scholarly journals Evaluation of malignancy-risk gene signature in breast cancer patients

2009 ◽  
Vol 120 (1) ◽  
pp. 25-34 ◽  
Author(s):  
Dung-Tsa Chen ◽  
Aejaz Nasir ◽  
Chinnambally Venkataramu ◽  
William Fulp ◽  
Mike Gruidl ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Gene Signature ◽  
Breast Cancer Patients ◽  
Malignancy Risk ◽  
Risk Gene

Integrated immune-related gene signature to predict clinical outcome for patients with luminal B breast cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e12526-e12526
Author(s):  
Xiaying Kuang ◽  
Du Cai ◽  
Ying Lin ◽  
Feng Gao
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Validation Cohort ◽  
Risk Groups ◽  
Gene Signature ◽  
Breast Cancer Patients ◽  
Training Cohort ◽  
Luminal B ◽  
Immune Related Gene ◽  
Immune Related Genes

e12526 Background: Luminal B breast cancer is always routinely treated with chemotherapy and endocrine therapy but heterogeneous with respect to sensitivity to treatment, identification of patients who may most benefit remains a matter of controversy. Immune-related genes (IRGs) was found to be associated with the prognosis of breast cancer. The aim of this study is to evaluate the impact of IRGs in predicting the outcome of luminal B breast cancer patients. Methods: According to the Metabric microarray dataset also as a training cohort, 488 luminal B breast cancer patients were selected for generation of immune-related gene signature (IRGS). Another independent dataset (n=250) of patients with complete prognostic information was analyzed as a validation cohort. Prognostic analysis was assessed to test the predictive value of IRGS. Results: A model of prognostic IRGS containing 12 immune-related genes was developed. In both training and validation cohorts, IRGS significantly stratified luminal B breast cancer patients into immune low- and high-risk groups in terms of disease free survival (DFS, HR=4.95, 95% CI=3.22-7.62, P<0.001 in training cohort, HR=2.47, 95% CI=1.29-4.75, P<0.001 in validation cohort). Multivariate analysis revealed IRGS as an independent prognostic factor (HR=4.96, 95% CI=3.00-8.18, P<0.001 in training cohort, HR=2.56, 95% CI=1.28-5.09, P=0.007 in validation cohort). Furthermore, those 12 genes mostly related with response to chemical, and the expression levels of them were completely opposite in patients of immune low- and high-risk groups. Conclusions: The proposed IRGS is a satisfactory prognostic model for estimating DFS of luminal B breast cancer patients. Further studies are needed to assess the clinical effectiveness of this system in predicting prognosis and treatment options for luminal B breast cancer patients. This work was supported by National Natural Science Foundation of China (No. 81602520), Natural Science Foundation of Guangdong Province (No. 2017A030313596).

Complementary role of Ki67 index and 70-gene signature (MammaPrint) high-risk patients in the St Gallen risk group with uncertain chemotherapy suggestion.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 579-579 ◽  
Author(s):  
Paolo Pronzato ◽  
Giorgio Mustacchi ◽  
Daniele Giulio Generali ◽  
Alberto Bottini
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Risk Group ◽  
Gene Signature ◽  
Endocrine Treatment ◽  
Average Risk ◽  
Intermediate Risk ◽  
Breast Cancer Patients ◽  
Proliferation Markers ◽  
Complementary Role

579 Background: St Gallen (SG) Consensus Panel on adjuvant treatment recommends the use of proliferation markers and multigene assays when choosing the appropriate treatment in addition to traditional parameters. Ki67 predictive/prognostic value is widely accepted; we tested its role in increasing the accuracy of risk prediction among Mammaprint (MP) /High(HR) or /Low Risk (LR) breast cancer patients with a SG risk uncertain for chemotherapy selection. Methods: MP was determined (courtesy of Agendia, Amsterdam) in 3 Italian Hospitals on 305 consecutive samples of breast cancer patients. We focused on SG “Intermediate Risk” Group (HER2 neg, no VI and: ER>10<50%, or G2 or Ki67 >15<30% or N1a or T>2<5cm), where the indication for adjuvant CHT still remains uncertain. In MP/HR cases, a “low” Ki67 value (<15%) was used to eliminate CHT suggestion and viceversa an “intermediate” Ki67 value (>15<30%) in MP/LR cases. Results: Overall, 72/305 pts (26.6%) were in SG intermediate risk group with a median age of 64 years (26-98). Ki67 was non available in 4.1% of cases, MP rejected in 16.6%, HR was detected in 39 (54.2%), whereas LR in 21 (29.2%). Ki67 resulted low in 23 MP/HR cases (59%), intermediate in 6 MP/LR cases (28.5%) and in 3 out 12 MP/Rejected cases (25%). The overall concordance between MP and Ki67 was 28/57 (49.1%), MP rejected excluded. Overall MP suggested 39/60 CHT (65%), Ki67 29/69 (42%). Conclusions: The risk of relapse is a continous variable and MP evaluates it in a dichotomy (low vs high) wich could decrease the accuracy of the test. In the selected SG Group, the average risk of 5 yr relapse is around 20% (EBCTCG, Lancet 2011) and MP HR cases in our experience account for 55%. A low Ki67 value could help avoid more than 20% of cytotoxic treatments suggested by MP. In the same way, according to an ER value <50% (2 cases), an intermediate Ki67 value could suggest a more aggressive treatment in a further 13% of cases MP LR (6/21) or Rejected (3/12). MP probably overestimates the risk in 20% of cases and underestimates it in further 20%. Ki67 could be usefull for a more personalized treatment in patients where the indication for adjuvant chemotherapy added to endocrine treatment is uncertain.

A novel IFNγ-stimulated gene signature is predictive of the survival and chemotherapy responsiveness of breast cancer patients

2014 ◽  
Vol 47 (12) ◽  
pp. 1145
Author(s):  
Mohamed Abou El Hassan ◽  
Katherine Huang ◽  
Jeffrey Liu ◽  
Tao Yu ◽  
Eldad Zacksenhaus ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Gene Signature ◽  
Breast Cancer Patients

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.

scholarly journals Identification of the prognostic value of ferroptosis-related gene signature in breast cancer patients

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ding Wang ◽  
Guodong Wei ◽  
Ju Ma ◽  
Shuai Cheng ◽  
Longyuan Jia ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
High Risk ◽  
Cancer Patients ◽  
Roc Curves ◽  
Risk Groups ◽  
Gene Signature ◽  
Breast Cancer Patients ◽  
Prognostic Signature ◽  
Kaplan Meier

Abstract Background Breast cancer (BRCA) is a malignant tumor with high morbidity and mortality, which is a threat to women’s health worldwide. Ferroptosis is closely related to the occurrence and development of breast cancer. Here, we aimed to establish a ferroptosis-related prognostic gene signature for predicting patients’ survival. Methods Gene expression profile and corresponding clinical information of patients from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. The Least absolute shrinkage and selection operator (LASSO)-penalized Cox regression analysis model was utilized to construct a multigene signature. The Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes, Genomes (KEGG) pathway and single-sample gene set enrichment analysis (ssGSEA) were performed for patients between the high-risk and low-risk groups divided by the median value of risk score. Results We constructed a prognostic signature consisted of nine ferroptosis-related genes (ALOX15, CISD1, CS, GCLC, GPX4, SLC7A11, EMC2, G6PD and ACSF2). The Kaplan-Meier curves validated the fine predictive accuracy of the prognostic signature (p < 0.001). The area under the curve (AUC) of the ROC curves manifested that the ferroptosis-related signature had moderate predictive power. GO and KEGG functional analysis revealed that immune-related responses were largely enriched, and immune cells, including activated dendritic cells (aDCs), dendritic cells (DCs), T-helper 1 (Th1), were higher in high-risk groups (p < 0.001). Oppositely, type I IFN response and type II IFN response were lower in high-risk groups (p < 0.001). Conclusion Our study indicated that the ferroptosis-related prognostic signature gene could serve as a novel biomarker for predicting breast cancer patients’ prognosis. Furthermore, we found that immunotherapy might play a vital role in therapeutic schedule based on the level and difference of immune-related cells and pathways in different risk groups for breast cancer patients.

scholarly journals An 11-Gene Signature Based on Treatment Responsiveness Predicts Radiation Therapy Survival Benefit Among Breast Cancer Patients

2022 ◽  
Vol 11 ◽  
Author(s):  
Junjie Shen ◽  
Derui Yan ◽  
Lu Bai ◽  
Ruirui Geng ◽  
Xulun Zhao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Radiation Therapy ◽  
Cancer Patients ◽  
Survival Benefit ◽  
Gene Signature ◽  
Breast Cancer Patients ◽  
Survival Prognosis ◽  
Resistant Group ◽  
Radio Sensitivity ◽  
Treatment Responsiveness

PurposeWe developed a strategy of building prognosis gene signature based on clinical treatment responsiveness to predict radiotherapy survival benefit in breast cancer patients.Methods and MaterialsAnalyzed data came from the public database. PFS was used as an indicator of clinical treatment responsiveness. WGCNA was used to identify the most relevant modules to radiotherapy response. Based on the module genes, Cox regression model was used to build survival prognosis signature to distinguish the benefit group of radiotherapy. An external validation was also performed.ResultsIn the developed dataset, MEbrown module with 534 genes was identified by WGCNA, which was most correlated to the radiotherapy response of patients. A number of 11 hub genes were selected to build the survival prognosis signature. Patients that were divided into radio-sensitivity group and radio-resistant group based on the signature risk score had varied survival benefit. In developed dataset, the 3-, 5-, and 10-year AUC of the signature were 0.814 (CI95%: 0.742–0.905), 0.781 (CI95%: 0.682–0.880), and 0.762 (CI95%: 0.626–0.897), respectively. In validation dataset, the 3- and 5-year AUC of the signature were 0.706 (CI95%: 0.523–0.889) and 0.743 (CI95%: 0.595–0.891). The signature had higher predictive power than clinical factors alone and had more clinical prognosis efficiency. Functional enrichment analysis revealed that the identified genes were mainly enriched in immune-related processes. Further immune estimated analysis showed the difference in distribution of immune micro-environment between radio-sensitivity group and radio-resistant group.ConclusionsThe 11-gene signature may reflect differences in tumor immune micro-environment that underlie the differential response to radiation therapy and could guide clinical-decision making related to radiation in breast cancer patients.

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