Identification of an immune-related long noncoding RNA signature that predicts prognosis in breast cancer patients

2021 ◽  
Vol 15 (3) ◽  
pp. 167-180
Author(s):  
Na Li ◽  
Zubin Li ◽  
Xin Li ◽  
Bingjie Chen ◽  
Huibo Sun ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Cox Regression ◽  
Expression Profiles ◽  
Characteristic Curve ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Patients ◽  
Cox Regression Analysis

Aim: The purpose of this study was to identify an immune-related long noncoding RNA (lncRNA) signature that predicts the prognosis of breast cancer. Materials & methods: The expression profiles of breast cancer were downloaded from The Cancer Genome Atlas. Cox regression analysis was used to identify an immune-related lncRNA signature. Results: The five immune-related lncRNAs could be used to construct a breast cancer survival prognosis model. The receiver operating characteristic curve evaluation found that the accuracy of the model for predicting the 1-, 3- and 5-year prognosis of breast cancer was 0.688, 0.708 and 0.686. Conclusion: This signature may have an important clinical significance for improving predictive results and guiding the treatment of breast cancer patients.

A novel three-long noncoding RNA risk score system for the prognostic prediction of triple-negative breast cancer

2021 ◽  
Vol 15 (1) ◽  
pp. 43-55
Author(s):  
Chao Yuan ◽  
Hongjun Yuan ◽  
Li Chen ◽  
Miaomiao Sheng ◽  
Wenru Tang
Keyword(s):  
Breast Cancer ◽  
Risk Score ◽  
Triple Negative Breast Cancer ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Triple Negative ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Gene Expression Omnibus ◽  
Prognostic Prediction

Background: Triple-negative breast cancer (TNBC) is characterized by fast tumor increase, rapid recurrence and natural metastasis. We aimed to identify a genetic signature for predicting the prognosis of TNBC. Materials & methods: We conducted a weighted correlation network analysis of datasets from the Gene Expression Omnibus. Multivariate Cox regression was used to construct a risk score model. Results: The multi-factor risk scoring model was meaningfully associated with the prognosis of patients with TBNC. The predictive power of the model was demonstrated by the time-dependent receiver operating characteristic curve and Kaplan–Meier curve, and verified using a validation set. Conclusion: We established a long noncoding RNA-based model for the prognostic prediction of TNBC.

scholarly journals Identification of a prognostic long non-coding RNA signature in breast cancer

2020 ◽  
Author(s):  
Gaochen Lan ◽  
Xiaoling Yu ◽  
Yanna Zhao ◽  
Jinjian Lan ◽  
Wan Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Cox Regression Analysis

Abstract Background: Breast cancer is the most common malignant disease among women. At present, more and more attention has been paid to long non-coding RNAs (lncRNAs) in the field of breast cancer research. We aimed to investigate the expression profiles of lncRNAs and construct a prognostic lncRNA for predicting the overall survival (OS) of breast cancer.Methods: The expression profiles of lncRNAs and clinical data with breast cancer were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were screened out by R package (limma). The survival probability was estimated by the Kaplan‑Meier Test. The Cox Regression Model was performed for univariate and multivariate analysis. The risk score (RS) was established on the basis of the lncRNAs’ expression level (exp) multiplied regression coefficient (β) from the multivariate cox regression analysis with the following formula: RS=exp a1 * β a1 + exp a2 * β a2 +……+ exp an * β an. Functional enrichment analysis was performed by Metascape.Results: A total of 3404 differentially expressed lncRNAs were identified. Among them, CYTOR, MIR4458HG and MAPT-AS1 were significantly associated with the survival of breast cancer. Finally, The RS could predict OS of breast cancer (RS=exp CYTOR * β CYTOR + exp MIR4458HG * β MIR4458HG + exp MAPT-AS1 * β MAPT-AS1). Moreover, it was confirmed that the three-lncRNA signature could be an independent prognostic biomarker for breast cancer (HR=3.040, P=0.000).Conclusions: This study established a three-lncRNA signature, which might be a novel prognostic biomarker for breast cancer.

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 ESR1-Stabilizing Long Noncoding RNA TMPO-AS1 Promotes Hormone-Refractory Breast Cancer Progression

2019 ◽  
Vol 39 (23) ◽  
Author(s):  
Yuichi Mitobe ◽  
Kazuhiro Ikeda ◽  
Takashi Suzuki ◽  
Kiyoshi Takagi ◽  
Hidetaka Kawabata ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Estrogen Receptor ◽  
Cancer Patients ◽  
Endocrine Therapy ◽  
Cancer Progression ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Breast Cancer Patients ◽  
Er Positive ◽  
Positive Breast Cancer

ABSTRACT Acquired endocrine therapy resistance is a significant clinical problem for breast cancer patients. In recent years, increasing attention has been paid to long noncoding RNA (lncRNA) as a critical modulator for cancer progression. Based on RNA-sequencing data of breast invasive carcinomas in The Cancer Genome Atlas database, we identified thymopoietin antisense transcript 1 (TMPO-AS1) as a functional lncRNA that significantly correlates with proliferative biomarkers. TMPO-AS1 positivity analyzed by in situ hybridization significantly correlates with poor prognosis of breast cancer patients. TMPO-AS1 expression was upregulated in endocrine therapy-resistant MCF-7 cells compared with levels in parental cells and was estrogen inducible. Gain and loss of TMPO-AS1 experiments showed that TMPO-AS1 promotes the proliferation and viability of estrogen receptor (ER)-positive breast cancer cells in vitro and in vivo. Global expression analysis using a microarray demonstrated that TMPO-AS1 is closely associated with the estrogen signaling pathway. TMPO-AS1 could positively regulate estrogen receptor 1 (ESR1) mRNA expression by stabilizing ESR1 mRNA through interaction with ESR1 mRNA. Enhanced expression of ESR1 mRNA by TMPO-AS1 could play a critical role in the proliferation of ER-positive breast cancer. Our findings provide a new insight into the understanding of molecular mechanisms underlying hormone-dependent breast cancer progression and endocrine resistance.

scholarly journals Treatment outcomes and its associated factors among breast cancer patients at Kitui Referral Hospital

2022 ◽  
Vol 10 ◽  
pp. 205031212110678
Author(s):  
Mwendwa Dickson Wambua ◽  
Amsalu Degu ◽  
Gobezie T Tegegne
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Confidence Interval ◽  
Treatment Outcomes ◽  
Cancer Patients ◽  
Medical Records ◽  
Cox Regression ◽  
Breast Cancer Patients ◽  
Cox Regression Analysis ◽  
Study Setting

Objectives: Despite breast cancer treatment outcomes being relatively poor or heterogeneous among breast cancer patients, there was a paucity of data in the African settings, especially in Kenya. Hence, this study aimed to determine treatment outcomes among breast cancer patients at Kitui Referral Hospital. Methods: A hospital-based retrospective cohort study design was conducted among adult patients with breast cancer. All eligible breast cancer patients undergoing treatment from January 2015 to June 2020 in the study setting were included. Hence, a total of 116 breast cancer patients’ medical records were involved in the study. Patients’ medical records were retrospectively reviewed using a predesigned data abstraction tool. The data were entered, cleaned, and analyzed using SPSS (Statistical Package for Social Sciences) version 26 software. Descriptive analysis—such as percentage, frequency, mean, and figures—was used to present the data. Kaplan–Meier survival analysis was used to estimate the mean survival estimate across different variables. A Cox regression analysis was employed to determine factors associated with mortality. Results: The study showed that the overall survival and mortality rate was 62.9% (73) and 37.1% (43), respectively. The regression analysis showed that patients who had an advanced stage of disease had a 3.82 times risk of dying (crude hazard ratio= 3.82, 95% confidence interval = 1.5–9.8) than an early stage of the disease. Besides, patients with distant metastasis had 4.4 times more hazards of dying than (crude hazard ratio = 4.4, 95% confidence interval = 2.1–9.4) their counterparts. Conclusion: The treatment outcome of breast cancer patients was poor, and its overall mortality among breast cancer patients was higher in the study setting. In the multivariate Cox regression analysis, the tumor size was the only statistically significant predictor of mortality among breast cancer patients. Stakeholders at each stage should, therefore, prepare a relevant strategy to improve treatment outcomes.

scholarly journals A six-gene-based signature for breast cancer radiotherapy sensitivity prediction

2020 ◽  
Author(s):  
Xing Chen ◽  
Junjie Zheng ◽  
Min ling Zhuo ◽  
Ailong Zhang ◽  
Zhenhui You
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Regression Analysis ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Breast Cancer Radiotherapy

Abstract Background: Breast cancer (BRCA) represents the most common malignancy among women worldwide that with high mortality. Radiotherapy is a prevalent therapeutic for BRCA that with heterogeneous effectiveness among patients. Methods: we proposed to develop a gene expression-based signature for BRCA radiotherapy sensitivity prediction. Gene expression profiles of BRCA samples from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) were obtained and used as training and independent testing dataset, respectively. Differential expression genes (DEGs) in BRCA tumor samples compared with their paracancerous samples in the training set were identified by using edgeR Bioconductor package followed by dimensionality reduction through autoencoder method and univariate Cox regression analysis to screen genes among DEGs that with significant prognosis significance in patients that were previously treated with radiation. LASSO Cox regression method was applied to screen optimal genes for constructing radiotherapy sensitivity prediction signature. Results: 603 DEGs were obtained in BRCA tumor samples, and seven out of which were retained after univariate cox regression analysis. LASSO Cox regression analysis finally remained six genes based on which the radiotherapy sensitivity prediction model was constructed. The signature was proved to be robust in both training and independent testing sets and an independent marker for BRCA radiotherapy sensitivity prediction. Conclusions: this study should be helpful for BRCA patients’ therapeutics selection and clinical decision.

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.

scholarly journals Prognostic factors and survival according to tumour subtype in women presenting with breast cancer bone metastases at initial diagnosis: a SEER-based study

2020 ◽  
Author(s):  
Xiao Li ◽  
Xiaoli Zhang ◽  
Shen Yin Zhong ◽  
Jie Liu
Keyword(s):  
Breast Cancer ◽  
Bone Metastases ◽  
Cancer Patients ◽  
Cox Regression ◽  
Stage Iv ◽  
Cox Proportional Hazards Model ◽  
Significant Prognostic Factor ◽  
Breast Cancer Patients ◽  
Cox Regression Analysis ◽  
Stage Iv Breast Cancer

Abstract Background: Tumour subtype has a significant effect on bone metastasis in breast cancer, but population-based estimates of the prognosis of patients with bone metastases at breast cancer diagnosis are lacking. The aim of this study was to analyse the influence of tumour subtype and other factors on the prognosis and survival of patients with bone metastases of breast cancer.Methods: Using the Surveillance, Epidemiology, and End Results (SEER) Program data from 2012 to 2016, a retrospective cohort study was conducted to investigate stage IV breast cancer patients with bone metastases. Stage IV patient characteristics according to subtype were compared using chi-square tests. Overall survival (OS) and prognostic factors were compared using the Kaplan-Meier method and the Cox proportional hazards model, respectively.Results: A total of 3384 stage IV patients were included in this study; 63.42% were HR+/HER2-, 19.86% were HR+/HER2+, 9.34% were HR-/HER2-, and 7.39% were HR-/HER2+. The median OS for the whole population was 38 months, and 33.9% of the patients were alive at five years. The median OS and five-year survival rate were significantly different among stage IV breast cancer patients with different molecular subtypes (p<0.05). Multivariate Cox regression analysis showed that age of 55-59 (HR=1.270), black race (HR=1.317), grade III or IV (HR=1.960), HR-/HER2- (HR=2.808), lung metastases (HR=1.378), liver metastases (HR=2.085), and brain metastases (HR=1.903) were independent risk factors for prognosis; married status (HR=0.819), HR+/HER2+ (HR=0.631), HR-/HER2+ (HR=0.716), insurance (HR=0.587) and surgery (HR=0.504) were independent protection factors of prognosis. There was an interaction between the HR+/HER2+ subtype and other metastases (except bone metastases, HR=0.694, 95% CI: 0.485-0.992), but the interaction between race and subtype did not reach significance for prognosis.Conclusions: There were substantial differences in OS according to tumour subtype. In addition to tumour subtype, other independent predictors of OS were age at diagnosis, race, marital status, insurance, grade, surgery and visceral metastases. There was an interaction between the HR+/HER2+ subtype and other metastases (except bone metastases) for prognosis. Tumour subtype, as a significant prognostic factor, warrants further investigation.

scholarly journals Identification and Validation of a Five-gene Signature Associated with Overall Survival in Breast Cancer Patients

2020 ◽  
Author(s):  
Xiaolong Wang ◽  
Chen Li ◽  
Tong Chen ◽  
Hanwen Zhang ◽  
Ying Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Individual Treatment ◽  
Breast Cancer Patients ◽  
Internal Validation ◽  
Novel Biomarkers

Abstract Background Recent years, attributed to early detection and new therapies, the mortality rates of breast cancer (BC) decreased. Nevertheless, the global prevalence was still high and the underlying molecular mechanisms were remained largely unknown. The investigation of prognosis-related genes as the novel biomarkers for diagnosis and individual treatment had become an urgent demand for clinical practice. Methods Gene expression profiles and clinical information of breast cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database and randomly divided into training (n = 514) and internal validation (n = 562) cohort by using a random number table. The differentially expressed genes (DEGs) were estimated by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. In the training set, the gene signature was constructed by the least absolute shrinkage and selection operator (LASSO) method based on DEGs screened by R packages. The results were further tested in the internal validation cohort and the entire cohort. Moreover, functions of five genes were explored by MTT, Colony-Formation, scratch and transwell assays. Western blot analysis was used to explore the mechanisms. Results In the training cohort, a total of 2805 protein coding DEGs were acquired through comparing breast cancer tissues (n = 514) with normal tissues (n = 113). A risk score formula involving five novel prognostic associated biomarkers (EDN2, CLEC3B, SV2C, WT1 and MUC2) were then constructed by LASSO. The prognostic value of the risk model was further confirmed in the internal validation set and the entire set. To explore the biological functions of the selected genes, in vitro assays were performed, indicating that these novel biomarkers could markedly influence breast cancer progression. Conclusion We established a predictive five-gene signature, which could be helpful for prognosis assessment and personalized management in breast cancer patients.

Breast Cancer in Mosul: A Survival Analysis

2020 ◽  
pp. 31-36
Keyword(s):  
Breast Cancer ◽  
Survival Analysis ◽  
Cancer Patients ◽  
Cox Regression ◽  
Hazard Ratio ◽  
Breast Cancer Patients ◽  
Cox Regression Analysis ◽  
Radical Removal ◽  
One Year ◽  
The Right

Breast cancer is the most common cancer in women, and the major cause of death. This study was conducted in order to make a descriptive study and survival analysis for breast cancer patients in Mosul. Two hundred forty-six early diagnosed women with breast cancer out of 290 patients were included during the period from March 2007 to February 2012. The average follows up was 36. months (range: 11-67 months). The patients were undergone modified radical removal of the breast, chemotherapy and deep radiation. Patients with estrogen positive were given tamoxifen for five years. Patients with Her2/neu positive were given trastuzumab with docytaxil for one year. Only 25 patients (10.2%) died during the study. The highest incidence of breast cancer (35.8%) was between the ages 51 ≥ 60 years. The presentation of cancer was high (90.1%) in the lumber. Tumor in the right side (66.35%) was significantly higher than the left side. Metastasis was high (25%) and most of them in the liver (19.1%). The percentage of patients with positive estrogen, progesterone, and Her2/neu receptors were not different from negative receptors. Cox regression analysis showed that metastasis had significant effect on death (hazard ratio=2.917). Age 31 ≥ 40 years was the least affected age (hazard ratio=0.034). In conclusion, survival rate of breast cancer patients in Mosul is high due to good management. The early detection of cancer is the best way for survival of the patients, by developing the educational programs.

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