Inflammatory Response-related Genes Research Articles

Predicting the prognosis of esophageal cancer based on extensive analysis of new inflammatory response-related signature.

The prognosis of esophageal cancer (ESCA) is very poor, with a 5-year survival rate of less than 20%. On the other hand, inflammation is the characteristic hallmark of ESCA; however, the prognostic relationship between inflammatory response-related genes and ESCA has not been clarified yet. Therefore, in the present manuscript, we intend to investigate the correlation and specific signature of inflammation for the prediction of the prognosis of ESCA. A total of 173 samples were obtained from The Cancer Genome Atlas(TCGA) database, including 162 tumors and 11 normal specimens. The prognostic signature was established by least absolute shrinkage and selection operator Cox regression analysis. The transcription factor regulatory network with genes of the prognostic signature was analyzed from the transcriptionalregulatoryrelationshipsunravelled bysentence-based text-mining database. Chemotherapy sensitivity and immunotherapy analysis were also performed. Multivariate Cox analysis showed that the signature was an independent prognostic risk factor. The low-risk group had poorer outcomes than the high-risk group. In the high-risk group, the infiltration of most immune cells was high and strongly correlated with the riskScore. In chemotherapeutic drug sensitivity analysis, OSM, AHR, and BTG2 were significantly correlated with the current chemotherapeutic drugs of ESCA. We have demonstrated a valid prognostic signature of inflammatory response-related genes and found strong associations with immune cells, targeted genes, and chemotherapeutic agents.

Supplementing Diets with Agriophyllum squarrosum Reduced Blood Lipids, Enhanced Immunity and Anti-Inflammatory Capacities, and Mediated Lipid Metabolism in Tan Lambs.

Agriophyllum squarrosum (sand rice), a widespread desert plant, possesses anti-hyperglycemic and anti-inflammatory properties, and has been used in traditional Chinese medicine for many years. However, its effects on ruminants are unknown. To fill this gap, we examined the effects of A. squarrosum on the immune and anti-inflammatory responses of lambs. A total of 23, 6-month-old Tan ewe-lambs (27.6 ± 0.47 kg) were divided into four groups and offered a basic diet (C-control), or a diet that contained 10%, 20%, or 30% A. squarrosum, on a dry matter basis, for 128 days. Serum concentrations of total cholesterol were lower (p = 0.004) in the 30% supplemented lambs than controls, while concentrations of high-density lipoprotein cholesterol were lower (p = 0.006) in the 10% and 20%, but not in 30% supplemented lambs than controls. Serum-cortisol concentrations were lower (p = 0.012) in the 30% supplemented lambs and free fatty acid concentrations were higher in the 10% and 20% supplemented lambs than in control lambs (p < 0.001). Supplementation with A. squarrosum decreased (p < 0.05) the area of adipocytes in subcutaneous adipose tissue, but there was no difference between the 20% and 30% diets. Conversely, the area in visceral adipose tissue (VAT) increased (p < 0.05), especially for the 10% and 20% supplemented diets. Supplementation with A. squarrosum also enriched immune and anti-inflammatory related and lipid and glucose-metabolic pathways and associated differentially expressed gene expressions in adipose tissue. A total of 10 differential triacylglycerol, 34 differential phosphatidylcholines and seven differential phosphatidylethanolamines decreased in the diet with 30% supplementation, when compared to the other diets. Finally, adipocyte-differentiation genes, and immune and inflammatory response-related gene expression levels decreased in lamb adipocytes cultured with an aqueous A. squarrosum extract. In conclusion, supplementing lamb diets with A. squarrosum reduced blood lipids, enhanced immunity and anti-inflammatory capacities, and mediated lipid metabolism in adipose tissue and adipocytes of Tan lambs. A level of approximately 10% is recommended, but further research is required to determine the precise optimal level.

Identifying crosstalk genetic biomarkers linking a neurodegenerative disease, Parkinson's disease, and periodontitis using integrated bioinformatics analyses.

To identify the genetic linkage mechanisms underlying Parkinson's disease (PD) and periodontitis, and explore the role of immunology in the crosstalk between both these diseases. The gene expression omnibus (GEO) datasets associated with whole blood tissue of PD patients and gingival tissue of periodontitis patients were obtained. Then, differential expression analysis was performed to identify the differentially expressed genes (DEGs) deregulated in both diseases, which were defined as crosstalk genes. Inflammatory response-related genes (IRRGs) were downloaded from the MSigDB database and used for dividing case samples of both diseases into different clusters using k-means cluster analysis. Feature selection was performed using the LASSO model. Thus, the hub crosstalk genes were identified. Next, the crosstalk IRRGs were selected and Pearson correlation coefficient analysis was applied to investigate the correlation between hub crosstalk genes and hub IRRGs. Additionally, immune infiltration analysis was performed to examine the enrichment of immune cells in both diseases. The correlation between hub crosstalk genes and highly enriched immune cells was also investigated. Overall, 37 crosstalk genes were found to be overlapping between the PD-associated DEGs and periodontitis-associated DEGs. Using clustering analysis, the most optimal clustering effects were obtained for periodontitis and PD when k = 2 and k = 3, respectively. Using the LASSO feature selection, five hub crosstalk genes, namely, FMNL1, MANSC1, PLAUR, RNASE6, and TCIRG1, were identified. In periodontitis, MANSC1 was negatively correlated and the other four hub crosstalk genes (FMNL1, PLAUR, RNASE6, and TCIRG1) were positively correlated with five hub IRRGs, namely, AQP9, C5AR1, CD14, CSF3R, and PLAUR. In PD, all five hub crosstalk genes were positively correlated with all five hub IRRGs. Additionally, RNASE6 was highly correlated with myeloid-derived suppressor cells (MDSCs) in periodontitis, and MANSC1 was highly correlated with plasmacytoid dendritic cells in PD. Five genes (i.e., FMNL1, MANSC1, PLAUR, RNASE6, and TCIRG1) were identified as crosstalk biomarkers linking PD and periodontitis. The significant correlation between these crosstalk genes and immune cells strongly suggests the involvement of immunology in linking both diseases.

Development and validation of novel inflammatory response-related gene signature for sepsis prognosis.

Due to the low specificity and sensitivity of biomarkers in sepsis diagnostics, the prognosis of sepsis patient outcomes still relies on the assessment of clinical symptoms. Inflammatory response is crucial to sepsis onset and progression; however, the significance of inflammatory response-related genes (IRRGs) in sepsis prognosis is uncertain. This study developed an IRRG-based signature for sepsis prognosis and immunological function. The Gene Expression Omnibus (GEO) database was retrieved for two sepsis microarray datasets, GSE64457 and GSE69528, followed by gene set enrichment analysis (GSEA) comparing sepsis and healthy samples. A predictive signature for IRRGs was created using least absolute shrinkage and selection operator (LASSO). To confirm the efficacy and reliability of the new prognostic signature, Cox regression, Kaplan-Meier (K-M) survival, and receiver operating characteristic (ROC) curve analyses were performed. Subsequently, we employed the GSE95233 dataset to independently validate the prognostic signature. A single-sample GSEA (ssGSEA) was conducted to quantify the immune cell enrichment score and immune-related pathway activity. We found that more gene sets were enriched in the inflammatory response in sepsis patient samples than in healthy patient samples, as determined by GSEA. The signature of nine IRRGs permitted the patients to be classified into two risk categories. Patients in the low-risk group showed significantly better 28-d survival than those in the high-risk group. ROC curve analysis corroborated the predictive capacity of the signature, with the area under the curve (AUC) for 28-d survival reaching 0.866. Meanwhile, the ssGSEA showed that the two risk groups had different immune states. The validation set and external dataset showed that the signature was clinically predictive. In conclusion, a signature consisting of nine IRRGs can be utilized to predict prognosis and influence the immunological status of sepsis patients. Thus, intervention based on these IRRGs may become a therapeutic option in the future.

Taraxasterol inhibits inflammation in osteoarthritis rat model by regulating miRNAs and NF-κB signaling pathway.

Osteoarthritis (OA) has a high incidence rate in the elderly population and is a cause of chronic degenerative joint disease. Current therapeutic approaches to OA are effective but come with some side effects. Therefore, it is urgent to find new safe and effective OA treatments. This study aimed to clarify the function of taraxasterol (TAX) isolated from Taraxacum officinale in the papain-induced rat OA model. We observed that TAX alleviated the typical OA-caused phenomena in the joint. The expression of serum inflammatory mediators such as TNF-α, IL-6, and IL-1β was also repressed by TAX. In addition, NF-κB signaling pathway was repressed by TAX. Furthermore, two microRNAs: miR-140 and miR-146a were elevated after TAX treatment in OA rat model. Interestingly, several common targets of miR-140 and miR-146a, including HSPA4L, ST5, and ERBB4, were confirmed to be regulated by TAX. Inflammatory response related genes including S100A8, CCL3, A2M, LBP, and CCR1 were repressed by TAX in OA rat model. In summary, TAX inhibits inflammation in osteoarthritis rat model. Inflammatory mediators, NF-κB pathway and miR-140/miR-146a targets mediate the function of TAX.

A Novel Inflammatory Response-Related Gene Signature Predicts Immune Status and Prognosis of Breast Cancer.

Breast cancer is the most common type of cancer and the leading cause of cancer-related death in women worldwide. In this study, we aimed to construct an inflammatory response-related gene model for predicting the immune status and prognosis of breast cancer patients. We obtained the inflammatory response-related genes from the Molecular Signatures Database. Furthermore, we used univariate Cox regression analysis, the least absolute shrinkage and selection operator (LASSO) regression analysis, and multivariate Cox regression to construct an inflammatory response-related gene signature (IRGS) model based on dataset obtained from The Cancer Genome Atlas (TCGA). Patients were consequently categorized into high-risk and low-risk groups. Kaplan-Meier analysis was used to compare the overall survival (OS) of high-risk and low-risk groups. Following that, we validated the model using a dataset (GSE96058) acquired from Gene Expression Omnibus (GEO) database. Univariate and multivariate Cox analyses were used to determine the independent prognostic value of the IRGS in the TCGA and GSE96058 cohorts. A nomogram was constructed to predict the OS in the TCGA cohort. Further, we used Gene Set Enrichment Analysis (GSEA), CIBERSORT, and single-sample Gene Set Enrichment Analysis (ssGSEA) to evaluate the associations of IRGS with immune-associated pathways and immune infiltration. Finally, the relationship between the expression of the signature genes and drug sensitivity was conducted using Pearson correlation analysis. We established an IRGS to stratify breast cancer patients into the low-risk and high-risk groups. In both the training and validation sets, patients in the high-risk group had significantly shorter OS than those in the low-risk group. The risk score was significantly correlated with the clinical characteristics and could be used as a tool to predict the prognosis of breast cancer. Moreover, we found that the IRGS risk score was an independent predictor of OS in breast cancer patients, and a nomogram model based on IRGS risk score and other clinical factors could effectively predict the prognosis of breast cancer patients. Furthermore, the IRGS risk score was correlated with immune characteristics and was inversely associated with the abundance of immune cell infiltration. Patients with a low IRGS risk score had higher expression levels of immune checkpoint genes, suggesting that IRGS can be used as a potential indicator for immunotherapy. Finally, we found that the expression levels of prognostic genes were significantly correlated with tumor cell sensitivity to chemotherapeutic drugs. Overall, these findings suggest that the IRGS can be used to predict the prognosis and immune status of breast cancer patients and provide new therapeutic targets for the treatment of these patients.

An Inflammatory Response-Related Gene Signature Can Predict the Prognosis and Impact the Immune Status of Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) accounts for a cancer with high heterogeneity and poor prognostic outcome. Nonetheless, it is still unknown about the relation between inflammatory response-related genes (IRGs) and LUAD. This study used LASSO-Cox regression for establishing the multigene prognostic signature based on TCGA and the GSE31210 cohorts. In addition, gene set enrichment analysis (GSEA) was performed for GO and KEGG analyses. By contrast, single-sample GSEA (ssGSEA) investigated immune cell infiltration scores as well as the immune pathway activity. We also conducted qRT-PCR and IHC to evaluate prognostic gene expression at protein and mRNA levels within LUAD and adjacent healthy samples. As a result, a novel prognostic signature involving 10 IRGs was identified. Furthermore, the signature has been validated as being important in functional analysis, TME, drug sensitivity, and prognosis prediction in LUAD. Moreover, prognostic genes showed significant expression at protein and mRNA levels in LUAD compared with normal samples. The signature involving 10 IRGs could potentially predict LUAD prognosis.

An integrated analysis of gut microbiota and the brain transcriptome reveals host-gut microbiota interactions following traumatic brain injury

ObjectivesRecent evidence suggests that there is a link between gut and brain via microbial, immune, endocrine and neural signaling pathways, but the changes of gut-brain axis following brain trauma has not yet been clearly shown. The aim of this study was to reveal the gut microbiota and transcriptomic profile of the cerebral cortex in traumatic brain injury (TBI) mice. MethodsA controlled cortical impact (CCI) device was used to establish a TBI model. Behavioral testing and histopathological analysis were performed. The gut microbiota was analyzed by 16S rRNA sequencing, and gene expression in the cerebral cortex was detected by whole-transcriptome sequencing (RNA-Seq) 7 days after TBI. ResultsThe analysis of 16S rRNA sequencing data indicated that TBI increased the relative abundance of Bifidobacterium. The TBI group showed a disturbance in intestinal flora. RNA-Seq analysis identified 523 differentially expressed genes (481 upregulated and 42 downregulated) in the cerebral cortex of the TBI group compared with the sham group. Cluster analysis revealed 93 immune system process-related genes and 55 inflammatory response-related genes that were differentially expressed. ConclusionsThis manuscript reports pathogenic changes via the gut-brain axis driven by TBI, which confer persistent symptoms and susceptibility to neurodegeneration.

Development and validation of a gene signature for pancreatic cancer: based on inflammatory response-related genes.

Pancreatic cancer (PC) is one of the most common malignant tumors in the world with a poor prognosis. There were limited studies investigating the genetic signatures associated with inflammatory responses, tumor microenvironment (TME), and tumor drug sensitivity prediction. In the Cancer Genome Atlas (TCGA) dataset, we constructed an inflammatory response-related genes prognostic signature for PC, and predictive ability of the model was assessed via the International Cancer Genome Consortium (ICGC) database. Then, we explored the differences of TME, immune checkpoint genes and drug resistance genes, and the cancer cell sensitivity to chemotherapy drugs between different risk score group. Based on the TCGA and ICGC databases, we constructed and validated a prognostic model, which consisted of 5 genes (including AHR, F3, GNA15, IL18, and INHBA). Moreover, the prognostic model was independent prognostic factors affecting overall survival (OS). The low-risk score group had better OS, and lower stromal score, compared with patients in the high-risk score group. The difference of antigen-presenting cells, T cell regulation, and drug resistance genes between different risk score groups was found. In addition, the immune checkpoint genes were positively correlation to risk score. The expression levels of AHR, GNA15, IL18, and INHBA were related to the sensitivity of anti-tumor chemotherapy drugs. Gene set enrichment analysis (GSEA) showed significant pathway such as calcium signaling pathway and p53 signaling pathway. We successfully constructed a 5-inflammatory response-related gene signature to predict survival, TME, and cancer cell sensitivity to chemotherapy drugs in PC patients. Furthermore, substantiation was warranted to verify the role of these genes in tumorigenesis.

Identification and Validation of an Inflammatory Response-Related Polygenic Risk Score as a Prognostic Marker in Hepatocellular Carcinoma.

Aims We hypothesized that the expression patterns of inflammatory response-related genes may be a potential tool for hepatocellular carcinoma (HCC) risk scoring. Background Inflammatory response plays a pivotal role in the pathogenesis of HCC. Objective To establish and validate a hallmark inflammatory response gene-based polygenic risk score as a prognostic tool in HCC. Methods We screened differentially expressed inflammatory response genes and established an inflammatory response-related polygenic risk score (IRPRS) in an HCC-related dataset. Patients with HCC were categorized into high- and low-risk groups according to the median IRPRS, and the overall survival between the two groups was compared. The IRPRS was validated in an independent external dataset. Tumor-infiltrating lymphocytes (TILs) in high- and low-risk groups were compared, and gene set enrichment analysis was performed to characterize high-risk HCC identified using this IRPRS. Results Four differentially expressed hallmark inflammatory response genes (CD14, AQP9, SERPINE1, and ITGA5) were identified to construct the IRPRS. Patients in the high-risk group had significantly shorter overall survival than those in the low-risk group in both the training set and the test set. Furthermore, the IRPRS remained an independent prognostic factor compared to the routine clinicopathological characteristics. Many cancer-related hallmark gene sets and TILs were significantly enriched in the high-risk group. Conclusions We established and validated a four-hallmark inflammatory response gene-based polygenic risk score, which could successfully divide patients with HCC into high-risk and low-risk groups. These two risk groups of HCC possess significantly distinct prognostic and biological characteristics.

Bioinformatics Analysis of Inflammation Gene Signature in Indicating Cholangiocarcinoma Prognosis

Aim We studied inflammatory response-related genes in cholangiocarcinoma by bioinformatics analysis. Methods The expression profiles and clinical information of cholangiocarcinoma patients were downloaded from the TCGA cohort and the Gene Expression Omnibus. The greatest absolute shrinking and selecting operator Cox analyses were utilized to build a multigene predictive signature. Results An inflammation response-related gene profile was generated using LASSO-Cox regression analysis of Homo sapiens bestrophin 1 (BEST1), Chemokine (C–C motif) ligand 2 (CCL2), and plasminogen activator, urokinase receptor (PLAUR). Individuals in the highest category had a significantly lower overall survival time than those from the low-risk group. A receiver operating curve analysis was used to demonstrate the predictive ability of the predictive gene signature. Through multivariate Cox analysis, the risk score was discovered to be a predictor of overall survival (OS). According to functional assessments, the immunological state and milieu of the two risk areas were significantly different. The expression levels of predictive genes were found to be strongly linked to the sensitivity of cancer cells to antitumor therapy. Conclusion A new signature made up of three respective response-relevant genes is found to be a promising indicator of prognosis by influencing the immune condition and tumor microenvironment.

Integrated analysis of inflammatory response subtype-related signature to predict clinical outcomes, immune status and drug targets in lower-grade glioma.

Background: The inflammatory response in the tumor immune microenvironment has implications for the progression and prognosis in glioma. However, few inflammatory response-related biomarkers for lower-grade glioma (LGG) prognosis and immune infiltration have been identified. We aimed to construct and identify the prognostic value of an inflammatory response-related signature, immune infiltration, and drug targets for LGG. Methods: The transcriptomic and clinical data of LGG samples and 200 inflammatory response genes were obtained from public databases. The LGG samples were separated into two inflammatory response-related subtypes based on differentially expressed inflammatory response genes between LGG and normal brain tissue. Next, inflammatory response-related genes (IRRGs) were determined through a difference analysis between the aforementioned two subtypes. An inflammatory response-related prognostic model was constructed using IRRGs by using univariate Cox regression and Lasso regression analyses and validated in an external database (CGGA database). ssGSEA and ESTIMATE algorithms were conducted to evaluate immune infiltration. Additionally, we performed integrated analyses to investigate the correlation between the prognostic signature and N 6-methyladenosine mRNA status, stemness index, and drug sensitivity. We finally selected MSR1 from the prognostic signature for further experimental validation. Results: A total of nine IRRGs were identified to construct the prognostic signature for LGG. LGG patients in the high-risk group presented significantly reduced overall survival than those in the low-risk group. An ROC analysis confirmed the predictive power of the prognostic model. Multivariate analyses identified the risk score as an independent predictor for the overall survival. ssGSEA revealed that the immune status was definitely disparate between two risk subgroups, and immune checkpoints such as PD-1, PD-L1, and CTLA4 were significantly expressed higher in the high-risk group. The risk score was strongly correlated with tumor stemness and m6A. The expression levels of the genes in the signature were significantly associated with the sensitivity of tumor cells to anti-tumor drugs. Finally, the knockdown of MSR1 suppressed LGG cell migration, invasion, epithelial–mesenchymal transition, and proliferation. Conclusion: The study constructed a novel signature composed of nine IRRGs to predict the prognosis, potential drug targets, and impact immune infiltration status in LGG, which hold promise for screening prognostic biomarkers and guiding immunotherapy for LGG.

A novel inflammatory response-related signature predicts the prognosis of cutaneous melanoma and the effect of antitumor drugs

Cutaneous melanoma (CM) is a skin cancer that is highly metastatic and aggressive, with a dismal prognosis. This is the first study to use inflammatory response-related genes to build a model and evaluate their predictive significance in CM. This study used public databases to download CM patients’ mRNA expression profiles and clinical data to create multigene prognostic markers in the UCSC cohort. We compared overall survival (OS) between high- and low-risk groups using the Kaplan-Meier curve and determined independent predictors using Cox analysis. We also used enrichment analysis to assess immune cell infiltration fraction and immune pathway-related activity using KEGG enrichment analysis. Furthermore, we detected prognostic genes’ mRNA and protein expression in CM and normal skin tissues using qRT-PCR and immunohistochemistry. Finally, we developed a 5-gene predictive model that showed that patients in the high-risk group had a considerably shorter OS than those in the low-risk group. The analysis of the receiver operating characteristic (ROC) curve proved the model’s predictive ability. We also conducted a drug sensitivity analysis and discovered that the expression levels of prognostic genes were substantially linked with cancer cell sensitivity to antitumor medicines. The findings show that the model we developed, which consists of five inflammatory response-related genes, can be used to forecast the prognosis and immunological state of CM, giving personalized and precision medicine a new goal and direction.

Expression Profile of Inflammation Response Genes and Potential Regulatory Mechanisms in Dilated Cardiomyopathy.

Background The inflammatory response is important in dilated cardiomyopathy (DCM). However, the expression of inflammatory response genes (IRGs) and regulatory mechanisms in DCM has not been well characterized. Methods We analyzed 27,665 cells of single-cell RNA sequencing dataset of four DCM samples and two healthy controls (HC). IRGs among differentially expressed genes (DEGs) of active cell clusters were screened from the Molecular Signatures Database (MSigDB). The bulk sequencing dataset of 166 DCM patients and 166 HC was analyzed to explore the common IRGs. The biological functions of the IRGs were analyzed according to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. IRG-related transcription factors (TFs) were determined using the TRRUST database. The protein–protein interaction (PPI) network was constructed using the STRING database. Then, we established the noncoding RNA (ncRNA) regulatory network based on the StarBase database. Finally, the potential drugs that target IRGs were explored using the Drug Gene Interaction Database (DGIdb). Results The proportions of dendritic cells (DCs), B cells, NK cells, and T cells were increased in DCM patients, whereas monocytes were decreased. DCs expressed more IRGs in DCM. The GO and KEGG analyses indicated that the functional characteristics of active cells mainly focused on the immune response. Thirty-nine IRGs were commonly expressed among active cell cluster DEGs, bulk RNA DEGs, and inflammatory response-related genes. ETS1 plays an important role in regulation of IRG expression. The competing endogenous RNA regulatory network showed the relationship between ncRNA and IRGs. Sankey diagram showed that arachidonate 5-lipoxygenase (ALOX5) played a major role in regulation between TFs and potential drugs. Conclusion DCs infiltrate into the myocardium and contribute to the immune response in DCM. The transcription factor ETS1 plays an important role in regulation of IRGs. Moreover, ALOX5 may be a potential therapeutic target for DCM.

Mesenchymal stem cell spheroids alleviate neuropathic pain by modulating chronic inflammatory response genes.

Chronic neuropathic pain is caused by dysfunction of the peripheral nerves associated with the somatosensory system. Mesenchymal stem cells (MSCs) have attracted attention as promising cell therapeutics for chronic pain; however, their clinical application has been hampered by the poor in vivo survival and low therapeutic efficacy of transplanted cells. Increasing evidence suggests enhanced therapeutic efficacy of spheroids formed by three-dimensional culture of MSCs. In the present study, we established a neuropathic pain murine model by inducing a chronic constriction injury through ligation of the right sciatic nerve and measured the therapeutic effects and survival efficacy of spheroids. Monolayer-cultured and spheroids were transplanted into the gastrocnemius muscle close to the damaged sciatic nerve. Transplantation of spheroids alleviated chronic pain more potently and exhibited prolonged in vivo survival compared to monolayer-cultured cells. Moreover, spheroids significantly reduced macrophage infiltration into the injured tissues. Interestingly, the expression of mouse-origin genes associated with inflammatory responses, Ccl11/Eotaxin, interleukin 1A, tumor necrosis factor B, and tumor necrosis factor, was significantly attenuated by the administration of spheroids compared to that of monolayer. These results suggest that MSC spheroids exhibit enhanced in vivo survival after cell transplantation and reduced the host inflammatory response through the regulation of main chronic inflammatory response-related genes.

SIG-1451, a Novel, Non-Steroidal Anti-Inflammatory Compound, Attenuates Light-Induced Photoreceptor Degeneration by Affecting the Inflammatory Process.

Age-related macular degeneration is a progressive retinal disease that is associated with factors such as oxidative stress and inflammation. In this study, we evaluated the protective effects of SIG-1451, a non-steroidal anti-inflammatory compound developed for treating atopic dermatitis and known to inhibit Toll-like receptor 4, in light-induced photoreceptor degeneration. SIG-1451 was intraperitoneally injected into rats once per day before exposure to 1000 lx light for 24 h; one day later, optical coherence tomography showed a decrease in retinal thickness, and electroretinogram (ERG) amplitude was also found to have decreased 3 d after light exposure. Moreover, SIG-1451 partially protected against this decrease in retinal thickness and increase in ERG amplitude. One day after light exposure, upregulation of inflammatory response-related genes was observed, and SIG-1451 was found to inhibit this upregulation. Iba-1, a microglial marker, was suppressed in SIG-1451-injected rats. To investigate the molecular mechanism underlying these effects, we used lipopolysaccharide (LPS)-stimulated rat immortalised Müller cells. The upregulation of C-C motif chemokine 2 by LPS stimulation was significantly inhibited by SIG-1451 treatment, and Western blot analysis revealed a decrease in phosphorylated I-κB levels. These results indicate that SIG-1451 indirectly protects photoreceptor cells by attenuating light damage progression, by affecting the inflammatory responses.

Inflammation in Preeclampsia: Genetic Biomarkers, Mechanisms, and Therapeutic Strategies.

ObjectivePreeclampsia is a common and serious complication of pregnancy, posing a threat to maternal and fetal safety due to the lack of effective biomarkers and treatment strategies. This study aimed to identify potential biomarkers that can be used to predict preeclampsia and identify the molecular mechanisms of preeclampsia pathogenesis and drug prediction at the transcriptome level.MethodsWe analyzed differential expression genes (DEGs) in preeclampsia and non-preeclampsia groups in the GSE75010 dataset, cross-linking with extracted inflammatory response-related genes to obtain differentially expressed inflammation-related genes (DINRGs). Enrichment analysis and protein-protein interaction (PPI) networks were constructed to understand the functions and enrichment pathways. Machine learning models were used to identify key genes associated with preeclampsia and build a nomogram in the training set, which was validated in the validation set. The R package RcisTarget was used to predict transcription factors, and Cytoscape was used to construct miRNA-mRNA pathways, which could identify the molecular mechanisms. Then, we conducted molecular docking of the obtained key genes INHBA (inhibin subunit beta A), OPRK1 (opioid receptor kappa 1), and TPBG (trophoblast glycoprotein), as well as predicted transcription factors with drug molecules. Additionally, the CIBERSORT method explored the differences in immune cell infiltration between preeclampsia and non-preeclampsia samples based on the GSE75010 dataset.ResultsA total of 69 DINRGs associated with preeclampsia patients were screened. INHBA, OPRK1, and TPBG were the key genes based on machine learning models. A nomogram for prediction was further constructed, and the receiver operating curves (ROCs) showed good performance. Based on the transcriptome level of key genes, we proposed that RELA-miR-548K/miR-1206-TPBG may be a potential RNA regulatory pathway regulating the progression of early preeclampsia. Molecular docking suggested the effectiveness of curcumin in the treatment of preeclampsia. Additionally, regulatory T cells (Tregs) and resting mast cells were significantly different between the two groups.ConclusionIn summary, we identified three key inflammation-associated genes, namely INHBA, OPRK1, and TPBG, which can be used as potential genetic biomarkers for preeclampsia prediction and treatment, and established a nomogram as a predictive model. Additionally, we provided insights into the mechanisms of preeclampsia development at the transcriptome level and performed corresponding drug predictions.

An Inflammatory Response-Related Gene Signature Reveals Distinct Survival Outcome and Tumor Microenvironment Characterization in Pancreatic Cancer.

Background: Desmoplasia or rich fibrotic stroma is a typical property of pancreatic cancer (PC), with a significant impact on tumor progression, metastasis, and chemotherapy response. Unusual inflammatory responses are considered to induce fibrosis of tissue, but the expression and clinical significance of inflammatory response-related genes in PC have not been clearly elucidated. Methods: Prognosis-related differentially expressed genes (DEGs) between tumor and normal tissues were identified by comparing the transcriptome data of PC samples based on The Cancer Genome Atlas (TCGA) portal and the Genotype Tissue Expression (GTEx) databases. Samples from the ArrayExpress database were used as an external validation cohort. Results: A total of 27 inflammatory response-related DEGs in PC were identified. Least absolute shrinkage and selection operator (LASSO) analysis revealed three core genes that served as an inflammatory response gene signature (IRGS), and a risk score was calculated. The diagnostic accuracy of the IRGS was validated in the training (n = 176) and validation (n = 288) cohorts, which reliably predicted the overall survival (OS) and disease-free survival (DFS) of patients with PC. Furthermore, multivariate analysis identified the risk score as an independent risk factor for OS and DFS. The comprehensive results suggested that a high IRGS score was correlated with decreased CD8+ T-cell infiltration, increased M2 macrophage infiltration, increased occurrence of stroma-activated molecular subtype and hypoxia, enriched myofibroblast-related signaling pathways, and greater benefit from gemcitabine. Conclusion: The IRGS was able to promisingly distinguish the prognosis, the tumor microenvironment characteristics, and the benefit from chemotherapy for PC.

Identification and Validation of lncRNA-SNHG17 in Lung Adenocarcinoma: A Novel Prognostic and Diagnostic Indicator.

BackgroundLung cancer has the highest death rate among cancers globally. Accumulating evidence has indicated that cancer-related inflammation plays an important role in the initiation and progression of lung cancer. However, the prognosis, immunological role, and associated regulation axis of inflammatory response-related gene (IRRGs) in non-small-cell lung cancer (NSCLC) remains unclear.MethodsIn this study, we perform comprehensive bioinformatics analysis and constructed a prognostic inflammatory response-related gene (IRRGs) and related competing endogenous RNA (ceRNA) network. We also utilized the Pearson’s correlation analysis to determine the correlation between IRRGs expression and tumor mutational burden (TMB), microsatellite instability (MSI), tumor-immune infiltration, and the drug sensitivity in NSCLC. Growth curve and Transwell assay used to verify the function of SNHG17 on NSCLC progression.ResultsFirst, we found that IRRGs were significantly upregulated in lung cancer, and its high expression was correlated with poor prognosis; high expression of IRRGs was significantly correlated with the tumor stage and poor prognosis in lung cancer patients. Moreover, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment indicated that these IRRGs are mainly involved in the inflammatory and immune response-related signaling pathway in the progression of NSCLC. We utilized 10 prognostic-related genes to construct a prognostic IRRGs model that could predict the overall survival of lung adenocarcinoma (LUAD) patients possessing high specificity and accuracy. Our evidence demonstrated that IRRGs expression was significantly correlated with the TMB, MSI, immune-cell infiltration, and diverse cancer-related drug sensitivity. Finally, we identified the upstream regulatory axis of IRRGs in NSCLC, namely, lncRNA MIR503HG/SNHG17/miR-330-3p/regulatory axis. Finally, knockdown of SNHG17 expression inhibited lung adenocarcinoma (LUAD) cell proliferation and migration. Our findings confirmed that SNHG17 is a novel oncogenic lncRNA and may be a biomarker for the prognosis and diagnosis of LUAD.ConclusionDNA hypomethylation/lncRNA MIR503HG/SNHG17/microRNA-330-3p/regulatory axis may be a valuable biomarker for prognosis and is significantly correlated with immune cell infiltration in lung cancer.

Polymethoxyflavone-rich Fraction from Citrus sunki Leaves Alleviates Renal Dysfunction in Mice with Unilateral Ureteral Obstruction

Polymethoxyflavones (PMFs) are flavonoid compounds present in citrus plants that are proposed to be advantageous to human health. However, the advantageous effects of PMFs in the context of renal dysfunction are unclear. In this study, we made a PMF-rich fraction (PRF) from the leaves of Citrus sunki Hort ex. Tanaka and identified its components using liquid chromatography and mass spectrometry. We then investigated the effect of PRF—comprising 9 types of PMF—on renal dysfunction induced by unilateral ureteral obstruction (UUO) in mice. Animals were divided into four experimental groups ( n = 7 per group): I) sham-operated group (Sham); II) UUO group (UUO); III) UUO + Enalapril 0.1 mg/1 mL (UUO + Enap); IV) UUO + PRF 100 mg/kg/day (UUO + PRF). All mice were orally administered with the drugs once a day from 7 days before UUO to 1 week after UUO. After the experiments were over, serum and tissues were taken for biochemical and histological analysis. PRF promoted the recovery of body weight in the background of UUO. Biochemical and histological analysis revealed that PRF ameliorated UUO-induced renal dysfunction and moderately reversed inflammation and tubulointerstitial fibrosis. Further, PRF inhibited the expression of endothelial nitric oxide synthase (eNOS), inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), transforming growth factor-β (TGF-β), collagen I (Col-I), and collagen IV (Col-IV). These results suggest that PRF improves UUO-induced renal dysfunction by regulating the expression of inflammatory and fibrotic response-related genes.