Inflammatory Response-related Genes Research Articles

Single and combined effects of polyethylene microplastics and acetochlor on accumulation and intestinal toxicity of zebrafish (Danio rerio)

The co-exposure of microplastics (MPs) and other contaminants has aroused extensive attention, but the combined impacts of MPs and pesticides remain poorly understood. Acetochlor (ACT), a widely used chloroacetamide herbicide, has raised concerns for its potential bio-adverse effects. This study evaluated the influences of polyethylene microplastics (PE-MPs) for acute toxicity, bioaccumulation, and intestinal toxicity in zebrafish to ACT. We found that PE-MPs significantly enhanced ACT acute toxicity. Also, PE-MPs increased the accumulation of ACT in zebrafish and aggravate the oxidative stress damage of ACT in intestines. Exposure to PE-MPs or/and ACT causes mild damage to the gut tissue of zebrafish and altered gut microbial composition. In terms of gene transcription, ACT exposure triggered a significant increase in inflammatory response-related gene expressions in the intestines, while some pro-inflammatory factors were found to be inhibited by PE-MPs. This study provides a new perspective on the fate of MPs in the environment and on the assessment of the combined effects of MPs and pesticides on organisms.

Development and validation of an inflammatory response-related signature in triple negative breast cancer for predicting prognosis and immunotherapy.

Inflammation is one of the most important characteristics of tumor tissue. Signatures based on inflammatory response-related genes (IRGs) can predict prognosis and treatment response in a variety of tumors. However, the clear function of IRGs in the triple negative breast cancer (TNBC) still needs to be explored. IRGs clusters were discovered via consensus clustering, and the prognostic differentially expressed genes (DEGs) across clusters were utilized to develop a signature using a least absolute shrinkage and selection operator (LASSO). Verification analyses were conducted to show the robustness of the signature. The expression of risk genes was identified by RT-qPCR. Lastly, we formulated a nomogram to improve the clinical efficacy of our predictive tool. The IRGs signature, comprised of four genes, was developed and was shown to be highly correlated with the prognoses of TNBC patients. In contrast with the performance of the other individual predictors, we discovered that the IRGs signature was remarkably superior. Also, the ImmuneScores were elevated in the low-risk group. The immune cell infiltration showed significant difference between the two groups, as did the expression of immune checkpoints. The IRGs signature could act as a biomarker and provide a momentous reference for individual therapy of TNBC.

Effects of dietary vitamin E on the growth performance, immunity and digestion of Epinephelus fuscoguttatus ♀ × Epinephelus lanceolatus ♂ by physiology, pathology and RNA-seq

Vitamin E (VE) is an essential nutrient for most animals. Investigating the requirement and effects of vitamin E for Epinephelus fuscoguttatus ♀ × Epinephelus lanceolatus ♂ would be meaningful. In the study, a 12-week feeding trial was conducted via the juvenile fish (initial body weight: 20.48 ± 0.20 g), and the physiological and biochemical indexes detection, histology observation and transcriptome analysis were operated to reach our goals. The results showed that vitamin E deficiency would result in bad effects on the growth, immunity and digestion, and induced some injury for the tissues. The analyse of quadratic regression curve and broken-line regression curve for some representative parameters revealed the optimal dosage of dietary VE for the fish was at 130.12–156.52 mg/kg. Comparative transcriptomic analyses revealed that VE mainly regulated the inflammatory response related genes(IL-1β, Irak3, Irak4, Nfkbia), immune related genes (Cd40, Fos, Cxcl9, Ccl20, Cxcr, Tnfaip3, Akt, Calca), membrane stability genes (Fgf1, Mrl and Gadd45b) and metabolism related genes (Gck and Ins) to influence the liver function and for the foregut, the functional metabolism related genes were Try3, Prss2, Cel, Pla2g1b, Ins Socs3 and Sqle and the Socs3 maybe also an important function gene in maintaining a good status of the foregut. Our study provided a new insight of VE function on the fish, meanwhile, it enriched the bioinformatic data of this species, as well as contributed to the healthy culture of the fish.

A novel predictive model based on inflammatory response-related genes for predicting endometrial cancer prognosis and its experimental validation.

Inflammatory response is an important feature of most tumors. Local inflammation promotes tumor cell immune evasion and chemotherapeutic drug resistance. We aimed to build a prognostic model for endometrial cancer patients based on inflammatory response-related genes (IRGs). RNA sequencing and clinical data for uterine corpus endometrial cancer were obtained from TCGA datasets. LASSO-penalized Cox regression was used to obtain the risk formula of the model: the score = esum(corresponding coefficient × each gene's expression). The "ESTIMATE" and "pRRophetic" packages in R were used to evaluate the tumor microenvironment and the sensitivity of patients to chemotherapy drugs. Data sets from IMvigor210 were used to evaluate the efficacy of immunotherapy in cancer patients. For experimental verification, 37 endometrial cancer and 43 normal endometrial tissues samples were collected. The mRNA expression of the IRGs was measured using qRT-PCR. The effects of IRGs on the malignant biological behaviors of endometrial cancer were detected using CCK-8, colony formation, Transwell invasion, and apoptosis assays. We developed a novel prognostic signature comprising 13 IRGs, which is an independent prognostic marker for endometrial cancer. A nomogram was developed to predict patient survival accurately. Three key IRGs (LAMP3, MEP1A, and ROS1) were identified in this model. Furthermore, we verified the expression of the three key IRGs using qRT-PCR. Functional experiments also confirmed the influence of the three key IRGs on the malignant biological behavior of endometrial cancer. Thus, a characteristic model constructed using IRGs can predict the survival, chemotherapeutic drug sensitivity, and immunotherapy response in patients with endometrial cancer.

Inflammatory response-related genes predict prognosis in patients with HNSCC

BackgroundHead and neck squamous cell carcinoma (HNSCC) is the most common malignancy of the head and neck, and the inflammatory microenvironment can impact the prognosis of HNSCC. However, the contribution of inflammation to tumour progression has not been fully elucidated. MethodsThe mRNA expression profiles and corresponding clinical data of HNSCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The least absolute shrinkage and selection operator (LASSO) Cox analysis model was used to identify prognostic genes. The overall survival (OS) between high- and low-risk patients was compared by Kaplan‒Meier analysis. The independent predictors of OS were determined by univariate and multivariate Cox analyses. Single-sample gene set enrichment analysis (ssGSEA) was used to assess immune cell infiltration and immune-related pathway activity. GSEA was used to analyse Gene Ontology (GO) terms and Kyoto encyclopaedia of Genes and Genomes (KEGG) pathways. The Gene Expression Profiling Interactive Analysis (GEPIA) database was used to examine prognostic genes in HNSCC patients. Immunohistochemistry was used to verify the protein expression of prognostic genes in HNSCC samples. ResultsAn inflammatory response-related gene signature was constructed by LASSO Cox regression analysis. HNSCC patients in the high-risk group showed significantly reduced OS compared with those in the low-risk group. The predictive capacity of the prognostic gene signature was confirmed by ROC curve analysis. Multivariate Cox analysis revealed that the risk score was an independent predictor for OS. Functional analysis indicated that the immune status was markedly different between the two risk groups. The risk score was significantly related to tumour stage and immune subtype. The expression levels of the prognostic genes were significantly related to the sensitivity of cancer cells to antitumour drugs. Furthermore, high expression of the prognostic genes significantly predicted poor prognosis of HNSCC patients. ConclusionsThe novel signature containing 9 inflammatory response-related genes reflects the immune status of HNSCC and can be used for prognosis prediction. Furthermore, the genes may be potential targets for HNSCC treatment.

Development of a machine learning-based signature utilizing inflammatory response genes for predicting prognosis and immune microenvironment in ovarian cancer.

Ovarian cancer (OC) represents a significant health challenge, characterized by a particularly unfavorable prognosis for affected women. Accumulating evidence supports the notion that inflammation-related factors impacting the normal ovarian epithelium may contribute to the development of OC. However, the precise role of inflammatory response-related genes (IRRGs) in OC remains largely unknown. To address this gap, we performed an integration of mRNA expression profiles from 7 cohorts and conducted univariate Cox regression analysis to screen 26 IRRGs. By utilizing these IRRGs, we categorized patients into subtypes exhibiting diverse inflammatory responses, with subtype B displaying the most prominent immune infiltration. Notably, the elevated abundance of Treg cells within subtype B contributed to immune suppression, resulting in an unfavorable prognosis for these patients. Furthermore, we validated the distribution ratios of stromal cells, inflammatory cells, and tumor cells using whole-slide digitized histological slides. We also elucidated differences in the activation of biological pathways among subtypes. In addition, machine learning algorithms were employed to predict the likelihood of survival in OC patients based on the expression of prognostic IRRGs. Through rigorous testing of over 100 combinations, we identified CXCL10 as a crucial IRRG. Single-cell analysis and vitro experiments further confirmed the potential secretion of CXCL10 by macrophages and its involvement in lymphangiogenesis within the tumor microenvironment. Overall, the study provides new insights into the role of IRRGs in OC and may have important implications for the development of novel therapeutic approaches.

Predicting the Progress of Tuberculosis by Inflammatory Response-Related Genes Based on Multiple Machine Learning Comprehensive Analysis.

Tuberculosis (TB), caused by the bacterium Mycobacterium tuberculosis, affects approximately one-quarter of the global population and is considered one of the most lethal infectious diseases worldwide. The prevention of latent tuberculosis infection (LTBI) from progressing into active tuberculosis (ATB) is crucial for controlling and eradicating TB. Unfortunately, currently available biomarkers have limited effectiveness in identifying subpopulations that are at risk of developing ATB. Hence, it is imperative to develop advanced molecular tools for TB risk stratification. The TB datasets were downloaded from the GEO database. Three machine learning models, namely LASSO, RF, and SVM-RFE, were used to identify the key characteristic genes related to inflammation during the progression of LTBI to ATB. The expression and diagnostic accuracy of these characteristic genes were subsequently verified. These genes were then used to develop diagnostic nomograms. In addition, single-cell expression clustering analysis, immune cell expression clustering analysis, GSVA analysis, immune cell correlation, and immune checkpoint correlation of characteristic genes were conducted. Furthermore, the upstream shared miRNA was predicted, and a miRNA-genes network was constructed. Candidate drugs were also analyzed and predicted. In comparison to LTBI, a total of 96 upregulated and 26 downregulated genes related to the inflammatory response were identified in ATB. These characteristic genes have demonstrated excellent diagnostic performance and significant correlation with many immune cells and immune sites. The results of the miRNA-genes network analysis suggested a potential role of hsa-miR-3163 in the molecular mechanism of LTBI progressing into ATB. Moreover, retinoic acid may offer a potential avenue for the prevention of LTBI progression to ATB and for the treatment of ATB. Our research has identified key inflammatory response-related genes that are characteristic of LTBI progression to ATB and hsa-miR-3163 as a significant node in the molecular mechanism of this progression. Our analyses have demonstrated the excellent diagnostic performance of these characteristic genes and their significant correlation with many immune cells and immune checkpoints. The CD274 immune checkpoint presents a promising target for the prevention and treatment of ATB. Furthermore, our findings suggest that retinoic acid may have a role in preventing LTBI from progressing to ATB and in treating ATB. This study provides a new perspective for differential diagnosis of LTBI and ATB and may uncover potential inflammatory immune mechanisms, biomarkers, therapeutic targets, and effective drugs in the progression of LTBI into ATB.

Establishment of a Nomogram Based on Inflammatory Response-Related Methylation Sites in Intraoperative Visceral Adipose Tissue to Predict EWL% at One Year After LSG.

Laparoscopic sleeve gastrectomy (LSG) is considered as an effective bariatric and metabolic surgery for patients with severe obesity. Chronic low-grade inflammation of adipose tissue is associated with obesity and obesity-related complications. This study intends to establish a nomogram based on inflammatoryresponse-related methylation sites in intraoperative visceral adipose tissue (VAT) to predict excess weight loss (EWL)% at one-year after LSG. Based on EWL% at one-year after LSG, patients were divided into two groups: the satisfied group (group-A, EWL%≥50%) and the unsatisfied group (group-B, EWL%<50%). Next, we defined genes corresponding to the methylation sites in the 850 K methylation microarray as methylation-related genes (MRGs). We then took the intersection of MRGs and inflammatoryresponse-related genes. After that, inflammatoryresponse-related methylation sites were identified based on overlapping genes. Moreover, difference analysis was carried out to obtain inflammatoryresponse-related differentially methylated sites (IRRDMSs) between group-A and group-B. LASSO analysis was used to identify the hub methylation sites. Finally, we developed a nomogram based on the hub methylation sites. There were 26 patients in the study, with 13 in group-A and 13 in group-B. After data filtering and difference analysis, 200 IRRDMSs were identified (143 hypermethylated sites and 57 hypomethylated sites). Then, we identified three hub methylation sites (cg03610073, cg03208951, and cg18746357) by LASSO analysis and built a predictive nomogram (Area under the curve=0.953). The predictive nomogram based on three inflammatory-related methylation sites (cg03610073, cg03208951, and cg18746357) in intraoperative visceral adipose tissue can predict one-year EWL% after LSG effectively.

ZNF667 Suppressed LPS-induced Macrophages Inflammation through mTORdependent Aerobic Glycolysis Regulation.

Macrophages participate in all stages of the inflammatory response, and the excessive release of inflammatory mediators and other cytokines synthesized and secreted by macrophages is fundamentally linked to an uncontrolled inflammatory response. The zinc finger 667 (ZNF667) protein, a novel DNAbinding protein, has been shown to play a vital role in oxidative stress. However, none of the target genes in macrophages or the potential roles of ZNF667 have been elucidated to date. The present study was designed to investigate the effects of ZNF667 on LPS-induced inflammation in macrophages. The RAW264.7 macrophage cell line was selected as a model system. Inflammatory response-related gene expression levels and phosphorylation levels of PI3K, AKT, and mTOR were detected in LPS-treated macrophages via RT-PCR and western blotting, respectively. We found that LPS resulted in the up-regulation of ZNF667 in macrophages and a peak response in ZNF667 protein expression levels when used at a concentration of 100 ng/mL. ZNF667 overexpression significantly inhibited the LPS-induced up-regulation of iNOS, and IL-1β mRNA and protein expression levels, together with the secretion of IL-1β, IL-6, and TNF-α. ZNF667 overexpression also inhibited PI3K, AKT, and mTOR hyperphosphorylation and had no effect on the phosphorylation of NF-κB p65, ERK1/2, MAPK p38, and the transcriptional activity of NF-κB in macrophages. The up-regulation of ZNF667 inhibited the levels of expression of HK2 and PFKFB3, glucose consumption, and lactate production in LPS-stimulated macrophages. The up-regulation of mRNA levels of LPS-induced glycolytic genes HK2 and PFKFB3 and the increased mRNA expression of pro-inflammatory cytokines (IL-1β and iNOS) were abolished by hexokinase inhibitor 2-DG in ZNF667-deficient macrophages. Meanwhile, glucose consumption and lactate production were abrogated in macrophages when cells were treated with the specific mTOR inhibitor RPM. Our results demonstrate that ZNF667 suppressed LPS-stimulated RAW264.7 macrophage inflammation by regulating mTOR-dependent aerobic glycolysis.

Development and validation of an inflammatory response-related gene and clinical factor-based signature for predicting prognosis in gastric cancer.

Gastric cancer (GC) is an aggressive disease that requires prognostic tools to aid in clinical management. The prognostic power of clinical features is unsatisfactory, which might be improved by combining mRNA-based signatures. Inflammatory response is widely associated with cancer development and treatment response. It is worth exploring the prognostic performance of inflammatory-related genes plus clinical factors in GC. An 11-gene signature was trained using the least absolute shrinkage and selection operator (LASSO) based on the messenger RNA (mRNA) and overall survival (OS) data of The Cancer Genome Atlas-stomach adenocarcinoma (TCGA-STAD) cohort. A nomogram was established using the signature and clinical factors with a significant linkage with OS and was validated in 3 independent cohorts (GSE15419, GSE13861, and GSE66229) via calculating the area under the receiver operator characteristic curve (AUC). The association between the signature and immunotherapy efficacy was explored in the ERP107734 cohort. A high risk score was associated with shorter OS in both the training and the validation sets (the AUC for 1-, 3-, 5-year in TCGA-STAD cohort: 0.691, 0.644, and 0.707; GSE15459: 0.602, 0.602, and 0.650; GSE13861: 0.648, 0.611, and 0.647; GSE66229: 0.661, 0.630, and 0.610). Its prognostic power was improved by combining clinical factors including age, sex, and tumor stage (the AUC for 1-, 3-, 5-year in TCGA-STAD cohort: 0.759, 0.706, and 0.742; GSE15459: 0.773, 0.786, and 0.803; GSE13861: 0.749, 0.881, and 0.795; GSE66229: 0.773, 0.735, and 0.722). Moreover, a low-risk score was associated with a favorable response to pembrolizumab monotherapy in the advanced setting (AUC =0.755, P=0.010). In GCs, the inflammatory response-related gene-based signature was related to immunotherapy efficacy, and its risk score plus clinical features yielded robust prognostic power. With prospective validation, this model may improve the management of GC by enabling risk stratification and the prediction of response to immunotherapy.

Identification of CCL20 as a Key Biomarker of Inflammatory Responses in the Pathogenesis of Intracerebral Hemorrhage.

Inflammatory responses after intracerebral hemorrhage (ICH) contribute to severe secondary brain injury, leading to poor clinical outcomes. However, the responsible genes for effective anti-inflammation treatment in ICH remain poorly elucidated. The differentially expressed genes (DEGs) of human ICH were explored by online GEO2R. Go and KEGG were used to explore the biological function of DEGs. Protein-protein interactions (PPI) were built in the String database. Critical modules of PPI were identified by a molecular complex detection algorithm (MCODE). Cytohubba was used to determine the hub genes. The mRNA-miRNA interaction network was built in the miRWalk database. The rat ICH model was applied to validate the key genes. A total of 776 DEGs were identified in ICH. Go and KEGG analyses indicated that DEGs were mainly involved in neutrophil activation and the TNF signaling pathway. GSEA analysis presented that DEGs were significantly enriched in TNF signaling and inflammatory response. PPI network was constructed in the 48 differentially expressed inflammatory response-related genes. The critical module of the PPI network was constructed by 7 MCODE genes and functioned as the inflammatory response. The top 10 hub genes with the highest degrees were identified in the inflammatory response after ICH. CCL20 was confirmed as a key gene and mainly expressed in neurons in the rat ICH model. The regulatory network between CCL20 and miR-766 was built, and the miR-766 decrease was confirmed in a human ICH dataset. CCL20 is a key biomarker of inflammatory response after intracerebral hemorrhage, providing a potential target for inflammatory intervention in ICH.

The prognostic and immune significance of C15orf48 in pan-cancer and its relationship with proliferation and apoptosis of thyroid carcinoma.

C15orf48 was recently identified as an inflammatory response-related gene; however there is limited information on its function in tumors. In this study, we aimed to elucidate the function and potential mechanism of action of C15orf48 in cancer. We evaluated the pan-cancer expression, methylation, and mutation data of C15orf48 to analyze its clinical prognostic value. In addition, we explored the pan-cancer immunological characteristics of C15orf48, especially in thyroid cancer (THCA), by correlation analysis. Additionally, we conducted a THCA subtype analysis of C15orf48 to determine its subtype-specific expression and immunological characteristics. Lastly, we evaluated the effects of C15orf48 knockdown on the THCA cell line, BHT101, by in vitro experimentation. The results of our study revealed that C15orf48 is differentially expressed in different cancer types and that it can serve as an independent prognostic factor for glioma. Additionally, we found that the epigenetic alterations of C15orf48 are highly heterogeneous in several cancers and that its aberrant methylation and copy number variation are associated with poor prognosis in multiple cancers. Immunoassays elucidated that C15orf48 was significantly associated with macrophage immune infiltration and multiple immune checkpoints in THCA, and was a potential biomarker for PTC. In addition, cell experiments showed that the knockdown of C15orf48 could reduce the proliferation, migration, and apoptosis abilities of THCA cells. The results of this study indicate that C15orf48 is a potential tumor prognostic biomarker and immunotherapy target, and plays an essential role in the proliferation, migration, and apoptosis of THCA cells.

An inflammation-related gene landscape predicts prognosis and response to immunotherapy in virus-associated hepatocellular carcinoma.

Due to the viral infection, chronic inflammation significantly increases the likelihood of hepatocellular carcinoma (HCC) development. Nevertheless, an inflammation-based signature aimed to predict the prognosis and therapeutic effect in virus-related HCC has rarely been established. Based on the integrated analysis, inflammation-associated genes (IRGs) were systematically assessed. We comprehensively investigated the correlation between inflammation and transcriptional profiles, prognosis, and immune cell infiltration. Then, an inflammation-related risk model (IRM) to predict the overall survival (OS) and response to treatment for virus-related HCC patients was constructed and verified. Also, the potential association between IRGs and tumor microenvironment (TME) was investigated. Ultimately, hub genes were validated in plasma samples and cell lines via qRT-PCR. After transfection with shCCL20 combined with overSLC7A2, morphological change of SMMC7721 and huh7 cells was observed. Tumorigenicity model in nude mouse was established. An inflammatory response-related gene signature model, containing MEP1A, CCL20, ADORA2B, TNFSF9, ICAM4, and SLC7A2, was constructed by conjoint analysis of least absolute shrinkage and selection operator (LASSO) Cox regression and gaussian finite mixture model (GMM). Besides, survival analysis attested that higher IRG scores were positively relevant to worse survival outcomes in virus-related HCC patients, which was testified by external validation cohorts (the ICGC cohort and GSE84337 dataset). Univariate and multivariate Cox regression analyses commonly proved that the IRG was an independent prognostic factor for virus-related HCC patients. Thus, a nomogram with clinical factors and IRG was also constructed to superiorly predict the prognosis of patients. Featured with microsatellite instability-high, mutation burden, and immune activation, lower IRG score verified a superior OS for sufferers. Additionally, IRG score was remarkedly correlated with the cancer stem cell index and drug susceptibility. The measurement of plasma samples further validated that CCL20 upexpression and SLC7A2 downexpression were positively related with virus-related HCC patients, which was in accord with the results in cell lines. Furthermore, CCL20 knockdown combined with SLC7A2 overexpression availably weakened the tumor growth in vivo. Collectively, IRG score, serving as a potential candidate, accurately and stably predicted the prognosis and response to immunotherapy in virus-related HCC patients, which could guide individualized treatment decision-making for the sufferers.

Identification of diagnostic markers related to oxidative stress and inflammatory response in diabetic kidney disease by machine learning algorithms: Evidence from human transcriptomic data and mouse experiments.

Diabetic kidney disease (DKD) is a long-term complication of diabetes and causes renal microvascular disease. It is also one of the main causes of end-stage renal disease (ESRD), which has a complex pathophysiological process. Timely prevention and treatment are of great significance for delaying DKD. This study aimed to use bioinformatics analysis to find key diagnostic markers that could be possible therapeutic targets for DKD. We downloaded DKD datasets from the Gene Expression Omnibus (GEO) database. Overexpression enrichment analysis (ORA) was used to explore the underlying biological processes in DKD. Algorithms such as WGCNA, LASSO, RF, and SVM_RFE were used to screen DKD diagnostic markers. The reliability and practicability of the the diagnostic model were evaluated by the calibration curve, ROC curve, and DCA curve. GSEA analysis and correlation analysis were used to explore the biological processes and significance of candidate markers. Finally, we constructed a mouse model of DKD and diabetes mellitus (DM), and we further verified the reliability of the markers through experiments such as PCR, immunohistochemistry, renal pathological staining, and ELISA. Biological processes, such as immune activation, T-cell activation, and cell adhesion were found to be enriched in DKD. Based on differentially expressed oxidative stress and inflammatory response-related genes (DEOIGs), we divided DKD patients into C1 and C2 subtypes. Four potential diagnostic markers for DKD, including tenascin C, peroxidasin, tissue inhibitor metalloproteinases 1, and tropomyosin (TNC, PXDN, TIMP1, and TPM1, respectively) were identified using multiple bioinformatics analyses. Further enrichment analysis found that four diagnostic markers were closely related to various immune cells and played an important role in the immune microenvironment of DKD. In addition, the results of the mouse experiment were consistent with the bioinformatics analysis, further confirming the reliability of the four markers. In conclusion, we identified four reliable and potential diagnostic markers through a comprehensive and systematic bioinformatics analysis and experimental validation, which could serve as potential therapeutic targets for DKD. We performed a preliminary examination of the biological processes involved in DKD pathogenesis and provide a novel idea for DKD diagnosis and treatment.

Prognostic analysis of inflammatory response-related genes and biomarkers in patients with urothelial carcinoma of ureter.

Ureteral urothelial carcinoma is a common urinary system tumor, accounting for 40% to 60% of all ureteral diseases. This study attempted to analyze the prognosis of patients with urothelial carcinoma, judging ureteral urothelial carcinoma by genes and biomarkers of inflammatory response. In this paper, co-expression network analysis and gene-based image fusion evaluation methods were proposed to obtain the prognosis results of patients with ureteral urothelial carcinoma. The experimental results showed that the levels of PLR and NLR increased, and the levels of HGB and HCT decreased; high PLR and high NLR levels, low HGB and low HCT levels were all risk factors affecting bladder urothelial carcinoma, and their ratios (OR) were 1.023, 1.611, 0.961, 0.859, 1.015, 1.072, 0.979, and 0.951, respectively. However, high PLR and high NLR levels were independent risk factors for bladder urothelial carcinoma, and their OR values were 1.497 and 1.071, respectively. Through biomarker diagnosis, the area under the curve, sensitivity, specificity and Youden index of hsa-mir-17, hsa-mir-93, hsa-mir-429 and hsa-mir-20a all exceeded 0.9, indicating that this is a potential diagnostic indicators. All in all, during the treatment of ureteral cancer, in order to reduce tumor recurrence, systemic therapy should be combined with ureteral cancer. In addition, this study also analyzed the prognosis of chemotherapy patients, and the results showed that immunotherapy may increase the risk of tumor cell reperfusion during chemotherapy.

Construction and Verification of the Molecular Subtype and a Novel Prognostic Signature Based on Inflammatory Response-Related Genes in Uveal Melanoma.

The significance of inflammation in tumorigenesis and progression has become prominent. This study aimed to construct and validate the molecular subtype and a novel prognostic signature based on inflammatory response-related genes in uveal melanoma (UM). Patients from the TCGA, GSE84976, and GSE22138 UM cohorts were enrolled. According to the consensus cluster analysis, patients were divided into two molecular subtypes, namely IC1 and IC2. Survival curves showed that patients in IC1 had a better prognosis. The IC2 subgroup had higher levels of immune cell infiltration and more enriched immunological pathways. There were statistical differences in the immune-inflammation microenvironment, immune checkpoint genes expression, and drug sensitivity. The prognostic signature constructed based on inflammatory response-related genes exhibited a stable predictive power. Multivariate analysis confirmed that the signature was a prognostic factor independent of clinical characteristics. Functional analyses showed that the high-risk group was associated with immunological response, inflammatory cell activation, and tumor-related signal pathways. The riskscore had a negative relationship with tumor purity and was positively correlated with immune and stromal scores. Furthermore, the prognostic signature could sensitively predict the response to drug treatments. In conclusion, the prognostic signature might aid in stratifying patients at risk premised on the prognosis and immunotherapy sensitivity.

Development and validation of an inflammatory response-related prognostic model and immune infiltration analysis in glioblastoma.

Despite receiving standard treatment, the prognosis of glioblastoma (GBM) patients is still poor. Considering the heterogeneity of each patient, it is imperative to identify reliable risk model that can effectively predict the prognosis of each GBM patient to guide the personalized treatment. Transcriptomic gene expression profiles and corresponding clinical data of GBM patients were downloaded from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Inflammatory response-related genes were extracted from Gene Set Enrichment Analysis (GSEA) website. Univariate Cox regression analysis was used for prognosis-related inflammatory genes (P<0.05). A polygenic prognostic risk model was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Validation was performed through CGGA cohort. Overall survival (OS) was compared by Kaplan-Meier analysis. A nomogram was plotted to accurately predict the prognosis for each patient. GSEA was used for the pathway enrichment analysis. The single sample GSEA (ssGSEA) algorithm was implemented to conduct the immune infiltration analysis. The potential role of oncostatin M receptor (OSMR) in GBM was investigated through the in vitro experiment. A prognostic risk model consisting of 4 genes (PTPRN, OSMR, MYD88, and EFEMP2) was developed. GBM patients in the high-risk group had worse OS. The time-dependent ROC curves showed an area under the curve (AUC) of 0.782, 0.765, and 0.784 for 1-, 2-, and 3-year survival in TCGA cohort, while the AUC in the CGGA cohort was 0.589, 0.684, and 0.785 at 1, 2, and 3 years, respectively. The risk score, primary-recurrent-secondary (PRS) type, and isocitrate dehydrogenase (IDH) mutation could predict the prognosis of GBM patients well. The nomogram accurately predicted the 1-, 2-, and 3-year OS for each patient. Immune cell infiltration was associated with the risk score and the model could predict immunotherapy responsiveness. The expression of the prognostic gene was correlated with the sensitivity to antitumor drugs. Interference of OSMR inhibited proliferation and migration and promoted apoptosis of GBM cells. The prognostic model based on 4 inflammatory response-related genes had reliable predictive power to effectively predict clinical outcome in GBM patients and provided the guide for the personalized treatment.

Identification of a Diagnosis and Therapeutic Inflammatory Response-Related Gene Signature Associated with Esophageal Adenocarcinoma.

The purpose of this study is to identify the key regulatory genes related to the inflammatory response of esophageal adenocarcinoma (EAC) and to find new diagnosis and therapeutic options. We downloaded the dataset GSE72874 from the Gene Expression Omnibus database for this study. Weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) analysis were used to find common inflammatory response-related genes (IRRGs) in EAC. The relationship between normal and tumor immune infiltration was analyzed using an online database of CIBERSORTx. Finally, 920 DEGs were identified, of which 5 genes were key IRRGs associated with EAC, including three down-regulated genes GNA15, MXD1, and NOD2, and two down-regulated genes PLAUR and TIMP1. Further research found that GNA15, MXD1, and NOD2 were down-regulated, PLAUR and TIMP1 were up-regulated in Barrett's esophagus (BE). In addition, we found that the expression of GNA15 and MXD1 in normal esophageal squamous epithelial cells decreased after ethanol treatment, while the expression of PLAUR and TIMP1 increased after ethanol treatment. Compared with normal esophageal tissue, immune cells infiltrated such as plasma cells, macrophages M0, macrophages M1, macrophages M2, dendritic cells activated, and mast cells activated were significantly increased in EAC, while immune cells infiltrated such as T cells CD4 memory resting, T cells follicular helper, NK cells resting, and dendritic cells resting were significantly reduced. The receiver operating characteristic curve indicated that GNA15, MXD1, NOD2, PLAUR and TIMP1 expression had a performed well in diagnosing EAC from healthy control. GNA15, MXD1, NOD2, PLAUR and TIMP1 were identified and validated as novel potential biomarkers for early diagnosis and may be new molecular targets for treatment of EAC.

Effects of zinc deficiency on the regeneration of olfactory epithelium in mice.

The olfactory epithelium can regenerate after damage; however, the regeneration process is affected by various factors, such as viral infections, head trauma, and medications. Zinc is an essential trace element that has important roles in organ development, growth, and maturation. Zinc also helps regulate neurotransmission in the brain; nevertheless, its relationship with olfactory epithelium regeneration remains unclear. Therefore, we used a severe zinc deficiency mouse model to investigate the effects of zinc deficiency on olfactory epithelium regeneration. Male wild-type C57BL/6 mice were divided into zinc-deficient and control diet groups at the age of 4 weeks, and methimazole was administered at the age of 8 weeks to induce severe olfactory epithelium damage. We evaluated the olfactory epithelium before and 7, 14, and 28 d after methimazole administration by histologically analyzing paraffin sections. RNA sequencing was also performed at the age of 8 weeks before methimazole administration to examine changes in gene expression caused by zinc deficiency. In the zinc-deficient group, the regenerated olfactory epithelium thickness was decreased at all time points, and the numbers of Ki-67-positive, GAP43-positive, and olfactory marker protein-positive cells (i.e., proliferating cells, immature olfactory neurons, and mature olfactory neurons, respectively) failed to increase at some time points. Additionally, RNA sequencing revealed several changes in gene expression, such as a decrease in the expression of extracellular matrix-related genes and an increase in that of inflammatory response-related genes, in the zinc-deficient group. Therefore, zinc deficiency delays olfactory epithelium regeneration after damage in mice.

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.