Characterization of hypoxia signature to evaluate the tumor immune microenvironment and predict prognosis in glioma.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14534-e14534
Author(s):  
Shihong Wu ◽  
Wanzun Lin ◽  
Youliang Weng ◽  
Yuhui Pan ◽  
Zongwei Huang ◽  
...  
Keyword(s):  
Survival Data ◽  
Poor Prognosis ◽  
Risk Model ◽  
Malignant Gliomas ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Immune Microenvironment ◽  
Molecular Features ◽  
Dismal Prognosis ◽  
Hypoxia Signature

e14534 Background: Glioma, the most common primary brain tumor, accounts for more than 50% of all primary brain tumors. Malignant gliomas, especially glioblastomas, are associated with a dismal prognosis. Hypoxia is a driver of the malignant phenotype in glioma; it triggers a cascade of immunosuppressive processes and malignant cellular responses (tumor progression, metastases, and resistance to chemoradiotherapy), which result in disease progression and poor prognosis. However, approaches to determine the extent of hypoxia in the tumor microenvironment are still unclear. Methods: Here, we enrolled 1626 glioma patients with RNA sequence and survival data in two independent cohorts, and developed a hypoxia risk model to reflect the immune microenvironment in glioma and predict prognosis. Results: High hypoxia risk score was associated with poor prognosis and indicated an immunosuppressive microenvironment. Hypoxia signature significantly correlated with clinical and molecular features and could serve as an independent prognostic factor for glioma patients. Moreover, Gene Set Enrichment Analysis showed that gene sets associated with the high-risk group were involved in carcinogenesis and immunosuppression signaling. Conclusions: In conclusion, we developed and validated a novel hypoxia risk model, which served as an independent prognostic indicator and reflected overall immune response intensity in the glioma microenvironment.

scholarly journals Retinoic acid metabolism-related enzyme signature identified prognostic and immunotherapeutic efficiency in sarcoma

2021 ◽  
Author(s):  
Huaiyuan Xu ◽  
JinXin Hu ◽  
YiJiang Song ◽  
HongMin Chen ◽  
YanYang Xu ◽  
...  
Keyword(s):  
Retinoic Acid ◽  
Cancer Progression ◽  
Poor Prognosis ◽  
Acid Metabolism ◽  
Risk Model ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
Data Set ◽  
Molecular Features ◽  
Prognosis Group

Abstract Background: Dynamic balance of retinoic acid metabolism plays a major role in a variety of biological functions including cell proliferation and differentiation, while its dysregulation often leads to cancer progression and disordered immunity. Targeting retinoic acid signaling has shown effectivity in re-educates tumor microenvironment so that they could enhance the efficacy of immunotherapies and received better outcome. However, a comprehensive analysis of retinoic acid metabolism abnormality in sarcoma is still lacking, which limits the development and application of related targeted drugs.Methods: The RA metabolism related enzymes data set was collected from several database. Then we systematically analyzed the molecular features of 19 retinoic acid metabolism-related enzymes based on TCGA/TARGET/GSE datasets and revealed two subtypes with distinct metabolic status and prognostic value. And we further generated a 7 genes signature to predict retinoic acid metabolism index based on LASSO-penalized Cox regression model.Results: Gene set enrichment analysis indicated a set of immune and oncogenic pathways were enriched in poor-prognosis group. Connectivity Map screened 56 potential small molecules specific to different sub-groups. Survival analysis demonstrated significant prognostic difference between high- and low-risk groups among all datasets. Several immune cells including CD8+ T cells, Treg cells, Monocytes, and Macrophages showed different abundance between these groups, and immune checkpoint blockade therapy response prediction indicated potential immunotherapeutic efficiency of poor-prognosis group.Conclusions: Taken together, our study elaborated two different retinoic acid metabolism status of sarcoma, which revealed the metabolic heterogeneity of sarcoma. Robust and powerful metabolic index risk model could provide insightful suggestions to explore the molecular functions and mechanisms of retinoic acid metabolism.

scholarly journals Comprehensive Analysis of Tumor Immune Microenvironment and Prognosis of m6A-Related lncRNAs in Gastric Cancer

2021 ◽  
Author(s):  
Yi Wang ◽  
Gui-Qi Zhu ◽  
Di Tian ◽  
Chang-Wu Zhou ◽  
Na Li ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Risk Model ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Immune Checkpoints ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment

Abstract Background N6-methyladenosine (m6A) modification and long non-coding RNAs (lncRNAs) play pivotal role in gastric cancer (GC) progression. The emergence of immunotherapy in GC has created a paradigm shift in the approach of treatment, whereas there is significant heterogeneity with regard to degree of treatment responses, which results from the variability of tumor immune microenvironment (TIME). How the interplay between them enrolled in the shaping of TIME remains unclear. Methods The RNA sequencing and clinical data of GC patients were collected from TCGA database. Pearson correlation test and univariate Cox analysis were used to screen out m6A-related lncRNAs. Consensus clustering was implemented to classify GC patients into 2 subtypes. Survival analysis, the infiltration of immune cells, Gene set enrichment analysis (GSEA) and the mutation profiles were analyzed and compared between two clusters. Then least absolute shrinkage and selection operator (LASSO) COX regression was implemented to select pivotal genes and risk score model was constructed accordingly. The prognosis value of the risk model was explored. In addition, the discrepancies of response to immune checkpoints inhibitor (ICIs) therapy were compared between different risk groups. Finally, we performed qRT-PCR to detect the expression pattern in 35 tumor tissues and paired adjacent normal tissue, and validated the prognostic value of risk model in the our cohort (N=35). Results The expression profiles of 23 lncRNAs were included to cluster patients into different subtypes. Cluster1 with worse prognosis harbored higher immune score, stromal score, ESTIMATE score and mutation rate of genes. Different immune cell infiltration pattern were also displayed between different clusters. GSEA showed that cluster1 was preferentially enriched with tumor hallmarks and tumor correlated biological pathways. Next, 9 lncRNAs were selected by LASSO regression model to construct risk model. Patients in the high risk group had poor prognosis. The prognosis value of this model was also validated in our cohort. As for predicting responses to the ICIs therapy, we found that patients from high risk group had lower TMB score and lower proportion of MSI-H subtype. Moreover, patients had distinct immunophenoscores in different risk groups. Conclusion Our study revealed that the potential interplay between m6A modification and lncRNAs might have critical role in predicting GC prognosis, sculpting TIME landscape and predicting the responses to immune checkpoints inhibitors therapy.

scholarly journals Overexpressed XRCC2 as an independent risk factor for poor prognosis in glioma patients

2021 ◽  
Vol 27 (1) ◽  
Author(s):  
Zhendong Liu ◽  
Wang Zhang ◽  
Xingbo Cheng ◽  
Hongbo Wang ◽  
Lu Bian ◽  
...  
Keyword(s):  
Risk Factor ◽  
Expression Pattern ◽  
Poor Prognosis ◽  
Independent Risk Factor ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Cellular Mechanisms ◽  
Molecular Features ◽  
Meta Analyses

Abstract Background XRCC2, a homologous recombination-related gene, has been reported to be associated with a variety of cancers. However, its role in glioma has not been reported. This study aimed to find out the role of XRCC2 in glioma and reveal in which glioma-specific biological processes is XRCC2 involved based on thousands of glioma samples, thereby, providing a new perspective in the treatment and prognostic evaluation of glioma. Methods The expression characteristics of XRCC2 in thousands of glioma samples from CGGA and TCGA databases were comprehensively analyzed. Wilcox or Kruskal test was used to analyze the expression pattern of XRCC2 in gliomas with different clinical and molecular features. The effect of XRCC2 on the prognosis of glioma patients was explored by Kaplan–Meier and Cox regression. Gene set enrichment analysis (GSEA) revealed the possible cellular mechanisms involved in XRCC2 in glioma. Connectivity map (CMap) was used to screen small molecule drugs targeting XRCC2 and the expression levels of XRCC2 were verified in glioma cells and tissues by RT-qPCR and immunohistochemical staining. Results We found the overexpression of XRCC2 in glioma. Moreover, the overexpressed XRCC2 was associated with a variety of clinical features related to prognosis. Cox and meta-analyses showed that XRCC2 is an independent risk factor for the poor prognosis of glioma. Furthermore, the results of GSEA indicated that overexpressed XRCC2 could promote malignant progression through involved signaling pathways, such as in the cell cycle. Finally, doxazosin, quinostatin, canavanine, and chrysin were identified to exert anti-glioma effects by targeting XRCC2. Conclusions This study analyzed the expression pattern of XRCC2 in gliomas and its relationship with prognosis using multiple datasets. This is the first study to show that XRCC2, a novel oncogene, is significantly overexpressed in glioma and can lead to poor prognosis in glioma patients. XRCC2 could serve as a new biomarker for glioma diagnosis, treatment, and prognosis evaluation, thus bringing new insight into the management of glioma.

scholarly journals RNA N6-Methyladenosine Regulators Contribute to Tumor Immune Microenvironment and Have Clinical Prognostic Impact in Breast Cancer

2022 ◽  
Vol 12 ◽  
Author(s):  
Lan-Xin Mu ◽  
You-Cheng Shao ◽  
Lei Wei ◽  
Fang-Fang Chen ◽  
Jing-Wei Zhang
Keyword(s):  
Breast Cancer ◽  
Immune Cell ◽  
Risk Model ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Impact ◽  
Immune Microenvironment ◽  
Model Based ◽  
Tumor Immune Microenvironment

Purpose: This study aims to reveal the relationship between RNA N6-methyladenosine (m6A) regulators and tumor immune microenvironment (TME) in breast cancer, and to establish a risk model for predicting the occurrence and development of tumors.Patients and methods: In the present study, we respectively downloaded the transcriptome dataset of breast cancer from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database to analyze the mutation characteristics of m6A regulators and their expression profile in different clinicopathological groups. Then we used the weighted correlation network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), and cox regression to construct a risk prediction model based on m6A-associated hub genes. In addition, Immune infiltration analysis and gene set enrichment analysis (GSEA) was used to evaluate the immune cell context and the enriched gene sets among the subgroups.Results: Compared with adjacent normal tissue, differentially expressed 24 m6A regulators were identified in breast cancer. According to the expression features of m6A regulators above, we established two subgroups of breast cancer, which were also surprisingly distinguished by the feature of the immune microenvironment. The Model based on modification patterns of m6A regulators could predict the patient’s T stage and evaluate their prognosis. Besides, the low m6aRiskscore group presents an immune-activated phenotype as well as a lower tumor mutation load, and its 5-years survival rate was 90.5%, while that of the high m6ariskscore group was only 74.1%. Finally, the cohort confirmed that age (p < 0.001) and m6aRiskscore (p < 0.001) are both risk factors for breast cancer in the multivariate regression.Conclusion: The m6A regulators play an important role in the regulation of breast tumor immune microenvironment and is helpful to provide guidance for clinical immunotherapy.

scholarly journals A Novel S100 Family-Based Signature Associated with Prognosis and Immune Microenvironment in Glioma

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Yifang Hu ◽  
Jiahang Song ◽  
Zhen Wang ◽  
Jingbao Kan ◽  
Yaoqi Ge ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Group ◽  
High Risk Group ◽  
Immune Microenvironment ◽  
S100 Family ◽  
Kaplan Meier ◽  
Dismal Prognosis ◽  
Family Based ◽  
Genome Atlas

Background. Glioma is the most common central nervous system (CNS) cancer with a short survival period and a poor prognosis. The S100 family gene, comprising 25 members, relates to diverse biological processes of human malignancies. Nonetheless, the significance of S100 genes in predicting the prognosis of glioma remains largely unclear. We aimed to build an S100 family-based signature for glioma prognosis. Methods. We downloaded 665 and 313 glioma patients, respectively, from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) database with RNAseq data and clinical information. This study established a prognostic signature based on the S100 family genes through multivariate COX and LASSO regression. The Kaplan–Meier curve was plotted to compare overall survival (OS) among groups, whereas Receiver Operating Characteristic (ROC) analysis was performed to evaluate model accuracy. A representative gene S100B was further verified by in vitro experiments. Results. An S100 family-based signature comprising 5 genes was constructed to predict the glioma that stratified TCGA-derived cases as a low- or high-risk group, whereas the significance of prognosis was verified based on CGGA-derived cases. Kaplan–Meier analysis revealed that the high-risk group was associated with the dismal prognosis. Furthermore, the S100 family-based signature was proved to be closely related to immune microenvironment. In vitro analysis showed S100B gene in the signature promoted glioblastoma (GBM) cell proliferation and migration. Conclusions. We constructed and verified a novel S100 family-based signature associated with tumor immune microenvironment (TIME), which may shed novel light on the glioma diagnosis and treatment.

scholarly journals A Hypoxia Gene-Based Signature to Predict the Survival and Affect the Tumor Immune Microenvironment of Osteosarcoma in Children

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Feng Jiang ◽  
Xiao-Lin Miao ◽  
Xiao-Tian Zhang ◽  
Feng Yan ◽  
Yan Mao ◽  
...  
Keyword(s):  
High Risk ◽  
Poor Prognosis ◽  
Gene Signature ◽  
Response Strength ◽  
Gene Expression Omnibus ◽  
Immune Microenvironment ◽  
Prognostic Predictor ◽  
Molecular Features ◽  
Tumor Immune Microenvironment ◽  
Immunosuppressive Microenvironment

Osteosarcoma is a quickly developing, malignant cancer of the bone, which is associated with a bad prognosis. In osteosarcoma, hypoxia promotes the malignant phenotype, which results in a cascade of immunosuppressive processes, poor prognosis, and a high risk of metastasis. Nonetheless, additional methodologies for the study of hyperoxia in the tumor microenvironment also need more analysis. We obtained 88 children patients with osteosarcoma from the Therapeutically Applicable Research to Generate Effective Treatment (TARGET) database and 53 children patients with RNA sequence and clinicopathological data from the Gene Expression Omnibus (GEO). We developed a four-gene signature related to hypoxia to reflect the immune microenvironment in osteosarcoma that predicts survival. A high-risk score indicated a poor prognosis and immunosuppressive microenvironment. The presence of the four-gene signature related to hypoxia was correlated with clinical and molecular features and was an important prognostic predictor for pediatric osteosarcoma patients. In summary, we established and validated a four-gene signature related to hypoxia to forecast recovery and presented an independent prognostic predictor representing overall immune response strength within the osteosarcoma microenvironment.

Identification of FPR3 as a unique biomarker for targeted therapy in the immune microenvironment of breast cancer

2020 ◽  
Author(s):  
Jian Qi ◽  
Yu Liu ◽  
Jiliang Hu ◽  
Li Lu ◽  
Zhen Dou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Poor Prognosis ◽  
Functional Module ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Ppi Network ◽  
Immune Microenvironment ◽  
Tumor Stages ◽  
Immune Score

Abstract Background Immunotherapy is in the ascendant, but its use in the treatment of breast cancer remains limited. Thus, identification and evaluation of prognostic biomarkers of tissue microenvironment will reveal new immune-based therapeutic strategies for breast cancer. Methods Using in silico bioinformatic approach, we investigated the tumor microenvironmental and genetic factors related to breast cancer. We calculated the Immune score, Stromal score, Estimate score, Tumor purity, Tumor mutation burden (TMB), Mutant-allele tumor heterogeneity (MATH) of breast cancer patients from the Cancer Genome Atlas (TCGA) using the ESTIMATE algorithm and Maftools. Weighted correlation network analysis (WGCNA) was used to identify gene patterns association with the Immune score. Then we use the MCODE plugin of Cytoscape to analyze the protein-protein interaction (PPI) network for mining the functional gene modules. Survival and Cox analysis was further performed to identify the key prognostic targets in immune microenvironment. Gene set enrichment analysis (GSEA) was utilized to explore the carcinogenic pathways associated with the target genes. Results Significant correlations between Immune/Stromal scores with breast cancer subtypes and tumor stages were established. Importantly, we found that the Immune score, but not the Stromal score, was significantly related to the patient's prognosis. WGCNA identified a pattern of gene function associated with Immune score, and that almost all of these genes (388 genes) are significantly upregulated in the higher Immune score group. PPI network analysis revealed the enrichment of immune checkpoint genes in the functional module but predicting a good prognosis by survival analysis. Among all the upregulated genes, FPR3, a G protein-coupled receptor essential for neutrophil activation, is the sole factor that predicts poor prognosis. GSEA analysis showed FRP3 upregulation synergizes with the activation of many pathways involved in carcinogenesis. Conclusions This study identified FPR3 as a key immune-related biomarker predicting a poor prognosis for breast cancer, revealing it as a promising targetable gene for immunotherapy.

scholarly journals A Hypoxia Signature for Predicting Prognosis and Tumor Immune Microenvironment in Adrenocortical Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xi Chen ◽  
Lijun Yan ◽  
Yu Lu ◽  
Feng Jiang ◽  
Ni Zeng ◽  
...  
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Adrenocortical Carcinoma ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Immune Microenvironment ◽  
Gene Sets ◽  
Tumor Immune Microenvironment ◽  
Hypoxia Signature

Adrenocortical carcinoma (ACC) is a rare malignancy with dismal prognosis. Hypoxia is one of characteristics of cancer leading to tumor progression. For ACC, however, no reliable prognostic signature on the basis of hypoxia genes has been built. Our study aimed to develop a hypoxia-associated gene signature in ACC. Data of ACC patients were obtained from TCGA and GEO databases. The genes included in hypoxia risk signature were identified using the Cox regression analysis as well as LASSO regression analysis. GSEA was applied to discover the enriched gene sets. To detect a possible connection between the gene signature and immune cells, the CIBERSORT technique was applied. In ACC, the hypoxia signature including three genes (CCNA2, COL5A1, and EFNA3) was built to predict prognosis and reflect the immune microenvironment. Patients with high-risk scores tended to have a poor prognosis. According to the multivariate regression analysis, the hypoxia signature could be served as an independent indicator in ACC patients. GSEA demonstrated that gene sets linked to cancer proliferation and cell cycle were differentially enriched in high-risk classes. Additionally, we found that PDL1 and CTLA4 expression were significantly lower in the high-risk group than in the low-risk group, and resting NK cells displayed a significant increase in the high-risk group. In summary, the hypoxia risk signature created in our study might predict prognosis and evaluate the tumor immune microenvironment for ACC.

scholarly journals Comprehensive Analysis of Inflammatory Response–Related Genes, and Prognosis and Immune Infiltration in Patients With Low-Grade Glioma

2021 ◽  
Vol 12 ◽  
Author(s):  
Tao Han ◽  
Zhifan Zuo ◽  
Meilin Qu ◽  
Yinghui Zhou ◽  
Qing Li ◽  
...  
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Immune Cell ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Grade ◽  
Immune Microenvironment ◽  
Cox Regression Analysis

Background: Although low-grade glioma (LGG) has a good prognosis, it is prone to malignant transformation into high-grade glioma. It has been confirmed that the characteristics of inflammatory factors and immune microenvironment are closely related to the occurrence and development of tumors. It is necessary to clarify the role of inflammatory genes and immune infiltration in LGG.Methods: We downloaded the transcriptome gene expression data and corresponding clinical data of LGG patients from the TCGA and GTEX databases to screen prognosis-related differentially expressed inflammatory genes with the difference analysis and single-factor Cox regression analysis. The prognostic risk model was constructed by LASSO Cox regression analysis, which enables us to compare the overall survival rate of high- and low-risk groups in the model by Kaplan–Meier analysis and subsequently draw the risk curve and survival status diagram. We analyzed the accuracy of the prediction model via ROC curves and performed GSEA enrichment analysis. The ssGSEA algorithm was used to calculate the score of immune cell infiltration and the activity of immune-related pathways. The CellMiner database was used to study drug sensitivity.Results: In this study, 3 genes (CALCRL, MMP14, and SELL) were selected from 9 prognosis-related differential inflammation genes through LASSO Cox regression analysis to construct a prognostic risk model. Further analysis showed that the risk score was negatively correlated with the prognosis, and the ROC curve showed that the accuracy of the model was better. The age, grade, and risk score can be used as independent prognostic factors (p < 0.001). GSEA analysis confirmed that 6 immune-related pathways were enriched in the high-risk group. We found that the degree of infiltration of 12 immune cell subpopulations and the scores of 13 immune functions and pathways in the high-risk group were significantly increased by applying the ssGSEA method (p < 0.05). Finally, we explored the relationship between the genes in the model and the susceptibility of drugs.Conclusion: This study analyzed the correlation between the inflammation-related risk model and the immune microenvironment. It is expected to provide a reference for the screening of LGG prognostic markers and the evaluation of immune response.

scholarly journals LncRNA FOXP4-AS1 Promotes Progression of Ewing Sarcoma and Is Associated With Immune Infiltrates

2021 ◽  
Vol 11 ◽  
Author(s):  
Jiachao Xiong ◽  
Liang Wu ◽  
Lu Huang ◽  
Chunyang Wu ◽  
Zhiming Liu ◽  
...  
Keyword(s):  
Ewing Sarcoma ◽  
Poor Prognosis ◽  
Es Cells ◽  
Enrichment Analysis ◽  
Extracellular Vesicle ◽  
Gene Set Enrichment Analysis ◽  
Immune Microenvironment ◽  
Primary Bone Tumor ◽  
Cerna Network ◽  
Tumor Immune Microenvironment

Ewing sarcoma (ES) is a highly malignant primary bone tumor with poor prognosis. Studies have shown that abnormal expression of lncRNA influences the prognosis of tumor patients. Herein, we established that FOXP4-AS1 was up-regulated in ES and this correlated with poor prognosis. Further analysis illustrated that FOXP4-AS1 down-regulation repression growth, migration, along with invasion of ES. On the contrary, up-regulation of FOXP4-AS1 promoted the growth, migration, as well as invasion of ES. To explore the mechanism of FOXP4-AS1, Spearman correlation analysis was carried out to determine genes that were remarkably linked to FOXP4-AS1 expression. The potential functions and pathways involving FOXP4-AS1 were identified by GO analysis, Hallmark gene set enrichment analysis, GSEA, and GSVA. The subcellular fractionation results illustrated that FOXP4-AS1 was primarily located in the cytoplasm of ES cells. Then a ceRNA network of FOXP4-AS1 was constructed. Analysis of the ceRNA network and GSEA yielded two candidate mRNAs for FOXP4-AS1. Results of the combined survival analysis led us to speculate that FOXP4-AS1 may affect the expression of TMPO by sponging miR-298, thereby regulating the malignant phenotype of ES. Finally, we found that FOXP4-AS1 may modulates the tumor immune microenvironment in an extracellular vesicle-mediated manner. In summary, FOXP4-AS1 correlates with poor prognosis of ES. It promotes the growth, migration, as well as invasion of ES cells and may modulate the tumor immune microenvironment.

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