To determine whether gross tumor volume (GTV) of adenocarcinoma of esophagogastric junction (AEG) corresponding to cT and cN stages measured on CT could help quantitatively determine resectability.343 consecutive patients with AEG, including 279 and 64 randomly enrolled in training cohort (TC) and validation cohort (VC), respectively, underwent preoperative contrast-enhanced CT. Univariate and multivariate analyses for TC were performed to determine factors associated with resectability. Receiver operating characteristic (ROC) analyses were to determine if GTV corresponding to cT and cN stages could help determine resectability. For VC, Cohen’s Kappa tests were to assess performances of the ROC models.3 in stage cT1-4N0-3 with an area under the ROC curve (AUC) of 0.901. Particularly, cutoffs of 27.67 and 32.77 cm3 in stages cT3 and cT4 obtained AUC values of 0.860 and 0.890, respectively; and cutoffs of 27.09, 33.32 and 37.39 cm3 in stages cN1, cN2 and cN3 obtained AUC values of 0.852, 0.821 and 0.902, respectively. In VC, Cohen’s Kappa tests verified that the ROC models had good performance in distinguishing between resectable and unresectable AEG .cT stage, cN stage and GTV were independently associated with resectability of AEG with odds ratios of 4.715, 4.534 and 1.107, respectively. For differentiating resectable and unresectable AEG, ROC analyses showed that cutoff GTV of 32.77 cmGTV, cT and cN stages could be independent determinants of resectability of AEG. And GTV corresponding to cT and cN stages can help quantitatively determine resectability. Adenocarcinoma of the esophagogastric junction (AEG) is one of the most common cancers in the world, with an estimated 1 million deaths each year . In 1998AEG usually adopts multimodal treatments, but the main treatment method is surgical resection . HoweverThis retrospective study was approved by the institutional ethics committee of our hospital , and written informed consent was obtained from each participant before the study.From October 2017 to November 2021, patients with AEG proved by endoscopic biopsy were enrolled into our study according to the following inclusion criteria: (a) the patient did not receive any tumor-related treatment  before undergoing enhanced CT scanning, (b) the quality of CT was sufficient, and (c) the AEG lesion was regarded unresectable and resectable according to the NCCN guidelines based on CT findings , 13. The2. Infiltration of periesophageal tissue was divided into stage T3 and was identified by high-density grounding of fat around the esophagus. Invasion of adjacent organs was staged as T4, and it is confirmed by the invasion of adjacent organs and the loss of intermediate fat layer. The diagnostic criteria of lymph node metastasis on CT was defined as lymph node enlargement with a short axis of more than 1 cm  tumors in the submucosa (T1b) or deeper, (c) T1-3 tumors were resectable even with regional nodal metastases, although bulky; and (d) T4a tumors with involvement of pericardium, pleura, or diaphragm. The criteria of unresectable tumor of Siewert types I/II were as follows: (a) cT4b tumors with involvement of the heart, great vessels, trachea, or adjacent organs including liver, pancreas, lung and spleen; (b) most patients with multi-station, bulky lymphadenopathy; (c) patients with supraclavicular lymph node involvement; and (d) patients with distant  metastases (stage IV). The criteria of unresectable AEG of Siewert type III were the tumors with locoregionally advanced and distant metastasis or peritoneal seeding . Exactly, disease infiltration of the root of the mesentery or para-aortic lymph node highly suspicious on imaging or confirmed by biopsy and invasion or encasement of major vascular structures (excluding the splenic vessels) were included in locoregionally advanced. If the AEG of Siewert type III was not regarded as unresectable, the cancer was considered resectable. According to the above staging criteria, the clinical characteristics, Siewert classification, cT stage, cN stage, vascular invasion and GTV are shown in All patients underwent enhanced CT scans of the thorax and upper abdomen. The resectable AEG lesions were clinically staged according to the AJCC staging system of AEG , and washan 1 cm . Accordihan 1 cm , 13, we The CT data were obtained with two 64-section multidetector computed tomography (MDCT) scanners . Before CT data acquisition, 800–1000 ml water was orally taken as negative contrast medium. All examinations were performed in the supine position. After routine unenhanced CT scanning, a total of 70–100 ml of contrast agent  calculated according to the proportion of 1.5 ml/kg body weight was customized at the rate of 3.0 ml/s through a 20-G needle with a pump injector  through a cubital vein, and subsequently 20 ml of saline was rinsed. The enhanced CT data of the arterial phase and portal venous phase were obtained 25 and 65 seconds after the injection of contrast agent, respectively. The scanning parameters are as follows: peak voltage of 120 kV, tube current of 200 mA (automatic exposure control), rotation time of 0.5 s, collimation of 64 × 0.6 mm, pitch of 0.9, matrix of 512 × 512 mm, and slice thickness of 5 mm. The coverage of CT scans were from the apex of the lungs though the liver to the middle of the right kidney in the arterial phase and from the right diaphragmatic dome to the middle of the right kidney in the portal venous phase. Data were transferred to the General Electric Advantage Workstation 4.4 for further data analysis.The GTV of AEG was measured on the above mentioned workstation, was calculated by multiplying the sum of all the tumor areas by the section thickness according to the method used in previous reports , 16. ForP value less than 0.05 was defined as statistically significant. The inter- and intraobserver intraclass correlation coefficient (ICC) was used to assess the reliability of measurements of GTV. ICCs less than 0.5, between 0.5 and 0.75, between 0.75 and 0.9, and greater than 0.90 are indicative of poor, moderate, good and excellent reliability, respectively . A test with ectively .2 test. If the variables with a P value less than 0.05 were regarded statistically different, they were included in the multivariate analysis, which was carried out through the binary logistic regression analysis to clarify the independent determinants of resectability of AEG. The Mann-Whitney U test was used to compare GTV corresponding to different cT and cN stages between patients with resectable and unresectable tumors. When the previous Mann-Whitney U test showed a significant difference, receiver operating characteristic (ROC) analyses were then carried out to determine whether the cutoff values of GTV corresponding to cT and cN stages could be helpful to determine resectability (The continuous variables were expressed as mean ± standard deviation (SD). Categorical variables were shown as numbers and percentages. For TC, the univariate association of GTV and clinical factors including age, sex, Siewert classification, cT, cN stages and vascular invasion were assessed by using χtability . In VC, tability .3. The intra- and interobserver ICC values of GTV measurement were 0.996  and 0.998 , respectively , indicating that measurement of GTV obtained excellent repeatability in TC. Therefore, the first measurement of the first observer was repeatable and could be used for further statistical analysis.In all the 279 patients with AEG in TC, the initial measured mean GTV of the first observer was 36.58 ± 26.61 cmP-values < 0.05).The clinical factors and GTV together with their correlations with resectability are summarized in P-values <0.0001, odds ratio = 4.715, 4.534 and 1.107, 95%CI of 2.016–11.026, 2.403–8.557 and 1.070–1.145, respectively).According to the above significant factors obtained by the univariate analysis, Siewert classification, vascular invasion, cT stage, cN stage and GTV were chosen as potential independent determinants of resectability, and the binary logistic regression analysis was carried out to determine the independent factors. Logistic regression analysis indicated that GTV, cT and cN stages were independent determinants between resectable and unresectable AEG .Mann-Whitney U test was used to analyze differences in GTV corresponding to cT and cN stages between resectable and unresectable AEG. Because numbers of patients with tumors in stage cT1-4N0-3 with the cutoff value of 32.77 cm3, especially in stages cT3 and cT4, or stages cN1, cN2 and cN3 with the cutoff values of 27.67 and 32.77, or 27.09, 33.32 and 37.39 cm3, respectively. The area under the ROC curve (AUC), specificity, sensitivity, predictive value and accuracy of GTV corresponding to cT and cN stages for determining resectability of AEG in TC and VC are shown in In order to assess GTV corresponding to cT and cN stages in determining the resectability of AEG, the ROC analysis was carried out. According to the ROC analysis Figure 21-4N0-3, especially in stages cT3 and cT4, or stages cN1, cN2 and cN3 to distinguish between resectable and unresectable AEG lesions in TC, Cohen’s Kappa tests were performed in VC according to the obtained cutoff values. The tests revealed that the models obtained good agreements in VC as shown in In order to verify the performance of our ROC models of GTV in stage cTOur study explored the potential determinants associated with the resectability of AEG including the clinical factors and GTV, and developed the combined ROC models to quantitatively identify the resectability. The current research showed that the cT stage, cN stage, Siewert classification, vascular invasion and GTV could be related to the resectability of AEG according to the univariate analysis. Based on our multivariate analysis, cT stage, cN stage and GTV were independently associated with the resectability. Considering the independent factors, we further demonstrated that GTV corresponding to cT and cN stages could help quantitatively determine the resectability.As shown in our study, the GTV of AEG measured on CT could be independently associated with the resectability. Gross tumor volume can be used as a comprehensive index to reflect the tumor diameter, length and depth of invasion. Previous study has shown that measuring the gross tumor volume of AEG by CT can help determine lymph node metastasis . For AEGOur study demonstrated that cT stage could be independently associated with the resectability of AEG. A published study has shown that the exact cT stage could be very crucial for the limited resection of AEG in early period, and it was also important to exclude patients with advanced diseases from unnecessary surgery . We can Another independent determinant of resectability of AEG was the cN stage in our study. Studies show that lymph node status affects the prognosis of patients with AEG, often complicated with vascular and nerve invasion , 24. It 4b) or by metastatic lymph nodes  could have no relationship with resectability, which may be explained by that the tumor with the higher cT stage and/or cN stage have yet been independent factors of resectability.As shown in our research, Siewert classification and vascular invasion could be potential independent determinants of resectability for AEG. However, our study revealed that the Siewert classification and vascular invasion of AEG were not independently associated with the resectability in the multivariate analysis. We usually determine the Siewert classification of AEG according to the location of the center of the tumor . Burkhar3 and cT4, or cN1, cN2 and cN3. Our study showed that the stratification of GTV according to cT and cN stages had a good decisive performance for determining the resectability. Corresponding to cT or cN stages, especially to stages cT3 and cT4, or to stages cN1, cN2 and cN3, the AUC values to determine resectability could be greater than 0.80. Moreover, the previous ROC models obtained good performance in distinguishing resectable and unresectable AEG with Cohen’s K values of greater than 0.71 in stage cT1-4N0-3 in VC, especially in stages cT3 and cT4, or N1, N2 and N3.Our study suggested that cT stage, cN stage and GTV of AEG may be independently associated with the resectability. Therefore, we compared GTV between resectable and unresectable tumors in different cT and cN stages, especially in stages cT4, however, our ROC quantitative method could be more effective in determining whether AEG can be resectable.In general, we developed a quantitative method to determine whether AEG could be resectable according to GTV in consideration of other independent determinants including cT or cN stages. Nonetheless, AEG of Siewert type III with lymph node infiltration at the root of the mesentery or para-aorta, and AEG of Siewert I/II with multi-station bulky lymph node metastasis should be considered unresectable demonstrably on CT. The patients with definite distant metastases could also be considered unresectable. For the patients with locally advanced tumors in the absence of distant metastases and multi-station lymph node metastases especially in stage TThere were several limitations in our study. The first limitation was the nature of single-center retrospective study. But the AUC values of our ROC models to determine resectability of AEG were greater than 0.8, indicating good performance in distinguishing between resectable and unresectable AEG. We will carry out a prospective multi-center study to confirm our results. Secondly, the measurement of GTV might be affected by the distention of the esophagus and stomach. In order to reduce the error affected by the expansion of esophagus and stomach during obtaining GTV, we measured the tumor volume by manual sketching the edge of the tumor independently by two experienced radiologists, and obtained excellent repeatability. Thirdly, some factors such as tumor biomarkers may affect the resectability of AEG. We will perform the relevant study focusing on the combination of imaging and laboratory data to improve our ROC models for determining the resectability in the future. Fourthly, it may be more complicated and time-consuming to obtain GTV than TNM stage to determine the resectability. Despite this limitation, we reported independent determinants to develop ROC models to quantitatively determine the resectability.In conclusion, we found that GTV, cT and cN stages could be independently associated with the resectability of AEG. And the GTV measured on CT corresponding to cT and cN stages may be helpful to determine resectability as shown by our ROC analyses. We hope that this study may help quantitatively determine whether AEG can be surgically removed, so that clinicians can choose the best treatment for individual cases.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the institutional ethics committee of Affiliated Hospital of North Sichuan Medical College. The patients/participants provided their written informed consent to participate in this study.T-WC, X-MZ, RL, and H-YZ: study conception and design. K-YL, JO, Z-YY, DG, and X-YY: data collection and analysis. K-YL, DG, and Z-YY: image processing and modeling. K-YL: manuscript writing. K-YL, JO, and X-YY: statistical analysis. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Osteoarthritis (OA) is a common chronic joint disease, but the association between molecular and cellular events and the pathogenic process of OA remains unclear.The study aimed to identify key molecular and cellular events in the processes of immune infiltration of the synovium in OA and to provide potential diagnostic and therapeutic targets.To identify the common differential expression genes and function analysis in OA, we compared the expression between normal and OA samples and analyzed the protein–protein interaction (PPI). Additionally, immune infiltration analysis was used to explore the differences in common immune cell types, and Gene Set Variation Analysis (GSVA) analysis was applied to analyze the status of pathways between OA and normal groups. Furthermore, the optimal diagnostic biomarkers for OA were identified by least absolute shrinkage and selection operator (LASSO) models. Finally, the key role of biomarkers in OA synovitis microenvironment was discussed through single cell and Scissor analysis.A total of 172 DEGs  associated with osteoarticular synovitis were identified, and these genes mainly enriched eight functional categories. In addition, immune infiltration analysis found that four immune cell types, including Macrophage, B cell memory, B cell, and Mast cell were significantly correlated with OA, and LASSO analysis showed that Macrophage were the best diagnostic biomarkers of immune infiltration in OA. Furthermore, using scRNA-seq dataset, we also analyzed the cell communication patterns of Macrophage in the OA synovial inflammatory microenvironment and found that CCL, MIF, and TNF signaling pathways were the mainly cellular communication pathways. Finally, Scissor analysis identified a population of M2-like Macrophages with high expression of CD163 and LYVE1, which has strong anti-inflammatory ability and showed that the TNF gene may play an important role in the synovial microenvironment of OA.Overall, Macrophage is the best diagnostic marker of immune infiltration in osteoarticular synovitis, and it can communicate with other cells mainly through CCL, TNF, and MIF signaling pathways in microenvironment. In addition, TNF gene may play an important role in the development of synovitis. It can significantly alter joint dysfunction in older people and lead to disability and reduced the quality of life , 2. FurtRecently, more research has shown that the synovium plays an important role in the progression of OA , 6. MoreTo describe the cellular and molecular events and reveal its inflammatory pathogenesis for OA, we attempted to find differentially expressed genes (DEGs) in OA by obtaining GEO datasets for integrated bioinformatics analysis. Then, we performed function enrichment analysis and PPI interaction analysis to reveal the key pathways of the DEGs. In addition, the LASSO method was used to identify the key cell immune subpopulations and key biomarkers for OA. Furthermore, single-cell dataset was performed to explore the relationship between different cells in the microenvironment and identify the possible key regulatory molecules. Thus, the study aimed to identify the key molecular and cellular events involved and the immune infiltration mechanism within the synovium of patients with OA, to provide potential novel diagnostic and therapeutic targets.22.1https://www.ncbi.nlm.nih.gov/geo/). All selected datasets were genome-wide expression data of OA or normal synovial membrane tissues. We obtained 5, 10, 10, and 12 normal samples and 5, 10, 9, and 12 OA samples from the array datasets GSE1919, GSE55235, GSE32317, and GSE55235, respectively. In addition, 28 and 3 normal samples and 22 and 5 OA samples were obtained from the RNA-seq dataset GSE89408 and GSE143514, and obtained 3 synovial samples of OA in GSE152805 single-cell dataset. The normal samples used in the study were all synovial tissues from accidental death or post-traumatic joint surgery or traumatic joint injury, whereas the samples in the OA group were synovial samples from patients diagnosed with synovitis who underwent open synovectomy and joint replacement. In total, we obtained 37 normal healthy samples and 36 OA patient samples from Array dataset, 31 normal healthy samples and 27 OA patient samples from RNA-seq dataset, and 3 OA patient samples from 10× Genomics single-cell sequencing dataset  database , which is an R package that uses a model based on the negative binomial distribution and which is widely used for RNA-seq data differential analysis  was used to identify the DEGs between OA synovial membranes and normal group in the GSE1919 and GSE55235 datasets . After banalysis . All the2.3P < 0.05 were considered significantly enriched  to perform functional cluster enrichment analysis, which is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. Enriched GO-based bp terms and pathways were considered statistically significant when the P-value was < 0.05.To investigate key mRNAs at molecular and functional levels, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway functional enrichment analyses were performed. GO analysis includes the categories of molecular function (MF), cellular component (CC), and biological processes (BPs). Pathway analysis is the process of classifying large genes by the KEGG database. In our study, GOseq uses the Wallenius non-central hypergeometric distribution model, thus taking gene length bias into account; therefore, it was used to perform GO enrichment analysis and GO terms with enriched . KEGG Orenriched  software2.4https://string-db.org/cgi/input.pl)  (nput.pl)  is an onnput.pl) . Then, tnput.pl) . Based onput.pl)  with defnput.pl) , then th2.5P-value threshold of 0.05 for statistical significance. The “ggplot2” package was used to draw box diagrams to visualize the differences in immune cell infiltration.To determine OA synovial membrane invasion from the expression data, normalized gene expression data were used to infer the relative proportions of several types of infiltrating immune cells using xcell method as previously reported . Briefly2.6P-value obtained from the correlation calculations was corrected by using the BH method, and the degree of correlations was shown by heat maps, where the asterisks were used to indicate the significance.Gene set variation analysis (GSVA) can estimate the relative enrichment of a gene set of interest over a sample population, which is used to observe the variation in the activity of a set of genes corresponding to a particular biological condition [21]. As previously described, The GSVA R package  was used2.7n = n (Coef.k×Cell.Prop.k), the Cell.Prop.k was the relative infiltrating proportion of the infiltrating cell type of the patient k, and Coef.k indicated the LASSO coefficient of gene k. Then, all the samples were randomly assigned to the training set (70%) and test set (30%). To evaluate the diagnostic ability of the above models, we evaluated the receiver operating characteristic (ROC) area under the curve (AUC), sensitivity, and specificity. In addition, two OA synovial inflammation RNA-seq datasets from GEO database were used to validate the sensitivity and specificity of our model.To identify the optimal diagnostic gene biomarkers for OA inflammatory response, we utilized the immune infiltrating cell types as the biomarkers to distinguish and predict the OA inflammatory responses. Six differential percentages of immune infiltrating cells obtained from all the Array datasets were used as feature variables to construct the least absolute shrinkage and selection operator (LASSO) model. The LASSO algorithm analysis was performed by the “glmnet” R software package to achieve our datasets shrinkage. The optimal value of λ was determined by 10-fold cross-validation, and the significant variables were selected for risk prediction. The coefficient risk score of each sample was obtained according to the proportion of each significant difference immune-infiltrating cell types and their correlation. The coefficient risk score was calculated as follows: ∑2.8The single-cell raw matrix data from GSE152805 were downloaded and imported using the Seurat package for the R programming language (version 4.0.2) , and the2.9P ≤ 0.05 was used as a threshold for significance, and Adjust P-value was corrected using the BH method.Cell–cell communications (CCCs) analysis assesses the expression of ligand-receptor pairs across cell types and revealed specific signaling pathways . CellCha2.10Single-Cell Identification of Subpopulations with bulk Sample phenotype correlation (Scissor) uses leveraging bulk data and phenotype information to identify biologically and clinically relevant cell subsets form single-cell RNA sequencing, with high accuracy and specificity . The cel2.11P values were calculated using Wilcox.test, and P < 0.05 was considered statistically significant.All the statistical analyses were executed with R software (version 4.0.2). 33.1To identify the DEGs in OA, we downloaded the OA synovial and normal tissue gene expression profiles of GSE1919 and GSE55235 from the GEO database. Then, we performed differential analysis, a total of 563 DEGs were identified in GSE1919 dataset, including 293 upregulated and 270 downregulated DEGs, and a total of 505 DEGs were identified in GSE55235 dataset, including 287 upregulated and 218 downregulated DEGs Table S23.2To further improve the biological understanding of the correlation between the 172 DEGs gene functions identified in our study, we next conducted the protein–protein interaction analysis. The STRING database was used to identify the PPI network analysis of 172 DEGs; the results showed that 121 nodes and 257 edges were established in the constructed PPI network and visualized by Cytoscape software , and the AUC value on the test set was 0.8262 (95% CI: 60.81–92.86%), the model had high AUC values in both the training set and the test set, indicating that the model has a robust sensitivity and specificity, and could be used as an optimal biomarker of OA , we further analyzed the communication of ligand-receptor pairs. Notably, five ligand-receptor pairs including CCL3-CCR1, CCL3-CCR5, CCL3L1-CCR1, CCL3L3-CCR1, and CCL4-CCR5, were the dominant contributors to CCL signaling pathways, which has the strongest communication between macrophage and T cells, and this result suggested that Macrophage uses this signaling pathway to recruit T cells in the microenvironment to cope with the occurrence and development of inflammation , we performed differentially expressed analysis between these cells and other Macrophage cells (M_Scissor0). As a result, 181 upregulated genes such as CD163, LYVE1, LYZ, S100A9, CD74, CD4, SEPP1, C1QB, and RNASE1 were significantly expressed in Scissor1 Macrophage group, whereas 287 downregulated genes such as MGP, CALD1, DCN, CLU, and SOX4 were significantly expressed in Scissor0 Macrophage group (|Foldchange|≥1.5) , HSPA6, MYC, IL6, MMP9, CXCL8, LCK, VCAM1, the enriched function such as cellular response of unfolded proteins, cell cycle and kinase activity, chemokine signaling pathway, metabolic processes, signal translocation and inflammation during biofilm processes, and antigen presentation and T-cell differentiation. As reported in OA, HSPA1A protected against OA by inhibiting chondrocyte apoptosis, which were specific upregulated by RNA-binding protein ZFP36L1 . The simThen, we performed Immune infiltration analysis on GSE55235, GSE1919, GSE32317, and GSE46750 datasets; the results showed that six immune-related cell types including B cell memory, Macrophage, B cell, B cell naïve, B cell plasma, and Mast cell were significantly correlated with the OA. These different immune cell types have been indicated that the immune infiltration degree might synergize with the occurrence and development of inflammation in OA , 61. Nexvia cellular communications by the major signaling pathways.Therefore, it is necessary to explore the specific mechanisms of Macrophage in the development of inflammation in OA synovial microenvironment. We used the CellChat tool to analyze the interaction between different cell types in the OA synovial microenvironment. The results showed that Macrophages had significant interactions with SIF, SSF, and ProIC cells. Three major signaling pathways including CCL (C-C motif Chemokine ligand), TNF-α (Tumor necrosis factor α), and MIF (Macrophage migration inhibitory factor) were the mainly signal pathway of Macrophage in OA synovial microenvironment. Among these, CCL signaling pathway is reported to perform a pleiotropic effect on multiple cell type–related OA aside from chemotaxis, with the most important chemokines such as CCL2, CCL3, CCL4, and CCL5 , 68. MorAt last, we performed Scissor analysis, which uses single-cell dataset, to identify associated cells consistent with the OA phenotype and function, such as identifying a population cell with greater proinflammatory capacity in M1 macrophages. The result showed that the identified cells were mainly distributed in Macrophage, SIF, and ProIC cells. Interestingly, we found that a group of cells were also identified in macrophages, and this group of cells highly expressed biomarkers of M2 macrophages, such as CD163 and LYVE1, and we also found that these cells had anti-inflammatory ability, which was consistent with the function of M2 macrophages in OA synovitis. For example, Alpha defensin-1 promotes M2 Macrophage polarization, indirectly effect on chondrocytes to attenuate the severity of OA . MoreoveThe lack of results from own samples and the absence of experimental validation is a major limitation of this study, and the differences among individuals in biological samples may also have some influence on the results. Although four microarray data, two RNA-seq data, and one single-cell data were used in our analysis to demonstrate our point, the combination of multiple data sets only can reduce the conclusion bias generated by a single data method. Expanding the sample size and obtaining clinical sample for biological verification and exploring the specific functions of macrophages can effectively prove our conclusion.In summary, our study combined integrated bioinformatics and machine learning methods to identify the immune regulation and immune infiltration during OA pathogenesis. The above results show that Macrophage could be a rational candidate cells for OA, and TNF gene may be a key molecule in the development of OA; however, the concrete mechanism should be elucidated in further studies.5In this study, a total of 172 DEGs were identified in the OA synovium, and Macrophage infiltration could be a rational biomarker. In addition, TNF may be a key molecule. However, the key genes and related mechanisms involved in immune regulation and immune infiltration in the pathogenesis of OA need to be further studied, and the function of macrophages needs to be further explored.The original contributions presented in the study are included in the article/GN and XF conceived and designed the experiments. SL, MY, YeW performed the experiments, analyzed the data, contributed reagents and materials, prepared figures and tables, authored or reviewed drafts of the paper. XF wrote the manuscript. All authors contributed to the article and approved the submitted version.


These findings will contribute to be better understanding of seasonal reproduction regulation in Tan sheep and will serve as a useful reference for molecular breeding of high fertility Tan sheep.Seasonal estrus is an important factor limiting the fertility of some animals such as sheep. Promoting estrus in the anestrus season is one of the major ways in improving the fecundity of seasonally breeding animals. The pineal-hypothalamus-pituitary-ovary (PHPO) axis plays a decisive role in regulating animal reproduction. However, the molecular mechanisms by which the PHPO axis regulates seasonal reproduction in animals are not well understood, especially in Tan sheep. To this end, we collected pineal, hypothalamus, pituitary and ovary tissues from Tan sheep during estrus and anestrus for RNA-Sequencing, and performed bioinformatics analysis on the entire regulatory axis of the pineal-hypothalamic-pituitary-ovary (PHPO). The results showed that 940, 1,638, 750, and 971 DEGs  were identified in pineal, hypothalamus, pituitary and ovary, respectively. GO analysis showed that DEGs from PHPO axis-related tissues were mainly enriched in “biological processes” such as transmembrane transport, peptide and amide biosynthesis and DNA synthesis. Meanwhile, KEGG enrichment analysis showed that the bile acid secretion pathway and the neuroactive ligand-receptor interaction pathway were significantly enriched. Additionally, four potential candidate genes related to seasonal reproduction  All experimental animal studies were reviewed and approved by the Animal Welfare Committee of Ningxia University (permit number NXUC20180306).In this study, 12 Tan sheep (14 months old) in similar body condition during the anestrus season (early April) were selected and kept individually for 1 week at a Tan sheep breeding farm in Ningxia Autonomous Region, and three female Tan sheep were proved not to be in heat by test rams again. Subsequently, frozen tubes of pineal, hypothalamus, pituitary and ovarian tissues were collected from each of the three Tan sheep after humanitarian execution at the slaughterhouse and immediately cast into liquid nitrogen for backup until RNA extraction. At the same time, three female Tan sheep were screened from the remaining nine Tan sheep during the same year's anestrus season  by means of a test ram, and the pineal, hypothalamus, pituitary gland and ovary tissues were collected in frozen tubes after slaughter at the slaughterhouse, and immediately placed in liquid nitrogen until RNA extraction.Total RNA was extracted from all samples according to the instructions of TRIZOL RNA Extraction Kit . The integrity and quality of sample RNA were assessed by the Agilent 2,100 bio analyze system , and samples RNA that passed the test were used for subsequent library construction.® Ultra™ RNA Library Prep Kit was used for library construction. mRNAs with polyA tails was enriched by Oligo (dT) magnetic beads and resulting RNA was subsequently randomly interrupted with divalent cations in NEB Fragmentation Buffer. The fragmented mRNA was used as a template, and random oligonucleotides were used as primers to synthesize the first strand of cDNA in M-MuLV reverse transcriptase system, followed by RNaseH degradation of RNA strands and dNTP synthesis in DNA polymerase I system. The purified double-stranded cDNAs were end-repaired, A-tailed and connected to sequencing adapters, and the cDNAs of about 200 bp were screened for PCR amplification using AMPure XP beads. After that, the PCR products were purified again using AMPure XP beads to finally obtain the library.The NEBNextAfter constructing the library, it was quantified with a Qubit 2.0 Fluorometer, diluted to 1.5 ng/μl, and tested for the insert size with an Agilent 2,100 bioanalyzer. Subsequently, qRT-PCR was used to accurately quantify the effective concentration of the library (above 2 nM) to ensure the library's quality.https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) was used to remove N-containing reads, filter reads with adapters and remove low-quality reads. To obtain the comparison efficiency of each sample reads and the position of reads on the genome, all trimmed data were mapped to the sheep reference genome Ovis aries .The sequenced fragments were converted into fastq format reads by CASAVA base identification of the images from the high-throughput sequencer. After obtaining the raw data to ensure quality and reliability of the data, Trim-galore software  tool in subread software, and gene expression values for RNA-seq were calculated using FPKM. DEGs were detected using DESeq2  with differential gene screening criteria of |log2(FoldChange)| ≥ 3 and padj ≤ 0.01. GO and KEGG analyzes were performed on the DAVID  website.Gene expression levels were quantified using the feature Counts  analysis program was used to identify significant gene expression profiles and to compare and visualize clusters of genes with similar expression trends in estrus and anestrus. Each gene was assigned to the closest profile using a Pearson correlation-based distance metric. To determine the significance level of the clustering profiles, alignment-based tests were used to quantify the expected number of genes that would be assigned to each profile. The gene expression levels used for analysis are expressed as FPKM values, with a P value < 0.05 in the clustering profile considered significantly enriched. Where the standardization method used for STEM analysis was chosen as “Log normalize data” and the number of trends for analysis using the “TEM Clustering Method” was chosen as “50,” other parameters were selected as default values.The STEM . Cluster 25, 24, 8, and 35 were examined with confidence criteria of 0.7, 0.92, 0.5, and 0.9, respectively. The Cytoscape tool  was used to further visualize the interaction networks. The CytoHubba plugin's network topology analysis approach (Degree) was used to determine the core genes.To elucidate the potential relationships between DEGs and different tissues over time, we analyzed colored Clusters using STEM. DEGs with significant differences and similar trends contained in colored clusters  were then subjected to protein-protein interaction (PPI) analysis in the STRING database  kit. For each tissue, six DEGs were randomly chosen to design unique primers, with the β-actin gene serving as an internal reference gene  kit. The reaction system was: 2 × TB Green Premix Ex Taq II 10 μl, Primer F 0.8 μl, Primer R 0.8 μl, RNase Free ddH2O 6.4 μl, cDNA template 2 μl; reaction conditions: pre-denaturation at 95 °C for 30 s, denaturation at 95 °C for 10 s, annealing at 60 °C 30 s, total 40 cycles. Three replicates of each sample were used to calculate the relative expression by the 2−ΔΔCt method. In order to confirm the accuracy of the RNA-Seq results, their results of RT-qPCR were compared with those of RNA-Seq.RNA was extracted from four tissue samples of six Tan sheep stored in liquid nitrogen, and after determination of satisfactory concentration and purity, diluted to 1 μg-μlnce gene . RT-qPCRt-test for independent samples was used to test for differences between the two groups. The differences were statistically significant (P < 0.05). Data are expressed as mean ± standard deviation.Statistical processing of RT-qPCR results was performed using SPSS . Student's Ovis aries genome, an average alignment rate of 89.49% was obtained for all samples, with a minimum value of 88.52% and a maximum value of 92.31% , 1,638 , 750 , and 971  DEGs were found in the pineal, hypothalamus, pituitary and ovary tissues in estrus and anestrus periods, respectively . AccordiTo understand the molecular functions of DEGs at the transcriptional level of the PHPO axis, DEGs were annotated using GO enrichment . The 940There are 1,638 DEGs enriched to 646 GO entries in the hypothalamus, including 325 BP, 84 CC and 237 MF. The enrichment of DEGs on the hypothalamus is associated with processes such as transport of neurotransmitters, ions and other substances, cell adhesion and other processes. on the CC, there are mainly components of the virion, shell, intrinsic and intrinsic components of the plasma membrane, extracellular regions, intermediate filaments and intermediate filament cytoskeleton. The MF category is associated with RNA-DNA hybrid ribonuclease activity, endonuclease activity, ion channel activity, substrate specific channel activity, and transmembrane transporter activity.In the pituitary, 750 DEGs were enriched to 514 GO entries, including 225 BP, 72 CC, and 187 MF. BP was mainly enriched to processes such as immune response, ion transport, DNA synthesis, regulation of phosphate metabolic processes and microtubule movement. It was mainly associated with components of ion and sodium channel transporter complexes, extracellular regions, supramolecular complexes, fibers and polymers On the CC. Additionally, there are some category mainly associated with G-protein coupled receptors, chemokine receptors, tetrapyrrole and heme binding, chemokines, cytokines, hormones and serine-type endopeptidase activity in the MF.In addition, 971 DEGs were enriched to 561 GO entries, including 259 BP, 89 CC, 213 MF in the ovaries. In BP, it was mainly enriched in processes such as movement of microtubules and cellular or subcellular components, peptide and amide synthesis and metabolism, cell surface receptor signaling pathways, cell-cell adhesion. It was mainly associated with ribosomes, ribonucleoproteins, and Intracellular ribonucleoprotein complexes, extracellular regions, skeletal fractions, supramolecular fibers, complexes and polymers, and other components in the CC. While in MF, DEG category is mainly enriched to molecular functions such as serine-type peptidase, hydrolase endopeptidase and endopeptidase inhibitor and regulator activity, nucleoside-triphosphatase and transmembrane receptor protein kinase activity. As a result, it is clear that DNA synthesis, extracellular regions, enzyme and hormone activities are the most common GO entries in the four tissues of the PHPO axis.To further determine the role of specific signaling pathways in the regulation of seasonal reproduction in Tan sheep, we completed an analysis of the KEGG pathway enrichment of DEGs in the PHPO axis of Tan sheep during estrus and anestrus, and the top 20 pathways in the four tissues are displayed in the graphs . The ribP value (P < 0.05), these seven significant modules were 25, 24, 8, 35, 39, 33, and 12, while the remaining white modules were not significantly different (P > 0.05). The gene expression trends in the colored modules 25, 24, 8, and 35 were similar, while those in the colored modules 8 and 39 were opposite. As a result, the DEGs discovered from the colored modules 25, 24, 8, and 35 with similar and considerably divergent gene expression trends can be subjected to bioinformatics analysis to further investigate the core genes regulating seasonal reproduction and core genes in Tan sheep  are a family of transcription factors that include endocrine receptors activated by steroids and thyroid hormones, which regulate target genes though binding its ligands and can play a role in biological processes such as energy metabolism, reproduction, and development . FXR, thvia the neuroactive ligand-receptor interaction pathway  during mitosis and can maintain the genome by controlling the spindle assembly checkpoint . The actprimates . TherefoPLCG2 (the gene encoding phospholipase Cγ2) was found to be located at the core of the PPI network. It was found that PLCG2 may be involved in the regulation of placental function in goats .SW analyzed the data and wrote the paper. XK, CY, FW, and TD performed the experiment, provided text and data for the methods, and contributed comments on the manuscript. XG, ZM, and CL participated in analysis of the data and writing of the manuscript. HZ designed and supervised the experiment. XD supervised the project. All authors read and approved the final manuscript.This research was funded by the National Natural Science Foundation of China (31760665) and the Ningxia Natural Science Foundation (2022AAC03057).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Humans are exposed to thousands of chemicals, including environmental chemicals. Unfortunately, little is known about their potential toxicity, as determining the toxicity remains challenging due to the substantial resources required to assess a chemical in vivo. Here, we present a novel hybrid neural network (HNN) deep learning method, called HNN-Tox, to predict chemical toxicity at different doses. To develop a hybrid HNN-Tox method, we combined two neural network frameworks, the Convolutional Neural Network (CNN) and the multilayer perceptron (MLP)-type feed-forward neural network (FFNN). Combining the CNN and FCNN in the field of environmental chemical toxicity prediction is a novel approach. We developed several binary and multiclass classification models to assess dose-range chemical toxicity that is trained based on thousands of chemicals with known toxicity. The performance of the HNN-Tox was compared with other machine-learning methods, including Random Forest (RF), Bootstrap Aggregation (Bagging), and Adaptive Boosting (AdaBoost). We also analyzed the model performance dependency on varying features, descriptors, dataset size, route of exposure, and toxic dose. The HNN-Tox model, trained on 59,373 chemicals annotated with known LD50 and routes of exposure, maintained its predictive ability with an accuracy of 84.9% and 84.1%, even after reducing the descriptor size from 318 to 51, and the area under the ROC curve (AUC) was 0.89 and 0.88, respectively. Further, we validated the HNN-Tox with several external toxic chemical datasets on a large scale. The HNN-Tox performed optimally or better than the other machine-learning methods for diverse chemicals. This study is the first to report a large-scale prediction of dose-range chemical toxicity with varying features. The HNN-Tox has broad applicability in predicting toxicity for diverse chemicals and could serve as an alternative methodology approach to animal-based toxicity assessment. Humans are exposed to thousands of potentially toxic chemicals, including environmental chemicals, such as industrial chemicals, microplastics, nanoplastics, food products, solvents, air pollutants, fertilizers, pesticides, insecticides, carcinogens, drugs, metals/metalloids, PFOS, burn pits, and other hazardous chemicals. Factors affecting the degree of toxicity are the route of exposure , duration of exposure, a dose of the chemical, age, gender, and health condition. The conventional in vivo and in vitro tests for finding the toxicity of chemicals are resource intensive. In this context, in silico toxicity prediction  is gainiToxicity predictions could be quantitative, predicting the quantity of chemical required for the adverse outcome or qualitative, predicting binary endpoint , or ordinary, predicting the categorical endpoint . Several computational models are developed using various machine-learning algorithms for various toxicity endpoints ,3,4. To Deep neural network (DNN) architecture is an ANN with more than one hidden layer. Convolutional Neural Network (CNN) is a class of deep neural network more suitable for image processing tasks, such as visual data recognition ,11,12. TTaken together, several computational and machine-learning methods are reported in the literature for chemical toxicity prediction ,16,17,18We collected thousands of chemicals annotated with experimentally known toxic dose data from different samples, attributes, properties, and sources to build the HNN-Tox and other machine-learning models. We used the following data sources: (i) ChemIDplus, (ii) Toxin and Toxin Target Database (T3DB), (iii) Environmental Protection Agency (EPA), and (iv) Tox21 Challenge. Models were developed for each dataset and the prediction capability was assessed. The T3DB and National Toxicology Program (NTP) datasets were used as an external validation set to test the models’ predictive ability.Datasets were annotated with toxicity classification at various LD50 cutoffs  to compare the impact of various dose cutoffs and resulting class imbalance on the predictive toxicity performance of the models.ftp://ftp.nlm.nih.gov/nlmdata/.chemidlease/ accessed on 12 October 2019. These CASRNs were used to retrieve the LD50 data from the ChemIDplus using the open stream method of URL class in Java’s java.net package. Through this Java package, we retrieved a total of 386,620 chemicals with CASRNs. We used 92,322 chemicals annotated with LD50 values, SMILES, and other physicochemical properties. The structconvert utility in Schrodinger software (version 2021) was used to convert the SMILES of the 92,322 chemicals to 2D structures in .sdf format. The chemicals were filtered further by removing metal-containing compounds and obtained a final set of 59,373 chemicals. Then, 3D minimization application in Schrodinger’s Canvas module was used to convert the 2D structures to a .sdf file containing 3D structures. We calculated 51 physicochemical descriptors for 59,373 chemicals using the QikProp application in the Schrodinger computational chemistry software https://www.schrodinger.com (accessed on 15 February 2020).CAS registry numbers (CASRNs) were obtained from the ChemIDplus database at Dataset 1(a) IP/IV/Subcutaneous/Oral—All animals: Out of 59,373 chemicals, 55,856 were annotated with LD50 values and the different routes of animal exposure, such as IP, IV, subcutaneous, and oral. This dataset was further annotated with 26,923 chemicals as nontoxic, and 28,933 were toxic based on the toxic-nontoxic cutoff of LD50 of 500 mg/kg. Randomly selected 5000 chemicals were used as a test set, while the remaining chemicals were used as a training set during each prediction.Dataset 1(b) Oral—Rat/Mouse: Out of 59,373 chemicals, 22,808 with LD50 were obtained by filtering rat and mouse species via the oral route of exposure. We calculated 31 ADMET  properties for the 22,792 chemicals using the ADMETlab platform . AdditioWe used the predictive toxicity models project dataset provided by the National Toxicology Program  as an ex2a. NTP data as an external validation set for ChemIDplus Oral Rat/Mouse dataThe same chemicals in NTP data that also existed in the Oral dataset from ChemIDplus (described in Section 1b) were removed. The final list of 1703 chemicals was obtained as an external validation set. The 1703 chemicals were used as an external validation set for the models developed with oral ChemIDplus training dataset with 51 QikProp descriptors.2b. NTP data as an external validation set for ChemIDplus IP/IV/Sub/Oral dataThe same chemicals in NTP data that also existed in the IP/IV/Sub/Oral dataset from ChemIDplus (described in Section 1a) were removed. The final list of 1648 chemicals was obtained as an external validation set. The 1648 chemicals were used as the external validation set for the models developed with IP/IV/Sub/Oral ChemIDplus training dataset with 51 QikProp descriptors.The 3673 chemicals with toxin data were curated from the T3DB database . Out of 3a. Oral exposure data: 687 chemicals with 62 descriptors were separated as Oral data.3b. IP/IV/Sub/Oral exposure data: 752 chemicals with 62 descriptors were separated as IP/IV/Subcutaneous/Oral data.We classified chemicals with different dose ranges for the development of categorical toxicity models. We classified 687 Oral data (obtained from Section 3a) into four categories: (a) LD50 < 50 mg/kg, (b) 50 mg/kg ≤ LD50 < 500 mg/kg, (c) 500 ≤ LD50 < 1000, and (d) LD50 ≥ 100.a. T3DB data as an external validation set for ChemIDplus Oral Rat/Mouse dataThe same chemicals in T3DB data (Section 3a) that also existed in the oral dataset from ChemIDplus (Section 1b) were removed. The final list of 636 chemicals was retained as the external validation set. Datasets were annotated with toxicity classification at various LD50 cutoffs at 250 mg/kg, 500 mg/kg, 750 mg/kg, and 1000 mg/kg. In total, 318 descriptors and 155 fingerprints (as described for a dataset in Section 1b) were calculated. These data were used as an external validation set for the models developed with the ChemIDplus oral training dataset, as described in Section 1b.b. EPA data as an external validation set for ChemIDplus Oral Rat/Mouse dataThe animal toxicity data of 980 chemicals were retrieved from the EPA. The lowest effect level (lel) chemical dose was considered to determine the toxicity. Various LD50 thresholds considered were 250 mg/kg, 500 mg/kg, 750 mg/kg, and 1000 mg/kg.http://bioinf.jku.at/research/DeepTox/tox21.html accessed on 14 November 2019) [Tox21 Challenge dataset of 12 different assays and their corresponding 801 descriptors were obtained from the online resource (er 2019) .C=CC#N is one-hot encoded as:SMILES was used as one of the key chemical attributes in our hybrid deep learning model. Raw texts cannot be directly used as input for the deep learning models but should be encoded as numbers. The entire list of SMILES strings was first fit onto the tokenizer to create a dictionary of the set of all the possible characters in the SMILES string and its corresponding index. We assumed that a dictionary D is created where D = {‘C’: 1, ‘=‘: 2, ‘’: 4, ‘#’: 5, ‘N’: 6, …, ‘ ‘: M}. This results in every character in the SMILES string being assigned a unique integer value which is the index of the character in the dictionary. Here, M is the total number of unique characters in the SMILES of our dataset. The SMILES entry for every chemical is then converted to a one-hot encoded 2D matrix. One-hot encoding results in each character of the SMILES string to be represented by M bits and the bit at a particular position assigned to that character by the dictionary is set to 1 whereas remaining bits remain as 0 for that character. As an example, acrylonitrile-d3 with SMILES string Thus, as mapped by the dictionary, 1st bit is set to 1 for A 3D matrix of size K × L × M is obtained eventually, where K is the number of chemicals, L is the maximum length of the SMILES string, and M is the number of all possible characters in the SMILES string from K chemicals (number of entries in the created dictionary). One-hot encoding means converting the integer value of each character in the SMILES to its equivalent binary vector of length M.The HNN-Tox model is developed in python using the Keras API with Tensorflow in the backend. The model consists of a Convolutional Neural Network (CNN) for deep learning based on structure attributes (SMILES) and a multilayer perceptron (MLP)-type feed-forward neural network (FFNN) for learning based on the remaining features of the chemicals . The basWe implemented hyperparameter tuning to improve the performance of the model. A Hyperopt package from python that uses Tree-structured Parzen Estimator (TPE) method was used for hyperparameter optimization. This approach requires defining the objective function that the fmin  function minimizes, the parameter space over which the search is performed, and the number of experiments to run. The Area Under the receiver operating characteristic Curve (AUC) was the metric used to evaluate each model’s performance. Class imbalance is a common problem while modeling toxicity data and AUC is a better metric to optimize than accuracy.To test HNN-Tox performance, we developed several other models based on other machine-learning algorithms, such as Random Forest and Bootstrap Aggregation (Bagging), Bagged Decision Tree, and Adaptive Boosting (AdaBoost).An ensemble of model predictions is considered an excellent option to optimize the model’s performance. To boost overall performance, we made ensemble predictions using the Random Forest, Bagging, and Adaboost methods. The ensemble method derived by Mayr et al.  for calcAll the results presented are the average of 10 simulation run repeats for the ChemIDplus datasets and 30 simulation repeats for the T3DB and Tox21 Challenge datasets. Thus, 20% of the dataset was randomly separated as the test set; the remaining data were used as the training set, which is similar to 10-fold cross-validation except that the test sets were randomly selected each time. The performance of each model was evaluated based on the accuracy and the Area Under the receiver operating characteristic Curve (AUC). The AUC gives the probability of a positive outcome being ranked before the negative outcome and is a better metric for evaluating a binary classifier than accuracy ,23. The In this study, we present a deep-learning-based novel hybrid neural network (HNN) method called HNN-Tox that consists of a CNN and FFNN framework to predict chemical toxicity at different doses and dose ranges. The overall workflow of the HNN-Tox process for chemical toxicity prediction is shown in The collected 386,620 chemicals from the ChemIDPlus online database, annotated with experimental LD50 data, are multispecies data. We separated the data annotated with oral rat and mouse acute toxicity (LD50) and obtained 22,792 chemicals. The 318 descriptors and 155 MACCS fingerprints were computed for 22,792 chemicals. The predictive models were developed with an LD50 threshold value of 500 mg/kg. For the HNN-Tox, the FFNN was developed based on the 318 descriptors and two CNNs were developed based on the 155 fingerprints and SMILES string separately. Independently, the RF, Bag, and Ada models were developed based on the 318 descriptors. The HNN-Tox, RF, and Bag models exhibit similar accuracy and AUC A,B. In cNext, to investigate the effect of descriptors and SMILES on the predictive performance, we developed models with 51 Schrodinger QikProp descriptors (instead of 318 descriptors) in addition to the SMILES for the CNN (without MACCS fingerprints). Notably, in the absence of many descriptors, such as ADMET and fingerprints, the accuracy and AUC of RF, Bagging, and Ada are decreased significantly . This meTo ensure the prediction accuracy of the models developed for the ChemIDplus dataset, the performance was evaluated by making predictions for external datasets that were not used to develop the models. The T3DB and NTP datasets (see Methods section) were used as external validation.A.T3DB data as an external validation setWe first removed the common chemicals present in both T3DB and ChemIDplus datasets from the ChemIDplus training dataset. The total chemicals in the training set then included 22,438 rat and mouse oral toxicity data and the test set included 636. The models predicted an accuracy of 76.94%, 75.69%, 72.76%, 74.37%, and 75.28% and an AUC of 0.83, 0.81, 0.79, 0.80, and 0.84 for the HNN-Tox, RF, Bag, Ada, and Ensemble, respectively . These rB.NTP data as an external validation setNext, we used the NTP dataset as the second external validation dataset with rat and mice oral toxicity. Models were developed with 1703 chemicals and 51 QikProp descriptors (Section 2a of the Methods section). The models predicted accuracy of 73.29%, 75.23%, 75.39%, 69.82%, and 75.44% for the HNN-Tox, RF, Bagging, AdaBoost, and Ensemble, respectively . The AUCExtending the domain coverage of the data enhances the model’s applicability. Thus, we sought to apply our models to a more generalized larger dataset that consists of 55,856 chemicals from the ChemIDplus data. These chemicals were annotated with the experimental LD50 of the animals and birds via the IP/IV/Subcutaneous/Oral route of administration. Molecular descriptors (51) were computed using the Schrodinger QikProp tool. The model’s predictive performance developed on this more generalized large dataset compared to the models developed on smaller rat and mice oral data; the accuracy decreased from 84.96%  to 78.43We next evaluated the prediction capability of the models for this more generalized larger set . The NTP data were used as an external validation dataset that contains 1648 chemicals with 51 descriptors (Section 2b of the Methods section). For this NTP external validation set, the accuracy was 60% for HNN-Tox, with much less accuracy predicted by other models . The AUCThe T3DB oral route of exposure data of 687 chemicals with 62 descriptors was used here (Section 3a in the Methods section). Models were developed using four different LD50 cutoffs: 250 mg/kg, 500 mg/kg, 750 mg/kg, and 1000 mg/kg. The accuracy is highest when the LD50 threshold is set at 250 mg/kg, whereas the AUC was lowest in this category A. The moTo study the effect on the toxicity prediction for the chemicals administered via various routes of exposure, the toxin data from the T3DB were separated to include intraperitoneal (IP), intravenous (IV), subcutaneous oral routes of exposure  data . The modNext, we investigated if achieving a larger dataset by combining chemicals from different sources and toxicity affects the model’s predictive ability. The animal LD50 data from the T3DB and the EPA data with lel dose values were combined to form a single dataset . Both daFurther, the 250 mg/kg threshold accuracy was the lowest compared to the 500 mg/kg and 750 mg/kg thresholds A,C. In cConsidering the dataset with the highest AUC, i.e., 1000 mg/kg threshold T3DB Oral data D and 750Next, we sought to assess how well HNN-Tox can predict compounds’ interference in biochemical pathways, an entirely unique and different dataset when compared to those used in the previous sections. The Tox21 Challenge data contains 12 different experimental assay data from the National Institute of Health database. These data were generated from nuclear receptor signaling and stress pathway assays run against Tox21’s 10,000-compound library (Tox21 10K). We obtained 801 molecular descriptors computed for this dataset from Mayr et al. . Then, tThe degree of toxicity  at a different dose range is important in the field of toxicology. The degree of toxicity can vary from substance to substance. Therefore, assessing the hazardous substances’ categorical toxicity is imperative. These substances can be categorized based on their toxicity severity level. To predict the categorical toxicity of substances, the toxins from the T3DB database were classified into four categories: (a) LD50 < 50 mg/kg, (b) 50 mg/kg ≤ LD50 < 500 mg/kg, (c) 500 ≤ LD50 < 1000, and (d) LD50 ≥ 1000 . The mulWe developed a hybrid neural network framework method called HNN-Tox to predict dose-range chemical toxicity. The performance of the HNN-Tox was compared with other machine-learning methods. The optimal performance of the HNN-Tox is evident in most cases. We also analyzed the HNN-Tox performance on the learning feature dependency, feature variations, route of administration, dose, and dataset size. The results suggest that the machine-learning methods depend on these variations and the imbalanced dataset. The HNN-Tox performed well, even after decreasing the number of chemical descriptors to train. For example, compared to the other methods, the HNN-Tox maintained similar accuracy and AUC when the chemical descriptors were decreased from 318 to 51 for the ChemIDplus data. Notably, adding the SMILES feature with the descriptors enriched the HNN-Tox model to learn based on the structure of a chemical and compensate for the missing descriptors. Further, the HNN-Tox demonstrates overall optimal toxicity-predicting capability with the external validation chemical sets.The binary classification models developed on a smaller dataset showed the impact of class imbalance. Accuracy is increased and the AUC is decreased with the increase in class imbalance. For example, the highest accuracy was achieved in the case of the combined dataset (EPA + T3DB) when the class imbalance ratio was 8.32:1 for the LD50 cutoff 1000 mg/kg; however, the AUC was lowest in this model. These models showed that optimal AUC could be achieved if the class is balanced (ratio between 1 and 2) for the smaller datasets. When the training set is enriched with additional data with highly balanced data, the accuracy increases without affecting the AUC. This means that the high accuracy achieved in the case of imbalanced data was due to overfitting.The models developed for the multiclass categorical classifications predicted chemical toxicity with moderate accuracy and higher AUC. The decrease in accuracy is due to the class imbalance, which is more evident in the case of multiclass classification, as separating chemicals into multiple categories increases the possibility of highly imbalanced classes, with an insufficient number of samples in some classes for training purposes. A random oversampling method was used to augment the model performance by counteracting the data imbalance, significantly improving the accuracy and the AUC. Additionally, the HNN-Tox performed optimally in predicting active and inactive to entirely different datasets obtained from the EPA Tox-21 data challenge containing 12 different target-based pathways’ cell assay data.In summary, in a large-scale prediction of dose-dependent and categorical toxicity, with different dataset sizes and varying features, the HNN-Tox method performed optimally well most of the time without needing many descriptors. Further, the HNN-Tox is not sensitive when applied to diverse chemicals and maintains optimal performance. Consequently, the HNN-Tox can be used to predict the toxicity of any chemical type with a broad applicability domain. Additionally, the HNN-Tox could act as a new approach method (NAM) to help prioritize the chemicals and avoid conducting a costlier and more time-consuming animal experiment to identify toxic chemicals. Due to the hybrid nature of the HNN-Tox method, it has broad applicability in predicting toxicity and carcinogenicity of chemical mixtures and those studies are in progress.The HNN-Tox, in its current form, has some limitations that are reflected in the lower performance in some cases. We used a random split in some datasets to split the data into training and test data. This may sometimes lead to overestimating the model’s performance because similar molecules could be in the training and the test set. We used various in vitro and in vivo datasets, multispecies, and various dataset sizes with varying features. Obviously, these data are very complex and require a significant optimization routine to obtain an excellent performance overall. Therefore, in many ways, we will improve the HNN-Tox method. We will introduce multi-task learning, as deep neural networks support multi-task learning, which generally boosts performance. We will add a fine-tuned, Fully Convolutional Neural Network (FCNN) framework. We will introduce more hyper-optimization parameters. We will introduce several optimization routines and extend the simulation run to 50. In the next routine, nested cross-validation will also be used for model comparison to give all approaches a chance to find a good hyperparameter. This would enable us to measure the performance of the methods without bias. We will use cluster-based nested cross-validation , which is based on molecular similarities.The Dataset 1(a) IP/IV/Subcutaneous/Oral—All animals, Dataset 1(b) Oral— Rat/Mouse, 2a NTP data as an external validation set for ChemIDplus Oral Rat/Mouse data. The dataset partition may be illogical because these datasets are from different animal models or different administration methods in the same dataset and may not correspond to the same endpoint. However, in this first version of proof-of-concept studies, we wanted to try the performance of the HNN-Tox method to all types of data partition in the initial phase of this project from small to large, different animal or species data and different administration data. Further, we focused on more generalized predictions. Thus, we made several data partitions starting from mouse only, rat only, mouse/rat, bird data, and other species data individually and combined. Similarly, we tried and made the data partition with appropriate administration data, such as IP, IV, subcutaneous, oral, etc. However, when we used the external validation set, we ensured most of them had no heterogeneous data among training and test datasets. In the next version, we will further fine tune the HNN-Tox to make more appropriate considerations about the dataset partition and focus on more specific examples with specific chemicals and classes, dose, administration, animal/species, and endpoints.


Gliomas are the most malignant central nervous system tumors. With the development of sequencing technology, more potential biomarkers related to the treatment, prognosis, and molecular classification of glioma have been identified. Here, we intend to investigate the potential biological function and clinical value of a new biomarker in glioma.via TIMER and CIBERSORT. Immunohistochemistry was conducted to confirm the KDELR1 expression and its correlation with immunity infiltration and prognosis.KDELR1 expression data and the corresponding clinical information were downloaded from public databases and then preprocessed using R language. Correlation, Kaplan–Meier survival, and Cox regression analyses were performed to explore the clinical significance of KDELR1 in glioma patients. Furthermore, the immune infiltration and microenvironment parameters were evaluated KDELR1 was upregulated in glioma samples compared with normal brain tissues, and its expression was significantly correlated with age, the World Health Organization (WHO) grade, recurrence, necrosis, microvascular proliferation, molecular classification, isocitrate dehydrogenase (IDH) mutation, and 1p/19q codeletion status. In addition, survival analysis showed that glioma patients with KDELR1 overexpression had shorter overall survival (OS) and disease-free survival times, and Cox regression analysis revealed that KDELR1 acted as an independent prognostic factor of OS in glioma patients. Gene set enrichment analysis indicated a significant enrichment of metabolism-associated pathways. KDELR1 expression was positively associated with immune infiltration  and microenvironment parameters  in gliomas. The expression of KDELR1 and its correlation with the tumor grade and prognosis were confirmed by immunohistochemistry in clinical samples .Taken together, these findings suggest that KDELR1 is correlated with the tumor grade, molecular classifications, and immune infiltration; highlighting that KDELR1 is a novel and promising biomarker for molecular classification, treatment, and prognostic assessment may further indicate the treating effect of immune therapy. Gliomas are the most common primary malignant tumors of the central nervous system (CNS), accounting for approximately 40%~60% of intracranial tumors, and have high morbidity and mortality . Low-graCompared with the World Health Organization (WHO) classification of CNS tumors published in 2007 and 2016, the main change in the 2021 classification is laying more emphasis on genetic parameters into the strategy of glioma diagnosis, breaking with the century-old standard of diagnosis based entirely on microscopy , 7 . TheGiven the limited effectiveness of traditional treatments, a series of new treating strategies have been tried in gliomas with the help of advancing genomics. In recent years, high hopes have been placed on tumor immunotherapy, which has not only gradually become a research hotspot in the scientific studies  but alsoKDEL Endoplasmic Reticulum Protein Retention Receptor 1 (KDELR1), a 24.5-kDa seven-transmembrane protein encoded by ERD2, which is located on 19q13.33, is responsible for the retrieval of soluble endoplasmic reticulum (ER) residents from the Golgi back to the ER . Some stIn this study, genetic and clinical data related to KDELR1 were downloaded from public databases, including TCGA, Gene Expression Omnibus (GEO), and Chinese Glioma Genome Atlas (CGGA). After data preprocessing was completed, correlation, survival, and Cox regression analyses were performed to explore the potential function of KDELR1. We aimed to develop a novel diagnostic biomarker for glioma, thus assisting disease stratification and precise treatment.https://cancergenome.nih.gov/), CGGA (http://www.cgga.org.cn/), and GEO (https://www.ncbi.nlm.nih.gov/geo/). The TCGA database classifies gliomas into two types: LGG and GBM; therefore, the three cohorts  were named TCGA_glioma, TCGA_LGG, and TCGA_GBM, respectively. These three datasets contained 698, 529, and 169 samples, respectively. For Chinese cohorts, CGGA contained three datasets: mRNA-array_301, mRNAseq_325, and mRNAseq_693, each with 301, 325, and 693 glioma samples, respectively. The CGGA database contains abundant clinical information, such as age, sex, WHO grade, TCGA subtype, histology, IDH mutation and 1p/19q codeletion status, and OS time and status. In addition, microarray datasets were obtained from GEO, including GSE4271 (generated from GPL96), GSE4290 (GPL570), GSE4412 (GPL96), GSE68848 (GPL570), and GSE13041 . After retrieval, all the gene expression profiles were preprocessed, including background correction, normalization, and log2 conversion, using R software (version 3.5.1). When a gene matched multiple probes, the average was computed and adopted for subsequent analyses.The datasets of expression profiles with corresponding clinical information were obtained from public databases, including TCGA (https://www.oncomine.org) with the criteria of P < 0.01, fold-change (FC) > 1.5 and gene rank = all (http://gepia.cancer-pku.cn/index.html), based on gene expression and clinical data from TCGA and GTEx, was used for the survival analyses of OS and disease-free survival (DFS)  and normal samples, the KDELR1 gene was submitted to Oncomine  . Gene seal (DFS) .The R package “ESTIMATE” was used to analyze the communities of immune and stromal cells according to the characteristics of gene expression and then to obtain immune, stromal, and ESTIMATE scores . The sigSamples from 119 patients  were collected from Shanghai OutDo Biotech. Co., Ltd., Shanghai, China. Informed consent was obtained from all patients, and the experimental protocols were approved by the Ethics Committee of Shanghai OutDo Biotech Co., Ltd.Antigen retrieval was performed by heating in the citrate buffer  for 10 min. The slides were incubated with the KDELR1 antibody , CD4 antibody , and CD8 antibody  for 1.5 h at room temperature. Immunoreactive elements were visualized using an EnVisio Detection kit  containing the secondary antibody and peroxidase/3,3-diaminobenzidine (DAB) chromogen. Next, the cell nuclei were counterstained with hematoxylin. Slides in which the primary antibody was omitted were used as negative controls. KDELR1 immunoreactivity scores (IRS) were calculated based on the staining intensity (SP) and the positive staining percentage (SI) of the cells . SI was scored as follows: 0: <5%; 1: 5%–25%; 2: 25%–50%; 3: 51%–75% and 4: 75%–100%. SP was subjectively scored as follows: 0, no staining; 1, weak but definite staining; 2, moderate staining; and 3, intense staining. The IRS was calculated as IRS = SP + SI. The total possible score was 7, and specimens were assigned to one of the 4 levels based on the score: 0–1 (–), 2–3 (+), 4–5 (++), and more than 6 (+++).via the R language. Immunoreactivity scores were analyzed and generated via SPSS Statistics 20. Differences with P<0.05 were considered statistically significant.GraphPad Prism 7.0, SPSS Statistics 20, and R language were used for statistical analysis. The box plots of the expression level of KDELR1 across different groups were generated and calculated by GraphPad Prism 7.0. Kaplan–Meier survival analysis and Cox regression analysis were performed and generated We initially evaluated KDELR1 transcription levels in different human tumors by analyzing TCGA RNA-seq data using the TIMER database   times than those with low KDELR1 expression  were different among the two groups  enrichment analysis revealed that these 100 co-expressed genes may be associated with chaperone-mediated protein folding, cell redox homeostasis, the regulation of mitochondrial membrane potential, Wnt signaling, planar cell polarity, extracellular exosome, focal adhesion, endoplasmic reticulum membrane, protein binding, glycoprotein binding, and protein disulfide isomerase activity . The level of expression was determined semiquantitatively by the staining index based on staining intensity (SP) and the positive staining percentage (SI). The pathological characteristics and IRS of 119 patients are summarized in Gliomas are the most common primary CNS tumor, accounting for more than 80% of primary brain tumors . Among tIt has been established that several clinical features, including age, recurrence, and pathology, are responsible for clinical prognosis in glioma patients –33. A prRecently, with the rapid advances in next-generation technology, research on the biomolecular markers and associated signaling pathways that are involved in the occurrence and development of gliomas has made substantial progress . After tIn 2010, a study classified GBMs into proneural, neuronal, classical, and mesenchymal subtypes according to the status of Platelet-derived growth factor alpha receptor (PDGFRA), isocitrate dehydrogenase 1 (IDH1), Epidermal growth factor receptor (EGFR), and Neurofibromin type 1 gene (NF1) . AdditioOur previous results have indicated that the increased expression of KDELR1 is strongly related to negative survival factors such as older age, a higher WHO grade, recurrence, IDH wild type, and 1p/19q non-codeletion status; therefore, we further investigated whether the expression of KDELR1 is related to the survival time of patients with gliomas. Survival analyses showed that gliomas with KDELR1 overexpression were associated with shorter OS and PFS times than gliomas with low KDELR1 expression. This finding may provide proof that KDELR1 can be used in predicting clinical prognosis.We further focused on the relation between KDELR1 and immune infiltration; CD8+ T cells, CD4+ T cells, and macrophages were found with high expression in HGGs along with the overexpression of KDELR1. The IHC expression of CD4 and CD8 in four different grades is consistent with the bioinformation results, and CD8 showed more participants in the tumoral immune microenvironment.In summary, our findings show that KDELR1 is upregulated in gliomas compared with normal brain tissues and that its expression is significantly associated with clinical features such as the WHO grade, recurrence, molecular classification, IDH mutation, and 1p/19q codeletion status. Moreover, the survival and Cox regression analyses of different datasets suggested that KDELR1 expression in gliomas could be an independent, unfavorable prognostic factor for survival time. On the other hand, KDELR1 expression was associated with immune infiltration  and microenvironment parameters  in gliomas. Collectively, these results indicate that KDELR1 could be a promising novel biomarker for molecular classification, immune treatment, and prognostic assessment in glioma.in vitro or in vivo assays, validating these findings, and as a biomarker, diagnostic tests will be conducted in future research. Second, bioinformation data collected from the online database were established and categorized based on the 2016 WHO CNS classification system. Since the 2021 WHO classification system has been published, a prospective study is underway to collect samples and improve this work.However, there were some limitations in this study. First, the research on KDELR1 in glioma is still in the early stage, and our research is limited to the bioinformatics database analysis and experimental verification of IHC. Thus, further studies based on surgical samples will be imperative to perform The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committee of Shanghai OutDo Biotech Co., LTD. and the Ethics Committee of Huashan Hospital. The patients/participants provided their written informed consent to participate in this study.YY, BY, and ZQ performed the research. ZH conducted the IHC staining. JC designed the research. YY and BY wrote the article. JS revised it. All authors contributed to the article and approved the submitted version.This work was supported by the Shanghai Municipal Science and Technology Major Project (No. 2018SHZDZX01) and ZJLab, the Shanghai Rising-Star Program (No. 18QA1400900), and Shanghai Hospital Development Center (No. SHDC2020CR3073B).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Embryonal tumors with multilayered rosettes (ETMRs) are a histologically heterogeneous entity and gather embryonal tumors with abundant neuropil and true rosettes (ETANTRs), ependymoblastoma, and medulloepithelioma. ETMRs are highly aggressive and associated with poorer clinical courses. However, cases of this entity are rare, and advances in molecular genetics and therapy are minor. The purpose of our study was to retrospectively analyze the clinical, pathological features, and prognostic factors of ETMRs.Our cohort consisted of 17 patients diagnosed with ETMRs in our hospital from 2018 to 2022, and two of them were lost to follow-up. Clinical data were retrieved, and immunohistochemistry and genetic analyses were performed.N-MYC copy numbers were detected in the primary tumor and recurrence of patient 8. Moreover, the incidence of metastatic disease was 100% in patients aged > 4 years and 18% in the younger group. For patients receiving chemotherapy, the median overall survival time was 7.4 months, while that of those who didn’t receive it was 1.2 months. Nevertheless, surgical approaches, radiotherapy, age at presentation, gender, tumor location, and metastatic status were not associated with independent prognosis.Among 17 cases, 16 were ETANTRs, and one was medulloepithelioma. Morphologically, tumor cells of ETANTRs could transform into anaplasia and lose the biphasic architecture during tumor progression. Immunohistochemistry of LIN28A revealed positive expression in 17 cases, and the expression of LIN28A was more intense and diffuse in the recurrent lesions than in primaries. The increased C19MC amplification and Lin28A antibody are indispensable for diagnosing ETMRs accurately. Children aged > 4 years tend to have a higher rate of metastasis in ETMRs. Chemotherapy is the only prognostic factor for ETMRs patients with a favorable prognosis. The biological nature and clinical patterns for recurrent diseases need to be further demonstrated to predict prognosis and guide treatment.ETANTR might not present as the typical morphologies during tumor progression, so analyses of  C19MC locus at the Chr 19q13.42  rosettes and small embryonal tumors. ETMRs could develop through the CNS but mainly involve the cerebral hemisphere , 9. TheySeventeen patients with ETMRs were diagnosed at our hospital from January 2018 to January 2022, including ETANTRs and medulloepithelioma. All diagnoses were made according to the 2021 WHO CNS tumor classification criteria and reviewed by two expertized neuropathologists. Surgery approaches were divided into biopsy, partial resection, and complete resection. Two patients were lost to follow-up; therefore, fifteen were included in our statistical analysis. Besides, we collected 31 patients with atypical teratoid/rhabdoid tumors (AT/RTs) and 50 patients with medulloblastomas (MB) as control groups.in situ hybridization (FISH) using an intermittent microwave irradiation method was performed on 4-um-thick FFPE tissue sections  clones as previously described (C19MC FISH were performed on ETMRs.Immunohistochemistry (IHC) was performed on 10% neutral buffered formalin-fixed, paraffin-embedded (FFPE) 4-um-thick tissue sections using the two-step Envision standard. The primary antibodies included: Lin28A , INI1 , glial fibrillary acidic protein , oligodendrocyte transcription factor 2 , synaptophysin , neuronal nuclei , and Ki-67 (Dako). Dual-color fluorescence sections . The C19p = 0.05. The statistical analyses were performed using SPSS 26.0  and Prism 9.1.1 .Log-rank analysis using the Kaplan Meier and Chi-square analysis was applied to compare survival time, including overall survival (OS) and event-free survival (EFS). Cox regression analysis was performed to identify independent predictors. The level of significance was The clinical data of all 17 patients are listed in . Three of these tumors contained single or multiple partially cystic appearances. Calcification was not found in our cohort.The initial site was supratentorial in 10 patients and infratentorial in 7 patients, with a ratio of 1.4:1. Initial magnetic resonance imaging (MRI) information was available for 15 cases, and the other two were unavailable as MRI was performed at a local hospital. In 15 cases, tumors were isointense or hypointense on T1-weighted imaging (T1WI) and isointense or hyperintense on T2 WI. In contrast, tumors exhibited slight or partial contrast enhancement and hyperintensity on diffusion-weighted imaging (DWI) , as well as sheets of poorly differentiated cells. ETANTRs showed biphasic architecture featuring dense clusters of small embryonal cells (or multilayered rosettes) and paucicellular neuropil-like areas . In ETANTRs, the neuropil-like areas were stained strongly and diffusely for the neuronal markers such as NeuN and Syn. In contrast, only a few positive cells were found within the multilayered rosettes and small cell areas . The glial markers GFAP and Olig2 focally expressed in some embryonal cells but were negative in the neuron differentiated cells . The Ki67 index ranged from 40% to 90%, which was relatively high in embryonal cells and lowered in the neuropil-like areas . All tumors demonstrated diffused nuclear expression of INI1 throughout all components. Lin28A expression was found in all cases (17/17), and its immunoreactivity was more predominant and intense in multilayered rosettes and poorly differentiated small cell areas compared with neuropil-like regions . In our three relapsed cases, the expression of LIN28A was much stronger and more intense in the recurrent lesions compared with primary tumors . Conversely, tumor cells of AT/RTs were partially or focally positive for Lin28A in13 cases , but all cases showed a nuclear loss of INI-1. In 50 cases of medulloblastoma, all tumor cells were negative for Lin28A and positive for INI-1. Besides, the increased copy number of MYC (N) was found in case 8 of both primary and recurrent lesions (NRAS and CDK6 aberrations were detected (. For the patient without the amplification of C19MC (case 9), we performed the FISH analysis of DICER1, and mutations were not detected.Among the 17 patients, tumor sizes varied from 0.5 to 10cm, with a median size of 2.5cm. Sixteen patients had ETANTRs, 1 had medulloepithelioma (case 9), and no ependymoblastoma. The pathological features of the 17 cases are listed in , as well, as well, as well, as well, as well, as well, as well, as well, as well, confirming the diagnosis of ETMR. IHC showed the diffuse expression of Lin28A  and focal expression of SYN and NeuN in tumor cells. FISH analysis revealed the amplification of C19MC. After 6.5 months, the tumor presented with recurrence demonstrated by MRI and undertook the complete resection. Histopathology showed tumor cells were three times larger than their neighboring cells with cellular pleomorphism and prominent nucleoli, indicating the anaplastic progression, but neither neuropils nor ependymoblastic rosettes were observed . Amplification at 19q13.42 and immunopositivity of LIN28A  still existed in the tumor cells, which demonstrated the recurrence of ETMRs.One case (1/8) occurred with the histological evolution towards anaplasia during tumor progression (case 4). At first surgery, histopathology revealed a biphasic architecture featuring small embryonal cells with round or polygonal nuclei and scant cytoplasm and neuropil-like areas with sparse neoplastic neurocytic and ganglion cells , confirm, confirm, confirm.All 17 patients received surgery, with five undergoing complete resections, four biopsies, and eight partial resections. Two patients were lost to follow-up. Therefore, therapy and follow-up data were only available for 15 patients p = 0.025, χ2 = 9.76; Five patients occurred with metastatic disease, two with recurrence, and one presented with both metastasis and recurrence. Among the six patients with metastatic disease, one developed spinal metastasis from the fourth ventricle, and two occurred with intracranial metastasis at initial presentation. All the metastatic lesions were visible by MRI, while no metastatic cells were found in the cerebrospinal fluid. The 2021 WHO indicates that ETMRs occur in children ≤ 4 years of age, and analyses performed with stratification for periods ≤ and > 4 years showed that two patients had intracranial metastasis in the younger group  and four in the elder group . The incidence of metastasis was significantly different between the two groups . Nine patients were treated with adjuvant treatment following surgery ; one patient received radiotherapy alone, four received chemotherapy alone, and four received radiotherapy plus chemotherapy. The median durations of OS and EFS were both 1.4 months. Analyses of OS and EFS showed patients who were treated with radiation treatment exhibited a higher survival rate than those who were not  rosettes are the characteristic and standard features of ETMRs , 13, 21.TP53 , 24, andTP53 . TherefoImmunohistochemical staining for Lin28A is more intense and prominent in the small cells and multilayered rosettes compared with neuron-differentiated cells in ETMRs irrespective of histological subgroups , 26. In C19MC amplification at the Chr 19q 13.42 and the LIN28A/Let-7 signaling pathways are considered crucial hallmarks for tumorigenesis . At the same time, WNT and MYCN signaling are downstream of the RNase III domain in DICER1 mutations, so ETMRs are molecularly similar whether they have C19MC amplification , according to the 2021 WHO CNS classification (N-MYC in case 8, which might play a role in tumor relapse (The origin cells and biology of ETMRs remain poorly elucidated. Recently, the igenesis , 26, 27.NA genes , 31. Comogenesis . Of notefication –35. Our To date, no standard therapeutic approaches have been established for ETMRs. The treatment now includes surgery, radiotherapy, and chemotherapy, but the prognosis of ETMRs remains poorer. In our analysis, the extent of surgical resection was not associated with overall survival. This result was different from the finding reported by Kirti et al. , in whicThe effectiveness and prognostic role of chemotherapy have not been reliably identified hitherto. Due to the lack of established protocols, the type of chemotherapeutic agents varies among patients. Our data found that patients who received chemotherapy tend to have a statistically better prognosis than those who weren’t treated with chemotherapy, similar to some findings , 37. TheR pathway. Another study proposed that SHH inhibitors could benefit ETMRs by activating the WNT pathway and upregulating the SHH pathway (C19MC amplification, DICER1 mutations, and LIN28 expression remain unclear. A more in-depth understanding of tumor biology is needed as well in ETMRs.In conclusion, our study covers the impressive number of patients diagnosed with ETMRs in a short time frame. Our data revealed that children over four years old have a higher rate of metastasis. The morphology of ETMRs varies, and characteristic features are not always presented along with tumor progression, which reminds us of the combination of the C19MC FISH and LIN28A IHC when making the diagnosis. Patients treated with chemotherapy tend to have a better prognosis. Since C19MC aberration has been detected for more than two decades, the understanding of ETMRs biology has improved as well as better diagnostic tools, more driving aberrations, and possible downstream targets. However, the survival of ETMRs patients has only minimally improved due to the lack of effective therapies; therefore, more case numbers and further studies are needed to demonstrate the involved signaling pathways and develop targeted molecular therapies.Due to the limited effects of the current therapeutic strategy for ETMRs, novel treatment options are needed. Tara et al.  found th pathway . Large-sThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Research Ethics Committee of the Shanghai Jiao Tong University School of Medicine. Written informed consent to participate in this study was provided by the patient/participants legal guardian/next of kin.KX contributed to the investigation and was a significant contributor to writing the manuscript; ZS designed the project and collected data; LW performed the data analysis and designed experiments; WG revised the manuscript; KX and ZS contributed equally to the work and share first authorship. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.