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 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 Development of a miR-3648-Targeted Immune-Related 4-Gene Signature as a Prognostic Biomarker in Esophageal Adenocarcinoma Based on ehe WGCNA Algorithm

2021 ◽  
Author(s):  
Donglei Zhang ◽  
Hang Yin ◽  
Ping Xu ◽  
Xiaozhe Qian
Keyword(s):  
Esophageal Adenocarcinoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Normal Tissues

Abstract Background: Esophageal adenocarcinoma (EA) has a poor prognosis and is a typical immunogenic malignant tumor. Abnormal expression of miR-3648 has been reported in EA, but its value in prognosis prediction and immune cell infiltration imbalance mediation is still unknown. We aimed to mine immune-related genes (IRGs) targeted by miR-3648 and construct a multigene signature to improve the prognostic prediction of EA.Methods: The gene expression data of EA tumor or normal tissues from The Cancer Genome Atlas (TCGA) database and GTEx database were downloaded. Weighted gene coexpression network analysis (WGCNA), the CIBERSORT algorithm and Cox regression analysis were applied to identify IRGs and to construct a prognostic signature and nomogram.Results: miR-3648 was obviously highly expressed in EA tumor tissues and was correlated with patient survival time [hazard ratio (HR) = 1.28, 95% confidence interval (CI): 1.09-1.49, p = 0.002]. A total of 70 miR-3648-targeted genes related to immune cell infiltration were identified, and a novel 4-gene signature (C10orf55, DLL4, PANX2 and NKAIN1) was established. The prognostic signature-based risk score has superior capability to predict overall survival (AUC = 0.740 for 1 year; AUC = 0.717 for 3 years; AUC = 0.622 for 5 years). A higher score was indicative of a poorer prognosis than a lower score (HR = 1.69, 95% CI: 1.08-2.64, p = 0.20, adjusted by TNM stage).Conclusion: miR-3648 might play a crucial role in the progression of EA. The novel miR-3648-targeted immune-related 4-gene signature is expected to become a potential prognostic marker in EA.

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 Identification and validation of tumor microenvironment-related lncRNA prognostic signature for uveal melanoma

2021 ◽  
Vol 14 (8) ◽  
pp. 1151-1159
Author(s):  
Chen-Lu Liao ◽  
◽  
Xing-Yu Sun ◽  
Qi Zhou ◽  
Min Tian ◽  
...  
Keyword(s):  
Prognostic Value ◽  
Prognostic Markers ◽  
Immune Cell ◽  
Cox Regression ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Prognostic Signature ◽  
Profile Data ◽  
Small Molecule Drugs

AIM: To investigate the role of tumor microenvironment (TME)-related long non-coding RNA (lncRNA) in uveal melanoma (UM), probable prognostic signature and potential small molecule drugs using bioinformatics analysis. METHODS: UM expression profile data were downloaded from the Cancer Genome Atlas (TCGA) and bioinformatics methods were used to find prognostic lncRNAs related to UM immune cell infiltration. The gene expression profile data of 80 TCGA specimens were analyzed using the single sample Gene Set Enrichment Analysis (ssGSEA) method, and the immune cell infiltration of a single specimen was evaluated. Finally, the specimens were divided into high and low infiltration groups. The differential expression between the two groups was analyzed using the R package ‘edgeR’. Univariate, multivariate and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analyses were performed to explore the prognostic value of TME-related lncRNAs. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analyses were also performed. The Connectivity Map (CMap) data set was used to screen molecular drugs that may treat UM. RESULTS: A total of 2393 differentially expressed genes were identified and met the criteria for the low and high immune cell infiltration groups. Univariate Cox analysis of lncRNA genes with differential expression identified 186 genes associated with prognosis. Eight prognostic markers of TME-included lncRNA genes were established as potentially independent prognostic elements. Among 269 differentially expressed lncRNAs, 69 were up-regulated and 200 were down-regulated. Univariate Cox regression analysis of the risk indicators and clinical characteristics of the 8 lncRNA gene constructs showed that age, TNM stage, tumor base diameter, and low and high risk indices had significant prognostic value. We screened the potential small-molecule drugs for UM, including W-13, AH-6809 and Imatinib. CONCLUSION: The prognostic markers identified in this study are reliable biomarkers of UM. This study expands our current understanding of the role of TME-related lncRNAs in UM genesis, which may lay the foundations for future treatment of this disease.

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.

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.

scholarly journals A  novel  comprehensive immune-related gene signature as a promising survival predictor for head and neck squamous cell carcinoma

2020 ◽  
Author(s):  
Ruihua Fang ◽  
Lin Chen ◽  
Jing Liao ◽  
Jierong Luo ◽  
Chenchen Zhang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Head And Neck ◽  
Cox Regression ◽  
Gene Signature ◽  
Related Gene ◽  
Prognostic Signature ◽  
Immune Microenvironment ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Immune Related Gene

Abstract Background: Head and neck squamous cell carcinoma (HNSCC), the most frequent subtype of head and neck cancer, continues to have a poor prognosis with no improvement. Growing evidence has demonstrated that the immune system plays a crucial role in the development and progression of HNSCC. The goal of our study was to develop an immune-related signature for accurately predicting the survival of HNSCC patients. Methods: Gene expression profiles were established from a total of 546 HNSCC and normal tissues to establish a training set and 83 HNSCC tissues for a validation set. Differentially expressed prognostic immune genes were identified by univariate Cox regression analysis and a corresponding network of differentially expressed transcription factors (TFs) were identified using Cytoscape. The immune-related gene signature was established and validated by univariate Cox regression analysis, least absolute shrinkage and selector operation (LASSO), and multivariate Cox regression analyses. In addition, the prognostic value of the immune-related signature was analyzed by survival and Cox regression analysis. Finally, the correlation between the immune-related signature and the immune microenvironment was established.Results: In this study, the TF-mediated network revealed that Foxp3 plays a central role in the regulatory mechanism of most immune genes. A prognostic signature based on 10 immune-related genes, which divided patients into high and low risk groups, was developed and successfully validated using two independent databases. Our prognostic signature was significantly related to worse survival and predicted prognosis in patients with different clinicopathological factors. A nomogram including clinical characteristics was also constructed for accurate prediction. Furthermore, it was determined that our prognostic signature may act as an independent factor for predicting the survival of HNSCC patients. ROC analysis also revealed that our signature had superior predictive value compared with TNM stage. As for the immune microenvironment, our signature showed a positive correlation with activated mast cells and M0 macrophages, a negative correlation with Tregs, and immune checkpoint molecules PD-1 and CLTA-4. Conclusions: Our study established an immune-related gene signature, which not only provides a promising biomarker for survival prediction, but may be evaluated as an indicator for personalized immunotherapy in patients with HNSCC.

Identification of an Immune-Related Prognostic Signature Associated with Immune Infiltration in Melanoma

2020 ◽  
Author(s):  
Nian Liu ◽  
Zijian Liu ◽  
Xinxin Liu ◽  
Xiaoru Duan ◽  
Yuqiong Huang ◽  
...  
Keyword(s):  
Immune Cell ◽  
Underlying Mechanism ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Prognostic Signature ◽  
Immune Infiltration ◽  
Mutation Burden ◽  
Melanoma Prognosis ◽  
Melanoma Patients ◽  
Immune Related Genes

Abstract Background: Melanoma is the leading cause of cancer-related death among skin tumors, with an increasing incidence worldwide. Few studies have effectively investigated the significance of an immune-related genes (IRGs) signature for melanoma prognosis. Methods: Here, we constructed an IRGs prognostic signature using bioinformatics methods and evaluated and validated its predictive capability. Then, immune cell infiltration and tumor mutation burden (TMB) landscapes associated with this signature in melanoma were analyzed comprehensively. Results: With the 10-IRG prognostic signature, melanoma patients in the low-risk group showed better survival with distinct features of high immune cell infiltration and TMB. Importantly, melanoma patients in this subgroup were significantly responsive to MAGE-A3 in the validation cohort. Conclusions: This immune-related prognostic signature is thus a reliable tool to predict melanoma prognosis; as the underlying mechanism of this signature is associated with immune infiltration and mutation burden, it might reflect the benefit of immunotherapy to patients.

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 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.

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