scholarly journals Comprehensive Analysis of Tumor-Infiltrating Immune Cells and Relevant Therapeutic Strategy in Esophageal Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Guangrong Lu ◽  
Liping Chen ◽  
Shengjie Wu ◽  
Yuao Feng ◽  
Tiesu Lin
Keyword(s):  
Esophageal Cancer ◽  
Immune Cells ◽  
Immune Cell ◽  
Risk Model ◽  
Risk Group ◽  
Tumor Stage ◽  
Tumor Location ◽  
High Risk Group ◽  
Comprehensive Analysis ◽  
Lasso Regression

A growing body of evidence has indicated that behaviors of cancers are defined by not only intrinsic activities of tumor cells but also tumor-infiltrating immune cells (TIICs) in the tumor microenvironment. However, it still lacks a well-structured and comprehensive analysis of TIICs and its therapeutic value in esophageal cancer (EC). The proportions of 22 TIICs were evaluated between 150 normal tissues and 141 tumor tissues of EC by the CIBERSORT algorithm. Besides, correlation analyses between proportions of TIICs and clinicopathological characters, including age, gender, histologic grade, tumor location, histologic type, LRP1B mutation, TP53 mutation, tumor stage, lymph node stage, and TNM stage, were conducted. We constructed a risk score model to improve prognostic capacity with 5 TIICs by least absolute shrinkage and selection operator (lasso) regression analysis. The risk score=−1.86∗plasma+2.56∗T cell follicular helper−1.37∗monocytes−3.64∗activated dendritic cells−2.24∗resting mast cells (immune cells in the risk model mean the proportions of immune cell infiltration in EC). Patients in the high-risk group had significantly worse overall survival than these in the low-risk group (HR: 2.146, 95% CI: 1.243-3.705, p=0.0061). Finally, we identified Semustine and Sirolimus as two candidate compounds for the treatment of EC based on CMap analysis. In conclusion, the proportions of TIICs may be important to the progression, prognosis, and treatment of EC.

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

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

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

scholarly journals Ferroptosis-related gene signature predicts the prognosis of papillary thyroid carcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jinyuan Shi ◽  
Pu Wu ◽  
Lei Sheng ◽  
Wei Sun ◽  
Hao Zhang
Keyword(s):  
High Risk ◽  
Immune Cells ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk ◽  
Lasso Regression ◽  
Papillary Thyroid ◽  
Related Gene ◽  
Normal Thyroid

Abstract Background Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer (TC), accounting for more than 80% of all cases. Ferroptosis is a novel iron-dependent and Reactive oxygen species (ROS) reliant type of cell death which is distinct from the apoptosis, necroptosis and pyroptosis. Considerable studies have demonstrated that ferroptosis is involved in the biological process of various cancers. However, the role of ferroptosis in PTC remains unclear. This study aims at exploring the expression of ferroptosis-related genes (FRG) and their prognostic values in PTC. Methods A ferroptosis-related gene signature was constructed using lasso regression analysis through the PTC datasets of the Cancer Genome Atlas (TCGA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to investigate the bioinformatics functions of significantly different genes (SDG) of ferroptosis. Additionally, the correlations of ferroptosis and immune cells were assessed through the single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT database. Finally, SDG were test in clinical PTC specimens and normal thyroid tissues. Results LASSO regression model was utilized to establish a novel FRG signature with 10 genes (ANGPTL7, CDKN2A, DPP4, DRD4, ISCU, PGD, SRXN1, TF, TFRC, TXNRD1) to predicts the prognosis of PTC, and the patients were separated into high-risk and low-risk groups by the risk score. The high-risk group had poorer survival than the low-risk group (p < 0.001). Receiver operating characteristic (ROC) curve analysis confirmed the signature's predictive capacity. Multivariate regression analysis identified the prognostic signature-based risk score was an independent prognostic indicator for PTC. The functional roles of the DEGs in the TGCA PTC cohort were explored using GO enrichment and KEGG pathway analyses. Immune related analysis demonstrated that the most types of immune cells and immunological function in the high-risk group were significant different with those in the low-risk group. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) verified the SDG have differences in expression between tumor tissue and normal thyroid tissue. In addition, cell experiments were conducted to observe the changes in cell morphology and expression of signature’s genes with the influence of ferroptosis induced by sorafenib. Conclusions We identified differently expressed FRG that may involve in PTC. A ferroptosis-related gene signature has significant values in predicting the patients’ prognoses and targeting ferroptosis may be an alternative for PTC’s therapy.

scholarly journals Prognostic Value of Immune-Related genes in the Tumor Microenvironment of Glioma - Analyzed by Integrated Bioinformatics

2021 ◽  
Author(s):  
Yali Zhong ◽  
Xiaobin Luo ◽  
Fubing Yang ◽  
Xinling Song
Keyword(s):  
T Cells ◽  
High Risk ◽  
Risk Prediction ◽  
Immune Cell ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Lasso Regression ◽  
Immune Related Genes

Abstract Object: Immune related genes play an important role in the process of tumor genesis and development. Therefore, we aim to find the Immune genes which are related to the prognosis of glioma patients, and to explore the infiltration of Immune cells in glioma microenvironment. Methods We downloaded the data of the glioma samples from the CGGA database, and performed batch correction to screen the primary glioma samples for subsequent analysis. Then the ESTIMATE algorithm was used to deal with the Stromal scores and Immune scores of the primary glioma samples, and the difference was analyzed. Then the common Immune related genes (IRGs) were obtained by intersecting with the Immune genes in the ImmPort database. Moreover, we used common IRGs to construct protein-protein interaction (PPI) networks, from which we screened the top 30 genes with high connectivity, and Lasso regression was used to screen the IRGs. Lastly, we obtained the combined genes, which were overlapped both in the top 30 high-connection genes and Lasso regression genes. The final genes were used to construct COX risk prediction models. The accuracy of the model were verified by the TCGA glioma data, and the model genes were analyzed for Immune-related pathways, as well as the Hallmark and KEGG enrichment. Additionally, we used CIBERSOFT algorithm to estimate the Immune cell content of the samples, and analyzed the differences, correlations and survival of the Immune cells in high and low risk groups. Results Firstly, a total of 117 IRGs were obtained from the gene sets, which were overlapped in the data of Stromal score, Immune score and ImmPort database. Secondly, the top 30 genes were selected after the PPI network, and another 26 genes were screened out after the Lasso regression algorithm. And then, six coexist IRGs were obtained from the intersecting sets. Furthermore, the COX risk prediction model was constructed and tested, showing that the overall survival rate of the high-risk group was about 50% of that of the low-risk group. We observed that the high-risk group were enriched in Immune response and Immune process. Most importantly, in KEGG pathways, the high-risk groups were mainly enriched in p53 signaling pathway, JAK-STAT signaling pathway, pathways in cancer and cell cycle. By estimating the Immune cell contents, we also found that the Immune cell Plasma cells, T cells CD8, T cells CD4 naïve, T cells regulatory (Tregs), Macrophages M0 and Neutrophils were higher in high-risk groups, when compared to the low-risk group, with significant difference. Finally, the correlation analysis showed that the degree of Immune infiltration in high-risk groups was related to T cells regulatory (Tregs), Macrophages M0 and Neutrophils. Conclusion A COX risk prediction model of 6 genes was successfully constructed, which was enriched in Immune-related pathways. Meanwhile, survival analysis and TCGA data validation revealed significant differences in the model genes in the overall survival of the glioma patients, and the degree of Immune infiltration in the model was associated with T cells regulatory (Tregs), Macrophages M0 and Neutrophils.

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 &lt; 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 &lt; 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 Comprehensive Analysis of Immune-Related Metabolic Genes in Lung Adenocarcinoma

2021 ◽  
Author(s):  
Fangfang Li ◽  
Chun Huang ◽  
Llingxiao Qiu ◽  
Ping Li ◽  
Guojun Zhang
Keyword(s):  
Lung Cancer ◽  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Immune Cells ◽  
Immune Cell ◽  
Comprehensive Analysis ◽  
Lasso Regression ◽  
Cell Functions ◽  
Qrt Pcr ◽  
Metabolic Genes

Abstract Purpose The immunotherapy of lung adenocarcinoma has received more and more attention. Different immune cells can affect other metabolic genes and lifespan, and cell metabolism directly regulates immune cell functions. Therefore, it is crucial to explore the role of immune-related metabolic genes in lung adenocarcinoma. Methods In this study, we divided immune-related metabolic genes into three categories based on different immune characteristics and researched immune and clinical pathology. LASSO regression analysis was used to screen immune-related metabolic genes, and a clinical prediction model of the screened genes was constructed. Finally, we selected the intersection of immune metabolism genes that are highly expressed in the tumor site and immune metabolism genes that are negatively related to survival, and used qRT-PCR for experimental verification. Results We first screened out immune-related metabolic genes that may affect lung cancer tumor progression, and screened out 9 pivot genes (TK1, TCN1, CAV1, ACMSD, HS3ST2, HS3ST5, AMN, ADRA2C, ACOXL) through LASSO regression analysis and constructed Prognosis model. Finally, through the screening of tumor-related immune metabolism genes, we obtained five pivot genes (HMMR, PFKP, RRM2, TCN1 and TK1). Our qRT-PCR results also show that RRM2 is positively correlated with CDK2, CDK4, CDK6, and CDK8, revealing the close relationship between RRM2 and immune cell tumor infiltration. Conclusion We conducted a comprehensive analysis of the immune infiltration of the tumor microenvironment of lung cancer, and finally determined RRM2 as a promising immune metabolism checkpoint for lung adenocarcinoma based on the high correlation of RRM2 with immune cells and CDK family.

scholarly journals Esophageal Cancer Associated Immune Genes as Biomarkers for Predicting Outcome in Upper Gastrointestinal Tumors

2021 ◽  
Vol 12 ◽  
Author(s):  
Chuanhui Zhu ◽  
Qianqian Xia ◽  
Bin Gu ◽  
Mengjing Cui ◽  
Xing Zhang ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Risk Group ◽  
High Risk Group ◽  
Prognostic Biomarkers ◽  
Lasso Regression ◽  
Training Set ◽  
Gastrointestinal Malignancies ◽  
Immune Genes ◽  
Predicting Outcome ◽  
Upper Gastrointestinal

Esophageal cancer (EC) is the seventh most common tumor in the world, ranking the sixth leading cause of cancer death, with a 5-year survival rate of 15-25%. Therefore, reliable prognostic biomarkers are needed to effectively predict the prognosis of EC. In this study, the gene profile information of the EC cohort served as a training set, which was derived from TCGA and Immport databases. GO and KEGG enrichment analysis was performed on the differential genes in normal and tumor groups of EC. The immune genes in differentially expressed genes (DEGs) were further obtained for univariate and multivariate Cox and Lasso regression analysis, and 6 independent immune genes (S100A3, STC2, HSPA6, CCL25, GPER1, and OSM) associated with prognosis were obtained to establish an immune risk score signature (IRSS). The signature was validated using head and neck cancers (HNSC) and gastric cancer (GC)in upper gastrointestinal malignancies as validation sets. The Kaplan-Meier results showed that the prognosis of the high-risk group was significantly favorable than that of the low-risk group in both the training set (P &lt; 0.001; HR = 3.68, 95% CI = 2.14−6.35) and the validation set (P = 0.010; HR = 1.43, 95% CI = 1.09−1.88). A nomogram combining multiple clinical information and IRSS was more effective than a single independent prognostic factor in predicting outcome. This study explored the potential link between immunity and EC, and established and validated prognostic biomarkers that can effectively predict the prognosis of EC, HNSC and GC based on six immune genes.

scholarly journals Immune Cell as a Promising Biomarker in the Diagnosis and Prognosis of Cutaneous Melanoma by Using Machine Learning

2021 ◽  
Author(s):  
Jing Tang ◽  
Hongqaun Ye ◽  
Wan qi
Keyword(s):  
Machine Learning ◽  
Immune Cells ◽  
Immune Cell ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Diagnostic Models ◽  
Melanoma Patients

Abstract Background: Tumor infiltration, is known to associate with various cancer initiations and progressions, is potential therapeutic target for this aggressive skin cancer.Methods: single sample gene set enrichment analysis (ssGSEA) algorithm was applied to assess the relative expression of 24 types of immune cell from public database. Firstly, the differentially expressed immune cells between melanomas and normal samples were identified. Next, multiple machine learning algorithms were performed to evaluate the efficiency of immune cells in diagnosis of melanoma. In addition, the feature selection in machine learning methods was used to figure out the most important prognostic immune cells for developing biomarker to predict the prognosis of melanoma.Results: In comparison with the expression of immune cells in tumors and normal controls, we built the immune diagnostic models in training dataset, which can accurately classify melanoma patients from normal (LR AUC= 0.965, RF AUC= 0.99, SVM AUC=0.963, LASSO AUC= 0.964 and NNET AUC=0.989). These diagnostic models also validated in three outside datasets and suggested over 90% sensitivity and specificity to distinguish melanomas from normal patients. Moreover, we also developed a robust immune cell biomarker which could estimate the prognosis of melanoma. This biomarker also further validated in internal and external datasets. Next, we constructed nomogram combined risk score of biomarker and clinical characteristics, which showed good accuracies in predicting 3 and 5 years’ survival. The decision curve of nomogram model manifested a higher net benefit than tumor stage. In addition, melanoma patients divided into high and low risk subgroups by applied risk score system. The high risk group have a significantly shorter survival time than the low risk subgroup. Gene Set Enrichment Analysis (GSEA) analysis revealed that complement, epithelial mesenchymal transition and inflammatory response and so on significantly activated in high risk group. Conclusions: We constructed immune cell related diagnostic and prognostic models, which could provide new clinical applications for diagnosing and predicting the survival of melanoma patients.

scholarly journals A Novel Framework to Predict Breast Cancer Prognosis Using Immune-Associated LncRNAs

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhijian Huang ◽  
Chen Xiao ◽  
Fushou Zhang ◽  
Zhifeng Zhou ◽  
Liang Yu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Immune Cells ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Comprehensive Analysis ◽  
Cancer Prognosis ◽  
Clinicopathological Factors ◽  
Cox Regression Analysis

Background: Breast cancer (BC) is one of the most frequently diagnosed malignancies among females. As a huge heterogeneity of malignant tumor, it is important to seek reliable molecular biomarkers to carry out the stratification for patients with BC. We surveyed immune- associated lncRNAs that may be used as potential therapeutic targets in BC.Methods: LncRNA expression data and clinical information of BC patients were downloaded from the TCGA database for a comprehensive analysis of candidate genes. A model consisting of immune-related lncRNAs enriched in BC cancerous tissues was established using the univariate Cox regression analysis and the iterative Lasso Cox regression analysis. The prognostic performance of this model was validated in two independent cohorts (GSE21653 and BC-KR), and compared with known prognostic biomarkers. A nomogram that integrated the immune-related lncRNA signature and clinicopathological factors was constructed to accurately assess the prognostic value of this signature. The correlation between the signature and immune cell infiltration in BC was also analyzed.Results: The Kaplan-Meier analysis showed that the OS of Patients in the low-risk group had significantly better survival than those in the high-risk group, Clinical subgroup analysis showed that the predictive ability was independent of clinicopathological factors. Univariate/multivariate Cox regression analysis showed immune lncRNA signature is an important prognostic factor and an independent prognostic marker. In addition, GSEA and GSVA analysis as well as comprehensive analysis of immune cells showed that the signature was significantly correlated with the infiltration of immune cells.Conclusion: We successfully constructed an immune-associated lncRNA signature that can accurately predict BC prognosis.

scholarly journals Development and Validation of an Immune-Related Signature for the Prediction of Recurrence Risk of Patients With Laryngeal Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Hang Zhang ◽  
Xudong Zhao ◽  
Jin Wang ◽  
Wenyue Ji
Keyword(s):  
Cancer Cells ◽  
Laryngeal Cancer ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Recurrence Risk ◽  
High Risk Group ◽  
Migration And Invasion ◽  
Lasso Regression ◽  
Immune Genes

ObjectiveOur purpose was to develop and verify an immune-related signature for predicting recurrence risk of patients with laryngeal cancer.MethodsRNA-seq data of 51 recurrence and 81 non-recurrence laryngeal cancer samples were downloaded from TCGA database, as the training set. Microarray data of 34 recurrence and 75 non-recurrence cancer samples were obtained from GEO dataset, as the validation set. Single factor cox regression was utilized to screen prognosis-related immune genes. After LASSO regression analysis, an immune-related signature was constructed. Recurrence free survival (RFS) between high- and low- recurrence risk patients was presented, followed by ROC. We also evaluated the correlation between immune infiltration and the signature using the CIBERSORT algorithm. The genes in the signature were validated in laryngeal cancer tissues by western blot or RT-qPCR. After RCN1 knockdown, migration and invasion of laryngeal cancer cells were investigated.ResultsTotally, 43 prognosis-related immune genes were identified for laryngeal cancer. Among them, eight genes were used for constructing a prognostic signature. High risk group exhibited a higher recurrence risk than low risk group. The AUC for 1-year was separately 0.803 and 0.715 in the training and verification sets, suggesting its well efficacy for predicting the recurrence. Furthermore, this signature was closely related to distinct immune cell infiltration. RCN1, DNAJA2, LASP1 and IBSP were up-regulated in laryngeal cancer. RCN1 knockdown restrained migrated and invasive abilities of laryngeal cancer cells.ConclusionOur findings identify a reliable immune-related signature that can predict the recurrence risk of patients with laryngeal cancer.

scholarly journals Characterization of Exosome-Related Gene Risk Model to Evaluate the Tumor Immune Microenvironment and Predict Prognosis in Triple-Negative Breast Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Pengjun Qiu ◽  
Qiaonan Guo ◽  
Qingzhi Yao ◽  
Jianpeng Chen ◽  
Jianqing Lin
Keyword(s):  
Triple Negative ◽  
Immune Cell ◽  
Risk Model ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Expression Levels ◽  
Related Risk ◽  
The Relationship

BackgroundAs a kind of small membrane vesicles, exosomes are secreted by most cell types from multivesicular endosomes, including tumor cells. The relationship between exosomes and immune response plays a vital role in the occurrence and development of tumors. Nevertheless, the interaction between exosomes and the microenvironment of tumors remains unclear. Therefore, we set out to study the influence of exosomes on the triple-negative breast cancer (TNBC) microenvironment.MethodOne hundred twenty-one exosome-related genes were downloaded from ExoBCD database, and IVL, CXCL13, and AP2S1 were final selected because of the association with TNBC prognosis. Based on the sum of the expression levels of these three genes, provided by The Cancer Genome Atlas (TCGA), and the regression coefficients, an exosome risk score model was established. With the median risk score value, the patients in the two databases were divided into high- and low-risk groups. R clusterProfiler package was employed to compare the different enrichment ways between the two groups. The ESTIMATE and CIBERSORT methods were employed to analyze ESTIMATE Score and immune cell infiltration. Finally, the correlation between the immune checkpoint-related gene expression levels and exosome-related risk was analyzed. The relationship between selected gene expression and drug sensitivity was also detected.ResultsDifferent risk groups exhibited distinct result of TNBC prognosis, with a higher survival rate in the low-risk group than in the high-risk group. The two groups were enriched by immune response and biological process pathways. A better overall survival (OS) was demonstrated in patients with high scores of immune and ESTIMATE rather than ones with low scores. Subsequently, we found that CD4+-activated memory T cells and M1 macrophages were both upregulated in the low-risk group, whereas M2 macrophages and activated mast cell were downregulated in the low-risk group in patients from the TCGA and GEO databases, respectively. Eventually, four genes previously proposed to be targets of immune checkpoint inhibitors were evaluated, resulting in the expression levels of CD274, CTLA4, LAG3, and TIM3 being higher in the low-risk group than high-risk group.ConclusionThe results of our study suggest that exosome-related risk model was related to the prognosis and ratio of immune cell infiltration in patients with TNBC. This discovery may make contributions to improve immunotherapy for TNBC.

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