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.

Objective:To explore the characteristics of intestinal microbiota in patients with systemic lupus erythematosus (SLE), and further explore the relationship between microbiota and CD4 +T lymphocyte subsets and disease activity. Methods:Fecal samples were collected from 96 patients with SLE, and 96 sex- and age-matched healthy controls (HCs). The gut microbiota were investigated via 16s rRNA sequencing. Flow cytometry was used to detect peripheral CD4 +T lymphocyte subsets of Th1, Th2, Th17 and Treg cells. Indicators of disease activity such as erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), complement C3 and C4, Systemic lupus erythematosus disease activity index(SLEDAI) for each patient were recorded. Differential abundance analysis was carried out using the edgeR algorithm. The Wilcoxon rank-sum test was used to compare alpha diversity indices, bacterial abundances, and the F/B ratio between groups. R (version 4.0.1) was used for comparative statistics, and Pearson′s correlation analysis was used to assess the correlations between the relative abundances of bacterial genera and serum levels of ESR, CRP, C3 and C4 in the samples. Results:The alpha estimators of richness (ACE and Chao 1) were significantly reduced in SLE feces samples compared with those of HCs ( P<0.01). Bacterial diversity estimators, including the Shannon ( P<0.01) and Simpson′s ( P<0.01) indices, were also significantly lower in SLE. Significant differences in gut microbiota composition between SLE and HCs were found using the edgeR algorithm. Compared with HC, 24 species of bacteria were significantly different in SLE patients at the genus level ( P<0.05). Moreover, there was a significant positive correlation between CRP and Coprococcus ( r=0.30, P=0.014), C4 and Corynebacterium ( r=0.31, P=0.013) and Faecalibacterium( r=0.25, P=0.048), Hemoglobin and Morganella( r=0.41, P=0.001), as well as SLIDA and Corynebacterium( r=0.25, P=0.047). In terms of lymphocyte subsets, there was significant positive correlation between B cells, Treg cells and Eubacterium eligens group, as well as CD8 +T, CD4 +T, NK cells and Corynebacterium. In additional, Th1 was positively correlated with Shigella Escherichia coli ( r=0.52, P=0.008), and Th2 was positively correlated with Dielma ( r=0.51, P<0.001). Conclusion:The abundance and diversity of intestinal flora in SLE patients were significantly reduced, and the differentially expressed bacteria were closely related to the CD4 +T lymphocyte subsets and disease activity indicators of patients.