ObjectiveTo explore the dose-effect relationship and safety of high， medium， and low doses of raw Astragali Radix in the modified Huangqi Chifengtang （MHCD） for treating proteinuria in immunoglobulin A （IgA） nephropathy， and to provide scientific evidence for the clinical use of high-dose Astragali Radix in the treatment of proteinuria in IgA nephropathy. MethodsA total of 120 patients with IgA nephropathy， diagnosed with Qi deficiency and blood stasis combined with wind pathogen and heat toxicity， were randomly divided into a control group and three treatment groups. The control group received telmisartan combined with a Chinese medicine placebo， while the treatment groups were given telmisartan combined with MHCD containing different doses of raw Astragali Radix （60， 30， 15 g）. Each group contained 30 patients， and the treatment period was 12 weeks. Changes in 24-hour urinary protein （24 hUTP）， traditional Chinese medicine （TCM） syndrome scores， effective rate， and renal function were observed before and after treatment. Safety was assessed by monitoring liver function and blood routine. ResultsAfter 12 weeks of treatment， 24 hUTP significantly decreased in the high， medium， and low-dose groups， as well as the control group （P<0.05， P<0.01）. The TCM syndrome scores in the high， medium， and low-dose groups also significantly decreased （P<0.01）. Comparisons between groups showed that the 24 hUTP in the high-dose group was significantly lower than in the medium， low-dose， and control groups （P<0.05， P<0.01）， and the 24 hUTP in the medium-dose group was significantly lower than in the control group （P<0.05）. The TCM syndrome scores in the high and medium-dose groups were significantly lower than in the low-dose and control groups （P<0.05， P<0.01）. The total effective rates for proteinuria in the high， medium， low-dose， and control groups were 92.59% （25/27）， 85.19% （23/27）， 60.71% （17/28）， and 57.14% （16/28）， respectively. The effective rates in the high and medium-dose groups were significantly higher than in the low-dose and control groups （χ²=13.185， P<0.05， P<0.01）. The effective rates for TCM syndrome scores in the high， medium， low-dose， and control groups were 88.89% （24/27）， 81.48% （22/27）， 71.43% （20/28）， and 46.43% （13/28）， respectively. The efficacy of TCM syndrome scores in the high and medium-dose groups was significantly higher than in the control group （χ²=14.053， P<0.01）. Compared with pre-treatment values， there was no statistically significant difference in eGFR and serum creatinine in the high and medium-dose groups. However， eGFR significantly decreased in the low-dose and control groups after treatment （P<0.05）， and serum creatinine levels increased significantly in the control group （P<0.05）. No statistically significant differences were observed in urea nitrogen， uric acid， albumin， total cholesterol， triglycerides， liver function， and blood routine before and after treatment in any group. ConclusionThere is a dose-effect relationship in the treatment of IgA nephropathy with high， medium， and low doses of raw Astragali Radix in MHCD. The high-dose group exhibited the best therapeutic effect and good safety profile.

Objective:To screen key genes that co-regulate immune and mitochondrial energy metabolism through bioinformatics methods and to investigate the relationship between the key genes and immune infiltration.Methods:A total of 671 glioma samples (the tumor group) and 5 non-tumor brain tissue samples (the control group) were collected from the Cancer Genome Atlas (TCGA) database on November 13, 2023. Through a comprehensive search of the GeneCards database and immune-related genes (IRG) and mitochondrial energy metabolism-related genes (MEMRG) in previous published literatures, 76 IRG and MEMRG (IR & MEMRG) were obtained by taking the intersection of IRG and MEMRG after merging and deduplicating. The limma package in R software was used to screen the differentially expressed genes (DEG) between the tumor group and the control group. Then, immune-related & mitochondrial energy metabolism-related differentially expressed genes (IR&MEMRDEG) were obtained by intersecting with IR & MEMRG. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted on the IR&MEMRDEG through the clusterProfiler package in R software. The STRINGv12.0 online database (https://cn.string-db.org/) was employed to construct a protein interaction network based on IR&MEMRDEG and to identify the top 5 key core genes. Single-sample gene-set enrichment analysis (ssGSEA) was used to determine the relative abundance of immune cell infiltration in all samples, and the immune cell infiltration matrices for both the tumor and the control groups were acquired. The expression differences in infiltration abundance of the immune cells in the tumor group and the control group were analyzing by using the ggplot2 package in R software. The heat map was drawn by utilizing the R software pheatmap package to show self-correlation of immune cells. The correlation between the top 5 key genes in the protein interaction network and immune cells was calculated by using the Spearman algorithm and the R software ggplot2 package.Results:A total of 3 623 DEGs were identified from the TCGA database in both groups, including 1 711 up-regulated genes and 1 912 down-regulated genes. After taking the intersection of DEG and IR&MEMRG, 11 IR&MEMRDEG were obtained including EIF4EBP1, TP53, IDH1, PRCKZ, CD200, GPI, PGM2, PKLR, AK2, ATP4A, and ALDH3B1. GO enrichment analysis results showed that 11 IR&MEMRDEG were mainly enriched in ADP metabolic process, ATP metabolic process, purine nucleoside diphosphate metabolic process, purine ribonucleoside diphosphate metabolic process, and ribonucleoside diphosphate metabolic process at the biological level; in the fibronectin-1 rich granule, secretory granule lumen, cytoplasmic vesiclelumen, vesiclelumen, and nuclear matrix at the cellular component level; in magnesium ion binding, potassium ion binding, and alkali metal ion binding at the molecular functional level. The KEGG enrichment analysis results showed that 11 IR&MEMRDEGs were mainly enriched in glycolysis/gluconeogenesis, carbon metabolism, insulin signaling pathway, pentose phosphate pathway, starch and sucrose metabolism signaling pathways. The protein interaction network analysis from the STRING database revealed that 5 highest scoring core proteins were identified, namely EIF4EBP1, TP53, IDH1, PRKCZ, and AK2.The immune infiltration abundances of 28 immune cells were calculated by using the ssGSEA algorithm. The infiltration abundance of 15 immune cells in the tumor group was higher than that in the control group, and the differences were statistically significant (all P < 0.05). The findings from the immune infiltration analysis indicated a positive correlation among 15 types of immune cells, in which there was a strongest correlation between effector memory CD8 + T cell and myeloid derived suppressor cells. EIF4EBP1, TP53, IDH1, and AK2 exhibited a positive correlation with a large number of immune cells (all P < 0.05), whereas PRKCZ demonstrated a negative correlation with more immune cells (all P < 0.05). Conclusions:PRKCZ, AK2, and EIF4EBP1 have the potential to be the new targets of immunotherapy for gliomas.

Objective:To investigate the effects of compound Duzhong Jiangu Granules on joint function and gut microbiota in patients with Kashin-Beck disease.Methods:A single group pre- and post-experimental design was conducted, the patients with Kashin-Beck disease were selected as the subjects in Xunyi County, Xianyang City, Shaanxi Province; and treated with oral administration of compound Duzhong Jiangu Granules (12 g/bag, 1 bag/time, 3 times/day) for a period of 1 month. The improvement of joint function was evaluated using the joint dysfunction index scoring method before and after treatment. Morning stool samples of patients were collected and the changes in gut microbiota were analyzed before and after treatment using 16S rDNA sequencing technology.Results:A total of 87 patients with Kashin-Beck disease were included, including 44 males and 43 females; the age was (60.38 ± 7.12) years old, and the body mass index was (23.67 ± 3.59) kg/m 2. The comprehensive scores of joint dysfunction index for patients with Kashin-Beck disease before and after treatment were (7.27 ± 2.05) and (5.86 ± 2.01) points, respectively, and the difference was statistically significant ( t = 5.88, P < 0.001). The sequencing results of gut microbiota showed that there were statistically significant differences in the alpha diversity (chao1, observed species index) and beta diversity of gut microbiota in patients with Kashin-Beck disease before and after treatment ( Z = - 5.08, - 5.03, R = 0.09, P < 0.001). In the distribution of gut microbiota, Firmicutes was the dominant phylum, with relative abundances of 50.21% and 52.09% before and after treatment, respectively; the Bifidobacterium was the dominant bacterial genus, with relative abundances of 16.83% and 18.81% before and after treatment, respectively. At the genus level, a total of 17 gut microbiota genera were screened out, among which the relative abundances of Hafnia-Obesumbacterium, Gammaproteobacteria_unclassified, Acinetobacter, Pantoea, Leuconostoc, and Akkermanisia were significantly higher than before treatment ( Z = - 2.40, - 2.24, - 2.06, - 3.59, - 2.24, - 2.11, P < 0.05). The relative abundances of Dubosiella, Selenomonas, Anaeroplasma, Lachnospiraceae_ NK4A136_group, Rikenella, Prevotella, Megasphaera, Lactobacillus, Prevotella-9, Phascolarctobacterium, and Desulfovibrio were significantly lower than before treatment ( Z = - 9.38, - 2.61, - 2.18, - 8.43, - 2.45, - 2.46, - 2.49, - 7.29, - 2.29, - 2.55, - 2.08, P < 0.05). Conclusions:Compound Duzhong Jiangu Granules can effectively improve the joint function of patients with Kashin-Beck disease, and alter the diversity and richness of the gut microbiota community. It may reduce clinical symptoms in patients by regulating the structure of gut microbiota.

Objective:To investigate the value of a deep learning model based on diffusion-weighted imaging (DWI) in quick identification of the TOAST etiology classification in patients with acute ischemic stroke (AIS).Methods:In this cross-sectional study, imaging and clinical data of 504 patients with AIS admitted to the First Affiliated Hospital of Xinjiang Medical University from March 2023 to February 2024 were retrospectively reviewed. Using the TOAST etiology classification, there were 252 large artery atherosclerosis type and 252 small-artery occlusion type. The 504 cases were divided into a training set ( n=302), a validation set ( n=101) and a test set ( n=101) using stratified randomization in the ratio of 6∶2∶2. All cases had DWI data. A DenseNet network framework was used to construct DenseNet models by optimizing the model configurations of different layers. Three DenseNet models with different layers (121, 169, 201) were constructed, named DenseNet169 model, DenseNet121 model, and DenseNet201 model. The data enhancement, Adam optimizer and cross-entropy loss function methods were used to improve the convergence speed and robustness of the model, and to balance the positive and negative sample imbalance problem. Independent sample t-test or χ2 was used to compare the clinical data of patients with large artery atherosclerosis type and small-artery occlusion type AIS. Receiver operating characteristic curves and area under the curve (AUC) were performed to evaluate the efficacy of each model in identification of patients with large artery atherosclerosis type and small-artery occlusion type AIS. Results:There were statistically significant differences in age, National Institutes of Health Stroke Scale score at admission, and stenosis or occlusion of large vessels between patients with large artery atherosclerosis type and small-artery occlusion (all P<0.05). In the test set, the AUC, sensitivity, accuracy, and F1 score values of the DenseNet201 model for discriminating patients with large artery atherosclerosis type AIS and small-artery occlusion type AIS (0.826, 0.902, 0.743, 0.780, respectively) were higher than those of DenseNet121 (0.801, 0.647, 0.723, 0.702, respectively) and DenseNet169 model (0.778, 0.882, 0.733, 0.769). Conclusions:The deep learning models based DWI constructed in this study can help with the TOAST etiology classification of AIS cases. DenseNet201 model shows the best and stable performance in the deep learning-based classification.

Objective:To compare the resting energy expenditure(REE)characteristics among young men with different body mass indexes(BMI).Methods:Thirty young men[average age was(26.93± 4.16)years]were enrolled in this study.They underwent resting metabolism tests in the Department of Sports Medicine of Peking University Third Hospital from December 2017 to June 2021.The resting meta-bolic rate(RMR)was measured by indirect calorimetry,the body composition was measured by bioresis-tance antibody component analyzer.The REE characteristics were analyzed,and 11 predictive equations were used to estimate RMR and compared with the measured value.The differences were analyzed by paired t-test and intra-class correlation coefficient(ICC).Results:The RMR of the overall 30 young men was(1 960.17±463.11)kcal/d(1 kcal=4.186 8 kJ).Including(1 744.33±249.62)kcal/d in those with normal BMI,which was significantly lower than that in those who were overweight or obese[(2 104.06±520.32)kcal/d,P＜0.01],but the weight-corrected RMR in those with normal BMI was significantly higher than that in those who were overweight or obese[(24.02±2.61)kcal/(kg·d)vs.(19.98±4.38)kcal/(kg·d),P＜0.01].The RMR was significantly and positively correlated with body weight,adiposity,lean body mass,body surface area,and extracellular fluid in the subjects with diffe-rent BMI(all P＜0.05).The predicted values of the 11 prediction equations were not in good agreement with the measured values(all ICC＜0.75),with relatively high agreement between the pre-dicted and measured values of the World Health Organization(WHO)equation in overweight obese young men(ICC=0.547,P＜0.01).Conclusion:There were significant differences in RMR among young men with different BMI,and the RMR after weight correction should be considered for those who were overweight or obese.The consistency between the predicted values of different prediction equations and the actual measured values of RMR was relatively poor,and it is recommended to accurately measure RMR by indirect calorimetry.For overweight or obese young men,the WHO prediction equation can be considered to calculate RMR,but it is necessary to establish an RMR prediction equation applicable to different BMI populations.

Zhigancao Tang （also known as Fumaitang） is a classic formula for treating "intermittent pulse and palpitations" and is widely used in clinical practice. Sanjia Fumaitang， included in the Catalogue of Ancient Classical Formulas （First Batch） published by the National Administration of Traditional Chinese Medicine of China in 2018， is derived from this formula. This paper employed bibliometric methods to comprehensively investigate and summarize the historical evolution， drug composition， herb origins and preparation， prescription meanings， and ancient and modern applications of Zhigancao Tang， analyzed the composition and usage of Zhigancao Tang， and discussed the reasons and applications of the "Fumaitang" variants created by Wu Jutong. A total of 47 valid pieces of data from 38 ancient texts were included. Results showe that Zhigancao Tang originates from the Treatise on Cold Damage （Shang Han Lun）， and the name "Fumaitang" is also recorded in the formula's description. Converted to modern measurements from the Han dynasty system， the recommended preparation for Zhigancao Tang includes 55.2 g of fried Glycyrrhizae Radix et Rhizoma， 41.4 g of Cinnamomi Ramulus， 27.6 g of Ginseng Radix et Rhizoma， 220 g of fresh Rehmannia glutinosa， 27.6 g of Asini Corii Colla， 53 g of Ophiopogonis Radix， 45 g of Cannabis Fructus， and 90 g of Jujubae Fructus. All herbs should be decocted with 1 400 mL of yellow rice wine and 1 600 mL of water until 600 mL. Once the Asini Corii Colla is fully dissolved， the decoction should be taken warm at a dosage of 200 mL， three times a day. Zhigancao Tang is effective for replenishing Qi， warming Yang， nourishing Yin， and nourishing blood and is primarily used to treat “intermittent pulse and palpitations” caused by deficiencies in heart Yin and Yang， as well as malnutrition of the heart meridian and conditions like lung atrophy. Modern applications mainly focus on cardiovascular and cerebrovascular diseases， including arrhythmias， coronary heart disease， and premature ventricular contractions. The findings from this research provide a reference for the further development of Zhigancao Tang.

To evaluate the safety and efficacy of neoadjuvant radiohormonal therapy for oligometastatic prostate cancer (OMPC), we conducted a 3 + 3 dose escalation, prospective, phase I/II, single-arm clinical trial (CHiCTR1900025743), in which long-term neoadjuvant androgen deprivation was adopted 1 month before radiotherapy, comprising intensity modulated radiotherapy to the pelvis, and stereotactic body radiation therapy to all extra-pelvic bone metastases for 4-7 weeks, at 39.6, 45, 50.4, and 54 Gy. Robotic-assisted radical prostatectomy was performed after 5-14 weeks. The primary outcome was treatment-related toxicities and adverse events; secondary outcomes were radiological treatment response, positive surgical margin (pSM), postoperative prostate-specific antigen (PSA), pathological down-grading and tumor regression grade, and survival parameters. Twelve patients were recruited from March 2019 to February 2020, aging 66.2 years in average (range, 52-80). Median baseline PSA was 62.0 ng/mL. All underwent RARP successfully without open conversions. Ten patients recorded pathological tumor down-staging (83.3%), and 5 (41.7%) with cN1 recorded negative regional lymph nodes on final pathology. 66.7% (8/12) recorded tumor regression grading (TRG) -I and 25% (3/12) recorded TRG-II. Median follow-up was 16.5 months. Mean radiological progression-free survival (RPFS) was 21.3 months, with 2-year RPFS of 83.3%. In all, neoadjuvant radiohormonal therapy is well tolerated for oligometastatic prostate cancer.
Male ; Humans ; Prostatic Neoplasms/radiotherapy* ; Prostate-Specific Antigen/therapeutic use* ; Neoadjuvant Therapy ; Androgen Antagonists/therapeutic use* ; Prospective Studies

Prostate cancer is one of the most common malignant tumors in the world.Without typical early manifestations in the early stage, it is often too late when found. Therefore, early diagnosis, treatment, and prognosis are critical to improving the survival rate of patients with prostate cancer. Over the past few years, artificial intelligence(AI) has developed rapidly in the field of prostate cancer. In terms of diagnosis, AI is used as a tool to screen the images to reduce the error caused by the professionalism and subjectivity of the technician and to improve the repeatability of the results; In the prediction of prognosis, the algorithm calculates and evaluates disease-related parameters such as recurrence rate, lymph node metastasis rate and mortality rate, so as to assistant clinicians in decision-making and treatment improvements. This reviews aims to introduce the application of artificial intelligence in the diagnosis, treatment and prognosis of prostate cancer in recent years, as well as the prospect and challenges faced by artificial intelligence in the medical field.

Objective:To analyze the incidence of stillbirth and the associated factors in pregnancy among pregnant residents in Wuhan.Methods:A previous birth cohort was retrospectively reviewed. The cohort was based on Wuhan Maternal and Child Information System, and the perinatal information of pregnant residents in Wuhan from January 1, 2011, to September 30, 2017 and information of selected cases was collected, including socio-demographic characteristics, pregnant history, and healthcare information during pregnancy and labor. Data on stillbirth, including fetal death in uterus and in labor, were selected for this study. Chi-square test was adopted for comparing the differences in pregnancy-related factors between live birth and stillbirth, and binary logistic regressions for exploring the influencing factors associated with the occurrence of stillbirth. Results:A total of 509 057 deliveries in Wuhan were included in this study, including 505 839 live births and 3 218 stillbirths (3 155 after exclusion of fetal death in labor), with an overall incidence of stillbirth of 6.32‰(3 218/509 057), and an annual incidence between 4.90‰ to 8.11‰. Statistically significant differences were found between the live birth and stillbirth group in the following items: maternal age [<25 years old: 19.28% (97 544/505 839) vs 19.36% (623/3 218); 25-30 years old: 48.45% (245 077/505 839) vs 45.15% (1 453/3 218); 30-35 years old: 26.09% (131 952/505 839) vs 26.29% (846/3 218); >35 years old: 6.18% (31 266/505 839) vs 9.20% (296/3 218)], educational background [middle school or below: 22.90% (115 833/505 839) vs 22.03% (709/3 218); high school: 36.37% (183 978/505 839) vs 38.72% (1 246/3 218); college or above: 40.73% (206 028/505 839) vs 39.25% (1 263/3 218)], occupation [brainworker or professionals: 33.51% (169 514/505 839) vs 31.54% (1 015/3 218); manual or freelance worker: 66.38% (335 763/505 839) vs 68.34% (2 199/3 218)], residential area [urban area: 70.00% (354 365/505 839) vs 76.32% (2 456/3 218); rural area: 30.00% (151 474/505 839) vs 23.68%(762/3 218)], and time of conception [spring (March to May): 24.27% (122 746/505 839) vs 24.08% (775/3 218); summer (June to August): 24.09% (121 867/505 839) vs 23.87% (768/3 218); fall (September to November): 26.69% (135 012/505 839) vs 25.08% (807/3 218); winter (December to next February): 24.95% (126 214/505 839) vs 26.97% (868/3 218)] (all P<0.05), but no significant difference was found in fetal gender ( P>0.05). Besides, gravidity [once: 49.32% (249 484/505 839) vs 47.02% (1 513/3 218); over twice: 50.68% (256 355/505 839) vs 52.98% (1 705/3 218)], parity [once: 73.60% (372 316/505 839) vs 77.07% (2 480/3 218); over twice: 26.40% (133 523/505 839) vs 22.93% (738/3 218)], history of stillbirth [0.33% (842/256 355) vs 0.65% (11/1 705)], hypertensive disorders in pregnancy [3.25% (16 464/505 839) vs 5.59% (180/3 218)], first trimester vaginal bleeding [2.02% (10 251/505 839) vs 2.61% (84/3 218)], placenta previa [0.98% (4 963/505 491) vs 2.64% (53/2 009)], and oligohydramnios [2.52% (12 764/505 839) vs 1.90% (61/3 218)] differed significantly between the two groups (all P<0.05). However, no significant difference was found between the two groups in terms of the proportion of women with gestational diabetes mellitus and previous spontaneous abortion (both P>0.05). After exclusion of fetal death in labor from the 3 218 stillbirths, the same results were achieved. Binary logistic regression analysis showed that women who were over 30 years old (30-35 years old: OR=1.42, 95% CI: 1.30-1.56; >35 years old: OR=2.59, 95% CI: 2.25-2.98), with a high school degree or below (middle school or below: OR=1.37, 95% CI: 1.21-1.55; high school: OR=1.28, 95% CI: 1.16-1.42), manual or freelance worker ( OR=1.18, 95% CI: 1.07-1.31), in the urban area ( OR=1.43, 95% CI:1.31-1.57), and gravidity ≥ 2 times ( OR=1.32, 95% CI: 1.21-1.43), primiparity ( OR=1.76, 95% CI: 1.58-1.96), gestational hypertension ( OR=2.80, 95% CI: 2.40-3.27), vaginal bleeding in the first trimester ( OR=1.35, 95% CI: 1.08-1.68), placenta previa ( OR=10.86, 95% CI: 8.84-13.35) and history of stillbirth ( OR=2.27, 95% CI: 1.30-3.98) were all risk factors of stillbirth. Conclusion:Pregnant women who were over 30 years old, less educated, manual worker or freelance or with a history of adverse pregnancy, vaginal bleeding in the first trimester, hypertension in pregnancy, and placenta previa are at higher risk of stillbirth

Objective:To screen the endoplasmic reticulum stress (ERS) signature-related differentially expressed genes (DEG) in gastric cancer and to construct a prognostic risk model based on a bioinformatics.Methods:Transcriptome sequencing data (RNA-seq) of 375 gastric cancer and 32 paracancerous tissue samples downloaded from The Cancer Genome Atlas (TCGA) database and the corresponding clinical information were obtained as training set samples; data of 387 gastric cancer patients (GSE84437) from Gene Expression Omnibus (GEO) database were downloaded as validation set samples. All data were obtained on December 25, 2021. A total of 785 ERS signature-related genes (ERS-RG) were obtained from the GeneCards database. DEG between gastric cancer tissues and paracancerous tissues in the TCGA database was analyzed. The identified gastric cancer DEG were intersected with ERS-RG from the GeneCards database to obtain gastric cancer ERS signature-related DEG, which were analyzed for gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Univariate Cox proportional risk model was used to screen ERS signature-related DEG with prognostic value in gastric cancer, and LASSO regression analysis was performed to construct a polygenic prognostic risk model, and to calculate the prognostic risk score. The patients in training set and validation set were divided into high-risk group and low-risk group according to the median of the prognostic risk score (2.369); Kaplan-Meier survival analysis was used to compare the overall survival (OS) and to draw time-dependent receiver operating characteristic (ROC) curves of patients in the two groups; nomogram was drawn based on the prognostic independent influencing factors of gastric cancer. The characteristic immune cell infiltration abundance between the two groups was analyzed by using the inverse convolution-based CIBERSORT algorithm. Cytolytic activity scores were calculated by using the geometric mean of granzyme A and perforin 1 expression. According to the median prognostic risk score (2.369) and median tumor mutation burden (TMB) (3.000), all patients with gastric cancer were divided into high risk score-high TMB group, high risk score-low TMB group, low risk score-high TMB group and low risk score-low TMB group to compare the OS of patients in each group.Results:A total of 444 ERS signature-related DEG in gastric cancer including 168 down-regulated genes and 276 up-regulated genes were obtained, which were mainly enriched in biological processes such as protein processing in the endoplasmic reticulum, extracellular matrix (ECM) receptor interactions and unfolded protein responses (all P < 0.05). Univariate Cox regression analysis showed that 12 prognostic-related ERS signature-related DEG in gastric cancer were screened out. LASSO regression analysis was performed to obtain a prognostic risk score = 0.052×NOS3+0.137×PON1+0.067×CXCR4+0.131×MATN3+0.116×ANXA5+0.090×SERPINE1. The results of Kaplan-Meier analysis showed that the OS of the low-risk group in both the training and validation sets was better than that of the high-risk group (all P < 0.01). The results of the time-dependent ROC curve analysis showed that the AUC for the 3-year, 5-year, 8-year OS rates was 0.695, 0.786, 0.698, respectively in the training set, while the AUC for the 3-year 5-year, 8-year OS rates was 0.580, 0.625, 0.627, respectively in the validation set. Multivariate Cox regression analysis showed that prognostic risk score ( HR = 3.598, 95% CI 2.290-5.655, P < 0.001) and tumor stage ( HR = 1.344, 95% CI 1.057-1.709, P < 0.05) were independent factors influencing the prognosis of gastric cancer. Among 375 gastric cancer patients in the TCGA database, the expression levels of ATF6, HSPA5, XBP1 and ATF4 in the high-risk group were higher than those in the low-risk group (all P < 0.05); CIBERSORT results showed that the abundance of activated CD4 memory T cells in the high-risk group was lower than that in the low-risk group, and the abundance of both M0 and M2 macrophages in the high-risk group was higher than that in the low-risk group (all P < 0.05). The expression levels of common immune checkpoints (CD274, CTLA4, TNFRSF9, TIGIT, PDCD1, LAG3) in the high-risk group were all higher than those in the low-risk group (all P < 0.05). Cytolytic activity score in the high-risk group was higher than that in the low-risk group ( P < 0.05). The prognostic risk score was negatively correlated with TMB ( r = -0.20, P < 0.001). Patients in the low-risk score-high TMB group had the best OS and those in the high-risk score-low TMB group had the worst OS (both P < 0.001). Conclusions:The prognostic risk score model is established based on 6 ERS signature-related DEG in gastric cancer and its prognostic risk score may be effective as an independent prognostic factor to predict the prognosis of gastric cancer patients.