scholarly journals Identification and Validation of Immune-Related Methylation Clusters for Predicting Immune Activity and Prognosis in Breast Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Anli Yang ◽  
Ying Zhou ◽  
Yanan Kong ◽  
Xiaoli Wei ◽  
Feng Ye ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
High Risk ◽  
Immune Cells ◽  
Immune Modulation ◽  
Risk Group ◽  
External Validation ◽  
High Risk Group ◽  
Breast Cancer Patients ◽  
Potential Marker

The role of DNA methylation of breast cancer-infiltrating immune cells has not been fully explored. We conducted a cohort-based retrospective study analyzing the genome-wide immune-related DNA methylation of 1057 breast cancer patients from the TCGA cohort and GSE72308 cohort. Based on patients’ overall survival (OS), a prognostic risk score system using 18 immune-related methylation genes (IRMGs) was established and further validated in an independent cohort. Kaplan–Meier analysis showed a clear separation of OS between the low- and high-risk groups. Patients in the low-risk group had a higher immune score and stromal score compared with the high-risk group. Moreover, the characteristics based on 18-IRMGs signature were related to the tumor immune microenvironment and affected the abundance of tumor-infiltrating immune cells. Consistently, the 18-IRMGs signatures showed similar influences on immune modulation and survival in another external validation cohort (GSE72308). In conclusion, the proposed 18-IRMGs signature could be a potential marker for breast cancer prognostication.

scholarly journals Comprehensive multi-omics analysis identifies an immune related predicting model in early stage breast cancer

2020 ◽  
Author(s):  
Xuan Li ◽  
Wei Jin
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Immune Cells ◽  
Risk Group ◽  
Early Stage ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
High Risk Group ◽  
Early Stage Breast Cancer ◽  
Breast Cancer Patients

Abstract Background Breast cancer is known the highest incidence cancer in women. Tumor-infiltrating immune cells were reported closely related to cancers’ fate but it’s function still not very clear in breast cancer. The aim of our study is to build a infiltrating immune cells based nomogram model to better predict patients survival and explore its relationship with immune features. Methods We first use CIBERSORT to analyze 22 immune cells’ status in two unrelated breast cancer cohorts (TCGA and METRABRIC). The univariate and multivariate Cox analyses were used to analyze the prognostic ability of immune cells and other clinicopathological factors. Nomogram model were built by using independent prognostic factors. Different immune signatures and gene enrichment analysis were performed in different nomogram risk groups. Results Multivariate cox analysis showed that Macrophages M2 with HR of 1.733 (95% CI: 1.013-2.966) in TCGA cohort and 1.334 (95% CI: 1.125-1.581) in METABRIC cohort is the only independent prognostic factor among the 22 immune cells in early stage breast cancer. Macrophages M2, age, TNM stage and molecular types were used to build the nomogram model. The AUC of the ROC of nomogram reached 0.732 in TCGA cohort and 0.702 in METABRIC cohort. Using the nomogram score can better classified patients to low and high risk group. High risk group showed higher malignant signatures and predicted immunotherapy response rates than low risk group which consistent with the gene entrenchment analysis. Conclusion In this study, we revealed that M2 macrophages could predict the OS of breast cancer patients. Based on M2 and other clinical features we established a nomogram model which were significantly different in immune features that can better assess the OS risk or be a predictor for the immunotherapy response in breast cancer for further research.

Clinical impact of LncRNA XIST and LncRNA NEAT1 for diagnosis of high-risk group breast cancer patients

2021 ◽  
pp. 100709
Author(s):  
Menha Swellam ◽  
Hekmat M EL Magdoub ◽  
May A Shawki ◽  
Marwa Adel ◽  
Mona M Hefny ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Group ◽  
High Risk Group ◽  
Clinical Impact ◽  
Breast Cancer Patients ◽  
Lncrna Xist ◽  
Lncrna Neat1

Adjuvant Chemotherapy Guidance for pT1-3N0-1 Breast Cancer Patients with HR+, HER2- subtype: a study based on SEER database

2021 ◽  
Author(s):  
juanjuan Qiu ◽  
Li Xu ◽  
Yu Wang ◽  
Jia Zhang ◽  
Jiqiao Yang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prognostic Factors ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Rank Test ◽  
Breast Cancer Patients ◽  
Group Score

Abstract Background Although the results of gene testing can guide early breast cancer patients with HR+, HER2- to decide whether they need chemotherapy, there are still many patients worldwide whose problems cannot be solved well by genetic testing. Methods 144 735 patients with HR+, HER2-, pT1-3N0-1 breast cancer from the Surveillance, Epidemiology, and End Results database were included from 2010 to 2015. They were divided into chemotherapy (n = 38 392) and no chemotherapy (n = 106 343) group, and after propensity score matching, 23 297 pairs of patients were left. Overall survival (OS) and breast cancer-specific survival (BCSS) were tested by Kaplan–Meier plot and log-rank test and Cox proportional hazards regression model was used to identify independent prognostic factors. A nomogram was constructed and validated by C-index and calibrate curves. Patients were divided into high- or low-risk group according to their nomogram score using X-tile. Results Patients receiving chemotherapy had better OS before and after matching (p < 0.05) but BCSS was not significantly different between patients with and without chemotherapy after matching: hazard ratio (HR) 1.005 (95%CI 0.897, 1.126). Independent prognostic factors were included to construct the nomogram to predict BCSS of patients without chemotherapy. Patients in the high-risk group (score > 238) can get better OS HR 0.583 (0.507, 0.671) and BCSS HR 0.791 (0.663, 0.944) from chemotherapy but the low-risk group (score ≤ 238) cannot. Conclusion The well-validated nomogram and a risk stratification model was built. Patients in the high-risk group should receive chemotherapy while patients in low-risk group may be exempt from chemotherapy.

scholarly journals Obesity and menopause modify the epigenomic profile of breast cancer

2017 ◽  
pp. 351-363 ◽  
Author(s):  
Ana B Crujeiras ◽  
Angel Diaz-Lagares ◽  
Olafur A Stefansson ◽  
Manuel Macias-Gonzalez ◽  
Juan Sandoval ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
High Risk ◽  
Risk Group ◽  
Menopausal Status ◽  
Breast Tumors ◽  
Methylation Pattern ◽  
High Risk Group ◽  
Cpg Sites ◽  
The Impact

Obesity is a high risk factor for breast cancer. This relationship could be marked by a specific methylome. The current work was aimed to explore the impact of obesity and menopausal status on variation in breast cancer methylomes. Data from Infinium 450K array-based methylomes of 64 breast tumors were coupled with information on BMI and menopausal status. Additionally, DNA methylation results were validated in 18 non-tumor and 81 tumor breast samples. Breast tumors arising in either pre- or postmenopausal women stratified by BMI or menopausal status alone were not associated with a specific DNA methylation pattern. Intriguingly, the DNA methylation pattern identified in association with the high-risk group (postmenopausal women with high BMI (>25) and premenopausal women with normal or low BMI < 25) exclusively characterized by hypermethylation of 1287 CpG sites as compared with the low-risk group. These CpG sites included the promoter region of fourteen protein-coding genes of which CpG methylation over the ZNF577 promoter region represents the top scoring associated event. In an independent cohort, the ZNF577 promoter methylation remained statistically significant in association with the high-risk group. Additionally, the impact of ZNF577 promoter methylation on mRNA expression levels was demonstrated in breast cancer cell lines after treatment with a demethylating agent (5-azacytidine). In conclusion, the epigenome of breast tumors is affected by a complex interaction between BMI and menopausal status. The ZNF577 methylation quantification is clearly relevant for the development of novel biomarkers of precision therapy in breast cancer.

scholarly journals A Ferroptosis-Related lncRNAs Signature Predicts Prognosis and Immune Microenvironment for Breast Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Kaiming Zhang ◽  
Liqin Ping ◽  
Tian Du ◽  
Gehao Liang ◽  
Yun Huang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Prognostic Value ◽  
Cox Regression ◽  
Validation Cohort ◽  
Risk Group ◽  
High Risk Group ◽  
Breast Cancer Patients ◽  
Immune Microenvironment ◽  
Cox Regression Analysis

Background: Ferroptosis, a regulated cell death which is driven by the iron-dependent peroxidation of lipids, plays an important role in cancer. However, studies about ferroptosis-related Long non-coding RNAs (lncRNAs) in breast cancer (BC) are limited. Besides, the prognostic role of ferroptosis-related lncRNAs and their relationship to immune microenvironment in breast cancer remain unclear. This study aimed to explore the potential prognostic value of ferroptosis-related lncRNAs and their relationship to immune microenvironment in breast cancer.Methods: RNA-sequencing data of female breast cancer patients were downloaded from TCGA database. 937 patients were randomly separated into training or validation cohort in 2:1 ratio. Ferroptosis-related lncRNAs were screened by Pearson correlation analysis with 239 reported ferroptosis-related genes. A ferroptosis-related lncRNAs signature was constructed with univariate and multivariate Cox regression analyses in the training cohort, and its prognostic value was further tested in the validation cohort.Results: An 8-ferroptosis-related-lncRNAs signature was developed by multivariate Cox regression analysis to divide patients into two risk groups. Patients in the high-risk group had worse prognosis than patients in the low-risk group. Multivariate Cox regression analysis showed the risk score was an independent prognostic indicator. Receiver operating characteristic curve (ROC) analysis proved the predictive accuracy of the signature. The area under time-dependent ROC curve (AUC) reached 0.853 at 1 year, 0.802 at 2 years, 0.740 at 5 years in the training cohort and 0.791 at 1 year, 0.778 at 2 years, 0.722 at 5 years in the validation cohort. Further analysis demonstrated that immune-related pathways were significantly enriched in the high-risk group. Analysis of the immune cell infiltration landscape showed that breast cancer in the high-risk group tended be immunologically “cold”.Conclusion: We identified a novel ferroptosis-related lncRNA signature which could precisely predict the prognosis of breast cancer patients. Ferroptosis-related lncRNAs may have a potential role in the process of anti-tumor immunity and serve as therapeutic targets for breast cancer.

scholarly journals Application of a 70-Gene Expression Profile to Japanese Breast Cancer Patients

Breast Care ◽  
2015 ◽  
Vol 10 (2) ◽  
pp. 118-122 ◽  
Author(s):  
Hideo Shimizu ◽  
Yoshiya Horimoto ◽  
Atsushi Arakawa ◽  
Hiroshi Sonoue ◽  
Mami Kurata ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
High Risk ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Risk Group ◽  
Japanese Patients ◽  
High Risk Group ◽  
Breast Cancer Patients ◽  
Luminal A

Background: As data on using MammaPrint®, a 70-gene expression profile for molecular subtyping of breast cancer, are limited in Japanese patients, we aimed to determine the gene profiles of Japanese patients using MammaPrint and to investigate its possible clinical application for selecting adjuvant treatments. Patients and Methods: 50 women treated surgically at our institution were examined. The MammaPrint results were compared with the St Gallen 2007 and intrinsic subtype risk categorizations. Results: Of 38 cases judged to be at intermediate risk based on the St Gallen 2007 Consensus, 11 (29%) were in the high-risk group based on MammaPrint. 1 of the 30 luminal A-like tumors (3%) was judged as high risk based on MammaPrint results, whereas 7 of the 20 tumors (35%) categorized as luminal B-like or triple negative were in the low-risk group. There have been no recurrences to date in the MammaPrint group, and this is possibly attributable to most of the high-risk patients receiving chemotherapy that had been recommended on the basis of their MammaPrint results. Conclusions: Our results indicate that MammaPrint is applicable to Japanese patients and that it is of potential value in current clinical practice for devising individualized treatments.

scholarly journals Detecting prognostic biomarkers of breast cancer by regularized Cox proportional hazards models

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Lingyu Li ◽  
Zhi-Ping Liu
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Proportional Hazards ◽  
Risk Group ◽  
External Validation ◽  
Cox Proportional Hazards ◽  
High Risk Group ◽  
Low Risk ◽  
Functional Enrichment ◽  
Prognostic Biomarkers

Abstract Background The successful identification of breast cancer (BRCA) prognostic biomarkers is essential for the strategic interference of BRCA patients. Recently, various methods have been proposed for exploring a small prognostic gene set that can distinguish the high-risk group from the low-risk group. Methods Regularized Cox proportional hazards (RCPH) models were proposed to discover prognostic biomarkers of BRCA from gene expression data. Firstly, the maximum connected network with 1142 genes by mapping 956 differentially expressed genes (DEGs) and 677 previously BRCA-related genes into the gene regulatory network (GRN) was constructed. Then, the 72 union genes of the four feature gene sets identified by Lasso-RCPH, Enet-RCPH, $$L_{0}$$ L 0 -RCPH and SCAD-RCPH models were recognized as the robust prognostic biomarkers. These biomarkers were validated by literature checks, BRCA-specific GRN and functional enrichment analysis. Finally, an index of prognostic risk score (PRS) for BRCA was established based on univariate and multivariate Cox regression analysis. Survival analysis was performed to investigate the PRS on 1080 BRCA patients from the internal validation. Particularly, the nomogram was constructed to express the relationship between PRS and other clinical information on the discovery dataset. The PRS was also verified on 1848 BRCA patients of ten external validation datasets or collected cohorts. Results The nomogram highlighted that the importance of PRS in guiding significance for the prognosis of BRCA patients. In addition, the PRS of 301 normal samples and 306 tumor samples from five independent datasets showed that it is significantly higher in tumors than in normal tissues ($$P<0.05$$ P < 0.05 ). The protein expression profiles of the three genes, i.e., ADRB1, SAV1 and TSPAN14, involved in the PRS model demonstrated that the latter two genes are more strongly stained in tumor specimens. More importantly, external validation illustrated that the high-risk group has worse survival than the low-risk group ($$P<0.05$$ P < 0.05 ) in both internal and external validations. Conclusions The proposed pipelines of detecting and validating prognostic biomarker genes for BRCA are effective and efficient. Moreover, the proposed PRS is very promising as an important indicator for judging the prognosis of BRCA patients.

A prognostic 10-miRNA risk score (10-miRNA RS) in predicting neoadjuvant chemotherapy sensitivity of luminal breast cancer.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 3139-3139
Author(s):  
Chang Gong ◽  
Luyuan Tan ◽  
Na You ◽  
Kai Chen ◽  
Weige Tan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
High Risk ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Luminal Breast Cancer ◽  
Breast Cancer Patients ◽  
Luminal B ◽  
Risk Patients

3139 Background: The 10-miRNA risk score is a prognostic 10-gene expression signature specifically developed in luminal breast cancer associated with relapse-free survival. Since high-risk patients identified by10-miRNA RS had worse prognosis but better outcome with chemotherapy than low-risk patients (Gong C et al, EBioMedicine. 2016), this model may facilitate personalized therapy-decision making for luminal breast cancer patients. Therefore, we seek to validate whether high-risk group are more sensitive to chemotherapy than low-risk group by assessing the predictive value of 10-miRNA RS for pathological complete response (pCR) in patients receiving neoadjuvant chemotherapy (NAC). Methods: The 10-miRNA gene expression and clinicopathological data were prospectively gathered from 251 pretreated biopsy-diagnosed luminal breast cancer patients from 4 breast cancer centers. Formalin-fixed paraffin-embedded tissues from basal line biopsy were used for the detection of 10-miRNA expression to calculate the RS. The correlation between pCR and the 10-miRNA RS classification were identified. Results: In this prospective, multicenter study, the overall pCR rate was 13.6% (34/251). The 10-miRNA RS of the pCR group was significantly higher than the non-pCR group ( P = 0.015). Fifty-one percent of patients were classified as low-risk according to the 10-miRNA RS classification and 49% as high-risk with a RS cut-off point of 2.144. The 10-miRNA RS classification was associated with a pCR rate of 9.4% in the low-risk group and 17.8% in the high-risk group ( P = 0.041). The correlation between the pCR and the 10-miRNA RS classification was significant in subgroup analysis stratified by molecular subtypes (8% vs. 13.2% in luminal B1; 14.7% vs. 30.1% in luminal B2; no pCR was observed in all 13 luminal A subtype). In multivariate analysis, the 10-miRNA RS remained significantly associated with pCR and independent from subtype, ki67 and other clinicopathological characteristics. Conclusions: 10-miRNA RS clearly defined that high-risk patients are more sensitive to chemotherapy which leads to a higher pCR rate in NAC patients. Thus, 10-miRNA RS is not only a prognostic factor but an effective method in determining whether a patient would undergo surgery or receive NAC prior to surgery. Clinical trial information: ChiCTR-DDD-17013651.

scholarly journals Integrated Multiparametric Radiomics and Informatics System for Characterizing Breast Tumor Characteristics with the OncotypeDX Gene Assay

Cancers ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2772
Author(s):  
Michael A. Jacobs ◽  
Christopher B. Umbricht ◽  
Vishwa S. Parekh ◽  
Riham H. El Khouli ◽  
Leslie Cope ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Risk Group ◽  
Area Under The Curve ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Intermediate Risk ◽  
Breast Cancer Patients ◽  
Gene Assay

Optimal use of multiparametric magnetic resonance imaging (mpMRI) can identify key MRI parameters and provide unique tissue signatures defining phenotypes of breast cancer. We have developed and implemented a new machine-learning informatic system, termed Informatics Radiomics Integration System (IRIS) that integrates clinical variables, derived from imaging and electronic medical health records (EHR) with multiparametric radiomics (mpRad) for identifying potential risk of local or systemic recurrence in breast cancer patients. We tested the model in patients (n = 80) who had Estrogen Receptor positive disease and underwent OncotypeDX gene testing, radiomic analysis, and breast mpMRI. The IRIS method was trained using the mpMRI, clinical, pathologic, and radiomic descriptors for prediction of the OncotypeDX risk score. The trained mpRad IRIS model had a 95% and specificity was 83% with an Area Under the Curve (AUC) of 0.89 for classifying low risk patients from the intermediate and high-risk groups. The lesion size was larger for the high-risk group (2.9 ± 1.7 mm) and lower for both low risk (1.9 ± 1.3 mm) and intermediate risk (1.7 ± 1.4 mm) groups. The lesion apparent diffusion coefficient (ADC) map values for high- and intermediate-risk groups were significantly (p < 0.05) lower than the low-risk group (1.14 vs. 1.49 × 10−3 mm2/s). These initial studies provide deeper insight into the clinical, pathological, quantitative imaging, and radiomic features, and provide the foundation to relate these features to the assessment of treatment response for improved personalized medicine.

scholarly journals Identification and Validation of an Autophagy-Related lncRNA Signature for Patients With Breast Cancer

2021 ◽  
Vol 10 ◽  
Author(s):  
Ruyue Zhang ◽  
Qingwen Zhu ◽  
Detao Yin ◽  
Zhe Yang ◽  
Jinxiu Guo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Cancer Patients ◽  
Roc Curve ◽  
Prognostic Model ◽  
Risk Group ◽  
High Risk Group ◽  
Potential Functions ◽  
Low Risk ◽  
Breast Cancer Patients

BackgroundAutophagy is a “self-feeding” phenomenon of cells, which is crucial in mammalian development. Long non-coding RNA (lncRNA) is a new regulatory factor for cell autophagy, which can regulate the process of autophagy to affect tumor progression. However, poor attention has been paid to the roles of autophagy-related lncRNAs in breast cancer.ObjectiveThis study aimed to construct an autophagy-related lncRNA signature that can effectively predict the prognosis of breast cancer patients and explore the potential functions of these lncRNAs.MethodsThe RNA sequencing (RNA-Seq) data of breast cancer patients was collected from The Cancer Genome Atlas (TCGA) database and the GSE20685 database. Multivariate Cox analysis was implemented to produce an autophagy-related lncRNA signature in the TCGA cohort. The signature was then validated in the GSE20685 cohort. The receiver operator characteristic (ROC) curve was performed to evaluate the predictive ability of the signature. Gene set enrichment analysis (GSEA) was used to explore the potential functions based on the signature. Finally, the study developed a nomogram and internal verification based on the autophagy-related lncRNAs.ResultsA signature composed of 9 autophagy-related lncRNAs was determined as a prognostic model, and 1,109 breast cancer patients were divided into high-risk group and low-risk group based on median risk score of the signature. Further analysis demonstrated that the over survival (OS) of breast cancer patients in the high-risk group was poorer than that in the low-risk group based on the prognostic signature. The area under the curve (AUC) of ROC curve verified the sensitivity and specificity of this signature. Additionally, we confirmed the signature is an independent factor and found it may be correlated to the progression of breast cancer. GSEA showed gene sets were notably enriched in carcinogenic activation pathways and autophagy-related pathways. The qRT-PCR identified 5 lncRNAs with significantly differential expression in breast cancer cells based on the 9 lncRNAs of the prognostic model, and the results were consistent with the tissues.ConclusionIn summary, our signature has potential predictive value in the prognosis of breast cancer and these autophagy-related lncRNAs may play significant roles in the diagnosis and treatment of breast cancer.

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