scholarly journals Establishment of a novel CNV-related prognostic signature predicting prognosis in patients with breast cancer

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Wei Hu ◽  
Mingyue Li ◽  
Qi Zhang ◽  
Chuan Liu ◽  
Xinmei Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
High Risk ◽  
Risk Score ◽  
Copy Number ◽  
Risk Group ◽  
Gene Signature ◽  
Low Risk ◽  
Breast Cancer Dataset ◽  
Cox Analysis

Abstract Background Copy number variation (CNVs) is a key factor in breast cancer development. This study determined prognostic molecular characteristics to predict breast cancer through performing a comprehensive analysis of copy number and gene expression data. Methods Breast cancer expression profiles, CNV and complete information from The Cancer Genome Atlas (TCGA) dataset were collected. Gene Expression Omnibus (GEO) chip data sets (GSE20685 and GSE31448) containing breast cancer samples were used as external validation sets. Univariate survival COX analysis, multivariate survival COX analysis, least absolute shrinkage and selection operator (LASSO), Chi square, Kaplan-Meier (KM) survival curve and receiver operating characteristic (ROC) analysis were applied to build a gene signature model and assess its performance. Results A total of 649 CNV related-differentially expressed gene obtained from TCGA-breast cancer dataset were related to several cancer pathways and functions. A prognostic gene sets with 9 genes were developed to stratify patients into high-risk and low-risk groups, and its prognostic performance was verified in two independent patient cohorts (n = 327, 246). The result uncovered that 9-gene signature could independently predict breast cancer prognosis. Lower mutation of PIK3CA and higher mutation of TP53 and CDH1 were found in samples with high-risk score compared with samples with low-risk score. Patients in the high-risk group showed higher immune score, malignant clinical features than those in the low-risk group. The 9-gene signature developed in this study achieved a higher AUC. Conclusion The current research established a 5-CNV gene signature to evaluate prognosis of breast cancer patients, which may innovate clinical application of prognostic assessment.

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 A ferroptosis-associated gene signature for the prediction of prognosis and therapeutic response in luminal-type breast carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yang Peng ◽  
Haochen Yu ◽  
Yingzi Zhang ◽  
Fanli Qu ◽  
Zhenrong Tang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Breast Carcinoma ◽  
Risk Score ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Luminal Type

AbstractFerroptosis is a new form of regulated cell death (RCD), and its emergence has provided a new approach to the progression and drug resistance of breast cancer (BRCA). However, there is still a great gap in the study of ferroptosis-related genes in BRCA, especially luminal-type BRCA patients. We downloaded the mRNA expression profiles and corresponding clinical data of BRCA patients from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and The Cancer Genome Atlas (TCGA) databases. Then, we built a prognostic multigene signature with ferroptosis-related differentially expressed genes (DEGs) in the METABRIC cohort and validated it in the TCGA cohort. The predictive value of this signature was investigated in terms of the immune microenvironment and the probability of a response to immunotherapy and chemotherapy. The patients were divided into a high-risk group and a low-risk group according to the ferroptosis-associated gene signature, and the high-risk group had a worse overall survival (OS). The risk score based on the 10 ferroptosis-related gene-based signature was determined to be an independent prognostic predictor in both the METABRIC and TCGA cohorts (HR, 1.41, 95% CI, 1.14–1.76, P = 0.002; HR, 2.19, 95% CI, 1.13–4.26, P = 0.02). Gene set enrichment analysis indicated that the term “cytokine-cytokine receptor interaction” was enriched in the high-risk score subgroup. Moreover, the immune infiltration scores of most immune cells were significantly different between the two groups, the low-risk group was much more sensitive to immunotherapy, and six drugs might have potential therapeutic implications in the high-risk group. Finally, a nomogram incorporating a classifier based on the 10 ferroptosis-related genes, tumor stage, age and histologic grade was established. This nomogram showed favorable discriminative ability and could help guide clinical decision-making for luminal-type breast carcinoma.

scholarly journals A ferroptosis-associated gene signature for the prediction of prognosis and therapeutic response in luminal-type breast carcinoma

2021 ◽  
Author(s):  
Yang Peng ◽  
Haochen Yu ◽  
Yingzi Zhang ◽  
Fanli Qu ◽  
Zhenrong Tang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Breast Carcinoma ◽  
Risk Score ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Luminal Type

Abstract Background: Ferroptosis is a new form of regulated cell death (RCD), and its emergence has provided a new approach to the progression and drug resistance of breast cancer (BRCA). However, there is still a great gap in the study of ferroptosis-related genes in BRCA, especially luminal-type BRCA patients.Methods: We downloaded the mRNA expression profiles and corresponding clinical data of BRCA patients from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and The Cancer Genome Atlas (TCGA) databases. Then, we built a prognostic multigene signature with ferroptosis-related differentially expressed genes (DEGs) in the METABRIC cohort and validated it in the TCGA cohort. The predictive value of this signature was investigated in terms of mutations, copy number variations (CNVs), the immune microenvironment and the probability of a response to immunotherapy and chemotherapy.Findings: The patients were divided into a high-risk group and a low-risk group by the ferroptosis-associated gene signature, and the high-risk group had a worse overall survival (OS). The risk score based on the 10 ferroptosis-related gene-based signature was determined to be an independent prognostic predictor in both the METABRIC and TCGA cohorts (HR, 1.41, 95% CI, 1.14-1.76, P = 0.002; HR, 2.19, 95% CI, 1.13-4.26, P= 0.02). Gene set enrichment analysis indicated that the term “cytokine-cytokine receptor interaction” was enriched in the high-risk score subgroup. Moreover, the immune infiltration scores of most immune cells were significantly different between the two groups, and the low-risk group was much more sensitive to immunotherapy and six drugs might have potential therapeutic implications in high- risk group. In addition, we found that amplifications on chromosome 11 accompanied by the deletion of chromosome 1 were enriched in the high-risk subgroup. Finally, a nomogram incorporating a classifier based on the 10 ferroptosis-related genes, tumor stage, age and histologic grade was established. This nomogram showed a favorable discriminating ability and might contribute to clinical decision-making for luminal-type breast carcinoma.

scholarly journals A Ferroptosis-associated Gene Signature for the Prediction of Prognosis and Therapeutic Response in Luminal-type Breast Carcinoma

2020 ◽  
Author(s):  
Yang Peng ◽  
Haochen Yu ◽  
Yingzi Zhang ◽  
Zhenrong Tang ◽  
Chi Qu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Breast Carcinoma ◽  
Risk Score ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk ◽  
Related Gene ◽  
Luminal Type

Abstract Background: Ferroptosis is a new form of regulated cell death (RCD), and its emergence has provided a new approach to the progression and drug resistance of breast cancer (BRCA). However, there is still a great gap in the study of ferroptosis-related genes in BRCA, especially luminal-type BRCA patients.Methods: We downloaded the mRNA expression profiles and corresponding clinical data of BRCA patients from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and The Cancer Genome Atlas (TCGA) databases. Then, we built a prognostic multigene signature with ferroptosis-related differentially expressed genes (DEGs) in the METABRIC cohort and validated it in the TCGA cohort. The predictive value of this signature was investigated in terms of mutations, copy number variations (CNVs), the immune microenvironment, tumor purity, related pathway and the probability of a response to immunotherapy and chemotherapy.Findings: The patients were divided into a high-risk group and a low-risk group by the ferroptosis-associated gene signature, and the high-risk group had a worse overall survival (OS). The risk score based on the 10 ferroptosis-related gene-based signature was determined to be an independent prognostic predictor in both the METABRIC and TCGA cohorts (HR, 1.41, 95% CI, 1.14-1.76, P = 0.002; HR, 2.19, 95% CI, 1.13-4.26, P= 0.02). Gene set enrichment analysis indicated that the term “cytokine-cytokine receptor interaction” was enriched in the high risk score subgroup. Moreover, the immune infiltration scores of most immune cells were significantly different between the two groups, and the low-risk group was much more sensitive to immunotherapy and chemotherapy. In addition, we found that amplifications on chromosome 12 accompanied by the deletion of chromosome 21 were enriched in the high-risk subgroup. Pathway score results suggest that the ferroptosis-related gene-based signature show differences in most breast cancer-associated phenotypes. Finally, a nomogram incorporating a classifier based on the 10 ferroptosis-related genes, tumor stage, age and histologic grade was established. This nomogram showed a favorable discriminating ability and might contribute to clinical decision-making for luminal-type breast carcinoma.

scholarly journals Prediction of Breast Cancer Recurrence Using a Deep Convolutional Neural Network Without Region-of-Interest Labeling

2021 ◽  
Vol 11 ◽  
Author(s):  
Nam Nhut Phan ◽  
Chih-Yi Hsu ◽  
Chi-Cheng Huang ◽  
Ling-Ming Tseng ◽  
Eric Y. Chuang
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Risk Score ◽  
High Performance ◽  
Cancer Recurrence ◽  
Region Of Interest ◽  
Gene Signature ◽  
Low Risk ◽  
Breast Cancer Recurrence ◽  
Testing Set

PurposeThe present study aimed to assign a risk score for breast cancer recurrence based on pathological whole slide images (WSIs) using a deep learning model.MethodsA total of 233 WSIs from 138 breast cancer patients were assigned either a low-risk or a high-risk score based on a 70-gene signature. These images were processed into patches of 512x512 pixels by the PyHIST tool and underwent color normalization using the Macenko method. Afterward, out of focus and pixelated patches were removed using the Laplacian algorithm. Finally, the remaining patches (n=294,562) were split into 3 parts for model training (50%), validation (7%) and testing (43%). We used 6 pretrained models for transfer learning and evaluated their performance using accuracy, precision, recall, F1 score, confusion matrix, and AUC. Additionally, to demonstrate the robustness of the final model and its generalization capacity, the testing set was used for model evaluation. Finally, the GRAD-CAM algorithm was used for model visualization.ResultsSix models, namely VGG16, ResNet50, ResNet101, Inception_ResNet, EfficientB5, and Xception, achieved high performance in the validation set with an overall accuracy of 0.84, 0.85, 0.83, 0.84, 0.87, and 0.91, respectively. We selected Xception for assessment of the testing set, and this model achieved an overall accuracy of 0.87 with a patch-wise approach and 0.90 and 1.00 with a patient-wise approach for high-risk and low-risk groups, respectively.ConclusionsOur study demonstrated the feasibility and high performance of artificial intelligence models trained without region-of-interest labeling for predicting cancer recurrence based on a 70-gene signature risk score.

scholarly journals Development a Prognostic Model Integrating lncRNA/ mRNA novel Biomarkers Identified by Bioinformatics Analysis and Experiments in Breast Cancer

2021 ◽  
Author(s):  
Jinrong Wei ◽  
Qianshu Dou ◽  
Futing Ba ◽  
Guo-Qin Jiang
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Risk Score ◽  
Risk Group ◽  
Low Risk ◽  
Breast Cancers ◽  
Rt Pcr ◽  
Cerna Network ◽  
Prognosis Model

Abstract Purpose: The purpose of this study is to established a prognosis model based on the expression profiles of lncRNAs and mRNAs for breast cancers.Methods: Single Variable Cox Proportional Risk Regression analysis and difference analysis were applied to screen survival-related and differently expressed lncRNAs and mRNAs between tumor and normal tissues from TCGA data. GO and KEGG analysis were applied for top 30 survival-related genes. LncRNA/mRNA co-expressed network was constructed based on correlation analysis. LASSO analysis and Multivariate Stepwise Cox Regression analysis were applied to establish the prognosis model. RT-PCR experiments were applied to verify the correctness of the analysis results. Relative components of the TME in breast cancers with high and low risk groups were analysed by xCell and Cox proportional risk regression analysis. The ceRNA network was constructed by calculating the Pearson correlation coefficient (PCC) for miRNA-mRNA and miRNA-lncRNA using paired miRNA, mRNA, and lncRNA expression profile data.Results:Venn diagrams showed that there were 60 genes and 54 lncRNAs that were differently expressed and related with survival. Through lncRNA/mRNA co-expressed network construction, 19 lncRNA and 16 mRNA hub genes were gained. The genes were then included in LASSO and multivariate Cox proportional hazard regression analysis, and finally, 3 lncRNAs (LINC01497, LINC02766, LINC02528) and 2 mRNAs (C20orf85, CST1) were selected as prognosis predictive genes. According to the median risk score of the 5 candidates, patients were divided into high-risk group and low-risk group. The results of RT-PCR were consistent with the analysis results. The proportions of Adipocytes, Endothelial cells, HSCs, Fibroblasts were significantly lower in low risk score tissues compared with the high risk score tissues, while the proportions of M1 macrophages, MSCs, Th2 cells were significantly higher. A lncRNA-miRNA-mRNA ceRNA network containing 3 lncRNAs, 2 mRNAs, and 158 miRNAs was finally constructed, preliminarily revealed a proper mechanism of the 5 molecules playing important roles in breast cancer progression and prognosis prediction.Conclusion: We found that LINC01497, LINC02766, LINC02528 and C20orf85, CST1 may serve as a powerful prognostic tool to optimize the prognosis evaluation system of breast cancer.

scholarly journals Identification of a Novel Glycolysis-Related Signature to Predict the Prognosis of Patients with Breast Cancer

2021 ◽  
Author(s):  
Menglin He ◽  
Cheng Hu ◽  
Jian Deng ◽  
Hui Ji ◽  
Weiqian Tian
Keyword(s):  
Breast Cancer ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis ◽  
Expression Levels ◽  
Incidence And Mortality

Abstract Background: Breast cancer (BC) is a kind of cancer with high incidence and mortality in female. Conventional clinical characteristics are far from accurate to predict individual outcomes. Therefore, we aimed to develop a novel signature to predict the survival of patients with BC. Methods: We analyzed the data of a training cohort from the TCGA database and a validation cohort from GEO database. After the applications of GSEA and Cox regression analyses, a glycolysis-related signature for predicting the survival of patients with BC was developed. The signature contains AK3, CACNA1H, IL13RA1, NUP43, PGK1, and SDC1. Then, we constructed a risk score formula to classify the patients into high and low-risk groups based on the expression levels of six-gene in patients. The receiver operating characteristic (ROC) curve and the Kaplan-Meier curve were used to assess the predicted capacity of the model. Next, a nomogram was developed to predict the outcomes of patients with risk score and clinical features in 1, 3, and 5 years. We further used Human Protein Atlas (HPA) database to validate the expressions of the six biomarkers in tumor and sample tissues.Results: We constructed a six-gene signature to predict the outcomes of patients with BC. The patients in high-risk group showed poor prognosis than that in low-risk group. The AUC values were 0.719 and 0.702, showing that the prediction performance of the signature is acceptable. Additionally, Cox regression analysis revealed that these biomarkers could independently predict the prognosis of BC patients without being affected by clinical factors. The expression levels of all six biomarkers in BC tissues were higher than that in normal tissues except AK3. Conclusion: We developed a six-gene signature to predict the prognosis of patients with BC. Our signature has been proved to have the ability to make an accurate and obvious prediction and might be used to expand the prediction methods in clinical.

scholarly journals A Four-Gene-Based Prognostic Model Predicts Overall Survival in Patients With Cutaneous Melanoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiaoxia Tong ◽  
Xiaofei Qu ◽  
Mengyun Wang
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Risk Score ◽  
Cutaneous Melanoma ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Rank Test ◽  
Score Model

BackgroundCutaneous melanoma (CM) is one of the most aggressive cancers with highly metastatic ability. To make things worse, there are limited effective therapies to treat advanced CM. Our study aimed to investigate new biomarkers for CM prognosis and establish a novel risk score system in CM.MethodsGene expression data of CM from Gene Expression Omnibus (GEO) datasets were downloaded and analyzed to identify differentially expressed genes (DEGs). The overlapped DEGs were then verified for prognosis analysis by univariate and multivariate COX regression in The Cancer Genome Atlas (TCGA) datasets. Based on the gene signature of multiple survival associated DEGs, a risk score model was established, and its prognostic and predictive role was estimated through Kaplan-Meier (K-M) analysis and log-rank test. Furthermore, the correlations between prognosis related genes expression and immune infiltrates were analyzed via Tumor Immune Estimation Resource (TIMER) site.ResultsA total of 103 DEGs were obtained based on GEO cohorts, and four genes were verified in TCGA datasets. Subsequently, four genes (ADAMDEC1, GNLY, HSPA13, and TRIM29) model was developed by univariate and multivariate Cox regression analyses. The K-M plots showed that the high-risk group was associated with shortened survival than that in the low-risk group (P < 0.0001). Multivariate analysis suggested that the model was an independent prognostic factor (high-risk vs. low-risk, HR= 2.06, P < 0.001). Meanwhile, the high-risk group was prone to have larger breslow depth (P< 0.001) and ulceration (P< 0.001).ConclusionsThe four-gene risk score model functions well in predicting the prognosis and treatment response in CM and will be useful for guiding therapeutic strategies for CM patients. Additional clinical trials are needed to verify our findings.

scholarly journals A 5-Gene Signature Is Closely Related to Tumor Immune Microenvironment and Predicts the Prognosis of Patients with Non-Small Cell Lung Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Jia Li ◽  
Huiyu Wang ◽  
Zhaoyan Li ◽  
Chenyue Zhang ◽  
Chenxing Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk ◽  
Egfr Mutations ◽  
Kaplan Meier ◽  
Tumor Immune Microenvironment ◽  
Nsclc Patients

Purpose. Establishing prognostic gene signature to predict clinical outcomes and guide individualized adjuvant therapy is necessary. Here, we aim to establish the prognostic efficacy of a gene signature that is closely related to tumor immune microenvironment (TIME). Methods and Results. There are 13,035 gene expression profiles from 130 tumor samples of the non-small cell lung cancer (NSCLC) in the data set GSE103584. A 5-gene signature was identified by using univariate survival analysis and Least Absolute Shrinkage and Selection Operator (LASSO) to build risk models. Then, we used the CIBERSORT method to quantify the relative levels of different immune cell types in complex gene expression mixtures. It was found that the ratio of dendritic cells (DCs) activated and mast cells (MCs) resting in the low-risk group was higher than that in the high-risk group, and the difference was statistically significant (P<0.001 and P=0.03). Pathway enrichment results which were obtained by performing Gene Set Variation Analysis (GSVA) showed that the high-risk group identified by the 5-gene signature had metastatic-related gene expression, resulting in lower survival rates. Kaplan–Meier survival results showed that patients of the high-risk group had shorter disease-free survival (DFS) and overall survival (OS) than those of the low-risk group in the training set (P=0.0012 and P<0.001). The sensitivity and specificity of the gene signature were better and more sensitive to prognosis than TNM (tumor/lymph node/metastasis) staging, in spite of being not statistically significant (P=0.154). Furthermore, Kaplan–Meier survival showed that patients of the high-risk group had shorter OS and PFS than those of the low-risk group (P=0.0035, P<0.001, and P<0.001) in the validating set (GSE31210, GSE41271, and TCGA). At last, univariate and multivariate Cox proportional hazard regression analyses were used to evaluate independent prognostic factors associated with survival, and the gene signature, lymphovascular invasion, pleural invasion, chemotherapy, and radiation were employed as covariates. The 5-gene signature was identified as an independent predictor of patient survival in the presence of clinical parameters in univariate and multivariate analyses (P<0.001) (hazard ratio (HR): 3.93, 95% confidence interval CI (2.17–7.1), P=0.001, (HR) 5.18, 95% CI (2.6995–9.945), P<0.001), respectively. Our 5-gene signature was also related to EGFR mutations (P=0.0111), and EGFR mutations were mainly enriched in low-risk group, indicating that EGFR mutations affect the survival rate of patients. Conclusion. The 5-gene signature is a powerful and independent predictor that could predict the prognosis of NSCLC patients. In addition, our gene signature is correlated with TIME parameters, such as DCs activated and MCs resting. Our findings suggest that the 5-gene signature closely related to TIME could predict the prognosis of NSCLC patients and provide some reference for immunotherapy.

scholarly journals Construction and Identification of a Novel 5-Gene Signature for Predicting the Prognosis in Breast Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Lingling Guo ◽  
Yu Jing
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Risk Model ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk ◽  
M2 Macrophage ◽  
Hub Genes ◽  
Cox Regression Analysis

Background: Breast cancer is one of the most common malignancies in women worldwide. The purpose of this study was to identify the hub genes and construct prognostic signature that could predict the survival of patients with breast cancer (BC).Methods: We identified differentially expressed genes between the responder group and non-responder group based on the GEO cohort. Drug-resistance hub genes were identified by weighted gene co-expression network analysis, and a multigene risk model was constructed by univariate and multivariate Cox regression analysis based on the TCGA cohort. Immune cell infiltration and mutation characteristics were analyzed.Results: A 5-gene signature (GP6, MAK, DCTN2, TMEM156, and FKBP14) was constructed as a prognostic risk model. The 5-gene signature demonstrated favorable prediction performance in different cohorts, and it has been confirmed that the signature was an independent risk indicater. The nomogram comprising 5-gene signature showed better performance compared with other clinical features, Further, in the high-risk group, high M2 macrophage scores were related with bad prognosis, and the frequency of TP53 mutations was greater in the high-risk group than in the low-risk group. In the low-risk group, high CD8+ T cell scores were associated with a good prognosis, and the frequency of CDH1 mutations was greater in the low-risk group than that in the high-risk group. At the same time, patients in the low risk group have a good response to immunotherapy in terms of immunotherapy. The results of immunohistochemistry showed that MAK, GP6, and TEMEM156 were significantly highly expressed in tumor tissues, and DCTN2 was highly expressed in normal tissues.Conclusions: Our study may find potential new targets against breast cancer, and provide new insight into the underlying mechanisms.

Sign in / Sign up

Export Citation Format

Share Document