scholarly journals Predictive Analyses of Prognostic-Related Immune Genes and Immune Infiltrates for Glioblastoma

Diagnostics ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 177
Author(s):  
Ping Liang ◽  
Yi Chai ◽  
He Zhao ◽  
Guihuai Wang
Keyword(s):  
Differential Expression ◽  
Roc Curve ◽  
Differential Expression Analysis ◽  
Model Performance ◽  
Predictive Biomarkers ◽  
Positive Interactions ◽  
Immune Genes ◽  
Kaplan Meier ◽  
Significant Difference ◽  
Immune Related Genes

Glioblastoma (GBM), the most common and aggressive brain tumor, has a very poor outcome and high tumor recurrence rate. The immune system has positive interactions with the central nervous system. Despite many studies investigating immune prognostic factors, there is no effective model to identify predictive biomarkers for GBM. Genomic data and clinical characteristic information of patients with GBM were evaluated by Kaplan–Meier analysis and proportional hazard modeling. Deseq2 software was used for differential expression analysis. Immune-related genes from ImmPort Shared Data and the Cistrome Project were evaluated. The model performance was determined based on the area under the receiver operating characteristic (ROC) curve. CIBERSORT was used to assess the infiltration of immune cells. The results of differential expression analyses showed a significant difference in the expression levels of 2942 genes, comprising 1338 upregulated genes and 1604 downregulated genes (p < 0.05). A population of 24 immune-related genes that predicted GBM patient survival was identified. A risk score model established on the basis of the expressions of the 24 immune-related genes was used to evaluate a favorable outcome of GBM. Further validation using the ROC curve confirmed the model was an independent predictor of GBM (AUC = 0.869). In the GBM microenvironment, eosinophils, macrophages, activated NK cells, and follicular helper T cells were associated with prognostic risk. Our study confirmed the importance of immune-related genes and immune infiltrates in predicting GBM patient prognosis.

scholarly journals Expression profiling of ileal mucosa in asthma reveals upregulation of innate immunity and genes characteristic of Paneth and goblet cells

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Jan K. Nowak ◽  
Marzena Dworacka ◽  
Nazgul Gubaj ◽  
Arystan Dossimov ◽  
Zhumabek Dossimov ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Differential Expression ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Goblet Cells ◽  
Ileal Mucosa ◽  
Significant Differential Expression ◽  
Immune Genes ◽  
Asthma Patients

Abstract Background The expression profiles of the intestinal mucosa have not been comprehensively investigated in asthma. We aimed to explore this in the Correlated Expression and Disease Association Research (CEDAR) patient cohort. Methods Differential expression analysis of ileal, transverse colon, and rectal biopsies were supplemented by a comparison of transcriptomes from platelets and leukocytes subsets, including CD4+, CD8+, CD14+, CD15+, and CD19+ cells. Asthma patients (n = 15) and controls (n = 15) had similar age (p = 0.967), body mass index (p = 0.870), similar numbers of females (80%) and smoking rates (13.3%). Results Significant differential expression was found in the ileum alone, and not in any other cell/tissue types. More genes were found to be overexpressed (1,150) than under-expressed (380). The most overexpressed genes included Fc Fragment of IgG Binding Protein (FCGBP, logFC = 3.01, pFDR = 0.015), Mucin 2 (MUC2, logFC = 2.78, pFDR = 0.015), and Alpha 1B Defensin (DEFA1B, logFC = 2.73, pFDR = 0.024). Gene ontology implicated the immune system, including interleukins 4 and 13, as well as antimicrobial peptides in this overexpression. There was concordance of gene over- (STAT1, XBP1) and underexpression (NELF, RARA) in asthma and Crohn’s disease ileum when our results were compared to another dataset (p = 3.66 × 10–7). Conclusion Ileal mucosa in asthma exhibits a specific transcriptomic profile, which includes the overexpression of innate immune genes, mostly characteristic of Paneth and goblet cells, in addition to other changes that may resemble Crohn’s disease.

scholarly journals Identification of an independent immune-genes prognostic index for renal cell carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Guangyao Li ◽  
Xiyi Wei ◽  
Shifeng Su ◽  
Shangqian Wang ◽  
Wei Wang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Risk Score ◽  
Roc Curve ◽  
Cox Regression ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Score Model ◽  
Set Up ◽  
Immune Related Genes ◽  
Survival Risk

Abstract Background Considerable evidence has indicated an association between the immune microenvironment and clinical outcome in ccRCC. The purpose of this study is to extensively figure out the influence of immune-related genes of tumors on the prognosis of patients with ccRCC. Methods Files containing 2498 immune-related genes were obtained from the Immunology Database and Analysis Portal (ImmPort), and the transcriptome data and clinical information relevant to patients with ccRCC were identified and downloaded from the TCGA data-base. Univariate and multivariate Cox regression analyses were used to screen out prognostic immune genes. The immune risk score model was established in light of the regression coefficient between survival and hub immune-related genes. We eventually set up a nomogram for the prediction of the overall survival for ccRCC. Kaplan-Meier (K-M) and ROC curve was used in evaluating the value of the predictive risk model. A P value of < 0.05 indicated statistically significant differences throughout data analysis. Results Via differential analysis, we found that 556 immune-related genes were expressed differentially between tumor and normal tissues (p < 0. 05). The analysis of univariate Cox regression exhibited that there was a statistical correlation between 43 immune genes and survival risk in patients with ccRCC (p < 0.05). Through Lasso-Cox regression analysis, we established an immune genetic risk scoring model based on 18 immune-related genes. The high-risk group showed a bad prognosis in K-M analysis. (p < 0.001). ROC curve showed that it was reliable of the immune risk score model to predict survival risk (5 year over survival, AUC = 0.802). The model indicated satisfactory AUC and survival correlation in the validation data set (5 year OS, Area Under Curve = 0.705, p < 0.05). From Multivariate regression analysis, the immune-risk score model plays an isolated role in the prediction of the prognosis of ccRCC. Under multivariate-Cox regression analysis, we set up a nomogram for comprehensive prediction of ccRCC patients’ survival rate. At last, it was identified that 18 immune-related genes and risk scores were not only tremendously related to clinical prognosis but also contained in a variety of carcinogenic pathways. Conclusion In general, tumor immune-related genes play essential roles in ccRCC development and progression. Our research established an unequal 18-immune gene risk index to predict the prognosis of ccRCC visually. This index was found to be an independent predictive factor for ccRCC.

scholarly journals Correlation analysis between preoperative systemic immune inflammation index and prognosis of patients after radical gastric cancer surgery: based on propensity score matching method

2022 ◽  
Vol 20 (1) ◽  
Author(s):  
Xu Zhaojun ◽  
Chen Xiaobin ◽  
An Juan ◽  
Yuan Jiaqi ◽  
Jiang Shuyun ◽  
...  
Keyword(s):  
Overall Survival ◽  
Risk Factor ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Roc Curve ◽  
Independent Risk Factor ◽  
Gastrointestinal Surgery ◽  
Kaplan Meier ◽  
Significant Difference ◽  
Immune Inflammation

Abstract Background To explore the correlation between the preoperative systemic immune inflammation index (SII) and the prognosis of patients with gastric carcinoma (GC). Methods The clinical data of 771 GC patients surgically treated in the Department of Gastrointestinal Surgery, Qinghai University Affiliated Hospital from June 2010 to June 2015 were retrospectively analyzed, and their preoperative SII was calculated. The optimal cut-off value of preoperative SII was determined using the receiver operating characteristic (ROC) curve, the confounding factors between the two groups were eliminated using the propensity score matching (PSM) method, and the correlation between preoperative SII and clinicopathological characteristics was assessed by chi-square test. Moreover, the overall survival was calculated using Kaplan-Meier method, the survival curve was plotted, and log-rank test was performed for the significance analysis between the curves. Univariate and multivariate analyses were also conducted using the Cox proportional hazards model. Results It was determined by the ROC curve that the optimal cut-off value of preoperative SII was 489.52, based on which 771 GC patients were divided into high SII (H-SII) group and low SII (L-SII) group, followed by PSM in the two groups. The results of Kaplan-Meier analysis showed that before and after PSM, the postoperative 1-, 3-, and 5-year survival rates in L-SII group were superior to those in H-SII group, and the overall survival rate had a statistically significant difference between the two groups (P < 0.05). Before PSM, preoperative SII [hazard ratio (HR) = 2.707, 95% confidence interval (CI) 2.074-3.533, P < 0.001] was an independent risk factor for the prognosis of GC patients. After 1:1 PSM, preoperative SII (HR = 2.669, 95%CI 1.881–3.788, P < 0.001) was still an independent risk factor for the prognosis of GC patients. Conclusions Preoperative SII is an independent risk factor for the prognosis of GC patients. The increase in preoperative SII in peripheral blood indicates a worse prognosis.

Gestational Age as a Predictor of Pregnancy Outcome After Repeat Cerclage

2021 ◽  
Author(s):  
Benshuo Cai ◽  
Xinni Na
Keyword(s):  
Survival Rate ◽  
Pregnancy Outcome ◽  
Gestational Age ◽  
Roc Curve ◽  
Univariate Analysis ◽  
Area Under The Curve ◽  
Roc Curve Analysis ◽  
Kaplan Meier ◽  
Significant Difference ◽  
The Mean

Abstract Background: The role of repeat cerclage (RC) as a remedy for patients with prolapsed membranes after prior cerclage remains controversial. We aimed to investigate whether gestational age (GA) could be used as a valuable factor for predicting pregnancy outcome following RC in women with prolapsed membranes after prior cerclage. Methods: We retrospectively investigated the clinical data of 29 patients who underwent RC resulting from prolapsed membrane after prior cerclage. Receiving operating characteristic (ROC) curve analysis and univariate analysis were performed to determine predictive factors. Patients were divided into two groups according to GA at RC, GA<24.2 weeks and GA≥24.2 weeks. Pregnancy outcomes were compared between groups.Results: The mean GA at prior cerclage was 16.5 weeks; mean GA at RC was 23.6 weeks. The mean GA at delivery was 27.8 weeks with a 69.0% neonatal survival rate. ROC curve and univariate analysis demonstrated that GA at RC was significantly predictive for neonatal survival (area under the curve: 0.928; p=0.000). Using a GA cut-off of ≥24.2 weeks at RC, the sensitivity and specificity of predicting neonatal survival were 93.75% and 61.54%, respectively. There was a significant difference in neonatal survival rate between the GA <24.2 weeks group and GA ≥24.2 weeks group (38.5% vs. 93.8%, p=0.003). Kaplan–Meier survival curves showed a lower incidence of neonatal death in the GA ≥24.2 weeks group (6.3%) compared with GA <24.2 weeks group (61.5%, p=0.023).Conclusions: GA could be a valuable factor for predicting pregnancy outcome post-RC in women with prolapsed membrane after prior cerclage.

scholarly journals Identification of prognostic immune genes in laryngeal cancer

2020 ◽  
Vol 48 (11) ◽  
pp. 030006052096466
Author(s):  
Huan Xiao ◽  
Qi-sheng Su ◽  
Chao-qian Li
Keyword(s):  
Laryngeal Cancer ◽  
Prognostic Model ◽  
Cox Regression ◽  
Area Under The Curve ◽  
Predictive Biomarkers ◽  
The Cancer Genome Atlas ◽  
Bioinformatics Analyses ◽  
Prediction Ability ◽  
Immune Genes ◽  
Immune Related Genes

Objective In this study, we aimed to identify prognostic immune-related genes and establish a prognostic model for laryngeal cancer based on these genes. Methods Transcriptome profiles and clinical data of patients with laryngeal cancer were downloaded from The Cancer Genome Atlas database. Integrated bioinformatics analyses were performed to identify genes associated with prognosis. Results Thirty prognostic immune-related genes for laryngeal cancer were identified. We constructed a regulatory network of prognosis comprising transcription factors and immune-related genes. Multivariate Cox regression analyses identified 15 immune-related genes in the network that were used to establish the prognostic model. The model exhibited excellent prognostic prediction ability with a high area under the curve value (0.916). The calculated risk score based on expression of the 15 immune-related genes was shown to be an independent prognostic factor for laryngeal cancer. Conclusion We identified prognostic immune-related genes and established a prognostic model for laryngeal cancer, which might help identify novel predictive biomarkers and therapeutic targets of laryngeal cancer.

scholarly journals Analysis of the role of METTL5 as a hub gene in lung adenocarcinoma based on a weighted gene co-expression network

2021 ◽  
Vol 18 (5) ◽  
pp. 6608-6619
Author(s):  
Xinwang Yan ◽  
◽  
Xiaowen Zhao ◽  
Qing Yan ◽  
Ye Wang ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Differential Expression ◽  
Epithelial Mesenchymal Transition ◽  
Differential Expression Analysis ◽  
Predictive Performance ◽  
Tumor Stage ◽  
Malignant Progression ◽  
Mesenchymal Transition ◽  
Kaplan Meier ◽  
Differential Expression Genes

<abstract> <p>Lung adenocarcinoma (LUAD) is a frequently diagnosed malignant tumor that is highly invasive and lethal. The prognosis of patients with LUAD still needs to be improved, as conventional treatment is remarkably well tolerated. In this study, the expression profile of LUAD in the TCGA database was used for differential expression analysis, and differential expression genes were determined to construct a weighted gene co-expression network analysis (WGCNA) for dividing and finding the gene modules with the highest correlation with tumor stage. Here, METTL5, DDX23, GPSM2, CEP95, WDCP, and METL17 were identified as hub genes. According to the relation degree, METTL5 was determined as the candidate gene in this study. Difference analysis and receiver operating characteristic (ROC) curve were applied to identify the predictive performance of METTL5 in LUAD, and Kaplan-Meier (KM) analysis showed that the prognosis of LUAD patients with high METTL5 expression was poor. Further GSEA analysis showed that high-expressed METTL5 was related to epithelial-mesenchymal transition and other pathways. Therefore, METTL5 may be involved in the occurrence and malignant progression of LUAD. The current findings provide an effective molecular target for early diagnosis of LUAD, helping monitor the malignant progression of LUAD and improve the prognosis of LUAD patients.</p> </abstract>

scholarly journals Crosstalk between Venous Thromboembolism and Periodontal Diseases: A Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Zheng He ◽  
Qilong Jiang ◽  
Fuping Li ◽  
Mingxiang Chen
Keyword(s):  
Venous Thromboembolism ◽  
Bioinformatics Analysis ◽  
Differential Expression Analysis ◽  
Functional Enrichment ◽  
Wilcoxon Test ◽  
Immune Gene ◽  
Ppi Network ◽  
Immune Genes ◽  
Immune Related Genes ◽  
Control Samples

Background. This current study applied bioinformatics analysis to reveal the crosstalk between venous thromboembolism (VTE) and periodontitis, as well as the potential role of immune-related genes in this context. Methods. Expression data were downloaded from the GEO database. Blood samples from venous thromboembolism (VTE) were used (GSE19151), while for periodontal disease, we used gingival tissue samples (GSE10334, GSE16134, and GSE23586). After batch correction, we used “limma” packages of R language for differential expression analysis ( p value < 0.05, ∣ logFC ∣ ≥ 0.5 ). We used Venn diagrams to extract the differentially expressed genes common to VTE and periodontitis as potential crosstalk genes and applied functional enrichment analysis (GO biological process and KEGG pathway). The protein-protein interaction (PPI) network of crosstalk genes was constructed by Cytoscape software. The immune-related genes were downloaded from the literature. The Wilcoxon test was used to test the scores of immune infiltrating cells. The crosstalk genes were further screened by LASSO Logistic Regression. Results. For periodontitis, 427 case and 136 control samples, and for VTE, 70 case and 63 control samples were included. The obtained PPI network had 1879 nodes and 2257 edges. Moreover, 782 immune genes and 28 cell types were included in the analysis. Over 90% of immune cells had different expressions in VTE and periodontitis. We obtained 12 significant pathways corresponding to crosstalk genes. CD3D, CSF3R, and CXCR4 acted as an immune gene and a crosstalk gene. We obtained a total of 12 shared biomarker crosstalk genes. Among those 12 biomarker crosstalk genes, 4 were immune genes (LGALS1, LSP1, SAMSN1, and WIPF1). Conclusion. Four biomarker crosstalk genes between periodontitis and VTE were also immune genes, i.e., LGALS1, LSP1, SAMSN1, and WIPF1. The findings of the current study need further validation and are a basis for development of biomarkers.

scholarly journals Differential Expression of Immune Genes between Two Closely Related Beetle Species with Different Immunocompetence following Attack by Asecodes parviclava

2020 ◽  
Vol 12 (5) ◽  
pp. 522-534 ◽  
Author(s):  
Xuyue Yang ◽  
Lisa Fors ◽  
Tanja Slotte ◽  
Ulrich Theopold ◽  
Mahesh Binzer-Panchal ◽  
...  
Keyword(s):  
Differential Expression ◽  
Evolutionary Dynamics ◽  
Healing Process ◽  
Differential Expression Analysis ◽  
Immune Defense ◽  
Differentially Expressed ◽  
Host Immune System ◽  
Beetle Species ◽  
Immune Genes ◽  
Galerucella Pusilla

Abstract Endoparasitoid wasps are important natural enemies of many insect species and are major selective forces on the host immune system. Despite increased interest in insect antiparasitoid immunity, there is sparse information on the evolutionary dynamics of biological pathways and gene regulation involved in host immune defense outside Drosophila species. We de novo assembled transcriptomes from two beetle species and used time-course differential expression analysis to investigate gene expression differences in closely related species Galerucella pusilla and G. calmariensis that are, respectively, resistant and susceptible against parasitoid infection by Asecodes parviclava parasitoids. Approximately 271 million and 224 million paired-ended reads were assembled and filtered to form 52,563 and 59,781 transcripts for G. pusilla and G. calmariensis, respectively. In the whole-transcriptome level, an enrichment of functional categories related to energy production, biosynthetic process, and metabolic process was exhibited in both species. The main difference between species appears to be immune response and wound healing process mounted by G. pusilla larvae. Using reciprocal BLAST against the Drosophila melanogaster proteome, 120 and 121 immune-related genes were identified in G. pusilla and G. calmariensis, respectively. More immune genes were differentially expressed in G. pusilla than in G. calmariensis, in particular genes involved in signaling, hematopoiesis, and melanization. In contrast, only one gene was differentially expressed in G. calmariensis. Our study characterizes important genes and pathways involved in different immune functions after parasitoid infection and supports the role of signaling and hematopoiesis genes as key players in host immunity in Galerucella against parasitoid wasps.

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