Immune-related Processes Research Articles

Identification of Two Novel Immune Subtypes Characterized by Distinct Prognosis and Tumor Microenvironment in Osteosarcoma.

Osteosarcoma is a kind of primary malignant tumor of bone. In recent years, its therapeutic effect and prognostic survival are dissatisfactory. The tumor immune microenvironment (TIME) reflects immune status of patients, but it is little known in osteosarcoma. Therefore, this study attempts to conduct a comprehensive analysis to explore TIME of osteosarcoma and identify TIME-related subtypes for clinical management and treatment. We successfully established two novel tumor immune infiltration clusters (TIIC) which are characterized by difference of microenvironment and immune-related biological processes. High tumor immune infiltration cluster (H-TIIC) subtypes with higher immune infiltration score shows a better overall survival. Further, the two immune subtypes are shown to differ in immunotherapy and chemotherapy. The results would be helpful for clinical decision in osteosarcoma.

Identification of potential drug targets for vascular dementia and carotid plaques by analyzing underlying molecular signatures shared by them.

BackgroundVascular dementia (VaD) and carotid atherosclerotic plaques are common in the elderly population, conferring a heavy burden on families and society. Accumulating evidence indicates carotid atherosclerotic plaques to be a risk factor for VaD. However, the underlying mechanisms for this association are mainly unknown.Materials and methodsWe analyzed temporal cortex gene expression data of the GSE122063 dataset and gene expression data of the GSE163154 dataset to identify commonly differentially expressed genes (DEGs). Then we performed functional enrichment analysis, immune cell infiltration and evaluation, correlation analysis between differentially expressed immune-related genes (DEIRGs) and immune cells, receiver operating characteristic (ROC) analysis, and drug-gene analysis.ResultsWe identified 41 overlapped DEGs between the VaD and carotid atherosclerosis plaque datasets. Functional enrichment analyses revealed that these overlapped DEGs were mainly enriched in inflammatory and immune-related processes. Immunocyte infiltration and evaluation results showed that M0 macrophages, M2 macrophages, and T cells gamma delta had a dominant abundance in carotid atherosclerosis plaque samples, and M0 macrophages showed a significantly different infiltration percentage between the early and advanced stage plaques group. Resting CD4 memory T cells, M2 macrophages, and naive B cells were the top three highest infiltrating fractions in VaD. Furthermore, B cells and NK cells showed a different infiltration percentage between VaD and matched controls. We identified 12 DEIRGs, and the result of correlation analysis revealed that these DEIRGs were closely related to differentially expressed immune cells. We identified five key DEIRGs based on ROC analysis. The drug-gene interaction analysis showed that four drugs (avacopan, CCX354, BMS-817399, and ASK-8007) could be potential drugs for VaD and carotid atherosclerotic plaques treatment.ConclusionCollectively, these findings indicated that inflammatory and immune-related processes be a crucial common pathophysiological mechanism shared by VaD and carotid plaques. This study might provide new insights into common molecular mechanisms between VaD and carotid plaques and potential targets for the treatment.

Integrated Single-Cell and RNA Sequencing Analysis Identifies Key Immune Cell and Dendritic Cells Associated Genes Participated in Myocarditis

Myocarditis is a complex disease characterized by myocardial inflammatory cell infiltration. The purpose of our study was to investigate the gene and single-cell signature to explore the involvement of immune cells in myocarditis. Gene expressions merged from GSE35182 and GSE35182 datasets were subjected to differential expression gene (DEG) analysis and PPI network construction. The correlation analysis of DEGs with immune cell infiltration was performed. Single-cell RNA sequencing (scRNA-seq) was downloaded from GSE174458. A total of 58 DEGs were identified, including 51 DEGs upregulated and 7 DEGs downregulated in the myocarditis group compared with the control group. GO and KEGG enrichment analyses revealed that myocarditis triggered DEGs mainly involved in immune-related processes and pathways. PPI network analysis identified 20 central hub genes. Occurrence of myocarditis induced significant enrichment of conventional dendritic cell 2 (cDC2), plasmacytoid DC, and plasma cell in myocardial tissue. Mmp12, Gpnmb, and Atp6v0d2 expressions were positively correlated with cDC abundance, of which only Mmp1 and Gpnmb were shared with hub gene list. A total of 20972 cells in scRNA-seq yielded 26 cell clusters and annotated 9 cell types, including fibroblasts, neutrophils, stromal cells, monocytes, basophils, B cells, natural killer T cells, innate lymphoid cells, and T cells, and only proportion of natural killer T cells and monocytes were higher in the myocarditis than in control. Monocytes annotated 3 subclusters including DC, macrophage, and monocytes. Hub genes of Ctss, Mpeg1, Cybb, H2-Ab1, Ly86, CD74, and Lgals3 were highly expressed in monocytes cluster. Among DC-correlated DEGs, Mmp12 was mainly expressed in monocyte cluster, and Gpnmb was mainly expressed in fibroblast cluster, whereas Atp6v0d2 expression has a weaker signal and weaker cell preference. In conclusion, DC infiltration and its associated pivotal genes may be responsible for progression of myocarditis. Our study expands and provides novel information on the immune cell engagement of myocarditis.

Molecular Changes in Chronic Myeloid Leukemia During Tyrosine Kinase Inhibitors Treatment. Focus on Immunological Pathways.

IntroductionThe aim of our research was to investigate changes in the molecular background of the immune response in the chronic phase (CP) of chronic myeloid leukaemia (CML) during treatment with tyrosine kinase inhibitors (TKIs).MethodsGlobal gene and miRNA expression profiles were assessed using genome-wide RNA and miRNA microarray technology in bone marrow mononuclear cells. Fifty-one patients were recruited, and bone marrow samples were taken at diagnosis before treatment with TKIs and after 3, 6, and 12 months of treatment with TKIs. The largest number of upregulated genes was observed when the 0-month group (time of diagnosis) was compared to the 3-month group; 1774 genes were significantly upregulated, and 390 genes were significantly downregulated.DiscussionUpregulated biological processes according to gene ontology (GO) classification involved basic cellular processes such as cell division, cell cycle, cell–cell adhesion, protein transport, mitotic nuclear division, apoptosis, and DNA replication. Differentially expressed miRNAs were annotated using GO classification to several immunity-related processes, including the T cell receptor signalling pathway, T cell costimulation, immune response, and inflammatory response. TKI therapy exerts a significant impact on cellular cycle processes and T-cell activation, which was proven at the molecular level.

Metabolic-related gene pairs signature analysis identifies ABCA1 expression levels on tumor-associated macrophages as a prognostic biomarker in primary IDHWT glioblastoma

BackgroundAlthough isocitrate dehydrogenase (IDH) mutation serves as a prognostic signature for routine clinical management of glioma, nearly 90% of glioblastomas (GBM) patients have a wild-type IDH genotype (IDHWT) and lack reliable signatures to identify distinct entities.MethodsTo develop a robust prognostic signature for IDHWT GBM patients, we retrospectively analyzed 4 public datasets of 377 primary frozen tumor tissue transcriptome profiling and clinical follow-up data. Samples were divided into a training dataset (204 samples) and a validation (173 samples) dataset. A prognostic signature consisting of 21 metabolism-related gene pairs (MRGPs) was developed based on the relative ranking of single-sample gene expression levels. GSEA and immune subtype analyses were performed to reveal differences in biological processes between MRGP risk groups. The single-cell RNA-seq dataset was used to examine the expression distribution of each MRG constituting the signature in tumor tissue subsets. Finally, the association of MRGs with tumor progression was biologically validated in orthotopic GBM models.ResultsThe metabolic signature remained an independent prognostic factor (hazard ratio, 5.71 [3.542-9.218], P < 0.001) for stratifying patients into high- and low-risk levels in terms of overall survival across subgroups with MGMTp methylation statuses, expression subtypes, and chemo/ratio therapies. Immune-related biological processes were significantly different between MRGP risk groups. Compared with the low-risk group, the high-risk group was significantly enriched in humoral immune responses and phagocytosis processes, and had more monocyte infiltration and less activated DC, NK, and γδ T cell infiltration. scRNA-seq dataset analysis identified that the expression levels of 5 MRGs (ABCA1, HMOX1, MTHFD2, PIM1, and PTPRE) in TAMs increased with metabolic risk. With tumor progression, the expression level of ABCA1 in TAMs was positively correlated with the population of TAMs in tumor tissue. Downregulation of ABCA1 levels can promote TAM polarization towards an inflammatory phenotype and control tumor growth.ConclusionsThe metabolic signature is expected to be used in the individualized management of primary IDHWT GBM patients.

Crosstalk of necroptosis and pyroptosis defines tumor microenvironment characterization and predicts prognosis in clear cell renal carcinoma.

Pyroptosis and necroptosis are two recently identified forms of immunogenic cell death in the tumor microenvironment (TME), indicating a crucial involvement in tumor metastasis. However, the characteristics of necroptosis and pyroptosis that define tumor microenvironment and prognosis in ccRCC patients remain unknown. We systematically investigated the transcriptional variation and expression patterns of Necroptosis and Pyroptosis related genes (NPRGs). After screening the necroptosis-pyroptosis clusters, the potential functional annotation for clusters was explored by GSVA enrichment analysis. The Necroptosis-Pyroptosis Genes (NPG) scores were used for the prognosis model construction and validation. Then, the correlations of NPG score with clinical features, cancer stem cell (CSC) index, tumor mutation burden (TMB), TME, and Immune Checkpoint Genes (ICGs) were also individually explored to evaluate the prognosis predictive values in ccRCC. Microarray screenings identified 27 upregulated and 1 downregulated NPRGs. Ten overall survival associated NPRGs were filtered to construct the NPG prognostic model indicating a better prognostic signature for ccRCC patients with lower NPG scores (P< 0.001), which was verified using the external cohort. Univariate and multivariate analyses along with Kaplan-Meier survival analysis demonstrated that NPG score prognostic model could be applied as an independent prognostic factor, and AUC values of nomogram from 1- to 5- year overall survival with good agreement in calibration plots suggested that the proposed prognostic signature possessed good predictive capabilities in ccRCC. A high-/sNPG score is proven to be connected with tumor growth and immune-related biological processes, according to enriched GO, KEGG, and GSEA analyses. Comparing patients with a high-NPG score to those with a low-NPG score revealed significant differences in clinical characteristics, growth and recurrence of malignancies (CSC index), TME cell infiltration, and immunotherapeutic response (P< 0.005), potentially making the NPG score multifunctional in the clinical therapeutic setting. Furthermore, AIM2, CASP4, GSDMB, NOD2, and RBCK1 were also found to be highly expressed in ccRCC cell lines and tumor tissues, and GASP4 and GSDMB promote ccRCC cells’ proliferation, migration, and invasion. This study firstly suggests that targeting the NPG score feature for TME characterization may lend novel insights into its clinical applications in the prognostic prediction of ccRCC.

Blood-based protein biomarkers for the diagnosis of acute stroke: A discovery-based SWATH-MS proteomic approach.

Background and purposesRecent developments in high-throughput proteomic approach have shown the potential to discover biomarkers for diagnosing acute stroke and to elucidate the pathomechanisms specific to different stroke subtypes. We aimed to determine blood-based protein biomarkers to diagnose total stroke (IS+ICH) from healthy controls, ischemic stroke (IS) from healthy controls, and intracerebral hemorrhage (ICH) from healthy control subjects within 24 h using a discovery-based SWATH-MS proteomic approach.MethodsIn this discovery phase study, serum samples were collected within 24 h from acute stroke (IS & ICH) patients and healthy controls and were subjected to SWATH-MS-based untargeted proteomics. For protein identification, a high-pH fractionated peptide library for human serum proteins (obtained from SCIEX) comprising of 465 proteins was used. Significantly differentially expressed (SDE) proteins were selected using the following criteria: >1.5-fold change for upregulated, < 0.67 for downregulated, p-value < 0.05, and confirmed/tentative selection using Boruta random forest. Protein–protein interaction network analysis and the functional enrichment analysis were conducted using STRING 11 online tool, g:Profiler tool and Cytoscape 3.9.0 software. The statistical analyses were conducted in R version 3.6.2.ResultsOur study included 40 stroke cases (20 IS, 20 ICH) within 24 h and 40 age-, sex-, hypertension-, and diabetes-matched healthy controls. We quantified 375 proteins between the stroke cases and control groups through SWATH-MS analysis. We observed 31 SDE proteins between total stroke and controls, 16 SDE proteins between IS and controls, and 41 SDE proteins between ICH and controls within 24 h. Four proteins [ceruloplasmin, alpha-1-antitrypsin (SERPINA1), von Willebrand factor (vWF), and coagulation factor XIII B chain (F13B)] commonly differentiated total stroke, IS, and ICH from healthy control subjects. The most common significant pathways in stroke cases involved complement and coagulation cascades, platelet degranulation, immune-related processes, acute phase response, lipid-related processes, and pathways related to extracellular space and matrix.ConclusionOur discovery phase study identified potential protein biomarker candidates for the diagnosis of acute stroke and highlighted significant pathways associated with different stroke subtypes. These potential biomarker candidates warrant further validation in future studies with a large cohort of stroke patients to investigate their diagnostic performance.

PSVIII-B-19 The Effect of Dietary Fatty Acid Source on Gene Expression in the Rumen of Cattle

Abstract The objective of this experiment was to ascertain how different fatty acid sources impacted gene expression in the rumen epithelium. The study utilized rumen cannulated Holstein steers (n = 8) in a crossover design with two 16-d periods with a 20-d washout between periods. The diets contained (DM-basis) dry-rolled corn (57%), Sweet Bran (25%), chopped prairie hay (8%), vitamin-mineral supplement (5%), and 5% of either high oleic soybean oil (Oleic) or 5% commodity soybean oil diet containing predominantly linoleic acid (Linoleic). The diets were provided ad libitum and refusals were collected and subsampled. Water access was unrestricted. Rumen papillae biopsies were taken on d-16 of each period and flash-frozen using dry ice and alcohol. RNA was extracted and sequenced using short-read sequencing. Differential gene expression (DEG) was analyzed using edgeR. Data were analyzed within periods to determine differences between treatments. In period 1, there were 25 DEG (False Discovery Rate (FDR) &amp;lt; 0.05) with 13 downregulated and 12 upregulated in the Oleic treatment. In period 2, there were 35 DEG (FDR &amp;lt; 0.05) with 12 downregulated and 23 upregulated in the Oleic treatment. The gene LILRA6, which is involved in immune function, was differentially expressed in both periods. Many of the KEGG pathways enriched were related to fatty acid metabolism, chemokine signaling, cytokine-cytokine receptor interactions, and immune function-related pathways. Gene ontology enrichment analysis highlighted inflammatory response, chemokine-mediated signaling, and other immune-related processes. These data indicated that dietary fat composition may impact gut inflammatory and immune function.

Reclassifying TNM stage I/II colorectal cancer into two subgroups with different overall survival, tumor microenvironment, and response to immune checkpoint blockade treatment

Background: The traditional TNM staging system is often insufficient to differentiate the survival discrepancies of colorectal cancer (CRC) patients at TNM stage I/II. Our study aimed to reclassify stage I/II CRC patients into several subgroups with different prognoses and explore their suitable therapeutic methods. Methods: Single-cell RNA (scRNA) sequencing data, bulk RNA sequencing data, and clinicopathological information of CRC patients were enrolled from the TCGA and GEO databases. The tumor microenvironment of CRC tissues was accessed by the ESTIMATE algorithm. The prognostic genes were identified by Cox regression analysis. GO and KEGG analyses were conducted in the DAVID database. GSEA analysis was performed for annotation of the correlated gene sets. Results: We successfully reclassified stage I/II CRC patients into two subgroups and discovered that patients in cluster-2 underwent worse overall survival than those in cluster-1. GSEA analysis showed that immune-associated gene sets were positively enriched in cluster-2. Besides, the differentially expressed genes (DEGs) between cluster-1 and cluster-2 patients also participated in immune-related biological processes and signaling pathways. Moreover, we found that more immune cells infiltrated the microenvironment of cluster-2 patients compared to that of cluster-1 patients, such as Tregs and tumor-associated macrophages. ScRNA sequencing analysis uncovered that most of the enriched immune-associated signaling in cluster-2 patients was mainly attributed to these upregulated immune cells whose infiltration levels were also high in CRC tissues rather than in normal tissues. In addition, we demonstrated that the expression of immune checkpoint genes was significantly higher in cluster-2 patients compared to cluster-1 patients. ScRNA sequencing analysis revealed that the infiltrated CD8+T cells in CRC were naïve T cells and can be activated into effector T cells after immune checkpoint blockade (ICB) treatment. Conclusion: TNM stage I/II CRC patients can be divided into two subgroups, which have different overall survival rates, tumor microenvironment, and response to ICB therapy.

Signature of seven cuproptosis-related lncRNAs as a novel biomarker to predict prognosis and therapeutic response in cervical cancer.

Background: Given the high incidence and high mortality of cervical cancer (CC) among women in developing countries, identifying reliable biomarkers for the prediction of prognosis and therapeutic response is crucial. We constructed a prognostic signature of cuproptosis-related long non-coding RNAs (lncRNAs) as a reference for individualized clinical treatment. Methods: A total of seven cuproptosis-related lncRNAs closely related to the prognosis of patients with CC were identified and used to construct a prognostic signature via least absolute shrinkage and selection operator regression analysis in the training set. The predictive performance of the signature was evaluated by Kaplan–Meier (K-M) analysis, receiver operating characteristic (ROC) analysis, and univariate and multivariate Cox analyses. Functional enrichment analysis and single-sample gene set enrichment analysis were conducted to explore the potential mechanisms of the prognostic signature, and a lncRNA–microRNA–mRNA network was created to investigate the underlying regulatory relationships between lncRNAs and cuproptosis in CC. The associations between the prognostic signature and response to immunotherapy and targeted therapy were also assessed. Finally, the prognostic value of the signature was validated using the CC tissues with clinical information in my own center. Results: A prognostic signature was developed based on seven cuproptosis-related lncRNAs, including five protective factors (AL441992.1, LINC01305, AL354833.2, CNNM3-DT, and SCAT2) and two risk factors (AL354733.3 and AC009902.2). The ROC curves confirmed the superior predictive performance of the signature compared with conventional clinicopathological characteristics in CC. The ion transport-related molecular function and various immune-related biological processes differed significantly between the two risk groups according to functional enrichment analysis. Furthermore, we discovered that individuals in the high-risk group were more likely to respond to immunotherapy and targeted therapies including trametinib and cetuximab than those in the low-risk group. Finally, CC tissues with clinical data from my own center further verify the robustness of the seven-lncRNA risk signature. Conclusion: We generated a cuproptosis-related lncRNA risk signature that could be used to predict prognosis of CC patients. Moreover, the signature could be used to predict response to immunotherapy and chemotherapy and thus could assist clinicians in making personalized treatment plans for CC patients.

Identification and validation of key molecules associated with humoral immune modulation in Parkinson's disease based on bioinformatics.

ObjectiveParkinson’s disease (PD) is the most common neurodegenerative movement disorder and immune-mediated mechanism is considered to be crucial to pathogenesis. Here, we investigated the role of humoral immune regulatory molecules in the pathogenesis of PD.MethodsFirstly, we performed a series of bioinformatic analyses utilizing the expression profile of the peripheral blood mononuclear cell (PBMC) obtained from the GEO database (GSE100054, GSE49126, and GSE22491) to identify differentially expressed genes related to humoral immune regulatory mechanisms between PD and healthy controls. Subsequently, we verified the results using quantitative polymerase chain reaction (Q-PCR) and enzyme-linked immunosorbent assay (ELISA) in clinical blood specimen. Lastly, receiver operating characteristic (ROC) curve analysis was performed to determine the diagnostic effects of verified molecules.ResultsWe obtained 13 genes that were mainly associated with immune-related biological processes in PD using bioinformatic analysis. Then, we selected PPBP, PROS1, and LCN2 for further exploration. Fascinatingly, our experimental results don’t always coincide with the expression profile. PROS1 and LCN2 plasma levels were significantly higher in PD patients compared to controls (p < 0.01 and p < 0.0001). However, the PPBP plasma level and expression in the PBMC of PD patients was significantly decreased compared to controls (p < 0.01 and p < 0.01). We found that PPBP, PROS1, and LCN2 had an area under the curve (AUC) of 0.663 (95%CI: 0.551–0.776), 0.674 (95%CI: 0.569–0.780), and 0.885 (95%CI: 0.814–0.955). Furthermore, in the biological process analysis of gene ontology (GO), the three molecules were all involved in humoral immune response (GO:0006959).ConclusionsIn general, PPBP, PROS1, and LCN2 were identified and validated to be related to PD and PPBP, LCN2 may potentially be biomarkers or therapeutic targets for PD. Our findings also provide some new insights on the humoral immune modulation mechanisms in PD.

A novel cuproptosis-related prognostic lncRNA signature for predicting immune and drug therapy response in hepatocellular carcinoma.

Intratumoral copper levels are closely associated with immune escape from diverse cancers. Cuproptosis-related lncRNAs (CRLs), however, have an unclear relationship with hepatocellular carcinoma (HCC). Gene expression data from 51 normal tissues and 373 liver cancer tissues from the Cancer Genome Atlas (TCGA) database were collected and analyzed. To identify CRLs, we employed differentially expressed protein-coding genes (DE-PCGs)/lncRNAs (DE-lncRNAs) analysis, Kaplan–Meier (K-M) analysis, and univariate regression. By univariate and Lasso Cox regression analyses, we screened 10 prognosis-related lncRNAs. Subsequently, five CRLs were identified by multivariable Cox regression analysis to construct the prognosis model. This feature is an independent prognostic indicator to forecast overall survival. According to Gene Set Variation Analysis (GSVA) and Gene Ontology (GO), both immune-related biological processes (BPS) and pathways have CRL participation. In addition, we found that the characteristics of CRLs were associated with the expression of the tumor microenvironment (TME) and crucial immune checkpoints. CRLs could predict the clinical response to immunotherapy based on the studies of tumor immune dysfunction and rejection (TIDE) analysis. Additionally, it was verified that tumor mutational burden survival and prognosis were greatly different between high-risk and low-risk groups. Finally, we screened potential sensitive drugs for HCC. In conclusion, this study provides insight into the TME status in patients with HCC and lays a basis for immunotherapy and the selection of sensitive drugs.

Characteristic of Molecular Subtypes in Lung Squamous Cell Carcinoma Based on Autophagy-Related Genes and Tumor Microenvironment Infiltration.

Background Recently, a large number of studies have sought personalized treatment for lung squamous cell carcinoma (LUSC) by dividing patients into different molecular subtypes. Autophagy plays an important role in maintaining the tumor microenvironment and immune-related biological processes. However, the molecular subtypes mediated by autophagy in LUSC are not clear. Methods Based on 490 LUSC samples, we systematically analyzed the molecular subtype modification patterns mediated by autophagy-related genes. The ssGSEA and CIBERSORT algorithm were utilized to quantify the relative abundance of TME cell infiltration. Principal component analysis was used to construct autophagy prognostic score (APS) model. Results We identified three autophagy subtypes in LUSC, and their clinical outcomes and TME cell infiltration had significant heterogeneity. Cluster A was rich in immune cell infiltration. The enrichment of EMT stromal pathways and immune checkpoint molecules were significantly enhanced, which may lead to its immunosuppression. Cluster B was characterized by relative immunosuppression and relative stromal activation. Cluster C was activated in biological processes related to repair. Patients with high APS were significantly positively correlated with TME stromal activity and poor survival. Meanwhile, high APS showed an advantage in response to anti-PD1 and anti-CTLA4 immunotherapy. Conclusion This study explored the autophagy molecular subtypes in LUSC. We also discovered the heterogeneity of TME cell infiltration driven by autophagy-related genes. The established APS model is of great significance for evaluating the prognosis of LUSC patients, the infiltration of TME cells, and the effect of immunotherapy.

Genetic Diversity and Selection Signatures in Jianchang Black Goats Revealed by Whole-Genome Sequencing Data

Simple SummaryJianchang Black goats are one of the indigenous black goat breeds in China. They are known for their crude feed tolerance but have a low growth rate and small body size. However, little is known about the genetic composition of Jianchang Black goats. The purpose of this study was to systematically examine the genetic composition of this breed by conducting multiple population genetic analyses. Our results revealed that Jintang Black and Yunshang Black goats showed a close genetic relationship with a non-negligible amount of gene flows but were genetically distant from Jianchang Black goats. Jianchang Black goats generally showed moderate inbreeding levels. Importantly, we identified several putatively selected genes that may be potentially related to immunity, behavior, and reproduction in this breed. The current study provides valuable references and genomic resources for the future breeding of Jianchang Black goats.Understanding the genetic composition of indigenous goats is essential to promote the scientific conservation and sustainable utilization of these breeds. The Jianchang Black (JC) goat, a Chinese native breed, is solid black and exhibits crude feed tolerance, but is characterized by a low growth rate and small body size. Based on the whole-genome sequencing data for 30 JC, 41 Jintang Black (JT), and 40 Yunshang Black (YS) goats, and 21 Bezoar ibexes, here, we investigated the genetic composition of JC goats by conducting analyses of the population structure, runs of homozygosity (ROH), genomic inbreeding, and selection signature. Our results revealed that JT and YS showed a close genetic relationship with a non-negligible amount of gene flows but were genetically distant from JC, apart from Bezoars. An average of 2039 ROHs were present in the autosomal genome per individual. The ROH-based inbreeding estimates in JC goats generally showed moderate values ranging from 0.134 to 0.264, mainly due to rapid declines in the effective population size during recent generations. The annotated genes (e.g., IL2, IL7, and KIT) overlapping with ROH islands were significantly enriched in immune-related biological processes. Further, we found 61 genes (e.g., STIM1, MYO9A, and KHDRBS2) under positive selection in JC goats via three complementary approaches, which may underly genetic adaptations to local environmental conditions. Our findings provided references for the conservation and sustainable utilization of JC goats.

Long Noncoding RNA and mRNA Expression Profiles in Rats with LPS-induced Myocardial Dysfunction.

Sepsis is an uncontrolled systemic inflammatory response. Long noncoding RNAs (lncRNAs) are involved in the pathogenesis of sepsis. However, little is known about the roles of lncRNAs in sepsis-induced myocardial dysfunction. We aimed to determine the regulatory mechanism of lncRNAs in sepsis-induced myocardial dysfunction. In this study, we analysed the lncRNA and mRNA expression profiles using microarray analysis. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, protein-protein interaction network, and gene set enrichment analysis were used to evaluate the data. We also constructed coding and noncoding coexpression and competing endogenous RNA networks to investigate the mechanisms. In vivo lipopolysaccharide -induced sepsis rat model was established. A total of 387 lncRNAs and 1,952 mRNAs were identified as significantly changed in the left ventricle. Kyoto Encyclopedia of Genes and Genomes analysis of mRNAs showed that the upregulated genes were mainly enriched in the "complement and coagulation cascade pathway" and "immune-related biological processes" terms. Eight significantly changed lncRNAs detected by RT-qPCR may be responsible for these processes. A competing endogenous RNA network was generated, and the results indicated that eight lncRNAs were related to the "calcium ion binding" process. These results demonstrate that crosstalk between lncRNAs and mRNAs may play important roles in the development of sepsis-induced myocardial dysfunction.

Identification of SLAMF1 as an immune-related key gene associated with rheumatoid arthritis and verified in mice collagen-induced arthritis model.

BackgroundRheumatoid arthritis (RA) is the most common inflammatory arthropathy. Immune dysregulation was implicated in the pathogenesis of RA. Thus, the aim of the research was to determine the immune related biomarkers in RA.MethodsWe downloaded the gene expression data of RA in GSE89408 and GSE45291 from Gene Expression Omnibus public database (GEO). Differentially expressed genes (DEGs) were identified between RA and control groups. Infiltrating immune cells related genes were obtained by ssGSEA and weighted gene co-expression network analysis (WGCNA). We performed functional enrichment analysis of differentially expressed immunity-related genes (DEIRGs) by “clusterProfiler” R package, key genes screening by protein-protein interaction (PPI) network of DEIRGs. And mice collagen-induced arthritis (CIA) model was employed to verify these key genes.ResultsA total of 1,885 up-regulated and 1,899 down-regulated DEGs were identified in RA samples. The ssGSEA analysis showed that the infiltration of 25 cells was significantly different. 603 immune related genes were obtained by WGCNA, and 270 DEIRGs were obtained by taking the intersection of DEGs and immune related genes. Enrichment analyses indicated that DEIRGs were associated with immunity related biological processes. 4 candidate biomarkers (CCR7, KLRK1, TIGIT and SLAMF1) were identified from the PPI network of DEIRGs and literature research.In mice CIA model, the immunohistochemical stain showed SLAMF1 has a significantly high expression in diseased joints. And flow cytometry analysis shows the expression of SLAMF1 on CIA mice-derived CTL cells, Th, NK cells, NKT cells, classical dendritic cell (cDCs) and monocytes/macrophages was also significantly higher than corresponding immune cells from HC mice.ConclusionOur study identified SMLAF1 as a key biomarker in the development and progression of RA, which might provide new insight for exploring the pathogenesis of RA.

Quantification of Salivary Nitric Oxide in Patients with Fixed Orthodontic Treatment

Nitric oxide (NO) is considered a regulator of bone response to mechanical stress that mediates adaptive bone formation, the pathological effects of lipopolysaccharides (LPS), tumour necrosis factor (TNF), interleukin 1 (IL-1) and other cytokines; regulates leukocytes and epithelial cell adhesion; inhibits T cell proliferation; and enhances natural killer (NK) cell activity, as well as other immune-related processes. The aim of the current study was to test the potential use of salivary NO as a biomarker of bone response that is specific and sensitive to local changes, following the application of different types of dental appliances. Material and methods: Salivary NO was determined in 30 patients divided into three groups with 10 participants each: control (C), fixed metal braces group (M), and aligners group (A). Salivary NO was determined four times in each group (before the procedure, at 2 weeks, 30 days, and 60 days after the procedure) using ELISA and rapid semi-quantitative assay with Nitric Oxide Saliva Test Strips (Berkeley, CA, USA). The mean results were compared with the ANOVA test, and the Pearson correlation index was calculated. The results show a significant increase in salivary NO levels by both methods only in the metal braces group, which is suggestive of oxidative damage, increased invasiveness, and bone response to metal braces. In conclusion, our study showed that metal brackets lead to a significant temporary increase in oral oxidative stress as an adaptive reaction to the presence of foreign bodies in the oral cavity. The subsequent concentration decrease at 60 days suggests a normalization of the body’s response to foreign bodies.

CCL8 as a promising prognostic factor in diffuse large B-cell lymphoma via M2 macrophage interactions: A bioinformatic analysis of the tumor microenvironment.

BackgroundsPrior investigations of the tumor microenvironment (TME) of diffuse large B-cell lymphoma (DLBCL) have shown that immune and stromal cells are key contributing factors to patients’ outcome. However, challenges remain in finding reliable prognostic biomarkers based on cell infiltration. In this study, we attempted to shed some light on chemokine C–C motif chemokine ligand 8 (CCL8) in DLBCL via interaction with M2 macrophages.MethodsThe Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm was applied to evaluate immune and stromal scores from transcriptomic profiles of 443 DLBCL samples from The Cancer Genome Atlas (TCGA) and GSE10846 datasets. Immune cell infiltration (ICI) clusters were obtained based on different immune cell infiltrations of each sample, and gene clusters were derived through differentially expressed genes (DEGs) between the distinct ICI clusters. Five immune-related hub genes related to overall survival (OS) and clinical stages were obtained by COX regression analysis and protein–protein interaction (PPI) network construction then verified by quantitative real-time PCR (qPCR) and immunofluorescence staining in the FFPE tissues. The Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and TIMER websites were employed to explore the biological functions of CCL8-related DEGs. Uni- and multivariable Cox regression analyses were performed to analyze CCL8 as an independent prognostic risk factor in GSE10846 and were verified in other independent GEO cohorts.ResultsA higher stromal score was associated with favorable prognosis in DLBCL. Patients in the ICI B cluster and gene B clusters had a better follow-up status with a higher programmed death ligand 1 (PD-L1) and cytotoxic T-lymphocyte antigen 4 (CTLA4) expression. Most of ICI-related DEGs were enriched for immune-related signaling pathways. Five hub genes with a distinct prognosis association were identified, including CD163, which is a biomarker of M2 macrophages, and CCL8. Abundant M2 macrophages were discovered in the high-CCL8 expression group. The functional analysis indicated that CCL8 is a key component of immune-related processes and secretory granule groups. Cox regression analysis and data from other GSE datasets yielded additional evidence of the prognostic value of CCL8 in DLBCL.ConclusionsCCL8 has been implicated in macrophage recruitment in several solid tumors, and only a few reports have been published on the role of CCL8 in the pathogenesis of hematological malignancies. This article attempted to find out TME-related genes that associated with the survival in DLBCL patients. CCL8 was identified to be involved in immune activities. Importantly, a series of bioinformatics analysis indicated that CCL8 might become an effective target for DLBCL, which interacts with M2 macrophage and immune checkpoint. The potential related mechanisms need to be further elucidated.

A m6A methyltransferase-mediated immune signature determines prognosis, immune landscape and immunotherapy efficacy in patients with lung adenocarcinoma.

As the most abundant modification in mRNA, the N6-methyladenosine (m6A) RNA modification is involved in the occurrence and development of various tumors. However, the underlying functions of this alteration in the immune microenvironment of lung adenocarcinoma (LUAD) remain unknown. We identified m6A-mediated immune genes by performing a correlation analysis. Next, a m6A-mediated immune model was constructed using multiple machine learning algorithms, including univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses. The potential of this model to predict the immune landscapes, drug sensitivities, and immunotherapy responses of different LUAD risk groups was studied. A m6A-mediated immune model containing 13 m6A-mediated immune genes was established and found to be an independent predictor of survival time. The prognosis of low-risk patients was significantly better than that of high-risk patients. These two risk groups displayed different immune environments, genomic backgrounds, chemotherapy responses and immunotherapy response tendencies. The low- and high-risk groups strongly corresponded to the immune-hot and immune-cold phenotypes, respectively. The low-risk group was more enriched in immune-related biological processes, and the high-risk group was more enriched in proliferation-related biological processes. Furthermore, low-risk patients responded better to immunotherapy based on the results obtained from the tumor immune dysfunction and exclusion (TIDE) algorithm and subclass mapping algorithm using five external independent immunotherapy cohorts. Our results suggest that the m6A modification participates in regulating the tumor microenvironment. The m6A-mediated immune model may be useful to predict the immunotherapy responses and outcomes of patients with LUAD.

Identification of immune-related key genes in the peripheral blood of ischaemic stroke patients using a weighted gene coexpression network analysis and machine learning

BackgroundThe immune system plays a vital role in the pathological process of ischaemic stroke. However, the exact immune-related mechanism remains unclear. The current research aimed to identify immune-related key genes associated with ischaemic stroke.MethodsCIBERSORT was utilized to reveal the immune cell infiltration pattern in ischaemic stroke patients. Meanwhile, a weighted gene coexpression network analysis (WGCNA) was utilized to identify meaningful modules significantly correlated with ischaemic stroke. The characteristic genes correlated with ischaemic stroke were identified by the following two machine learning methods: the support vector machine-recursive feature elimination (SVM-RFE) algorithm and least absolute shrinkage and selection operator (LASSO) logistic regression.ResultsThe CIBERSORT results suggested that there was a decreased infiltration of naive CD4 T cells, CD8 T cells, resting mast cells and eosinophils and an increased infiltration of neutrophils, M0 macrophages and activated memory CD4 T cells in ischaemic stroke patients. Then, three significant modules (pink, brown and cyan) were identified to be significantly associated with ischaemic stroke. The gene enrichment analysis indicated that 519 genes in the above three modules were mainly involved in several inflammatory or immune-related signalling pathways and biological processes. Eight hub genes (ADM, ANXA3, CARD6, CPQ, SLC22A4, UBE2S, VIM and ZFP36) were revealed to be significantly correlated with ischaemic stroke by the LASSO logistic regression and SVM-RFE algorithm. The external validation combined with a RT‒qPCR analysis revealed that the expression levels of ADM, ANXA3, SLC22A4 and VIM were significantly increased in ischaemic stroke patients and that these key genes were positively associated with neutrophils and M0 macrophages and negatively correlated with CD8 T cells. The mean AUC value of ADM, ANXA3, SLC22A4 and VIM was 0.80, 0.87, 0.91 and 0.88 in the training set, 0.85, 0.77, 0.86 and 0.72 in the testing set and 0.87, 0.83, 0.88 and 0.91 in the validation samples, respectively.ConclusionsThese results suggest that the ADM, ANXA3, SLC22A4 and VIM genes are reliable serum markers for the diagnosis of ischaemic stroke and that immune cell infiltration plays a crucial role in the occurrence and development of ischaemic stroke.