Construction of an immune-related gene signature for overall survival prediction and immune infiltration in gastric cancer

BACKGROUND Treatment options for patients with gastric cancer(GC)continue to improve,but the overall prognosis is poor.The use of PD-1 inhibitors has also brought benefits to patients with advanced GC and has gradually become the new standard treatment option at present,and there is an urgent need to identify valuable biomarkers to classify patients with different characteristics into subgroups.AIM To determined the effects of differentially expressed immune-related genes(DEIRGs)on the development,prognosis,tumor microenvironment(TME),and treatment response among GC patients with the expectation of providing new biomarkers for personalized treatment of GC populations.METHODS Gene expression data and clinical pathologic information were downloaded from The Cancer Genome Atlas(TCGA),and immune-related genes(IRGs)were searched from ImmPort.DEIRGs were extracted from the intersection of the differentially-expressed genes(DEGs)and IRGs lists.The enrichment pathways of key genes were obtained by analyzing the Kyoto Encyclopedia of Genes and Genomes(KEGGs)and Gene Ontology(GO)databases.To identify genes associated with prognosis,a tumor risk score model based on DEIRGs was constructed using Least Absolute Shrinkage and Selection Operator and multivariate Cox regression.The tumor risk score was divided into high-and lowrisk groups.The entire cohort was randomly divided into a 2:1 training cohort and a test cohort for internal validation to assess the feasibility of the risk model.The infiltration of immune cells was obtained using‘CIBERSORT,’and the infiltration of immune subgroups in high-and low-risk groups was analyzed.The GC immune score data were obtained and the difference in immune scores between the two groups was analyzed.RESULTS We collected 412 GC and 36 adjacent tissue samples,and identified 3627 DEGs and 1311 IRGs.A total of 482 DEIRGs were obtained.GO analysis showed that DEIRGs were mainly distributed in immunoglobulin complexes,receptor ligand activity,and signaling receptor activators.KEGG pathway analysis showed that the top three DEIRGs enrichment types were cytokine-cytokine receptors,neuroactive ligand receptor interactions,and viral protein interactions.We ultimately obtained an immune-related signature based on 10 genes,including 9 risk genes(LCN1,LEAP2,TMSB15A mRNA,DEFB126,PI15,IGHD3-16,IGLV3-22,CGB5,and GLP2R)and 1 protective gene(LGR6).Kaplan-Meier survival analysis,receiver operating characteristic curve analysis,and risk curves confirmed that the risk model had good predictive ability.Multivariate COX analysis showed that age,stage,and risk score were independent prognostic factors for patients with GC.Meanwhile,patients in the low-risk group had higher tumor mutation burden and immunophenotype,which can be used to predict the immune checkpoint inhibitor response.Both cytotoxic T lymphocyte antigen4+and programmed death 1+patients with lower risk scores were more sensitive to immunotherapy.CONCLUSION In this study a new prognostic model consisting of 10 DEIRGs was constructed based on the TME.By providing risk factor analysis and prognostic information,our risk model can provide new directions for immunotherapy in GC patients.

Immune-related gene characteristics:A new chapter in precision treatment of gastric cancer

Gastric cancer ranks as the sixth most prevalent cancer worldwide.In recent research within the realm of gastric cancer treatment,the identification and application of immune-related genetic features have emerged as groundbreaking advancements.The study by Ma et al,which developed a prognostic model based on 10 genes,categorizes patients into high and low-risk groups to predict their responsiveness to immune checkpoint inhibitor therapy.This research underscores the potential of immune-related genes as biomarkers for personalized treatment,offering insights into tumor mutation burden and immune phenotype scores.We advocate for further validation,understanding of biological mechanisms,and integration of diverse datasets to enhance the model's predictive accuracy and clinical application,marking a significant step towards personalized and precise treatment for gastric cancer.

Construction of an immune-related prognostic model to predict prognosis and immunotherapy in liver cancer patients with hepatitis B virus-infected

Background:Hepatocellular carcinoma(HCC)appears to be strongly associated with immune-related genes.However,immune-related genes are not well understood as a prognostic marker in HCC caused by the hepatitis B virus(HBV).The purpose of this study was to investigate the prognostic significance of immune-related genes in HBV-infected HCC.Methods:Gene expression data from 114 HBV-infected HCC and 50 normal tissues were integrated into The Cancer Genome Atlas.Differentially expressed immune-associated genes were analyzed to identify immune-associated differential genes associated with overall survival.Least Absolute Shrinkage and Selection Operator and multivariate Cox regressions were used to constructing immunoprognostic models.An independent prognostic factor analysis using multiple Cox regressions was also performed for HBV-infected HCCs.Immunocorrelation analysis markers and immune cell infiltration were also investigated.Results:We found 113 differentially expressed immune-associated genes.Immune-related differential genes were significantly correlated with the overall survival of HCC patients.We constructed an immune-based prognostic model using multivariate Cox regression analysis including seven immune-related genes.According to further analysis,immune-related prognostic factors may serve as independent prognostic indicators in the clinical setting.There is also evidence that the 7-gene prognostic model reflects the tumor immune microenvironment as a result of the risk score model and immune cell infiltration.Conclusions:As a result of our study,we screened immune-related genes for prognosis in HBV-infected HCC and developed a novel immune-based prognostic model.The research not only provides new prognostic biomarkers but also offers insight into the tumor immune microenvironment and lays the theoretical groundwork for immunotherapy.

Screening of Postpartum Depression Diagnostic Markers Based on Immune-Related Genes and Immune Infiltration

Background:Postpartum depression(PPD)is a mild to severe non-psychotic depressive episode,one of the main factors leading to pregnancy-related morbidity and mortality,and a mental disorder that has not been fully diagnosed and treated.Compared with women without polycystic ovary syndrome,women with polycystic ovary syndrome are more likely to have a variety of pregnancy complications,including PPD.However,there is currently limited research on whether polycystic ovary syndrome is related to anxiety and depression during pregnancy,and whether this increases the risk of postpartum depression in women.Study design:The GSE10558 data set gene expression profile matrix was used for PPD expression profiles from Gene Expression Synthesis(GEO).The differentially expressed genes were selected and analyzed.Perform gene ontology(GO)enrichment and gene set variation analysis(GSVA)for annotation,visualization,and integrated discovery.At the same time,CIBERSORT and ESTIMATE were used to analyze the immune infiltration situation of the GSE10558 expression profile matrix,including the immune infiltration pattern of ovarian samples,and construct the immune cell infiltration(ICI)score.Then we screened the differentially expressed genes(DEGs)clustered with three groups of immune subtypes,and constructed a protein-protein interaction(PPI)and mRNA-miRNA-TF molecular interaction network.And further predicted the drug target of the hub gene and the target of small molecule compounds,and constructed a network.Based on the intersection of the phenotypic gene set,the pivot gene was identified.Finally,evaluate the expression differences of Hub genes between the data set groups,and generate receiver operating characteristic(ROC)curves to verify the diagnostic value of differentially expressed genes(DEG).Finally,genes with high area under the curve(AUC)values are validated.Results:We analyzed 222 DEGs with statistically significant differences in the GSE10558 data set by bioinformatics methods,of which 18 DEGs have significant differences.GO analysis showed that most of the 18 significantly differentially expressed genes were rich in receptor ligand activity and cytokine receptor binding.It is worth noting that these genes are also enriched in functional areas related to immune inflammatory response and immune cell regulation.The GSVA package was used for GSVA analysis,and the results showed that it was significantly enriched in growth factor binding and other aspects.And according to the ssGSEA analysis to obtain immune clustering groupings,the DEGs found in the high,medium,and low immune score groups are mainly enriched in immune inflammatory response and immune cell regulation through GO analysis.CIBERSORT analysis found that there are significant differences in memory B cells of 22 types of immune cells in ovarian samples.By mining the phenotypic gene set,the DEGs that are significantly related to PPD are intersected respectively,and four overlapping genes APOA1,PLN,PRKCZ,and TRPV2 are obtained as the most important pivot genes.We also use box plots to show the expression differences between tissue samples.The results show that there are significant differences in expression of these genes between groups,which may serve as new potential targets for the diagnosis and treatment of PPD.Subsequently,the ROC curve analysis of the four APOA1,PLN,PRKCZ,and TRPV2 that are significantly related to PPD showed significant prediction accuracy,and all AUCs were above 0.9,indicating that these new biomarkers can be further developed in PPD Research.Conclusion:The molecular markers APOA1,PLN,PRKCZ and TRPV2,which are closely related to immune cell function,can efficiently identify PPD.A diagnostic prediction model composed of these four immune function-related genes can distinguish PPD patients with different immune status.This discovery contributes to a more comprehensive understanding of the molecular mechanisms driving the occurrence and development of PPD,which is critical for improving the diagnosis,prognosis and treatment of this disease.

Identification of an immune-related gene-based signature to predict prognosis of patients with gastric cancer

BACKGROUND Gastric cancer(GC)is the most commonly diagnosed malignancy worldwide.Increasing evidence suggests that it is necessary to further explore genetic and immunological characteristics of GC.AIM To construct an immune-related gene(IRG)signature for accurately predicting the prognosis of patients with GC.METHODS Differentially expressed genes(DEGs)between 375 gastric cancer tissues and 32 normal adjacent tissues were obtained from The Cancer Genome Atlas(TCGA)GDC data portal.Then,differentially expressed IRGs from the ImmPort database were identified for GC.Cox univariate survival analysis was used to screen survival-related IRGs.Differentially expressed survival-related IRGs were considered as hub IRGs.Genetic mutations of hub IRGs were analyzed.Then,hub IRGs were selected to conduct a prognostic signature.Receiver operating characteristic(ROC)curve analysis was used to evaluate the prognostic performance of the signature.The correlation of the signature with clinical features and tumor-infiltrating immune cells was analyzed.RESULTS Among all DEGs,70 hub IRGs were obtained for GC.The deletions and amplifications were the two most common types of genetic mutations of hub IRGs.A prognostic signature was identified,consisting of ten hub IRGs(including S100A12,DEFB126,KAL1,APOH,CGB5,GRP,GLP2R,LGR6,PTGER3,and CTLA4).This prognostic signature could accurately distinguish patients into highand low-risk groups,and overall survival analysis showed that high risk patients had shortened survival time than low risk patients(P<0.0001).The area under curve of the ROC of the signature was 0.761,suggesting that the prognostic signature had a high sensitivity and accuracy.Multivariate regression analysis demonstrated that the prognostic signature could become an independent prognostic predictor for GC after adjustment for other clinical features.Furthermore,we found that the prognostic signature was significantly correlated with macrophage infiltration.CONCLUSION Our study proposed an immune-related prognostic signature for GC,which could help develop treatment strategies for patients with GC in the future.

Integrated bioinformatics analysis identifies immune-related epithelial-mesenchymal transition prognostic biomarkers and immune infiltrates in patients with lung adenocarcinoma

Background:Lung cancer,particularly lung adenocarcinoma(LUAD),is highly lethal.Understanding the critical interaction between epithelial-mesenchymal transition(EMT)and the immune status of patients is imperative for clinical assessment.Methods:We conducted bioinformatics analysis to identify potential immune-related EMT(iEMT)prognostic genes and explored the immune status in LUAD.Using data from The CancerGenome Atlas andGSE68465,differentially expressed genes,were identified,and a risk modelwas constructed.Cluster analysis was conducted using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways.Results:Our findings revealed 69 differentially expressed iEMT genes,with risk values demonstrating independent prognostic significance for both The Cancer Genome Atlas and GSE68465 samples.The risk value was positively correlated with tumor stage.Immune cell infiltration analysis showed a significant decrease in resting dendritic cells and an increase in CD4 memory T cells in high-risk groups with poor survival prognoses.The immunotherapy analysis revealed weak immunotherapeutic effects in the high-risk group.Conclusions:This study provides insights into potential aberrant differential iEMT genes and risk models and explores immune landscapes that inform personalized immunotherapy in patients with LUAD.

Weighted gene co-expression network analysis identifies a novel immune-related gene signature and nomogram to predict the survival and immune infiltration status of breast cancer

Breast cancer is one of the most common cancers in the world and seriously threatens the health of women worldwide.Prognostic models based on immune-related genes help to improve the prognosis prediction and clinical treatment of breast cancer patients.In the study,we used weighted gene co-expression network analysis to construct a co-expression network to screen out highly prognostic immune-related genes.Subsequently,the prognostic immunerelated gene signature was successfully constructed from highly immune-related genes through COX regression and LASSO COX analysis.Survival analysis and time receiver operating characteristic curves indicate that the prognostic signature has strong predictive performance.And we developed a nomogram by combing the risk score with multiple clinical characteristics.CIBERSORT and TIMER algorithms confirmed that there are significant differences in tumorinfiltrating immune cells in different risk groups.In addition,gene set enrichment analysis shows 6 pathways that differ between high-and low-risk group.The immune-related gene signature effectively predicts the survival and immune infiltration of breast cancer patients and is expected to provide more effective immunotherapy targets for the prognosis prediction of breast cancer.

Construction and Analysis of an Immune-Related Gene Prognostic Index for Bladder Cancer

Objective: To construct an Immune-Related Gene Prognostic Index (IRGPI) for bladder cancer using a bioanalytical approach to analyze its molecular and immunological characteristics, as well as to assess the benefit of Immune Checkpoint Inhibitor (ICI) therapy in the IRGPI-defined bladder cancer subgroup. Methods: Twenty-nine immune-related pivotal genes were identified by Weighted Gene Co-expression Network Analysis (WGCNA) based on The Cancer Genome Atlas (TCGA) bladder cancer immune dataset (n = 433). Six genes were identified using a multifactorial Cox regression approach to construct the IRGPI and validated against the Gene Expression Omnibus (GEO) dataset (n = 256). Then, molecular and immunological features in the subgroups defined by IRGPI were synthesized by GSEA, Kaplan-Meier survival curves, and other methods, and the benefit of ICI treatment was assessed. Results: IRGPI was constructed based on six genes including AHNAK, ILK, OGN, PDGFD, PPARGC1B, and JAM3. Patients with low IRGPI had better Overall Survival (OS) than those with high IRGPI, which was confirmed in the validation cohort of GEO. Pooled analysis showed that the low IRGPI subgroup was associated with higher infiltration of CD8 T cells, activated memory CD4 T cells, and could benefit from ICI treatment. Meanwhile, high IRGPI subgroups were associated with higher resting memory CD4T cells, M0 macrophages, and M2 macrophage content, immunosuppression, and benefited less from ICI treatment. Conclusion: IRGPI is a novel biomarker with better efficacy in differentiating the prognosis of bladder cancer, molecular and immune features, and evaluation of ICI therapy for individualized treatment of bladder cancer.

桔梗总皂苷通过抑制IRG-1对呼吸道合胞病毒肺炎小鼠治疗作用的研究

目的:探讨桔梗总皂苷通过抑制免疫反应基因1 (IRG-1)对呼吸道合胞病毒(RSV)肺炎小鼠的治疗作用。方法:将60只小鼠分为对照组、模型组、利巴韦林组和桔梗总皂苷低、中、高剂量组,每组10只。除对照组外,其余各组制备RSV肺炎小鼠模型。模型制备后第2天利巴韦林组按0.150 g/(kg·d)的剂量给予利巴韦林灌胃,桔梗总皂苷低、中、高剂量组依次按照0.014 g/(kg·d)、0.028 g/(kg·d)、0.056 g/(kg·d)的剂量给予桔梗总皂苷灌胃,对照组、模型组给予等体积生理盐水。连续给药5天,计算小鼠肺指数,进行病理学检查,检测支气管肺泡灌洗液中白细胞介素-6 (IL-6)、白细胞介素-1β(IL-1β)、活性氧(ROS)、肿瘤坏死因子-α(TNF-α)及血清中IRG-1 mRNA的含量。结果:对照组小鼠肺组织未见明显异常;模型组小鼠肺组织见肺泡壁增厚,支气管和细支气管周围有大量淋巴细胞浸润,腔内有大量脱落炎细胞及渗出物,存在肺实变表现;利巴韦林组小鼠肺组织病理改变与对照组接近;桔梗总皂苷各剂量组小鼠肺泡壁部分增厚,肺泡内炎症细胞渗出较模型组轻,呈剂量依赖性。与对照组比较,模型组小鼠肺指数、IL-6、IL-1β、TNF-α、ROS含量及血清中IRG-1 mRNA相对表达量均显著增高(P<0.05)。与模型组比较,利巴韦林组和桔梗总皂苷低、中、高剂量组小鼠肺指数、IL-6、IL-1β、TNF-α、ROS含量及血清中IRG-1 mRNA相对表达量均显著降低(P<0.05)。与利巴韦林组比较,桔梗总皂苷各剂量组小鼠肺指数、IL-6、IL-1β、TNF-α、ROS含量及血清中IRG-1 mRNA相对表达量均增高,除桔梗总皂苷高剂量组肺指数及TNF-α外,差异均有统讨学意义(P<0.05)。结论:桔梗总皂苷能改善RSV感染所致的小鼠肺炎炎症反应,其机制可能与抑制IRG-1的表达有关。

Molecular Cloning and Expression of PoIR2,a Novel Gene Involved in Immune Response in Japanese Flounder (Paralichthys olivaceus)

A novel immune-related gene was expressed in Japanese flounder （Paralichthys olivaceus） injected with Vibrio anguillarum. The complete cDNA contained a 169 bp 5＇UTR, a 336 bp open reading frame （ORF） encoding 111 amino acids and a 556bp 3＇UTR. Six exons and five introns were identified in the PoIR2 gene. Blastp similarity comparison showed its encoding protein had 50% similarity to Danio rerio neuromedin S （NMS）, but further alignment indicated they did not have NMS C-terminal conservational signature domain. So it was not defined as an NMS homologue. Protein structure analysis indicated it had a 26aa sig- nal peptide and was a secretory pathway protein. RT-PCR demonstrated that the expression of PoIR2 was quickly induced and drastically increased in liver, kidney, spleen, gills, intestine, heart, and skeletal muscle after infected with V. anguillarurn. These results indicated that the PolR2 might play some important role in Japanese flounder immune response system. This gene was named PolR2 （P.olivaceus immune-related gene 2, GenBank accession number： EU224372）. The mature PoIR2 peptide was expressed in BL21 （DE3） pLysS using pET-32a（＋） vector and a great part of the recombinant mature peptide existed as soluble type.

急性髓性白血病预后免疫相关基因的生物信息学分析

急性髓性白血病(acute myeloid leukemia,AML)是一种造血干细胞的恶性克隆性疾病,复发率高,预后差,临床缺乏准确而稳健的预后标志物。本研究旨在构建一个免疫相关基因(immune-related genes,IRGs)的拟合模型来预测AML患者的预后。首先,从TCGA中下载AML样本的基因表达谱数据和临床相关信息矩阵,从Imm Port数据库获取IRGs列表,利用R语言提取并筛选IRGs的表达矩阵;然后,对IRGs进行单变量Cox回归生存分析,筛选出预后IRGs;随后,对预后IRGs进行GO和KEGG富集分析、PPI网络构建以及关键IRGs的筛选;最后,构建AML预后的最优IRGs拟合。经过筛选共得到302个预后IRGs,这些预后IRGs涉及细胞趋化反应、白细胞黏附以及细胞因子-细胞因子受体相互作用和JAK-STAT等信号通路。最重要的是,文中通过迭代Lasso的算法构建了由9个IRGs组成的预后风险模型。该模型对AML预后的高低风险具有很高的诊断效能(AUC=0.91)。总之,本文开发了一种最优IRGs拟合模型,其能准确预测AML患者的预后,该模型可能有助于指导临床医生对高风险AML患者进行个性化管理。

Identification and Validation of an Immune-related Prognostic Signature for Hepatocellular Carcinoma

Background and Aims:The immune system plays vital roles in hepatocellular carcinoma(HCC)initiation and progression.The present study aimed to construct an immune-gene related prognostic signature(IRPS)for predicting the prognosis of HCC patients.Methods:Gene expression data were retrieved from The Cancer Genome Atlas database.The IRPS was established via least absolute shrinkage and selection operator(LASSO)and multivariate Cox regression analysis.The prognostic values of the IRPS were further validated using the International Cancer Genome Consortium(ICGC)dataset.Results:A total of 62 genes were identified as candidate immune-related prognostic genes.According to the results of Lasso and multivariate Cox regression analysis,we established an IRPS and confirmed its stability and reliability in the ICGC dataset.The IRPS was significantly associated with advanced clinicopathological characteristics.Both Cox regression analyses revealed that the IRPS could be independent risk factors influencing prognosis of HCC patients.The relationships between the IRPS and infiltration of immune cells demonstrated that the IRPS was associated with immune cell infiltration.Furthermore,a nomogram was constructed to estimate the survival probability of HCC patients.Conclusions:The IRPS was effective for predicting prognosis of HCC patients,which might serve as novel prognostic and therapeutic biomarkers for HCC.

Survival-associated alternative splicing events interact with the immune microenvironment in stomach adenocarcinoma

BACKGROUND Alternative splicing(AS)increases the diversity of mRNA during transcription;it might play a role in alteration of the immune microenvironment,which could influence the development of immunotherapeutic strategies against cancer.AIM To obtain the transcriptomic and clinical features and AS events in stomach adenocarcinoma(STAD)from the database.The overall survival data associated with AS events were used to construct a signature prognostic model for STAD.METHODS Differentially expressed immune-related genes were identified between subtypes on the basis of the prognostic model.In STAD,2042 overall-survival-related AS events were significantly enriched in various pathways and influenced several cellular functions.Furthermore,the network of splicing factors and overallsurvival-associated AS events indicated potential regulatory mechanisms underlying the AS events in STAD.RESULTS An eleven-AS-signature prognostic model(CD44|14986|ES,PPHLN1|21214|AT,RASSF4|11351|ES,KIAA1147|82046|AP,PPP2R5D|76200|ES,LOH12CR1|20507|ES,CDKN3|27569|AP,UBA52|48486|AD,CADPS|65499|AT,SRSF7|53276|RI,and WEE1|14328|AP)was constructed and significantly related to STAD overall survival,immune cells,and cancer-related pathways.The differentially expressed immune-related genes between the high-and low-risk score groups were significantly enriched in cancer-related pathways.CONCLUSION This study provided an AS-related prognostic model,potential mechanisms for AS,and alterations in the immune microenvironment(immune cells,genes,and pathways)for future research in STAD.

布鲁菌感染小鼠RAW264.7细胞对IRG1基因的表达调控作用

针对小鼠RAW264.7细胞IRG1基因设计4个RNA干扰靶位,筛选出最佳干扰序列构建shRNA慢病毒载体质粒并包装获得慢病毒颗粒,进而经嘌呤霉素筛选获得稳转细胞系,实现IRG1基因在RAW264.7细胞基因表达的沉默。并通过布鲁菌16M株及M5株感染基因沉默细胞对IRG1基因在布鲁菌感染中的作用进行研究。结果表明,慢病毒介导的shRNA高效、稳定地沉默了IRG1基因的表达,布鲁菌侵染RAW264.7细胞后IRG1基因表达上调。本试验为IRG1基因及相关调控基因抗布鲁菌病作用研究奠定了基础。

Host gut-derived Bacillus probiotics supplementation improves growth performance, serum and liver immunity, gut health, and resistive capacity against Vibrio harveyi infection in hybrid grouper(♀Epinephelus fuscoguttatus × ♂Epinephelus lanceolatus)

Several reports have revealed the vital role that probiotics play in fish growth and health.However,few works are available for host gut-derived probiotics on the growth,immunity,and gut microbiota of fish,especially in hybrid grouper (♀Epinephelus fuscoguttatus×♂Epinephelus lanceolatus) due to their isolation difficulty and functional verification.This study aimed at assessing 3 host gut-derived Bacillus species?effects on the growth,immune and antioxidant-biochemical responses,haematological parameters,intestinal morphology,immune-related gene expression,gut microbiota,and disease resistance against Vibrio harveyi in hybrid grouper.A total of 480 hybrid grouper (initial weight=9.03±0.02 g) were randomly allotted into 4 groups,namely,the group fed a basal diet without probiotic inclusion (control,B0),the group fed the basal diet with Bacillus velezensis GPSAK4 (BV),the group fed the basal diet with Bacillus subtilis GPSAK9 (BS),and the group fed the basal diet with Bacillus tequilensis GPSAK2 (BT) strains at 1.0×10^(9)CFU/g.After a 6-week feeding trial,the results revealed significant improvements (P<0.05) in the growth performance,whole fish-body proximate composition,blood haematological parameters,serum,liver,and intestinal biochemical indexes,intestinal morphology,and protection against V.harveyi pathogen in the probiotic-treated groups compared with the untreated.Additionally,the expressions of intestinal tight junction genes (occludin and ZO1),pro-and anti-inflammatory genes,including IL1β,IL6,IL8,TNFa,MyD88,IL10,and TGFβ,were upregulated (P<0.05) after Bacillus species administration.Host gut-derived Bacillus supplementation shaped the gut microbiota by significantly increasing (P<0.05) the relative abundance of Proteobacteria,Bacteroidetes,Actinobacteria (except the BS group),Acidobacteria(except the BT group),Cyanobacteria (except the BV and BT groups),and Verrucomicrobia phyla,as well as known beneficial genera (Romboutsia,Turicibacter,Epulopiscium,Clostridium_sensu_stricto 1 and 13,Lactobacillus,and Bacillus),but significantly decreased (P<0.05) the abundance of Firmicutes,Chloroflexi,and Fusobacteria phyla,and purported pathogenic genera (Staphylococcus and Photobacterium) compared with the control group.Collectively,the results suggest that B.velezensis GPSAK4,B.subtilis GPSAK9(especially this strain),B.tequilensis GPSAK2 dietary supplementation at 1.0×10^(9)CFU/g has positive effects on the intestinal health of hybrid grouper via microbial composition modulation,thus enhancing the assimilation and absorption of nutrients to boost fish growth,immunity,and disease resistance.

FGF13 suppresses acute myeloid leukemia by regulating bone marrow niches

Fibroblast growth factor 13(FGF13)is aberrantly expressed in multiple cancer types,suggesting its essential role in tumorigenesis.Hence,we aimed to explore its definite role in the development of acute myeloid leukemia(AML)and emphasize its associations with bone marrow niches.Results showed that FGF13 was lowly expressed in patients with AML and that its elevated expression was related to prolonged overall survival(OS).Univariate and multivariate Cox regression analyses identified FGF13 as an independent prognostic factor.A prognostic nomogram integrating FGF13 and clinicopathologic variables was constructed to predict 1-,3-,and 5-year OS.Gene mutation and functional analyses indicated that FGF13 was not associated with AML driver mutations but was related to bone marrow niches.As for immunity,FGF13 was remarkably associated with T cell count,immune checkpoint genes,and cytokines.In addition,FGF13 overexpression substantially inhibited the growth and significantly induced the early apoptosis of AML cells.The xenograft study indicated that FGF13 overexpression prolonged the survival of recipient mice.Overall,FGF13 could serve as an independent prognostic factor for AML,and it was closely related to the bone marrow microenvironment.

A novel immunogenomic signature to predict prognosis and reveal immune infiltration characteristics in pancreatic ductal adenocarcinoma

Background:The immune response in the tumor microenvironment(TME)plays a crucial role in cancer progression and recurrence.We aimed to develop an immune-related gene(IRG)signature to improve prognostic predictive power and reveal the immune infiltration characteristics of pancreatic ductal adenocarcinoma(PDAC).Methods:The Cancer Genome Atlas(TCGA)PDAC was used to construct a prognostic model as a training cohort.The International Cancer Genome Consortium(ICGC)and the Gene Expression Omnibus(GEO)databases were set as validation datasets.Prognostic genes were screened by using univariate Cox regression.Then,a novel optimal prognostic model was developed by using least absolute shrinkage and selection operator(LASSO)Cox regression.Cell type identification by estimating the relative subsets of RNA transcripts(CIBERSORT)and estimation of stromal and immune cells in malignant tumors using expression data(ESTIMATE)algorithms were used to characterize tumor immune infiltrating patterns.The tumor immune dysfunction and exclusion(TIDE)algorithm was used to predict immunotherapy responsiveness.Results:A prognostic signature based on five IRGs(MET,ERAP2,IL20RB,EREG,and SHC2)was constructed in TCGA-PDAC and comprehensively validated in ICGC and GEO cohorts.Multivariate Cox regression analysis demonstrated that this signature had an independent prognostic value.The area under the curve(AUC)values of the receiver operating characteristic(ROC)curve at 1,3,and 5 years of survival were 0.724,0.702,and 0.776,respectively.We further demonstrated that our signature has better prognostic performance than recently published ones and is superior to traditional clinical factors such as grade and tumor node metastasis classification(TNM)stage in predicting survival.Moreover,we found higher abundance of CD8+T cells and lower M2-like macrophages in the low-risk group of TCGA-PDAC,and predicted a higher proportion of immunotherapeutic responders in the low-risk group.Conclusions:We constructed an optimal prognostic model which had independent prognostic value and was comprehensively validated in external PDAC databases.Additionally,this five-genes signature could predict immune infiltration characteristics.Moreover,the signature helped stratify PDAC patients who might be more responsive to immunotherapy.