scholarly journals A Signature of Autophagy-Related Long Non-coding RNA to Predict the Prognosis of Breast Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiaoping Li ◽  
Jishang Chen ◽  
Qihe Yu ◽  
Hui Huang ◽  
Zhuangsheng Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Lasso Regression ◽  
Cox Regression Analysis ◽  
Risk Scoring

Background: A surge in newly diagnosed breast cancer has overwhelmed the public health system worldwide. Joint effort had beed made to discover the genetic mechanism of these disease globally. Accumulated research has revealed autophagy may act as a vital part in the pathogenesis of breast cancer.Objective: Aim to construct a prognostic model based on autophagy-related lncRNAs and investigate their potential mechanisms in breast cancer.Methods: The transcriptome data and clinical information of patients with breast cancer were obtained from The Cancer Genome Atlas (TCGA) database. Autophagy-related genes were obtained from the Human Autophagy Database (HADb). Long non-coding RNAs (lncRNAs) related to autophagy were acquired through the Pearson correlation analysis. Univariate Cox regression analysis as well as the least absolute shrinkage and selection operator (LASSO) regression analysis were used to identify autophagy-related lncRNAs with prognostic value. We constructed a risk scoring model to assess the prognostic significance of the autophagy-related lncRNAs signatures. The nomogram was then established based on the risk score and clinical indicators. Through the calibration curve, the concordance index (C-index) and receiver operating characteristic (ROC) curve analysis were evaluated to obtain the model's predictive performance. Subgroup analysis was performed to evaluate the differential ability of the model. Subsequently, gene set enrichment analysis was conducted to investigate the potential functions of these lncRNAs.Results: We attained 1,164 breast cancer samples from the TCGA database and 231 autophagy-related genes from the HAD database. Through correlation analysis, 179 autophagy-related lncRNAs were finally identified. Univariate Cox regression analysis and LASSO regression analysis further screened 18 prognosis-associated lncRNAs. The risk scoring model was constructed to divide patients into high-risk and low-risk groups. It was found that the low-risk group had better overall survival (OS) than those of the high-risk group. Then, the nomogram model including age, tumor stage, TNM stage and risk score was established. The evaluation index (C-index: 0.78, 3-year OS AUC: 0.813 and 5-year OS AUC: 0.785) showed that the nomogram had excellent predictive power. Subgroup analysis showed there were difference in OS between high-risk and low-risk patients in different subgroups (stage I-II, ER positive, Her-2 negative and non-TNBC subgroups; all P < 0.05). According to the results of gene set enrichment analysis, these lncRNAs were involved in the regulation of multicellular organismal macromolecule metabolic process in multicellular organisms, nucleotide excision repair, oxidative phosphorylation, and TGF-β signaling pathway.Conclusions: We identified 18 autophagy-related lncRNAs with prognostic value in breast cancer, which may regulate tumor growth and progression in multiple ways.

Immune-related gene based prognostic signature for the risk stratification analysis of breast cancer

2021 ◽  
Vol 16 ◽  
Author(s):  
Dongqing Su ◽  
Qianzi Lu ◽  
Yi Pan ◽  
Yao Yu ◽  
Shiyuan Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Breast Cancer Patients ◽  
Cox Regression Analysis ◽  
Score Model

Background: Breast cancer has plagued women for many years and caused many deaths around the world. Method: In this study, based on the weighted correlation network analysis, univariate Cox regression analysis and least absolute shrinkage and selection operator, 12 immune-related genes were selected to construct the risk score for breast cancer patients. The multivariable Cox regression analysis, gene set enrichment analysis and nomogram were also conducted in this study. Results: Good results were obtained in the survival analysis, enrichment analysis, multivariable Cox regression analysis and immune-related feature analysis. When the risk score model was applied in 22 breast cancer cohorts, the univariate Cox regression analysis demonstrated that the risk score model was significantly associated with overall survival in most of the breast cancer cohorts. Conclusion: Based on these results, we could conclude that the proposed risk score model may be a promising method, and may improve the treatment stratification of breast cancer patients in the future work.

scholarly journals Comprehensive Analysis of Ferroptosis-Related LncRNAs in Breast Cancer Patients Reveals Prognostic Value and Relationship With Tumor Immune Microenvironment

2021 ◽  
Vol 8 ◽  
Author(s):  
Zhengjie Xu ◽  
Suxiao Jiang ◽  
Juan Ma ◽  
Desheng Tang ◽  
Changsheng Yan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
T Cells ◽  
Regression Analysis ◽  
Cox Regression ◽  
Comprehensive Analysis ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Immune Microenvironment ◽  
Cox Regression Analysis

Background: Breast cancer (BC) is a heterogeneous malignant tumor, leading to the second major cause of female mortality. This study aimed to establish an in-depth relationship between ferroptosis-related LncRNA (FRlncRNA) and the prognosis as well as immune microenvironment of the patients with BC.Methods: We downloaded and integrated the gene expression data and the clinical information of the patients with BC from The Cancer Genome Atlas (TCGA) database. The co-expression network analysis and univariate Cox regression analysis were performed to screen out the FRlncRNAs related to prognosis. A cluster analysis was adopted to explore the difference of immune microenvironment between the clusters. Furthermore, we determined the optimal survival-related FRLncRNAs for final signature by LASSO Cox regression analysis. Afterward, we constructed and validated the prediction models, which were further tested in different subgroups.Results: A total of 31 FRLncRNAs were filtrated as prognostic biomarkers. Two clusters were determined, and C1 showed better prognosis and higher infiltration level of immune cells, such as B cells naive, plasma cells, T cells CD8, and T cells CD4 memory activated. However, there were no significantly different clinical characters between the clusters. Gene Set Enrichment Analysis (GSEA) revealed that some metabolism-related pathways and immune-associated pathways were exposed. In addition, 12 FRLncRNAs were determined by LASSO analysis and used to construct a prognostic signature. In both the training and testing sets, patients in the high-risk group had a worse survival than the low-risk patients. The area under the curves (AUCs) of receiver operator characteristic (ROC) curves were about 0.700, showing positive prognostic capacity. More notably, through the comprehensive analysis of heatmap, we regarded LINC01871, LINC02384, LIPE-AS1, and HSD11B1-AS1 as protective LncRNAs, while LINC00393, AC121247.2, AC010655.2, LINC01419, PTPRD-AS1, AC099329.2, OTUD6B-AS1, and LINC02266 were classified as risk LncRNAs. At the same time, the patients in the low-risk groups were more likely to be assigned to C1 and had a higher immune score, which were consistent with a better prognosis.Conclusion: Our research indicated that the ferroptosis-related prognostic signature could be used as novel biomarkers for predicting the prognosis of BC. The differences in the immune microenvironment exhibited by BC patients with different risks and clusters suggested that there may be a complementary synergistic effect between ferroptosis and immunotherapy.

scholarly journals A Six-Immune-Related Genes Prognostic Signature for Glioblastoma

2021 ◽  
Author(s):  
Kate Huang ◽  
Changjun Rao ◽  
Jianglong Lu ◽  
Zhangzhang Zhu ◽  
Chengde Wang ◽  
...  
Keyword(s):  
High Risk ◽  
Predictive Model ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Lasso Regression ◽  
Prognostic Signature ◽  
Data Resource ◽  
Immune Activity

Abstract Background: Glioblastoma (GBM) multiforme is a common malignant brain tumor with high mortality. It is urgently necessary to develop a new treatment because traditional approaches have reached a bottleneck.Purpose: Here we created an immune-related gene (IRGs)-based prognostic signature to comprehensively define the prognosis of glioblastoma (GBM).Methods: Glioblastoma samples were abstracted from the Chinese Glioma Genome Atlas (CGGA) and the Gene Expression Omnibus (GEO). We retrieved IRGs from the ImmProt data resource. Univariate Cox analysis was adopted to determine the prognostically remarkable IRGs for individual with GBM. The prognostically optimal IRGs were determined via LASSO regression, and predictive model created. Besides, the association of specific factors with the overall survival (OS) of individuals with GBM was explored via multivariate Cox-regression. Lastly, we constructed a predictive nomogram integrating the independent predictive factors to determine the one-, two-, and three-year OS likelihoods of individuals with GBM. Additionally,gene set enrichment analysis(GSEA) and single sample GSEA(ssGSEA) were performed to understand the correlation between the risk score and immune activity.Results: Overall, 273 IRGs which exhibited differential expression were identified in GBM tumor in contrast with the non-malignant samples. Of these 273 IRGs, only six were remarkably linked to OS of individuals with GBM, which were employed in constructing the predictive signature. The GBM were categorized into either the high-risk GBM group or the low-risk GBM group. There were remarkable differences between the high-risk GBM and the low-risk GBM groups regarding OS. The AUC for predicting one-,two-, and three-year OS in training set was 0.610,0.698 and 0.694.In line with the AUC of validation set was 0.608,0.692 and 0.678.Besides,the results of ssGSEA showed the score of prognostic signature is closely related to immune activity.Conclusion: Herein, a robust predictive model based on IRGs was created to estimate the diversity of OS likelihoods in GBM patients, as well as aid future clinical research.

scholarly journals A Five-Immune-Related lncRNA Signature Predicts Survival of Patients With Osteosarcoma

2021 ◽  
Author(s):  
Jie Huang ◽  
Hongyi Lai ◽  
Wentao Qin ◽  
Zhandong Bo ◽  
Zhen Tan ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Data Matrix ◽  
Lasso Regression ◽  
Training Set ◽  
Cox Regression Analysis ◽  
Validation Set

Abstract Background: Osteosarcoma (OS) is the most common primary solid malignant bone tumor, and its metastasis is a prominent cause of high mortality in patients.Methods: A risk signature was constructed based on re-annotating the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) data matrix, of the lncRNAs related to OS prognosis and immunity. From the OS transcription data, which is downloaded from the TARGET, the 1126 lncRNAs those harbour co-expressions with immunity genes were selected by Pearson correlation test and later divided into the training set (n=44) and validation set (n=41) with the caret package of R. With the training set we build the model related to Osteosarcoma prognosis by the univariate and multivariate Cox, and the Lasso regression analysis, and in combination with the clinical factors we conducted the multivariate Cox regression analysis to build the 1-year, 3-year and 5-year survival rate nomograms. Afterwards, we validated the ROC and the calibration curve of the subjects with the validation set and the whole dataset. Lastly, we performed functional enrichment analysis with the GSEA, GO and KEGG to figure out the biological functions of the prognosis genes.Results: The training set was performed in univariate and multivariate Cox regression analysis, identifying 25 lncRNAs correlated with prognosis. Eleven lncRNAs were selected by the least absolute shrinkage and selection operator (LASSO) regression for multivariate cox analysis and Kaplan-Meier (KM) survival analysis. Finally, lncRNAs (RP11-69E11.4, SNHG6, MIR210HG, RP11-750H9.5 and CTD-2341M24.1) risk signature was constructed, and the validation set and the whole dataset were used to evaluate the prediction stability and accuracy of the signature. The survival times of high- and low-risk groups were significantly different in the training set, validation set and the whole dataset. Further, function enrichment and gene set enrichment analysis revealed that the lncRNAs in the signature may affect the proliferation, migration, chemotaxis and combination of Osteosarcoma-related immune cells, and involve in every pathways of OS metabolism. Conclusion: The five lncRNAs survival risk signature could potentially predict the prognosis of OS patients, additionally, may provide novel insights for future clinical diagnosis and treatment of OS.

scholarly journals Construction and Analysis of a Prognostic Model of Osteosarcoma Based on m6A-Related lncRNA

2021 ◽  
Author(s):  
Zhian Ling ◽  
Yuting Liang ◽  
Suping Wei ◽  
Yuanming Chen ◽  
Jinmin Zhao
Keyword(s):  
Regression Analysis ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Lasso Regression ◽  
Gene Set Enrichment ◽  
Cox Regression Analysis

Abstract Background N6-methylandenosine (m6A) methylation is one of the most common methylation modifications in RNA. At present, a large number of studies have found that m6A methylation can regulate the occurrence and development of tumors by modifying mRNA. However, it is still unclear how m6A modifies Long non-coding RNA (lncRNA) that regulates mRNA expression by interacting with miRNA to affect the occurrence and development of osteosarcoma(OS). Therefore, exploring the lncRNAs related to m6A methylation and identifying lncRNAs that have both prognostic effects and immune functions are things that need to be solved urgently. Methods The published gene expression data of OS and complete clinical annotation files were obtained from the TARGET database. LncRNAs with P <0.001 from the results of Pearson correlation coefficient analysis as m6A-related lncRNAs were screened. Single-factor Cox regression analysis was used to screening prognostic- related lncRNA combined with the clinical information of patients and constructed a prognostic model based on lasso regression analysis. Then we explored the differences in survival and immune function of different subtypes that be obtained using the Consensus Cluster. The enrichment of differential genes between high and low risk groups in the KEGG pathway is achieved through Gene set enrichment analysis(GSEA). Results We obtained 706 lncRNAs in the TARGET database. Consensus clustering method were used to divide patients with OS into subgroups based on the expression of 26 prognostic-related lncRNAs. Through Kaplan-Meier survival analysis, there are significant differences between the two subgroups. The average immune score (P = 0.02), stromal score(P =0.027), and estimate score༈P = 0.015༉were higher in cluster 1 than in cluster 2. We found that compared with cluster 2, SIGLEC15, HAVCR2, LAG3, and PDCD1 were highly expressed in cluster 1.We obtain a prognostic model by lasso regression analysis. In the training group and the text group, the OS curve showed that patients in the high-risk group had a poorer prognosis than those in the low-risk group. In the training set, univariate Cox regression analysis and multivariate Cox regression analysis showed that the risk score was correlated with the prognosis of OS patients. In the high-risk group, the Linoleic acid metabolism and the Glycine, serine and threonine metabolism pathway were mainly involved by Gene Set Enrichment analysis. The abundance of Mast cells activated (P ≦0.024) and T cells CD4 (P ≦0.0044) naive were positively association the risk score. Conclusions This study clarified the important role of m6A-related lncRNAs in the prognosis and immune microenvironment of patients with OS, and indicate that m6A-related prognostic lncRNA signals may provide new targets for the diagnosis and treatment of OS.

scholarly journals Identification of IGF2BP2 as a Prognostic Biomarker and Immunotherapeutic Target Based on Alternative Splicing Events in Glioblastoma

2021 ◽  
Author(s):  
Xin-Yu Li ◽  
Lei Hou ◽  
Lu-yu Zhang ◽  
Xue-yuan Li ◽  
xi-tao Yang
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Prognostic Model ◽  
Prognostic Markers ◽  
Risk Model ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Low Risk ◽  
Lasso Regression ◽  
Cox Regression Analysis

Abstract Aim: A glioblastoma (GBM) prognostic model was developed with GBM -related alternative splicing (AS) data and prognostic markers were identified. Methods: AS data and clinical data of GBM patients were retrieved from The Cancer Genome Atlas (TCGA) SpliceSeq database and TCGA database, respectively. The data from these two databases were intersected to screen the prognosis-associated AS events, which was subsequently examined in Univariate Cox regression models. To avoid model overfitting, LASSO regression analysis was conducted. On the basis of these AS events, we established a prognostic model of GBM with the use of multivariate Cox regression analysis. On the strength of this model, the patients were assigned into high-risk and low-risk groups with a median risk score as the threshold. Kaplan-Meier survival, receiver operating characteristic (ROC), and calibration curves were applied to evaluate the performance of this model. Finally, combined with the risk model and clinicopathological characteristics, Cox regression analysis was utilized to identify the independent prognostic markers of GBM, and a nomogram was constructed. Results: The AS and clinical data of 169 GBM patients from the TCGA SpliceSeq and TCGA databases were collected. Univariate Cox regression analysis identified 1000 prognosis-related AS events in GBM, and then Lasso regression analysis identified 16 AS events. A GBM prognostic risk model was constructed based on AS events of 7 genes (FAM86B1, ZNF302, C19orf57, RPL39L, CBLL1, RWDD1, IGF2BP2). Through this model, we found lower overall survival (OS) rates of the high-risk population versus the low-risk population (p < 0.05). ROC and calibration curve analyses demonstrated the good ability of this model to predict the OS of GBM patients. Cox regression analysis suggested risk score as an independent prognostic factor for GBM. We also found that IGF2BP2 is associated with patient prognosis and have a strong relationship with immunotherapy response. Conclusion: The prognostic model based on AS events can significantly distinguish the survival rate of high-risk and low-risk GBM patients and IGF2BP2 were identified as a novel prognostic biomarker and immunotherapeutic target.

scholarly journals Identification and development of an independent immune-related genes prognostic model for breast cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lin Chen ◽  
Yuxiang Dong ◽  
Yitong Pan ◽  
Yuhan Zhang ◽  
Ping Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
Risk Scores ◽  
Validation Dataset ◽  
Immune Gene ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Immune Related Genes

Abstract Background Breast cancer is one of the main malignant tumors that threaten the lives of women, which has received more and more clinical attention worldwide. There are increasing evidences showing that the immune micro-environment of breast cancer (BC) seriously affects the clinical outcome. This study aims to explore the role of tumor immune genes in the prognosis of BC patients and construct an immune-related genes prognostic index. Methods The list of 2498 immune genes was obtained from ImmPort database. In addition, gene expression data and clinical characteristics data of BC patients were also obtained from the TCGA database. The prognostic correlation of the differential genes was analyzed through Survival package. Cox regression analysis was performed to analyze the prognostic effect of immune genes. According to the regression coefficients of prognostic immune genes in regression analysis, an immune risk scores model was established. Gene set enrichment analysis (GSEA) was performed to probe the biological correlation of immune gene scores. P < 0.05 was considered to be statistically significant. Results In total, 556 immune genes were differentially expressed between normal tissues and BC tissues (p < 0. 05). According to the univariate cox regression analysis, a total of 66 immune genes were statistically significant for survival risk, of which 30 were associated with overall survival (P < 0.05). Finally, a 15 immune genes risk scores model was established. All patients were divided into high- and low-groups. KM survival analysis revealed that high immune risk scores represented worse survival (p < 0.001). ROC curve indicated that the immune genes risk scores model had a good reliability in predicting prognosis (5-year OS, AUC = 0.752). The established risk model showed splendid AUC value in the validation dataset (3-year over survival (OS) AUC = 0.685, 5-year OS AUC = 0.717, P = 0.00048). Moreover, the immune risk signature was proved to be an independent prognostic factor for BC patients. Finally, it was found that 15 immune genes and risk scores had significant clinical correlations, and were involved in a variety of carcinogenic pathways. Conclusion In conclusion, our study provides a new perspective for the expression of immune genes in BC. The constructed model has potential value for the prognostic prediction of BC patients and may provide some references for the clinical precision immunotherapy of patients.

scholarly journals Identification and Validation of Immune-Related LncRNA Prognostic Signature for Lung Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Guomin Wu ◽  
Qihao Wang ◽  
Ting Zhu ◽  
Linhai Fu ◽  
Zhupeng Li ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Clinical Features ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Immune Infiltration

This study aimed to establish a prognostic risk model for lung adenocarcinoma (LUAD). We firstly divided 535 LUAD samples in TCGA-LUAD into high-, medium-, and low-immune infiltration groups by consensus clustering analysis according to immunological competence assessment by single-sample gene set enrichment analysis (ssGSEA). Profile of long non-coding RNAs (lncRNAs) in normal samples and LUAD samples in TCGA was used for a differential expression analysis in the high- and low-immune infiltration groups. A total of 1,570 immune-related differential lncRNAs in LUAD were obtained by intersecting the above results. Afterward, univariate COX regression analysis and multivariate stepwise COX regression analysis were conducted to screen prognosis-related lncRNAs, and an eight-immune-related-lncRNA prognostic signature was finally acquired (AL365181.2, AC012213.4, DRAIC, MRGPRG-AS1, AP002478.1, AC092168.2, FAM30A, and LINC02412). Kaplan–Meier analysis and ROC analysis indicated that the eight-lncRNA-based model was accurate to predict the prognosis of LUAD patients. Simultaneously, univariate COX regression analysis and multivariate COX regression analysis were undertaken on clinical features and risk scores. It was illustrated that the risk score was a prognostic factor independent from clinical features. Moreover, immune data of LUAD in the TIMER database were analyzed. The eight-immune-related-lncRNA prognostic signature was related to the infiltration of B cells, CD4+ T cells, and dendritic cells. GSEA enrichment analysis revealed significant differences in high- and low-risk groups in pathways like pentose phosphate pathway, ubiquitin mediated proteolysis, and P53 signaling pathway. This study helps to treat LUAD patients and explore molecules related to LUAD immune infiltration to deeply understand the specific mechanism.

scholarly journals Establishment and Validation of a Novel Metabolism-related Gene Signature for Predicting Overall Survival of Patients with Breast Cancer

2021 ◽  
Author(s):  
Jian Li ◽  
Yang Liu ◽  
Fei Liu ◽  
Qiang Tian ◽  
Baojiang Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cox Regression ◽  
Treatment Strategies ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Current Status ◽  
Lasso Regression ◽  
Related Gene

Abstract It is well known that Breast cancer is a heterogeneous disease.Although the current recurrence and mortality rate have been greatly improved, many people still suffer relapse and metastasis.Metabolic reprograming is currently considered to be a new hallmark of cancer.Therefore,in this study, we comprehensively analyzed the prognostic effect of metabolic-related gene signatures in breast cancer and its relationship with the immune microenvironment.We constructed a novel metabolic-related gene signature containing 6 genes to distinguish between high and low risk groups by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression, and validated its robustness and accuracy through multiple databases.The metabolic gene signature may be an independent risk factor for BC both in the training and the testing set,the nomogram has a moderately accurate performance,and the C index was 0.757 and 0.728 respectively.The signature can reveal metabolic characteristics based on gene set enrichment analysis and at the same time monitor the status of TME.This gene signature can be used as a promising independent prognostic marker for BC patients, and can indicate the current status of TME, providing more clues for exploring new diagnostic and treatment strategies.

scholarly journals Development and Validation of a Novel Hypoxia-Related Long Noncoding RNA Model With Regard to Prognosis and Immune Features in Breast Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Peng Gu ◽  
Lei Zhang ◽  
Ruitao Wang ◽  
Wentao Ding ◽  
Wei Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis

Background: Female breast cancer is currently the most frequently diagnosed cancer in the world. This study aimed to develop and validate a novel hypoxia-related long noncoding RNA (HRL) prognostic model for predicting the overall survival (OS) of patients with breast cancer.Methods: The gene expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 200 hypoxia-related mRNAs were obtained from the Molecular Signatures Database. The co-expression analysis between differentially expressed hypoxia-related mRNAs and lncRNAs based on Spearman’s rank correlation was performed to screen out 166 HRLs. Based on univariate Cox regression and least absolute shrinkage and selection operator Cox regression analysis in the training set, we filtered out 12 optimal prognostic hypoxia-related lncRNAs (PHRLs) to develop a prognostic model. Kaplan–Meier survival analysis, receiver operating characteristic curves, area under the curve, and univariate and multivariate Cox regression analyses were used to test the predictive ability of the risk model in the training, testing, and total sets.Results: A 12-HRL prognostic model was developed to predict the survival outcome of patients with breast cancer. Patients in the high-risk group had significantly shorter median OS, DFS (disease-free survival), and predicted lower chemosensitivity (paclitaxel, docetaxel) compared with those in the low-risk group. Also, the risk score based on the expression of the 12 HRLs acted as an independent prognostic factor. The immune cell infiltration analysis revealed that the immune scores of patients in the high-risk group were lower than those of the patients in the low-risk group. RT-qPCR assays were conducted to verify the expression of the 12 PHRLs in breast cancer tissues and cell lines.Conclusion: Our study uncovered dozens of potential prognostic biomarkers and therapeutic targets related to the hypoxia signaling pathway in breast cancer.

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