues. After cardiac perfusion, the brains were collected and fixed in 4% PFA at 4 °C for a minimum of 24 h under light protection. The fixed brain tissue was then dehydrated in a solution of 30% sucrose. After 48 h, the brain tissue sank to the bottom of the liquid and was cut into coronal sections that were 25 μm thick using a cryomicrotome . The intact hippocampal sections were stored in cryosolution containing PBS, ethylene glycol, and glycerol at a ratio of 5:3:2.®488) ) was applied for one hour at room temperature, and, finally, the slices were stained with DAPI  for ten minutes. After being washed several times in PBS, the samples were analyzed using fluorescence microscopy , and images were captured using MicroImaging System . Finally, the acquired images were analyzed and quantified using ImageJ software(1.54d).The brain slices were penetrated with 0.3% Triton X-100  for 20 min. Following blocking with a solution of bovine serum albumin diluted to a concentration of 5% in PBS at room temperature for one hour, the slices were incubated overnight at 4 °C with primary antibodies , rabbit anti-GFAP ). Subsequently, the secondary antibody . High-quality reads were aligned to the mouse reference genome using Hisat2 (v2.0.3). We identified DEGs between the samples and performed clustering analysis and functional annotation via R studio (v 4.2.3) and DESeq2 (v1.40.2). Trimmomatic (v 0.39) was used for quality control to assess the integrity of the analyzed RNA.t-test was employed to determine significant differences between groups. A one-way ANOVA and the original FDR method of Benjamini and Hochberg were used to compare multiple independent groups. Statistical significance is denoted by * when the p-value is < 0.05.The histograms and line charts were generated using GraphPad Prism 8.0 software. The results are presented as mean ± SEM (standard error of the mean). To ensure consistency, a minimum of three biological replicates were performed for all experiments. The unpaired Student’s 1–42  treatment regimen was initiated. The Morris water maze (MWM) test was utilized to evaluate the cognitive functions of the mice , and brain-derived neurotrophic factor (BDNF) are pivotal for neuronal survival and regeneration . In Aβ1–r levels G,H. In e1–42-treated mice. Our data demonstrated that CBD significantly reduced mRNA levels of inflammatory agents, such as TNF-α and MCP-1, compared to Aβ1–42-treated mice  of gene ontology (GO) on RNA-seq data from CBD-treated mice to decipher the molecular mechanisms driving CBD’s therapeutic efficacy, identifying differential pathways through Limma. Based on the 58 genes A and 10558 genes B were fo58 genes C. RT-PCR58 genes D,E. Spec2+-dependent PKC/p38MAPK/NF-κB pathway [Furthermore, CBD’s modulatory effects showered a decline in PLCG2 and CTSC expression, both implicated in the positive regulation of the neuroinflammatory response pathway D,F. Plcg pathway . Its exp pathway . CBD dowIn summation, our data advocate for CBD’s potential to attenuate cognitive and memory deficits by modulating inflammatory and neuroinflammatory pathways. This dual action, encompassing synaptic restoration and neuroinflammation inhibition, bolsters cognitive function and mitigates memory challenges in AD mice .This study used in vitro methodologies to validate CBD’s safety profile and neuroprotective attributes. In vivo experiments further showcased CBD’s capacity to repair cognitive deficits and rejuvenate the learning and memory capabilities which had been compromised by Aβ in mice. Our analyses illuminated CBD’s ability to mitigate synaptic damage, neuronal attrition, and the hyperactivation of microglia and astrocytes while concurrently decreasing neuroinflammatory markers. RNA-seq data further underscored CBD’s anti-inflammatory prowess in terms of enhancing neuronal protection and memory function.Synaptic plasticity is intrinsically tied to cognitive faculties, particularly learning and memory. Early AD manifestations include cognitive deficits, aberrant synaptic AMPAR distribution, and perturbed LTP/LTD, which are pivotal cellular mechanisms underpinning memory and learning. Aβ disrupts CaMKII activity, impairing AMPAR trafficking and, subsequently, LTP/LTD ,46. Our Neuronal loss, which disrupts neural signaling circuits, is intimately linked to memory impairment. BDNF, with its broad neuroprotective effects in AD models, can reverse synaptic loss, restore cellular signaling, and prevent neuronal death . GDNF ovIn AD brains, Aβ polymers activate microglia and astrocytes, leading to an excessive release of proinflammatory agents and causing synaptic damage and memory impairment. Our data indicate that CBD suppresses this glial activation and reduces proinflammatory cytokine levels, notably TNF-alpha and MCP1. Sequencing data have further revealed CBD’s modulation of key inflammatory regulatory pathways and genes, including Plcg2, Ctsc, Slamf8, Prdx2, and Grn.Chronic systemic inflammation may exacerbate the deposition of Aβ, increasing the risk of significant cognitive decline . Immune Nonetheless, there are inherent limitations involved in considering CBD as a therapeutic molecule for AD. A deeper understanding of CBD’s modulation of glial activation and its comprehensive effects on synaptic repair and neuronal protection is imperative. However, there is a paucity of research on glial cells, synapses, and neurons in relation to CBD, and this knowledge gap is crucial for the progression of CBD-based AD treatments . Our resOur study has meticulously dissected the multifaceted neuroprotective and anti-inflammatory properties of CBD. We have demonstrated its capacity to counteract Aβ-induced cognitive and memory impairments, inhibit the hyperactivation of microglia and astrocytes, and augment the release of neurotrophic factors. Furthermore, our RNA-seq analyses have provided invaluable insights into CBD’s role in modulating critical genes within the inflammatory cascade, underscoring its robust anti-inflammatory potential.Currently, cannabidiol (CBD), which is recognized for its non-addictive and non-toxic properties, is esteemed as a nutraceutical, offering therapeutic benefits across a spectrum of ailments, from ameliorating insomnia and alleviating depressive symptoms to optimizing lipid profiles and enhancing cardiovascular metabolism ,55,56,57


However, their multicomponent  program. Next, we optimized these structures with density functional theory (DFT) using parameters compatible with the Materials Project (MP) database. Our dataset contains these optimized structures and their formation (ΔHf) and decomposition enthalpies (ΔHd) computed relative to MP tabulated elemental references and competing phases, respectively. This dataset can be mined, used to train machine learning models, and rapidly and systematically expanded by optimizing more SPuDS-generated Perovskite oxides (ternary chemical formula ABO They have been used in solid-oxide fuel cells10, photocatalysis13 and to mediate CO2-to-CO16 and steam-to-hydrogen solar thermal conversions18, and could underlie future industrial production of these renewable fuels. Perovskite oxides are defined by networks of corner-sharing octahedra formed from transition metal or metalloid B-site cations coordinated by six oxygen anions (BO6). Many cations  can occupy the A-site positions between octahedra and are accommodated through coordinated distortions of the octahedral network that are referred to as octahedral tilting. Glazer derived 23 tilting modes — as defined by the phases and magnitudes of the octahedral tilting relative to the unit cell axes — unique to ternary perovskites19, 15 of which were determined to be symmetrically distinct20. The stabilities, electronic properties, redox properties, etc. are strongly influenced by octahedral tilting.Perovskite oxides , extension to just the stoichiometrically restricted Perovskite properties can be accurately predicted using plane wave-based quantum mechanical methods, such as density functional theory (DFT) when computed for the appropriate Glazer tilt. In particular, high-throughput (HT) DFT investigations have explored hundreds and even thousands of theoretical perovskite oxides. For example, Castelli 25, which reliably predicts the probability that a composition forms a stable perovskite and can therefore guide HT investigators through the vast multinary space. τ does not, however, predict perovskite structures for DFT optimizations. While techniques such as topotactic structure templating — whereby existing perovskite structures are transmuted with elemental substitutions — can generate theoretical perovskites, significant optimization is required if the initial structures poorly approximate the optimized structures. This is exacerbated in increasingly complex multinary perovskites, where more elements are substituted and optimization expense can therefore compound rapidly in HT investigations. Fortunately, structural templating can be avoided for some multinaries using Structure Prediction and Diagnostic Software (SPuDS), which estimates the lattices, site positions, and octahedral tilting of perovskites with minimal computational expense and generally reproduces experimental structures26. Optimizing SPuDS predicted structures using DFT is thus not unlike the optimization of experimental crystal structures by DFT to generate the entries of the Materials Project (MP) computational materials database27. Therefore, SPuDS structures should provide reasonable initial estimates of structures that lower the expense of DFT geometry optimizations.One such tool is the machine-learned tolerance factor, τΔHf) and decomposition enthalpies (ΔHd) computed relative to MP-tabulated elemental references and competing phases for the 66,516 structures converged with DFT. We show that SPuDS structures match or are in the same structural family as DFT-optimized structures for 56,716 compositions but differ for 6,808 of the 66,516 converged structures (~10%) that relaxed out of the perovskite phase. Finally, we benchmark our dataset’s DFT energies against the MP database and show that our dataset greatly expands the MP’s multinary oxide compositions. The HT SPuDS python wrapper used to generate structures is available on GitHub (see Code Availability) and all structures are available as tabulated entries in the MP with the complete metadata available on MPContribs28.In this work, we first generated 69,407 et al., which is briefly summarized here. To make at least one combination of common oxidation states that charge-balanced a multinary composition, its tolerance factor τ was computed using Eq. 29, which SPuDS needs to generate structures.We identified 69,407 multinary perovskite oxide compositions for DFT optimization using the method described by Bartel mentclass2pt{minimmentclass2pt{minimrA* and rB* are the stoichiometrically-averaged Shannon ionic radii30 of the A/A’- and B/B’-site cations, rX is the Shannon ionic radius of the X-site cation (oxygen), and nA* is the stoichiometrically-averaged A/A’-site oxidation state. For each composition, the cation(s) with the largest ionic radii were assigned to the perovskite A- and/or A’-sites, as larger cations tend to occupy the larger A-sites over the smaller B-sites. The remaining cation(s) were assigned to the perovskite B- and B’-sites. 12-fold, 6-fold and 6-fold coordinated Shannon ionic radii were preferentially chosen for the A/A’-, B/B’- and X-site radii, respectively, to approximate the cubic perovskite structure’s site coordination numbers. In the event that these Shannon radii were not tabulated, τ was computed using the closest available radii. As all compositions considered are within or near the predicted stable perovskite region of τ < 4.18, we generated 69,407 total perovskite structures for DFT optimization.In Eq. GII) to predict perovskite B-O bond lengths and BO6 tilting magnitudes. Morelock et al. showed that GII correlates with the DFT energies of competing Glazer tilts for ternary ABO3 perovskites31, meaning that SPuDS computed ternary perovskite oxide structures lower the optimization expense when used as starting structures for DFT optimization. SPuDS constrains structures with multiple B-sites, i.e., a−b+a− Glazer tilting mode, a decision we justify in the Comparison to the Materials Project database sub-section of the Technical Validation section. SPuDS does not predict octahedral tilting for 6,523 compositions, meaning these structures were generated in the cubic a0a0a0 Glazer mode. Using the MP-compatible framework detailed in Computational Methodology, we converged structures for 66,516 of these 69,407 compositions, which we report here. The remaining 2,891 structures  had unphysical (exploded) unit cells and/or unreasonable energetics, even after multiple re-optimization attempts, and are not reported in this work.Perovskite structures were generated using PySPuDS, a custom high-throughput python wrapper to the SPuDS program. SPuDS minimizes the BVM Global Instability Index (34 with the Perdew-Burke-Ernzerhof (PBE)35 exchange-correlation functional and projector augmented wave (PAW) pseudopotentials37 under periodic boundary conditions. All calculations are compatible with the MP database, which tabulates the structures, energetics, and other properties of inorganic materials computed at the PBE (GGA and GGA + U) level of theory. The electronic wave functions were expanded in a plane wave basis set with an energy cutoff of 520 eV. The Brillouin zones were sampled during geometry optimization using the Monkhorst-Pack algorithm38 to automatically generate a Γ-point centered k-point mesh with a grid density of at least 1000/(atoms/unit cell). We used pseudopotentials, Hubbard U parameters39, magnetic moments, and energy convergence criteria that are consistent with pymatgen’s40 MPRelaxSet, which is the default relaxation parameter set for the MP. Consistent with MP’s convergence methodology, we performed two consecutive spin-polarized geometry optimizations, the first of which was initialized in the high-spin ferromagnetic configuration. These geometry optimizations relaxed both the unit cell lattice vectors and atomic positions. ΔHf and ΔHd include MP2020 anion corrections that are based on the structure type , as well as element-specific corrections for compatibility between GGA and GGA + U calculations.GGA + U DFT calculations were performed using the Vienna Ab-initio Simulation Program (VASP 5.4.1)31. Thus, explicit consideration of BO6 tilting should better approximate ground state perovskite energetics, and more reliably predict perovskite phase stabilities and synthesizability. Finally, we include non-perovskite structures in the dataset and report each structure’s perovskite/non-perovskite classification in the metadata. This is intended to direct materials scientists towards compositions where the perovskite phase is preserved by DFT optimization so that data scientists can use this designation for machine learning. To our knowledge, high-throughput DFT investigations typically do not confirm nor repudiate phase preservation in their metadata, and this has certainly not been previously reported for a multinary perovskite oxide dataset of this size.Currently, the MP database contains only 3,904 entries composed of the 39 elements considered herein with the chemical formulae site positions relative to DFT. This postulate is evaluated in the Comparison of SPuDS and DFT-optimized structures sub-section of the Technical Validation section, where we show that site positions predicted by SPuDS approximate DFT optimized site positions for ~85% of compositions in our dataset. The Goldschmidt tolerance factor41 predicts experimental perovskite synthesizability for ternary (ABX3) compositions from their cation and anion radii. We computed Goldschmidt tolerance factors for multinary compositions in our dataset by stoichiometrically averaging their A- and B-site Shannon ionic radii:Figure t < 1.059 reported range of Goldschmidt tolerance factors t expected to form perovskites25. This suggests that the perovskite phase might not be preferred for ~9% of compositions in our dataset, which we elaborate upon in the Perovskite or non-Perovskite classification sub-section of the Technical Validation section. Finally, Fig. ΔHf and are thus stable relative to their elemental reference states. Figure ΔHd < 650 meV/atom above the convex hull defined by competing phases in the MP, with a mean ΔHd of 170 meV/atom. In the Comparison to the Materials Project database sub-section of the Technical Validation, we indicate when our ΔHf and ΔHd values most reliably estimate DFT energetics and justify choosing the a−b+a− Glazer tilt as a surrogate for the ground state structure in HT, theoretical multinary perovskite oxide investigations.As shown in Fig. https://contribs.materialsproject.org/projects/Multinary_Oxides42) and can be accessed using the MPContribs API and/or downloaded in JSON or CSV formats directly from the host webpage. Each of the 66,516 data entries have a unique identifier, which is the composition ordered by A, A’, B and B’ sites, with ‘_’ appended to single-letter element abbreviations  Each entry also has an associated id for querying its pymatgen Structure object and metadata from the MPContribs API , and a tabulated formula for each entry, e.g., TbYFeCoO6. The four sub-headers: elements, oxidation, tolerance and dH are placed under the data header for each entry. The elements sub-header includes the columns A1, A2, B1 and B2, which tabulate the A, A’, B and B’ cation element identities, e.g., A1 = “Tb”, A2 = “Y”, B1 = “Fe” and B2 = “Co”. The oxidation sub-header includes the columns A1, A2, B1 and B2, which tabulate the numerical, positive oxidation states of each cation used to generate SPuDS structures for DFT optimization, e.g., A1 = 4, A2 = 3, B1 = 2 and B2 = 3 for “TbY_FeCoO6.” The tolerance sub-header includes the columns t and tau, which are the Goldschmidt41 and Bartel25 perovskite tolerance factors, respectively  computed using Eq. dH includes the columns formation [eV] and decomposition [eV], which tabulate the formation and decomposition enthalpies  of the DFT-optimized structures computed relative to only elemental and all competing phases in the Materials Project database, respectively  The perovskite column is under the data sub-header, which specifies “Yes” or “No” if the structure is a perovskite following DFT optimization according to the classification method outlined in the Perovskite or non-perovskiteclassification sub-section of the Technical Validation section (“Yes” for “TbY_FeCoO6.”) The SPuDS structures optimized with DFT for each entry are available for download as Crystallographic Information Framework (.cif) files under the structures sub-header. All entries in our dataset also have unique “mp-id” identifiers for the Materials Project database, meaning that their DFT-optimized structures and calculation details can be found in the main MP repository.All metadata is hosted by MPContribs  and structural similarity metrics (ε) of SPuDS and DFT-optimized structures, as more similar structures should result in lower DFT optimization expense. As implemented in the Matminer python package44, a structural fingerprint is a vector of statistical information, i.e., minimum, maximum, mean, and standard deviation, derived from a structure’s coordination environment distributions. The SFPD is the normed difference between two structural fingerprints and quantifies two structures’ similarity; according to the MP, structures with 0 ≤ SFPD ≤ 0.9 are typically similar, whereas structures with SFPD > 0.9 are typically different. As implemented by the AFLOW45 software framework’s Xtal-Finder46, ε is a non-negative, scalar metric that measures the structural similarity between two structures by computing deviations between crystal lattices (εlatt) and mapped site positions (εcoord). It also includes a failure term (εfail) for incompatible structures. Hicks et al. reported that structures with 0 ≤ ε ≤ 0.1 match and structures with 0.1 < ε ≤ 0.2 reside in the same family, whereas structures with ε > 0.2 are different. Because SFPD and ε are interpretable, scalar quantities, but are derived independently from one another, we compared SPuDS structures to DFT-optimized structures using both metrics.SPuDS accurately predicts perovskite oxide structures with multiple octahedral cations relative to experimental structures47, we find that 0 ≤ SFPD ≤ 0.522 generally corresponds with 0 ≤ ε ≤ 0.1 and 0.522 < SFPD ≤ 0.927 generally corresponds with 0.1 < ε ≤ 0.2, which supports the MP claim regarding SFPD. 25,349 of the 29,969 compositions with 0 ≤ SFPD ≤ 0.522 have 0 ≤ ε ≤ 0.1 (~85%), while 42,305 of the 45,250 compositions with 0 ≤ SFPD ≤ 0.927 have 0.1 < ε ≤ 0.2 (~93%). Xtal-Finder did not map DFT-optimized structures to their starting SPuDS structures for the remaining 17,774 compositions. This does not necessarily mean that the structures for these compositions are dissimilar, however, as 13,549 of the 17,774 unmapped structures have SFPD ≤ 0.927  stoichiometry”, although “deviations from this ABX3 stoichiometry can be obtained when the A and B cation sites… are replaced by a combination of other cations” such as in multinary The standard MP relaxation set, MPRelaxSet, used for our high-throughput investigation optimizes all internal degrees of freedom  with DFT, which can relax the input SPuDS structure out of the perovskite phase. We classified structures as perovskite or non-perovskite following DFT optimization using the guidelines proposed by Akkerman and Manna for 3D perovskites49. We assigned oxidation states for CrystalNN using the algorithm reported by Bartel et al.25, meaning that the oxidation states assigned to DFT-optimized structures were the same oxidation states used to generate initial SPuDS structures. This assumes that Bartel’s assignment algorithm is accurate, even though it does not account for charge disproportionation, e.g., mixed valence Bi in Ba(Bi3+/Bi5+)O350, that can be present in perovskite oxide systems. Even when cations with multiple valences are not present, assigning formal oxidation states to DFT-optimized multinary metal oxides is nontrivial and beyond the scope of this work. As a result, perovskite/non-perovskite classifications in our dataset can change depending upon the cation and anion oxidation states assigned, as well as the nearest neighbor finder51 and/or the cutoff radius used, although benchmarking suggests that perovskite/non-perovskite classifications only change for a small subset (<1%) of highly sensitive structures.To check if octahedral connectivity was preserved, we constructed B-site cation octahedral  coordination environments from anion nearest neighbors identified using pymatgen’s CrystalNN classAll 66,516 SPuDS structures input to VASP were deemed perovskite according to our classification methodology. However, only 59,708 DFT-optimized structures were classified as perovskite, whereas the remaining 6,808 structures were classified as non-perovskite (~10%). All 39 elements considered are present in both perovskite and non-perovskite structures. The frequencies of each element’s A- and B-site occupations in perovskites and non-perovskites are tabulated in Supplementary Table 6 composition  with Ba2+, Ce4+, Sn2+, and Se4+ cations, in which SeO6 and SnO6 octahedra optimize to SeO3 and SnO3 complexes. Supplementary Table 4+ have non-perovskite DFT optimized structures, while only 1,770 compositions have perovskite structures. This suggests that many Se-containing multinary oxide compositions might preferentially form non-perovskite structures and is consistent with the oxide coordination environment analysis of Waroquiers et al.53, which determined that Se4+ prefers the trigonal non-coplanar environments present in DFT-optimized BaCeSnSeO6.DFT optimization can cause under- or over-coordinated B-sites and/or disruption of octahedral connectivities such that not all oxygens remain shared between octahedra, resulting in non-perovskite classifications. For instance, this occurs for the BaCeSnSeO54, which auto-generates text-based descriptions of crystal structures analogous to a “real-life crystallographer”, to classify structures as perovskite or non-perovskite. Robocrystallographer classified 26,781 of the 66,516 DFT-optimized structures in our dataset as perovskite and 5,907 structures as non-perovskite, with non-perovskite mineral type classifications that include ilmenite , PbZr0.5Ti0.48O3 , and hausmannite (255). However, 3,940 of the 4,065 ilmenite structures, 492 of the 1,201 PbZr0.5Ti0.48O3-type structures, and all 255 hausmannite structures are perovskites according to their octahedral network connectivities. Some of these structures have large BO6 tilting angles that reduce the A-site coordination numbers to less than eight, which Robocrystallographer interpreted to be “non-perovskite”-like. It therefore returned a non-perovskite mineral type classification, such as ilmenite, even if the octahedral network connectivity was preserved in the structure. Another consequence of Robocrystallographer’s mineral type assignment algorithm is that it did not classify 33,828 structures, which is greater than half of our dataset. For this reason, we report perovskite classifications using only the octahedral connectivity rules, and we conclude that ~90% of the converged structures in our dataset are perovskites.We also used the Robocrystallographer python packaget, which predicts that ~91% should form perovskites. However, t and DFT do not necessarily agree for all compositions in our dataset. For the 59,708 compositions with structures that remain perovskites following DFT, t predicts “perovskite” for 54,918 compositions (~92%) and “non-perovskite” for 4,790 compositions. Agreement between t and DFT is significantly worse for the 6,808 compositions that relax out of the perovskite structure following DFT, as t predicts “non-perovskite” for only 1,155 compositions (~17%) and “perovskite” for 5,653 compositions. As previously discussed, improperly assigned oxidation states---resulting in Goldschmidt tolerance factors calculated from the wrong Shannon radii---could contribute to this observed disagreement. Additionally, t does not account for variations in bonding strength or the preferred coordination environments of different elements, and stoichiometrically averaged A/A’ and B/B’ radii might not fully capture the effects of mixed cation sites on octahedral network formation. We also did not optimize non-perovskite starting structures in this work, meaning that some structures that remain perovskite following DFT optimization could have more stable non-perovskite structures that were missed. This is even more likely for cubic a0a0a0 structures where the SPuDS-generated unit cell symmetries limit the atomic degrees of freedom during HT optimization (VASP tag ISYM = 1). Optimizing supercells with more degrees of freedom could improve the poor agreement between t and DFT for non-perovskite predictions, albeit by incurring a far greater computational expense.The proportion of compositions in our dataset with structures that are perovskites following DFT optimization generally agrees with the Goldschmidt tolerance factor, ΔHf and ΔHd computed relative to MP elemental references and competing phases, respectively, which assumes that the DFT-computed energetics are consistent with the MP and can thus reliably predict perovskite phase stabilities. We quantified the dataset’s MP compatibility by comparing our DFT energetics to MP energetics for structures present in both datasets. There are very few compositions present in the MP repeated in our dataset, let alone repeated structures: of the 66,516 multinary oxide compositions reported, only 425 (<1%) have any structural entries, perovskite or non-perovskite, in the MP. Of these 425 overlapping compositions, the MP tabulates at least one perovskite structure for 391 compositions according to the classification rules described in Comparison of SPuDS and DFT-optimized structures. We compared energetics for 213 of these 391 compositions, or for the compositions where our perovskite structure matches an MP perovskite structure according to pymatgen’s StructureMatcher class. Because the MP uses spin-polarized calculations, which means that spin magnitudes and orientations are simultaneously optimized with atomic positions, energetics can differ depending upon site spin orderings. For example, Horton et al. showed that magnetic sampling with different starting spin initializations was necessary to correctly capture non-ferromagnetic spin orderings for a benchmarking set of 64 materials, 50 of which are mixed metal oxides55. Likewise, Bare et al. magnetically sampled Gd-containing multinary oxides in their HT DFT investigation because, on average, magnetic sampling stabilized these compositions by ~60 meV/atom relative to initial ferromagnetic ordering24. We therefore separately compared our structures that matched or did not match site magnetic moments according to pymatgen’s CollinearMagneticStructureAnalyzer class.We report 56. However, when matching structures with different magnetisms were compared, the RMSD increased to 95.3 meV/atom. This nearly four-fold increase confirms that the DFT energetics of our multinary oxides are sensitive to magnetic ordering, most likely because these compositions can have multiple magnetic elements with many unique spin magnitudes and orientations. Due to the unprecedented size of our dataset---where we report more than 80 times as many optimized bulk structures compared to our previous investigation of Gd-containing perovskite oxide redox mediators for solar thermochemical hydrogen production24---we forewent magnetic sampling during the first structure optimization and instead restricted all initial spin configurations to be high-spin ferromagnetic to reduce computational expense. The magnetisms for the second structural optimizations were initialized from the final magnetisms of the first structure optimizations and thus were not spin sampled. Because the RMSD attributable to slight differences in final structures, which could arise from spin initialization during structure optimization, is 24.3 meV/atom, or within DFT error (~30 meV/atom), we conclude that high-spin ferromagnetic initializations should be sufficient for structure optimizations. However, high-spin ferromagnetic initializations do not necessarily capture the most accurate DFT total energetics for magnetic structures. Therefore, we recommend single-point magnetic sampling of our converged structures to maximize compatibility with the MP database, with the caveat that sensitivities to magnetic ordering will vary based on the magnetic elements and the number of magnetic sites present in each structure.For the 104 compositions in our dataset with structures and magnetisms that matched the MP, the RMSD between DFT and MP energetics is 24.3 meV/atom, which approaches one standard deviation of GGA + U DFT error for oxide formation enthalpiesa−b+a− Glazer tilt or the SPuDS a0a0a0 Glazer tilt if no tilting was predicted for the a−b+a− Glazer mode. The SPuDS program already estimates Glazer tilt stability using the BVM Global Instability Index; however, because the GII does not consider unrealistically short anion-anion (O-O) distances, the a−a−a− Glazer tilt is always the lowest GII structure, even though the a−b+a− Glazer tilt is more often observed experimentally57. Furthermore, our investigation into experimental ABO3 perovskite compositions showed that the a−b+a− Glazer tilt optimized to the DFT ground state perovskite for more than 95% of ternaries31, and we expected this would also apply to multinaries. To evaluate our choice, we counted the number of compositions in the dataset with more than one perovskite entry tabulated in the MP where the DFT-optimized SPuDS structure matched the MP ground state perovskite. A visual depiction of our benchmarking is shown in Fig. a−b+a− Glazer tilt, while the remaining 37 compositions have structures optimized from the a0a0a0 Glazer tilt. Only 39 of the 176 compositions optimized from the a−b+a− Glazer tilt have more than one unique perovskite structure tabulated in the MP, but of these, we matched the ground state in 33 compositions (~85%). This indicates that, when present, multinary perovskite oxide structures derived from the a−b+a− Glazer tilt are frequently the lowest energy perovskite tabulated in the MP. We also matched the MP ground state perovskite for 10 of the 13 compositions optimized from the a0a0a0 Glazer tilt. This suggests that octahedral tilting does not typically stabilize multinary perovskite oxide structures when SPuDS predicts no tilting.We also benchmarked our choice to constrain all starting structures for DFT optimization to be either the SPuDS ΔHd cutoff energy. Importantly, this dataset can be rapidly and systematically expanded by optimizing additional SPuDS-generated The dataset reported in this study is the largest compilation of multinary perovskite oxides to date. This dataset can be data-mined and/or used to train machine-learning models that can elucidate structural and stability trends that will accelerate the screening of potentially interesting compositions for targeted applications. For example, the structures reported, which were optimized with GGA+U DFT, can be used as starting structures for further optimization using higher-level methods, such as SCAN or RPA. Furthermore, this dataset can be used to screen for potentially stable perovskite compositions using a Supplementary Table 1


Nasopharyngeal Carcinoma (NPC) is a malignancy of epithelium of epithelium of the nasopharynx, with the highest incidence of otolaryngeal malignancies. A growing number of studies confirm that Circular RNA (circRNA) plays an important role in tumor development, including Hsa_circ_0013561. This study aims to elucidate the process and mechanism of NPC regulation hsa_circ_0013561.In this study, circRNA expression nodes and subcellular localization in NPC tissues were analyzed by fluorescence in situ hybridization. The expression of hsa_circ_0013561 in NPC cells was further clarified by RT-qPCR. At the same time, the lentivirus vector interfered by hsa_circ_0013561 was constructed and transfected. The cell proliferation was detected by CCK-8 method, EdU assay and plate cloning assay. The cell cycle and apoptosis were detected by flow cytometry, and the cell migration ability was detected by wound healing assay and Transwell assay. Western blotting examined the expression of apoptosis, Epithelial-Mesenchymal Transition (EMT)-associated proteins, and Janus Kinase/Signal Transductor and Activator of Transcription (JAK/STAT) signaling pathway-related proteins.The results showed that the expression of hsa_circ_0013561 in NPC samples was significantly upregulated and hsa_circ_0013561 localized in the cytoplasm. After down-regulating hsa_circ_0013561 expression, it significantly inhibited the proliferation and metastasis ability of NPC, inhibited EMT progression, and promoted apoptosis. Further studies showed that interference hsa_circ_0013561 significantly inhibited JAK2/STAT3 signaling pathway activation and induced the expression of apoptosis-related proteins.In summary, we found that hsa_circ_0013561 is a pro-tumor circRNA in NPC, which can reduce the activation of JAK2/STAT3 pathway by knocking down hsa_circ_0013561, thereby slowing down the malignant progression of NPC.Level 4. Nasopharyngeal Carcinoma (NPC) has ethnic susceptibility and regional concentration, and the incidence area is concentrated in South China, East China, and West China, which is a cancer with 'Chinese characteristics'. In 2020, there were about 133,354 newly diagnosed cases of NPC worldwide, and 80,008 new deaths in the same year. More than 70% of these cases occurred in endemic regions, and the incidence of men in Southeast Asia ranked first in the world.Janus Kinase (JAK)/Signal Transducer and Activator of Transcription (STAT) pathway plays a central role in immune response, cell proliferation and differentiation, and studies have shown that overexpression and overactivation of components in the JAK/STAT pathway are associated with the development of different types of cancer.Circular RNAs (circRNAs) are a special class of non-coding RNA molecules that are formed by a non-canonical splicing mechanism of reverse splicing, connecting the 3' end of the exon to the 5' end of the same or upstream exon, forming a closed loop.Here, we aim to explore the biological role of hsa_circ_0013561 in NPC. We speculate that hsa_circ_0013561 regulates the role of JAK2/STAT3 signaling pathway in the progression of NPC, and we will further dissect the mechanism.Paraffin samples of carcinoma and paracancer tissues were collected from 3 patients with NPC who were pathologically diagnosed and had complete clinical data during 2021‒2022 from the Department of Pathology of Shanghai Gongli Hospital, and the paraffin samples were used for immunohistochemical experiments. This research was approved by the Ethics Committee of Shanghai Gongli Hospital, and all patients signed the informed consent.We tested hsa_circ_0013561 with the help of Geneseed Biotech . We used Cy3-bound anti-digoxin and FitC-bound anti-biotin antibodies to explore the signal. We used 4,6-Diamino-2- Phenylindole (DAPI) to invert the nucleus. After that, we used a Leica microscope to get the images.2.We purchased Human Nasal mucosal Epithelial Cells (HNEpC) from Shanghai phytobiotechnology Company  and received a donation from the NPC cell line HNE1 from the Naval Medical University. HNEpC cells were then inoculated in DMEM/F-12  medium and HNE1 cells in RPMI-1640  medium with 10% Fetal Bovine Serum  and 1% streptomycin/penicillin antibiotics, and cultured at 37 °C and 5% COWe constructed lentivirus-stabilized hsa_circ_0013561 knockdown cells known as sh-circ0013561-HNE1. After cell passage, RNA was extracted to verify transfection efficiency.-ΔΔCt method is used for estimation of relative gene expression. Real-time PCR was performed in the StepOnePlus RT-PCR system (Thermo Fisher Scientific) using SYBR Green PCR Master Mix . The thermal cycle conditions of PCR were: 0.5 min at 95℃, 10 s at 95 ℃, 30 s at 60 ℃, and 40 cycles.Total RNA was isolated by the Fastagen kit (RNAfast200), and the amount of total RNA was measured by using the NanoDrop device . Complementary DNA (cDNA) is synthesized using the cDNA synthesis kit . Primers were synthesized by Sangon Biotech  using Primer software based on the sequence of each gene in circBase. The hsa_circ_0013561 and β-actin primers used in this study are as follows: hsa_circ_0013561 , β-actin . β-actin is an endogenous control, while the 2450nm) was measured at 450 nm for each sample using a microplate reader.According to the manufacturer's protocols, relative cell viability was measured at 24 h, 48 h, and 72 h after transfection using CCK-8 . The cells were inoculated in 96-well plates with 4,000 cells per well and 3 repeat wells. After 10 μL CCK-8 solution was added to each well, the plates were incubated at 37 °C for 1 h away from light. After 0 h, 24 h, 48 h, and 72 h, Optical Density  to DAPI-positive cells .We evaluated cell proliferation using the EdU assay kit . Normal Control Cells (HNE1-NC) and sh-circ0013561-HNE1 cells in the logarithmic growth phase were inoculated in 24-well plates, inoculated with 4 × 10The transfected cells were inoculated into six-well plates at a density of 1000 cells per well and cultured in RPMI-1640 medium containing 10% FBS for 7 days. Washed with Phosphate Buffered Saline (PBS) twice, fixed with 4% paraformaldehyde for 20 min, and dyed with 0.5% crystal violet for 20 min. Colonies consisting of at least 50 cells were counted and compared with NC.6 cells and add 100 μL of 1× Binding Buffer and transfer them to Eppendorf (EP) tubes, add 5 μL of Annexin V-APC and 5 μL PI to each tube. After mixing, incubate for 15 min at room temperature in the dark, add 500 μL of 1× Binding Buffer to terminate the reaction, and then detect apoptosis with flow cytometry.We assessed apoptosis using apoptosis assays . First, collect 1 × 106 cells and add 500 μL staining buffer, 25 μL propidium iodide staining solution (20×) and 10 μL RNase A (50×) and transfer them to EP tubes, mix well and incubate at room temperature protected from light for 30 min. Measure the cell cycle with flow cytometry.Cell cycle was assessed using a cell cycle assay kit . First, collect 1 × 105 cells/well and cultured in RPMI-1640 medium containing 10% FBS. When confluence was reached at 90%‒95%, create a “scratch” using a 1 mL pipette tip and the scratched cells were washed off with PBS. Subsequently, basic medium without FBS was added for culture. At 0 h, 24 h, 48 h and 72 h, the wound area was measured under the microscope, and the wound area was measured with Image J software, and the cell scratch healing rate was calculated = [(scratch area at 0 h − scratch area at a certain time point)/scratch area at 0 h] × 100%.Control cells and transfected cells were inoculated into six-well plates at a density of 3.5 × 104 cells/well and inoculated in the upper chamber of Transwell plate with 8 μm well. In addition, 600 μL medium containing 15% FBS was placed in the lower chamber as chemotactic agent. After 1 day of culture, it was fixed with 4% paraformaldehyde, stained with 0.1% crystal violet, and counted with 3 random fields of view under the microscope.Control cells and transfected cells were suspended in 200 μL serum-free medium at a density of 2 × 10Total proteins were isolated from transfected cells using RIPA Lysis Buffer  lysate with a protease inhibitor . Protein concentration was determined with a BCA protein kit . Electrophoretic protein samples (15 μg) in SDS-PAGE (10%) gel and transfer them to PVDF membranes. Next, block with skim milk powder (5%) for 1 h and then incubate the primary antibody overnight in a 4 °C shaker. The corresponding primary antibody: anti-BAX , anti-BCL-2 , anti-E-cadherin , anti-N-cadherin , anti-JAK2 , anti-p-JAK2 , anti-STAT3 , anti- p-STAT3  and anti-β-actin . β-actin was used as internal reference. On the second day, incubated with peroxidase labeled secondary antibodies on a shaker for 1 h, the corresponding second antibody: Goat Anti-Mouse IgG  and Goat Anti-Rabbit IgG . Then ECL Western Blotting kit  was used to analyze the chromogenic protein bands. Finally, the bands were analyzed using Image J software.t-test, and the data were expressed as averages. We considered with p <  0.05 considered statistically significant. All statistical analyses were performed using GraphPad Prism.Differences between the groups were evaluated using the unpaired p <  0.05, To reveal the correlation between circRNA expression and nasopharyngeal carcinoma progression, we performed in situ hybridization on paraffin specimens from 3 nasopharyngeal carcinoma patients and analyzed nasopharyngeal carcinoma cells by qRT-PCR. FISH results showed that the hsa_circ_0013561 was significantly upregulated in cancerous tissue compared to paracancerous tissue and most of the hsa_circ_0013561 was distributed in the cytoplasm A and B. p <  0.05, Nasopharyngeal carcinoma cell line HNE1 was established, hsa_circ_0013561 knockdown virus model was constructed, and hsa_circ_0013561 was stably transfected into HNE1. After transfection, observe under a fluorescence microscope and take pictures A. The rep <  0.05). In the EdU test, the count EdU/DAPI ratio was the percentage of cell proliferation, which was significantly decreased in knockdown group compared with the mean percentage of cell proliferation in NC group (**p <  0.05). Compared with the NC group, the average number of clone colonies in knockdown group was significantly decreased (**p < 0.05).CCK-8 A, EdU tep < 0.05), and the apoptosis ability of cells was enhanced after transfection knockdown hsa_circ_0013561. It has been shown that knocking down hsa_circ_0013561 promotes apoptosis in HNE1 cells. Cell cycle (p <  0.05). The results showed that compared with the NC group, the mean S phase cell ratio was significantly decreased in the knock down group (****p <  0.05). The results indicated that knockdown of hsa_circ_0013561 mediated the arrest of HNE1 cells in G1 phase. Western-blot assay was used to detect the expression of apoptosis-related proteins in the two groups . In the results of transwell (p <  0.05). The expression of EMT-related protein in the two groups of cells was detected by the Western-blot method  plays an important role in cancer metastasis. EMT procedures can enhance metastasis, chemical resistance, and tumor stemness, mainly characterized by reduced expression of cell adhesion molecules such as E-cadherin. E-cadherin is a potential marker of malignancy.In this study, we found that hsa_circ_0013561 was significantly upregulated in NPC tissues and hsa_circ_0013561 was highly expressed in NPC cells HNE1 compared to HNEpC. These findings suggest that hsa_circ_0013561 may play a role in NPC progression. In order to further explore the function, expression and mechanism of hsa_circ_0013561 in NPC, we further carry out research. Firstly, knocking down the hsa_circ_0013561 in HNE1 cells found that it would inhibit cell proliferation, while the expression level of BAX protein was upregulated and the expression level of BCL-2 protein was down-regulated, which promoted cell apoptosis. Change the cell cycle and block cells in G1 phase inhibit migration ability, high expression of E-cadherin and low expression of N-cadherin inhibit EMT progression. The results show that hsa_circ_0013561 can promote the proliferation and migration of NPC.In conclusion, these data indicate that the expression of hsa_circ_0013561 in NPC is increased and closely related to the malignant progression of NPC. Knocking down hsa_circ_0013561 inhibits NPC progression through the JAK2/STAT3 signaling pathway . TherefoResearch Grant for Health Science and Technology of Shanghai Municipal Health Commission [grant number 202140405]; and the Integrated construction of thyroid tumor prevention and treatment in Pudong New Area [grant number PW2019D-4]; and the Key Subspecialty Program of Pudong Health Bureau of Shanghai [grant number PWZy2020-06]; and the Clinical Characteristic Subject of Pudong Health Bureau of Shanghai [grant number PWYts2021-15].This work was supported by the The authors declare no conflicts of interest.


Objective: The tumor microenvironment plays a critical role in the radiotherapy and immunotherapy response of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). Radioresistance is a key factor in treatment failure among patients who receive radical radiotherapy. Thus, new immune-related biomarkers associated with radiotherapy response in CESC are needed.Methods: In this study, the CIBERSORT and ESTIMATE methods were applied to determine the percentage of tumor-infiltrating cells and the number of immune components in 103 CESCs treated with radiotherapy from The Cancer Genome Atlas (TCGA) database. The main dysregulated genes were subjected to multivariate and univariate analyses. The prognostic value of this system was studied via receiver operating characteristic curve and survival analysis. For further confirmation, the biomarkers’ expression levels and predictive value were validated by immunohistochemistry (IHC) and qRT-PCR. The CIBERSORT algorithm was used to calculate the compositional patterns of 22 types of immune cells in cervical cancer patients treated with radiation therapy.Results: Data for 17 radioresistant and 86 radiosensitive tumors were obtained from the The Cancer Genome Atlas database. 53 immune-related DEGs were identified. GO and KEGG analyses revealed that the DEGs were enriched in protein kinase B signaling, growth factors in cytokines, the MAPK pathway and the PI3K-Akt pathway. Then, 14 key immune-related genes built a risk scoring model were deemed prognostic in CESC with radiotherapy. The area under the curve (AUC) of the model was 0.723, and the high-risk group presented worse outcomes than the low-risk group. In addition, the high-risk group tended to have persistent tumors (p = 0.001). The high expression of WT1 and SPOUYT4 were associated with relapse, the high expression of Angiotensinogen and MIEN1 were associated with nonrelapse. Analysis of the immune microenvironment indicated that M0 macrophages, M2 macrophages, activated mast cells and resting memory CD4+ T cells were positively correlated with the risk score (p < 0.05).Conclusion: The novel immune-related risk scoring system has some advantages in predicting the prognosis and treatment response of cervical cancer patients treated with radiotherapy. Moreover, it might provide novel clues for providing targeted immune therapy to these patients. Cervical cancer (CC) is the first incidence of female neoplasms in 23 countries worldwide, and also the first death of female neoplasms in 36 countries worldwide , with thThe tumor microenvironment (TME), which includes factors such as tumor-infiltrating lymphocytes, is closely related to the biological behavior of tumors and tumor prognosis . ImmunotHerein, to further explore the immune-related characteristics and mechanism of cervical cancer radiotherapy resistance, we explored the differentially expressed genes (DEGs) between cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) samples stratified by immune infiltration level and radiotherapy response and established a risk prediction system for cervical cancer radiotherapy resistance that reflects the alteration of TME in CESC treated with radiotherapy. With this strategy, we aimed to provide a new option for the selection of individualized immune-related treatments for radioresistant CESC.p < 0.05 were considered significantly enriched.Transcriptome RNA-seq data of 105 CESC cases  and the matching clinical-pathological data were downloaded from the TCGA CESC dataset. For each sample, the percentage of immune components in the TME was evaluated, and the ESTIMATE algorithm was applied with the R language (version 3.6.3) and the ESTIMATE package to determp < 0.05). Independent prognostic genes were selected by establishing a multivariate Cox hazards regression model. A regression coefficient (β) was obtained from the multivariate Cox proportional hazards regression for each gene; then, we added the products of the gene expression values multiplied by the β values to construct a prognostic gene signature. The following formula was used: risk score = expression of gene1 × β1gene1 + … expression of genen × βngenen  were selected by quality filtering for subsequent analysis.For all tumor samples, we used the CIBERSORT computational method to estimate the TIC abundance profile, and then only cancer samples  from June 2019 to December 2022. The main treatment of all patients underwent radiotherapy. The research was approved by the Research Ethics Committee of The Second Affiliated Hospital of Fujian Medical University prior to the study.IHC staining was operated as previously described . The pri−ΔΔCT method. qRT-PCR for each sample was repeated in three independent experiments. The primer sequences are shown below:Total RNA was extracted from frozen CC tissue utilizing Tirzol , and then, cDNA was prepared according to the protocol . The detailed procedure is presented in the Supplementary Methods. GAPDH was used as an internal reference, relative mRNA expression of WT1, AGT, MIEN1 and SPOUTY4 was calculated by the 2GAPDHForward: 5′- GAACGGGAAGCTCACTGG-3′,Reverse: 5′- GCCTGCTTCACCACCTTCT -3′.WT1Forward: 5′- GGGTACGAGAGCGATAACCAC -3′Reverse: 5′- CCGTGCGTGTGTATTCTGTATTG -3’.AGTForward: 5′- CCCACGCTCTCTGGACTTCA-3′,Reverse: 5′- GGACGTAGGTGTTGAAAGCCA -3′.MIEN1Forward: 5′- CAGGGTAAGTGCCCACGAA-3′Reverse: 5′- TACTGCCAATAGCTGACATTGC -3′.SPOUTY4Forward: 5′- AGTGAGTCCAAGCCTCGTTCC-3′,Reverse: 5′- CCCGAGAGTTAGTTGATGCAGTT -3′.t-test was used as described above. Differences with p < 0.05 were considered statistically significant for all statistical analyses, unless otherwise specified.We used R software (v.3.6.3) for all statistical analyses. We applied the Wilcoxon signed-rank test, Kolmogorov-Smirnov test and logistic regression to analyze the relation of the risk score to clinicopathological characteristics. For survival analysis, we utilized the Cox multivariate system and the Kaplan-Meier method. The median value was used as the cutoff value of the risk score. Unpaired We show the analysis procedure of our study in versus low ImmuneScores (according to the median)  . In tota median) . A total median) . To detep < 0.05. A risk score algorithm consisting of 14 key genes  was established (p < 0.001) (p < 0.05) but not to age, differentiation degree or tumor-node-metastasis (TNM) stage (p > 0.05) (p < 0.001) and risk score  were independent prognostic factors in the multivariate analysis  . In addi > 0.05) . To furtanalysis .p < 0.05), high expression of AGT and MIEN1 were associated with nonrelapse (p < 0.05). For further clinical validation, we assessed the expression of WT1, AGT, MIEN1 and SPOUTY4 using qRT-PCR and revealed that low expression of AGT and MIEN1 were associated with relapse, conversely, high expression of WT1 and SPOUTY4 were associated with relapse (p < 0.05). The above results indicating that the WT1, AGT, MIEN1 and SPOUTY4 had a higher predicitive capacity to radiosensitivity.We assessed the expression of WT1, AGT, MIEN1 and SPOUTY4 across cervical cancer tissues via IHC. To determine the clinical importance of WT1, AGT, MIEN1 and SPOUTY4 in CC, we detected WT1, AGT, MIEN1 and SPOUTY4 protein levels in 69 samples from CC patients who underwent radiotherapy as a primary treatment regimen . We founp < 0.05) (p < 0.05)  . The com< 0.05) (. Further< 0.05) . The expression of MIEN1 was related to age, differentiation degree and lymph node metastasis. Kaplan-Meier survival analysis showed that the overall survival rate in patients with positive expression of MIEN1 protein was lower than that of those with negative expression of MIEN1 protein; in addition, overexpression of MIEN1 may contribute to the migration and invasion of breast cancer. In our study, MIEN1 expression was negatively correlated with radioresistance, possibly because our research subjects had cervical cancer treated with radiotherapy, and the expression of MIEN1 may change after radiotherapy. Angiotensinogen (AGT) is a rate-limiting substrate of the renin-angiotensin system (RAS). Sun et al.  , immature dendritic cells, etc., are abundant in the tumor microenvironment, which prevents the activation of T cells   . Our stu T cells . Our stu T cells , but som T cells . Our stu T cells . Howevertivated) . FurtherMast cells are innate immune cells that play a central control role in natural immunity, and as sentinel cells and effectors recruit cells, they regulate the host’s response to cancer and can be used to predict the prognosis of patients . ActivatThe importance of individual TICs in predicting the therapeutic effect has been the focus of research, and it is certain immune subgroups that play a major role rather than the overall scope of immune infiltration . ClassicOur study has some limitations. First, the number of cases in the TCGA was low, especially radioresistance patients. Second, the reproducibility and function of the biomarkers and the related immune cell infiltration in CC should be further validated by prospective studies with larger sample sizes.+ T cells were positively correlated with the risk score. Therefore, our data might explore new biomarkers to predict sensitivity to radiotherapy in CC, and then for targeted therapy in CC with radiation.In conclusion, based on the TCGA database, we analyzed the immune characteristics of CESC treated with radiotherapy, 53 immune-related DEGs were identified. GO and KEGG analyses were applied to analyse the function and pathway of DEGs, and then 14 key immune-related genes established a novel risk scoring system that has predictive value. The high expression of WT1 and SPOUYT4 were associated with relapse, the high expression of AGT and MIEN1 were associated with nonrelapse. Analysis of the immune microenvironment indicated that M0 macrophages, M2 macrophages, activated mast cells and resting memory CD4