scholarly journals Identification of an Exosome-Related Signature Associated with Prognosis and Immune Infiltration in Breast Cancer

2022 ◽  
Author(s):  
Qiaonan Guo ◽  
Pengjun Qiu ◽  
Kelun Pan ◽  
Jianpeng Chen ◽  
Jianqing Lin
Keyword(s):  
Breast Cancer ◽  
T Cells ◽  
High Risk ◽  
Immune Cell ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Immune Cell Infiltration ◽  
Immune Microenvironment ◽  
Expression Levels

Abstract Background: Exosomes are nanosized vesicles, play a vital role in breast cancer (BC) occurrence, development, invasion, metastasis, and drug resistance. Nevertheless, studies about exosome-related genes in breast cancer are limited. Besides, the interaction between the exosomes and tumor immune microenvironment (TIME) in BC are still unclear. Hence, we procced to study the potential prognostic value of exosome-related genes and their relationship to immune microenvironment in BC. Methods: 121 exosome-related genes were provided by ExoBCD database and 7 final genes were selected from the intersection of 33 differential expression genes (DEGs) and 19 prognostic genes in BC. Based on the expression levels of the 7 genes, downloaded from The Cancer Genome Atlas (TCGA) database, as well as the regression coefficients, the exosome-related signature was constructed. As a result, the patients in TCGA and GEO database were separated into low- and high- risk groups, respectively. Subsequently, R clusterProfiler package was applied to identify the distinct enrichment pathways between high-risk group and low-risk group. The ESTIMATE method was used to calculate ESTIMATE Score and CIBERSORT was applied to evaluate the immune cell infiltration. Eventually, the different expression levels of immune checkpoint related genes were analyzed between the two risk groups. Results: Results of BC prognosis vary from different risk groups. The low-risk groups were identified with higher survival rate both in TCGA and GEO cohort. The DEGs between high- and low- risk groups were found to enrich in immunity, biological processes, and inflammation pathways. The BC patients with higher ESTIMATE scores were revealed to have better overall survival (OS). Subsequently, CD8+ T cells, naive B cells, CD4+ resting memory T cells, monocytes, and neutrophils were upregulated, while M0 macrophages and M2 macrophages were downregulated in the low-risk group. At last, 4 genes reported as the targets of immune checkpoint inhibitors were further analyzed. The low-risk groups in TCGA and GEO cohorts were indicated with higher expression levels of LAG-3, CD274, TIGIT and CTLA-4. Conclusion: According to this study, exosomes are closely associated with the prognosis and immune cell infiltration of BC patients. These findings may make contributions to improve immunotherapy and bring a new sight for BC treatment strategies.

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 Immune‐related Long Noncoding RNA Signature for Patients with Cervical Cancer

2020 ◽  
Author(s):  
Jianfeng Zheng ◽  
Jinyi Tong ◽  
Benben Cao ◽  
Xia Zhang ◽  
Zheng Niu
Keyword(s):  
Cervical Cancer ◽  
High Risk ◽  
Risk Score ◽  
Immune Cell ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Training Set ◽  
Score Model

Abstract Background: Cervical cancer (CC) is a common gynecological malignancy for which prognostic and therapeutic biomarkers are urgently needed. The signature based on immune‐related lncRNAs(IRLs) of CC has never been reported. This study aimed to establish an IRL signature for patients with CC.Methods: The RNA-seq dataset was obtained from the TCGA, GEO, and GTEx database. The immune scores（IS）based on single-sample gene set enrichment analysis (ssGSEA) were calculated to identify the IRLs, which were then analyzed using univariate Cox regression analysis to identify significant prognostic IRLs. A risk score model was established to divide patients into low-risk and high-risk groups based on the median risk score of these IRLs. This was then validated by splitting TCGA dataset(n=304) into a training-set(n=152) and a valid-set(n=152). The fraction of 22 immune cell subpopulations was evaluated in each sample to identify the differences between low-risk and high-risk groups. Additionally, a ceRNA network associated with the IRLs was constructed.Results: A cohort of 326 CC and 21 normal tissue samples with corresponding clinical information was included in this study. Twenty-eight IRLs were collected according to the Pearson’s correlation analysis between immune score and lncRNA expression (P < 0.01). Four IRLs (BZRAP1-AS1, EMX2OS, ZNF667-AS1, and CTC-429P9.1) with the most significant prognostic values (P < 0.05) were identified which demonstrated an ability to stratify patients into low-risk and high-risk groups by developing a risk score model. It was observed that patients in the low‐risk group showed longer overall survival (OS) than those in the high‐risk group in the training-set, valid-set, and total-set. The area under the curve (AUC) of the receiver operating characteristic curve (ROC curve) for the four IRLs signature in predicting the one-, two-, and three-year survival rates were larger than 0.65. In addition, the low-risk and high-risk groups displayed different immune statuses in GSEA. These IRLs were also significantly correlated with immune cell infiltration. Conclusions: Our results showed that the IRL signature had a prognostic value for CC. Meanwhile, the specific mechanisms of the four-IRLs in the development of CC were ascertained preliminarily.

scholarly journals Immune-Related Four-lncRNA Signature for Patients with Cervical Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Jianfeng Zheng ◽  
Benben Cao ◽  
Xia Zhang ◽  
Zheng Niu ◽  
Jinyi Tong
Keyword(s):  
Cervical Cancer ◽  
High Risk ◽  
Immune Cell ◽  
Risk Group ◽  
Pearson Correlation ◽  
Characteristic Curve ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Gynecological Malignancy

Cervical cancer (CC) is a common gynecological malignancy for which prognostic and therapeutic biomarkers are urgently needed. The signature based on immune-related lncRNAs (IRLs) of CC has never been reported. This study is aimed at establishing an IRL signature for patients with CC. A cohort of 326 CC and 21 normal tissue samples with corresponding clinical information was included in this study. Twenty-eight IRLs were collected according to the Pearson correlation analysis between the immune score and lncRNA expression ( p < 0.01 ). Four IRLs (BZRAP1-AS1, EMX2OS, ZNF667-AS1, and CTC-429P9.1) with the most significant prognostic values ( p < 0.05 ) were identified which demonstrated an ability to stratify patients into the low-risk and high-risk groups by developing a risk score model. It was observed that patients in the low-risk group showed longer overall survival (OS) than those in the high-risk group in the training set, valid set, and total set. The area under the curve (AUC) of the receiver operating characteristic curve (ROC curve) for the four-IRL signature in predicting the one-, two-, and three-year survival rates was larger than 0.65. In addition, the low-risk and high-risk groups displayed different immune statuses in GSEA. These IRLs were also significantly correlated with immune cell infiltration. Our results showed that the IRL signature had a prognostic value for CC. Meanwhile, the specific mechanisms of the four IRLs in the development of CC were ascertained preliminarily.

scholarly journals The ferroptosis and iron-metabolism signature robustly predicts clinical diagnosis, prognosis and immune microenvironment for hepatocellular carcinoma

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Bufu Tang ◽  
Jinyu Zhu ◽  
Jie Li ◽  
Kai Fan ◽  
Yang Gao ◽  
...  
Keyword(s):  
High Risk ◽  
Iron Metabolism ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Models ◽  
Integrated Analysis ◽  
Immune Microenvironment ◽  
Diagnostic Models

Abstract Background In this study, we comprehensively analyzed genes related to ferroptosis and iron metabolism to construct diagnostic and prognostic models and explore the relationship with the immune microenvironment in HCC. Methods Integrated analysis, cox regression and the least absolute shrinkage and selection operator (LASSO) method of 104 ferroptosis- and iron metabolism-related genes and HCC-related RNA sequencing were performed to identify HCC-related ferroptosis and iron metabolism genes. Results Four genes (ABCB6, FLVCR1, SLC48A1 and SLC7A11) were identified to construct prognostic and diagnostic models. Poorer overall survival (OS) was exhibited in the high-risk group than that in the low-risk group in both the training cohort (P < 0.001, HR = 0.27) and test cohort (P < 0.001, HR = 0.27). The diagnostic models successfully distinguished HCC from normal samples and proliferative nodule samples. Compared with low-risk groups, high-risk groups had higher TMB; higher fractions of macrophages, follicular helper T cells, memory B cells, and neutrophils; and exhibited higher expression of CD83, B7H3, OX40 and CD134L. As an inducer of ferroptosis, erastin inhibited HCC cell proliferation and progression, and it was showed to affect Th17 cell differentiation and IL-17 signaling pathway through bioinformatics analysis, indicating it a potential agent of cancer immunotherapy. Conclusions The prognostic and diagnostic models based on the four genes indicated superior diagnostic and predictive performance, indicating new possibilities for individualized treatment of HCC patients. Graphical abstract

scholarly journals A Ferroptosis-Related Signature Robustly Predicts Clinical Outcomes and Associates With Immune Microenvironment for Thyroid Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Mingqin Ge ◽  
Jie Niu ◽  
Ping Hu ◽  
Aihua Tong ◽  
Yan Dai ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
High Risk ◽  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Low Risk ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Immune Microenvironment ◽  
Cox Regression Analysis

Objective: This study aimed to construct a prognostic ferroptosis-related signature for thyroid cancer and probe into the association with tumor immune microenvironment.Methods: Based on the expression profiles of ferroptosis-related genes, a LASSO cox regression model was established for thyroid cancer. Kaplan-Meier survival analysis was presented between high and low risk groups. The predictive performance was assessed by ROC. The predictive independency was validated via multivariate cox regression analysis and stratified analysis. A nomogram was established and verified by calibration curves. The enriched signaling pathways were predicted via GSEA. The association between the signature and immune cell infiltration was analyzed by CIBERSORT. The ferroptosis-related genes were validated in thyroid cancer tissues by immunohistochemistry and RT-qPCR.Results: A ferroptosis-related eight gene model was established for predicting the prognosis of thyroid cancer. Patients with high risk score indicated a poorer prognosis than those with low risk score (p = 1.186e-03). The AUCs for 1-, 2-, and 3-year survival were 0.887, 0.890, and 0.840, respectively. Following adjusting other prognostic factors, the model could independently predict the prognosis (p = 0.015, HR: 1.870, 95%CI: 1.132–3.090). A nomogram combining the signature and age was constructed. The nomogram-predicted probability of 1-, 3-, and 5-year survival approached the actual survival time. Several ferroptosis-related pathways were enriched in the high-risk group. The signature was distinctly associated with the immune cell infiltration. After validation, the eight genes were abnormally expressed between thyroid cancer and control tissues.Conclusion: Our findings established a prognostic ferroptosis-related signature that was associated with the immune microenvironment for thyroid cancer.

scholarly journals Complete Identification of Autophagy-Related lncRNA Prognostic Signature for Breast Cancer

2021 ◽  
Author(s):  
Wenxi Wang ◽  
Na Li ◽  
Lin Shen ◽  
Qin Zhou ◽  
Zhanzhan Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prognostic Model ◽  
Immune Cell ◽  
Risk Group ◽  
Pearson Correlation ◽  
Risk Groups ◽  
Cell Types ◽  
High Risk Group ◽  
Low Risk ◽  
High Morbidity

Abstract Purpose: Breast cancer (BC) has a relatively high morbidity and mortality for women. The research about BC prognosis is significant. Autophagy is an essential process for tumor progression, which could play its role with lncRNA, a kind of ncRNA that have regulatory roles in multiple tumors. Therefore, constructing an autophagy-related prognostic model for breast cancer is meaningful.Methods: We download data from the TCGA and HADb. Pearson correlation analysis was performed to excavate autophagy-related lncRNA. Then with gene expression difference analysis, etc. we explored the relationship between clinical features and the signature. We applied Cytoscape as well as KEGG, etc. to explore expression condition. And the autophagy status of our signature was investigated by GSEA, etc. We also searched the immune distinction by CIBERSORTx to extend our study and preliminarily verified our study in the end.Results: Firstly, we got an independent autophagy-related lncRNA prognostic model, by which BC patients were divided into high- and low-risk groups. We found that the OS of high-risk group is significantly lower than that of low-risk group, which was identical to those within various clinical subgroups. Then, the KEGG and GO analysis enriched several pathways including autophagy. PCA and GSEA analysis demonstrated the autophagy status. Several distinguishing immune cell types in separated groups were revealed by immunity analysis. Then the verification in the end proved the feasibility of our signature.Conclusion: In this study, we acquired an independent autophagy-related lncRNA signature involving 12 lncRNAs, which contributes to the prediction of prognosis of BC patients.

scholarly journals The ferroptosis and iron-metabolism signature robustly predicts clinical diagnosis, prognosis and immune microenvironment for hepatocellular carcinoma

2020 ◽  
Author(s):  
Jiansong Ji ◽  
Bufu Tang ◽  
Jinyu Zhu ◽  
Jie Li ◽  
Kai Fan ◽  
...  
Keyword(s):  
High Risk ◽  
Iron Metabolism ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Models ◽  
Integrated Analysis ◽  
Immune Microenvironment ◽  
Diagnostic Models

Abstract Background : In this study, we comprehensively analyzed genes related to ferroptosis and iron metabolism to construct diagnostic and prognostic models and explore the relationship with the immune microenvironment in HCC. Methods : Integrated analysis, cox regression and the least absolute shrinkage and selection operator (LASSO) method of 104 ferroptosis- and iron metabolism-related genes and HCC-related RNA sequencing were performed to identify HCC-related ferroptosis and iron metabolism genes. Results : four genes (ABCB6, FLVCR1, SLC48A1 and SLC7A11) were identified to construct prognostic and diagnostic models. Poorer overall survival (OS) was exhibited in the high-risk group than that in the low-risk group in both the training cohort (P < 0.001, HR = 0.27) and test cohort (P < 0.001, HR = 0.27). The diagnostic models successfully distinguished HCC from normal samples and proliferative nodule samples. Compared with low-risk groups, high-risk groups had higher TMB; higher fractions of macrophages, follicular helper T cells, memory B cells, and neutrophils; and exhibited higher expression of CD83, B7H3, OX40 and CD134L. As an inducer of ferroptosis, erastin inhibited HCC cell proliferation and progression, and it was showed to affect Th17 cell differentiation and IL-17 signaling pathway through bioinformatics analysis, indicating it a potential agent of cancer immunotherapy. Conclusions: The prognostic and diagnostic models based on the four genes indicated superior diagnostic and predictive performance, indicating new possibilities for individualized treatment of HCC patients.

scholarly journals The ferroptosis and iron-metabolism signature robustly predicts clinical diagnosis, prognosis and immune microenvironment for hepatocellular carcinoma

2020 ◽  
Author(s):  
Jiansong Ji ◽  
Bufu Tang ◽  
Jinyu Zhu ◽  
Jie Li ◽  
Kai Fan ◽  
...  
Keyword(s):  
High Risk ◽  
Iron Metabolism ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Models ◽  
Integrated Analysis ◽  
Immune Microenvironment ◽  
Diagnostic Models

Abstract Background : In this study, we comprehensively analyzed genes related to ferroptosis and iron metabolism to construct diagnostic and prognostic models and explore the relationship with the immune microenvironment in HCC. Methods : Integrated analysis, cox regression and the least absolute shrinkage and selection operator (LASSO) method of 104 ferroptosis- and iron metabolism-related genes and HCC-related RNA sequencing were performed to identify HCC-related ferroptosis and iron metabolism genes. Results : four genes (ABCB6, FLVCR1, SLC48A1 and SLC7A11) were identified to construct prognostic and diagnostic models. Poorer overall survival (OS) was exhibited in the high-risk group than that in the low-risk group in both the training cohort (P < 0.001, HR = 0.27) and test cohort (P < 0.001, HR = 0.27). The diagnostic models successfully distinguished HCC from normal samples and proliferative nodule samples. Compared with low-risk groups, high-risk groups had higher TMB; higher fractions of macrophages, follicular helper T cells, memory B cells, and neutrophils; and exhibited higher expression of CD83, B7H3, OX40 and CD134L. As an inducer of ferroptosis, erastin inhibited HCC cell proliferation and progression, and it was showed to affect Th17 cell differentiation and IL-17 signaling pathway through bioinformatics analysis, indicating it a potential agent of cancer immunotherapy. Conclusions: The prognostic and diagnostic models based on the four genes indicated superior diagnostic and predictive performance, indicating new possibilities for individualized treatment of HCC patients.

scholarly journals Development and validation of a ferroptosis-related prognostic model in pancreatic cancer

2021 ◽  
Author(s):  
Chen-jie Qiu ◽  
Xue-bing Wang ◽  
Zi-ruo Zheng ◽  
Chao-zhi Yang ◽  
Kai Lin ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
T Cell ◽  
High Risk ◽  
Risk Score ◽  
Prognostic Model ◽  
Immune Cell ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Immune Cell Infiltration

Abstract Background: The purpose of this study was to identify ferroptosis-related genes (FRGs) associated with the prognosis of pancreatic cancer and to construct a prognostic model based on FRGs. Methods: Based on pancreatic cancer data obtained from The Cancer Genome Atlas database, we established the prognostic model from 232 FRGs. A nomogram was constructed by combining the prognostic model and clinicopathological features. Gene Expression Omnibus datasets and tissue samples obtained from our center were utilized to validate the model. Relationship between risk score and immune cell infiltration was explored by CIBERSORT and TIMER.Results: The prognostic model was established based on four FRGs (ENPP2, ATG4D, SLC2A1 and MAP3K5) and can be an independent risk factor in pancreatic cancer (HR 1.648, 95% CI 1.335-2.035, p < 0.001). Based on the median risk score, patients were divided into a high-risk group and a low-risk group. The prognosis of the low-risk group was significantly better than that of the high-risk group. In the high-risk group, patients treated with chemotherapy had a better prognosis. The nomogram showed that the model was the most important element. Gene set enrichment analysis identified three key pathways, namely, TGFβ signaling, HIF signaling pathway and adherens junction. The prognostic model can also affect the immune cell infiltration, such as macrophages M0, M1, CD4+T cell and CD8+T cell. Conclusion: A ferroptosis-related prognostic model can be employed to predict the prognosis of pancreatic cancer. Ferroptosis can be an important marker and immunotherapy can be a potential therapeutic target for pancreatic cancer.

scholarly journals Hypoxia-Related Long Non-Coding RNA Signature Predicts the Prognosis of Breast Cancer

2021 ◽  
Author(s):  
Jinlong Huo ◽  
Shuang Shen ◽  
Chen Chen ◽  
Rui Qu ◽  
Youming Guo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
T Cell ◽  
High Risk ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Cytolytic Activity ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas

Abstract Background: Breast cancer(BC) is the most common tumour in women. Hypoxia stimulates metastasis in cancer and is linked to poor patient prognosis.Methods: We screened prognostic-related lncRNAs(Long Non-Coding RNAs) from the Cancer Genome Atlas (TCGA) data and constructed a prognostic signature based on hypoxia-related lncRNAs in BC.Results: We identified 21 differentially expressed lncRNAs associated with BC prognosis. Kaplan Meier survival analysis indicated a significantly worse prognosis for the high-risk group(P<0.001). Moreover, the ROC-curve (AUC) of the lncRNAs signature was 0.700, a performance superior to other traditional clinicopathological characteristics. Gene set enrichment analysis (GSEA) showed many immune and cancer-related pathways and in the low-risk group patients. Moreover, TCGA revealed that functions including activated protein C (APC)co-inhibition, Cinnamoyl CoA reductase(CCR),check-point pathways, cytolytic activity, human leukocyte antigen (HLA), inflammation-promotion, major histocompatibility complex(MHC) class1, para-inflammation, T cell co-inhibition, T cell co-stimulation, and Type Ⅰ and Ⅱ Interferons (IFN) responses were significantly different in the low-risk and high-risk groups. Immune checkpoint molecules such as ICOS, IDO1, TIGIT, CD200R1, CD28, PDCD1(PD-1), were also expressed differently between the two risk groups. The expression of m6A-related mRNA indicated that YTHDC1, RBM15, METTL3, and FTO were significantly between the high and low-risk groups.Additionally, immunotherapy in patients with BC from the low-risk group yielded a higher frequency of clinical responses to anti-PD-1/PD-L1 therapy or a combination of anti-PD-1/PD-L1and anti-CTLA4 therapies.Except for lapatinib, the results also show that a high-risk score is related to a higher half-maximal inhibitory concentration (IC50) of chemotherapy drugs.Conclusion: A novel hypoxia-related lncRNAs signature may serve as a prognostic model for BC.

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