scholarly journals ISE: An Algorithm to Screen out the high-risk Group of Breast Cancer

2017 ◽  
Author(s):  
Fei Chang ◽  
Rui Wang
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Risk Group ◽  
High Risk Group

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 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 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.

scholarly journals Risk-Adapted Postmastectomy Radiotherapy Decision Based on Prognostic Nomogram for pT1-2N1M0 Breast Cancer: A Multicenter Study

2020 ◽  
Vol 10 ◽  
Author(s):  
Ming Li ◽  
Jinbo Yue ◽  
Xiangbo Wan ◽  
Bin Hua ◽  
Qiuan Yang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Locoregional Recurrence ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Moderate Risk ◽  
Primary Tumor Site ◽  
Free Survival ◽  
Significant Difference

PurposeThe aim of this study was to develop a widely accepted prognostic nomogram and establish a risk-adapted PMRT strategy based on locoregional recurrence for pT1-2N1M0 breast cancer.Methods and MaterialsA total of 3,033 patients with pT1-2N1M0 breast cancer treated at 6 participating institutions between 2000 and 2016 were retrospectively reviewed. A nomogram was developed to predicted locoregional recurrence-free survival (LRFS). A propensity score-matched (PSM) analyses was performed in risk-adapted model.ResultsWith the median follow-up of 65.0 months, the 5-year overall survival (OS), disease free survival (DFS) and LRFS were 93.0, 84.8, and 93.6%, respectively. There was no significant difference between patients who received PMRT or not for the entire group. A nomogram was developed and validated to estimate the probability of 5-year LRFS based on five independent factors including age, primary tumor site, positive lymph nodes number, pathological T stage, and molecular subtype that were selected by a multivariate analysis of patients who did not receive PMRT in the primary cohort. According to the total nomogram risk scores, the entire patients were classified into low- (40.0%), moderate- (42.4%), and high-risk group (17.6%). The 5-year outcomes were significantly different among these three groups (P&lt;0.001). In low-risk group, patients who received PMRT or not both achieved a favorable OS, DFS, and LRFS. In moderate-risk group, no differences in OS, DFS, and LRFS were observed between PMRT and no PMRT patients. In high-risk group, compared with no PMRT, PMRT resulted in significantly different OS (86.8 vs 83.9%, P = 0.050), DFS (77.2 vs 70.9%, P = 0.049), and LRFS (90.8 vs. 81.6%, P = 0.003). After PSM adjustment, there were no significant differences in OS, DFS, and LRFS in low-risk and moderate-risk groups. However, in the high-risk group, PMRT still resulted in significantly better OS, DFS and improved LRFS.ConclusionsThe proposed nomogram provides an individualized risk estimate of LRFS in patients with pT1-2N1M0 breast cancer. Risk-adapted PMRT for high-risk patients is a viable effective strategy.

Brain metastases from breast cancer: identification of a high-risk group

2004 ◽  
Vol 16 (5) ◽  
pp. 345-349 ◽  
Author(s):  
A.J Evans ◽  
J.J James ◽  
E.J Cornford ◽  
S.Y Chan ◽  
H.C Burrell ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Brain Metastases ◽  
Risk Group ◽  
High Risk Group
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