scholarly journals Profiles of immune infiltration and its relevance to survival outcome in meningiomas

2020 ◽  
Vol 40 (5) ◽  
Author(s):  
Xiaodong Chen ◽  
Fen Tian ◽  
Peng Lun ◽  
Yugong Feng
Keyword(s):  
Immune Cells ◽  
Clustering Analysis ◽  
Immune Cell ◽  
Cox Regression ◽  
Cell Types ◽  
Survival Outcome ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
Online Databases ◽  
The Relationship

Abstract Tumor-infiltrating immune cells play a decisive part in prognosis and survival. Until now, previous researches have not made clear about the diversity of cell types involved in the immune response. The objective of this work was to confirm the composition of tumor-infiltrating immune cells and their correlation with prognosis in meningiomas based on a metagene approach (known as CIBERSORT) and online databases. A total of 22 tumor-infiltrating immune cells were detected to determine the relationship between the immune infiltration pattern and survival. The proportion of M2 macrophages was more abundant in 68 samples, reaching more than 36%. Univariate Cox regression analysis displayed that the proportion of dendritic cells was obviously related to prognosis. Hierarchical clustering analysis identified two clusters by the method of within sum of squares errors, which exhibited different infiltrating immune cell composition and survival. To summarize, our results indicated that proportions of tumor-infiltrating immune cells as well as cluster patterns were associated with the prognosis, which offered clinical significance for research of meningiomas.

scholarly journals Identification of recurrence marker associated with immune infiltration in prostate cancer with radical resection and build prognostic nomogram

BMC Cancer ◽  
2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Xin Rui ◽  
Siliang Shao ◽  
Li Wang ◽  
Jiangyong Leng
Keyword(s):  
Prostate Cancer ◽  
Immune Cells ◽  
Prediction Models ◽  
Immune Cell ◽  
Cox Regression ◽  
Cancer Recurrence ◽  
Th2 Cells ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
Prostate Cancer Recurrence

Abstract Background Some historic breakthroughs have been made in immunotherapy of advanced cancer. However, there is still little research on immunotherapy in prostate cancer. We explored the relationship between immune cell infiltration and prostate cancer recurrence and tried to provide new ideas for the treatment of prostate cancer. Methods Prostate cancer RNA-seq data and clinical information were downloaded from the TCGA database and GEO database. The infiltration of 24 immune cells in tissues was quantified by ssGSEA. Univariate Cox regression analysis was used to screen for immune cell types associated with tumor recurrence, weighted gene co-expression network analysis (WGCNA) and LASSO were used to identify hub genes which regulate prognosis in patients through immune infiltration. Then, the nomogram was constructed based on the hub gene to predict the recurrence of prostate cancer, and the decision curve analysis (DCA) was used to compare the accuracy with the PSA and Gleason prediction models. Result Analysis showed that Th2 cells and Tcm related to prostate cancer recurrence after radical prostatectomy, and they are independent protective factors for recurrence. Through WGCNA and Lasso, we identified that NDUFA13, UQCR11, and USP34 involved in the infiltration of Th2 cells and Tcm in tumor tissues, and the expression of genes is related to the recurrence of patients. Based on the above findings, we constructed a clinical prediction model and mapped a nomogram, which has better sensitivity and specificity for prostate cancer recurrence prediction, and performed better in comparison with PSA and Gleason’s predictions. Conclusion The immune cells Th2 cells and Tcm are associated with recurrence of PCa. Moreover, the genes NDUFA13, UQCR11, and USP34 may affect the recurrence of PCa by affecting the infiltration of Th2 cells and Tcm. Moreover, nomogram can make prediction effectively.

scholarly journals Prognostic Signature of Osteosarcoma Based on 14 Autophagy-Related Genes

2021 ◽  
Vol 27 ◽  
Author(s):  
Wei Qi ◽  
Qian Yan ◽  
Ming Lv ◽  
Delei Song ◽  
Xianbin Wang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Risk Score ◽  
Clinical Decision Making ◽  
Immune Cell ◽  
Cox Regression ◽  
Clinical Decision ◽  
Cell Types ◽  
Survival Outcome ◽  
Risk Scores ◽  
Cox Regression Analysis

Background: Osteosarcoma is a common malignancy of bone with inferior survival outcome. Autophagy can exert multifactorial influence on tumorigenesis and tumor progression. However, the specific function of genes related to autophagy in the prognosis of osteosarcoma patients remains unclear. Herein, we aimed to explore the association of genes related to autophagy with the survival outcome of osteosarcoma patients.Methods: The autophagy-associated genes that were related to the prognosis of osteosarcoma were optimized by LASSO Cox regression analysis. The survival of osteosarcoma patients was forecasted by multivariate Cox regression analysis. The immune infiltration status of 22 immune cell types in osteosarcoma patients with high and low risk scores was compared by using the CIBERSORT tool.Results: The risk score model constructed according to 14 autophagy-related genes (ATG4A, BAK1, BNIP3, CALCOCO2, CCL2, DAPK1, EGFR, FAS, GRID2, ITGA3, MYC, RAB33B, USP10, and WIPI1) could effectively predict the prognosis of patients with osteosarcoma. A nomogram model was established based on risk score and metastasis.Conclusion: Autophagy-related genes were identified as pivotal prognostic signatures, which could guide the clinical decision making in the treatment of osteosarcoma.

scholarly journals The Role of CBLL1 in the Prognosis and Immune Infiltration of Pan-Cancer

2020 ◽  
Author(s):  
Qiang Guo ◽  
Dan Li ◽  
Yanmei Ji ◽  
Jialong Guo
Keyword(s):  
Mrna Expression ◽  
Immune Cells ◽  
Cox Regression ◽  
Expression Level ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
The Relationship ◽  
Pan Cancer ◽  
Ucsc Database

Abstract ObjectiveThis study aims to explore the role of CBLL1 in pan-carcinoma and tumor immune infiltrates. MethodsDownload mRNA expression, mutation and clinical data in UCSC database, to analyze the relationship between CBLL1 expression and clinicopathological vlaue, and immune microenvironment in pan-cancer. CIBERSORT was used to analyze the relationship between CBLL1 expression and the infiltration of pan-carcinoma immune cells. The mRNA expression data of UCSC database were used to analyze the correlation between CBLL1 expression and pan-cancer immunomodulations, checkpoints and receptor molecules. ResultsThe levels of CBLL1 mRNA expression in pan-cancer tissues were abnormal. The level of CBLL1 is related to the age, race, clinical stage and treatment effect of patients with pan-carcinoma and associated with the prognosis of patients with KIRC, LUSC, THCA, THYM, MESO, PRAD, STAD, and UVM. Univariate COX regression analysis showed that expression of CBLL1 was a risk factor for poor prognosis in patients with KICH, KIRC, LAML, THYM, KIRC, PCPG, OV, PRAD, STAD, GBM and UVM. The expression level of CBLL1 was correlated with BLCA, BRCA, COAD, LAML, LGG, LUAD, LUSC, SARC, STAD, THCA, THYM and UVM tumor mutational burden, and with ACC, BRCA, CESC, COAD, DLBC, HNSC, PRAD, READ, SARC, STAD, TGCT, THCA and UCEC microsatellite instability. The expression level of CBLL1 was correlated with cancer stromal cells and immune cells. The expression of CBLL1 is related to pan-cancer immunomodulators, checkpoints and receptor molecules. ConclusionCBLL1 is abnormally expressed in patients with pan-carcinoma, which is expected to be a biomarker for prognosis, mutation and immune infiltration in patients with pan-carcinoma.

scholarly journals SERPINE1 associated with remodeling of the tumor microenvironment in colon cancer progression: a novel therapeutic target

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shaokun Wang ◽  
Li Pang ◽  
Zuolong Liu ◽  
Xiangwei Meng
Keyword(s):  
Colorectal Cancer ◽  
Colon Cancer ◽  
Immune Cells ◽  
Immune Cell ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
The Relationship

Abstract Background The change of immune cell infiltration essentially influences the process of colorectal cancer development. The infiltration of immune cells can be regulated by a variety of genes. Thus, modeling the immune microenvironment of colorectal cancer by analyzing the genes involved can be more conducive to the in-depth understanding of carcinogenesis and the progression thereof. Methods In this study, the number of stromal and immune cells in malignant tumor tissues were first estimated by using expression data (ESTIMATE) and cell-type identification with relative subsets of known RNA transcripts (CIBERSORT) to calculate the proportion of infiltrating immune cell and stromal components of colon cancer samples from the Cancer Genome Atlas database. Then the relationship between the TMN Classification and prognosis of malignant tumors was evaluated. Results By investigating differentially expressed genes using COX regression and protein-protein interaction network (PPI), the candidate hub gene serine protease inhibitor family E member 1 (SERPINE1) was found to be associated with immune cell infiltration. Gene Set Enrichment Analysis (GSEA) further projected the potential pathways with elevated SERPINE1 expression to carcinogenesis and immunity. CIBERSORT was subsequently utilized to investigate the relationship between the expression differences of SERPINE1 and immune cell infiltration and to identify eight immune cells associated with SERPINE1 expression. Conclusion We found that SERPINE1 plays a role in the remodeling of the colon cancer microenvironment and the infiltration of immune cells.

scholarly journals Development of a Novel Immune Infiltration-Based Gene Signature to Predict Prognosis and Immunotherapy Response of Patients With Cervical Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Sihui Yu ◽  
Xi Li ◽  
Jiawen Zhang ◽  
Sufang Wu
Keyword(s):  
Cervical Cancer ◽  
Immune Cell ◽  
Cox Regression ◽  
Patient Survival ◽  
Checkpoint Inhibitors ◽  
Cell Types ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Cervical Lesions ◽  
Immune Infiltration

Predictive models could indicate the clinical outcome of patients with carcinoma. Cervical cancer is one of the most frequently diagnosed female malignancies. Herein, we proposed an immune infiltration-related gene signature that predicts prognosis of patients with cervical cancer and depicts the immune landscape as well. We utilized the transcriptome data of The Cancer Genome Atlas (TCGA) and estimated the infiltration level of 28 immune cell types. We screened out four immune cell types conducive to patient survival and recognized their shared differentially expressed genes (DEGs). Four core genes (CHIT1, GTSF1L, PLA2G2D, and GNG8) that composed the ultimate signature were identified via univariate and multivariate Cox regression. The optimal model we built up could distinguish patients with cervical cancer into high-score and low-score subgroups. These two subgroups showed disparity in aspects of patient survival, immune infiltration landscape, and response to immune checkpoint inhibitors. Additionally, we found that GTSF1L was decreased gradually along with the severity of cervical lesions, and its potential role in immune contexture and clinical practice were also demonstrated. Our results suggested that the Immunoscore based on four immune-related genes could serve as a supplementary criterion to effectively foresee the survival outcome, tumor infiltration status, and immunotherapy efficacy of cervical cancer patients.

scholarly journals Development of an Immune-Related LncRNA Prognostic Signature for Glioma

2021 ◽  
Vol 12 ◽  
Author(s):  
Yudong Cao ◽  
Hecheng Zhu ◽  
Jun Tan ◽  
Wen Yin ◽  
Quanwei Zhou ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Clinical Outcome ◽  
Immune Cell ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Immune Infiltration

IntroductionGlioma is the most common primary cancer of the central nervous system with dismal prognosis. Long noncoding RNAs (lncRNAs) have been discovered to play key roles in tumorigenesis in various cancers, including glioma. Because of the relevance between immune infiltrating and clinical outcome of glioma, identifying immune-related lncRNAs is urgent for better personalized management.Materials and methodsSingle-sample gene set enrichment analysis (ssGSEA) was applied to estimate immune infiltration, and glioma samples were divided into high immune cell infiltration group and low immune cell infiltration group. After screening differentially expressed lncRNAs in two immune groups, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct an immune-related prognostic signature. Additionally, we explored the correlation between immune infiltration and the prognostic signature.ResultsA total of 653 samples were appropriate for further analyses, and 10 lncRNAs were identified as immune-related lncRNAs in glioma. After univariate Cox regression and LASSO Cox regression analysis, six lncRNAs were identified to construct a prognostic signature for glioma, which could be taken as independent prognostic factors in both univariate and multivariate Cox regression analyses. Moreover, risk score was significantly correlated with all the 29 immune-related checkpoint expression (p < 0.05) in ssGSEA except neutrophils (p = 0.43).ConclusionThe study constructed an immune-related prognostic signature for glioma, which contributed to improve clinical outcome prediction and guide immunotherapy.

scholarly journals The Role of CXCL10 in Prognosis of Patients with Colon Cancer and Tumor Microenvironment Remodeling

2020 ◽  
Author(s):  
Hongli Yin ◽  
Weiwei Song ◽  
Chenguang Han ◽  
Qiantai Mao ◽  
Zhaoshuai Ji ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Immune Cells ◽  
Immune Cell ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Ppi Network ◽  
Cox Regression Analysis ◽  
Immune Cell Subsets ◽  
Intersection Analysis ◽  
Immune Score

Abstract Background: In the past few years, tumor microenvironment (TME) has gradually become a hot topic in tumor research, which has important significance in the diagnosis, prevention and prognosis of tumors. Importantly, the immune system is a major contributing factor in TME, and studies have shown that tumors are partially infiltrated with various immune cell subsets. The immune characteristics of the TME play an essential role in evaluating the prognosis of patients. The immune scoring system based on the distribution of tumor local immune cell subsets and cell density has been an essential indicator in the evaluation of patient prognosis and has been verified in various tumor studies. TME is indispensable in the occurrence and development of Colorectal cancer (CRC). However, understanding the dynamic regulation of immunity and matrix components in TME of CRC is still a challenge and should be investigated further.Methods: In this study, we collected transcriptome RNA-seq data of 521 Colon adenocarcinoma (COAD) patients from The Cancer Genome Atlas (TCGA) data portal. We then estimate the fraction of stromal and immune cells in COAD cases by ESTIMATE algorithms [1]. A total of 1109 stromal-immune score-related differentially expressed genes (DEGs) were identified and used to generate a high-confidence protein–protein intersection (PPI) network and univariate COX regression analysis. Intersection analysis of the data from PPI network and univariate COX regression analysis showed the core gene. Then we performed Gene set enrichment analysis (GSEA), survival analysis and clinical analysis for CXCL10, and applied CIBERSORT algorithms to estimate the tumor-infiltrating immune cells (TICs) proportion in COAD cases.Results: The proportion of immune and stromal components in TME are associated with the progression of COAD. For example, tumor metastasis is inversely proportional to immune score. A total of 1109 DEGs were obtained by analyzing the low-score shared genes and the high-score shared genes by intersection analysis which might be the determinant of TME status. The GO enrichment analysis indicated that DEGs are associated with immune-related terms. KEGG pathway enrichment analysis showed that these DEGs are mainly enriched in cytokine cytokine receptor signaling pathway etc. Therefore, DEGs are related to immune regulation, which indicates that the participation of immune factors is the main characteristic of TME in COAD. Moreover, the expression level of CXCL10 has significantly connection with the prognosis of patients and the progression of COAD. Conclusion: Taken together, we conducted a comprehensive analysis of the TME in COAD, and predicted a prognostic indicator for COAD, which provided a novel insight for therapeutics of COAD.

scholarly journals Recognition of Immune Microenvironment Landscape and Immune-Related Prognostic Genes in Breast Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Huiling Wang ◽  
Shuo You ◽  
Meng Fang ◽  
Qian Fang
Keyword(s):  
Breast Cancer ◽  
T Cells ◽  
Regression Analysis ◽  
Clustering Algorithm ◽  
Immune Cell ◽  
Cox Regression ◽  
Therapeutic Potential ◽  
Enrichment Analysis ◽  
Cox Regression Analysis ◽  
Immune Infiltration

Background. Breast cancer (BC) is the most common malignant tumor in women. The immunophenotype of tumor microenvironment (TME) has shown great therapeutic potential in tumor. Method. The transcriptome was obtained from TCGA and GEO data. Immune infiltration was analyzed by single-sample gene set enrichment (ssGSEA). The immune feature was constructed by Cox regression analysis. In addition, the coexpression of differential expression genes (DEGs) was identified. Through enrichment analysis, the function and pathway of module genes were identified. The somatic mutations related to immune characteristics were analyzed by Maftools. By using the consistency clustering algorithm, the molecular subtypes were constructed, and the overall survival time (OS) was predicted. Results. Immune landscape can be divided into low immune infiltration and high immune infiltration. Cox regression analysis identified seven immune cells as protective factors of BC. In the coexpression modules for DEGs of high and low immune infiltration, module 1 was related to T cells and high immune infiltration. In particular, the area under the curve (AUC) value of hub gene SASH3 was the highest, and the correlation with T cells was stronger in the high immune infiltration. Enrichment analysis found that oxidative stress, T cell aggregation, and apoptosis were observed in high immune infiltration. In addition, TP53 was identified as the most important somatic gene mutation related to immune characteristics. Importantly, we also constructed seven immune cell-based breast cancer subtypes to predict OS. Conclusion. We evaluated the immune landscape of BC and constructed the gene characteristics related to the immune landscape. The potential of T cells and SASH3 in immunotherapy of BC was revealed, which may guide the development of new clinical treatment strategies.

scholarly journals GBP4 is an Accurate Diagnostic Biomarker and a Potential Treatment Target for Crohn’s Disease

2021 ◽  
Author(s):  
Heng Shi ◽  
Qin Peng ◽  
Xian-Ling Zhou ◽  
Shi-Ping Zhu ◽  
Sheng-Yun Sun
Keyword(s):  
Intestinal Mucosa ◽  
Immune Cells ◽  
Immune Cell ◽  
Cell Types ◽  
Strongly Correlated ◽  
Healthy Individuals ◽  
Hub Genes ◽  
M1 Macrophages ◽  
Pathway Enrichment ◽  
The Relationship

Abstract Background: Extensive evidence has shown that immune cell infiltration is associated with the pathogenesis of Crohn’s disease (CD). In the present study, we explored the potential mechanism underlying the pathogenesis biomarkers for CD.Methods: The GSE179285 dataset containing sequence data for intestinal mucosal was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in the intestinal mucosa of CD patients and healthy individuals were then identified. The infiltration pattern of 22 immune cell types was assessed using the CIBERSORT algorithm. The DEGs and 22 immune cell types were combined to find the key gene network using weighted gene co-expression network analysis (WGCNA), and pathway enrichment analyzes were performed on the hub module in the WGCNA. A linear regression model for the relationship between the expression of the hub genes in CD patients and infiltration of immune cells were also developed. The utility and accuracy of the hub genes for CD diagnosis were assessed using receiver operating characteristic (ROC) analysis. The accuracy of the model was validated using GSE20881 dataset. Results: There were 1135 DEGs between the intestinal mucosal tissue of CD patients and healthy individuals. Of these DEGs, 711 genes were upregulated, whereas 424 of them were downregulated. There was also a significant difference in the infiltration of immune cells to the intestinal mucosal between the CD patients and healthy individuals. WGCNA revealed that the turquoise module genes were strongly correlated with the infiltration of M1 macrophages (cor=0.68, p=10-16). Pathway enrichment analysis further showed the genes in the turquoise module mainly regulated the secretion of interferon-gamma and other immune effector molecules. Finally, the expression of GBP4, the identified hub gene, strongly correlated with the infiltration of M1 macrophages (adjusted r-squared=0.661, p<2x10-16), and is a relatively good marker for CD diagnostic prediction (AUC=0.736). The relationship between GBP4 expression and infiltration of M1 macrophages (adjusted r-squared=0.435, p<2x10-16) and prognostic value of the gene (AUC=0.702) were verified using the GSE20881 validation dataset.Conclusion: GBP4 is a potential biomarker for accurate CD diagnosis. The expression of GBP4 promotes the infiltration of M1 macrophages to the intestinal mucosa of CD patients.

scholarly journals TRIM68, PIKFYVE, and DYNLL2: The Possible Novel Autophagy- and Immunity-Associated Gene Biomarkers for Osteosarcoma Prognosis

2021 ◽  
Vol 11 ◽  
Author(s):  
Jie Jiang ◽  
Dachang Liu ◽  
Guoyong Xu ◽  
Tuo Liang ◽  
Chaojie Yu ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Immune Cells ◽  
Immune Cell ◽  
Cox Regression ◽  
Clinical Information ◽  
Lasso Regression ◽  
Osteosarcoma Cells ◽  
Screening Process ◽  
Cox Regression Analysis ◽  
Human Osteosarcoma Cells

IntroductionOsteosarcoma is among the most common orthopedic neoplasms, and currently, there are no adequate biomarkers to predict its prognosis. Therefore, the present study was aimed to identify the prognostic biomarkers for autophagy-and immune-related osteosarcoma using bioinformatics tools for guiding the clinical diagnosis and treatment of this disease.Materials and MethodsThe gene expression and clinical information data were downloaded from the Public database. The genes associated with autophagy were extracted, followed by the development of a logistic regression model for predicting the prognosis of osteosarcoma using univariate and multivariate COX regression analysis and LASSO regression analysis. The accuracy of the constructed model was verified through the ROC curves, calibration plots, and Nomogram plots. Next, immune cell typing was performed using CIBERSORT to analyze the expression of the immune cells in each sample. For the results obtained from the analysis, we used qRT-PCR validation in two strains of human osteosarcoma cells.ResultsThe screening process identified a total of three genes that fulfilled all the screening criteria. The survival curves of the constructed prognostic model revealed that patients with the high risk presented significantly lower survival than the patients with low risk. Finally, the immune cell component analysis revealed that all three genes were significantly associated with the immune cells. The expressions of TRIM68, PIKFYVE, and DYNLL2 were higher in the osteosarcoma cells compared to the control cells. Finally, we used human pathological tissue sections to validate the expression of the genes modeled in osteosarcoma and paracancerous tissue.ConclusionThe TRIM68, PIKFYVE, and DYNLL2 genes can be used as biomarkers for predicting the prognosis of osteosarcoma.

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