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 A glycolysis-related gene expression signature in predicting recurrence of breast cancer

2020 ◽  
Author(s):  
Jianing Tang ◽  
Gaosong Wu
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Group ◽  
Gene Signature ◽  
Low Risk ◽  
Individual Treatment ◽  
Breast Cancer Patients ◽  
Related Gene

Abstract Background Metabolic change is the hallmark of cancer. Even in the presence of oxygen, cancer cells reprogram their glucose metabolism to enhance glycolysis and reduce oxidative phosphorylation. In the present study, we aimed to develop a glycolysis-related gene signature to predict the prognosis of breast cancer patients.Methods Gene expression profiles and clinical data of breast cancer patients were obtained from the GEO database. Univariate, Lasso-penalized, and multivariate Cox analysis were performed to construct the glycolysis-related gene signature.Results A four-gene based signature (ALDH2, PRKACB, STMN1 and ZNF292) was developed to separate patients into high-risk and low-risk groups. Kaplan-Meier survival analysis demonstrated that patients in low-risk group had significantly better prognosis than those in the high-risk group. Time-dependent ROC analysis demonstrated that the glycolysis-related gene signature had excellent prognostic accuracy. We further confirmed the expression of the four prognostic genes in breast cancer and paracancerous tissues samples using qRT-PCR analysis. Expression level of PRKACB was higher in paracancerous tissues, while STMN1 and ZNF292 were overexpressed in tumor samples. No difference was found in ALDH2 expression. The same results were observed in the IHC data from the human protein atlas. Global proteome data of 105 TCGA breast cancer samples obtained from the Clinical Proteomic Tumor Analysis Consortium were used to evaluate the prognostic value of their protein levels. Consistently, high expression of PRKACB protein level was associated with better prognosis, while high ZNF292 and STMN1 protein expression levels indicated poor prognosis.Conclusions The glycolysis-related gene signature might provide an effective prognostic predictor and a new view for individual treatment of breast cancer patients.

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).

scholarly journals Expression and Prognostic Characteristics of m6 A RNA Methylation Regulators in Breast Cancer

2020 ◽  
Vol 11 ◽  
Author(s):  
Bo Zhang ◽  
Yanlin Gu ◽  
Guoqin Jiang
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Patients ◽  
High Expression ◽  
Cancer Gene ◽  
Breast Cancer Patients ◽  
Rna Methylation ◽  
Kaplan Meier ◽  
Her 2 ◽  
Kaplan Meier Plotter

PurposeN6-methyladenosine (m6A) is the most prevalent modification in mRNA methylation which has a wide effect on biological functions. This study aims to figure out the efficacy of m6A RNA methylation regulator-based biomarkers with prognostic significance in breast cancer.Patients and MethodsThe 23 RNA methylation regulators were firstly analyzed through ONCOMINE, then relative RNA-seq transcriptome and clinical data of 1,096 breast cancer samples and 112 normal tissue samples were acquired from The Cancer Gene Atlas (TCGA) database. The expressive distinction was also showed by the Gene Expression Omnibus (GEO) database. The gene expression data of m6A RNA regulators in human tissues were acquired from the Genotype-Tissue Expression (GTEx) database. The R v3.5.1 and other online tools such as STRING, bc-GeneExminer v4.5, Kaplan-Meier Plotter were applied for bioinformatics analysis.ResultsResults from ONCOMINE, TCGA, and GEO databases showed distinctive expression and clinical correlations of m6A RNA methylation regulators in breast cancer patients. The high expression of YTHDF3, ZC3H13, LRPPRC, and METTL16 indicated poor survival rate in patients with breast cancer, while high expression of RBM15B pointed to a better survival rate. Both univariate and multivariate Cox regression analyses revealed that age and risk scores were related to overall survival (OS). Univariate analysis also delineated that stage, tumor (T) status, lymph node (N) status, and metastasis (M) status were associated with OS. From another perspective, Kaplan-Meier Plotter platform showed that the relatively high expression of YTHDF3 and LRPPRC and the relatively low expression of RBM15B, ZC3H13, and METTL16 in breast cancer patients had worse Relapse-Free Survival (RFS). Breast Cancer Gene-Expression Miner v4.5 showed that LRPPRC level was negatively associated with ER and PR expression, while METTL16, RBM15B, ZC3H13 level was positively linked with ER and PR expression. In HER-2 (+) breast cancer patients, the expression of LRPPRC, METTL16, RBM15B, and ZC3H13 were all lower than the HER-2 (−) group.ConclusionThe significant difference in expression levels and prognostic value of m6A RNA methylation regulators were analyzed and validated in this study. This signature revealed the potential therapeutic value of m6A RNA methylation regulators in breast cancer.

Impact of the EndoPredict-clin score on risk stratification in ER-positive, HER2-negative breast cancer after considering clinical guidelines.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 542-542
Author(s):  
Martin Filipits ◽  
Peter Christian Dubsky ◽  
Margaretha Rudas ◽  
Jan C. Brase ◽  
Ralf Kronenwett ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Cancer Patients ◽  
Endocrine Therapy ◽  
Clinical Guidelines ◽  
Risk Groups ◽  
Low Risk ◽  
Breast Cancer Patients ◽  
Free Survival ◽  
Er Positive

542 Background: Many ER-positive, HER2-negative breast cancer patients are treated by adjuvant chemotherapy according to current clinical guidelines. We retrospectively assessed whether the combined gene expression/ clinicopathological EndoPredict-clin (EPclin) score improved the accuracy of risk classification in addition to considering clinical guidelines. Methods: Three clinical breast cancer guidelines (National Comprehensive Cancer Center Network (NCCN), German S3 and St. Gallen 2011), and the EPclin score - assessed by quantitative RT-PCR in formalin-fixed paraffin-embedded tissue - were used to assign risk groups in 1,702 ER-positive, HER2-negative breast cancer patients from two randomized phase III trials (Austrian Breast and Colorectal Cancer Study Group 6 and 8) treated with endocrine therapy only. Results: Although all analyzed clinical guidelines identified a low-risk group with improved metastasis-free survival, the overwhelming majority of all patients (81-94%) were classified as intermediate / high risk. In contrast to that, the EPclin classified only 37% of all patients as high risk and that stratification resulted in the best separation between low and high risk groups (p < 0.001, HR = 5.11 (3.48-7.51). Consequently, the majority of all patients deemed intermediate / high risk by the clinical guidelines was re-classified as low risk by the EPclin score. Kaplan Meier analyses demonstrated that the re-classified subgroups (47 to 57% of all patients) had an excellent 10-year metastasis-free survival of 95% comparable to the clinical assigned low-risk groups although encompassing a higher proportion of the trial patients. Conclusions: The EPclin score predicted distant recurrence more accurately than all three clinical guidelines and is especially useful to reclassify patients considered as intermediate / high risk by the guidelines. The data suggests that the EPclin score provides clinically useful prognostic information beyond common clinical guidelines and can be used to accurately identify the clinically relevant group of patients who are adequately and sufficiently treated with adjuvant endocrine therapy alone.

Comparative performance of Breast Cancer Index (BCIN+) and CTS5 in hormone receptor-positive (HR+) lymph node-positive (N+) breast cancer patients with one to three positive nodes (N1).

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 555-555
Author(s):  
Dennis Sgroi ◽  
Yi Zhang ◽  
Catherine A. Schnabel
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Cancer Patients ◽  
Endocrine Therapy ◽  
Tumor Size ◽  
Risk Groups ◽  
Cox Proportional Hazards ◽  
Low Risk ◽  
Intermediate Risk ◽  
Breast Cancer Patients

555 Background: Identification of N+ breast cancer patients with a limited risk of recurrence improves selection of those for which chemotherapy and/or extended endocrine therapy (EET) may be most appropriate to reduce overtreatment. BCIN+ integrates gene expression with tumor size and grade, and is highly prognostic for overall (0-10yr) and late (5-10yr) distant recurrence (DR) in N1 patients. Clinical Treatment Score post-5-years (CTS5) is a prognostic model based on clinicopathological factors (nodes, age, tumor size and grade) and significantly prognostic for late DR. The current analysis compares BCIN+ and CTS5 for risk of late DR in N1 patients. Methods: 349 women with HR+, N1 disease and recurrence-free for ≥5 years were included. BCIN+ results were determined blinded to clinical outcome. CTS5 was calculated as previously described (Dowsett et al, JCO 2018; 36:1941). Kaplan-Meier analysis and Cox proportional hazards regression for late DR (5-15y) were evaluated. Results: 64% of patients were > 50 years old, 34% with tumors > 2cm, 79% received adjuvant chemotherapy and 64% received up to 5 years of ET. BCIN+ stratified 23% of patients as low-risk with 1.3% risk for late DR vs those classified as high-risk with 16.1% [HR 12.4 (1.7-90.4), p = 0.0014]. CTS5 classified patients into 3 risk groups: 29% of patients as low-risk (4.2% DR), 37% as intermediate-risk (10.6% DR), and 34% as high-risk (22.1% DR) [HR intermediate vs. low: 2.3 (0.7-7.0), p = 0.16; high vs. low: 5.3 (1.8-15.5), p = 0.002]. In a subset of patients who completed 5 years of ET (N = 223), BCIN+ identified 22% of patients as low-risk with a late DR rate of 2.1%, while CTS5 identified 29% and 37% of patients as low- and intermediate-risk with late DR rates of 5.2% and 10.3%, respectively. Conclusions: BCIN+ classified N1 patients into binary risk groups and identified 20% patients with limited risk of late DR ( < 2%) that may be advised to forego EET and its attendant toxicities/side effects. In comparison, CTS5 classified patients into 3 risk groups, with low- and intermediate-risk of late DR of 4-5% and 10%, wherein the risk-benefit profile for extension of endocrine therapy is less clear.

Identification of high risk breast-cancer patients by gene expression profiling

The Lancet ◽  
2002 ◽  
Vol 359 (9301) ◽  
pp. 131-132 ◽  
Author(s):  
André Ahr ◽  
Thomas Karn ◽  
Christine Solbach ◽  
Tanja Seiter ◽  
Klaus Strebhardt ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Cancer Patients ◽  
Expression Profiling ◽  
Breast Cancer Patients ◽  
High Risk Breast Cancer ◽  
High Risk Breast ◽  
Risk Breast Cancer

Validation of an RT-PCR multi-gene prognostic signature for distant metastasis in node negative (N-), ER positive (ER+) breast cancer patients using FFPE sections

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 21009-21009
Author(s):  
A. Tutt ◽  
A. Wang ◽  
R. Springall ◽  
K. Lau ◽  
K. Ryder ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Distant Metastasis ◽  
Tumor Size ◽  
Cox Regression ◽  
Risk Groups ◽  
Breast Cancer Patients ◽  
Prognostic Signature ◽  
Rt Pcr ◽  
Free Survival

21009 Background: We sought to validate a previously developed 14-gene prognostic signature and a metastasis score (MS) that predicted distant metastasis in N-, ER+ breast cancer patients in an independent sample set of patients without systemic treatment. The genes consisted primarily of proliferation genes involved in p53 and TNF signaling pathways. Methods: : A cohort of 294 N-, ER+ breast cancer patients from Guy's Hospitals, London, UK without systemic therapy were tested. The cohort had a mean age of 55.5 yrs with 49% > 55 yrs, mean tumor size of 1.93 (max. 3) cm, and a median follow up of 14.3 yrs. The primary endpoint was distant metastasis free survival. RT-PCR was carried out on fixed sections. The MS was calculated. Results: The mean MS (SD) was 0.44 (0.59) with a range of -1.31 to 2.0. Hazard of distant metastasis increased 3.02 fold (95% CI 1.91–4.76, p <0.0001) per unit increase in MS from Cox model. The pre-determined cut point of zero was used to stratify patients into low- and high-risk groups. The 5-yr distant-metastasis-free survival rate (DMFSR) for low- and high-risk groups were 1 and 0.86 (SE 0.024); the 10-yr DMFSR were 0.97 (0.021) and 0.77 (0.030), respectively. Univariate Cox regression analyses indicated that MS (hazard ratio (HR) 5.65, 95% CI 2.05–15.56, p=0.008), tumor size (HR 1.62, p=0.0047) and tumor grade (HR 2.52, p=0.036) were significant but age was not. Multivariate Cox regression indicated that the signature had independent prognostic value with a HR of 4.71 (1.42–15.61, p=0.011) after adjusting for age, tumor size and grade. AUC of MS at 5-and 10-yr were 0.78 (0.71–0.85, p<0.001) and 0.73 (0.66–0.80, p<0.001) with sensitivities of 1 and 0.96 and specificities of 0.31 and 0.31 at zero cut point, respectively. The differential risk between the median MS scores of the lowest and highest deciles was 8-fold. Conclusions: A previously defined RT-PCR prognostic signature for N-, ER+ patients has been confirmed. A metastasis score that quantifies distant metastasis risk, not confounded with treatment effect can complement treatment response predictors. Insight on natural history of tumors is critical for evaluating impact of therapy. No significant financial relationships to disclose.

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