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

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiaolong Wang ◽  
Chen Li ◽  
Tong Chen ◽  
Wenhao Li ◽  
Hanwen Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Cancer Progression ◽  
Molecular Mechanisms ◽  
Risk Model ◽  
Expression Profiles ◽  
External Validation ◽  
Gene Signature ◽  
Breast Cancer Patients ◽  
Study Gene Expression

BackgroundRecent years, the global prevalence of breast cancer (BC) was still high and the underlying molecular mechanisms remained largely unknown. The investigation of prognosis-related biomarkers had become an urgent demand.ResultsIn this study, gene expression profiles and clinical information of breast cancer patients were downloaded from the TCGA database. The differentially expressed genes (DEGs) were estimated by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. 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 TCGA entire cohort and an independent external validation cohort. 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.ConclusionsWe established a predictive five-gene signature, which could be helpful for a personalized management in 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 An epithelial-mesenchymal-amoeboid transition gene signature reveals subtypes of breast cancer progression and metastasis

2017 ◽  
Author(s):  
Amin Emad ◽  
Tania Ray ◽  
Tor W. Jensen ◽  
Meera Parat ◽  
Rachael Natrajan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Cancer Patients ◽  
Cancer Progression ◽  
Gene Signature ◽  
Breast Cancer Progression ◽  
Breast Cancer Patients ◽  
Primary Tumors ◽  
Node Negative ◽  
Node Negative Breast Cancer

AbstractCancer cells within a tumor are known to display varying degrees of metastatic propensity but the molecular basis underlying such heterogeneity remains unclear. We analyzed genome-wide gene expression data obtained from primary tumors of lymph node-negative breast cancer patients using a novel metastasis biology-based Epithelial-Mesenchymal-Amoeboid Transition (EMAT) gene signature, and identified subtypes associated with distinct prognostic profiles. EMAT subtype status improved prognosis accuracy of clinical parameters and statistically outperformed traditional breast cancer intrinsic subtypes even after adjusting for treatment variables. Additionally, analysis of 3D spheroids from an in vitro isogenic model of breast cancer progression reveals that EMAT subtypes display progression from premalignant to malignant and pre-invasive to invasive cancer. EMAT classification is a biologically informed method to assess metastasis risk in early stage, lymph node-negative breast cancer patients.

scholarly journals Gene Expression Profiles for Predicting Metastasis in Breast Cancer: A Cross-Study Comparison of Classification Methods

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Mark Burton ◽  
Mads Thomassen ◽  
Qihua Tan ◽  
Torben A. Kruse
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Patients ◽  
Expression Profiles ◽  
External Validation ◽  
Gene Expression Profiles ◽  
Microarray Gene Expression Data ◽  
Classification Methods ◽  
Breast Cancer Patients ◽  
Microarray Gene Expression

Machine learning has increasingly been used with microarray gene expression data and for the development of classifiers using a variety of methods. However, method comparisons in cross-study datasets are very scarce. This study compares the performance of seven classification methods and the effect of voting for predicting metastasis outcome in breast cancer patients, in three situations: within the same dataset or across datasets on similar or dissimilar microarray platforms. Combining classification results from seven classifiers into one voting decision performed significantly better during internal validation as well as external validation in similar microarray platforms than the underlying classification methods. When validating between different microarray platforms, random forest, another voting-based method, proved to be the best performing method. We conclude that voting based classifiers provided an advantage with respect to classifying metastasis outcome in breast cancer patients.

scholarly journals Differences in gene-expression profiles in breast cancer between African and European-ancestry women

2020 ◽  
Vol 41 (7) ◽  
pp. 887-893 ◽  
Author(s):  
Jie Ping ◽  
Xingyi Guo ◽  
Fei Ye ◽  
Jirong Long ◽  
Loren Lipworth ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Patients ◽  
Expression Profiles ◽  
Breast Cancer Mortality ◽  
Tumor Biology ◽  
Gene Expression Profiles ◽  
Gene Signature ◽  
European Ancestry ◽  
Breast Cancer Patients

Abstract African American (AA) women have an excess breast cancer mortality than European American (EA) women. To investigate the contribution of tumor biology to this survival health disparity, we compared gene expression profiles in breast tumors using RNA sequencing data derived from 260 AA and 155 EA women who were prospectively enrolled in the Southern Community Cohort Study (SCCS) and developed breast cancer during follow-up. We identified 59 genes (54 protein-coding genes and 5 long intergenic non-coding RNAs) that were expressed differently between EA and AA at a stringent false discovery rate (FDR) < 0.01. A gene signature was derived with these 59 genes and externally validated using the publicly available Cancer Genome Atlas (TCGA) data from180 AA and 838 EA breast cancer patients. Applying C-statistics, we found that this 59-gene signature has a high discriminative ability in distinguishing AA and EA breast cancer patients in the TCGA dataset (C-index = 0.81). These findings may provide new insight into tumor biological differences and the causes of the survival disparity between AA and EA breast cancer patients.

scholarly journals A Novel Melatonergic Signature Predicts Reccurence Risk and Therapeutic Response in Breast Cancer Patients

2018 ◽  
Author(s):  
Hoa Quynh Tran ◽  
Phuc Loi Luu ◽  
Van Thai Than ◽  
Declan Clarke ◽  
Hanh Ngoc Lam ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Cancer Progression ◽  
Expression Profiles ◽  
Endocrine Resistance ◽  
Low Risk ◽  
Tamoxifen Treatment ◽  
Breast Cancer Patients ◽  
Survival Times ◽  
Free Survival

AbstractASMT is a key determinant of the levels of released melatonin. Though melatonin has been shown to exhibit anti-cancer activity and prevents endocrine resistance in breast cancer, the role of ASMT in breast cancer progression remains unclear. In this retrospective study, we analyzed gene expression profiles from thousands of patients and found thatASMTexpression was significantly lower in breast cancer tumors relative to healthy tissue. Among cancer patients, those with greater expression had better relapse-free survival outcomes and longer metastasis-free survival times, and they also experienced longer periods before relapse or distance recurrence following tamoxifen treatment. Administration of melatonin, in combination with tamoxifen, further promoted cancer cell death by promoting apoptosis. Motivated by these results, we devised an ASMT gene signature that identifies low-risk cases with great accuracy. This signature was validated using both mRNA array and RNAseq datasets. Intriguingly, patients who are classified as high-risk benefit from adjuvant chemotherapy, and those with grade II tumors who are classified as low-risk exhibit improved overall survival and distance relapse-free outcomes following endocrine therapy. Our findings more clearly elucidate the roles ofASMT,provide strategies for improving the efficacy of tamoxifen treatment, and help to identify those patients who may maximally benefit from adjuvant or endocrine therapies.

Overexpression of microRNA-21 in the Serum of Breast Cancer Patients

MicroRNA ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 58-63
Author(s):  
Batool Savari ◽  
Sohrab Boozarpour ◽  
Maryam Tahmasebi-Birgani ◽  
Hossein Sabouri ◽  
Seyed Mohammad Hosseini
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Cancer Progression ◽  
Tumor Resection ◽  
Tumor Grade ◽  
Healthy Controls ◽  
Breast Cancer Patients ◽  
Serum Samples ◽  
Post Surgery ◽  
Polymerase Chain

Background: Breast cancer is the most common cancer diagnosed in women worldwide. So it seems that there's a good chance of recovery if it's detected in its early stages even before the appearances of symptoms. Recent studies have shown that miRNAs play an important role during cancer progression. These transcripts can be tracked in liquid samples to reveal if cancer exists, for earlier treatment. MicroRNA-21 (miR-21) has been shown to be a key regulator of carcinogenesis, and breast tumor is no exception. Objective: The present study was aimed to track the miR-21 expression level in serum of the breast cancer patients in comparison with that of normal counterparts. Methods: Comparative real-time polymerase chain reaction was applied to determine the levels of expression of miR-21 in the serum samples of 57 participants from which, 42 were the patients with breast cancer including pre-surgery patients (n = 30) and post-surgery patients (n = 12), and the others were the healthy controls (n = 15). Results: MiR-21 was significantly over expressed in the serum of breast cancer patients as compared with healthy controls (P = 0.002). A significant decrease was also observed following tumor resection (P < 0.0001). Moreover, it was found that miR-21 overexpression level was significantly associated with tumor grade (P = 0.004). Conclusion: These findings suggest that miR-21 has the potential to be used as a novel breast cancer biomarker for early detection and prognosis, although further experiments are needed.

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

scholarly journals Targeting CD82/KAI1 for Precision Therapeutics in Surmounting Metastatic Potential in Breast Cancer

Cancers ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 4486
Author(s):  
Maximillian Viera ◽  
George Wai Cheong Yip ◽  
Han-Ming Shen ◽  
Gyeong Hun Baeg ◽  
Boon Huat Bay
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Cancer Progression ◽  
Therapeutic Approach ◽  
Traditional Approach ◽  
Unmet Need ◽  
Great Promise ◽  
Metastasis Suppressor ◽  
Precision Oncology ◽  
Breast Cancer Patients

Metastasis is the main cause of mortality in breast cancer patients. There is an unmet need to develop therapies that can impede metastatic spread. Precision oncology has shown great promise for the treatment of cancers, as the therapeutic approach is tailored to a specific group of patients who are likely to benefit from the treatment, rather than the traditional approach of “one size fits all”. CD82, also known as KAI1, a glycoprotein belonging to the tetraspanin family and an established metastasis suppressor, could potentially be exploited to hinder metastases in breast cancer. This review explores the prospect of targeting CD82 as an innovative therapeutic approach in precision medicine for breast cancer patients, with the goal of preventing cancer progression and metastasis. Such an approach would entail the selection of a subset of breast cancer patients with low levels of CD82, and instituting an appropriate treatment scheme tailored towards restoring the levels of CD82 in this group of patients. Proposed precision treatment regimens include current modalities of treating breast cancer, in combination with either clinically approved drugs that could restore the levels of CD82, CD82 peptide mimics or non-coding RNA-based therapeutics.

scholarly journals Salivary Protein Profiles among HER2/neu-Receptor-Positive and -Negative Breast Cancer Patients: Support for Using Salivary Protein Profiles for Modeling Breast Cancer Progression

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Charles F. Streckfus ◽  
Daniel Arreola ◽  
Cynthia Edwards ◽  
Lenora Bigler
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Cancer Progression ◽  
Salivary Protein ◽  
Differentially Expressed Proteins ◽  
Protein Profiles ◽  
Breast Cancer Patients ◽  
Itraq Reagent ◽  
Pooled Samples ◽  
Positive Breast Cancer

Purpose. The objective of this study was to compare the salivary protein profiles from individuals diagnosed with breast cancer that were either HER2/neu receptor positive or negative.Methods. Two pooled saliva specimens underwent proteomic analysis. One pooled specimen was from women diagnosed with stage IIa HER2/neu-receptor-positive breast cancer patients (n=10) and the other was from women diagnosed with stage IIa HER2/neu-receptor-negative cancer patients (n=10). The pooled samples were trypsinized and the peptides labeled with iTRAQ reagent. Specimens were analyzed using an LC-MS/MS mass spectrometer.Results. The results yielded approximately 71 differentially expressed proteins in the saliva specimens. There were 34 upregulated proteins and 37 downregulated proteins.

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).

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