scholarly journals Construction and Validation of an Immune Infiltration-Related Gene Signature for the Prediction of Prognosis and Therapeutic Response in Breast Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Yang Peng ◽  
Haochen Yu ◽  
Yudi Jin ◽  
Fanli Qu ◽  
Haoyu Ren ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Stratification ◽  
Cancer Progression ◽  
Gene Signature ◽  
Significant Heterogeneity ◽  
Breast Cancer Patients ◽  
Related Gene ◽  
Immune Infiltration ◽  
Patient Prognosis ◽  
Prediction Of Prognosis

Breast cancer patients show significant heterogeneity in overall survival. Current assessment models are insufficient to accurately predict patient prognosis, and models for predicting treatment response are lacking. We evaluated the relationship between various immune cells and breast cancer and confirmed the association between immune infiltration and breast cancer progression. Different bioinformatics and statistical approaches were combined to construct a robust immune infiltration-related gene signature for predicting patient prognosis and responses to immunotherapy and chemotherapy. Our research found that a higher immune infiltration-related risk score (IRS) indicates that the patient has a worse prognosis and is not very sensitive to immunotherapy. In addition, a new nomogram was constructed based on the gene signature and clinicopathological features to improve the risk stratification and quantify the risk assessment of individual patients. Our study might contribute to the optimization of the risk stratification for survival and the personalized management of breast cancer.

scholarly journals Prognostic Value of MUC1 and its Correlation with Tumor-Infiltrating Immune Cells in Breast Cancer

2021 ◽  
Author(s):  
Xiaomin Xi ◽  
Yilin You ◽  
Weidong Huang ◽  
jicheng zhan
Keyword(s):  
Breast Cancer ◽  
T Cells ◽  
Cancer Progression ◽  
Macrophage Polarization ◽  
Muc1 Expression ◽  
Breast Cancer Patients ◽  
Gene Markers ◽  
Clinical Prognosis ◽  
Luminal B ◽  
Immune Infiltration

Abstract Background: MUC1 is a transmembrane glycoprotein, aberrantly glycosylated and overexpressed in a variety of epithelial cancers, and plays a crucial role in cancer progression, especially in breast cancer. It is also an essential regulator for immune functionality, but the mechanisms whereby it effects immune infiltration in breast cancer remain uncertain. Methods: In this research, MUC1 expression was analyzed by the Oncomine and TIMER database. The association between MUC1 and prognosis was evaluated by Kaplan-Meier plotter database. The correlations of MUC1 with immune infiltration and immunological markers were assessed by TIMER database. Results: We found that MUC1 expression was significantly correlated with outcomes in multiple cancers, with the effect being particularly pronounced in breast cancer. Pathologically, elevated MUC1 expression was related with worse prognosis depending on intrinsic subtypes, ER status, patient stage, lymph node and TP53 mutation status in breast cancer. Specifically, low level of MUC1 seemed to be more favorable to luminal B patients with systemic treatment. MUC1 expression had significant negative correlations with infiltrating levels of CD8+ T cells, CD4+ T cells, B cells, macrophages, neutrophils, monocytes and dendritic cells (DCs) in breast cancer. Besides, MUC1 displayed strong regulations on macrophage polarization and diverse immunological gene markers. The patients with lower MUC1 and deeper immune infiltration predicted better prognosis. Conclusions: MUC1 is significantly associated with clinical prognosis and potentially plays an essential role in modulating cytotoxic T lymphocytes (CTLs), tumor associated macrophages (TAMs), natural killer cells (NK cells) and DCs in breast tumors. Collectively, MUC1 could be served as a valuable biomarker of predicting prognosis and immune infiltration for breast cancer patients.

scholarly journals The m6A-related gene signature for predicting the prognosis of breast cancer

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11561
Author(s):  
Shanliang Zhong ◽  
Zhenzhong Lin ◽  
Huanwen Chen ◽  
Ling Mao ◽  
Jifeng Feng ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
High Risk ◽  
Cancer Survival ◽  
Risk Group ◽  
Gene Signature ◽  
Breast Cancer Survival ◽  
Breast Cancer Patients ◽  
Related Gene ◽  
Rna Methylation

N6-methyladenosine (m6A) modification has been shown to participate in tumorigenesis and metastasis of human cancers. The present study aimed to investigate the roles of m6A RNA methylation regulators in breast cancer. We used LASSO regression to identify m6A-related gene signature predicting breast cancer survival with the datasets downloaded from Gene Expression Omnibus and The Cancer Genome Atlas (TCGA). RNA-Seq data of 3409 breast cancer patients from GSE96058 and 1097 from TCGA were used in present study. A 10 m6A-related gene signature associated with prognosis was identified from 22 m6A RNA methylation regulators. The signature divided patients into low- and high-risk group. High-risk patients had a worse prognosis than the low-risk group. Further analyses indicated that IGF2BP1 may be a key m6A RNA methylation regulator in breast cancer. Survival analysis showed that IGF2BP1 is an independent prognostic factor of breast cancer, and higher expression level of IGF2BP1 is associated with shorter overall survival of breast cancer patients. In conclusion, we identified a 10 m6A-related gene signature associated with overall survival of breast cancer. IGF2BP1 may be a key m6A RNA methylation regulator in breast cancer.

scholarly journals Identification of a Novel Glycolysis-Related Gene Signature for Predicting Breast Cancer Survival

2021 ◽  
Vol 10 ◽  
Author(s):  
Dai Zhang ◽  
Yi Zheng ◽  
Si Yang ◽  
Yiche Li ◽  
Meng Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Survival ◽  
Cox Regression ◽  
Gene Signature ◽  
Breast Cancer Survival ◽  
Breast Cancer Patients ◽  
Related Gene ◽  
Training Set ◽  
Cox Regression Analysis ◽  
Kaplan Meier

To identify a glycolysis-related gene signature for the evaluation of prognosis in patients with breast cancer, we analyzed the data of a training set from TCGA database and four validation cohorts from the GEO and ICGC databases which included 1,632 patients with breast cancer. We conducted GSEA, univariate Cox regression, LASSO, and multiple Cox regression analysis. Finally, an 11-gene signature related to glycolysis for predicting survival in patients with breast cancer was developed. And Kaplan–Meier analysis and ROC analyses suggested that the signature showed a good prognostic ability for BC in the TCGA, ICGC, and GEO datasets. The analyses of univariate Cox regression and multivariate Cox regression revealed that it’s an important prognostic factor independent of multiple clinical features. Moreover, a prognostic nomogram, combining the gene signature and clinical characteristics of patients, was constructed. These findings provide insights into the identification of breast cancer patients with a poor prognosis.

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.

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 Development and validation of a hypoxia-related gene signature to predict overall survival in early-stage lung adenocarcinoma patients

2020 ◽  
Vol 12 ◽  
pp. 175883592093790
Author(s):  
Jing Sun ◽  
Tianyu Zhao ◽  
Di Zhao ◽  
Xin Qi ◽  
Xuanwen Bao ◽  
...  
Keyword(s):  
Overall Survival ◽  
Risk Factor ◽  
Decision Tree ◽  
Risk Stratification ◽  
Lung Adenocarcinoma ◽  
Early Stage ◽  
Staging System ◽  
Gene Signature ◽  
Significant Heterogeneity ◽  
Related Gene

Background: Patients with early-stage lung adenocarcinoma (LUAD) exhibit significant heterogeneity in overall survival. The current tumour-node-metastasis staging system is insufficient to provide precise prediction for prognosis. Methods: We quantified the levels of various hallmarks of cancer and identified hypoxia as the primary risk factor for overall survival in early-stage LUAD. Different bioinformatic and statistical methods were combined to construct a robust hypoxia-related gene signature for prognosis. Furthermore, a decision tree and a nomogram were constructed based on the gene signature and clinicopathological features to improve risk stratification and quantify risk assessment for individual patients. Results: The hypoxia-related gene signature discriminated high-risk patients at an early stage in our investigated cohorts. Survival analyses demonstrated that our gene signature served as an independent risk factor for overall survival. The decision tree identified risk subgroups powerfully, and the nomogram exhibited high accuracy. Conclusions: Our study might contribute to the optimization of risk stratification for survival and personalized management of early-stage LUAD.

scholarly journals Development and Validation of Tumor Immunogenicity Based Gene Signature for Skin Cancer Risk Stratification

2021 ◽  
Vol 22 (21) ◽  
pp. 12025
Author(s):  
Maryam Yavartanoo ◽  
Gwan-Su Yi
Keyword(s):  
Decision Making ◽  
Overall Survival ◽  
Skin Cancer ◽  
Risk Stratification ◽  
Gene Signature ◽  
Clinicopathological Features ◽  
Survival Prediction ◽  
Significant Heterogeneity ◽  
Related Gene ◽  
Melanoma Patients

Melanoma is one of the most aggressive types of skin cancer, with significant heterogeneity in overall survival. Currently, tumor-node-metastasis (TNM) staging is insufficient to provide accurate survival prediction and appropriate treatment decision making for several types of tumors, such as those in melanoma patients. Therefore, the identification of more reliable prognosis biomarkers is urgently essential. Recent studies have shown that low immune cells infiltration is significantly associated with unfavorable clinical outcome in melanoma patients. Here we constructed a prognostic-related gene signature for melanoma risk stratification by quantifying the levels of several cancer hallmarks and identify the Wnt/β-catenin activation pathway as a primary risk factor for low tumor immunity. A series of bioinformatics and statistical methods were combined and applied to construct a Wnt-immune-related prognosis gene signature. With this gene signature, we computed risk scores for individual patients that can predict overall survival. To evaluate the robustness of the result, we validated the signature in multiple independent GEO datasets. Finally, an overall survival-related nomogram was established based on the gene signature and clinicopathological features. The Wnt-immune-related prognostic risk score could better predict overall survival compared with standard clinicopathological features. Our results provide a comprehensive map of the oncogene-immune-related gene signature that can serve as valuable biomarkers for better clinical decision making.

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.

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 Stromal CCL5 Promotes Breast Cancer Progression by Interacting with CCR3 in Tumor Cells

2021 ◽  
Vol 22 (4) ◽  
pp. 1918
Author(s):  
Mio Yamaguchi ◽  
Kiyoshi Takagi ◽  
Koki Narita ◽  
Yasuhiro Miki ◽  
Yoshiaki Onodera ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Carcinoma ◽  
Tumor Progression ◽  
Tumor Cells ◽  
Cancer Progression ◽  
Stromal Cells ◽  
Human Breast Carcinoma ◽  
Breast Cancer Patients ◽  
Breast Carcinomas ◽  
Human Breast

Chemokines secreted from stromal cells have important roles for interactions with carcinoma cells and regulating tumor progression. C-C motif chemokine ligand (CCL) 5 is expressed in various types of stromal cells and associated with tumor progression, interacting with C-C chemokine receptor (CCR) 1, 3 and 5 expressed in tumor cells. However, the expression on CCL5 and its receptors have so far not been well-examined in human breast carcinoma tissues. We therefore immunolocalized CCL5, as well as CCR1, 3 and 5, in 111 human breast carcinoma tissues and correlated them with clinicopathological characteristics. Stromal CCL5 immunoreactivity was significantly correlated with the aggressive phenotype of breast carcinomas. Importantly, this tendency was observed especially in the CCR3-positive group. Furthermore, the risk of recurrence was significantly higher in the patients with breast carcinomas positive for CCL5 and CCR3 but negative for CCR1 and CCR5, as compared with other patients. In summary, the CCL5-CCR3 axis might contribute to a worse prognosis in breast cancer patients, and these findings will contribute to a better understanding of the significance of the CCL5/CCRs axis in breast carcinoma microenvironment.

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