scholarly journals Identification the prognostic value of immune gene signature and infiltrating immune cells of esophageal cancer patients

2020 ◽  
Author(s):  
Lin Wang ◽  
Qian Wei ◽  
Ming Zhang ◽  
Lianze Chen ◽  
Zinan Li ◽  
...  
Keyword(s):  
T Cells ◽  
Esophageal Cancer ◽  
Signaling Pathway ◽  
Immune Cells ◽  
Roc Curves ◽  
Survival Outcome ◽  
Immune Gene ◽  
Prognostic Signature ◽  
Immune Genes ◽  
Validation Set

Abstract Background Esophageal cancer (ESCA) is one of the deadliest solid malignancies with worse survival in the world. The poor prognosis of ESCA is not only related to malignant cells, but also affected by the microenvironment. We aimed to establish prognostic signature consisting of immune genes to predict the survival outcome of patients and estimate the prognosis value of infiltrating immune cells in tumor microenvironment (TME). Methods Based on integrated analysis of gene expression profiling and immune gene database, differentially immune-related genes were filtered out. Then, stepwise Cox regression analysis was applied to identify survival related immune genes and construct prognosis signature. Functional enrichment analysis was performed to explore biology function. Kaplan-Meier (K-M) curves and receiver operating characteristic (ROC) curves were performed to validate the predictive effect of predictive signature. We also verified the clinical value of prognostic signature under the influence of different clinical parameters. For deeper analysis, we evaluated the correlation between prognosis signature and infiltrating immune cells by Tumor Immune Estimation Resource (TIMER) and CIBERSORT. Results Finally, we identified 303 differentially immune genes as candidate and constructed immune prognosis signature composed of six immune genes. Furthermore, we observed that the prognosis signature was enriched in cytokine-mediated signaling pathway, lymphocyte activation, immune effector process, cancer pathway, NF-kappa B signaling pathway. K-M survival curves showed that the prognosis signature indeed have good predictive ability in entire ESCA set ( P =0.003), validation set 1 ( P =0.008) and validation set 2 ( P =0.036). The area under the curve (AUC) of ROC curves validated the predictive accuracy of immune signature in three cohorts (AUC=0.757, 0.800 and 0.701), respectively. In addition, we identified the prognosis value of infiltrating-immune cells including activated memory CD4 T cells, T cells follicular helper cells and monocytes and provided a landscape of TME. Conclusions The results indicated that immune prognosis signature can be a novel biomarker to predict survival outcome, which can provide new targets for immunotherapy and individualized therapies in ESCA and open up a new prospect for improving the prognosis of ESCA patients in the era of immunotherapy.

scholarly journals Comprehensive analysis of tumor mutation burden and immune microenvironment in gastric cancer

2021 ◽  
Vol 41 (2) ◽  
Author(s):  
Jie Yu ◽  
QianYun Zhang ◽  
MengChuan Wang ◽  
SiJia Liang ◽  
HongYun Huang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
T Cells ◽  
Immune Cells ◽  
Survival Outcome ◽  
Prognostic Role ◽  
High Group ◽  
Tumor Mutation Burden ◽  
Immune Genes ◽  
Mutation Burden

Abstract Tumor mutation burden (TMB) was a promising marker for immunotherapy. We aimed to investigate the prognostic role of TMB and its relationship with immune cells infiltration in gastric cancer (GC). We analyzed the mutation landscape of all GC cases and TMB of each GC patient was calculated and patients were divided into TMB-high and TMB-low group. Differentially expressed genes (DEGs) between the two groups were identified and pathway analysis was performed. The immune cells infiltration in each GC patient was evaluated and Kaplan–Meier analysis was performed to investigate the prognostic role of immune cells infiltration. At last, hub immune genes were identified and a TMB prognostic risk score (TMBPRS) was constructed to predict the survival outcome of GC patients. The relationships between mutants of hub immune genes and immune infiltration level in GC was investigated. We found higher TMB was correlated with better survival outcome and female patients, patients with T1-2 and N0 had higher TMB score. Altogether 816 DEGs were harvested and pathway analysis demonstrated that patients in TMB-high group were associated with neuroactive ligand–receptor interaction, cAMP signaling pathway, calcium signaling pathway. The infiltration of activated CD4+ memory T cells, follicular helper T cells, resting NK cells, M0 and M1 macrophages and neutrophils in TMB-high group were higher compared than that in TMB-low group and high macrophage infiltration was correlated with inferior survival outcome of GC patients. Lastly, the TMBPRS was constructed and GC patients with high TMBPRS had poor prognosis.

scholarly journals Identification and Validation of an Immune-Related eRNA Prognostic Signature for Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Shenglan Cai ◽  
Xingwang Hu ◽  
Ruochan Chen ◽  
Yiya Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cells ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Roc Curves ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Immune Genes ◽  
Normal Tissues

BackgroundEnhancer RNAs (eRNAs) are intergenic long non-coding RNAs (lncRNAs) that participate in the progression of malignancies by targeting tumor-related genes and immune checkpoints. However, the potential role of eRNAs in hepatocellular carcinoma (HCC) is unclear. In this study, we aimed to construct an immune-related eRNA prognostic model that could be used to prospectively assess the prognosis of patients with HCC.MethodsGene expression profiles of patients with HCC were downloaded from The Cancer Genome Atlas (TCGA). The eRNAs co-expressed from immune genes were identified as immune-related eRNAs. Cox regression analyses were applied in a training cohort to construct an immune-related eRNA signature (IReRS), that was subsequently used to analyze a testing cohort and combination of the two cohorts. Kaplan-Meier and receiver operating characteristic (ROC) curves were used to validate the predictive effect in the three cohorts. Gene Set Enrishment Analysis (GSEA) computation was used to identify an IReRS-related signaling pathway. A web-based cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) computation was used to evaluate the relationship between the IReRS and infiltrating immune cells.ResultsA total of sixty-four immune-related eRNAs (IReRNAs) was identified in HCC, and 14 IReRNAs were associated with overall survival (OS). Five IReRNAs were used for constructing an immune-related eRNA signature (IReRS), which was shown to correlate with poor survival and to be an independent prognostic biomarker for HCC. The GSEA results showed that the IReRS was correlated to cancer-related and immune-related pathways. Moreover, we found that IReRS was correlated to infiltrating immune cells, including CD8+ T cells and M0 macrophages. Finally, differential expressions of the five risk IReRNAs in tumor tissues vs. adjacent normal tissues and their prognostic values were verified, in which the AL445524.1 may function as an oncogene that affects prognosis partly by regulating CD4-CLTA4 related genes.ConclusionOur results suggest that the IReRS could serve as a biomarker for predicting prognosis in patients with HCC. Additionally, it may be correlated to the tumor immune microenvironment and could also be used as a biomarker in immunotherapy for HCC.

scholarly journals Development of a Multi-gene-based Immune Prognostic Signature in Ovarian Cancer

2020 ◽  
Author(s):  
tiefeng cao ◽  
huimin shen
Keyword(s):  
Ovarian Cancer ◽  
Network Analysis ◽  
Prognostic Biomarkers ◽  
Survival Prediction ◽  
Immune Gene ◽  
Prognostic Signature ◽  
Data Set ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Immune Related Genes

Abstract Background：Chemotherapeutic resistance is responsible for treatment failure. Immunotherapy is important in ovarian cancer (OC). Systematic exploration of immunogenic landscape and reliable immune gene-based prognostic biomarkers or signature is necessary to be identified. This study aims to identify the immune gene-based prognostic biomarkers and regulatory factors, further to develop an individualized prediction signature.Methods: This study systematically explored the gene expression profiles from RNA-seq data set for The Cancer Genome Atlas (TCGA) ovarian cancer. Differentially expressed and survival-associated immune genes and transcription factors (TFs) were identified using immune genes from ImmPort dataset and TFs from Cistoma database. We developed the prognostic signature based on survival associated immune genes with LASSO (Least absolute shrinkage and selection operator) Cox regression analysis. Further, Network analysis was performed to uncover the potential molecular mechanisms of immune-related genes with the help of computational biology. Results: The prognostic signature, a weighted combination of the 21 immune-related genes, performed moderately in survival prediction with AUC was 0.746, 0.735, and 0.749 for 1, 3, and 5 year overall survival, respectively. Network analysis uncovered the regulatory role of TFs in immune genes. Intriguingly, the prognostic signature reflected infiltration of some immune cell subtypes.Conclusions: We first constructed a signature with 21 immune genes of clinical significance, which showed promising predictive value in the surveillance, prognosis, even immunotherapy response of OC patients.

scholarly journals TMIC-60. COMPREHENSIVE SPATIAL CHARACTERIZATION OF IMMUNE CELLS IN THE CNS BRAIN TUMOR MICROENVIRONMENT

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi261-vi261
Author(s):  
Cynthia Kassab ◽  
Daniel Zamler ◽  
Pravesh Gupta ◽  
Visish Srinivasan ◽  
Ganesh Rao ◽  
...  
Keyword(s):  
Dendritic Cells ◽  
Brain Tumors ◽  
Immune Cells ◽  
Lung Metastases ◽  
Necrotic Core ◽  
Tumor Segmentation ◽  
Immune Gene ◽  
Cell Reactivity ◽  
Gene Signatures ◽  
Immune Genes

Abstract Previous immune profiling in brain tumors has mostly focused on the high-density tumor areas, and as such, little is known about the nature and types of immunological responses that occur across the tumor landscape, including at the tumor-central nervous system (CNS) interface. En bloc resections of glioblastomas (n=10) and CNS lung metastases (n=10) were oriented on slides as whole mount wedges spanning three anatomical areas including the invasive edge, tumor region, and necrotic core. Tumor segmentation was performed and regional differences were immunologically analyzed for 770 immune genes using the NanoString nCounter System with CIBERSORT analysis to delineate immune gene signatures. The analysis was validated using multiplex immunohistochemistry (IHC). The top upregulated immune genes in the GBM necrotic core were associated with macrophages, including the CD163 lineage marker, chemotactic factors (such as CCL18 and SAA1), and the phagocytosis stimulatory factors (such as IL-8 and MARCO). The necrotic core downregulates GBM antigens (such as IL13RA2 and MAGEB2), markers of dendritic cells (such as LILRA4), and immune stimulatory processes including MHC, IFN, IL-12, TNF, and ICOS expression. In direct contrast, the infiltrating edge of the GBM relative to the tumor is enriched with stimulators for NK cytotoxicity (i.e., CD244, the fractalkine receptor for immune cells), chemokines for thymocytes and dendritic cells, and immune stimulatory IL-12 receptors. Glioblastoma has rare focal isolated areas of CD3 T-cell reactivity within the tumor. Similar to GBM, the necrotic center of lung metastases is enriched in immune suppressive macrophages, as reflected by CD163 IHC staining and arginase expression; however, they are more frequently infiltrated with M1 macrophages. Yet the majority of lung cancers are more diffusely infiltrated with CD3 T cells, especially at the infiltrating edge. In general, we noted distinct inter- and intratumoral immune gene signatures, with macrophages dominating the brain tumors, especially the necrotic core.

scholarly journals Development of an Immune Infiltration-Related Eight-Gene Prognostic Signature in Colorectal Cancer Microenvironment

2020 ◽  
Vol 2020 ◽  
pp. 1-43
Author(s):  
Beilei Wu ◽  
Lijun Tao ◽  
Daqing Yang ◽  
Wei Li ◽  
Hongbo Xu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Score ◽  
Immune Cells ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Roc Curves ◽  
Functional Enrichment ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Score Model

Objective. Stromal cells and immune cells have important clinical significance in the microenvironment of colorectal cancer (CRC). This study is aimed at developing a CRC gene signature on the basis of stromal and immune scores. Methods. A cohort of CRC patients (n=433) were adopted from The Cancer Genome Atlas (TCGA) database. Stromal/immune scores were calculated by the ESTIMATE algorithm. Correlation between prognosis/clinical characteristics and stromal/immune scores was assessed. Differentially expressed stromal and immune genes were identified. Their potential functions were annotated by functional enrichment analysis. Cox regression analysis was used to develop an eight-gene risk score model. Its predictive efficacies for 3 years, 5 years, overall survival (OS), and progression-free survival interval (PFI) were evaluated using time-dependent receiver operating characteristic (ROC) curves. The correlation between the risk score and the infiltering levels of six immune cells was analyzed using TIMER. The risk score was validated using an independent dataset. Results. Immune score was in a significant association with prognosis and clinical characteristics of CRC. 736 upregulated and two downregulated stromal and immune genes were identified, which were mainly enriched into immune-related biological processes and pathways. An-eight gene prognostic risk score model was conducted, consisting of CCL22, CD36, CPA3, CPT1C, KCNE4, NFATC1, RASGRP2, and SLC2A3. High risk score indicated a poor prognosis of patients. The area under the ROC curves (AUC) s of the model for 3 years, 5 years, OS, and PFI were 0.71, 0.70, 0.73, and 0.66, respectively. Thus, the model possessed well performance for prediction of patients’ prognosis, which was confirmed by an external dataset. Moreover, the risk score was significantly correlated with immune cell infiltration. Conclusion. Our study conducted an immune-related prognostic risk score model, which could provide novel targets for immunotherapy of CRC.

scholarly journals Development of a Multi-gene-based Immune Prognostic Signature in Ovarian Cancer

2021 ◽  
Author(s):  
tiefeng cao ◽  
huimin shen
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
Network Analysis ◽  
Survival Prediction ◽  
Immune Gene ◽  
Prognostic Signature ◽  
Data Set ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Immune Related Genes

Abstract Background： Various components of the immune system play a critical role in the prognosis and treatment response in ovarian cancer (OC). Immunotherapy has been recognized as a hallmark of cancer but the effect is contradictional. Reliable immune gene-based prognostic biomarkers or regulatory factors are necessary to be systematically explored to develop an individualized prediction signature.Methods: This study systematically explored the gene expression profiles in patients with ovarian cancer from RNA-seq data set for The Cancer Genome Atlas (TCGA). Differentially expressed immune genes and transcription factors (TFs) were identified using the collected immune genes from ImmPort dataset and TFs from Cistoma database. Survival associated immune genes and TFs were identified in terms of overall survival. The prognostic signature was developed based on survival associated immune genes with LASSO (Least absolute shrinkage and selection operator) Cox regression analysis. Further, we performed network analysis to uncover the potential regulators of immune-related genes with the help of computational biology. Results: The prognostic signature, a weighted combination of the 21 immune-related genes, performed moderately in survival prediction with AUC was 0.746, 0.735, and 0.749 for 1, 3, and 5 year overall survival, respectively. Network analysis uncovered the regulatory role of TFs in immune genes. Intriguingly, the prognostic signature reflected the immune cells landscape and infiltration of some immune cell subtypes.Conclusions: We first constructed a signature with 21 immune genes of clinical significance, which showed promising predictive value in the surveillance, and prognosis of OC patients.

scholarly journals Identification of prognostic hypoxia-related genes signature on the tumor microenvironment in esophageal cancer

2021 ◽  
Vol 18 (6) ◽  
pp. 7743-7758
Author(s):  
Linlin Tan ◽  
◽  
Dingzhuo Cheng ◽  
Jianbo Wen ◽  
Kefeng Huang ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
High Risk ◽  
Immune Cells ◽  
Immune Cell ◽  
Risk Group ◽  
Roc Curves ◽  
Risk Groups ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas

<abstract> <sec><title>Background</title><p>Hypoxia is a crucial factor in the development of esophageal cancer. The relationship between hypoxia and immune status in the esophageal cancer microenvironment is becoming increasingly important in clinical practice. This study aims to clarify and investigate the possible connection between immunotherapy and hypoxia in esophageal cancer.</p> </sec> <sec><title>Methods</title><p>The Cancer Genome Atlas databases are used to find two types of esophageal cancer cases. Cox regressions analyses are used to screen genes for hypoxia-related traits. After that, the genetic signature is validated by survival analysis and the construction of ROC curves. GSEA is used to compare differences in enrichment in the two groups and is followed by the CIBERSORT tool to investigate a potentially relevant correlation between immune cells and gene signatures.</p> </sec> <sec><title>Results</title><p>We found that the esophageal adenocarcinoma hypoxia model contains 3 genes (PGK1, PGM1, SLC2A3), and the esophageal squamous cell carcinoma hypoxia model contains 2 genes (EGFR, ATF3). The findings demonstrated that the survival rate of patients in the high-risk group is lower than in the lower-risk group. Furthermore, we find that three kinds of immune cells (memory activated CD4+ T cells, activated mast cells, and M2 macrophages) have a marked infiltration in the tissues of patients in the high-risk group. Moreover, we find that PD-L1 and CD244 are highly expressed in high-risk groups.</p> </sec> <sec><title>Conclusions</title><p>Our data demonstrate that oxygen deprivation is correlated with prognosis and the incidence of immune cell infiltration in patients with both types of esophageal cancer, which provides an immunological perspective for the development of personalized therapy.</p> </sec> </abstract>

scholarly journals Development of an Individualized Immune Prognostic Signature for Clear Cell Renal Cell Carcinoma through the Identification of Differential Immune Genes

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Jianfeng Wang ◽  
Chaozhi Tang ◽  
Xiaowu Liu
Keyword(s):  
Renal Cell Carcinoma ◽  
T Cells ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Immune Gene ◽  
Cell Renal Cell Carcinoma ◽  
Immune Genes ◽  
Tumor Microenvironments ◽  
Prognosis Model

Increasing evidence has shown that tumor microenvironments are an important feature in clear cell renal cell carcinoma (ccRCC) carcinogenesis and therapeutic efficacy. In this study, two subtypes of ccRCC, high- and low-immune groups, were identified based on the immune gene datasets, of which the differential immune genes were identified accordingly. Furthermore, we constructed a risk prognosis model using five immune genes, specifically, AQP9, KIAA1429, HAMP, CCL13, and CCL21. This model was highly predictive of ccRCC clinical characteristics and showed potential for use in immunotherapy. Furthermore, the five identified genes were highly correlated with the abundance of B cells, CD4 T cells, CD8 T cells, macrophages, neutrophils, and dendritic cells in the tumor microenvironments. Among them, AQP9, KIAA1429, and HAMP exhibited significant prognostic potential. These findings indicate that monitoring and operating tumor microenvironments are of great significance for ccRCC prognosis and precise immunotherapy.

scholarly journals A Four-Immune Gene Prognostic Risk Model for Colorectal Adenocarcinoma Based on the TCGA and Immport Data Sets

2020 ◽  
Author(s):  
Boyang Xu ◽  
Ziqi Peng ◽  
Yue An ◽  
Xue Yao ◽  
Mingjun Sun
Keyword(s):  
T Cells ◽  
Colorectal Adenocarcinoma ◽  
Immune Cell ◽  
Risk Model ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Data Sets ◽  
Immune Gene ◽  
Immune Genes ◽  
Follicular Helper

Abstract BackgroundAs one of the hot spots in oncology field, immune research provides new ideas for the diagnosis and treatment of tumors. Different histological types of colorectal cancer are different. Adenocarcinoma, as the type with the highest proportion, has a high research value. This study aims to build an immune gene prognostic risk model for colorectal adenocarcinoma to improve the diagnosis and prognosis prediction of colorectal adenocarcinoma.MethodsThe differentially expressed immune genes could be obtained from the gene expression data downloaded from The Cancer Genome Atlas (TCGA) and the immune gene data downloaded from the ImmPort Database. Univariate COX and multivariate COX analyses were used to construct the immune gene prognostic risk model of and the clinical application potential of this model. The correlation between the model and the immune cells infiltration and the influence of each immune cell on the survival were analyzed.Results5975 differentially expressed genes were obtained, and 497 differentially expressed immune genes were selected by combining the information of immune genes. Among them, 36 immune genes were associated with prognosis, and 4 immune genes (THRB, IL1RL2, LGR6, LTB4R2) were included in the prognostic risk model of immune genes. Patients with higher Risk Score had shorter survival. Compared with gender, age and pathological stage, the model has better prediction potential. In addition, the model was correlated with Macrophages M0, Macrophages M1, T cells follicular helper and NK cells activated. Among them, T cells follicular helper and Macrophages M0 were related to the survival of patients.ConclusionWe developed a prognostic risk model containing four immune genes, THRB, IL1RL2, LGR6 and LTB4R2, which accurately described the prognosis of the patient, and affected the survival of patients by influencing the infiltration of Macrophages M0 and T cells follicular helper.

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