scholarly journals Immune-related gene data-based molecular subtyping related to the prognosis of breast cancer patients

Breast Cancer ◽  
2020 ◽  
Author(s):  
Guoyu Mu ◽  
Hong Ji ◽  
Hui He ◽  
Hongjiang Wang
Keyword(s):  
Breast Cancer ◽  
Prediction Model ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Tumor Development ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Patients ◽  
Prognostic Prediction ◽  
Immune Related Genes ◽  
Accuracy And Stability

Abstract Background Breast cancer (BC), which is the most common malignant tumor in females, is associated with increasing morbidity and mortality. Effective treatments include surgery, chemotherapy, radiotherapy, endocrinotherapy and molecular-targeted therapy. With the development of molecular biology, immunology and pharmacogenomics, an increasing amount of evidence has shown that the infiltration of immune cells into the tumor microenvironment, coupled with the immune phenotype of tumor cells, will significantly affect tumor development and malignancy. Consequently, immunotherapy has become a promising treatment for BC prevention and as a modality that can influence patient prognosis. Methods In this study, samples collected from The Cancer Genome Atlas (TCGA) and ImmPort databases were analyzed to investigate specific immune-related genes that affect the prognosis of BC patients. In all, 64 immune-related genes related to prognosis were screened, and the 17 most representative genes were finally selected to establish the prognostic prediction model of BC (the RiskScore model) using the Lasso and StepAIC methods. By establishing a training set and a test set, the efficiency, accuracy and stability of the model in predicting and classifying the prognosis of patients were evaluated. Finally, the 17 immune-related genes were functionally annotated, and GO and KEGG signal pathway enrichment analyses were performed. Results We found that these 17 genes were enriched in numerous BC- and immune microenvironment-related pathways. The relationship between the RiskScore and the clinical characteristics of the sample and signaling pathways was also analyzed. Conclusions Our findings indicate that the prognostic prediction model based on the expression profiles of 17 immune-related genes has demonstrated high predictive accuracy and stability in identifying immune features, which can guide clinicians in the diagnosis and prognostic prediction of BC patients with different immunophenotypes.

scholarly journals Immune-related Gene Data-based Molecular Subtyping Related to the Prognosis for Breast Cancer Patients

2020 ◽  
Author(s):  
Guoyu Mu ◽  
Hong Ji ◽  
Hui He ◽  
Hongjiang Wang
Keyword(s):  
Breast Cancer ◽  
Prediction Model ◽  
Expression Profiles ◽  
Pathway Enrichment Analysis ◽  
Breast Cancer Patients ◽  
Prognosis Prediction ◽  
Patient Prognosis ◽  
Prognosis Prediction Model ◽  
Immune Related Genes ◽  
Accuracy And Stability

Abstract Background Breast cancer (BC), the most frequently seen malignant tumor in female, is associated with increasing morbidity and mortality year by year. Generally, the available treatments for BC include surgery, chemotherapy, radiotherapy, endocrinotherapy and molecular targeted therapy. Typically, as molecular biology, immunology and pharmacogenomics develop, a growing amount of evidence has suggested that immunocyte infiltration into tumor microenvironment, together with the immunophenotype of tumor cells, would remarkably influence the development and malignant transformation of tumor; as a result, immunotherapy has become a promising therapy for treating BC, which would also affect patient prognosis.Methods In this study, samples collected from TCGA and ImmPort database would be analyzed to search for specific immune-related genes affecting BC patient prognosis. A total of 64 immune-related genes with significant correlation with patient prognosis had been screened and performed shrinkage estimate, among which, 29 most representative ones with significant correlation with patient prognosis had been selected and utilized to establish the prognosis prediction model for BC patients (as referred to as the RiskScore equation). Thereafter, samples in both training set and test set would be substituted into the model, respectively; meanwhile, BC patients would also be divided based on the median RiskScore to assess the efficiency, accuracy and stability of the model in predicting and classifying patient prognosis. Subsequently, functional annotations, GO and KEGG signaling pathway enrichment analysis would be carried out among the 29 as-screened immune-related genes.Results The results found that, these 29 genes could be mainly enriched to numerous BC- and immune microenvironment-related pathways. Eventually, the relationship between RiskScore and the sample clinical features as well as the signaling pathways would be analyzed.Conclusions Our findings indicate that, the prognosis prediction model RiskScore established on the basis of the expression profiles of 29 immune-related genes has displayed high prediction accuracy and stability in identifying the immune features, which can guide the clinicians to diagnose and predict the prognosis for different immunophenotypes, in the meantime of offering numerous therapeutic targets for precisely treating BC in clinic using the identified subtype-specific immune molecules.

scholarly journals Identification and Validation of a Predictive Immune-related Genes Signature for the Prognosis of Cholangiocarcinoma

2020 ◽  
Author(s):  
Rui Zhang ◽  
Chen Chen ◽  
Qi Li ◽  
Jialu Fu ◽  
Dong Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
High Risk ◽  
Risk Score ◽  
Risk Model ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Immune Related Genes

Abstract Background: Immune-related genes (IRGs) play a crucial role in the initiation and progression of cholangiocarcinoma (CCA). However, immune signatures have rarely been used to predict prognosis of CCA. The aim of this study was to construct a novel model for CCA to predict survival based on IRGs expression data.Methods: The gene expression profiles and clinical data of CCA patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were integrated to establish and validate prognostic IRG signatures. Differentially expressed immune-related genes were screened. Univariate and multivariate Cox analysis were performed to identify prognostic IRGs, and the risk model that predicts outcomes was constructed. Furthermore, receiver operating characteristic (ROC) and Kaplan-Meier curve were plotted to examine predictive accuracy of the model, and a nomogram was constructed based on IRGs signature, combining with other clinical characteristics. Finally, CIBERSORT was used to analyze the association of immune cells infiltration with risk score.Results: We identified that 223 IRGs were significantly dysregulated in patients with CCA, among which five IRGs (AVPR1B, CST4, TDGF1, RAET1E and IL9R) were identified as robust indicators for overall survival (OS), and a prognostic model was built based on the IRGs signature. Meanwhile, patients with high risk had worse OS in training and validation cohort, and the area under the ROC was 0.898 and 0.846, respectively. Nomogram demonstrated that immune risk score contributed much more points than other clinicopathological variables, with a C-index of 0.819 (95% CI, 0.727-0.911). Finally, we found that IRGs signature was positively correlated with the proportion of CD8+ T cells, neurophils and T gamma delta, while negatively with that of CD4+ memory resting T cells.Conclusions: We established and validated an effective five IRGs-based prediction model for CCA, which could accurately classify patients into groups with low and high risk of poor prognosis.

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 Molecular characterization of breast cancer: a potential novel immune-related lncRNAs signature

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Jianguo Lai ◽  
Bo Chen ◽  
Guochun Zhang ◽  
Xuerui Li ◽  
Hsiaopei Mok ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Stratification ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Treatment Strategies ◽  
Staging System ◽  
Tnm Staging ◽  
The Cancer Genome Atlas ◽  
Tnm Staging System

Abstract Background Accumulating evidence has demonstrated that immune-related lncRNAs (IRLs) are commonly aberrantly expressed in breast cancer (BC). Thus, we aimed to establish an IRL-based tool to improve prognosis prediction in BC patients. Methods We obtained IRL expression profiles in large BC cohorts (N = 911) from The Cancer Genome Atlas (TCGA) database. Then, in light of the correlation between each IRL and recurrence-free survival (RFS), we screened prognostic IRL signatures to construct a novel RFS nomogram via a Cox regression model. Subsequently, the performance of the IRL-based model was evaluated through discrimination, calibration ability, risk stratification ability and decision curve analysis (DCA). Results A total of 52 IRLs were obtained from TCGA. Based on multivariate Cox regression analyses, four IRLs (A1BG-AS1, AC004477.3, AC004585.1 and AC004854.2) and two risk parameters (tumor subtype and TNM stage) were utilized as independent indicators to develop a novel prognostic model. In terms of predictive accuracy, the IRL-based model was distinctly superior to the TNM staging system (AUC: 0.728 VS 0.673, P = 0.010). DCA indicated that our nomogram had favorable clinical practicability. In addition, risk stratification analysis showed that the IRL-based tool efficiently divided BC patients into high- and low-risk groups (P < 0.001). Conclusions A novel IRL-based model was constructed to predict the risk of 5-year RFS in BC. Our model can improve the predictive power of the TNM staging system and identify high-risk patients with tumor recurrence to implement more appropriate treatment strategies.

scholarly journals Identifies Immune-Related Gene Pair Signature Associated with Breast Cancer Prognosis

2021 ◽  
Author(s):  
Tianwei Sun ◽  
Qixing Tan ◽  
Changyuan Wei
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Gene Pair ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Future Research ◽  
Breast Cancer Patients ◽  
Related Gene ◽  
Immune Related Gene ◽  
Immune Related Genes

Abstract Background: Breast cancer (BC) is the cancer with the largest number of deaths in women. There is growing evidence that immunity plays an important role in the prognosis of breast cancer. Methods: In this study, we developed and validated an immune-related gene pair signature (IRGPs) to predict the survival of breast cancer patients. Screening immune-related genes from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database for the construction of IRGPs, and patients with breast cancer in these two cohorts were assigned to low- and high- risk subgroups. Additionally, we used Kaplan-Meier survival analysis, univariate and multivariate Cox analysis to investigate IRGPs and their individualized prognostic characteristics, and analysis of immune cell infiltration in breast cancer. Results: A 47-IRGP signature was constructed from 2498 immune genes, which could significantly predict the overall survival (OS) of breast cancer patients in the TCGA and GEO cohorts. Immune infiltration analysis showed that a variety of immune cells are significantly related to the prognostic effects of IRGP characteristics in breast cancer patients, especially CD8+ T cells and macrophages. Conclusions: The IRGP signature constructed in this study can help determine the prognosis of breast cancer and provide new ideas and basis for future research on the role of immune-related genes in breast cancer patients.

scholarly journals Integrative analysis of DNA methylation and gene expression profiles identified potential breast cancer-specific diagnostic markers

2020 ◽  
Vol 40 (5) ◽  
Author(s):  
Xinhua Liu ◽  
Yonglin Peng ◽  
Ju Wang
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Dna Methylation ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Diagnostic Model ◽  
Breast Cancer Patients ◽  
Gene Expressions ◽  
Normal Tissues

Abstract Breast cancer is a common malignant tumor among women whose prognosis is largely determined by the period and accuracy of diagnosis. We here propose to identify a robust DNA methylation-based breast cancer-specific diagnostic signature. Genome-wide DNA methylation and gene expression profiles of breast cancer patients along with their adjacent normal tissues from the Cancer Genome Atlas (TCGA) were obtained as the training set. CpGs that with significantly elevated methylation level in breast cancer than not only their adjacent normal tissues and the other ten common cancers from TCGA but also the healthy breast tissues from the Gene Expression Omnibus (GEO) were finally remained for logistic regression analysis. Another independent breast cancer DNA methylation dataset from GEO was used as the testing set. Lots of CpGs were hyper-methylated in breast cancer samples compared with adjacent normal tissues, which tend to be negatively correlated with gene expressions. Eight CpGs located at RIIAD1, ENPP2, ESPN, and ETS1, were finally retained. The diagnostic model was reliable in separating BRCA from normal samples. Besides, chromatin accessibility status of RIIAD1, ENPP2, ESPN and ETS1 showed great differences between MCF-7 and MDA-MB-231 cell lines. In conclusion, the present study should be helpful for breast cancer early and accurate diagnosis.

scholarly journals Hormone Receptor-status Prediction in Breast Cancer Using Gene Expression Profiles and Their Macroscopic Landscape

2020 ◽  
Author(s):  
Seokhyun Yoon ◽  
Hye Sung Won ◽  
Keunsoo Kang ◽  
Kexin Qiu ◽  
Woong June Park ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Hormone Receptor ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Receptor Expression ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Patients ◽  
Intrinsic Subtypes ◽  
Receptor Status

AbstractThe cost of next-generation sequencing technologies is rapidly declining, making RNA-seq-based gene expression profiling (GEP) an affordable technique for predicting receptor expression status and intrinsic subtypes in breast cancer (BRCA) patients. Based on the expression levels of co-expressed genes, GEP-based receptor-status prediction can classify clinical subtypes more accurately than can immunohistochemistry (IHC). Using data from the cancer genome atlas TCGA BRCA and METABRIC datasets, we identified common predictor genes found in both datasets and performed receptor-status prediction based on these genes. By assessing the survival outcomes of patients classified using GEP- or IHC-based receptor status, we compared the prognostic value of the two methods. We found that GEP-based HR prediction provided higher concordance with the intrinsic subtypes and a stronger association with treatment outcomes than did IHC-based hormone receptor (HR) status. GEP-based prediction improved the identification of patients who could benefit from hormone therapy, even in patients with non-luminal BRCA. We also confirmed that non-matching subgroup classification affected the survival of BRCA patients and that this could be largely overcome by GEP-based receptor-status prediction. In conclusion, GEP-based prediction provides more reliable classification of HR status, improving therapeutic decision making for breast cancer patients.

Identification of an immune-related long noncoding RNA signature that predicts prognosis in breast cancer patients

2021 ◽  
Vol 15 (3) ◽  
pp. 167-180
Author(s):  
Na Li ◽  
Zubin Li ◽  
Xin Li ◽  
Bingjie Chen ◽  
Huibo Sun ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Cox Regression ◽  
Expression Profiles ◽  
Characteristic Curve ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Patients ◽  
Cox Regression Analysis

Aim: The purpose of this study was to identify an immune-related long noncoding RNA (lncRNA) signature that predicts the prognosis of breast cancer. Materials & methods: The expression profiles of breast cancer were downloaded from The Cancer Genome Atlas. Cox regression analysis was used to identify an immune-related lncRNA signature. Results: The five immune-related lncRNAs could be used to construct a breast cancer survival prognosis model. The receiver operating characteristic curve evaluation found that the accuracy of the model for predicting the 1-, 3- and 5-year prognosis of breast cancer was 0.688, 0.708 and 0.686. Conclusion: This signature may have an important clinical significance for improving predictive results and guiding the treatment of breast cancer patients.

NMR-metabolite-resonance signature to predict HR+ breast cancer patient response to CDK4/6 inhibitors.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 3043-3043
Author(s):  
Bo Zhang ◽  
Jason Warner ◽  
Christopher Pinto ◽  
Dejan Juric ◽  
Elizabeth ODay
Keyword(s):  
Breast Cancer ◽  
Clinical Outcomes ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Metastatic Breast ◽  
Endocrine Treatment ◽  
Breast Cancer Patients ◽  
Patient Response ◽  
Estrogen Receptor Positive ◽  
Low Levels

3043 Background: Advanced ER+ breast cancer patients have reported prolongation of stable disease when treated with a CDK4/6 inhibitor as a monotherapy or in combination with endocrine treatment. However ~20% of patients are intrinsically resistant and all patients eventually acquire resistance to these therapies. There is a critical need to identify biomarkers that accurately predict response and resistance to CDK4/6 inhibitors. ER-positivity, luminal patterns of gene expression, Rb function, overexpression of cyclin D1, cyclin E, CDK6 and low levels of p16 are biomarkers that do not accurately match clinical outcomes. Methods: We performed a retrospective study analyzing plasma-based metabolites from a baseline (pre-dose) and ~2 months post treatment of 21 women with estrogen-receptor-positive (ER+) metastatic breast cancer treated with CDK4/6 inhibitors. Results: By correlating the metabolite expression profiles to clinical outcomes we were able to identify a metabolic signature that could differentiate the CDK4/6 responders and resistant patients with a predictive accuracy of > 90%. Further we were able to identify independent signatures predictive of response for individual CDK4/6 inhibitors palbociclib and ribociclib. Conclusions: The results of this study could lead to a paradigm shift in the administration of CDK4/6 inhibitors wherein prior to treatment and during treatment patient plasma is screened to determine whether that individual patient is responsive or resistant to a CDK4/6 inhibitor.

scholarly journals Analysis of LAGEs Family Gene Signature and Prognostic Relevance in Breast Cancer

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 726
Author(s):  
Hoang Dang Khoa Ta ◽  
Wan-Chun Tang ◽  
Nam Nhut Phan ◽  
Gangga Anuraga ◽  
Sz-Ying Hou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Expression Profiles ◽  
Family Members ◽  
Gene Expression Profiles ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Breast Cancer Patients ◽  
Kaplan Meier

Breast cancer (BRCA) is one of the most complex diseases and involves several biological processes. Members of the L-antigen (LAGE) family participate in the development of various cancers, but their expressions and prognostic values in breast cancer remain to be clarified. High-throughput methods for exploring disease progression mechanisms might play a pivotal role in the improvement of novel therapeutics. Therefore, gene expression profiles and clinical data of LAGE family members were acquired from the cBioportal database, followed by verification using the Oncomine and The Cancer Genome Atlas (TCGA) databases. In addition, the Kaplan-Meier method was applied to explore correlations between expressions of LAGE family members and prognoses of breast cancer patients. MetaCore, GlueGo, and GluePedia were used to comprehensively study the transcript expression signatures of LAGEs and their co-expressed genes together with LAGE-related signal transduction pathways in BRCA. The result indicated that higher LAGE3 messenger (m)RNA expressions were observed in BRCA tissues than in normal tissues, and they were also associated with the stage of BRCA patients. Kaplan-Meier plots showed that overexpression of LAGE1, LAGE2A, LAGE2B, and LAGE3 were highly correlated to poor survival in most types of breast cancer. Significant associations of LAGE family genes were correlated with the cell cycle, focal adhesion, and extracellular matrix (ECM) receptor interactions as indicated by functional enrichment analyses. Collectively, LAGE family members’ gene expression levels were related to adverse clinicopathological factors and prognoses of BRCA patients; therefore, LAGEs have the potential to serve as prognosticators of BRCA patients.

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