scholarly journals SEMA6D Expression and Patient Survival in Breast Invasive Carcinoma

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Dongquan Chen ◽  
Yufeng Li ◽  
Lizhong Wang ◽  
Kai Jiao
Keyword(s):  
Breast Cancer ◽  
Expression Profile ◽  
Heart Development ◽  
Invasive Carcinoma ◽  
Patient Survival ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Patients ◽  
Genomic Expression ◽  
Cancer Genome Atlas ◽  
Public Datasets

Breast cancer (BC) is the second most common cancer diagnosed in American women and is also the second leading cause of cancer death in women. Research has focused heavily on BC metastasis. Multiple signaling pathways have been implicated in regulating BC metastasis. Our knowledge of regulation of BC metastasis is, however, far from complete. Identification of new factors during metastasis is an essential step towards future therapy. Our labs have focused on Semaphorin 6D (SEMA6D), which was implicated in immune responses, heart development, and neurogenesis. It will be interesting to know SEMA6D-related genomic expression profile and its implications in clinical outcome. In this study, we examined the public datasets of breast invasive carcinoma from The Cancer Genome Atlas (TCGA). We analyzed the expression of SEMA6D along with its related genes, their functions, pathways, and potential as copredictors for BC patients’ survival. We found 6-gene expression profile that can be used as such predictors. Our study provides evidences for the first time that breast invasive carcinoma may contain a subtype based on SEMA6D expression. The expression of SEMA6D gene may play an important role in promoting patient survival, especially among triple negative breast cancer patients.

scholarly journals Simulation of Chemotherapy Effects in Older Breast Cancer Patients With High Recurrence Scores

2019 ◽  
Vol 112 (6) ◽  
pp. 574-581 ◽  
Author(s):  
Young Chandler ◽  
Jinani C Jayasekera ◽  
Clyde B Schechter ◽  
Claudine Isaacs ◽  
Christopher J Cadham ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Endocrine Therapy ◽  
Expression Profile ◽  
Early Stage ◽  
Breast Cancer Mortality ◽  
Sensitivity Analyses ◽  
Absolute Difference ◽  
Breast Cancer Patients ◽  
Genomic Expression ◽  
Life Years

Abstract Background Tumor genomic expression profile data are used to guide chemotherapy choice, but there are gaps in evidence for women aged 65 years and older. We estimate chemotherapy effects by age and comorbidity level among women with early-stage, hormone receptor–positive, human epidermal growth factor receptor 2 (HER2)–negative breast cancers and Oncotype DX scores of 26 or higher. Methods A discrete-time stochastic state transition simulation model synthesized data from population studies and clinical trials to estimate outcomes over a 25-year horizon for subgroups based on age (65–69, 70–74, 75–79, and 80–89 years) and comorbidity levels (no or low, moderate, severe). Outcomes were discounted at 3%, and included quality-adjusted life-years (QALYs), life-years, and breast cancer and other-cause mortality with chemoendocrine vs endocrine therapy. Sensitivity analysis tested the effect of varying uncertain parameters. Results Women aged 65–69 years with no or low comorbidity gained 0.16 QALYs with chemo-endocrine and reduced breast cancer mortality from 34.8% to 29.7%, for an absolute difference of 5.1%; this benefit was associated with a 12.8% rate of grade 3–4 toxicity. Women aged 65–69 years with no or low or moderate comorbidity levels, and women aged 70–74 years with no or low comorbidity had small chemotherapy benefits. All women aged 75 years and older experienced net losses in QALYs with chemo-endocrine therapy. The results were robust in sensitivity analyses. Chemotherapy had greater benefits as treatment effectiveness increased, but toxicity reduced the QALYs gained. Conclusion Among women aged 65–89 years whose tumors indicate a high recurrence risk, only those aged 65–74 years with no or low or moderate comorbidity have small benefits from adding chemotherapy to endocrine therapy. Genomic expression profile testing (and chemotherapy use) should be reserved for women aged younger than 75 years without severe comorbidity.

Metabolic gene signature for predicting breast cancer recurrence using transcriptome analysis

2021 ◽  
Author(s):  
Juan Feng ◽  
Jun Ren ◽  
Qingfeng Yang ◽  
Lingxia Liao ◽  
Le Cui ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Recurrence ◽  
Recurrence Risk ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Patients ◽  
Metabolic Gene ◽  
Limma Package ◽  
Cancer Genome Atlas

Background: The study aimed at identifying a metabolic gene signature for stratifying the risk of recurrence in breast cancer. Materials & Methods: The data of patients were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The limma package was used to identify differentially expressed metabolic genes, and a metabolic gene signature was constructed. Results: A five-gene metabolic signature was established that demonstrated satisfactory accuracy and predictive power in both training and validation cohorts. Also, a nomogram for predicting recurrence-free survival was established using a combination of the metabolism gene risk score and the clinicopathological features. Conclusions: The proposed metabolic gene signature and nomogram have a significant prognostic value and may improve the recurrence risk stratification for breast cancer patients.

scholarly journals Leptin Signaling Affects Survival and Chemoresistance of Estrogen Receptor Negative Breast Cancer

2020 ◽  
Vol 21 (11) ◽  
pp. 3794 ◽  
Author(s):  
Crystal C. Lipsey ◽  
Adriana Harbuzariu ◽  
Robert W. Robey ◽  
Lyn M. Huff ◽  
Michael M. Gottesman ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Estrogen Receptor ◽  
Leptin Receptor ◽  
Patient Survival ◽  
The Cancer Genome Atlas ◽  
Leptin Signaling ◽  
Cancer Genome Atlas ◽  
Targeted Gene Expression ◽  
Estrogen Receptor Negative

Estrogen-receptor-negative breast cancer (BCER−) is mainly treated with chemotherapeutics. Leptin signaling can influence BCER− progression, but its effects on patient survival and chemoresistance are not well understood. We hypothesize that leptin signaling decreases the survival of BCER− patients by, in part, inducing the expression of chemoresistance-related genes. The correlation of expression of leptin receptor (OBR), leptin-targeted genes (CDK8, NANOG, and RBP-Jk), and breast cancer (BC) patient survival was determined from The Cancer Genome Atlas (TCGA) mRNA data. Leptin-induced expression of proliferation and chemoresistance-related molecules was investigated in triple-negative BC (TNBC) cells that respond differently to chemotherapeutics. Leptin-induced gene expression in TNBC was analyzed by RNA-Seq. The specificity of leptin effects was assessed using OBR inhibitors (shRNA and peptides). The results show that OBR and leptin-targeted gene expression are associated with lower survival of BCER− patients. Importantly, the co-expression of these genes was also associated with chemotherapy failure. Leptin signaling increased the expression of tumorigenesis and chemoresistance-related genes (ABCB1, WNT4, ADHFE1, TBC1D3, LL22NC03, RDH5, and ITGB3) and impaired chemotherapeutic effects in TNBC cells. OBR inhibition re-sensitized TNBC to chemotherapeutics. In conclusion, the co-expression of OBR and leptin-targeted genes may be used as a predictor of survival and drug resistance of BCER− patients. Targeting OBR signaling could improve chemotherapeutic efficacy.

scholarly journals Comprehensive Transcriptomic Analysis Identifies ST8SIA1 as a Survival-Related Sialyltransferase Gene in Breast Cancer

Genes ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1436
Author(s):  
Jung-Yu Kan ◽  
Sin-Hua Moi ◽  
Wen-Chun Hung ◽  
Ming-Feng Hou ◽  
Fang-Ming Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Area Under The Curve ◽  
Disease Free Survival ◽  
Comprehensive Analysis ◽  
The Cancer Genome Atlas ◽  
Rna Seq ◽  
Breast Cancer Patients ◽  
Clinical Database ◽  
Kaplan Meier ◽  
Cancer Genome Atlas

Hypersialylation caused by the overexpression of sialyltransferases (STs) is a common feature in cancer that is associated with several characteristics of tumorigenesis. Thus, identifying cancer-associated STs is critical for cancer therapy. However, ST screening has been frequently conducted in cell line models. In this study, we conducted a comprehensive analysis of STs in the clinical database and identified the STs related with the survival of breast cancer patients. RNA sequencing (RNA-Seq) data of 496 patients were obtained from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA). Of the eight mapped STs, ST3GAL5, and ST8SIA1 met the acceptable area under the curve (AUC) criteria for overall survival (OS). Using Kaplan–Meier methods, we determined that high expression of ST8SIA1 was associated with poor 10-year OS in all patients, triple-negative breast cancer (TNBC), and non-TNBC patients, and poor disease-free survival (DFS) rates particularly in TNBC. ST8SIA1 also had superior AUC values in terms of OS/DFS. High ST8SIA1 levels showed a higher risk for poor OS in different groups of patients and a higher risk for poor DFS particularly in TNBC. In summary, we conducted a comprehensive analysis of STs from the clinical database and identified ST8SIA1 as a crucial survival-related ST, which might be a potential therapeutic target for breast cancer and TNBC patients.

The Low Expression of MEOX2 is Associated With Poorer Survival in Breast Cancer

2021 ◽  
Author(s):  
Huxia Wang ◽  
Yanan Tang ◽  
Meixia Wang ◽  
Caixia Ding ◽  
Xiaomin Yang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Cancer Progression ◽  
Cox Regression ◽  
Histological Grade ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Patients ◽  
Normal Tissues ◽  
Cancer Genome Atlas ◽  
And Function

Abstract The regulation of vertebrate limb myogenesis gene, Mesenchyme Homeobox 2 (MEOX2), has been reported to be associated with most cancer progression closely. However, its role and function in breast cancer are unidentified. Here, we aim to investigate the association of MEOX2 expression with clinicopathological features and the survival probability of breast cancer. The MEOX2 expression in breast cancer was first analyzed from The Cancer Genome Atlas (TCGA) database. Then, the association of MEOX2 with patients’ clinicopathological variables and prognostic probability were detected by bioinformatics analysis. Moreover, a high-throughput tissue microarray containing 135 cases of breast cancer was used to further clarify the expression of MEOX2 in breast cancer patients. The expression of MEOX2 is inhibited in breast cancer than in normal tissues, and the lower MEOX2 expression indicates the poorer prognosis of breast cancer patients. In addition, the histological grade of MEOX2 expression is negatively correlated with the Ki67 level. Multivariate COX regression also verified that MEOX2 was an independent prognostic factor in breast cancer patients. Based on our results, we can conclude that lower MEOX2 expression was related to tumor proliferation and could be a new diagnostic and prognostic biomarker of breast cancer.

scholarly journals Identified metabolic related genes to construct a prognostic model of Breast cancer patients

2020 ◽  
Author(s):  
Yu-Yuan Ma ◽  
Han Wang ◽  
Jie Qi ◽  
Jie Zhu ◽  
Yue-Qing Huang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prognostic Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Patients ◽  
Cox Regression Analysis ◽  
Earth Observations ◽  
Cancer Genome Atlas ◽  
Worse Prognosis ◽  
Geo Database

Abstract Background: More and more evidence confirms that there are many metabolic disorders in the tumor. The occurrence and development of breast cancer (BC) is closely related to metabolism. Methods: A metabolic related genes table was obtained by the Kyoto Encyclopedia of Genes and Genomes (KEGG) related metabolic pathway. The edgeR package was used to identify differentially expressed genes (DEGs) of The Cancer Genome Atlas (TCGA) breast cancer. We established a prognostic model by univariate Cox regression analysis and lasso-penalized Cox regression. The validation prognostic model was built through the Group on Earth Observations (GEO) database. Use the nomogram and Receiver Operating Characteristic (ROC) curve to verify the accuracy of models. Result: We identified 178 DEGs and 14 prognostic-related genes to construct a prognostic model. In the TCGA prognostic model and the GEO validation prognostic model, patients were divided into high riskscore group and low riskscore group, the high riskscore group had worse prognosis.Conclusion: We constructed a prognostic model of metabolic related genes and verified the feasibility and accuracy of the model. It is hoped that the model can provide a basis and biomarker for breast cancer related metabolic therapy and prognosis.

scholarly journals PEG3 mutation is associated with elevated tumor mutation burden and poor prognosis in breast cancer

2020 ◽  
Vol 40 (8) ◽  
Author(s):  
Min Zhang ◽  
Jin Zhang
Keyword(s):  
Breast Cancer ◽  
The Cancer Genome Atlas ◽  
Confounding Factors ◽  
Breast Cancer Patients ◽  
Wild Type ◽  
Tumor Mutation Burden ◽  
Mutation Burden ◽  
Cancer Genome Atlas ◽  
Brca1 Brca2 ◽  
Common Malignancy

Abstract Background: Breast cancer is the second most common malignancy in women and considered as a severe health burden. PEG3 mutations have been observed in several cancers. However, the associations of PEG3 mutation with tumor mutation burden (TMB) and prognosis in breast cancer have not been investigated. Methods: In our study, the somatic mutation data of 986 breast cancer patients from The Cancer Genome Atlas (TCGA) were analyzed. Results: It showed that PEG3 had a relatively high mutation rate (2%). After calculated the TMB in PEG3 mutant and PEG3 wild-type groups, we found the TMB value was significantly higher in PEG3 mutant samples than that in PEG3 wild-type samples (P = 5.6e-07), which was independent of the confounding factors including age, stage, mutations of BRCA1, BRCA2 and POLE (odd ratio, 0.45; 95% CI, 0.20–0.98; P=0.044). Survival analysis revealed that PEG3 mutant samples had inferior survival outcome compared with the PEG3 wild-type samples after adjusted for the confounding factors above (hazard ratio, 0.27; 95% CI: 0.12–0.57; P<0.001). Conclusion: These results illustrated that PEG3 mutation was associated with high TMB and inferior prognosis, suggesting PEG3 mutation might play a guiding role in prognosis prediction and immunotherapy selection in breast cancer.

scholarly journals DNA Methylation Based Molecular Subtypes Predict Prognosis in Breast Cancer Patients

2021 ◽  
Vol 28 ◽  
pp. 107327482098851
Author(s):  
Zeng-Hong Wu ◽  
Yun Tang ◽  
Yan Zhou
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Molecular Subtypes ◽  
Methylation Status ◽  
The Cancer Genome Atlas ◽  
Training Dataset ◽  
Consensus Clustering ◽  
Breast Cancer Patients ◽  
Cancer Subtypes ◽  
Novel Biomarkers

Background: Epigenetic changes are tightly linked to tumorigenesis development and malignant transformation’ However, DNA methylation occurs earlier and is constant during tumorigenesis. It plays an important role in controlling gene expression in cancer cells. Methods: In this study, we determining the prognostic value of molecular subtypes based on DNA methylation status in breast cancer samples obtained from The Cancer Genome Atlas database (TCGA). Results: Seven clusters and 204 corresponding promoter genes were identified based on consensus clustering using 166 CpG sites that significantly influenced survival outcomes. The overall survival (OS) analysis showed a significant prognostic difference among the 7 groups (p<0.05). Finally, a prognostic model was used to estimate the results of patients on the testing set based on the classification findings of a training dataset DNA methylation subgroups. Conclusions: The model was found to be important in the identification of novel biomarkers and could be of help to patients with different breast cancer subtypes when predicting prognosis, clinical diagnosis and management.

scholarly journals CSIG-03. RECONCILING TUMOR HETEROGENEITY IN GLIOBLASTOMA USING A PATHWAY-BASED APPROACH

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii28-ii28
Author(s):  
Alvaro Alvarado ◽  
Kaleab Tessema ◽  
Kunal Patel ◽  
Riki Kawaguchi ◽  
Richard Everson ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Patient Survival ◽  
Molecular Subtype ◽  
Gene List ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Molecular Architecture ◽  
Survival Pathways ◽  
Cancer Genome Atlas

Abstract Despite efforts to gain a deeper understanding of its molecular architecture, glioblastoma (GBM) remains uniformly fatal. While genome-based molecular subtyping has revealed that GBMs may be parsed into several molecularly distinct categories, this insight has yielded little progress towards extending patient survival. In particular, the great phenotypic heterogeneity of GBM – both inter and intratumorally – has hindered therapeutic efforts. To this end, we interrogated tumor samples using a pathway-based approach to resolve tumoral heterogeneity. Gene set enrichment analysis (GSEA) was applied to gene expression data and used to provide an overview of each sample that can be compared to other samples by generating sample clusters based on overall patterns of enrichment. The Cancer Genome Atlas (TCGA) samples were clustered using the canonical and oncogenic signatures and in both cases the clustering was distinct from the molecular subtype previously reported and clusters were informative of patient survival. We also analyzed single cell RNA sequencing datasets and uniformly found two clusters of cells enriched for cell cycle regulation and survival pathways. We have validated our approach by generating gene lists from common elements found in the top contributing genesets for a particular cluster and testing the top targets in appropriate gliomasphere patient-derived lines. Samples enriched for cell cycle related genesets showed a decrease in sphere formation capacity when E2F1, out top target, was silenced and when treated with fulvestrant and calcitriol, which were identified as potential drugs targeting this genelist. Conversely, no changes were observed in samples not enriched for this gene list. Finally, we interrogated spatial heterogeneity and found higher enrichment of the proliferative signature in contrast enhancing compared with non-enhancing regions. Our studies relate inter- and intratumoral heterogeneity to critical cellular pathways dysregulated in GBM, with the ultimate goal of establishing a pipeline for patient- and tumor-specific precision medicine.

scholarly journals Cytotoxic T-lymphocyte infiltration and chemokine predict long-term patient survival independently of tumor mutational burden in triple-negative breast cancer

2021 ◽  
Vol 13 ◽  
pp. 175883592110066
Author(s):  
Eriko Katsuta ◽  
Li Yan ◽  
Mateusz Opyrchal ◽  
Pawel Kalinski ◽  
Kazuaki Takabe
Keyword(s):  
Breast Cancer ◽  
Triple Negative ◽  
Patient Survival ◽  
Cytotoxic T Lymphocyte ◽  
Breast Cancer Patients ◽  
Mutational Burden ◽  
Cancer Immunity ◽  
Anti Cancer ◽  
Tumor Mutational Burden ◽  
Patient Prognosis

Background: Cytotoxic T-lymphocyte (CTL) infiltration into tumor is a positive prognostic factor in breast cancer. High tumor mutational burden (TMB) is also considered as a predictor of tumor immunogenicity and response to immunotherapy. However, it is unclear whether the infiltration of functional CTL simply reflects the TMB or represents an independent prognostic value. Methods: Utilizing The Cancer Genome Atlas (TCGA) breast cancer cohort, we established the Functional Hotness Score (FHS). The associations of FHS and breast cancer patient prognosis as well as distinct immunity markers were analyzed in a total of 3011 breast cancer patients using TCGA, METABRIC and metastatic breast cancer (MBC) cohort GSE110590. Results: We established FHS, based on CD8A, GZMB and CXCL10 gene expression levels of bulk tumors, which delivered the best prognostic value among some gene combinations. Breast cancer patients with the high-FHS tumors showed significantly better survival. FHS was lower in the MBCs. Triple-negative breast cancer (TNBC) showed the highest FHS among subtypes. FHS predicted patient survival in hormone receptor (HR)-negative, especially in TNBC, but not in HR-positive breast cancer. FHS predicted patient prognosis independently in TNBC. The high-FHS TNBCs showed not only higher CD8+ T cell infiltration, but also enhanced broader type-1 anti-cancer immunity. The patients with the high-FHS tumors showed better prognosis not only in high-TMB tumors but also in low-TMB TNBCs. The combination of high-TMB with high-FHS identified a unique subset of patients who do not recur over time in TNBC. Conclusion: TNBCs with high FHS based on the expression levels of CD8A, GZMB and CXCL10 showed improved prognosis with enhanced anti-cancer immunity regardless of TMB. FHS constitutes an independent prognostic marker of survival, particularly robustly when combined with TMB in TNBC.

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