Journal of Inflammation Research最新文献

Purpose: Previous studies have shown that long non-coding RNA GUSBP11 is abnormally expressed in patients with periodontitis, but the specific mechanism remains to be investigated. The purpose of this study was to explore the role of GUSBP11/miR-185-5p in chronic periodontitis (CP) and its potential mechanism, so as to provide a basis for elucidating the pathogenesis of CP.

Patients and methods: The expression trends of GUSBP11 and miR-185-5p in gingival crevicular fluid of CP patients and control group were analyzed by RT-qPCR. Human gingival fibroblasts (HGF) induced by 10μg/mL LPS were used to construct CP cell models in vitro. The level of intracellular gene expression is regulated by cell transfection. The cell viability of HGF was evaluated by CCK-8 method, and the expression of HGF inflammatory factors was evaluated by ELISA. The targeting relationship between GUSBP11 and miR-185-5p was confirmed by luciferase reporter gene. The target genes of miR-185-5p were predicted using an online database, and the intersection target genes were obtained by constructing Venn diagram. Then GO analysis and KEGG pathway enrichment analysis were performed.

Results: Compared with the control group, the expression levels of GUSBP11 and miR-185-5p in gingival crevicular fluid of CP patients were up-regulated and down-regulated, respectively (P < 0.001). The levels of GUSBP11 and miR-185-5p increased and decreased with the severity of CP, respectively (P < 0.01). LPS induces the decrease of HGF activity and the activation of inflammatory response, and the decrease of GUSBP11 may prevent the adverse effect of LPS on HGF (P < 0.001). Dual luciferase reporter genes showed that miR-185-5p interacts with GUSBP11. The increase of miR-185-5p also significantly improved the negative effect of LPS induction on HGF (P < 0.001).

Conclusion: GUSBP11 promotes the inflammatory response and proliferation inhibition of human gingival fibroblasts induced by LPS by down-regulating miR-185-5p, thus promoting the development of CP.

Purpose: Immunometabolism is pivotal in rheumatoid arthritis (RA) pathogenesis, yet the intricacies of its pathological regulatory mechanisms remain poorly understood. This study explores the complex immunometabolic landscape of RA to identify potential therapeutic targets.

Patients and methods: We integrated genome-wide association study (GWAS) data involving 1,400 plasma metabolites, 731 immune cell traits, and RA outcomes from over 58,000 participants. Mendelian randomization (MR) and mediation analyses were applied to evaluate causal relationships among plasma metabolites, immune cells, and RA. We further analyzed single-cell and bulk transcriptomes to investigate differential gene expression, immune cell interactions, and relevant biological processes. Machine learning models identified hub genes, which were validated via quantitative real-time PCR (qRT-PCR). Then, potential small-molecule drugs were screened using the Connectivity Map (CMAP) and molecular docking. Finally, a phenome-wide association study (PheWAS) was conducted to evaluate potential side effects of drugs targeting the hub genes.

Results: Causalities were found between six plasma metabolites, five immune cells and RA in genetically determined levels. Notably, DC mediated 18% of the protective effect of PE on RA. Autophagy-related scores were elevated in both RA and DC subsets in PE-associated biological processes. Through observation in the functional differences in cellular interactions between the identified clusters, DCs with high autophagy scores may process such as necroptosis and the activation of the Jak-STAT signaling pathway in contributing the pathogenesis of RA. Explainable machine learning, PPI network analysis, and qPCR jointly identified four hub genes (PFN1, SRP14, S100A11, and SAP18). CMAP, molecular docking, and PheWAS analysis further highlighted vismodegib as a promising therapeutic candidate.

Conclusion: This study clarifies the key immunometabolic mechanisms in RA, pinpointing promising paths for better prevention, diagnosis, and treatment.

Background: As a novel inflammatory-nutritional biomarker, the C-reactive protein-albumin-lymphocyte (CALLY) index has demonstrated significant prognostic value in various malignancies. However, research on its association with the prognosis of ampullary carcinoma (AC) is rare. This study aims to investigate the relationship between the CALLY index and the prognosis of patients with AC.

Methods: We retrospectively analyzed data from 201 patients with AC at Sun Yat-sen University Cancer Center. Several clinicopathological factors and biomarkers were included in the study. Univariate and multivariate Cox regression analyses, along with competing risk analysis, were performed to identify prognostic factors for AC after pancreaticoduodenectomy (PD). Only factors with significant results in univariate analysis were included in multivariate analysis. To ensure the robustness of our findings, propensity score matching (PSM) analyses were conducted to assess survival differences according to the CALLY index.

Results: The univariate and multivariate Cox regression analyses revealed that pathological type, N stage, T stage, postoperative chemotherapy regimen, and the CALLY index were all statistically significant prognostic factors for patients with AC after PD (all P values < 0.05). Taking into account non-cancer-related mortality as competing hazards, these factors remained significant predictors (all P values < 0.05). After PSM, the survival advantage observed between the low and high CALLY groups remained discernible and consistent.

Conclusion: This study indicated that a reduced CALLY index correlates with a poorer cancer-specific survival in AC patients after PD, highlighting its utility as a prognostic marker for this condition.

[This corrects the article DOI: 10.2147/JIR.S498775.].

Purpose: Intestinal ischemia-reperfusion injury (IIRI) occurs as a result of temporary blood flow interruption, leading to tissue damage upon reperfusion. Oxidative stress plays a critical role in this process, instigating inflammation and cell death. Identifying and characterizing genes associated with the oxidative stress response can offer valuable insights into potential therapeutic targets for managing IIRI.

Patients and methods: The IIRI dataset was sourced from the NCBI Gene Expression Omnibus Database (GEO), while oxidative stress genes were obtained from the Genecards database. Following the acquisition of differentially expressed genes in IIRI, they were cross-linked with oxidative stress genes to yield IIRI oxidative stress related genes (IOSRGs). The least absolute shrinkage and selection operator, as well as the support vector machine with random forest algorithm, were utilized for machine learning. Subsequently, the PPI network was established, and the Degree and MNC algorithms of the Cytohuba plugin were integrated with the genes obtained through the machine learning algorithms to identify hub IOSRGs (HIOSRGs). A mouse IIRI model and ROC curve were employed to verify the accuracy of HIOSRGs. Finally, siRNA was utilized to suppress the expression of HDAC3 in Caco2 cells, and the changes in oxidative stress levels before and after hypoxia-reoxygenation in Caco2 cells were observed.

Results: A total of 277 OSRGs and 4 HIOSRGs were obtained. Concurrently, in vivo experimental results of IIRI in C57BL/6 mice, and the establishment of ROC curves, reflected the accuracy and specificity of HIOSRGs. The knockdown of HDAC3 in Caco2 cells resulted in increased oxidative stress levels before and after hypoxia-reoxygenation, underscoring the significant role of HDAC3 in IIRI.

Conclusion: This study elucidates the interplay between oxidative stress genes and IIRI, offering novel insights into the potential pathogenesis of IIRI and medical interventions for IIRI.

Background: Accurate identification of the risk of postoperative pneumonia (POP) in aneurysmal subarachnoid hemorrhage (aSAH) is essential for the implementation of stratified treatment. This study evaluated the relevance and utility of the Systemic Immuno-inflammatory Index (SII) and the Systemic Inflammatory Response Index (SIRI) in predicting pneumonia after aSAH.

Methods: Two hundred and forty patients undergoing aSAH intervention were included. Differences in SII and SIRI between patient groups were analyzed by propensity score matching (PSM). Receiver Operating Characteristic curves (ROC) were used to evaluate the predictive validity of SII and SIRI and to determine their predictive thresholds. The association of these indices with POP risk was assessed by multivariate logistic regression and restricted cubic spline (RCS), and subgroup analyses were performed.

Results: The overall POP prevalence was 60%, with 37.5% males and 62.5% females. PSM analyses showed statistically significant differences between the two groups for SII (P=0.032) and SIRI (P=0.02). They had a high predictive accuracy for predicting POP, with AUC values of 0.643 and 0.644, respectively. SII and SIRI were positively associated with the POP risk, independent of other confounders. Moreover, further sensitivity analysis and RCS supported the stability of this finding. Subgroup analyses showed that the relationship was stable across subgroups.

Conclusion: This study reveals the potential role of SII and SIRI in predicting the risk of postoperative pneumonia in patients with aSAH, and provides a strong basis for early identification and stratification of patients who are at high risk of postoperative pneumonia in aSAH.

Background: Nonalcoholic steatohepatitis (NASH) has recently garnered increased attention due to immune infiltration. However, the role of membrane-associated protein 17 (MAP17) in NASH remains unclear, which prompted this study to explore its relationship with immune infiltration and its regulatory mechanisms.

Methods: We employed weighted correlation network analysis (WGCNA) to construct a gene co-expression network aimed at identifying key genes associated with NASH progression. Our further analyses included differential expression evaluation, protein-protein interaction (PPI) network analysis, and Venn diagram analysis to discover novel targets. The CIBERSORT algorithm assessed the correlation between MAP17 and immune cell infiltration within the tumor microenvironment (TME), while the TIDE algorithm predicted responses to immunotherapy. Additionally, we conducted gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) to elucidate the mechanisms by which MAP17 operates. The expression of MAP17 was validated using liver tissues obtained from NASH patients and mice with diet-induced NASH or CCl4-induced liver fibrosis.

Results: Our findings identified MAP17 as a novel target in the progression of NASH. Correlation analyses demonstrated a positive association between MAP17 and M1 macrophage infiltration, as well as a negative association with M2 infiltration. TIDE results positioned MAP17 as a potential biomarker for predicting responses to immune checkpoint blockade. Mechanistic studies revealed that MAP17 induced oxidative stress, which subsequently activated the p53, PI3K-AKT, and Wnt signaling pathways. Validation analyses confirmed that MAP17 levels significantly increased in liver tissues of mice with diet-induced NASH or CCl4-induced liver fibrosis, as well as in NASH patients.

Conclusion: MAP17 is a novel biomarker linked to macrophage infiltration and immunotherapy responses in NASH patients. The oxidative stress induced by MAP17 activates the p53, PI3K-AKT, and Wnt pathways, all of which contribute to the progression of NASH.

Purpose: The albumin to fibrinogen ratio (AFR), a biomarker associated with inflammatory, nutritional, and coagulation status, and the CALLY index, a biomarker combining C-reactive protein, albumin, and lymphocyte count, have been suggested to correlate with prognosis in a variety of diseases in previous studies; however, studies of these two markers in ulcerative colitis (UC) are lacking. The aim of this study was to evaluate the clinical significance of AFR and CALLY index in UC.

Methods: The study included 109 UC patients and 126 healthy controls. For UC patients treated with Vedolizumab (50 patients), they were categorized into mucosal healing group (MH group) and non- mucosal healing group (non-MH group) based on Mayo endoscopic score (MES) after 14 weeks of treatment. The differences in AFR and CALLY index were compared between the UC group and the healthy control group, and between the MH group and the non-MH group. Then, the correlation of the AFR and CALLY index with UC activity was assessed, and the predictive value of the AFR and CALLY index was evaluated using the receiver operating characteristic (ROC) curve.

Results: The results showed that both AFR and CALLY index were significantly decreased in the UC group compared with the healthy control group (both p<0.001); the area under the curve (AUC) of the AFR and CALLY index differentiating between the healthy control group and the UC group were 0.782 and 0.773, respectively. For Vedolizumab treatment, the non-MH group had significantly lower baseline AFR and CALLY index compared to the MH group; the AUC for baseline AFR and CALLY index discriminating the MH group from the non-MH group were 0.665 and 0.721, respectively. In addition, AFR and CALLY index were negatively correlated with the MES and inflammatory load of UC. The results of multivariate logistic regression analysis showed that the CALLY index was an independent predictor of UC diagnosis and mucosal healing after 14 weeks of Vedolizumab treatment.

Conclusion: AFR and CALLY index can be used as novel serologic markers for diagnosing UC and predicting the efficacy of Vedolizumab treatment.

Background: Autophagy and immunity play important regulatory roles in lung developmental disorders. However, there is currently a lack of bioinformatics analysis on autophagy-related genes (ARGs) and immune infiltration in bronchopulmonary dysplasia (BPD). We aim to screen and validate the signature genes of BPD by bioinformatics and in vivo experiment.

Methods: GSE8586 was obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using the R program. Using cell-type identification with CIBERSORT to analyze the inflammatory and immune status of BPD. Subsequently, the hub genes were identified by Lasso and Cytoscape with three machine-learning algorithms (MCC, Degree and MCODE). In addition, hub genes were validated with ROC, single-cell sequence and IHC in hyperoxia rats. Finally, we searched the drug targets of these hub genes, and established a nomogram model for predicting the risk of BPD.

Results: There were 73 the differentially expressed and autophagy-related genes (DE-ARGs) by overlapping the DEGs in GSE8586 and ARGs. Five hub genes, BRIX1, JUN, PES1, NR4A1 and RRP9, were lowly expressed in the BPD group and had high diagnostic value in the diagnostic model. All hub genes are mainly located in B cell, epithelial cell, fibroblast, endothelial cell, smooth muscle cell and pneumocyte in lung single-cell sequencing. Moreover, immune infiltration analysis showed immune cells were higher in the BPD group and were closely associated with hub genes. We also predict the drug targets of the genes. Finally, the IHC result in rats showed that expression of PES1, BRX1, RRP9, JUN, NR4A1 was lower in the hyperoxia group compared to the normoxia group.

Conclusion: BRIX1, JUN, PES1, NR4A1, RRP9, may be promising therapeutic targets for BPD. Our findings provided researchers and clinicians with more evidence regarding immunotherapeutic strategies for BPD treatment.

Purpose: Interleukin-6 (IL-6) is a central factor linking inflammation to cancer. This study aimed to provide a comprehensive assessment of the prognostic value of IL-6 and its immunotherapeutic features using a population-based pan-cancer analysis and comprehensive bioinformatic analysis.

Patients and methods: In the cohort study, 540 patients were included to explore the prognostic value of serum IL-6 levels in cancer. The differential expression of IL-6 and its association with survival and immune cell infiltration were investigated using the TCGA database. The SangerBox database was used to analyze the correlation between IL-6 expression and immune checkpoint (ICP), tumor mutation burden (TMB), and microsatellite instability (MSI) in cancer. Genomic changes in the IL-6 levels were studied using the c-BioPortal database. The IL-6 co-expression network was analyzed using the LinkedOmics database.

Results: Serum IL-6 is an independent prognostic factor for cancer, especially gastrointestinal cancers. Compared to other serum inflammatory markers, serum IL-6 is an optimal biomarker for cancer prognosis. A comprehensive bioinformatics analysis showed higher IL-6 expression in human cancers than in the paired normal tissues. The IL-6 expression is closely associated with prognosis, ICP, TMB, and MSI. In addition, it is also strongly correlated with tumor-infiltrating cells. IL-6 levels are significantly associated with the prognosis of stomach adenocarcinoma (STAD). The IL-6 co-expression network in STAD is mainly involved in regulating inflammatory pathways and cell communication.

Conclusion: IL-6 is a potential prognostic and immune biomarker of cancer. Compared to other clinical inflammatory biomarkers, IL-6 demonstrates superior prognostic efficacy.