scholarly journals Establishment of a novel glycolysis-related prognostic gene signature for ovarian cancer and its relationships with immune infiltration of the tumor microenvironment

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Jianlei Bi ◽  
Fangfang Bi ◽  
Xue Pan ◽  
Qing Yang
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Tumor Microenvironment ◽  
Immune Cell ◽  
Risk Groups ◽  
Cell Types ◽  
Gene Signature ◽  
Immune Cell Infiltration ◽  
Related Gene ◽  
Immune Related Genes

Abstract Background Glycolysis affects tumor growth, invasion, chemotherapy resistance, and the tumor microenvironment. In this study, we aimed to construct a glycolysis-related prognostic model for ovarian cancer and analyze its relationship with the tumor microenvironment’s immune cell infiltration. Methods We obtained six glycolysis-related gene sets for gene set enrichment analysis (GSEA). Ovarian cancer data from The Cancer Genome Atlas (TCGA) database and two Gene Expression Omnibus (GEO) datasets were divided into two groups after removing batch effects. We compared the tumor environments' immune components in high-risk and low-risk groups and analyzed the correlation between glycolysis- and immune-related genes. Then, we generated and validated a predictive model for the prognosis of ovarian cancer using the glycolysis-related genes. Results Overall, 27/329 glycolytic genes were associated with survival in ovarian cancer, 8 of which showed predictive value. The tumor cell components in the tumor microenvironment did not differ between the high-risk and low-risk groups; however, the immune score differed significantly between groups. In total, 13/24 immune cell types differed between groups, including 10 T cell types and three other immune cell types. Eight glycolysis-related prognostic genes were related to the expression of multiple immune-related genes at varying degrees, suggesting a relationship between glycolysis and immune response. Conclusions We identified eight glycolysis-related prognostic genes that effectively predicted survival in ovarian cancer. To a certain extent, the newly identified gene signature was related to the tumor microenvironment, especially immune cell infiltration and immune-related gene expression. These findings provide potential biomarkers and therapeutic targets for ovarian cancer.

scholarly journals Comprehensive Analysis of Tumor Microenvironment Identified Prognostic Immune-Related Gene Signature in Ovarian Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Na Li ◽  
Biao Li ◽  
Xianquan Zhan
Keyword(s):  
Ovarian Cancer ◽  
Immune Cells ◽  
Prognostic Model ◽  
Immune Cell ◽  
Gene Signature ◽  
Differentially Expressed ◽  
Immune Cell Infiltration ◽  
Related Gene ◽  
Immune Related Gene ◽  
Immune Related Genes

BackgroundAccumulating evidence demonstrated that tumor microenvironmental cells played important roles in predicting clinical outcomes and therapeutic efficacy. We aimed to develop a reliable immune-related gene signature for predicting the prognosis of ovarian cancer (OC).MethodsSingle sample gene-set enrichment analysis (ssGSEA) of immune gene-sets was used to quantify the relative abundance of immune cell infiltration and develop high- and low-abundance immune subtypes of 308 OC samples. The presence of infiltrating stromal/immune cells in OC tissues was calculated as an estimate score. We estimated the correlation coefficients among the immune subtype, clinicopathological feature, immune score, distribution of immune cells, and tumor mutation burden (TMB). The differentially expressed immune-related genes between high- and low-abundance immune subtypes were further used to construct a gene signature of a prognostic model in OC with lasso regression analysis.ResultsThe ssGSEA analysis divided OC samples into high- and low-abundance immune subtypes based on the abundance of immune cell infiltration, which was significantly related to the estimate score and clinical characteristics. The distribution of immune cells was also significantly different between high- and low-abundance immune subtypes. The correlation analysis showed the close relationship between TMB and the estimate score. The differentially expressed immune-related genes between high- and low-abundance immune subtypes were enriched in multiple immune-related pathways. Some immune checkpoints (PDL1, PD1, and CTLA-4) were overexpressed in the high-abundance immune subtype. Furthermore, the five-immune-related-gene-signature prognostic model (CCL18, CXCL13, HLA-DOB, HLA-DPB2, and TNFRSF17)-based high-risk and low-risk groups were significantly related to OC overall survival.ConclusionImmune-related genes were the promising predictors of prognosis and survival, and the comprehensive landscape of tumor microenvironmental cells of OC has potential for therapeutic schedule monitoring.

scholarly journals Identification of Immune-Related Gene Signatures in Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Na Li ◽  
Jiahong Wang ◽  
Xianquan Zhan
Keyword(s):  
Prognostic Model ◽  
Immune Cell ◽  
Lung Squamous Cell Carcinoma ◽  
Risk Groups ◽  
Gene Signature ◽  
Low Risk ◽  
Prognostic Models ◽  
Related Gene ◽  
Immune Related Gene ◽  
Immune Related Genes

Accumulating evidence indicates that immunotherapy helped to improve the survival and quality-of-life of patients with lung adenocarcinoma (LUAD) or lung squamous cell carcinoma (LUSC) besides chemotherapy and gene targeting treatment. This study aimed to develop immune-related gene signatures in LUAD and LUSC subtypes, respectively. LUAD and LUSC samples were divided into high- and low-abundance groups of immune cell infiltration (Immunity_H and Immunity_L) based on the abundance of immune cell infiltrations. The distribution of immune cells was significantly different between the high- and low-immunity subtypes in LUAD and LUSC samples. The differentially expressed genes (DEGs) between those two groups in LUAD and LUSC contain some key immune-related genes, such as PDL1, PD1, CTLA-4, and HLA families. The DEGs were enriched in multiple immune-related pathways. Furthermore, the seven-immune-related-gene-signature (CD1B, CHRNA6, CLEC12B, CLEC17A, CLNK, INHA, and SLC14A2) prognostic model-based high- and low-risk groups were significantly associated with LUAD overall survival and clinical characteristics. The eight-immune-related-gene-signature (C4BPB, FCAMR, GRAPL, MAP1LC3C, MGC2889, TRIM55, UGT1A1, and VIPR2) prognostic model-based high- and low-risk groups were significantly associated with LUSC overall survival and clinical characteristics. The prognostic models were tested as good ones by receiver operating characteristic, principal component analysis, univariate and multivariate analysis, and nomogram. The verifications of these two immune-related-gene-signature prognostic models showed consistency in the train and test cohorts of LUAD and LUSC. In addition, patients with LUAD in the low-risk group responded better to immunotherapy than those in the high-risk group. This study revealed two reliable immune-related-gene-signature models that were significantly associated with prognosis and tumor microenvironment cell infiltration in LUAD and LUSC, respectively. Evaluation of the integrated characterization of multiple immune-related genes and pathways could help to predict the response to immunotherapy and monitor immunotherapy strategies.

scholarly journals Identification and Verification of Immune-Related Gene Prognostic Signature Based on ssGSEA for Osteosarcoma

2020 ◽  
Vol 10 ◽  
Author(s):  
Bo Xiao ◽  
Liyan Liu ◽  
Aoyu Li ◽  
Cheng Xiang ◽  
Pingxiao Wang ◽  
...  
Keyword(s):  
Immune Cell ◽  
Cox Regression ◽  
Gene Signature ◽  
Cell Infiltration ◽  
Significant Prognostic Factor ◽  
Immune Cell Infiltration ◽  
Related Gene ◽  
Prognostic Signature ◽  
Immune Related Gene ◽  
Immune Related Genes

Osteosarcoma is the most common malignant bone tumor in children and adolescence. Multiple immune-related genes have been reported in different cancers. The aim is to identify an immune-related gene signature for the prospective evaluation of prognosis for osteosarcoma patients. In this study, we evaluated the infiltration of immune cells in 101 osteosarcoma patients downloaded from TARGET using the ssGSEA to the RNA-sequencing of these patients, thus, high immune cell infiltration cluster, middle immune cell infiltration cluster and low immune cell infiltration cluster were generated. On the foundation of high immune cell infiltration cluster vs. low immune cell infiltration cluster and normal vs. osteosarcoma, we found 108 common differentially expressed genes which were sequentially submitted to univariate Cox and LASSO regression analysis. Furthermore, GSEA indicated some pathways with notable enrichment in the high- and low-immune cell infiltration cluster that may be helpful in understanding the potential mechanisms. Finally, we identified seven immune-related genes as prognostic signature for osteosarcoma. Kaplan-Meier analysis, ROC curve, univariate and multivariate Cox regression further confirmed that the seven immune-related genes signature was an innovative and significant prognostic factor independent of clinical features. These results of this study offer a means to predict the prognosis and survival of osteosarcoma patients with uncovered seven-gene signature as potential biomarkers.

scholarly journals Development of an Immune-Related Gene Signature for Prognosis in Melanoma

2021 ◽  
Vol 10 ◽  
Author(s):  
Jia-An Zhang ◽  
Xu-Yue Zhou ◽  
Dan Huang ◽  
Chao Luan ◽  
Heng Gu ◽  
...  
Keyword(s):  
T Cells ◽  
Immune Cell ◽  
Treatment Method ◽  
Gene Signature ◽  
Cell Infiltration ◽  
Venn Diagram ◽  
Immune Cell Infiltration ◽  
Tumor Purity ◽  
New Treatment ◽  
Immune Related Genes

Melanoma remains a potentially deadly malignant tumor. The incidence of melanoma continues to rise. Immunotherapy has become a new treatment method and is widely used in a variety of tumors. Original melanoma data were downloaded from TCGA. ssGSEA was performed to classify them. GSVA software and the "hclust" package were used to analyze the data. The ESTIMATE algorithm screened DEGs. The edgeR package and Venn diagram identified valid immune-related genes. Univariate, LASSO and multivariate analyses were used to explore the hub genes. The "rms" package established the nomogram and calibrated the curve. Immune infiltration data were obtained from the TIMER database. Compared with that of samples in the high immune cell infiltration cluster, we found that the tumor purity of samples in the low immune cell infiltration cluster was higher. The immune score, ESTIMATE score and stromal score in the low immune cell infiltration cluster were lower. In the high immune cell infiltration cluster, the immune components were more abundant, while the tumor purity was lower. The expression levels of TIGIT, PDCD1, LAG3, HAVCR2, CTLA4 and the HLA family were also higher in the high immune cell infiltration cluster. Survival analysis showed that patients in the high immune cell infiltration cluster had shorter OS than patients in the low immune cell infiltration cluster. IGHV1-18, CXCL11, LTF, and HLA-DQB1 were identified as immune cell infiltration-related DEGs. The prognosis of melanoma was significantly negatively correlated with the infiltration of CD4+ T cells, CD8+ T cells, dendritic cells, neutrophils and macrophages. In this study, we identified immune-related melanoma core genes and relevant immune cell subtypes, which may be used in targeted therapy and immunotherapy of melanoma.

scholarly journals MIXTURE: an improved algorithm for immune tumor microenvironment estimation based on gene expression data

2019 ◽  
Author(s):  
Elmer A. Fernández ◽  
Yamil D. Mahmoud ◽  
Florencia Veigas ◽  
Darío Rocha ◽  
Mónica Balzarini ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Immune Cell ◽  
Therapy Response ◽  
Cell Types ◽  
Gene Signature ◽  
Response To Therapy ◽  
Support Vector ◽  
Data Sets ◽  
Cell Type ◽  
Before And After

AbstractRNA sequencing has proved to be an efficient high-throughput technique to robustly characterize the presence and quantity of RNA in tumor biopsies at a given time. Importantly, it can be used to computationally estimate the composition of the tumor immune infiltrate and to infer the immunological phenotypes of those cells. Given the significant impact of anti-cancer immunotherapies and the role of the associated immune tumor microenvironment (ITME) on its prognosis and therapy response, the estimation of the immune cell-type content in the tumor is crucial for designing effective strategies to understand and treat cancer. Current digital estimation of the ITME cell mixture content can be performed using different analytical tools. However, current methods tend to over-estimate the number of cell-types present in the sample, thus under-estimating true proportions, biasing the results. We developed MIXTURE, a noise-constrained recursive feature selection for support vector regression that overcomes such limitations. MIXTURE deconvolutes cell-type proportions of bulk tumor samples for both RNA microarray or RNA-Seq platforms from a leukocyte validated gene signature. We evaluated MIXTURE over simulated and benchmark data sets. It overcomes competitive methods in terms of accuracy on the true number of present cell-types and proportions estimates with increased robustness to estimation bias. It also shows superior robustness to collinearity problems. Finally, we investigated the human immune microenvironment of breast cancer, head and neck squamous cell carcinoma, and melanoma biopsies before and after anti-PD-1 immunotherapy treatment revealing associations to response to therapy which have not seen by previous methods.

Identification of a Metabolism-Related Risk Signature to Predict the Outcome and Immune Infiltration of Esophageal Squamous Cell Carcinoma

2020 ◽  
Author(s):  
Yu Liu ◽  
Liyu Wang ◽  
Hengchang Liu ◽  
He Tian ◽  
Tao Fan ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Plasma Cells ◽  
Immune Cell ◽  
Gene Signature ◽  
Metabolic Reprogramming ◽  
Immune Cell Infiltration ◽  
Related Gene ◽  
Prognostic Signature ◽  
Immune Infiltration

Abstract Background: Metabolic reprogramming is associated with tumor heterogeneity and progression. Understanding the characteristics of metabolic reprogramming in esophageal squamous cell carcinoma (ESCC) might help us to uncover new biomarkers for patient outcomes and targets for therapies.Methods: In this study, metabolism-related genes were screened from mRNA microarray data (GSE53624, GSE53622). Consensus clustering analysis was used to divide tumors into subgroups. Survival analysis and univariate Cox analysis were performed to select prognostic genes. A metabolism-related gene signature was established with multivariate Cox proportional hazards regression (PHR) analysis in the training group (GSE53624). Gene set enrichment analysis (GSEA) and CIBERSORT were used to analyze functional enrichment and immune cell infiltration.The gene signature and immune infiltration were verified in two public databases (GSE53622, TCGA-ESCC) and in two independent cohorts, 95 and 119 ESCC patients, by immunohistochemistry (IHC) analysis. Results: Based on prognosis-related metabolic gene expression, three cluster subgroups (k = 3) were identified with significantly different immune cell infiltration patterns, clinical features and overall survival (OS) times. Then, we developed a multigene (INPP5E, CD38 and POLR3G) prognostic signature that showed better predictive ability and was found to be an independent prognostic risk factor in ESCC database and two public database analyses. This result could also be verified by multicenter IHC experiment and indicated the clinical application potential. In addition, GSEA showed that several tumor-related pathways were associated with the prognostic signature. Furthermore, the immune cell infiltration of regulatory T cells (Tregs) and plasma cells displayed an obvious correlation with prognostic signature and IHC experiment in 119 cohort also support this result.Conclusions: Our study indicated that the metabolism-related prognostic gene could stratify patients into subgroups and was associated with immune infiltration, clinical features and outcomes. The three-gene prognostic signature from metabolic-related gene displays a good ability to predict OS and the infiltration of immunosuppressive Tregs and plasma cells.

Profiles of immune‐related genes and immune cell infiltration in the tumor microenvironment of diffuse lower‐grade gliomas

2020 ◽  
Vol 235 (10) ◽  
pp. 7321-7331 ◽  
Author(s):  
Xiangyang Deng ◽  
Dongdong Lin ◽  
Xiaojia Zhang ◽  
Xuchao Shen ◽  
Zelin Yang ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Lower Grade ◽  
Immune Cell Infiltration ◽  
Immune Related Genes

scholarly journals An Immune-Related Gene Signature for Predicting Survival and Immunotherapy Efficacy in Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Yifei Dai ◽  
Weijie Qiang ◽  
Kequan Lin ◽  
Yu Gui ◽  
Xun Lan ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cell ◽  
Prognostic Index ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Related Gene ◽  
Immune Related Gene ◽  
Clinicopathologic Factors ◽  
Immune Related Genes

Abstract Background: Hepatocellular carcinoma (HCC) ranks the fourth in terms of cancer-related mortality globally. Herein, in this research, we attempted to develop a novel immune-related gene signature that could predict survival and efficacy of immunotherapy for HCC patients.Methods: The transcriptomic and clinical data of HCC samples were downloaded from The Cancer Genome Atlas (TCGA) and GSE14520 datasets, followed by acquisition of immune-related genes from the ImmPort database. Afterwards, an immune-related gene-based prognostic index (IRGPI) was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression model. Kaplan-Meier survival curves as well as time-dependent receiver operating characteristic (ROC) curve were performed to evaluate its predictive capability. Besides, both univariate and multivariate analysis on overall survival for the IRGPI and multiple clinicopathologic factors were carried out, followed by the construction of nomogram. Finally, we explored the possible correlation of IRGPI with immune cell infiltration or immunotherapy efficacy. Results: Analysis of 365 HCC samples identified 11 differentially expressed genes, which were selected to establish the IRGPI. Notably, it can predict survival of HCC patients more accurately than published biomarkers. Furthermore, IRGPI can predict the infiltration of immune cells in the tumor microenvironment of HCC, as well as the response of immunotherapy.Conclusion: Collectively, the currently established IRGPI can accurately predict survival, reflect the immune microenvironment, and predict the efficacy of immunotherapy among HCC patients.

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