scholarly journals Comprehensive analysis of prognostic gene signatures based on immune infiltration of ovarian cancer

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Shibai Yan ◽  
Juntao Fang ◽  
Yongcai Chen ◽  
Yong Xie ◽  
Siyou Zhang ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
T Cells ◽  
Immune Cells ◽  
Malignant Tumors ◽  
Treatment Strategies ◽  
Risk Groups ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Immune Infiltration

Abstract Background Ovarian cancer (OV) is one of the most common malignant tumors of gynecology oncology. The lack of effective early diagnosis methods and treatment strategies result in a low five-year survival rate. Also, immunotherapy plays an important auxiliary role in the treatment of advanced OV patient, so it is of great significance to find out effective immune-related tumor markers for the diagnosis and treatment of OV. Methods Based on the consensus clustering analysis of single-sample gene set enrichment analysis (ssGSEA) score transformed via The Cancer Genome Atlas (TCGA) mRNA profile, we obtained two groups with high and low levels of immune infiltration. Multiple machine learning methods were conducted to explore prognostic genes associated with immune infiltration. Simultaneously, the correlation between the expression of mark genes and immune cells components was explored. Results A prognostic classifier including 5 genes (CXCL11, S1PR4, TNFRSF17, FPR1 and DHRS95) was established and its robust efficacy for predicting overall survival was validated via 1129 OV samples. Some significant variations of copy number on gene loci were found between two risk groups and it showed that patients with fine chemosensitivity has lower risk score than patient with poor chemosensitivity (P = 0.013). The high and low-risk groups showed significantly different distribution (P < 0.001) of five immune cells (Monocytes, Macrophages M1, Macrophages M2, T cells CD4 menory and T cells CD8). Conclusion The present study identified five prognostic genes associated with immune infiltration of OV, which may provide some potential clinical implications for OV treatment.

scholarly journals Identification of a gene set correlated with immune status in ovarian cancer by transcriptome-wide data mining

2020 ◽  
Author(s):  
Lili Fan ◽  
Han Lei ◽  
Ying Lin ◽  
Zhengwei Zhou ◽  
Guang Shu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Immune Cell ◽  
Good Prognosis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Survival Prognosis ◽  
Immune Activity ◽  
Gene Set ◽  
Immune Infiltration ◽  
Keywords Ovarian Cancer

Abstract Background : Ovarian cancer (OC) is a serious tumor disease in gynecology. Many papers have reported that high tumor mutational burden (TMB) can generate many neoantigens to result in a higher degree of tumor immune infiltration, so our study aims to predict the key molecules in OC immunotherapy by combined TMB with immunoactivity-related gene. Method: We divided OC cases into two groups: the low & high TMB group hinged on the somatic mutation data from the Cancer Genome Atlas (TCGA). We also used single-sample gene set enrichment analysis (ssGSEA) scores of immune cell types to conduct unsupervised clustering of OC patients in the TCGA cohort and some of them were defined as the low & high immunity group. Besides, to further understand the function of these genes, we conducted Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway, protein-protein interaction network, survival prognosis analysis and immune infiltration analysis. Finally, the effects on prognosis and immunotherapy in OC patients were explored by the Group on Earth Observations verification the patients' responses to immunotherapy. Results: We found that the higher the TMB was associated with the higher OC grades. Moreover, both high TMB and high immunity were significantly correlated with a good prognosis of OC. Then, 14 up-regulated differential expression genes (Up-DEGs) that were closely related to the prognosis of OC patients were screened according to the high TMB group and the high immunity group. Next, pathway analysis revealed that Up-DGEs were mainly involved in immune response and T cell proliferation. Finally, four genes had a good prognosis and were validated in the GEO dataset which included CXCL13, FCRLA, PLA2G2D, and MS4A1. We also identified that four genes had a good prognosis in melanoma patients treated with anti-PD-L1 and anti-CTLA-4 in the TIDE database. Conclusion: High TMB can promote immune cell infiltration and increases immune activity. And our analysis also demonstrated that the higher the TMB, the higher the immune activity, the better the prognosis of OC. Altogether, we found that CXCL13, FCRLA, PLA2G2D, and MS4A1 may be biomarkers for OC immunotherapy. Keywords: ovarian cancer, TMB, immune cells infiltration, survival prognosis.

scholarly journals APOC1 is a Potential Prognostic Biomarker and Correlated With Immune Infiltration in ESCC

2020 ◽  
Author(s):  
Jia-yi XIE ◽  
Ming Liu ◽  
Yaxin Luo ◽  
Zhen Wang ◽  
Zhenghong Lu ◽  
...  
Keyword(s):  
T Cells ◽  
Transcription Factors ◽  
Cd8 T Cells ◽  
Immune Cells ◽  
Poor Prognosis ◽  
Killer Cell ◽  
Gene Set Enrichment Analysis ◽  
Immune Infiltration ◽  
Kaplan Meier ◽  
Kaplan Meier Plotter

Abstract PurposeEsophageal cancer (EC) is the sixth leading cause of cancer death worldwide. Esophageal squamous cell carcinoma (ESCC) is a predominant subtype of EC. Identifying diagnostic biomarkers for ESCC is necessary for cancer practice. Increasing evidence illustrates that apolipoprotein C-1 (APOC1) participates in the carcinogenesis. However, the biological function of APOC1 in ESCC remains unclear. Patients and methodsWe investigated the expression level of APOC1 using TIMER2.0 and GEO databases, the prognostic value of APOC1 in ESCC using Kaplan-Meier plotter and TCGA databases. We used LinkedOmics to identify co-expressed genes with APOC1 and perform GO and KEGG pathway analysis. The target networks of kinases, miRNAs and transcription factors were predicted by gene set enrichment analysis (GSEA). The correlations between APOC1 and immune infiltration were calculated using TIMER2.0 and CIBERSORT databases. We further performed the prognostic analysis based on APOC1 expression levels in related immune cells subgroups via Kaplan-Meier plotter database. ResultsAPOC1 was found overexpressed in tumor tissues in multiple ESCC cohorts and high APOC1 expression was related to a dismal prognosis. Multivariate analysis confirmed that APOC1 overexpression was an independent indicator of poor OS. Functional network analysis indicated that APOC1 might regulate the natural killer cell mediated cytotoxicity, phagosome, AMPK and hippo signaling through pathways involving some cancer-related kinases, miRNA and transcription factors. Immune infiltration analysis showed that APOC1 was significantly positively correlated with M0 macrophages cells, M1 macrophages cells and activated NK cells, negatively correlated with regulatory T cells, CD8 T cells, neutrophils and monocytes. High APOC1 expression had a poor prognosis in server immune cells subgroups in ESCC, including decreased CD8+ T cells subgroups. ConclusionThese findings suggest that increased expression of APOC1 is related to poor prognosis and immune infiltration in ESCC. APOC1 holds promise for serving as a valuable diagnostic and prognostic marker in ESCC.

scholarly journals Glycolysis Define Two Prognostic Subgroups of Lung Adenocarcinoma With Different Mutation Characteristics and Immune Infiltration Signatures

2021 ◽  
Vol 9 ◽  
Author(s):  
Chen Huo ◽  
Meng-Yu Zhang ◽  
Rui Li ◽  
Ting-Ting Liu ◽  
Jian-Ping Li ◽  
...  
Keyword(s):  
Mast Cells ◽  
Lung Adenocarcinoma ◽  
Immune Cell ◽  
Malignant Tumors ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Activated T Cells ◽  
Biological Variables ◽  
Cluster 2

Increasing studies have proved that malignant tumors are associated with energy metabolism. This study was aimed to explore biological variables that impact the prognosis of patients in the glycolysis-related subgroups of lung adenocarcinoma (LUAD). The mRNA expression profiling and mutation data in large LUAD samples were collected from the Cancer Genome Atlas (TCGA) database. Then, we identified the expression level and prognostic value of glycolysis-related genes, as well as the fractions of 22 immune cells in the tumor microenvironment. The differences between glycolysis activity, mutation, and immune infiltrates were discussed in these groups, respectively. Two hundred fifty-five glycolysis-related genes were identified from gene set enrichment analysis (GSEA), of which 43 genes had prognostic values (p &lt; 0.05). Next, we constructed a glycolysis-related competing endogenous RNA (ceRNA) network which related to the survival of LUAD. Then, two subgroups of LUAD (clusters 1 and 2) were identified by applying unsupervised consensus clustering to 43 glycolysis-related genes. The survival analysis showed that the cluster 1 patients had a worse prognosis (p &lt; 0.001), and upregulated differentially expressed genes (DEGs) are interestingly enriched in malignancy-related biological processes. The differences between the two subgroups are SPTA1, KEAP1, USH2A, and KRAS among top 10 mutated signatures, which may be the underlying mechanism of grouping. Combined high tumor mutational burden (TMB) with tumor subgroups preferably predicts the prognosis of LUAD patients. The CIBERSORT algorithm results revealed that low TMB samples were concerned with increased infiltration level of memory resting CD4+ T cell (p = 0.03), resting mast cells (p = 0.044), and neutrophils (p = 0.002) in cluster 1 and high TMB samples were concerned with increased infiltration level of memory B cells, plasma cells, CD4 memory-activated T cells, macrophages M1, and activated mast cells in cluster 2, while reduced infiltration of monocytes, resting dendritic cells, and resting mast cells was captured in cluster 2. In conclusion, significant different gene expression characteristics were pooled according to the two subgroups of LUAD. The combination of subgroups, TMB and tumor-infiltrating immune cell signature, might be a novel prognostic biomarker in LUAD.

scholarly journals CLDN6 and CLDN10 are Associated with Immune Infiltration of Ovarian Cancer: A Study of Claudin Family

2020 ◽  
Author(s):  
Peipei Gao ◽  
Ting Peng ◽  
Canhui Cao ◽  
Shitong Lin ◽  
Ping Wu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
T Cell ◽  
Tumor Microenvironment ◽  
Immune Cells ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Gene Markers ◽  
Immune Infiltration ◽  
Kaplan Meier ◽  
The Relationship

Abstract Background: Claudin family is a group of membrane proteins related to tight junction. There are many studies about them in cancer, but few studies pay attention to the relationship between them and the tumor microenvironment. In our research, we mainly focused on the genes related to the prognosis of ovarian cancer, and explored the relationship between them and the tumor microenvironment of ovarian cancer.Methods: The cBioProtal provided the genetic variation pattern of claudin gene family in ovarian cancer. The ONCOMINE database and Gene Expression Profiling Interactive Analysis (GEPIA) were used to exploring the mRNA expression of claudins in cancers. The prognostic potential of these genes was examined via Kaplan-Meier plotter. Immunologic signatures were enriched by gene set enrichment analysis (GSEA). The correlations between claudins and the tumor microenvironment of ovarian cancer were investigated via Tumor Immune Estimation Resource (TIMER).Results: In our research, claudin genes were altered in 363 (62%) of queried patients/samples. Abnormal expression levels of claudins were observed in various cancers. Among them, we found that CLDN3, CLDN4, CLDN6, CLDN10, CLDN15 and CLDN16 were significantly correlated with overall survival of patients with ovarian cancer. GSEA revealed that CLDN6 and CLDN10 were significantly enriched in immunologic signatures about B cell, CD4 T cell and CD8 T cell. What makes more sense is that CLDN6 and CLDN10 were found related to the tumor microenvironment. CLDN6 expression was negatively correlated with immune infiltration level in ovarian cancer, and CLDN10 expression was positively correlated with immune infiltration level in ovarian cancer. Further study revealed the CLDN6 expression level was negatively correlated with gene markers of various immune cells in ovarian cancer. And, the expression of CLDN10 was positive correlated with gene markers of immune cells in ovarian cancer.Conclusions: CLDN6 and CLDN10 were prognostic biomarkers, and correlated with immune infiltration in ovarian cancer. Our results revealed new roles for CLDN6 and CLDN10, and they were potential therapeutic targets in the treatment of ovarian cancer.

scholarly journals CXCL13 Correlates with Prognosis, Immune Infiltration, and T Cell Exhaustion in Ovarian Cancer

2021 ◽  
Author(s):  
Hailing Duan ◽  
Ying Lv ◽  
Pan Liao ◽  
Yiming Wang ◽  
Zhifang Zheng ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
T Cells ◽  
Cell Adhesion ◽  
T Cell ◽  
Immune Cells ◽  
Gene Marker ◽  
T Cell Exhaustion ◽  
Cell Exhaustion ◽  
Immune Infiltration ◽  
Prognosis Prediction

Abstract Background: CXCL13 is an important chemotactic factor closely related to the biology of cancer cells. The presence work focused on exploring the significance of CXCL13 in prognosis prediction and analyzing the associations of CXCL13 with T cell function and immune infiltration in various cancers, especially ovarian cancer (OV).Purpose: CXCL13 is associated with prognosis, immune infiltration, and T cell failure of ovarian cancer.Methods: The Oncomine, GEPIA2 and HPA databases were utilized for analyzing CXCL13 levels within diverse cancers. The significance of CXCL13 in prognosis prediction was explored through Kaplan-Meier Plotter, TCGAportal, and GEPIA2. Meanwhile, the associations of CXCL13 with clinical stage, gene marker sets, and immune infiltration were examined through TISIDB, GEPIA2, and TIMER databases. Besides, CXCL13 was screened to analyze the biological processes (BPs) and KEGGs enriched by co-expression genes. The miRWalk database was employed for analyzing the gene-miRNA interaction network of CXCL13 within OV.Results: CXCL13 expression decreased in many cancers, which predicted the dismal survival of OV. CXCL13 upregulation was in direct proportion to the increased immune infiltration degrees of many functional T cells (like exhausted T cells) and immune cells. Additionally, some critical genes of exhausted T cells, such as TIM-3, PD-1, LAG3, TIGIT, GZMB, and CXCL13, were closely associated with CXCL13. Moreover, CXCL13 was related to immune response regulatory signaling pathway, leukocyte cell-cell adhesion, cell adhesion molecules (CAMs), and hematopoietic cell lineage. Conclusion: CXCL13 can serve as a biomarker to predict cancer prognosis, particularly OV. CXCL13 upregulation remarkably elevates the immune infiltration degrees of numerous immune cells, like mast cells, CD8+ T cells, natural killer (NK) cells, and dendritic cells (DCs). Furthermore, CXCL13 is suggested to be closely related to exhausted T cells, which may be used as a candidate regulating factor for T cell exhaustion within OV. Detecting CXCL13 levels contributes to prognosis prediction and CXCL13 regulation within exhausted T cells, which provides a new approach to maximizing the anti-OV efficacy of immunotherapy.

Association of Th2 high tumors with aggressive features of breast cancer.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e12584-e12584
Author(s):  
Yoshihisa Tokumaru ◽  
Lan Le ◽  
Masanori Oshi ◽  
Eriko Katsuta ◽  
Nobuhisa Matsuhashi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
T Cells ◽  
Cancer Progression ◽  
Immune Cells ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Th2 Cells ◽  
Immune Microenvironment ◽  
Adaptive Immune Cells ◽  
Tumor Immune Microenvironment

e12584 Background: Recent studies have shown that infiltrating T-lymphocytes have been implicated in the promotion of breast cancer progression. Upon activation, these antigen-presenting cells then recruit adaptive immune cells. It has been proposed that polarization of CD4+ effector T-cells towards the immunosuppressive Th2 cells induce cytokine release and T-cell anergy, which lead to polarization of M2 tumor-associated macrophages (TAM’s), providing a protumorigenic microenvironment. We hypothesized that there is a correlation between high levels of Th2 cells and aggressive features of breast cancer and unfavorable tumor immune environment. Methods: Clinicopathological data and overall survival information was obtained on 1069 breast cancer patients from The Cancer Genome Atlas (TCGA) database. We defined Th2 high and low levels with the median cutoff. Results: Analysis of cell composition of the immune cells within tumor immune microenvironment demonstrated that Th2 high tumors did not consistently associated with unfavorable tumor immune microenvironment. Pro-cancer immune cells, such as macrophage M2 cells were increased with Th2 high tumors whereas, regulatory T cells were decreased with Th2 high tumors (p < 0.01 and p < 0.001 respectively). On the contrary, infiltration of anti-cancer cells, such as macrophage M1 was increased whereas CD8 T cells were decreased with Th2 high tumors (p < 0.05 and p < 0.001 respectively). Th2 was not shown to have correlation with IL-4, IL-6, IL-10 and IL-13, all of which has been reported to associate with Th2 cells. Th2 levels were associated with advanced grades. Also, correlation analysis demonstrated that there was a strong correlation between Th2 levels and Ki-67. These results were further validated with gene set enrichment analysis (GSEA). GSEA revealed that in Th2 high tumors enriched the gene sets associated with cell proliferation and cell cycle. Conclusions: High expression of immunosuppressive Th2 cells was associated with highly proliferative features of breast cancer, but not with unfavorable tumor immune microenvironment.

scholarly journals LncRNA PAXIP1-AS1 is a Prognostic Biomarker and Correlated with Immune Infiltrates in Ovarian Cancer

2021 ◽  
Author(s):  
Buze Chen ◽  
Xiaoyuan Lu ◽  
Qingmei Zhou ◽  
Qing Chen ◽  
Siyan Zhu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
T Cells ◽  
Signaling Pathway ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
Th2 Cells ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
The Relationship

Abstract Background: The long non-coding RNA (LncRNA) PAXIP1 antisense RNA 1 (PAXIP1-AS1) was found to promote proliferation, migration, EMT, and apoptosis of ovarian cancer (OC) cells in OC cell lines, but the relationship between PAXIP1-AS1 expression and clinical characteristics, prognosis, and immune infiltration of OC patients and its regulatory network are unclear. Methods: QRT-PCR, Kruskal-Wallis test, Wilcoxon sign-rank test, logistic regression, Kaplan-Meier method, Cox regression analysis, Gene set enrichment analysis (GSEA), and immuno-infiltration analysis were used to evaluate the relationship between clinical characteristics and PAXIP1-AS1 expression, prognostic factors, and determine the significant involvement of PAXIP1-AS1 in function. Results: Low PAXIP1-AS1 expression in OC was associated with age (P=0.045), histological grade (P=0.011), and lymphatic invasion (P=0.004). Low PAXIP1-AS1 expression predicted a poorer overall survival (OS) (HR: 0.71; 95% CI: 0.55–0.92; P=0.009), progression free interval (PFS) (HR: 1.776; 95% CI: 1.067–2.955; P=0.001) and disease specific survival (DSS) (HR: 0.67; 95% CI: 0.51–0.89; P=0.006). And PAXIP1-AS1 expression (HR: 0.711; 95% CI: 0.542-0.934; P=0.014) was independently correlated with PFS in OC patients. GSEA demonstrated that neutrophil degranulation, signaling by Interleukins, GPCR-ligand binding, G alpha I signaling events, VEGFAVEGFR-2 signaling pathway, naba secreted factors, Class A 1 Rhodopsin-Like Receptors, PI3K-Akt signaling pathway, and Focal Adhesion-PI3K-Akt-mTOR-signaling pathway were differentially enriched in PAXIP1-AS1 high expression phenotype. PAXIP1-AS1 may inhibit the function of aDC, B cells, CD8 T cells, Cytotoxic cells, DC, iDC, Macrophages, Mast cells, Neutrophils, NK CD56dim cells, T cells, TFH, Tgd, Th1 cells, Th2 cells and Treg. Conclusions: Low expression of PAXIP1-AS1 was significantly associated with poor survival and immune infiltration in OC. PAXIP1-AS1 could be a promising prognosis biomarker for OC.

scholarly journals As a Newly-Discovered Component in Tumor Microenvironment RUFY4 Participates in Regulation of T cell via Cytokine-Interaction and Predicts Prognosis and Immunotherapy Responsiveness of Clear Cell Renal Carcinoma Patients

2020 ◽  
Author(s):  
Daojia Miao ◽  
Jian Shi ◽  
Zhiyong Xiong ◽  
Changfei Yuan ◽  
Wen Xiao ◽  
...  
Keyword(s):  
Overall Survival ◽  
T Cells ◽  
Tumor Microenvironment ◽  
Immune Cells ◽  
Clear Cell ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Sequencing Data ◽  
Cell Renal Cell Carcinoma ◽  
Immune Infiltration

Abstract Background: clear cell renal cell carcinoma (ccRCC) is one of the most lethal kinds of malignancies in urinary system and the existing immunotherapy have not achieved satisfactory outcomes. Therefore, this study aims to establish a brand-new gene signature for immune-infiltration and clinical outcome (overall survival and immunotherapy responsiveness) of patients with ccRCC. Methods: Based on RNA sequencing data and clinical information in the Cancer Genome Atlas Project (TCGA) database, we investigated proportions of immune cells in 611 samples by an online tool CIBERSORTx. Multivariate survival analysis was used to determine crucial survival-associated immune cells and immune-infiltration-related genes (IIRGs). Next ROC analysis was carried on to evaluate the ability of IIRGs to distinguish patients and functional enrichment analysis were implemented to explore potential interaction network between immune cells and IIRGs. Results: T cells follicular helper (TFHs) and T cells regulatory (Tregs) were highly infiltrated in the tumor microenvironment and their abundance ratios were independent prognostic factors for overall survival. Among IIRGs of TFHs and TREGs, RUFY4 was found to be highly activated in tumor microenvironment and its co-expression network was enriched in regulation of T cells via cytokine-cytokine receptor interactions.Conclusion: These two cells and RUFY4, considered prognostic biomarkers and immunotherapeutic predictors of ccRCC patients, might also simultaneously affect the regulatory network in tumor microenvironment (TME) through cytokine interactions.

scholarly journals A Prognostic Microenvironment-Related Immune Signature via ESTIMATE (PROMISE Model) Predicts Overall Survival of Patients With Glioma

2020 ◽  
Vol 10 ◽  
Author(s):  
Huaide Qiu ◽  
Yongqiang Li ◽  
Shupeng Cheng ◽  
Jiahui Li ◽  
Chuan He ◽  
...  
Keyword(s):  
Immune Cells ◽  
Cox Regression ◽  
Risk Groups ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Data Sets ◽  
Data Set ◽  
Cox Regression Analysis ◽  
Immune Signature ◽  
Genome Atlas

ObjectiveIn the development of immunotherapies in gliomas, the tumor microenvironment (TME) needs to be investigated. We aimed to construct a prognostic microenvironment-related immune signature via ESTIMATE (PROMISE model) for glioma.MethodsStromal score (SS) and immune score (IS) were calculated via ESTIMATE for each glioma sample in the cancer genome atlas (TCGA), and differentially expressed genes (DEGs) were identified between high-score and low-score groups. Prognostic DEGs were selected via univariate Cox regression analysis. Using the lower-grcade glioma (LGG) data set in TCGA, we performed LASSO regression based on the prognostic DEGs and constructed a PROMISE model for glioma. The model was validated with survival analysis and the receiver operating characteristic (ROC) in TCGA glioma data sets (LGG, glioblastoma multiforme [GBM] and LGG+GBM) and Chinese glioma genome atlas (CGGA). A nomogram was developed to predict individual survival chances. Further, we explored the underlying mechanisms using gene set enrichment analysis (GSEA) and Cibersort analysis of tumor-infiltrating immune cells between risk groups as defined by the PROMISE model.ResultsWe obtained 220 upregulated DEGs and 42 downregulated DEGs in both high-IS and high-SS groups. The Cox regression highlighted 155 prognostic DEGs, out of which we selected 4 genes (CD86, ANXA1, C5AR1, and CD5) to construct a PROMISE model. The model stratifies glioma patients in TCGA as well as in CGGA with distinct survival outcome (P&lt;0.05, Hazard ratio [HR]&gt;1) and acceptable predictive accuracy (AUCs&gt;0.6). With the nomogram, an individualized survival chance could be predicted intuitively with specific age, tumor grade, Isocitrate dehydrogenase (IDH) status, and the PROMISE risk score. ROC showed significant discrimination with the area under curves (AUCs) of 0.917 and 0.817 in TCGA and CGGA, respectively. GSEA between risk groups in both data sets were significantly enriched in multiple immune-related pathways. The Cibersort analysis highlighted four immune cells, i.e., CD 8 T cells, neutrophils, follicular helper T (Tfh) cells, and Natural killer (NK) cells.ConclusionsThe PROMISE model can further stratify both LGG and GBM patients with distinct survival outcomes.These findings may help further our understanding of TME in gliomas and shed light on immunotherapies.

scholarly journals CLEC10A is a Potential Prognostic Biomarker and Immune Regulator of Tumor Microenvironment in Breast Cancer

2021 ◽  
Author(s):  
Jincheng He ◽  
Lei Jiang ◽  
Jun Wang ◽  
Guangtao Min ◽  
Xiangwen Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
T Cells ◽  
B Cells ◽  
Tumor Microenvironment ◽  
Immune Cells ◽  
Cox Regression ◽  
Clinical Stage ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis

Abstract The communication between tumor cells and immune cells influences the ecology of the tumor microenvironment in breast cancer, as well as the disease progression and clinical outcome. The aim of this study was to investigate the prognostic value of the immunomodulatory factor CLEC10A in breast cancer. We applied the CIBERSORT and ESTIMATE calculation methods to calculate the proportion of tumor-infiltrating immune cells (TICs) and the amount of immune and stromal components in 1053 BRCA cases from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were analyzed by COX regression analysis and protein-protein interaction (PPI) network construction. Then, CLEC10A was identified as a prognostic factor by the intersection analysis of univariate COX and PPI. Further analysis revealed that CLEC10A expression was negatively correlated with the clinical pathologic characteristics (age, clinical stage) and positively correlated with survival of BRCA patients. Gene set enrichment analysis (GSEA) showed that genes in the high CLEC10A expression group were mainly enriched in immune-related activities. Genes in the low CLEC10A expression group were enriched in biochemical functions. CIBERSORT analysis of the proportion of TICs revealed that Macrophages M1, B cells memory, B cells naive, T cells CD4+ memory activated, T cells CD8+, and T cells gamma delta were positively correlated with CLEC10A expression, and Macrophages M0, Macrophages M2, Neutrophils, and NK cells resting were positively correlated with CLEC10A expression was negatively correlated, suggesting that CLEC10A may be an important factor in the immune regulation of the tumor microenvironment, especially in mediating the anti-tumor immune response of tumor-infiltrating immune cells at the tumor initiation stage. Therefore, CLEC10A expression may contribute to the prognosis of BRCA patients and provide a new idea for the immunotherapy of BRCA.

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